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Available online , doi: 10.11999/JEIT231278
Abstract:
In active electrical scanning millimeter-wave security imaging, the uniform array antenna has the bottleneck of uncontrolled cost and high complexity, which is difficult to be widely applied in practices. To this end, a near-field focused sparse array design algorithm for high sparsity and low sidelobes is proposed in this paper. It applies an improved three dimensional (3D) time-domain imaging algorithm to achieve high-accuracy 3D reconstruction. Firstly, the near-field focusing sparse array antenna model is constructed by taking the near-field focusing position and peak sidelobe level as constraints, where the\begin{document}$ {\ell _p} $\end{document} \begin{document}$ p $\end{document}
In active electrical scanning millimeter-wave security imaging, the uniform array antenna has the bottleneck of uncontrolled cost and high complexity, which is difficult to be widely applied in practices. To this end, a near-field focused sparse array design algorithm for high sparsity and low sidelobes is proposed in this paper. It applies an improved three dimensional (3D) time-domain imaging algorithm to achieve high-accuracy 3D reconstruction. Firstly, the near-field focusing sparse array antenna model is constructed by taking the near-field focusing position and peak sidelobe level as constraints, where the
Available online , doi: 10.11999/JEIT240299
Abstract:
As Moore’s Law comes to an end, it is more and more difficult to improve the chip manufacturing process, and chiplet technology has been widely adopted to improve the chip performance. However, new design parameters introduced into the chiplet architecture pose significant challenges to the computer architecture simulator. To fully support exploration and evaluation of chiplet architecture, SEEChiplet (System-level Exploration and Evaluation simulator for chiplet), a framework based on gem5 simulator, is developed in this paper. Firstly, three design parameters concerned about chiplet chip design are summarized in this paper, including: (1) chiplet cache system design; (2) Packaging simulation; (3) Interconnection networks between chiplet. Secondly, in view of the above three design parameters, in this paper: (1) a new private last level cache system are designed and implemented to expand the cache system design space; (2) existing gem5 global directory is modified to adapt to new private Last Level cache (LLC) system; (3) two common packaging methods of chiplet and inter-chiplet network are modeled. Finally, a chiplet-based processor is simulated with PARSEC 3.0 benchmark program running on it, which proves that SEEChiplet can explore and evaluate the design space of chiplet.
As Moore’s Law comes to an end, it is more and more difficult to improve the chip manufacturing process, and chiplet technology has been widely adopted to improve the chip performance. However, new design parameters introduced into the chiplet architecture pose significant challenges to the computer architecture simulator. To fully support exploration and evaluation of chiplet architecture, SEEChiplet (System-level Exploration and Evaluation simulator for chiplet), a framework based on gem5 simulator, is developed in this paper. Firstly, three design parameters concerned about chiplet chip design are summarized in this paper, including: (1) chiplet cache system design; (2) Packaging simulation; (3) Interconnection networks between chiplet. Secondly, in view of the above three design parameters, in this paper: (1) a new private last level cache system are designed and implemented to expand the cache system design space; (2) existing gem5 global directory is modified to adapt to new private Last Level cache (LLC) system; (3) two common packaging methods of chiplet and inter-chiplet network are modeled. Finally, a chiplet-based processor is simulated with PARSEC 3.0 benchmark program running on it, which proves that SEEChiplet can explore and evaluate the design space of chiplet.
Available online , doi: 10.11999/JEIT240407
Abstract:
The application of Deep Reinforcement Learning (DRL) in intelligent driving decision-making is increasingly widespread, as it effectively enhances decision-making capabilities through continuous interaction with the environment. However, DRL faces challenges in practical applications due to low learning efficiency and poor data-sharing security. To address these issues, a Directed Acyclic Graph (DAG)blockchain-assisted deep reinforcement learning Intelligent Driving Strategy Optimization (D-IDSO) algorithm is proposed. First, a dual-layer secure data-sharing architecture based on DAG blockchain is constructed to ensure the efficiency and security of model data sharing. Next, a DRL-based intelligent driving decision model is designed, incorporating a multi-objective reward function that optimizes decision-making by jointly considering safety, comfort, and efficiency. Additionally, an Improved Prioritized Experience Replay with Twin Delayed Deep Deterministic Policy Gradient (IPER-TD3) method is proposed to enhance training efficiency. Finally, braking and lane-changing scenarios are selected in the CARLA simulation platform to train Connected and Automated Vehicles (CAVs). Experimental results demonstrate that the proposed algorithm significantly improves model training efficiency in intelligent driving scenarios, while ensuring data security and enhancing the safety, comfort, and efficiency of intelligent driving.
The application of Deep Reinforcement Learning (DRL) in intelligent driving decision-making is increasingly widespread, as it effectively enhances decision-making capabilities through continuous interaction with the environment. However, DRL faces challenges in practical applications due to low learning efficiency and poor data-sharing security. To address these issues, a Directed Acyclic Graph (DAG)blockchain-assisted deep reinforcement learning Intelligent Driving Strategy Optimization (D-IDSO) algorithm is proposed. First, a dual-layer secure data-sharing architecture based on DAG blockchain is constructed to ensure the efficiency and security of model data sharing. Next, a DRL-based intelligent driving decision model is designed, incorporating a multi-objective reward function that optimizes decision-making by jointly considering safety, comfort, and efficiency. Additionally, an Improved Prioritized Experience Replay with Twin Delayed Deep Deterministic Policy Gradient (IPER-TD3) method is proposed to enhance training efficiency. Finally, braking and lane-changing scenarios are selected in the CARLA simulation platform to train Connected and Automated Vehicles (CAVs). Experimental results demonstrate that the proposed algorithm significantly improves model training efficiency in intelligent driving scenarios, while ensuring data security and enhancing the safety, comfort, and efficiency of intelligent driving.
Available online , doi: 10.11999/JEIT240427
Abstract:
Edge computing provides computing resources and caching services at the network edge, effectively reducing execution latency and energy consumption. However, due to user mobility and network randomness, caching services and user tasks frequently migrate between edge servers, increasing system costs. The migration computation model based on pre-caching is constructed and the joint optimization problem of resource allocation, service caching and migration decision-making is investigated. To address this mixed-integer nonlinear programming problem, the original problem is decomposed to optimize the resource allocation using Karush-Kuhn-Tucker condition and bisection search iterative method. Additionally, a Joint optimization algorithm for Migration decision-making and Service caching based on a Greedy Strategy (JMSGS) is proposed to obtain the optimal migration and caching decisions. Simulation results show the effectiveness of the proposed algorithm in minimizing the weighted sum of system energy consumption and latency.
Edge computing provides computing resources and caching services at the network edge, effectively reducing execution latency and energy consumption. However, due to user mobility and network randomness, caching services and user tasks frequently migrate between edge servers, increasing system costs. The migration computation model based on pre-caching is constructed and the joint optimization problem of resource allocation, service caching and migration decision-making is investigated. To address this mixed-integer nonlinear programming problem, the original problem is decomposed to optimize the resource allocation using Karush-Kuhn-Tucker condition and bisection search iterative method. Additionally, a Joint optimization algorithm for Migration decision-making and Service caching based on a Greedy Strategy (JMSGS) is proposed to obtain the optimal migration and caching decisions. Simulation results show the effectiveness of the proposed algorithm in minimizing the weighted sum of system energy consumption and latency.
Available online , doi: 10.11999/JEIT240388
Abstract:
With generative adversarial networks have attracted much attention because they provide new ideas for blind super-resolution reconstruction. Considering the problem that the existing methods do not fully consider the low-frequency retention characteristics during image degradation, but use the same processing method for high and low-frequency components, which lacks the effective use of frequency details and is difficult to obtain better reconstruction results, a frequency separation generative adversarial super-resolution reconstruction network based on dense residuals and quality assessment is proposed. The idea of frequency separation is adopted by the network to process the high-frequency and low-frequency information of the image separately, so as to improve the ability of capturing high-frequency information and simplify the processing of low-frequency features. The base block in the generator is designed to integrate the spatial feature transformation layer into the dense wide activation residuals, which enhances the ability of deep feature representation while differentiating the local information. In addition, no-reference quality assessment network is designed specifically for super-resolution reconstructed images using Visual Geometry Group (VGG), which provides a new quality assessment loss for the reconstruction network and further improves the visual effect of reconstructed images. The experimental results show that the method has better reconstruction effect on multiple datasets than the current state-of-the-art similar methods. It is thus shown that super-resolution reconstruction using generative adversarial networks with the idea of frequency separation can effectively utilize the image frequency components and improve the reconstruction effect.
With generative adversarial networks have attracted much attention because they provide new ideas for blind super-resolution reconstruction. Considering the problem that the existing methods do not fully consider the low-frequency retention characteristics during image degradation, but use the same processing method for high and low-frequency components, which lacks the effective use of frequency details and is difficult to obtain better reconstruction results, a frequency separation generative adversarial super-resolution reconstruction network based on dense residuals and quality assessment is proposed. The idea of frequency separation is adopted by the network to process the high-frequency and low-frequency information of the image separately, so as to improve the ability of capturing high-frequency information and simplify the processing of low-frequency features. The base block in the generator is designed to integrate the spatial feature transformation layer into the dense wide activation residuals, which enhances the ability of deep feature representation while differentiating the local information. In addition, no-reference quality assessment network is designed specifically for super-resolution reconstructed images using Visual Geometry Group (VGG), which provides a new quality assessment loss for the reconstruction network and further improves the visual effect of reconstructed images. The experimental results show that the method has better reconstruction effect on multiple datasets than the current state-of-the-art similar methods. It is thus shown that super-resolution reconstruction using generative adversarial networks with the idea of frequency separation can effectively utilize the image frequency components and improve the reconstruction effect.
Available online , doi: 10.11999/JEIT240417
Abstract:
Convolutional Neural Networks (CNNs) exhibit translation invariance but lack rotation invariance. In recent years, rotating encoding for CNNs becomes a mainstream approach to address this issue, but it requires a significant number of parameters and computational resources. Given that images are the primary focus of computer vision, a model called Offset Angle and Multibranch CNN (OAMC) is proposed to achieve rotation invariance. Firstly, the model detect the offset angle of the input image and rotate it back accordingly. Secondly, feed the rotated image into a multibranch CNN with no rotation encoding. Finally, Response module is used to output the optimal branch as the final prediction of the model. Notably, with a minimal parameter count of 8k, the model achieves a best classification accuracy of 96.98% on the rotated handwritten numbers dataset. Furthermore, compared to previous research on remote sensing datasets, the model achieves up to 8% improvement in accuracy using only one-third of the parameters of existing models.
Convolutional Neural Networks (CNNs) exhibit translation invariance but lack rotation invariance. In recent years, rotating encoding for CNNs becomes a mainstream approach to address this issue, but it requires a significant number of parameters and computational resources. Given that images are the primary focus of computer vision, a model called Offset Angle and Multibranch CNN (OAMC) is proposed to achieve rotation invariance. Firstly, the model detect the offset angle of the input image and rotate it back accordingly. Secondly, feed the rotated image into a multibranch CNN with no rotation encoding. Finally, Response module is used to output the optimal branch as the final prediction of the model. Notably, with a minimal parameter count of 8k, the model achieves a best classification accuracy of 96.98% on the rotated handwritten numbers dataset. Furthermore, compared to previous research on remote sensing datasets, the model achieves up to 8% improvement in accuracy using only one-third of the parameters of existing models.
Available online , doi: 10.11999/JEIT240428
Abstract:
In the era of big data, table widely exist in various document images, and table detection is of great significance for the reuse of table information. In response to issues such as limited receptive field, reliance on predefined proposals, and inaccurate table boundary localization in existing table detection algorithms based on convolutional neural network, a table detection network based on DINO model is proposed in this paper. Firstly, an image preprocessing method is designed to enhance the corner and line features of table, enabling more precise table boundary localization and effective differentiation between table and other document elements like text. Secondly, a backbone network SwTNet-50 is designed, and Swin Transformer Blocks (STB) are introduced into ResNet to effectively combine local and global features, and the feature extraction ability of the model and the detection accuracy of table boundary are improved. Finally, to address the inadequacies in encoder feature learning in one-to-one matching and insufficient positive sample training in the DINO model, a collaborative hybrid assignments training strategy is adopted to improve the feature learning ability of the encoder and detection precision. Compared with various table detection methods based on deep learning, our model is better than other algorithms on the TNCR table detection dataset, with F1-Scores of 98.2%, 97.4%, and 93.3% for IoU thresholds of 0.5, 0.75, and 0.9, respectively. On the IIIT-AR-13K dataset, the F1-Score is 98.6% when the IoU threshold is 0.5.
In the era of big data, table widely exist in various document images, and table detection is of great significance for the reuse of table information. In response to issues such as limited receptive field, reliance on predefined proposals, and inaccurate table boundary localization in existing table detection algorithms based on convolutional neural network, a table detection network based on DINO model is proposed in this paper. Firstly, an image preprocessing method is designed to enhance the corner and line features of table, enabling more precise table boundary localization and effective differentiation between table and other document elements like text. Secondly, a backbone network SwTNet-50 is designed, and Swin Transformer Blocks (STB) are introduced into ResNet to effectively combine local and global features, and the feature extraction ability of the model and the detection accuracy of table boundary are improved. Finally, to address the inadequacies in encoder feature learning in one-to-one matching and insufficient positive sample training in the DINO model, a collaborative hybrid assignments training strategy is adopted to improve the feature learning ability of the encoder and detection precision. Compared with various table detection methods based on deep learning, our model is better than other algorithms on the TNCR table detection dataset, with F1-Scores of 98.2%, 97.4%, and 93.3% for IoU thresholds of 0.5, 0.75, and 0.9, respectively. On the IIIT-AR-13K dataset, the F1-Score is 98.6% when the IoU threshold is 0.5.
Available online , doi: 10.11999/JEIT240645
Abstract:
To solve the degradation problems such as color distortion, low brightness, and detail loss in images under transformer oil, a multi-scale weighted Retinex algorithm for image enhancement is proposed in this paper. Firstly, in order to alleviate the color distortion problem of image under transformer oil, a hybrid dynamic color channel compensation algorithm is proposed, which dynamically compensates according to the attenuation state of each channel of the captured image. Then, in order to solve the problem of detail loss, a sharpening weight strategy is proposed. Finally, pyramid multi-scale fusion strategy is used to weighted fuse different-scale Retinex reflection components and corresponding weight maps to obtain clear images under transformer oil. Experimental results demonstrate that the algorithm proposed in this paper can effectively solve the complex degradation problem of image under transformer oil.
To solve the degradation problems such as color distortion, low brightness, and detail loss in images under transformer oil, a multi-scale weighted Retinex algorithm for image enhancement is proposed in this paper. Firstly, in order to alleviate the color distortion problem of image under transformer oil, a hybrid dynamic color channel compensation algorithm is proposed, which dynamically compensates according to the attenuation state of each channel of the captured image. Then, in order to solve the problem of detail loss, a sharpening weight strategy is proposed. Finally, pyramid multi-scale fusion strategy is used to weighted fuse different-scale Retinex reflection components and corresponding weight maps to obtain clear images under transformer oil. Experimental results demonstrate that the algorithm proposed in this paper can effectively solve the complex degradation problem of image under transformer oil.
Available online , doi: 10.11999/JEIT240411
Abstract:
It can effectively overcome the limitations of the ground environment, expand the network coverage and provide users with convenient computing services, through constructing the air-ground integrated edge computing network with Unmanned Aerial Vehicle (UAV) as the relay. In this paper, with the objective of maximizing the task completion amount, the joint optimization problem of UAV deployment, user-server association and bandwidth allocation is investigated in the context of the UAV assisted multi-user and multi-server edge computing network. The formulated joint optimization problem contains both continuous and discrete variables, which makes itself hard to solve. To this end, a Block Coordinated Descent (BCD) based iterative algorithm is proposed in this paper, involving the optimization tools such as differential evolution and particle swarm optimization. The original problem is decomposed into three sub-problems with the proposed algorithm, which can be solved independently. The optimal solution of the original problem can be approached through the iteration among these three subproblems. Simulation results show that the proposed algorithm can greatly increase the amount of completed tasks, which outperforms the other benchmark algorithms.
It can effectively overcome the limitations of the ground environment, expand the network coverage and provide users with convenient computing services, through constructing the air-ground integrated edge computing network with Unmanned Aerial Vehicle (UAV) as the relay. In this paper, with the objective of maximizing the task completion amount, the joint optimization problem of UAV deployment, user-server association and bandwidth allocation is investigated in the context of the UAV assisted multi-user and multi-server edge computing network. The formulated joint optimization problem contains both continuous and discrete variables, which makes itself hard to solve. To this end, a Block Coordinated Descent (BCD) based iterative algorithm is proposed in this paper, involving the optimization tools such as differential evolution and particle swarm optimization. The original problem is decomposed into three sub-problems with the proposed algorithm, which can be solved independently. The optimal solution of the original problem can be approached through the iteration among these three subproblems. Simulation results show that the proposed algorithm can greatly increase the amount of completed tasks, which outperforms the other benchmark algorithms.
Available online , doi: 10.11999/JEIT240521
Abstract:
Under the non-complete and highly dynamic active jamming confrontation combat environment, the static model optimized and trained for multiple types of single active jamming samples in the library at this stage is unable to update the model quickly and difficult to cope with the problem of imbalance of test samples in the face of compound jamming outside the library of diverse types, variable parameters and multiple combinations. Considering this problem, a zero-memory incremental learning-based radar compound active jamming recognition method is proposed in this paper. Firstly, a meta-learning training model is utilized to learn a prototype of a single jamming within the library, and an efficient feature extractor is trained so that it has the ability to effectively extract the features of the compound jamming outside the library. Further, based on the hyperdimensional space and cosine similarity calculation, a Zero-Memory Incremental Learning Network (ZMILN) is constructed to map the compound jamming prototype vectors into the hyperdimensional space and store them, so as to realize the dynamic update of the recognition model. In addition, in order to solve the compound jamming recognition problem under sample imbalance, the Transductive Information Maximization (TIM) test module is designed to further strengthen the training of the recognition model to cope with the imbalanced test samples by introducing scatter constraints in the mutual information loss function. Experimental results show that the method proposed in this paper achieves an average recognition accuracy of 93.62% after incremental learning for four types of single jamming and seven types of compound jamming under imbalance test conditions. The method realizes fast and dynamic recognition of compound jamming outside the library under multiple combination conditions by fully extracting the knowledge of multiple types of single jamming in the library.
Under the non-complete and highly dynamic active jamming confrontation combat environment, the static model optimized and trained for multiple types of single active jamming samples in the library at this stage is unable to update the model quickly and difficult to cope with the problem of imbalance of test samples in the face of compound jamming outside the library of diverse types, variable parameters and multiple combinations. Considering this problem, a zero-memory incremental learning-based radar compound active jamming recognition method is proposed in this paper. Firstly, a meta-learning training model is utilized to learn a prototype of a single jamming within the library, and an efficient feature extractor is trained so that it has the ability to effectively extract the features of the compound jamming outside the library. Further, based on the hyperdimensional space and cosine similarity calculation, a Zero-Memory Incremental Learning Network (ZMILN) is constructed to map the compound jamming prototype vectors into the hyperdimensional space and store them, so as to realize the dynamic update of the recognition model. In addition, in order to solve the compound jamming recognition problem under sample imbalance, the Transductive Information Maximization (TIM) test module is designed to further strengthen the training of the recognition model to cope with the imbalanced test samples by introducing scatter constraints in the mutual information loss function. Experimental results show that the method proposed in this paper achieves an average recognition accuracy of 93.62% after incremental learning for four types of single jamming and seven types of compound jamming under imbalance test conditions. The method realizes fast and dynamic recognition of compound jamming outside the library under multiple combination conditions by fully extracting the knowledge of multiple types of single jamming in the library.
Available online , doi: 10.11999/JEIT240561
Abstract:
Considering the limitations of traditional joint beamforming methods in optimizing Reconfigurable Intelligent Surface (RIS)-assisted Unmanned Aerial Vehicle (UAV) communication systems, such as solely focusing on the phase shift matrix optimization of RIS and the lack of universality in the optimization approach, a joint beamforming method based on Cooperative Co-Evolutionary Algorithm (CCEA) for the RIS-assisted UAV multi-user communication system is proposed. This method decomposes the joint beamforming problem into subproblems involving RIS reflection beam design and transmitter beam design, which are solved through information exchange and collaboration during the independent evolutionary process of two subpopulations. Simulation results demonstrate that compared to joint beamforming optimization only considering RIS phase shift matrix design, CCEA changes the energy distribution of the reflection wave in three-dimensional space by optimizing the RIS reflection wave shape, leading to improved reception-side signal-to-interference-plus-noise ratio (SINR) and spectral efficiency. Additionally, CCEA generates more diverse solutions that effectively cover user directions at various UAV and user positions, avoiding local optima and exhibiting greater applicability across different scenarios compared to traditional methods.
Considering the limitations of traditional joint beamforming methods in optimizing Reconfigurable Intelligent Surface (RIS)-assisted Unmanned Aerial Vehicle (UAV) communication systems, such as solely focusing on the phase shift matrix optimization of RIS and the lack of universality in the optimization approach, a joint beamforming method based on Cooperative Co-Evolutionary Algorithm (CCEA) for the RIS-assisted UAV multi-user communication system is proposed. This method decomposes the joint beamforming problem into subproblems involving RIS reflection beam design and transmitter beam design, which are solved through information exchange and collaboration during the independent evolutionary process of two subpopulations. Simulation results demonstrate that compared to joint beamforming optimization only considering RIS phase shift matrix design, CCEA changes the energy distribution of the reflection wave in three-dimensional space by optimizing the RIS reflection wave shape, leading to improved reception-side signal-to-interference-plus-noise ratio (SINR) and spectral efficiency. Additionally, CCEA generates more diverse solutions that effectively cover user directions at various UAV and user positions, avoiding local optima and exhibiting greater applicability across different scenarios compared to traditional methods.
Available online , doi: 10.11999/JEIT240503
Abstract:
A novel approach is proposed to integrate the Detection and Avoidance Alerting Logic for Unmanned Aircraft Systems (DAIDALUS) with the Markov Decision Process (MDP) to address the decision-making challenges associated with collision avoidance in Unmanned Aerial Vehicle (UAV) Detection and Avoidance (DAA) systems. The guidance logic inherent in the DAIDALUS algorithm is utilized to compute drone collision avoidance strategies based on the current state space. These strategies are subsequently employed as the action space for the MDP, with suitable reward functions and state transition probabilities defined to establish an MDP model. The model is then used to investigate the effects of various discount factors on the UAV flight collision avoidance process. The simulation results show that compared to DAIDALUS, the efficiency of this method has increased by 27.2%;when the discount factor is set to 0.99, it can balance long-term and short-term returns; The net intrusion rate is 5.8%, and the closest distance between the threatening aircraft and the local aircraft is 343 meters, which can meet the requirements of collision avoidance during drone flight.
A novel approach is proposed to integrate the Detection and Avoidance Alerting Logic for Unmanned Aircraft Systems (DAIDALUS) with the Markov Decision Process (MDP) to address the decision-making challenges associated with collision avoidance in Unmanned Aerial Vehicle (UAV) Detection and Avoidance (DAA) systems. The guidance logic inherent in the DAIDALUS algorithm is utilized to compute drone collision avoidance strategies based on the current state space. These strategies are subsequently employed as the action space for the MDP, with suitable reward functions and state transition probabilities defined to establish an MDP model. The model is then used to investigate the effects of various discount factors on the UAV flight collision avoidance process. The simulation results show that compared to DAIDALUS, the efficiency of this method has increased by 27.2%;when the discount factor is set to 0.99, it can balance long-term and short-term returns; The net intrusion rate is 5.8%, and the closest distance between the threatening aircraft and the local aircraft is 343 meters, which can meet the requirements of collision avoidance during drone flight.
Available online , doi: 10.11999/JEIT240092
Abstract:
Synthetic Aperture Radar (SAR) is a microwave remote sensing imaging radar. In recent years, with the advancement of digital technology and radio frequency electronic technology, the jamming technology of SAR imaging is developed rapidly. The active jamming such as deception jamming based on Digital Radio Frequency Memory (DRFM) technology brings serious challenges to SAR imaging systems for civil use and military use. For research on SAR anti-jamming imaging against deception jamming, firstly, orthogonal waveform diversity design and waveform optimization is carried out for Orthogonal Frequency Division Multiplexing waveforms with Cyclic Prefixes (CP-OFDM). And the CP-OFDM wide band orthogonal waveform set with excellent autocorrelation peak sidelobe level and cross-correlation peak level is obtained. Then the sparse SAR imaging theory is introduced, which is combined with CP-OFDM. By using the sparse reconstruction method, the high-quality and high-precision imaging with anti-jamming capability is realized. Finally, simulation based on point targets, surface targets and real data is conducted, and it is proved that the method can completely remove the false targets generated by deception jamming, suppress sidelobes and achieve high-precision imaging.
Synthetic Aperture Radar (SAR) is a microwave remote sensing imaging radar. In recent years, with the advancement of digital technology and radio frequency electronic technology, the jamming technology of SAR imaging is developed rapidly. The active jamming such as deception jamming based on Digital Radio Frequency Memory (DRFM) technology brings serious challenges to SAR imaging systems for civil use and military use. For research on SAR anti-jamming imaging against deception jamming, firstly, orthogonal waveform diversity design and waveform optimization is carried out for Orthogonal Frequency Division Multiplexing waveforms with Cyclic Prefixes (CP-OFDM). And the CP-OFDM wide band orthogonal waveform set with excellent autocorrelation peak sidelobe level and cross-correlation peak level is obtained. Then the sparse SAR imaging theory is introduced, which is combined with CP-OFDM. By using the sparse reconstruction method, the high-quality and high-precision imaging with anti-jamming capability is realized. Finally, simulation based on point targets, surface targets and real data is conducted, and it is proved that the method can completely remove the false targets generated by deception jamming, suppress sidelobes and achieve high-precision imaging.
Available online , doi: 10.11999/JEIT240029
Abstract:
Aiming at the problem that current network topology deception methods only make decisions in the spatial dimension without considering how to perform spatio-temporal multi-dimensional topology deception in cloud-native network environments, a multi-stage Flipit game topology deception method with deep reinforcement learning to obfuscate reconnaissance attacks in cloud-native networks. Firstly, the topology deception defense-offense model in cloud-native complex network environments is analyzed. Then, by introducing a discount factor and transition probabilities, a multi-stage game-based network topology deception model based on Flipit. Furthermore is constructed, under the premise of analyzing the defense-offense strategies of game models. A topology deception generation method is developed based on deep reinforcement learning to solve the topology deception strategy of multi-stage game models. Finally, through experiments, it is demonstrated that the proposed method can effectively model, and the topology deception defense-offense scenarios in cloud-native networks is analyzed. It is shown that the algorithm has significant advantages compared to other algorithms.
Aiming at the problem that current network topology deception methods only make decisions in the spatial dimension without considering how to perform spatio-temporal multi-dimensional topology deception in cloud-native network environments, a multi-stage Flipit game topology deception method with deep reinforcement learning to obfuscate reconnaissance attacks in cloud-native networks. Firstly, the topology deception defense-offense model in cloud-native complex network environments is analyzed. Then, by introducing a discount factor and transition probabilities, a multi-stage game-based network topology deception model based on Flipit. Furthermore is constructed, under the premise of analyzing the defense-offense strategies of game models. A topology deception generation method is developed based on deep reinforcement learning to solve the topology deception strategy of multi-stage game models. Finally, through experiments, it is demonstrated that the proposed method can effectively model, and the topology deception defense-offense scenarios in cloud-native networks is analyzed. It is shown that the algorithm has significant advantages compared to other algorithms.
Available online , doi: 10.11999/JEIT240087
Abstract:
In order to improve the accuracy of emotion recognition models and solve the problem of insufficient emotional feature extraction, this paper conducts research on bimodal emotion recognition involving audio and facial imagery. In the audio modality, a feature extraction model of a Multi-branch Convolutional Neural Network (MCNN) incorporating a channel-space attention mechanism is proposed, which extracts emotional features from speech spectrograms across time, space, and local feature dimensions. For the facial image modality, a feature extraction model using a Residual Hybrid Convolutional Neural Network (RHCNN) is introduced, which further establishes a parallel attention mechanism that concentrates on global emotional features to enhance recognition accuracy. The emotional features extracted from audio and facial imagery are then classified through separate classification layers, and a decision fusion technique is utilized to amalgamate the classification results. The experimental results indicate that the proposed bimodal fusion model has achieved recognition accuracies of 97.22%, 94.78%, and 96.96% on the RAVDESS, eNTERFACE’05, and RML datasets, respectively. These accuracies signify improvements over single-modality audio recognition by 11.02%, 4.24%, and 8.83%, and single-modality facial image recognition by 4.60%, 6.74%, and 4.10%, respectively. Moreover, the proposed model outperforms related methodologies applied to these datasets in recent years. This illustrates that the advanced bimodal fusion model can effectively focus on emotional information, thereby enhancing the overall accuracy of emotion recognition.
In order to improve the accuracy of emotion recognition models and solve the problem of insufficient emotional feature extraction, this paper conducts research on bimodal emotion recognition involving audio and facial imagery. In the audio modality, a feature extraction model of a Multi-branch Convolutional Neural Network (MCNN) incorporating a channel-space attention mechanism is proposed, which extracts emotional features from speech spectrograms across time, space, and local feature dimensions. For the facial image modality, a feature extraction model using a Residual Hybrid Convolutional Neural Network (RHCNN) is introduced, which further establishes a parallel attention mechanism that concentrates on global emotional features to enhance recognition accuracy. The emotional features extracted from audio and facial imagery are then classified through separate classification layers, and a decision fusion technique is utilized to amalgamate the classification results. The experimental results indicate that the proposed bimodal fusion model has achieved recognition accuracies of 97.22%, 94.78%, and 96.96% on the RAVDESS, eNTERFACE’05, and RML datasets, respectively. These accuracies signify improvements over single-modality audio recognition by 11.02%, 4.24%, and 8.83%, and single-modality facial image recognition by 4.60%, 6.74%, and 4.10%, respectively. Moreover, the proposed model outperforms related methodologies applied to these datasets in recent years. This illustrates that the advanced bimodal fusion model can effectively focus on emotional information, thereby enhancing the overall accuracy of emotion recognition.
Available online , doi: 10.11999/JEIT240113
Abstract:
Multi-exposure image fusion is used to enhance the dynamic range of images, resulting in higher-quality outputs. However, for blurred long-exposure images captured in fast-motion scenes, such as autonomous driving, the image quality achieved by directly fusing them with low-exposure images using generalized fusion methods is often suboptimal. Currently, end-to-end fusion methods for combining long and short exposure images with motion blur are lacking. To address this issue, a Deblur Fusion Network (DF-Net) is proposed to solve the problem of fusing long and short exposure images with motion blur in an end-to-end manner. A residual module combined with wavelet transform is proposed for constructing the encoder and decoder, where a single encoder is designed for the feature extraction of short exposure images, a multilevel structure based on encoder and decoder is built for feature extraction of long exposure images with blurring, a residual mean excitation fusion module is designed for the fusion of the long and short exposure features, and finally the image is reconstructed by the decoder. Due to the lack of a benchmark dataset, a multi-exposure fusion dataset with motion blur based on the dataset SICE is created for model training and testing. Finally, the designed model and method are experimentally compared with other state-of-the-art step-by-step optimization methods for image deblurring and multi-exposure fusion from both qualitative and quantitative perspectives to verify the superiority of the model and method in this paper for multi-exposure image fusion with motion blur. The validation is also conducted on a multi-exposure dataset acquired from a moving vehicle, and the effectiveness of the proposed method in solving practical problems is demonstrated by the results.
Multi-exposure image fusion is used to enhance the dynamic range of images, resulting in higher-quality outputs. However, for blurred long-exposure images captured in fast-motion scenes, such as autonomous driving, the image quality achieved by directly fusing them with low-exposure images using generalized fusion methods is often suboptimal. Currently, end-to-end fusion methods for combining long and short exposure images with motion blur are lacking. To address this issue, a Deblur Fusion Network (DF-Net) is proposed to solve the problem of fusing long and short exposure images with motion blur in an end-to-end manner. A residual module combined with wavelet transform is proposed for constructing the encoder and decoder, where a single encoder is designed for the feature extraction of short exposure images, a multilevel structure based on encoder and decoder is built for feature extraction of long exposure images with blurring, a residual mean excitation fusion module is designed for the fusion of the long and short exposure features, and finally the image is reconstructed by the decoder. Due to the lack of a benchmark dataset, a multi-exposure fusion dataset with motion blur based on the dataset SICE is created for model training and testing. Finally, the designed model and method are experimentally compared with other state-of-the-art step-by-step optimization methods for image deblurring and multi-exposure fusion from both qualitative and quantitative perspectives to verify the superiority of the model and method in this paper for multi-exposure image fusion with motion blur. The validation is also conducted on a multi-exposure dataset acquired from a moving vehicle, and the effectiveness of the proposed method in solving practical problems is demonstrated by the results.
Available online , doi: 10.11999/JEIT240090
Abstract:
The modular high-voltage power supply, characterized by high efficiency, reliability, and reconfigurability, has found widespread application in high-power high-voltage devices. Among them, the input series output series topology based on the series-parallel resonant converter is suitable for high-frequency high-voltage operating environments, offering advantages such as reduced power losses, winding dielectric losses, and utilizing parasitic parameters of multi-stage transformer. It has broad prospects for application. Current research on this topology primarily focuses on theoretical analysis and efficiency optimization. In practical high-voltage environments, the high-voltage isolation issues between windings of multi-stage transformers have not been effectively addressed. In this paper, a design of shared primary windings for multi-stage transformers is proposed to simplify the high-voltage isolation issues inherent in traditional transformer single-stage winding methods. However, this winding scheme can lead to non-uniform voltage distribution and voltage divergence in multi-stage transformers. Therefore, based on utilizing the parasitic parameters of diodes in transformers and voltage doubling rectifier circuits, an improved topology design is proposed to effectively address the uneven voltage distribution issue. Simulation and experimental validations were conducted, and the results from both simulations and experiments confirm the effectiveness of the proposed high-voltage isolation structure with shared primary windings and the improved topology.
The modular high-voltage power supply, characterized by high efficiency, reliability, and reconfigurability, has found widespread application in high-power high-voltage devices. Among them, the input series output series topology based on the series-parallel resonant converter is suitable for high-frequency high-voltage operating environments, offering advantages such as reduced power losses, winding dielectric losses, and utilizing parasitic parameters of multi-stage transformer. It has broad prospects for application. Current research on this topology primarily focuses on theoretical analysis and efficiency optimization. In practical high-voltage environments, the high-voltage isolation issues between windings of multi-stage transformers have not been effectively addressed. In this paper, a design of shared primary windings for multi-stage transformers is proposed to simplify the high-voltage isolation issues inherent in traditional transformer single-stage winding methods. However, this winding scheme can lead to non-uniform voltage distribution and voltage divergence in multi-stage transformers. Therefore, based on utilizing the parasitic parameters of diodes in transformers and voltage doubling rectifier circuits, an improved topology design is proposed to effectively address the uneven voltage distribution issue. Simulation and experimental validations were conducted, and the results from both simulations and experiments confirm the effectiveness of the proposed high-voltage isolation structure with shared primary windings and the improved topology.
Available online , doi: 10.11999/JEIT240253
Abstract:
Sea surface temperature is one of the key elements of the marine environment, which is of great significance to the marine dynamic process and air-sea interaction. Buoy is a commonly used method of sea surface temperature observation. However, due to the irregular distribution of buoys in space, the sea surface temperature data collected by buoys also show irregularity. In addition, it is inevitable that sometimes the buoy is out of order, so that the sea surface temperature data collected is incomplete. Therefore, it is of great significance to reconstruct the incomplete irregular sea surface temperature data. In this paper, the sea surface temperature data is established as a time-varying graph signal, and the graph signal processing method is used to solve the problem of missing data reconstruction of sea surface temperature. Firstly, the sea surface temperature reconstruction model is constructed by using the low rank data and the joint variation characteristics of time-domain and graph-domain. Secondly, a time-varying graph signal reconstruction method based on Low Rank and Joint Smoothness (LRJS) constraints is proposed to solve the optimization problem by using the framework of alternating direction multiplier method, and the computational complexity and the theoretical limit of the estimation error of the method are analyzed. Finally, the sea surface temperature data of the South China Sea and the Pacific Ocean are used to evaluate the effectiveness of the method. The results show that the LRJS method proposed in this paper can improve the reconstruction accuracy compared with the existing missing data reconstruction methods.
Sea surface temperature is one of the key elements of the marine environment, which is of great significance to the marine dynamic process and air-sea interaction. Buoy is a commonly used method of sea surface temperature observation. However, due to the irregular distribution of buoys in space, the sea surface temperature data collected by buoys also show irregularity. In addition, it is inevitable that sometimes the buoy is out of order, so that the sea surface temperature data collected is incomplete. Therefore, it is of great significance to reconstruct the incomplete irregular sea surface temperature data. In this paper, the sea surface temperature data is established as a time-varying graph signal, and the graph signal processing method is used to solve the problem of missing data reconstruction of sea surface temperature. Firstly, the sea surface temperature reconstruction model is constructed by using the low rank data and the joint variation characteristics of time-domain and graph-domain. Secondly, a time-varying graph signal reconstruction method based on Low Rank and Joint Smoothness (LRJS) constraints is proposed to solve the optimization problem by using the framework of alternating direction multiplier method, and the computational complexity and the theoretical limit of the estimation error of the method are analyzed. Finally, the sea surface temperature data of the South China Sea and the Pacific Ocean are used to evaluate the effectiveness of the method. The results show that the LRJS method proposed in this paper can improve the reconstruction accuracy compared with the existing missing data reconstruction methods.
Available online , doi: 10.11999/JEIT231394
Abstract:
To address the issues of insufficient multi-scale feature expression ability and insufficient utilization of shallow features in memory network algorithms, a Video Object Segmentation (VOS) algorithm based on multi-scale feature enhancement and global local feature aggregation is proposed in this paper. Firstly, the multi-scale feature enhancement module fuses different scale feature information from reference mask branches and reference RGB branches to enhance the expression ability of multi-scale features; At the same time, a global local feature aggregation module is established, which utilizes convolution operations of different sizes of receptive fields to extract features, through the feature aggregation module, the features of the global and local regions are adaptively fused. This fusion method can better capture the global features and detailed information of the target, improving the accuracy of segmentation; Finally, a cross layer fusion module is designed to improve the accuracy of masks segmentation by utilizing the spatial details of shallow features. By fusing shallow features with deep features, it can better capture the details and edge information of the target. The experimental results show that on the public datasets DAVIS2016, DAVIS2017, and YouTube 2018, the comprehensive performance of our algorithm reaches 91.8%, 84.5%, and 83.0%, respectively, and can run in real-time on both single and multi-objective segmentation tasks.
To address the issues of insufficient multi-scale feature expression ability and insufficient utilization of shallow features in memory network algorithms, a Video Object Segmentation (VOS) algorithm based on multi-scale feature enhancement and global local feature aggregation is proposed in this paper. Firstly, the multi-scale feature enhancement module fuses different scale feature information from reference mask branches and reference RGB branches to enhance the expression ability of multi-scale features; At the same time, a global local feature aggregation module is established, which utilizes convolution operations of different sizes of receptive fields to extract features, through the feature aggregation module, the features of the global and local regions are adaptively fused. This fusion method can better capture the global features and detailed information of the target, improving the accuracy of segmentation; Finally, a cross layer fusion module is designed to improve the accuracy of masks segmentation by utilizing the spatial details of shallow features. By fusing shallow features with deep features, it can better capture the details and edge information of the target. The experimental results show that on the public datasets DAVIS2016, DAVIS2017, and YouTube 2018, the comprehensive performance of our algorithm reaches 91.8%, 84.5%, and 83.0%, respectively, and can run in real-time on both single and multi-objective segmentation tasks.
Available online , doi: 10.11999/JEIT240188
Abstract:
The central symmetry based on the virtual array is a necessary fundamental assumption for the structure transformation of Uniform Circular Arrays (UCAs). In this paper, the virtual signal model for circular arrays is used to make an eigen analysis, and an efficient two-dimensional direction finding algorithm is proposed for arbitrary UCAs and Non Uniform Circular Arrays (NUCAs), where the structure transformation of linear arrays is avoided. As such, the Forward/Backward average of the Array Covariance Matrix (FBACM) and the sum-difference transformation method after separating the real and imaginary parts are both utilized to obtain the manifold and real-valued subspace with matching dimensions. Moreover, the linear relationship between the obtained real-valued subspace and the original complex-valued subspace is revealed, where the spatial spectrum is reconstructed without fake targets. The proposed method can be generalized to NUCAs, enhancing the adaptability of real-valued algorithms to circular array structures. Numerical simulations are applied to demonstrate that with significantly reduced complexity, the proposed method in this paper can provide similar performances and better angle resolution as compared to the traditional UCAs based on the mode-step. Meanwhile, the proposed method demonstrates high robustness with amplitude and phase errors in practical scenarios.
The central symmetry based on the virtual array is a necessary fundamental assumption for the structure transformation of Uniform Circular Arrays (UCAs). In this paper, the virtual signal model for circular arrays is used to make an eigen analysis, and an efficient two-dimensional direction finding algorithm is proposed for arbitrary UCAs and Non Uniform Circular Arrays (NUCAs), where the structure transformation of linear arrays is avoided. As such, the Forward/Backward average of the Array Covariance Matrix (FBACM) and the sum-difference transformation method after separating the real and imaginary parts are both utilized to obtain the manifold and real-valued subspace with matching dimensions. Moreover, the linear relationship between the obtained real-valued subspace and the original complex-valued subspace is revealed, where the spatial spectrum is reconstructed without fake targets. The proposed method can be generalized to NUCAs, enhancing the adaptability of real-valued algorithms to circular array structures. Numerical simulations are applied to demonstrate that with significantly reduced complexity, the proposed method in this paper can provide similar performances and better angle resolution as compared to the traditional UCAs based on the mode-step. Meanwhile, the proposed method demonstrates high robustness with amplitude and phase errors in practical scenarios.
Available online , doi: 10.11999/JEIT240049
Abstract:
As a new generation of flow-based microfluidics, Fully Programmable Valve Array (FPVA) biochips have become a popular biochemical experimental platform that provide higher flexibility and programmability. Due to environmental and human factors, however, there are usually some physical faults in the manufacturing process such as channel blockage and leakage, which, undoubtedly, can affect the results of bioassays. In addition, as the primary stage of architecture synthesis, high-level synthesis directly affects the quality of sub-sequent design. The fault tolerance problem in the high-level synthesis stage of FPVA biochips is focused on for the first time in this paper, and dynamic fault-tolerant techniques, including a cell function conversion method, a bidirectional redundancy scheme, and a fault mapping method, are presented, providing technical guarantee for realizing efficient fault-tolerant design. By integrating these techniques into the high-level synthesis stage, a high-quality fault-tolerance-oriented high-level synthesis algorithm for FPVA biochips is further realized in this paper, including a fault-aware real-time binding strategy and a fault-aware priority scheduling strategy, which lays a good foundation for the robustness of chip architecture and the correctness of assay outcomes. Experimental results confirm that a high-quality and fault-tolerant high-level synthesis scheme of FPVA biochips can be obtained by the proposed algorithm, providing a strong guarantee for the subsequent realization of a fault-tolerant physical design scheme.
As a new generation of flow-based microfluidics, Fully Programmable Valve Array (FPVA) biochips have become a popular biochemical experimental platform that provide higher flexibility and programmability. Due to environmental and human factors, however, there are usually some physical faults in the manufacturing process such as channel blockage and leakage, which, undoubtedly, can affect the results of bioassays. In addition, as the primary stage of architecture synthesis, high-level synthesis directly affects the quality of sub-sequent design. The fault tolerance problem in the high-level synthesis stage of FPVA biochips is focused on for the first time in this paper, and dynamic fault-tolerant techniques, including a cell function conversion method, a bidirectional redundancy scheme, and a fault mapping method, are presented, providing technical guarantee for realizing efficient fault-tolerant design. By integrating these techniques into the high-level synthesis stage, a high-quality fault-tolerance-oriented high-level synthesis algorithm for FPVA biochips is further realized in this paper, including a fault-aware real-time binding strategy and a fault-aware priority scheduling strategy, which lays a good foundation for the robustness of chip architecture and the correctness of assay outcomes. Experimental results confirm that a high-quality and fault-tolerant high-level synthesis scheme of FPVA biochips can be obtained by the proposed algorithm, providing a strong guarantee for the subsequent realization of a fault-tolerant physical design scheme.
Available online , doi: 10.11999/JEIT240257
Abstract:
Considering the issues of limited receptive field and insufficient feature interaction in vision-language tracking framework combineing bi-levelrouting Perception and Scattering Visual Trans-formation (BPSVTrack) is proposed in this paper. Initially, a Bi-level Routing Perception Module (BRPM) is designed which combines Efficient Additive Attention(EAA) and Dual Dynamic Adaptive Module(DDAM) in parallel to enable bidirectional interaction for expanding the receptive field. Consequently, enhancing the model’s ability to integrate features between different windows and sizes efficiently, thereby improving the model’s ability to perceive objects in complex scenes. Secondly, the Scattering Vision Transform Module(SVTM) based on Dual-Tree Complex Wavelet Transform(DTCWT) is introduced to decompose the image into low frequency and high frequency information, aiming to capture the target structure and fine-grained details in the image, thus improving the robustness and accuracy of the model in complex environments. The proposed framework achieves accuracies of 86.1%, 64.4%, and 63.2% on TNL2K, LaSOT, and OTB99 tracking datasets respectively. Moreover, it attains an accuracy of 70.21% on the RefCOCOg dataset, the performance in tracking and locating surpasses that of the baseline model.
Considering the issues of limited receptive field and insufficient feature interaction in vision-language tracking framework combineing bi-levelrouting Perception and Scattering Visual Trans-formation (BPSVTrack) is proposed in this paper. Initially, a Bi-level Routing Perception Module (BRPM) is designed which combines Efficient Additive Attention(EAA) and Dual Dynamic Adaptive Module(DDAM) in parallel to enable bidirectional interaction for expanding the receptive field. Consequently, enhancing the model’s ability to integrate features between different windows and sizes efficiently, thereby improving the model’s ability to perceive objects in complex scenes. Secondly, the Scattering Vision Transform Module(SVTM) based on Dual-Tree Complex Wavelet Transform(DTCWT) is introduced to decompose the image into low frequency and high frequency information, aiming to capture the target structure and fine-grained details in the image, thus improving the robustness and accuracy of the model in complex environments. The proposed framework achieves accuracies of 86.1%, 64.4%, and 63.2% on TNL2K, LaSOT, and OTB99 tracking datasets respectively. Moreover, it attains an accuracy of 70.21% on the RefCOCOg dataset, the performance in tracking and locating surpasses that of the baseline model.
Available online , doi: 10.11999/JEIT240300
Abstract:
Physical Unclonable Functions (PUFs), as well as Exclusive OR (XOR) operations, play an important role in the field of information security. In order to break through the functional barrier between PUF and logic operation, an integrated design scheme of PUF and multi-bit parallel XOR operation circuit based on the random process deviation of Differential Cascode Voltage Switch Logic (DCVSL) XOR gate cascade unit is proposed by studying the working mechanism of PUF and DCVSL. By adding a pre-charge tube at the differential output of the DCVSL XOR gate and setting a control gate at the ground end, three operating modes of the PUF feature information extraction, XOR/ Negated Exclusive OR (XNOR) operation and power control can be switched freely. Meanwhile, for the PUF response stability problem, the unstable bit hybrid screening technique with extreme and golden operating point participation labeling was proposed. Based on TSMC process of 65 nm, a fully customized layout design for a 10-bit input bit-wide circuit with an area of 38.76 μm2 was carried out. The experimental results show that the1024 -bit output response can be generated in PUF mode, and a stable key of more than 512 bit can be obtained after hybrid screening, which has good randomness and uniqueness; In the operation mode, 10-bit parallel XOR and XNOR operations can be achieved simultaneously, with power consumption and delay of 2.67 μW and 593.52 ps, respectively. In power control mode, the standby power consumption is only 70.5 nW. The proposed method provides a novel way to break the function-wall of PUF.
Physical Unclonable Functions (PUFs), as well as Exclusive OR (XOR) operations, play an important role in the field of information security. In order to break through the functional barrier between PUF and logic operation, an integrated design scheme of PUF and multi-bit parallel XOR operation circuit based on the random process deviation of Differential Cascode Voltage Switch Logic (DCVSL) XOR gate cascade unit is proposed by studying the working mechanism of PUF and DCVSL. By adding a pre-charge tube at the differential output of the DCVSL XOR gate and setting a control gate at the ground end, three operating modes of the PUF feature information extraction, XOR/ Negated Exclusive OR (XNOR) operation and power control can be switched freely. Meanwhile, for the PUF response stability problem, the unstable bit hybrid screening technique with extreme and golden operating point participation labeling was proposed. Based on TSMC process of 65 nm, a fully customized layout design for a 10-bit input bit-wide circuit with an area of 38.76 μm2 was carried out. The experimental results show that the
Available online , doi: 10.11999/JEIT240316
Abstract:
Currently, traditional explicit scene representation Simultaneous Localization And Mapping (SLAM) systems discretize the scene and are not suitable for continuous scene reconstruction. A RGB-D SLAM system based on hybrid scene representation of Neural Radiation Fields (NeRF) is proposed in this paper. The extended explicit octree Signed Distance Functions (SDF) prior is used to roughly represent the scene, and multi-resolution hash coding is used to represent the scene with different details levels, enabling fast initialization of scene geometry and making scene geometry easier to learn. In addition, the appearance color decomposition method is used to decompose the color into diffuse reflection color and specular reflection color based on the view direction to achieve reconstruction of lighting consistency, making the reconstruction result more realistic. Through experiments on the Replica and TUM RGB-D dataset, the scene reconstruction completion rate of the Replica dataset reaches 93.65%. Compared with the Vox-Fusion positioning accuracy, it leads on average by 87.50% on the Replica dataset and by 81.99% on the TUM RGB-D dataset.
Currently, traditional explicit scene representation Simultaneous Localization And Mapping (SLAM) systems discretize the scene and are not suitable for continuous scene reconstruction. A RGB-D SLAM system based on hybrid scene representation of Neural Radiation Fields (NeRF) is proposed in this paper. The extended explicit octree Signed Distance Functions (SDF) prior is used to roughly represent the scene, and multi-resolution hash coding is used to represent the scene with different details levels, enabling fast initialization of scene geometry and making scene geometry easier to learn. In addition, the appearance color decomposition method is used to decompose the color into diffuse reflection color and specular reflection color based on the view direction to achieve reconstruction of lighting consistency, making the reconstruction result more realistic. Through experiments on the Replica and TUM RGB-D dataset, the scene reconstruction completion rate of the Replica dataset reaches 93.65%. Compared with the Vox-Fusion positioning accuracy, it leads on average by 87.50% on the Replica dataset and by 81.99% on the TUM RGB-D dataset.
Available online , doi: 10.11999/JEIT240342
Abstract:
In modern electronic countermeasures, grouping of multiple joint radar and communication systems can improve the detection efficiency and collaborative detection capability of the single joint radar and communication system. Due to the high peak to average power ratio of the joint radar and communication signal itself, the signal is easy to be intercepted, and the system’s survivability is seriously threatened. In order to improve the Low Probability of Intercept (LPI) performance of the joint radar and communication signal, a time-frequency structure of grouping LPI joint radar and communication signal with communication subcarrier grouping power optimization and radar subcarrier interleaving equal power optimization under the framework of filter bank multicarrier is proposed in this paper. Then, from the perspective of the information theory, the paper unifies the performance assessment metrics of the system; On this basis, minimizing the intercepted information divergence of the interceptor is taken as the optimization objective, and an LPI optimization model of the group network joint radar and communication signal is established. The paper converts this optimization model into a convex optimization problem and solves it using the Karush-Kuhn-Tucker condition. The simulation results show that the radar interference of the network LPI joint radar and communication signal designed in this paper has inter-node radar interference as low as nearly –60 dB when detecting moving targets, and the communication bit error rate satisfies 10–6 order of magnitude, while the signal-to-noise ratio of the intercepted signal is effectively reduced.
In modern electronic countermeasures, grouping of multiple joint radar and communication systems can improve the detection efficiency and collaborative detection capability of the single joint radar and communication system. Due to the high peak to average power ratio of the joint radar and communication signal itself, the signal is easy to be intercepted, and the system’s survivability is seriously threatened. In order to improve the Low Probability of Intercept (LPI) performance of the joint radar and communication signal, a time-frequency structure of grouping LPI joint radar and communication signal with communication subcarrier grouping power optimization and radar subcarrier interleaving equal power optimization under the framework of filter bank multicarrier is proposed in this paper. Then, from the perspective of the information theory, the paper unifies the performance assessment metrics of the system; On this basis, minimizing the intercepted information divergence of the interceptor is taken as the optimization objective, and an LPI optimization model of the group network joint radar and communication signal is established. The paper converts this optimization model into a convex optimization problem and solves it using the Karush-Kuhn-Tucker condition. The simulation results show that the radar interference of the network LPI joint radar and communication signal designed in this paper has inter-node radar interference as low as nearly –60 dB when detecting moving targets, and the communication bit error rate satisfies 10–6 order of magnitude, while the signal-to-noise ratio of the intercepted signal is effectively reduced.
Available online , doi: 10.11999/JEIT240161
Abstract:
Most of the existing lipreading models use a combination of single-layer 3D convolution and 2D convolutional neural networks to extract spatio-temporal joint features from lip video sequences. However, due to the limitations of single-layer 3D convolutions in capturing temporal information and the restricted capability of 2D convolutional neural networks in exploring fine-grained lipreading features, a Multi-Scale Lipreading Network (MS-LipNet) is proposed to improve lip reading tasks. In this paper, 3D spatio-temporal convolution is used to replace traditional two-dimensional convolution in Res2Net network to better extract spatio-temporal joint features, and a spatio-temporal coordinate attention module is proposed to make the network focus on task-related important regional features. The effectiveness of the proposed method was verified through experiments conducted on the LRW and LRW-1000 datasets.
Most of the existing lipreading models use a combination of single-layer 3D convolution and 2D convolutional neural networks to extract spatio-temporal joint features from lip video sequences. However, due to the limitations of single-layer 3D convolutions in capturing temporal information and the restricted capability of 2D convolutional neural networks in exploring fine-grained lipreading features, a Multi-Scale Lipreading Network (MS-LipNet) is proposed to improve lip reading tasks. In this paper, 3D spatio-temporal convolution is used to replace traditional two-dimensional convolution in Res2Net network to better extract spatio-temporal joint features, and a spatio-temporal coordinate attention module is proposed to make the network focus on task-related important regional features. The effectiveness of the proposed method was verified through experiments conducted on the LRW and LRW-1000 datasets.
Available online , doi: 10.11999/JEIT240242
Abstract:
Ground Penetrating Radar (GPR) is identified as a non-destructive method usable for the identification of underground targets. Existing methods often struggle with variable target sizes, complex image recognition, and precise target localization. To address these challenges, an innovative method is introduced that leverages a dual YOLOv8-pose model for the detection and precise localization of hyperbolic keypoint. This method, termed Dual YOLOv8-pose Keypoint Localization (DYKL), offers a sophisticated solution to the challenges inherent in GPR-based target identification and positioning. The proposed model architecture includes two-stages: firstly, the YOLOv8-pose model is employed for the preliminary detection of GPR targets, adeptly identifying regions that are likely to contain these targets. Secondly, building upon the training weights established in the first phase, the model further hones the YOLOv8-pose network. This refinement is geared towards the precise detection of keypoints within the candidate target features, thereby facilitating the automated identification and exact localization of underground targets with enhanced accuracy. Through comparison with four advanced deep-learning models— Cascade Region-based Convolutional Neural Networks (Cascade R-CNN), Faster Region-based Convolutional Neural Networks (Faster R-CNN), Real-Time Models for object Detection (RTMDet), and You Only Look Once v7(YOLOv7-face)—the proposed DYKL model exhibits an average recognition accuracy of 98.8%, surpassing these models. The results demonstrate the DYKL model’s high recognition accuracy and robustness, serving as a benchmark for the precise localization of subterranean targets.
Ground Penetrating Radar (GPR) is identified as a non-destructive method usable for the identification of underground targets. Existing methods often struggle with variable target sizes, complex image recognition, and precise target localization. To address these challenges, an innovative method is introduced that leverages a dual YOLOv8-pose model for the detection and precise localization of hyperbolic keypoint. This method, termed Dual YOLOv8-pose Keypoint Localization (DYKL), offers a sophisticated solution to the challenges inherent in GPR-based target identification and positioning. The proposed model architecture includes two-stages: firstly, the YOLOv8-pose model is employed for the preliminary detection of GPR targets, adeptly identifying regions that are likely to contain these targets. Secondly, building upon the training weights established in the first phase, the model further hones the YOLOv8-pose network. This refinement is geared towards the precise detection of keypoints within the candidate target features, thereby facilitating the automated identification and exact localization of underground targets with enhanced accuracy. Through comparison with four advanced deep-learning models— Cascade Region-based Convolutional Neural Networks (Cascade R-CNN), Faster Region-based Convolutional Neural Networks (Faster R-CNN), Real-Time Models for object Detection (RTMDet), and You Only Look Once v7(YOLOv7-face)—the proposed DYKL model exhibits an average recognition accuracy of 98.8%, surpassing these models. The results demonstrate the DYKL model’s high recognition accuracy and robustness, serving as a benchmark for the precise localization of subterranean targets.
Available online , doi: 10.11999/JEIT240210
Abstract:
With the advancement of robot automatic navigation technology, software-based path planning algorithms can no longer satisfy the needs in scenarios of many real-time applications. Fast and efficient hardware customization of the algorithm is required to achieve low-latency performance acceleration. In this work, High Level Synthesis (HLS) of classic A* algorithm is studied. Hardware-oriented data structure and function optimization, varying design constraints are explored to pick the right architecture, which is then followed by FPGA synthesis. Experimental results show that, compared to the conventional Register Transfer Level (RTL) method, the HLS-based FPGA implementation of the A* algorithm can achieve better productivity, improved hardware performance and resource utilization efficiency, which demonstrates the advantages of high level synthesis in hardware customization in algorithm-centric applications.
With the advancement of robot automatic navigation technology, software-based path planning algorithms can no longer satisfy the needs in scenarios of many real-time applications. Fast and efficient hardware customization of the algorithm is required to achieve low-latency performance acceleration. In this work, High Level Synthesis (HLS) of classic A* algorithm is studied. Hardware-oriented data structure and function optimization, varying design constraints are explored to pick the right architecture, which is then followed by FPGA synthesis. Experimental results show that, compared to the conventional Register Transfer Level (RTL) method, the HLS-based FPGA implementation of the A* algorithm can achieve better productivity, improved hardware performance and resource utilization efficiency, which demonstrates the advantages of high level synthesis in hardware customization in algorithm-centric applications.
Available online , doi: 10.11999/JEIT240201
Abstract:
The Multi-Model Gaussian Mixture-Probability Hypothesis Density (MM-GM-PHD) filter is widely used in uncertain maneuvering target tracking, but it does not maintain parallel estimates under different models, leading to the model-related likelihood lagging behind unknown target maneuvers. To solve this issue, a Joint Multi-Gaussian Mixture PHD (JMGM-PHD) filter is proposed and applied to bearings-only multi-target tracking in this paper. Firstly, a JMGM model is derived, where each single-target state estimate is described by a set of parallel Gaussian functions with model probabilities, and the probability of this state estimate is characterized by a nonegative weight. The weights, model-related probabilities, means and covariances are collectively called JMGM components. According to the Bayesian rule, the updating method of the JMGM components is derived. Then, the multi-target PHD is approximated using the JMGM model. According to the Interactive Multi-Model (IMM) rule, the interacting, prediction and estimation methods of the JMGM components are derived. When addressing Bearings-Only Tracking (BOT), a method based on the derivative rule for composite functions is derived to compute the linearized observation matrix of observers that simultaneously perform translations and rotations. The proposed JMGM-PHD filter preserves the form of regular single-model PHD filter but can adaptively track uncertain maneuvering targets. Simulations show that our algorithm overcomes the likelihood lag issue and outperforms the MM-GM-PHD filter in terms of tracking accuracy and computation cost.
The Multi-Model Gaussian Mixture-Probability Hypothesis Density (MM-GM-PHD) filter is widely used in uncertain maneuvering target tracking, but it does not maintain parallel estimates under different models, leading to the model-related likelihood lagging behind unknown target maneuvers. To solve this issue, a Joint Multi-Gaussian Mixture PHD (JMGM-PHD) filter is proposed and applied to bearings-only multi-target tracking in this paper. Firstly, a JMGM model is derived, where each single-target state estimate is described by a set of parallel Gaussian functions with model probabilities, and the probability of this state estimate is characterized by a nonegative weight. The weights, model-related probabilities, means and covariances are collectively called JMGM components. According to the Bayesian rule, the updating method of the JMGM components is derived. Then, the multi-target PHD is approximated using the JMGM model. According to the Interactive Multi-Model (IMM) rule, the interacting, prediction and estimation methods of the JMGM components are derived. When addressing Bearings-Only Tracking (BOT), a method based on the derivative rule for composite functions is derived to compute the linearized observation matrix of observers that simultaneously perform translations and rotations. The proposed JMGM-PHD filter preserves the form of regular single-model PHD filter but can adaptively track uncertain maneuvering targets. Simulations show that our algorithm overcomes the likelihood lag issue and outperforms the MM-GM-PHD filter in terms of tracking accuracy and computation cost.
Available online , doi: 10.11999/JEIT240224
Abstract:
Blind signal detection is of great significance in large-scale communication networks and has been widely used. How to quickly obtain blind signal detection results is an urgent need for the new generation of real-time communication networks. Considering this demand, a Complex-valued Hopfield Neural Network (CHNN) circuit is designed that can accelerate blind signal detection from an analog circuit perspective, the proposed circuit can accelerate the blind signal detection by rapidly performing massively parallel calculation in one step. At the same time, the circuit can be programmable by adjusting the conductance and input voltage of the memristor. The Pspice simulation results show that the computing accuracy of the proposed circuit can exceed 99%. Compared with Matlab software simulation, the proposed circuit is three orders of magnitude faster in terms of computing time. And the accuracy can be maintained at more than 99% even under the interference of 20% noise.
Blind signal detection is of great significance in large-scale communication networks and has been widely used. How to quickly obtain blind signal detection results is an urgent need for the new generation of real-time communication networks. Considering this demand, a Complex-valued Hopfield Neural Network (CHNN) circuit is designed that can accelerate blind signal detection from an analog circuit perspective, the proposed circuit can accelerate the blind signal detection by rapidly performing massively parallel calculation in one step. At the same time, the circuit can be programmable by adjusting the conductance and input voltage of the memristor. The Pspice simulation results show that the computing accuracy of the proposed circuit can exceed 99%. Compared with Matlab software simulation, the proposed circuit is three orders of magnitude faster in terms of computing time. And the accuracy can be maintained at more than 99% even under the interference of 20% noise.
Available online , doi: 10.11999/JEIT240488
Abstract:
When the locations of the Base Station (BS) and user are fixed and the sum of the distances from BS to the Intelligent Reflecting Surface (IRS) and from the IRS to the user is given, the optimal placement of passive and active IRSs based on the maximizing achievable rate criterion under line-of-sight and Rayleigh channels are analyzed in this paper. First, the phase alignment and the law of large numbers are employed to derive the close-form expressions of the achievable rates of passive and active IRS-assisted wireless networks. Then, the effects of the path loss exponent\begin{document}${\beta _1}$\end{document} \begin{document}${\beta _2}$\end{document} \begin{document}${\beta _1} \gt {\beta _2}$\end{document} \begin{document}${\beta _1}$\end{document} \begin{document}${\beta _2}$\end{document} \begin{document}${\beta _1} < {\beta _2}$\end{document} \begin{document}${\beta _1} = {\beta _2}$\end{document}
When the locations of the Base Station (BS) and user are fixed and the sum of the distances from BS to the Intelligent Reflecting Surface (IRS) and from the IRS to the user is given, the optimal placement of passive and active IRSs based on the maximizing achievable rate criterion under line-of-sight and Rayleigh channels are analyzed in this paper. First, the phase alignment and the law of large numbers are employed to derive the close-form expressions of the achievable rates of passive and active IRS-assisted wireless networks. Then, the effects of the path loss exponent
Available online , doi: 10.11999/JEIT240590
Abstract:
In response to the temporary and emergent issue of poor communication in rural and remote areas, an adaptive multi-Unmanned Aerial Vehicle (UAV)-assisted Integrated Sensing And Communication (ISAC) mechanism is proposed in this paper. In scenarios where ground users and sensing targets are randomly distributed in clusters, the mechanism achieves comprehensive communication coverage by rationally scheduling multiple UAVs, providing a novel solution and scheme for UAV-enabled ISAC systems. The spatial deployment of UAVs and their beamforming directed towards ground equipment are primarily addressed in this paper. Under the constraints of the air-ground association policy, the system can maximize the lower bound of the users’ transmission reachable rate by optimizing the set of communication and sensing beamforming variables for the UAVs, while ensuring the basic requirements of ISAC. To solve the considered non-convex optimization problems, the Mean Shift (MS) algorithm based on Gaussian kernels to manage the mixed-integer linear issues within the association strategy is first employed. Additionally, combining the quadratic transformation and Successive Convex Approximation (SCA), the optimization of beamforming is conducted via the Block Coordinate Descent (BCD) method, thereby securing a suboptimal solution. Numerical results validate the effectiveness of the adaptive mechanism.
In response to the temporary and emergent issue of poor communication in rural and remote areas, an adaptive multi-Unmanned Aerial Vehicle (UAV)-assisted Integrated Sensing And Communication (ISAC) mechanism is proposed in this paper. In scenarios where ground users and sensing targets are randomly distributed in clusters, the mechanism achieves comprehensive communication coverage by rationally scheduling multiple UAVs, providing a novel solution and scheme for UAV-enabled ISAC systems. The spatial deployment of UAVs and their beamforming directed towards ground equipment are primarily addressed in this paper. Under the constraints of the air-ground association policy, the system can maximize the lower bound of the users’ transmission reachable rate by optimizing the set of communication and sensing beamforming variables for the UAVs, while ensuring the basic requirements of ISAC. To solve the considered non-convex optimization problems, the Mean Shift (MS) algorithm based on Gaussian kernels to manage the mixed-integer linear issues within the association strategy is first employed. Additionally, combining the quadratic transformation and Successive Convex Approximation (SCA), the optimization of beamforming is conducted via the Block Coordinate Descent (BCD) method, thereby securing a suboptimal solution. Numerical results validate the effectiveness of the adaptive mechanism.
Available online , doi: 10.11999/JEIT240399
Abstract:
Wireless through-the-earth communication provides a solution for information transmission in heavily shielded space. The received current field signal has low Signal-to-Noise Ratio (SNR), is easily distorted, and is greatly affected by carrier frequency offset, making signal acquisition difficult. In this paper, a long synchronization signal frame structure is designed and a two-stage long correlation signal acquisition algorithm is proposed that combines coarse and fine frequency offset estimation. In the first stage, the training symbols in the received time-domain signal are used for coarse estimation of sampling interval deviation based on the maximum likelihood algorithm, and the coarse estimation value of the sampling point compensation interval is calculated. In the second stage, the coarse estimation value and the received SNR are combined to determine the traversal range of the fine estimation value of the sampling point compensation interval. A long correlation template signal with local compensation is designed to achieve accurate acquisition of the current field signal. The algorithm’s performance is verified in a heavily shielded space located 30.26 m below the ground. Experimental results show that compared to traditional sliding correlation algorithms, the proposed algorithm has a higher acquisition success probability.
Wireless through-the-earth communication provides a solution for information transmission in heavily shielded space. The received current field signal has low Signal-to-Noise Ratio (SNR), is easily distorted, and is greatly affected by carrier frequency offset, making signal acquisition difficult. In this paper, a long synchronization signal frame structure is designed and a two-stage long correlation signal acquisition algorithm is proposed that combines coarse and fine frequency offset estimation. In the first stage, the training symbols in the received time-domain signal are used for coarse estimation of sampling interval deviation based on the maximum likelihood algorithm, and the coarse estimation value of the sampling point compensation interval is calculated. In the second stage, the coarse estimation value and the received SNR are combined to determine the traversal range of the fine estimation value of the sampling point compensation interval. A long correlation template signal with local compensation is designed to achieve accurate acquisition of the current field signal. The algorithm’s performance is verified in a heavily shielded space located 30.26 m below the ground. Experimental results show that compared to traditional sliding correlation algorithms, the proposed algorithm has a higher acquisition success probability.
Available online , doi: 10.11999/JEIT240330
Abstract:
Many traditional imbalanced learning algorithms suitable for low-dimensional data do not perform well on image data. Although the oversampling algorithm based on Generative Adversarial Networks (GAN) can generate high-quality images, it is prone to mode collapse in the case of class imbalance. Oversampling algorithms based on AutoEncoders (AE) are easy to train, but the generated images are of lower quality. In order to improve the quality of samples generated by the oversampling algorithm in imbalanced images and the stability of training, a Balanced oversampling method with AutoEncoders and Generative Adversarial Networks (BAEGAN) is proposed by this paper, which is based on the idea of GAN and AE. First, a conditional embedding layer is introduced in the Autoencoder, and the pre-trained conditional Autoencoder is used to initialize the GAN to stabilize the model training; then the output structure of the discriminator is improved, and a loss function that combines Focal Loss and gradient penalty is proposed to alleviate the impact of class imbalance; and finally the Synthetic Minority Oversampling TEchnique (SMOTE) is used to generate high-quality images from the distribution map of latent vectors. Experimental results on four image data sets show that the proposed algorithm is superior to oversampling methods such as Auxiliary Classifier Generative Adversarial Networks (ACGAN) and BAlancing Generative Adversarial Networks (BAGAN) in terms of image quality and classification performance after oversampling and can effectively solve the class imbalance problem in image data.
Many traditional imbalanced learning algorithms suitable for low-dimensional data do not perform well on image data. Although the oversampling algorithm based on Generative Adversarial Networks (GAN) can generate high-quality images, it is prone to mode collapse in the case of class imbalance. Oversampling algorithms based on AutoEncoders (AE) are easy to train, but the generated images are of lower quality. In order to improve the quality of samples generated by the oversampling algorithm in imbalanced images and the stability of training, a Balanced oversampling method with AutoEncoders and Generative Adversarial Networks (BAEGAN) is proposed by this paper, which is based on the idea of GAN and AE. First, a conditional embedding layer is introduced in the Autoencoder, and the pre-trained conditional Autoencoder is used to initialize the GAN to stabilize the model training; then the output structure of the discriminator is improved, and a loss function that combines Focal Loss and gradient penalty is proposed to alleviate the impact of class imbalance; and finally the Synthetic Minority Oversampling TEchnique (SMOTE) is used to generate high-quality images from the distribution map of latent vectors. Experimental results on four image data sets show that the proposed algorithm is superior to oversampling methods such as Auxiliary Classifier Generative Adversarial Networks (ACGAN) and BAlancing Generative Adversarial Networks (BAGAN) in terms of image quality and classification performance after oversampling and can effectively solve the class imbalance problem in image data.
Available online , doi: 10.11999/JEIT240431
Abstract:
Salient Object Detection (SOD) aims to mimic the human visual system’s attention and cognitive mechanisms to automatically extract prominent objects from a scene. While existing Convolutional Neural Network (CNN)- or Transformer-based models continuously advance performance in this field, there is less research addressing two specific issues: (1) Most methods in this domain commonly use a pixel-wise dense prediction approach to obtain pixel saliency values. However, this approach does not align with the human visual system’s scene analysis mechanism, where the human eye usually performs a holistic analysis of semantic regions rather than focusing on pixel-level information. (2) Enhancing contextual information association is widely emphasized in SOD tasks, but acquiring long-range contextual features through a Transformer backbone does not necessarily offer advantages. SOD should focus more on the center-neighborhood differences within appropriate regions rather than global long-range dependencies. To address these issues, we propose a novel salient object detection model that integrates CNN-based adaptive attention and masked attention into the network to enhance SOD performance. The proposed algorithm designs a mask-aware decoding module that perceives image features by restricting cross-attention to the predicted mask region, helping the network better focus on the entire region of salient objects. Additionally, we design a convolutional attention-based contextual feature enhancement module, which, unlike Transformers that establish long-range relationships layer by layer, captures appropriate contextual associations only in the highest-level features, avoiding the introduction of irrelevant global information. We conducted experimental evaluations on four widely used datasets, and the results demonstrate that our proposed method achieves significant performance improvements across different scenarios, showcasing good generalization ability and stability.
Salient Object Detection (SOD) aims to mimic the human visual system’s attention and cognitive mechanisms to automatically extract prominent objects from a scene. While existing Convolutional Neural Network (CNN)- or Transformer-based models continuously advance performance in this field, there is less research addressing two specific issues: (1) Most methods in this domain commonly use a pixel-wise dense prediction approach to obtain pixel saliency values. However, this approach does not align with the human visual system’s scene analysis mechanism, where the human eye usually performs a holistic analysis of semantic regions rather than focusing on pixel-level information. (2) Enhancing contextual information association is widely emphasized in SOD tasks, but acquiring long-range contextual features through a Transformer backbone does not necessarily offer advantages. SOD should focus more on the center-neighborhood differences within appropriate regions rather than global long-range dependencies. To address these issues, we propose a novel salient object detection model that integrates CNN-based adaptive attention and masked attention into the network to enhance SOD performance. The proposed algorithm designs a mask-aware decoding module that perceives image features by restricting cross-attention to the predicted mask region, helping the network better focus on the entire region of salient objects. Additionally, we design a convolutional attention-based contextual feature enhancement module, which, unlike Transformers that establish long-range relationships layer by layer, captures appropriate contextual associations only in the highest-level features, avoiding the introduction of irrelevant global information. We conducted experimental evaluations on four widely used datasets, and the results demonstrate that our proposed method achieves significant performance improvements across different scenarios, showcasing good generalization ability and stability.
Research on Combined Navigation Algorithm Based on Adaptive Interactive Multi-Kalman Filter Modeling
Available online , doi: 10.11999/JEIT240426
Abstract:
Practical applications struggle to obtain prior knowledge about inertial systems and sensors, affecting information fusion and positioning accuracy in combined navigation systems. To address the degradation of integrated navigation performance due to satellite signal quality changes and system nonlinearity in vehicle navigation, a Fuzzy Adaptive Interactive Multi-Model algorithm based on Multiple Kalman Filters (FAIMM-MKF) is proposed. It integrates a Fuzzy Controller based on satellite signal quality (Fuzzy Controller) and an Adaptive Interactive Multi-Model (AIMM). Improved Kalman filters such as Unscented Kalman Filter (UKF), Iterated Extended Kalman Filter (IEKF), and Square-Root Cubature Kalman Filter (SRCKF) are designed to match vehicle dynamics models. The method’s performance is verified through in-vehicle semi-physical simulation experiments. Results show that the method significantly improves vehicle positioning accuracy in complex environments with varying satellite signal quality compared to traditional interactive multi-model algorithms.
Practical applications struggle to obtain prior knowledge about inertial systems and sensors, affecting information fusion and positioning accuracy in combined navigation systems. To address the degradation of integrated navigation performance due to satellite signal quality changes and system nonlinearity in vehicle navigation, a Fuzzy Adaptive Interactive Multi-Model algorithm based on Multiple Kalman Filters (FAIMM-MKF) is proposed. It integrates a Fuzzy Controller based on satellite signal quality (Fuzzy Controller) and an Adaptive Interactive Multi-Model (AIMM). Improved Kalman filters such as Unscented Kalman Filter (UKF), Iterated Extended Kalman Filter (IEKF), and Square-Root Cubature Kalman Filter (SRCKF) are designed to match vehicle dynamics models. The method’s performance is verified through in-vehicle semi-physical simulation experiments. Results show that the method significantly improves vehicle positioning accuracy in complex environments with varying satellite signal quality compared to traditional interactive multi-model algorithms.
Electromagnetic Sensitivity Analysis of Curved Boundaries under the Method of Accompanying Variables
Available online , doi: 10.11999/JEIT240432
Abstract:
Sensitivity analysis an evaluation method for the influence with variations of the design parameters on electromagnetic performance, which is utilized to calculate sensitivity information. This information guides the analysis of structural models to ensure compliance with design specifications. In the optimization design of electromagnetic structures by commercial software, traditional algorithms are often employed, involving adjustments to the geometry. However, this approach is known to be extensive in terms of computational time and resource consumption. In order to enhance the efficiency of model design, a stable and efficient processing scheme is proposed in the paper, known as the Adjoint Variable Method (AVM). This method achieves estimation of 1st～2nd order sensitivity on parameter transformations with only two algorithmic simulation conditions required. The application of AVM has predominantly been confined to the sensitivity analysis of rectangular boundary parameters, with this paper making the first extension of AVM to the sensitivity analysis of arc boundary parameters. Efficient analysis of the electromagnetic sensitivity of curved structures is accomplished based on the conditions designed for three distinct scenarios: fixed intrinsic parameters, frequency-dependent objective functions, and transient impulse functions. Compared to the Finite-Difference Method (FDM), a significant enhancement in computational efficiency is achieved by the proposed method. The effective implementation of the method substantially expands the application scope of AVM to curved boundaries, which can be utilized in optimization problems such as the electromagnetic structures of plasma models and the edge structures of complex antenna models. When computational resources are limited, the reliability and stability of electromagnetic structure optimization can be ensured by the application of the proposed method.
Sensitivity analysis an evaluation method for the influence with variations of the design parameters on electromagnetic performance, which is utilized to calculate sensitivity information. This information guides the analysis of structural models to ensure compliance with design specifications. In the optimization design of electromagnetic structures by commercial software, traditional algorithms are often employed, involving adjustments to the geometry. However, this approach is known to be extensive in terms of computational time and resource consumption. In order to enhance the efficiency of model design, a stable and efficient processing scheme is proposed in the paper, known as the Adjoint Variable Method (AVM). This method achieves estimation of 1st～2nd order sensitivity on parameter transformations with only two algorithmic simulation conditions required. The application of AVM has predominantly been confined to the sensitivity analysis of rectangular boundary parameters, with this paper making the first extension of AVM to the sensitivity analysis of arc boundary parameters. Efficient analysis of the electromagnetic sensitivity of curved structures is accomplished based on the conditions designed for three distinct scenarios: fixed intrinsic parameters, frequency-dependent objective functions, and transient impulse functions. Compared to the Finite-Difference Method (FDM), a significant enhancement in computational efficiency is achieved by the proposed method. The effective implementation of the method substantially expands the application scope of AVM to curved boundaries, which can be utilized in optimization problems such as the electromagnetic structures of plasma models and the edge structures of complex antenna models. When computational resources are limited, the reliability and stability of electromagnetic structure optimization can be ensured by the application of the proposed method.
Available online , doi: 10.11999/JEIT240524
Abstract:
The Draco algorithm is an example of a stream cipher based on the Consisting of the Initial Value and Key-prefix (CIVK) scheme, claiming to have provable security against Time Memory Data TradeOff (TMDTO) attacks. However, its selection function has structural flaws, which have been exploited attackers to provide analyses that break its security boundaries. Addressing the security vulnerabilities and other issues in the Draco algorithm, an improved algorithm called Draco-F is proposed in this paper, which is based on state bit indexing and dynamic initialization. Firstly, the Draco-F algorithm extends the period of the selection function and reduces its hardware cost by employing the method of state bit indexing. Secondly, while ensuring the uniform usage of Nonlinear Feedback Shift Register (NFSR) state bits, the Draco-F algorithm further reduces the hardware cost of the algorithm by simplifying the output function. Finally, Draco-F introduces dynamic initialization techniques to prevent key backtracking. Security analysis and software-hardware testing results on the Draco-F algorithm show that, compared to the Draco algorithm, Draco-F avoids the security vulnerabilities in Draco, providing a 128 bit security level with an actual 128 bit internal state. Furthermore, the Draco-F algorithm has higher key stream throughput and a smaller circuit area.
The Draco algorithm is an example of a stream cipher based on the Consisting of the Initial Value and Key-prefix (CIVK) scheme, claiming to have provable security against Time Memory Data TradeOff (TMDTO) attacks. However, its selection function has structural flaws, which have been exploited attackers to provide analyses that break its security boundaries. Addressing the security vulnerabilities and other issues in the Draco algorithm, an improved algorithm called Draco-F is proposed in this paper, which is based on state bit indexing and dynamic initialization. Firstly, the Draco-F algorithm extends the period of the selection function and reduces its hardware cost by employing the method of state bit indexing. Secondly, while ensuring the uniform usage of Nonlinear Feedback Shift Register (NFSR) state bits, the Draco-F algorithm further reduces the hardware cost of the algorithm by simplifying the output function. Finally, Draco-F introduces dynamic initialization techniques to prevent key backtracking. Security analysis and software-hardware testing results on the Draco-F algorithm show that, compared to the Draco algorithm, Draco-F avoids the security vulnerabilities in Draco, providing a 128 bit security level with an actual 128 bit internal state. Furthermore, the Draco-F algorithm has higher key stream throughput and a smaller circuit area.
Available online , doi: 10.11999/JEIT240236
Abstract:
As a new technology to reconfigure wireless communication environment by intelligently controlling signal reflection via algorithms, Intelligent Reflecting Surface (IRS) has attracted lots of attention in recent years. Compared with the conventional relay system, the relay system aided by IRS can effectively save the cost and energy consumption, and significantly enhance the system performance. However, the phase quantization error generated by IRS with discrete phase shifter may degrade the performance of the receiver. To analyze the performance loss arising from IRS phase quantization error, in accordance with the weak law of large numbers and Rayleigh distribution, the closed-form expressions for the Signal-To-Noise Ratio (SNR) performance loss and achievable rate of the double IRS-aided amplify-and-forward relay network, which are associated with the number of phase shifter quantization bits, are derived in the Rayleigh channels. In addition, their approximate performance loss closed-form expressions are also derived based on the Taylor series expansion. Simulation results show that the performance losses of SNR and achievable rate decrease gradually with the number of quantization bits, and increase gradually with the number of IRS phase shift elements. When the number of IRS phase shift elements is 4, the performance losses of SNR and reachable rate are less than 0.06 dB and 0.03 bits/(s·Hz), respectively.
As a new technology to reconfigure wireless communication environment by intelligently controlling signal reflection via algorithms, Intelligent Reflecting Surface (IRS) has attracted lots of attention in recent years. Compared with the conventional relay system, the relay system aided by IRS can effectively save the cost and energy consumption, and significantly enhance the system performance. However, the phase quantization error generated by IRS with discrete phase shifter may degrade the performance of the receiver. To analyze the performance loss arising from IRS phase quantization error, in accordance with the weak law of large numbers and Rayleigh distribution, the closed-form expressions for the Signal-To-Noise Ratio (SNR) performance loss and achievable rate of the double IRS-aided amplify-and-forward relay network, which are associated with the number of phase shifter quantization bits, are derived in the Rayleigh channels. In addition, their approximate performance loss closed-form expressions are also derived based on the Taylor series expansion. Simulation results show that the performance losses of SNR and achievable rate decrease gradually with the number of quantization bits, and increase gradually with the number of IRS phase shift elements. When the number of IRS phase shift elements is 4, the performance losses of SNR and reachable rate are less than 0.06 dB and 0.03 bits/(s·Hz), respectively.
Available online , doi: 10.11999/JEIT240446
Abstract:
In the future, 6G will realize a new era of intelligent interconnection of everything and the combina-tion of virtual and reality, which can not be separated from the development of communication and perception technology. However, due to the scarcity of frequency resources, the sharing of frequency resources between them is an urgent problem to be solved. Integrated Sensing and Communication (ISAC) technology provides a new way to solve this problem. It allows communication and perception to share a set of equipment and frequency resources, and can simultaneously complete target detection and information communication, which is considered to be one of the key technologies of 6G. At the same time, China is powerful in the ocean with abundant marine resources and the demands for marine communication and marine targets perception have increased greatly. In this paper, ISAC technology in marine environment was studied, and a weighted waveform optimization design method is proposed. The simulation results show that when the power ratio of communication to perception is within the range of [0.2, 0.5], the integrated waveform not only has good communication performance, but also has good perception performance. Finally, the future work is prospected.
In the future, 6G will realize a new era of intelligent interconnection of everything and the combina-tion of virtual and reality, which can not be separated from the development of communication and perception technology. However, due to the scarcity of frequency resources, the sharing of frequency resources between them is an urgent problem to be solved. Integrated Sensing and Communication (ISAC) technology provides a new way to solve this problem. It allows communication and perception to share a set of equipment and frequency resources, and can simultaneously complete target detection and information communication, which is considered to be one of the key technologies of 6G. At the same time, China is powerful in the ocean with abundant marine resources and the demands for marine communication and marine targets perception have increased greatly. In this paper, ISAC technology in marine environment was studied, and a weighted waveform optimization design method is proposed. The simulation results show that when the power ratio of communication to perception is within the range of [0.2, 0.5], the integrated waveform not only has good communication performance, but also has good perception performance. Finally, the future work is prospected.
Available online , doi: 10.11999/JEIT240377
Abstract:
The Flying Ad-hoc NETworks (FANETs) are widely used in emergency rescue scenarios due to their high mobility and self-organization advantages. In emergency scenarios, a large number of user paging requests lead to a challenging coordination between the surge in local traffic and the limited spectrum resources, significant channel interference issues in FANETs are resulted from. There is an urgent need to extend the high spectrum utilization advantage of Partially Overlapping Channels (POCs) to emergency scenarios. However, the adjacent channel characteristics of POCs leads to complex interference that is difficult to characterize. Therefore, partial overlapping channel allocation methods in FANETs are studied in this paper. By utilizing geometric prediction to reconstruct time-varying interference graphs and characterizing the POCs interference model with the interference-free minimum channel spacing matrix, a Dynamic Channel Allocation algorithm for POCs based on Upper Confidence Bounds (UCB-DCA) is proposed. This algorithm aims to solve for an approximately optimal channel allocation scheme through distributed decision-making. Simulation results demonstrate that the algorithm achieves a trade-off between network interference and channel switching times, and has good convergence performance.
The Flying Ad-hoc NETworks (FANETs) are widely used in emergency rescue scenarios due to their high mobility and self-organization advantages. In emergency scenarios, a large number of user paging requests lead to a challenging coordination between the surge in local traffic and the limited spectrum resources, significant channel interference issues in FANETs are resulted from. There is an urgent need to extend the high spectrum utilization advantage of Partially Overlapping Channels (POCs) to emergency scenarios. However, the adjacent channel characteristics of POCs leads to complex interference that is difficult to characterize. Therefore, partial overlapping channel allocation methods in FANETs are studied in this paper. By utilizing geometric prediction to reconstruct time-varying interference graphs and characterizing the POCs interference model with the interference-free minimum channel spacing matrix, a Dynamic Channel Allocation algorithm for POCs based on Upper Confidence Bounds (UCB-DCA) is proposed. This algorithm aims to solve for an approximately optimal channel allocation scheme through distributed decision-making. Simulation results demonstrate that the algorithm achieves a trade-off between network interference and channel switching times, and has good convergence performance.
Available online , doi: 10.11999/JEIT240302
Abstract:
As the scale of Unmanned Aerial Vehicle (UAV) systems and the demand for higher communication rates continue to grow, UAV Optical Mobile Communications (UAV-OMC) has emerged as a promising technical direction. However, it is difficult for traditional UAV-OMC to support multiple UAVs’ communications. In this paper, based on the Optical Intelligent Reflecting Surface (OIRS) technology, we propose a distributed OMC system for UAV clusters. By setting the OIRS on a specific UAV, we utilize OIRS to spread the optical signal from a single UAV node to multiple UAV nodes. While retaining the high energy efficiency and high speed of the UAV-OMC system, this system can support the communication of distributed UAV clusters. This paper conducts mathematical modeling of the proposed system. When modeling the system, we took into account a series of realistic factors, such as OIRS beam control, relative motion between UAVs, UAV jitter, which fit the actual system. Closed-form expressions for the system's Bit Error Rate (BER) and asymptotic outage probability are also derived. Based on theoretical analysis and simulation results, the effect of each parameter and system design have been discussed.
As the scale of Unmanned Aerial Vehicle (UAV) systems and the demand for higher communication rates continue to grow, UAV Optical Mobile Communications (UAV-OMC) has emerged as a promising technical direction. However, it is difficult for traditional UAV-OMC to support multiple UAVs’ communications. In this paper, based on the Optical Intelligent Reflecting Surface (OIRS) technology, we propose a distributed OMC system for UAV clusters. By setting the OIRS on a specific UAV, we utilize OIRS to spread the optical signal from a single UAV node to multiple UAV nodes. While retaining the high energy efficiency and high speed of the UAV-OMC system, this system can support the communication of distributed UAV clusters. This paper conducts mathematical modeling of the proposed system. When modeling the system, we took into account a series of realistic factors, such as OIRS beam control, relative motion between UAVs, UAV jitter, which fit the actual system. Closed-form expressions for the system's Bit Error Rate (BER) and asymptotic outage probability are also derived. Based on theoretical analysis and simulation results, the effect of each parameter and system design have been discussed.
Available online , doi: 10.11999/JEIT240518
Abstract:
Reconfigurable Intelligent Surfaces (RIS) is considered as one of the potential key technologies for 6G mobile communications, which offers advantages such as low cost, low energy consumption, and easy deployment. By integrating RIS technology into marine wireless channels, it has the capability to convert the unpredictable wireless transmission environment into a manageable one. However, current channel models are struggling to accurately depict the unique signal transmission mechanisms of RIS-enabled base station to ship channels in marine communication scenarios, resulting in challenges in achieving a balance between accuracy and complexity for channel characterization and theoretical establishment. Therefore, this paper develops a segmented channel modeling method for near-field RIS-enabled marine communications, and then proposed a multi-domain joint parameterized statistical channel model for RIS-enabled marine communications. This approach focus on addressing the technical bottleneck of existing RIS channel modeling methods that face difficulties in achieving a balance between accuracy and efficiency, ultimately facilitating the rapid development of the 6G mobile communication industry in China.
Reconfigurable Intelligent Surfaces (RIS) is considered as one of the potential key technologies for 6G mobile communications, which offers advantages such as low cost, low energy consumption, and easy deployment. By integrating RIS technology into marine wireless channels, it has the capability to convert the unpredictable wireless transmission environment into a manageable one. However, current channel models are struggling to accurately depict the unique signal transmission mechanisms of RIS-enabled base station to ship channels in marine communication scenarios, resulting in challenges in achieving a balance between accuracy and complexity for channel characterization and theoretical establishment. Therefore, this paper develops a segmented channel modeling method for near-field RIS-enabled marine communications, and then proposed a multi-domain joint parameterized statistical channel model for RIS-enabled marine communications. This approach focus on addressing the technical bottleneck of existing RIS channel modeling methods that face difficulties in achieving a balance between accuracy and efficiency, ultimately facilitating the rapid development of the 6G mobile communication industry in China.
Available online , doi: 10.11999/JEIT240359
Abstract:
Influenced by factors such as observation conditions and acquisition scenarios, underwater optical image data usually presents the characteristics of high-dimensional small samples and is easily accompanied with noise interference, resulting in many dimension reduction methods lacking robust performance in their recognition process. To solve this problem, a novel 2DPCA method for underwater image recognition, called Dual Flexible Metric Adaptive Weighted 2DPCA (DFMAW-2DPCA), is proposed. DFMAW-2DPCA not only utilizes a flexible robust distance metric mechanism in establishing a dual-layer relationship between reconstruction error and variance, but also adaptively learn matching weights based on the actual state of each sample, which effectively enhances the robustness of the model in underwater noise interference environments and improves recognition accuracy. In this paper, a fast nongreedy algorithm for obtaining the optimal solution is designed and has good convergence. The extensive experimental results on three underwater image databases show that DFMAW-2DPCA has more outstanding overall performance than other 2DPCA-based methods.
Influenced by factors such as observation conditions and acquisition scenarios, underwater optical image data usually presents the characteristics of high-dimensional small samples and is easily accompanied with noise interference, resulting in many dimension reduction methods lacking robust performance in their recognition process. To solve this problem, a novel 2DPCA method for underwater image recognition, called Dual Flexible Metric Adaptive Weighted 2DPCA (DFMAW-2DPCA), is proposed. DFMAW-2DPCA not only utilizes a flexible robust distance metric mechanism in establishing a dual-layer relationship between reconstruction error and variance, but also adaptively learn matching weights based on the actual state of each sample, which effectively enhances the robustness of the model in underwater noise interference environments and improves recognition accuracy. In this paper, a fast nongreedy algorithm for obtaining the optimal solution is designed and has good convergence. The extensive experimental results on three underwater image databases show that DFMAW-2DPCA has more outstanding overall performance than other 2DPCA-based methods.
Available online , doi: 10.11999/JEIT240003
Abstract:
As a new information communication technology based on software and hardware resource sharing and information sharing, Integration of Sensing and Communication (ISAC) can integrate wireless sensing into Wi-Fi platforms, providing an efficient method for low-cost indoor localization. Focusing on the problem of real-time and accuracy of indoor positioning parameter estimation, a joint parameter estimation algorithm based on three Dimensional (3D) Matrix Pencil (MP) is proposed. First, the Channel State Information (CSI) data is analyzed and a 3D matrix containing Angle of Arrival (AoA), Time of Flight (ToF), and Doppler Frequency Shift (DFS) is constructed. Secondly, the 3D matrix is smoothed and the 3D MP algorithm is used for parameter estimation, the direct path is found by clustering. Finally, the triangulation method is used for positioning to verify the effectiveness of the proposed algorithm. Experimental results show that compared with the MUltiple SIgnal Classification (MUSIC) parameter estimation algorithm, there is no need for complicated peak search steps, and the computational complexity is reduced by 90%. Compared with the two-dimensional MP algorithm, adding DFS can effectively improve the resolution and accuracy of parameter estimation. The actual test verifies that the proposed algorithm can achieve an average positioning accuracy of 0.56 m at a confidence level of 67% indoors. Therefore, the proposed algorithm effectively improves the real-time and accuracy of the existing indoor positioning parameter estimation.
As a new information communication technology based on software and hardware resource sharing and information sharing, Integration of Sensing and Communication (ISAC) can integrate wireless sensing into Wi-Fi platforms, providing an efficient method for low-cost indoor localization. Focusing on the problem of real-time and accuracy of indoor positioning parameter estimation, a joint parameter estimation algorithm based on three Dimensional (3D) Matrix Pencil (MP) is proposed. First, the Channel State Information (CSI) data is analyzed and a 3D matrix containing Angle of Arrival (AoA), Time of Flight (ToF), and Doppler Frequency Shift (DFS) is constructed. Secondly, the 3D matrix is smoothed and the 3D MP algorithm is used for parameter estimation, the direct path is found by clustering. Finally, the triangulation method is used for positioning to verify the effectiveness of the proposed algorithm. Experimental results show that compared with the MUltiple SIgnal Classification (MUSIC) parameter estimation algorithm, there is no need for complicated peak search steps, and the computational complexity is reduced by 90%. Compared with the two-dimensional MP algorithm, adding DFS can effectively improve the resolution and accuracy of parameter estimation. The actual test verifies that the proposed algorithm can achieve an average positioning accuracy of 0.56 m at a confidence level of 67% indoors. Therefore, the proposed algorithm effectively improves the real-time and accuracy of the existing indoor positioning parameter estimation.
Available online , doi: 10.11999/JEIT240275
Abstract:
To solve the bottleneck problem of constrained spectrum resource for Unmanned Aerial Vehicles (UAVs) in unlicensed bands, a co-optimization scheme high spectral efficiency in underlay mechanism is proposed for UAV-assisted monitoring communication networks in urban environment. Considering the high maneuverability of UAVs, the air-to-ground channel is modeled as a probabilistic Line-of-Sight (LoS) channel, and the co-channel interference and maximum speed constraints are adopted to formulate a hybrid resource optimization model for power allocation and trajectory planning, enabling UAVs to construct the fast transmission scheme for monitoring data with occupied spectrum within the given time. The original problem is an NP-hard and non-convex integer problem, which is first decomposed into a two-layer programming problem, and then solved by applying the slack variable and Successive Convex Approximation (SCA) technologies to transform the trajectory design problem into a convex programming problem. Compared with the Particle Swarm Optimization (PSO) algorithm, the proposed joint optimization scheme is verified to improve the spectral efficiency by up to about 19% in simulations. For high-dimensional trajectory planning problems, the SCA-based algorithm is proved to have lower complexity and faster convergence.
To solve the bottleneck problem of constrained spectrum resource for Unmanned Aerial Vehicles (UAVs) in unlicensed bands, a co-optimization scheme high spectral efficiency in underlay mechanism is proposed for UAV-assisted monitoring communication networks in urban environment. Considering the high maneuverability of UAVs, the air-to-ground channel is modeled as a probabilistic Line-of-Sight (LoS) channel, and the co-channel interference and maximum speed constraints are adopted to formulate a hybrid resource optimization model for power allocation and trajectory planning, enabling UAVs to construct the fast transmission scheme for monitoring data with occupied spectrum within the given time. The original problem is an NP-hard and non-convex integer problem, which is first decomposed into a two-layer programming problem, and then solved by applying the slack variable and Successive Convex Approximation (SCA) technologies to transform the trajectory design problem into a convex programming problem. Compared with the Particle Swarm Optimization (PSO) algorithm, the proposed joint optimization scheme is verified to improve the spectral efficiency by up to about 19% in simulations. For high-dimensional trajectory planning problems, the SCA-based algorithm is proved to have lower complexity and faster convergence.
Available online , doi: 10.11999/JEIT240018
Abstract:
Simultaneous Transmitting and Reflecting Reconfigurable Intelligent Surfaces (STAR-RIS) is able to create an all-space intelligent radio environment to effectively improve the performance of wireless communication systems, thus it has vast research potential. Therefore, in this paper, a large-scale STAR-RIS-assisted near-field Integrated Sensing and Communication (ISAC) approach is proposed. Cramér-Rao Bound (CRB) of the three-dimensional estimation of the sensing target is optimized. First, the near-field system model is built and then beam steering vectors between base station, STAR-RIS, communication users, sensing target and sensor are derived respectively. Second, the sensing performance is optimized by designing the transmit beamforming matrix, the covariance matrix of transmit signal and the STAR-RIS coefficients. Third, a non-convex optimization problem is solved via semi-definite relaxation approach. The simulation results show the effectiveness of our proposed ISAC approach, and the positioning performance advantage brought by the extra distance freedom of near field.
Simultaneous Transmitting and Reflecting Reconfigurable Intelligent Surfaces (STAR-RIS) is able to create an all-space intelligent radio environment to effectively improve the performance of wireless communication systems, thus it has vast research potential. Therefore, in this paper, a large-scale STAR-RIS-assisted near-field Integrated Sensing and Communication (ISAC) approach is proposed. Cramér-Rao Bound (CRB) of the three-dimensional estimation of the sensing target is optimized. First, the near-field system model is built and then beam steering vectors between base station, STAR-RIS, communication users, sensing target and sensor are derived respectively. Second, the sensing performance is optimized by designing the transmit beamforming matrix, the covariance matrix of transmit signal and the STAR-RIS coefficients. Third, a non-convex optimization problem is solved via semi-definite relaxation approach. The simulation results show the effectiveness of our proposed ISAC approach, and the positioning performance advantage brought by the extra distance freedom of near field.
Available online , doi: 10.11999/JEIT240398
Abstract:
Establishing accurate short-term forecasting models for electrical load is crucial for the stable operation and intelligent advancement of power systems. Traditional methods have not adequately addressed the issues of data volatility and model uncertainty. In this paper, a multi-dimensional adaptive short-term forecasting method for electrical load based on Bayesian Autoformer network is proposed. Specifically, an adaptive feature selection method is designed to capture multi-dimensional features. By capturing multi-scale features and time-frequency localized information, the model is enhanced to handle high volatility and nonlinear features in load data. Subsequently, an adaptive probabilistic forecasting model based on Bayesian Autoformer network is proposed. It captures relationships of significant subsequence features and associated uncertainties in load time series data, and dynamically updates the probability prediction model and parameter distributions through Bayesian optimization. The proposed model is subjected to a series of experimental analyses (comparative analysis, adaptive analysis, robustness analysis) on real load datasets of three different magnitudes (GW, MW, and KW). The model exhibits superior performance in adaptability and accuracy, with average improvements in Root Mean Square Error (RMSE), Pinball Loss, and Continuous Ranked Probability Score (CRPS) of 1.9%, 24.2%, and 4.5%, respectively.
Establishing accurate short-term forecasting models for electrical load is crucial for the stable operation and intelligent advancement of power systems. Traditional methods have not adequately addressed the issues of data volatility and model uncertainty. In this paper, a multi-dimensional adaptive short-term forecasting method for electrical load based on Bayesian Autoformer network is proposed. Specifically, an adaptive feature selection method is designed to capture multi-dimensional features. By capturing multi-scale features and time-frequency localized information, the model is enhanced to handle high volatility and nonlinear features in load data. Subsequently, an adaptive probabilistic forecasting model based on Bayesian Autoformer network is proposed. It captures relationships of significant subsequence features and associated uncertainties in load time series data, and dynamically updates the probability prediction model and parameter distributions through Bayesian optimization. The proposed model is subjected to a series of experimental analyses (comparative analysis, adaptive analysis, robustness analysis) on real load datasets of three different magnitudes (GW, MW, and KW). The model exhibits superior performance in adaptability and accuracy, with average improvements in Root Mean Square Error (RMSE), Pinball Loss, and Continuous Ranked Probability Score (CRPS) of 1.9%, 24.2%, and 4.5%, respectively.
Available online , doi: 10.11999/JEIT240059
Abstract:
Recently, metasurface antenna technology has raised meticulous attention from scholars in the communications, radar, and antenna communities, owing to its great capability in flexible control of electromagnetic waves. In particular, the active tunable device used in the metasurface antenna element is one of the most significant components that affect the performance of the entire system. In this paper, a 95 to 105 GHz digitally controlled attenuator with 5-bit resolution is designed in a 0.13 μm SiGe BiCMOS process. The attenuator employs two different topological structures, reflective and simplified T-type. The 4 dB and 8 dB reflective attenuation units utilize cross-coupled broadband couplers instead of traditional 3 dB couplers or directional couplers, achieving high attenuation precision and low insertion loss. On the other hand, the 0.5 dB, 1 dB, and 2 dB attenuation units adopt a simplified T-type structure. Furthermore, the utilization of RC positive and negative slope correction networks applied separately to different attenuation units enables phase compensation, significantly improving the additional phase shift of the attenuator. Within the desired frequency range of 95～105 GHz, the attenuator achieves an attenuation range of 0～15.5 dB with a step of 0.5 dB in a compact size of 0.12 mm2. It exhibits a simulated insertion loss below 2.5 dB, a simulated amplitude Root Mean Square (RMS) error less than 0.25 dB, and a simulated phase RMS error is better than 2.2°. The proposed W-band attenuator can serve as a key component empowering the hardware implementation of an integrated Transmit/Receive (T/R) metasurface antenna system with simultaneous radiation and scattering control.
Recently, metasurface antenna technology has raised meticulous attention from scholars in the communications, radar, and antenna communities, owing to its great capability in flexible control of electromagnetic waves. In particular, the active tunable device used in the metasurface antenna element is one of the most significant components that affect the performance of the entire system. In this paper, a 95 to 105 GHz digitally controlled attenuator with 5-bit resolution is designed in a 0.13 μm SiGe BiCMOS process. The attenuator employs two different topological structures, reflective and simplified T-type. The 4 dB and 8 dB reflective attenuation units utilize cross-coupled broadband couplers instead of traditional 3 dB couplers or directional couplers, achieving high attenuation precision and low insertion loss. On the other hand, the 0.5 dB, 1 dB, and 2 dB attenuation units adopt a simplified T-type structure. Furthermore, the utilization of RC positive and negative slope correction networks applied separately to different attenuation units enables phase compensation, significantly improving the additional phase shift of the attenuator. Within the desired frequency range of 95～105 GHz, the attenuator achieves an attenuation range of 0～15.5 dB with a step of 0.5 dB in a compact size of 0.12 mm2. It exhibits a simulated insertion loss below 2.5 dB, a simulated amplitude Root Mean Square (RMS) error less than 0.25 dB, and a simulated phase RMS error is better than 2.2°. The proposed W-band attenuator can serve as a key component empowering the hardware implementation of an integrated Transmit/Receive (T/R) metasurface antenna system with simultaneous radiation and scattering control.
Available online , doi: 10.11999/JEIT240297
Abstract:
With the continuous emergence of new applications, the issue of spectrum congestion is becoming increasingly severe. Dual-Functional Radar-Communication (DFRC) is consideredas a key enabling technology for many emerging applications and is one of the essential approaches to addressing the issue of spectrum congestion. However, how to solve the mutual interference between communication and radar and achieve high communication rate is a fundamental challenge that urgently needs to be solved in DFRC system. Based on multi carrier complementary coded division multiple access technology, a DFRC signal suitable for multi-user scenarios is designed in this paper. Theoretical analysis and simulation results show that compared with typical spread spectrum schemes, the proposed scheme can achieve non-interference transmission between communication and radar, with low bit error rate and high user communication rate.
With the continuous emergence of new applications, the issue of spectrum congestion is becoming increasingly severe. Dual-Functional Radar-Communication (DFRC) is consideredas a key enabling technology for many emerging applications and is one of the essential approaches to addressing the issue of spectrum congestion. However, how to solve the mutual interference between communication and radar and achieve high communication rate is a fundamental challenge that urgently needs to be solved in DFRC system. Based on multi carrier complementary coded division multiple access technology, a DFRC signal suitable for multi-user scenarios is designed in this paper. Theoretical analysis and simulation results show that compared with typical spread spectrum schemes, the proposed scheme can achieve non-interference transmission between communication and radar, with low bit error rate and high user communication rate.
Available online , doi: 10.11999/JEIT240083
Abstract:
In order to solve the problems of information security, and spectrum limitation in Integrated Sensing And Communications (ISAC) systems, a secure resource allocation scheme in Intelligent Reflecting Surface (IRS)-assisted ISAC systems is investigated in this paper. To start with, in this IRS-ISAC system, where the user is being maliciously attacked by eavesdroppers, the security of the system is ensured by incorporating a jammer and deploying an IRS that utilizes its intelligent regulation of the wireless environment. Then, a secrecy rate maximization problem that subjects to the maximum transmit power constraints of the base station and the jammer, the IRS reflecting phase shift constraints, and the radar’s signal-to-noise ratio constraints is formulated by jointly designing the transmit beamforming of base station, jammer precoding vectors, and IRS phase shifts. Next, utilizing techniques such as alternating optimization and Semi-Definite Relaxation (SDR) algorithm, the original non-convex optimization problem is reformulated into a convex optimization problem, capable of determining a definitive solution. Finally, simulation results verify the security and effectiveness of the proposed algorithm and the superiority of the IRS-ISAC system.
In order to solve the problems of information security, and spectrum limitation in Integrated Sensing And Communications (ISAC) systems, a secure resource allocation scheme in Intelligent Reflecting Surface (IRS)-assisted ISAC systems is investigated in this paper. To start with, in this IRS-ISAC system, where the user is being maliciously attacked by eavesdroppers, the security of the system is ensured by incorporating a jammer and deploying an IRS that utilizes its intelligent regulation of the wireless environment. Then, a secrecy rate maximization problem that subjects to the maximum transmit power constraints of the base station and the jammer, the IRS reflecting phase shift constraints, and the radar’s signal-to-noise ratio constraints is formulated by jointly designing the transmit beamforming of base station, jammer precoding vectors, and IRS phase shifts. Next, utilizing techniques such as alternating optimization and Semi-Definite Relaxation (SDR) algorithm, the original non-convex optimization problem is reformulated into a convex optimization problem, capable of determining a definitive solution. Finally, simulation results verify the security and effectiveness of the proposed algorithm and the superiority of the IRS-ISAC system.
Available online , doi: 10.11999/JEIT240012
Abstract:
The Integrated Sensing And Communication (ISAC) requires that communication and sensing share the same radio frequency band and hardware resource. The characteristics of multi bands, large bandwidth, communication and sensing’s different requirements for hardware put forward higher requirements for ISAC hardware design. The hardware designs, verification technologies and systemic hardware verification platforms of beyond 5G, 6G and WiFi ISACs are summarized. The relevant hardware designs and verification researches at home and abroad in recent years are summarized also. The hardware design challenges such as the hardware requirement contradictions between communication and sensing systems, the In Band Full Duplex (IBFD) Self-Interference Cancellation (SIC), the Power Amplifier (PA) efficiency, and the more accurate modeling required by circuit performance are paid attention to. First of all, the design of ISAC transceiver architectures in existing researches are summarized and compared. Then, the existing ISAC IBFD self-interference suppression schemes, the low Peak to Average Power Ratio (PAPR) waveform or high-performance PA designs, the high precision device modeling methods and the systemic hardware verification platforms are introduced and analyzed. At last, the full text is summarized, the future open issues for ISAC hardware design are analyzed.
The Integrated Sensing And Communication (ISAC) requires that communication and sensing share the same radio frequency band and hardware resource. The characteristics of multi bands, large bandwidth, communication and sensing’s different requirements for hardware put forward higher requirements for ISAC hardware design. The hardware designs, verification technologies and systemic hardware verification platforms of beyond 5G, 6G and WiFi ISACs are summarized. The relevant hardware designs and verification researches at home and abroad in recent years are summarized also. The hardware design challenges such as the hardware requirement contradictions between communication and sensing systems, the In Band Full Duplex (IBFD) Self-Interference Cancellation (SIC), the Power Amplifier (PA) efficiency, and the more accurate modeling required by circuit performance are paid attention to. First of all, the design of ISAC transceiver architectures in existing researches are summarized and compared. Then, the existing ISAC IBFD self-interference suppression schemes, the low Peak to Average Power Ratio (PAPR) waveform or high-performance PA designs, the high precision device modeling methods and the systemic hardware verification platforms are introduced and analyzed. At last, the full text is summarized, the future open issues for ISAC hardware design are analyzed.
Available online , doi: 10.11999/JEIT240051
Abstract:
General low-complexity joint beamforming designs are proposed for Reconfigurable Intelligent Surface (RIS) assisted multi-user systems. First, the non-convex optimization problem of joint beamforming design is analyzed to maximize sum data rate for RIS-aided multi-user systems. Second, the RIS reflection matrix is designed by using the approximation orthogonality of the beam steering vectors, and the transmit beamforming at the base station is derived from the zero forcing method, and the power allocation is optimized for multiple users. Finally, it is found that the proposed scheme has wide applicability and an order of magnitude reduction on computational complexity than that of existing work. Numerical results show that the proposed beamforming design can achieve high sum data rate, which can be further improved by employing the optimal power allocation. Besides, both the simulation results and theoretical analysis indicate that the sum data rate changes with the RIS location, which provides reference standards for the selection of RIS location.
General low-complexity joint beamforming designs are proposed for Reconfigurable Intelligent Surface (RIS) assisted multi-user systems. First, the non-convex optimization problem of joint beamforming design is analyzed to maximize sum data rate for RIS-aided multi-user systems. Second, the RIS reflection matrix is designed by using the approximation orthogonality of the beam steering vectors, and the transmit beamforming at the base station is derived from the zero forcing method, and the power allocation is optimized for multiple users. Finally, it is found that the proposed scheme has wide applicability and an order of magnitude reduction on computational complexity than that of existing work. Numerical results show that the proposed beamforming design can achieve high sum data rate, which can be further improved by employing the optimal power allocation. Besides, both the simulation results and theoretical analysis indicate that the sum data rate changes with the RIS location, which provides reference standards for the selection of RIS location.
Available online , doi: 10.11999/JEIT221203
Abstract:
To overcome the limitation of the Federated Learning (FL) when the data and model of each client are all heterogenous and improve the accuracy, a personalized Federated learning algorithm with Collation game and Knowledge distillation (pFedCK) is proposed. Firstly, each client uploads its soft-predict on public dataset and download the most correlative of the k soft-predict. Then, this method apply the shapley value from collation game to measure the multi-wise influences among clients and quantify their marginal contribution to others on personalized learning performance. Lastly, each client identify it’s optimal coalition and then distill the knowledge to local model and train on private dataset. The results show that compared with the state-of-the-art algorithm, this approach can achieve superior personalized accuracy and can improve by about 10%.
To overcome the limitation of the Federated Learning (FL) when the data and model of each client are all heterogenous and improve the accuracy, a personalized Federated learning algorithm with Collation game and Knowledge distillation (pFedCK) is proposed. Firstly, each client uploads its soft-predict on public dataset and download the most correlative of the k soft-predict. Then, this method apply the shapley value from collation game to measure the multi-wise influences among clients and quantify their marginal contribution to others on personalized learning performance. Lastly, each client identify it’s optimal coalition and then distill the knowledge to local model and train on private dataset. The results show that compared with the state-of-the-art algorithm, this approach can achieve superior personalized accuracy and can improve by about 10%.
Available online , doi: 10.11999/JEIT210265
Abstract:
The application of Frequency Diverse Array and Multiple Input Multiple Output (FDA-MIMO) radar to achieve range-angle estimation of target has attracted more and more attention. The FDA can simultaneously obtain the degree of freedom of transmitting beam pattern in angle and range. However, its performance is degraded due to the periodicity and time-varying of the beam pattern. Therefore, an improved Estimating Signal Parameter via Rotational Invariance Techniques (ESPRIT) algorithm to estimate the target’s parameters based on a new waveform synthesis model of the Time Modulation and Range Compensation FDA-MIMO (TMRC-FDA-MIMO) radar is proposed. Finally, the proposed method is compared with identical frequency increment FDA-MIMO radar system, logarithmically increased frequency offset FDA-MIMO radar system and MUltiple SIgnal Classification (MUSIC) algorithm through the Cramer Rao lower bound and root mean square error of range and angle estimation, and the excellent performance of the proposed method is verified.
The application of Frequency Diverse Array and Multiple Input Multiple Output (FDA-MIMO) radar to achieve range-angle estimation of target has attracted more and more attention. The FDA can simultaneously obtain the degree of freedom of transmitting beam pattern in angle and range. However, its performance is degraded due to the periodicity and time-varying of the beam pattern. Therefore, an improved Estimating Signal Parameter via Rotational Invariance Techniques (ESPRIT) algorithm to estimate the target’s parameters based on a new waveform synthesis model of the Time Modulation and Range Compensation FDA-MIMO (TMRC-FDA-MIMO) radar is proposed. Finally, the proposed method is compared with identical frequency increment FDA-MIMO radar system, logarithmically increased frequency offset FDA-MIMO radar system and MUltiple SIgnal Classification (MUSIC) algorithm through the Cramer Rao lower bound and root mean square error of range and angle estimation, and the excellent performance of the proposed method is verified.
Available online , doi: 10.11999/JEIT240286
Abstract:
A novel and effective information geometry-based method for detecting radar targets is proposed. Based on the theory of matrix information geometry. Due to the poor discriminative power between the target and the clutter on matrix manifold under complex heterogeneous clutter background with low Signal-to-Clutter Ratio (SCR), in this study, the problem of unsatisfactory performance for the conventional information geometry detector is considered, Therefore, to address this issue, a manifold transformation-based information geometry detector is proposed. Concretely, a manifold-to-manifold mapping scheme is designed, and a joint optimization method based on the geometric distance between the Cell Under Test (CUT) and the clutter centroid is presented to enhance the discriminative power between the target and the clutter on the mapped manifold. Finally, the superior performance of the proposed method is evaluated using simulated and real clutter data. The results of simulated data show that the detection probability of the proposed method is over 60% when the SCR exceeds 1 dB. Meanwhile, the real data results confirm that the proposed method can achieve SCR improvement about 3～6 dB compared with the conventional information geometry detector.
A novel and effective information geometry-based method for detecting radar targets is proposed. Based on the theory of matrix information geometry. Due to the poor discriminative power between the target and the clutter on matrix manifold under complex heterogeneous clutter background with low Signal-to-Clutter Ratio (SCR), in this study, the problem of unsatisfactory performance for the conventional information geometry detector is considered, Therefore, to address this issue, a manifold transformation-based information geometry detector is proposed. Concretely, a manifold-to-manifold mapping scheme is designed, and a joint optimization method based on the geometric distance between the Cell Under Test (CUT) and the clutter centroid is presented to enhance the discriminative power between the target and the clutter on the mapped manifold. Finally, the superior performance of the proposed method is evaluated using simulated and real clutter data. The results of simulated data show that the detection probability of the proposed method is over 60% when the SCR exceeds 1 dB. Meanwhile, the real data results confirm that the proposed method can achieve SCR improvement about 3～6 dB compared with the conventional information geometry detector.
Available online , doi: 10.11999/JEIT240389
Abstract:
This paper studies the design and optimization issues of secure downlink transmission scheme for two users based on rate-splitting multiple access. Considering a scenario where partial messages sent to two users need to be kept confidential between users, the sum rate of non-confidential messages is maximized while ensuring the transmission rate of confidential messages. The common stream only carries the non-confidential messages, while the private streams carry both the non-confidential and confidential messages in a time-sharing manner. Transmit precoding vectors for each message flow, rate splitting, transmission time allocation for the private streams of non-confidential and confidential messages are jointly optimized. By decomposing the original problem into a two-level optimization problem and using methods such as binary search, relaxation variables, and successive convex approximation, the original problem is transformed and solved. The simulation results show that the proposed scheme can achieve higher non-confidential sum rate compared to the rate-splitting multiple access, where the private streams carry only the confidential messages, and space division multiple access with time-sharing between non-confidential messages and confidential messages.
This paper studies the design and optimization issues of secure downlink transmission scheme for two users based on rate-splitting multiple access. Considering a scenario where partial messages sent to two users need to be kept confidential between users, the sum rate of non-confidential messages is maximized while ensuring the transmission rate of confidential messages. The common stream only carries the non-confidential messages, while the private streams carry both the non-confidential and confidential messages in a time-sharing manner. Transmit precoding vectors for each message flow, rate splitting, transmission time allocation for the private streams of non-confidential and confidential messages are jointly optimized. By decomposing the original problem into a two-level optimization problem and using methods such as binary search, relaxation variables, and successive convex approximation, the original problem is transformed and solved. The simulation results show that the proposed scheme can achieve higher non-confidential sum rate compared to the rate-splitting multiple access, where the private streams carry only the confidential messages, and space division multiple access with time-sharing between non-confidential messages and confidential messages.
Available online , doi: 10.11999/JEIT240295
Abstract:
Video compressed sensing reconstruction is a highly underdetermined problem, where the low-quality of initial reconstructed and the single-motion estimation approach limit the effective modeling of inter-frames correlations. To improve video reconstruction performance, the Static and Dynamic-domain Prior Enhancement Two-stage reconstruction Network (SDPETs-Net) is proposed. Firstly, a strategy of reconstructing second-order static-domain residuals using reference frame measurements is proposed, and a corresponding Static-domain Prior Enhancement Network (SPE-Net) is designed to provide a reliable basis for dynamic-domain prior modeling. Secondly, the Pyramid Deformable-convolution Combined with Attention-search Network (PDCA-Net) is designed, which combines the advantages of deformable-convolution and attention mechanisms, and a pyramid cascade structure is constructed to effectively model and utilize dynamic-domain prior knowledge. Lastly, the Multi-Feature Fusion Residual Reconstruction Network (MFRR-Net) extracts and fuses key information of each feature from multiple scales to reconstruct residues, alleviating the instability of model training caused by the coupling of the two stages and suppressing feature degradation. Simulation results show that the Peak Signal-to-Noise Ratio (PSNR) is improved by an average of 3.34 dB compared to the representative two-stage network JDR-TAFA-Net under the UCF101 test set, and by an average of 0.79 dB compared to the recent multi-stage network DMIGAN.
Video compressed sensing reconstruction is a highly underdetermined problem, where the low-quality of initial reconstructed and the single-motion estimation approach limit the effective modeling of inter-frames correlations. To improve video reconstruction performance, the Static and Dynamic-domain Prior Enhancement Two-stage reconstruction Network (SDPETs-Net) is proposed. Firstly, a strategy of reconstructing second-order static-domain residuals using reference frame measurements is proposed, and a corresponding Static-domain Prior Enhancement Network (SPE-Net) is designed to provide a reliable basis for dynamic-domain prior modeling. Secondly, the Pyramid Deformable-convolution Combined with Attention-search Network (PDCA-Net) is designed, which combines the advantages of deformable-convolution and attention mechanisms, and a pyramid cascade structure is constructed to effectively model and utilize dynamic-domain prior knowledge. Lastly, the Multi-Feature Fusion Residual Reconstruction Network (MFRR-Net) extracts and fuses key information of each feature from multiple scales to reconstruct residues, alleviating the instability of model training caused by the coupling of the two stages and suppressing feature degradation. Simulation results show that the Peak Signal-to-Noise Ratio (PSNR) is improved by an average of 3.34 dB compared to the representative two-stage network JDR-TAFA-Net under the UCF101 test set, and by an average of 0.79 dB compared to the recent multi-stage network DMIGAN.
Available online , doi: 10.11999/JEIT240114
Abstract:
Given that the performance of the Deep Image Prior (DIP) denoising model highly depends on the search space determined by the target image, a new improved denoising model called RS-DIP (Relatively clean image Space-based DIP) is proposed by comprehensively improving its network input, backbone network, and loss function.Initially, two state-of-the-art supervised denoising models are employed to preprocess two noisy images from the same scene, which are referred to as relatively clean images. Furthermore, these two relatively clean images are combined as the network input using a random sampling fusion method. At the same time, the noisy image is replaced with two relatively clean images, which serve as dual-target images. This strategy narrows the search space, allowing exploration of potential images that closely resemble the ground-truth image. Finally, the multi-scale U-shaped backbone network in the original DIP model is simplified to a single scale. Additionally, the inclusion of Transformer modules enhances the network's ability to effectively model distant pixels. This augmentation bolsters the model’s performance while preserving the network's search capability. Experimental results demonstrate that the proposed denoising model exhibits significant advantages over the original DIP model in terms of both denoising effectiveness and execution efficiency. Moreover, regarding denoising effectiveness, it surpasses mainstream supervised denoising models.
Given that the performance of the Deep Image Prior (DIP) denoising model highly depends on the search space determined by the target image, a new improved denoising model called RS-DIP (Relatively clean image Space-based DIP) is proposed by comprehensively improving its network input, backbone network, and loss function.Initially, two state-of-the-art supervised denoising models are employed to preprocess two noisy images from the same scene, which are referred to as relatively clean images. Furthermore, these two relatively clean images are combined as the network input using a random sampling fusion method. At the same time, the noisy image is replaced with two relatively clean images, which serve as dual-target images. This strategy narrows the search space, allowing exploration of potential images that closely resemble the ground-truth image. Finally, the multi-scale U-shaped backbone network in the original DIP model is simplified to a single scale. Additionally, the inclusion of Transformer modules enhances the network's ability to effectively model distant pixels. This augmentation bolsters the model’s performance while preserving the network's search capability. Experimental results demonstrate that the proposed denoising model exhibits significant advantages over the original DIP model in terms of both denoising effectiveness and execution efficiency. Moreover, regarding denoising effectiveness, it surpasses mainstream supervised denoising models.
Available online , doi: 10.11999/JEIT240183
Abstract:
The XEX-based Tweaked-codebook mode with ciphertext Stealing (XTS) is widely used in storage encryption. With the emergence and application of big data computing and novel side-channel analysis methods, the security of the XTS encryption mode has become a matter of concern. Recent studies have attempted side-channel analysis on the XTS mode, aiming to narrow down the key search space by identifying partial keys and tweak values, but a comprehensive analysis of the XTS mode system has not been achieved. In this paper, a side-channel analysis technique targeting the SM4-XTS circuit is proposed. By combining traditional Correlation Power Analysis (CPA) with a multi-stage fusion CPA technique, the technique addresses the binary number shifting issue caused by the iterative modulation multiplication of the tweak values, enabling precise extraction of both the tweak values and keys. To validate the effectiveness of this analytical technique, an SM4-XTS encryption module is implemented on an FPGA to simulate real-world encryption memory scenarios. Experimental results demonstrate that the technique can successfully extract partial tweak values and keys from the target encryption circuit using only 10 000 power traces.
The XEX-based Tweaked-codebook mode with ciphertext Stealing (XTS) is widely used in storage encryption. With the emergence and application of big data computing and novel side-channel analysis methods, the security of the XTS encryption mode has become a matter of concern. Recent studies have attempted side-channel analysis on the XTS mode, aiming to narrow down the key search space by identifying partial keys and tweak values, but a comprehensive analysis of the XTS mode system has not been achieved. In this paper, a side-channel analysis technique targeting the SM4-XTS circuit is proposed. By combining traditional Correlation Power Analysis (CPA) with a multi-stage fusion CPA technique, the technique addresses the binary number shifting issue caused by the iterative modulation multiplication of the tweak values, enabling precise extraction of both the tweak values and keys. To validate the effectiveness of this analytical technique, an SM4-XTS encryption module is implemented on an FPGA to simulate real-world encryption memory scenarios. Experimental results demonstrate that the technique can successfully extract partial tweak values and keys from the target encryption circuit using only 10 000 power traces.
Available online , doi: 10.11999/JEIT240219
Abstract:
With the rapid development of integrated circuit technology, chips are easily implanted with malicious hardware Trojan logic in the process of design, production and packaging. Current security detection methods for IP soft core are logically complex, prone to errors and omissions, and unable to detect encrypted IP soft core. The paper uses the feature differences of non-controllable IP soft core and hardware Trojan Register Transfer Level (RTL) code grayscale map, proposing a hardware Trojan detection method for IP soft cores based on graph feature analysis, through the map conversion and map enhancement to get the standard map, using the texture feature extraction matching algorithm to achieve hardware Trojan detection. The experimental subjects are functional logic units with seven types of typical Trojans implanted during the design phase, and the detection results show that the detection correct rate of seven types of typical hardware Trojans has reached more than 90%, and the average growth rate of the number of successful feature point matches after the image enhancement has reached 13.24%, effectively improving the effectiveness of hardware Trojan detection.
With the rapid development of integrated circuit technology, chips are easily implanted with malicious hardware Trojan logic in the process of design, production and packaging. Current security detection methods for IP soft core are logically complex, prone to errors and omissions, and unable to detect encrypted IP soft core. The paper uses the feature differences of non-controllable IP soft core and hardware Trojan Register Transfer Level (RTL) code grayscale map, proposing a hardware Trojan detection method for IP soft cores based on graph feature analysis, through the map conversion and map enhancement to get the standard map, using the texture feature extraction matching algorithm to achieve hardware Trojan detection. The experimental subjects are functional logic units with seven types of typical Trojans implanted during the design phase, and the detection results show that the detection correct rate of seven types of typical hardware Trojans has reached more than 90%, and the average growth rate of the number of successful feature point matches after the image enhancement has reached 13.24%, effectively improving the effectiveness of hardware Trojan detection.
Available online , doi: 10.11999/JEIT240162
Abstract:
As chip manufacturing has advanced to the sub-micro-nanometer scale, shrinking technology nodes are accelerating link failures in on-chip network, and the growth of failure links reduces the number of available routing paths and might lead to severe traffic congestion or even system crashes. The difficulty in maintaining the correctness of the on-chip system dramatically rises as the technology node shrinks. Previous schemes typically utilize deflection algorithms to bypass packets. However, they incur additional transmission latency due to hop count and raise the probability of deadlock. In order to achieve normal packet transmission encountering faulty links, a self-adaptive Reconfigurable Fault-tolerant Link NoC design (RFL_NoC) is proposed, which redirects packets encountering a faulty link to another reversible link. The scheme contains a specific implementation of the reversible link and the associated distributed control protocol. The dynamic fault-tolerant link design maximizes the idle, available link and ensures that the network communication is not interrupted in case of link failures. Compared with the advanced fault-tolerant deflection routing algorithm QFCAR-W, RFL_NoC can reduce the average delay by 10%, the area overhead by 14.2%, and the power overhead by 9.3%.
As chip manufacturing has advanced to the sub-micro-nanometer scale, shrinking technology nodes are accelerating link failures in on-chip network, and the growth of failure links reduces the number of available routing paths and might lead to severe traffic congestion or even system crashes. The difficulty in maintaining the correctness of the on-chip system dramatically rises as the technology node shrinks. Previous schemes typically utilize deflection algorithms to bypass packets. However, they incur additional transmission latency due to hop count and raise the probability of deadlock. In order to achieve normal packet transmission encountering faulty links, a self-adaptive Reconfigurable Fault-tolerant Link NoC design (RFL_NoC) is proposed, which redirects packets encountering a faulty link to another reversible link. The scheme contains a specific implementation of the reversible link and the associated distributed control protocol. The dynamic fault-tolerant link design maximizes the idle, available link and ensures that the network communication is not interrupted in case of link failures. Compared with the advanced fault-tolerant deflection routing algorithm QFCAR-W, RFL_NoC can reduce the average delay by 10%, the area overhead by 14.2%, and the power overhead by 9.3%.
Available online , doi: 10.11999/JEIT240284
Abstract:
Computing-in-Memory (CiM) architectures based on Resistive Random Access Memory (ReRAM) have been recognized as a promising solution to accelerate deep learning applications. As intelligent applications continue to evolve, deep learning models become larger and larger, which imposes higher demands on the computational and storage resources on processing platforms. However, due to the non-idealism of ReRAM, large-scale ReRAM-based computing systems face severe challenges of low yield and reliability. Chiplet-based architectures assemble multiple small chiplets into a single package, providing higher fabrication yield and lower manufacturing costs, which has become a primary trend in chip design. However, compared to on-chip wiring, the expensive inter-chiplet communication becomes a performance bottleneck for chiplet-based systems which limits the chip’s scalability. As the countermeasure, a novel scaling framework for chiplet-based CiM accelerators, SMCA (SMT-based CiM chiplet Acceleration) is proposed in this paper. This framework comprises an adaptive deep learning task partition strategy and an automated SMT-based workload deployment to generate the most energy-efficient DNN workload scheduling strategy with the minimum data transmission on chiplet-based deep learning accelerators, achieving effective improvement in system performance and efficiency. Experimental results show that compared to existing strategies, the SMCA-generated automatically schedule strategy can reduce the energy costs of inter-chiplet communication by 35%.
Computing-in-Memory (CiM) architectures based on Resistive Random Access Memory (ReRAM) have been recognized as a promising solution to accelerate deep learning applications. As intelligent applications continue to evolve, deep learning models become larger and larger, which imposes higher demands on the computational and storage resources on processing platforms. However, due to the non-idealism of ReRAM, large-scale ReRAM-based computing systems face severe challenges of low yield and reliability. Chiplet-based architectures assemble multiple small chiplets into a single package, providing higher fabrication yield and lower manufacturing costs, which has become a primary trend in chip design. However, compared to on-chip wiring, the expensive inter-chiplet communication becomes a performance bottleneck for chiplet-based systems which limits the chip’s scalability. As the countermeasure, a novel scaling framework for chiplet-based CiM accelerators, SMCA (SMT-based CiM chiplet Acceleration) is proposed in this paper. This framework comprises an adaptive deep learning task partition strategy and an automated SMT-based workload deployment to generate the most energy-efficient DNN workload scheduling strategy with the minimum data transmission on chiplet-based deep learning accelerators, achieving effective improvement in system performance and efficiency. Experimental results show that compared to existing strategies, the SMCA-generated automatically schedule strategy can reduce the energy costs of inter-chiplet communication by 35%.
Available online , doi: 10.11999/JEIT201066
Abstract:
To solve the problem of Group Repetition Interval (GRI) selection in the construction of the enhanced LORAN (eLORAN) system supplementary transmission station, a screening algorithm based on cross interference rate is proposed mainly from the mathematical point of view. Firstly, this method considers the requirement of second information, and on this basis, conducts a first screening by comparing the mutual Cross Rate Interference (CRI) with the adjacent Loran-C stations in the neighboring countries. Secondly, a second screening is conducted through permutation and pairwise comparison. Finally, the optimal GRI combination scheme is given by considering the requirements of data rate and system specification. Then, in view of the high-precision timing requirements for the new eLORAN system, an optimized selection is made in multiple optimal combinations. The analysis results show that the average interference rate of the optimal combination scheme obtained by this algorithm is comparable to that between the current navigation chains and can take into account the timing requirements, which can provide referential suggestions and theoretical basis for the construction of high-precision ground-based timing system.
To solve the problem of Group Repetition Interval (GRI) selection in the construction of the enhanced LORAN (eLORAN) system supplementary transmission station, a screening algorithm based on cross interference rate is proposed mainly from the mathematical point of view. Firstly, this method considers the requirement of second information, and on this basis, conducts a first screening by comparing the mutual Cross Rate Interference (CRI) with the adjacent Loran-C stations in the neighboring countries. Secondly, a second screening is conducted through permutation and pairwise comparison. Finally, the optimal GRI combination scheme is given by considering the requirements of data rate and system specification. Then, in view of the high-precision timing requirements for the new eLORAN system, an optimized selection is made in multiple optimal combinations. The analysis results show that the average interference rate of the optimal combination scheme obtained by this algorithm is comparable to that between the current navigation chains and can take into account the timing requirements, which can provide referential suggestions and theoretical basis for the construction of high-precision ground-based timing system.
