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Available online ,
doi: 10.11999/JEIT240082
Abstract:
Aggressive scaling of CMOS technologies can cause the reliability issues of circuits. Two highly reliable Radiation Hardened By Design (RHBD) 10T and 12T Static Random-Access Memory (SRAM) cells are presented in this paper, which can protect against Single Node Upsets (SNUs) and Double Node Upsets (DNUs). The 10T cell mainly consists of two cross-coupled input-split inverters and the cell can robustly keep stored values through a feedback mechanism among its internal nodes. It also has a low cost in terms of area and power consumption, since it uses only a few transistors. Based on the 10T cell, a 12T cell is proposed that uses four parallel access transistors. The 12T cell has a reduced read/write access time with the same soft error tolerance when compared to the 10T cell. Simulation results demonstrate that the proposed cells can recover from SNUs and a part of DNUs. Moreover, compared with the state-of-the-art hardened SRAM cells, the proposed 10T cell can save 28.59% write access time, 55.83% read access time, and 4.46% power dissipation at the cost of 4.04% silicon area on average.
Aggressive scaling of CMOS technologies can cause the reliability issues of circuits. Two highly reliable Radiation Hardened By Design (RHBD) 10T and 12T Static Random-Access Memory (SRAM) cells are presented in this paper, which can protect against Single Node Upsets (SNUs) and Double Node Upsets (DNUs). The 10T cell mainly consists of two cross-coupled input-split inverters and the cell can robustly keep stored values through a feedback mechanism among its internal nodes. It also has a low cost in terms of area and power consumption, since it uses only a few transistors. Based on the 10T cell, a 12T cell is proposed that uses four parallel access transistors. The 12T cell has a reduced read/write access time with the same soft error tolerance when compared to the 10T cell. Simulation results demonstrate that the proposed cells can recover from SNUs and a part of DNUs. Moreover, compared with the state-of-the-art hardened SRAM cells, the proposed 10T cell can save 28.59% write access time, 55.83% read access time, and 4.46% power dissipation at the cost of 4.04% silicon area on average.
Available online ,
doi: 10.11999/JEIT230879
Abstract:
A rateless coding scheme based on Bernoulli random construction is proposed for strong interference communication environments, which differs from the traditional Luby Transform (LT) rateless codes. The scheme utilizes the Locally Constrained Ordered Statistic Decoding (LC-OSD) algorithm at the receiver to effectively combat strong interference noise and achieve adaptive and ultra-reliable transmission. To reduce the communication resource consumption at both the transmitter and receiver, three effective decoding criteria are proposed: (1) a startup criterion based on the Random Code Union (RCU) bound, which initiates decoding only when the number of received symbols exceeds a threshold derived from RCU; (2) an early stopping criterion based on soft weights, which stops decoding early when the soft weights exceed a preset threshold; and (3) a skipping criterion based on the comparison between the codeword and the hard decision sequence, which skips the current decoding process when the hard decision of the newly received sequence satisfies the recoding check. Simulation results show that the performance of the rateless random codes is significantly better than that of LT codes in a channel with block erasures and additive noise. Moreover, due to the adaptive channel quality capability of rateless codes, their performance is also significantly better than fixed-rate codes. The simulation results also show that the proposed startup, early stopping, and skipping criteria effectively reduce transmission resources and computational complexity for both the sender and receiver.
A rateless coding scheme based on Bernoulli random construction is proposed for strong interference communication environments, which differs from the traditional Luby Transform (LT) rateless codes. The scheme utilizes the Locally Constrained Ordered Statistic Decoding (LC-OSD) algorithm at the receiver to effectively combat strong interference noise and achieve adaptive and ultra-reliable transmission. To reduce the communication resource consumption at both the transmitter and receiver, three effective decoding criteria are proposed: (1) a startup criterion based on the Random Code Union (RCU) bound, which initiates decoding only when the number of received symbols exceeds a threshold derived from RCU; (2) an early stopping criterion based on soft weights, which stops decoding early when the soft weights exceed a preset threshold; and (3) a skipping criterion based on the comparison between the codeword and the hard decision sequence, which skips the current decoding process when the hard decision of the newly received sequence satisfies the recoding check. Simulation results show that the performance of the rateless random codes is significantly better than that of LT codes in a channel with block erasures and additive noise. Moreover, due to the adaptive channel quality capability of rateless codes, their performance is also significantly better than fixed-rate codes. The simulation results also show that the proposed startup, early stopping, and skipping criteria effectively reduce transmission resources and computational complexity for both the sender and receiver.
Available online ,
doi: 10.11999/JEIT240140
Abstract:
Improving the generalization performance of methods under limited sample conditions is an important research direction in Synthetic Aperture Radar Automatic Target Recognition (SAR ATR). Addressing the fundamental problem in this field, a causal model is established in this paper for SAR ATR, demonstrating that interferences in SAR images, such as background and speckle, can be neglected under sufficient sample conditions. However, under limited sample conditions, these factors become confounding variables, introducing spurious correlations into the extracted SAR image features and affecting the generalization of SAR ATR. To accurately identify and eliminate these spurious effects in the features, this paper proposes a limited-sample SAR ATR method via dual consistency, which includes an intra-class feature consistency loss and effect-consistency loss. Firstly, based on the principle that discriminative features should have intra-class consistency and inter-class differences, the intra-class feature consistency loss is used to capture the consistent discriminative features of the target, subtracting the confounded part in the target features, and identifying the spurious effects introduced by interferences. Secondly, based on the idea of invariant risk minimization, the effect-consistency loss transforms the data requirement of Empirical Risk Minimization into a need for labeling the similarity among effects of different samples, reducing the data demand for eliminating spurious effects and removing the spurious effects in the features. Thus, the limited-sample SAR ATR method proposed in this paper achieves true causal feature extraction and accurate recognition performance. Experiments on two benchmark datasets validate the effectiveness of this method which can achieve superior performance of SAR target recognition with limited sample.
Improving the generalization performance of methods under limited sample conditions is an important research direction in Synthetic Aperture Radar Automatic Target Recognition (SAR ATR). Addressing the fundamental problem in this field, a causal model is established in this paper for SAR ATR, demonstrating that interferences in SAR images, such as background and speckle, can be neglected under sufficient sample conditions. However, under limited sample conditions, these factors become confounding variables, introducing spurious correlations into the extracted SAR image features and affecting the generalization of SAR ATR. To accurately identify and eliminate these spurious effects in the features, this paper proposes a limited-sample SAR ATR method via dual consistency, which includes an intra-class feature consistency loss and effect-consistency loss. Firstly, based on the principle that discriminative features should have intra-class consistency and inter-class differences, the intra-class feature consistency loss is used to capture the consistent discriminative features of the target, subtracting the confounded part in the target features, and identifying the spurious effects introduced by interferences. Secondly, based on the idea of invariant risk minimization, the effect-consistency loss transforms the data requirement of Empirical Risk Minimization into a need for labeling the similarity among effects of different samples, reducing the data demand for eliminating spurious effects and removing the spurious effects in the features. Thus, the limited-sample SAR ATR method proposed in this paper achieves true causal feature extraction and accurate recognition performance. Experiments on two benchmark datasets validate the effectiveness of this method which can achieve superior performance of SAR target recognition with limited sample.
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/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/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/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.
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/JEIT240138
Abstract:
The existing Synthetic Aperture Radar (SAR) target recognition methods are mostly limited to the closed-set assumption, which considers that the training target categories in training template library cover all the categories to be tested and is not suitable for the open environment with the presence of both known and unknown classes. To solve the problem of SAR target recognition in the case of incomplete target categories in the training template library, an openset SAR target recognition method that combines unknown feature generation with classification score modification is proposed in this paper. Firstly, a prototype network is exploited to get high recognition accuracy of known classes, and then potential unknown features are generated based on prior knowledge to enhance the discrimination of known and unknown classes. After the prototype network being updated, the boundary features of each known class are selected and the distance of each boundary feature to the corresponding class prototype, i.e., maximum distance, is calculated, respectively. Subsequently the maximum distribution area for each known class is determined by the probability fitting of maximum distances for each known class by using extreme value theory. In the testing phase, on the basis of predicting closed-set classification scores by measuring the distance between the testing sample features and each known class prototype, the probability of each distance in the distribution of the corresponding known class’s maximum distance is calculated, and the closed-set classification scores are corrected to automatically determine the rejection probability. Experiments on measured MSTAR dataset show that the proposed method can effectively represent the distribution of unknown class features and enhance the discriminability of known and unknown class features in the feature space, thus achieving accurate recognition for both known class targets and unknown class targets.
The existing Synthetic Aperture Radar (SAR) target recognition methods are mostly limited to the closed-set assumption, which considers that the training target categories in training template library cover all the categories to be tested and is not suitable for the open environment with the presence of both known and unknown classes. To solve the problem of SAR target recognition in the case of incomplete target categories in the training template library, an openset SAR target recognition method that combines unknown feature generation with classification score modification is proposed in this paper. Firstly, a prototype network is exploited to get high recognition accuracy of known classes, and then potential unknown features are generated based on prior knowledge to enhance the discrimination of known and unknown classes. After the prototype network being updated, the boundary features of each known class are selected and the distance of each boundary feature to the corresponding class prototype, i.e., maximum distance, is calculated, respectively. Subsequently the maximum distribution area for each known class is determined by the probability fitting of maximum distances for each known class by using extreme value theory. In the testing phase, on the basis of predicting closed-set classification scores by measuring the distance between the testing sample features and each known class prototype, the probability of each distance in the distribution of the corresponding known class’s maximum distance is calculated, and the closed-set classification scores are corrected to automatically determine the rejection probability. Experiments on measured MSTAR dataset show that the proposed method can effectively represent the distribution of unknown class features and enhance the discriminability of known and unknown class features in the feature space, thus achieving accurate recognition for both known class targets and unknown class targets.
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. 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. 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/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/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.
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.
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/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.
Display Method:
2024, 46(9): 3445-3455.
doi: 10.11999/JEIT240062
Abstract:
The correction in Digital Elevation Models (DEMs) has always been a crucial aspect of remote sensing geoscience research. The burgeoning development of new machine learning methods in recent years has provided novel solutions for the correction of DEM elevation errors. Given the reliance of machine learning and other artificial intelligence methods on extensive training data, and considering the current lack of publicly available, unified, large-scale, and standardized multisource DEM elevation error prediction datasets for large areas, the multi-source DEM Elevation Error Prediction Dataset (DEEP-Dataset) is introduced in this paper. This dataset comprises four sub-datasets, based on the TerraSAR-X add-on for Digital Elevation Measurements (TanDEM-X) DEM and Advanced land observing satellite World 3D-30 m (AW3D30) DEM in the Guangdong Province study area of China, and the Shuttle Radar Topography Mission (SRTM) DEM and Advanced Spaceborne Thermal Emission and reflection Radiometer (ASTER) DEM in the Northern Territory study area of Australia. The Guangdong Province sample comprises approximately 40 000 instances, while the Northern Territory sample includes about 1 600 000 instances. Each sample in the dataset consists of ten features, encompassing geographic spatial information, land cover types, and topographic attributes. The effectiveness of the DEEP-Dataset in actual model training and DEM correction has been validated through a series of comparative experiments, including machine learning model testing, DEM correction, and feature importance assessment. These experiments demonstrate the dataset’s rationality, effectiveness, and comprehensiveness.
The correction in Digital Elevation Models (DEMs) has always been a crucial aspect of remote sensing geoscience research. The burgeoning development of new machine learning methods in recent years has provided novel solutions for the correction of DEM elevation errors. Given the reliance of machine learning and other artificial intelligence methods on extensive training data, and considering the current lack of publicly available, unified, large-scale, and standardized multisource DEM elevation error prediction datasets for large areas, the multi-source DEM Elevation Error Prediction Dataset (DEEP-Dataset) is introduced in this paper. This dataset comprises four sub-datasets, based on the TerraSAR-X add-on for Digital Elevation Measurements (TanDEM-X) DEM and Advanced land observing satellite World 3D-30 m (AW3D30) DEM in the Guangdong Province study area of China, and the Shuttle Radar Topography Mission (SRTM) DEM and Advanced Spaceborne Thermal Emission and reflection Radiometer (ASTER) DEM in the Northern Territory study area of Australia. The Guangdong Province sample comprises approximately 40 000 instances, while the Northern Territory sample includes about 1 600 000 instances. Each sample in the dataset consists of ten features, encompassing geographic spatial information, land cover types, and topographic attributes. The effectiveness of the DEEP-Dataset in actual model training and DEM correction has been validated through a series of comparative experiments, including machine learning model testing, DEM correction, and feature importance assessment. These experiments demonstrate the dataset’s rationality, effectiveness, and comprehensiveness.
2024, 46(9): 3456-3466.
doi: 10.11999/JEIT240167
Abstract:
Event Knowledge Graph (EKG) is a special knowledge graph that can learn the evolution laws of events, which has the functions of reasoning and prediction. In view of the characteristics of large amount of data, multiple modes and interactive coupling of power dispatching business, this paper describes in detail the dataset construction, mainstream methods, technical architecture, evaluation indexes, and applicable scenarios of the event knowledge graph for power dispatching, focuses on the feasibility of each scenario, and gives solutions in terms of application process, input and output, technical architecture, etc., and finally looks forward to the difficulties and possible research directions faced in the long-term development of power dispatching business. This paper provides a reference for the study of the characteristics of the field of power dispatching, the advantages of event knowledge graph and the combination of the two, and provides a guiding idea for the application direction of event knowledge graph in the field of power dispatching.
Event Knowledge Graph (EKG) is a special knowledge graph that can learn the evolution laws of events, which has the functions of reasoning and prediction. In view of the characteristics of large amount of data, multiple modes and interactive coupling of power dispatching business, this paper describes in detail the dataset construction, mainstream methods, technical architecture, evaluation indexes, and applicable scenarios of the event knowledge graph for power dispatching, focuses on the feasibility of each scenario, and gives solutions in terms of application process, input and output, technical architecture, etc., and finally looks forward to the difficulties and possible research directions faced in the long-term development of power dispatching business. This paper provides a reference for the study of the characteristics of the field of power dispatching, the advantages of event knowledge graph and the combination of the two, and provides a guiding idea for the application direction of event knowledge graph in the field of power dispatching.
2024, 46(9): 3467-3473.
doi: 10.11999/JEIT240086
Abstract:
The Deep Reinforcement Learning (DRL) has received widespread attention, which has potential in Reconfigurable Intelligent Surface (RIS) assisted Integrated Sensing And Communication (ISAC) network. However, due to the high cost of data offloading and model training, the existing RIS-assisted ISAC frameworks still face great challenges. To overcome this problem, the paper analyzes the main technology of DRL in the field of ISAC networks and its solution, which can solve the of high complexity, high-frequency transmission and limited coverage problems. To promote the implementation of these technologies, this paper further discusses the future development trends of DRL technologies in RIS-assisted ISAC networks, including potential applications and problems to be solved.
The Deep Reinforcement Learning (DRL) has received widespread attention, which has potential in Reconfigurable Intelligent Surface (RIS) assisted Integrated Sensing And Communication (ISAC) network. However, due to the high cost of data offloading and model training, the existing RIS-assisted ISAC frameworks still face great challenges. To overcome this problem, the paper analyzes the main technology of DRL in the field of ISAC networks and its solution, which can solve the of high complexity, high-frequency transmission and limited coverage problems. To promote the implementation of these technologies, this paper further discusses the future development trends of DRL technologies in RIS-assisted ISAC networks, including potential applications and problems to be solved.
2024, 46(9): 3474-3489.
doi: 10.11999/JEIT240091
Abstract:
The anonymity network represented by The onion router(Tor) is one of the most widely used encrypted communication networks, criminals utilize encrypted networks to conceal their illegal activities, posing significant challenges to network regulation and cybersecurity. The emergence of website fingerprinting attack has made the analysis of encrypted traffic possible, enabling supervisors to identify Tor traffic and infer the web pages being visited by users by utilizing features such as packet direction and so on. In this paper, a wide survey and analysis of website fingerprinting attack and defense methods on Tor are conducted. Firstly, relevant techniques of website fingerprinting attacks on Tor are summarized and compared. The emphasis is placed on website fingerprinting attacks based on traditional machine learning and deep learning technologies. Secondly, a comprehensive survey and analysis of various existing defense methods are conducted. The limitations in the field of website fingerprinting attack methods on Tor are analyzed and summarized, and the future development directions and prospects are looked forward to.
The anonymity network represented by The onion router(Tor) is one of the most widely used encrypted communication networks, criminals utilize encrypted networks to conceal their illegal activities, posing significant challenges to network regulation and cybersecurity. The emergence of website fingerprinting attack has made the analysis of encrypted traffic possible, enabling supervisors to identify Tor traffic and infer the web pages being visited by users by utilizing features such as packet direction and so on. In this paper, a wide survey and analysis of website fingerprinting attack and defense methods on Tor are conducted. Firstly, relevant techniques of website fingerprinting attacks on Tor are summarized and compared. The emphasis is placed on website fingerprinting attacks based on traditional machine learning and deep learning technologies. Secondly, a comprehensive survey and analysis of various existing defense methods are conducted. The limitations in the field of website fingerprinting attack methods on Tor are analyzed and summarized, and the future development directions and prospects are looked forward to.
2024, 46(9): 3490-3502.
doi: 10.11999/JEIT231457
Abstract:
Logic synthesis plays a crucial role in the modern electronic design automation process. With the continuous enhancement of computational capabilities and the emergence of new computing paradigms, various efficient Boolean SATisfiability (SAT) solvers and circuit simulators have been developed and applied in the context of logic synthesis. First, the overview of the Boolean Satisfiability problem and circuit logic simulator is briefly described. Subsequently, the historical development of the matrix semi-tensor product is reviewed, and based on the fundamental principles of the semi-tensor product, its research progress in inference engines and logic synthesis is expounded. Finally, a prospective analysis is conducted on emerging technologies that may significantly impact logic synthesis in the future.
Logic synthesis plays a crucial role in the modern electronic design automation process. With the continuous enhancement of computational capabilities and the emergence of new computing paradigms, various efficient Boolean SATisfiability (SAT) solvers and circuit simulators have been developed and applied in the context of logic synthesis. First, the overview of the Boolean Satisfiability problem and circuit logic simulator is briefly described. Subsequently, the historical development of the matrix semi-tensor product is reviewed, and based on the fundamental principles of the semi-tensor product, its research progress in inference engines and logic synthesis is expounded. Finally, a prospective analysis is conducted on emerging technologies that may significantly impact logic synthesis in the future.
2024, 46(9): 3503-3509.
doi: 10.11999/JEIT240120
Abstract:
The Residual Self-Interference (RSI) caused by Co-frequency and Co-time Full Duplex Device-to-Device (CCFD-D2D) and the interference introduced by spectrum sharing between D2D User (DU) and Cellular User (CU) lead to a degradation in the quality of experience for CUs. Therefore, the CCFD-D2D underlaying cellular system is considered and two algorithms are proposed, that is Maximizing Sum-rate of CU (MaxSumCU) and Maximizing Minimum-rate of CU (MaxMinCU) algorithm, to enhance the experience for CUs while spectral efficiency of the system is improved. For the MaxSumCU algorithm, an optimization problem is investigated to maximize the sum rate of CUs in the system, and formulate it as a Mixed Integer NonLinear Programming problem (MINLP) which is NP-hard in mathematics. MaxSumCU is designed to decompose it into two sub-problems as power control and spectral resource allocation. The power control is solved by geometric programming, and the resource allocation is achieved by employing Kuhn-Munkres algorithm to determine the spectrum sharing pairs of CUs and DUs. To provide a more uniform rate performance across all CUs, the MaxMinCU algorithm is designed to maximize the minimum rate among the CUs. The novel spectrum resource allocation algorithm based on bisection searching and Kuhn-Munkres minimum-weight algorithm is proposed to solve this optimization problem. Numerical results show that, compared with Maximizing Sum-rate of Cell (MaxSumCell) design, our proposed algorithm effectively optimize the CU’s experience while improve the spectral efficiency of system in CCFD-D2D underlaying cellular networks.
The Residual Self-Interference (RSI) caused by Co-frequency and Co-time Full Duplex Device-to-Device (CCFD-D2D) and the interference introduced by spectrum sharing between D2D User (DU) and Cellular User (CU) lead to a degradation in the quality of experience for CUs. Therefore, the CCFD-D2D underlaying cellular system is considered and two algorithms are proposed, that is Maximizing Sum-rate of CU (MaxSumCU) and Maximizing Minimum-rate of CU (MaxMinCU) algorithm, to enhance the experience for CUs while spectral efficiency of the system is improved. For the MaxSumCU algorithm, an optimization problem is investigated to maximize the sum rate of CUs in the system, and formulate it as a Mixed Integer NonLinear Programming problem (MINLP) which is NP-hard in mathematics. MaxSumCU is designed to decompose it into two sub-problems as power control and spectral resource allocation. The power control is solved by geometric programming, and the resource allocation is achieved by employing Kuhn-Munkres algorithm to determine the spectrum sharing pairs of CUs and DUs. To provide a more uniform rate performance across all CUs, the MaxMinCU algorithm is designed to maximize the minimum rate among the CUs. The novel spectrum resource allocation algorithm based on bisection searching and Kuhn-Munkres minimum-weight algorithm is proposed to solve this optimization problem. Numerical results show that, compared with Maximizing Sum-rate of Cell (MaxSumCell) design, our proposed algorithm effectively optimize the CU’s experience while improve the spectral efficiency of system in CCFD-D2D underlaying cellular networks.
2024, 46(9): 3510-3519.
doi: 10.11999/JEIT240254
Abstract:
In railway emergency scenarios with ground infrastructure vulnerable to damage from harsh natural environments, an Unmanned Aerial Vehicle (UAV) integrated communication and sensing wireless access network architecture is proposed in this paper, enabling real-time environmental sensing and information backhaul. Given the limited endurance of UAVs, a train braking distance model and a UAV energy consumption model are established, which are then jointly utilized to adjust the UAV flight speed and communication transmit power, optimizing overall UAV energy consumption while satisfying communication performance requirements during information backhaul and environmental sensing. Analysis reveals that this optimization problem conforms to the Markov Decision Process (MDP). Consequently, an intelligent wireless resource allocation algorithm for UAV integrated communication and sensing, grounded in the Double Deep Q Network (DDQN), is proposed to tackle the problem. The simulation results demonstrate that the proposed algorithm exhibits excellent convergence performance and maximizes the operational duration of UAV communications, while meeting the requirements for environmental sensing and information backhaul in railway emergency scenarios.
In railway emergency scenarios with ground infrastructure vulnerable to damage from harsh natural environments, an Unmanned Aerial Vehicle (UAV) integrated communication and sensing wireless access network architecture is proposed in this paper, enabling real-time environmental sensing and information backhaul. Given the limited endurance of UAVs, a train braking distance model and a UAV energy consumption model are established, which are then jointly utilized to adjust the UAV flight speed and communication transmit power, optimizing overall UAV energy consumption while satisfying communication performance requirements during information backhaul and environmental sensing. Analysis reveals that this optimization problem conforms to the Markov Decision Process (MDP). Consequently, an intelligent wireless resource allocation algorithm for UAV integrated communication and sensing, grounded in the Double Deep Q Network (DDQN), is proposed to tackle the problem. The simulation results demonstrate that the proposed algorithm exhibits excellent convergence performance and maximizes the operational duration of UAV communications, while meeting the requirements for environmental sensing and information backhaul in railway emergency scenarios.
2024, 46(9): 3520-3527.
doi: 10.11999/JEIT231460
Abstract:
The high-quality generation and precise phase synchronization of high-speed modulated baseband signals are key technologies of space optical communication ranging system. Traditional approaches relying on FPGA or Digital Signal Processor (DSP) and high-speed Digital to Analog Convertor (DAC) technology often suffer from limited phase synchronization accuracy and high hardware complexity. A method for high-speed optical communication baseband signal generation and phase synchronization is proposed and a phase-locked dynamic control loop is designed in this paper. By dynamically adjusting the phase of the high-speed signal transmission clock in real time, the deterministic relationship between the I/Q high-speed baseband signal phase and the external reference clock phase can be achieved. The experimental results demonstrate impressive performance metrics: When the code rate of the Quadrature Phase Shift Keying (QPSK) optical modulated signal is 5 Gbit/s, the phase synchronization accuracy is less than 2 ps and the Error Vector Magnitude (EVM) is less than 8%; the bit error rate is 10–7 at a 5 Gbit/s optical communication rate, the receiver sensitivity is better than –47 dBm, and the ranging accuracy is better than 2 mm. Compared with traditional methods, both sensitivity and ranging accuracy are significantly improved.
The high-quality generation and precise phase synchronization of high-speed modulated baseband signals are key technologies of space optical communication ranging system. Traditional approaches relying on FPGA or Digital Signal Processor (DSP) and high-speed Digital to Analog Convertor (DAC) technology often suffer from limited phase synchronization accuracy and high hardware complexity. A method for high-speed optical communication baseband signal generation and phase synchronization is proposed and a phase-locked dynamic control loop is designed in this paper. By dynamically adjusting the phase of the high-speed signal transmission clock in real time, the deterministic relationship between the I/Q high-speed baseband signal phase and the external reference clock phase can be achieved. The experimental results demonstrate impressive performance metrics: When the code rate of the Quadrature Phase Shift Keying (QPSK) optical modulated signal is 5 Gbit/s, the phase synchronization accuracy is less than 2 ps and the Error Vector Magnitude (EVM) is less than 8%; the bit error rate is 10–7 at a 5 Gbit/s optical communication rate, the receiver sensitivity is better than –47 dBm, and the ranging accuracy is better than 2 mm. Compared with traditional methods, both sensitivity and ranging accuracy are significantly improved.
2024, 46(9): 3528-3536.
doi: 10.11999/JEIT240176
Abstract:
The performance differences are investigated for terrestrial relay-assisted Integrated Satellite-Terrestrial Networks (ISTNs) when satellite employs different decoding schemes. Initially, a multi-user pairing scheme leveraging location information is proposed, and multiple users within the coverage area of satellite beam are divided into several groups. To improve transmission reliability and spectral efficiency, users communicate with satellite employing hybrid multiple access. Additionally, assuming that user-terrestrial relay links obey Nakagami-m fading while terrestrial relay-satellite link follows correlated shadowed-Rician fading, the closed-form expressions of outage probability and throughput are derived when the satellite adopts Successive Interference Cancellation (SIC) and Joint Decoding (JD) respectively. Finally, computer simulations validate the correctness of the theoretical analysis and the superiority of the proposed scheme compared with Orthogonal Multiple Access (OMA), and reveal that JD decoding scheme is feasible in the ISTNs.
The performance differences are investigated for terrestrial relay-assisted Integrated Satellite-Terrestrial Networks (ISTNs) when satellite employs different decoding schemes. Initially, a multi-user pairing scheme leveraging location information is proposed, and multiple users within the coverage area of satellite beam are divided into several groups. To improve transmission reliability and spectral efficiency, users communicate with satellite employing hybrid multiple access. Additionally, assuming that user-terrestrial relay links obey Nakagami-m fading while terrestrial relay-satellite link follows correlated shadowed-Rician fading, the closed-form expressions of outage probability and throughput are derived when the satellite adopts Successive Interference Cancellation (SIC) and Joint Decoding (JD) respectively. Finally, computer simulations validate the correctness of the theoretical analysis and the superiority of the proposed scheme compared with Orthogonal Multiple Access (OMA), and reveal that JD decoding scheme is feasible in the ISTNs.
2024, 46(9): 3537-3543.
doi: 10.11999/JEIT240007
Abstract:
A sum-rate maximization method is proposed for two Simultaneously Transmitting And Reflecting Reconfigurable Intelligent Surface (STAR-RIS) assisted downlink Non-Orthogonal Multiple Access (NOMA) systems. Firstly, the optimization problem for maximizing sum rate is constructed, with STAR-RIS phase shifts, power allocation and time allocation as optimization parameters. Then the Semi-Definite Programming method (SDP) is used to optimize the phase shifts of these two STAR-RISs. Finally, the power allocation and time allocation are optimized alternately by iterative method. In each iteration, Lagrange dual decomposition method is used to optimize power allocation and function extremum method is used to optimize time allocation. Simulation results show that the sum rate of the dual STAR-RIS-assisted NOMA system is higher than that of the single STAR-RIS-assisted NOMA system.
A sum-rate maximization method is proposed for two Simultaneously Transmitting And Reflecting Reconfigurable Intelligent Surface (STAR-RIS) assisted downlink Non-Orthogonal Multiple Access (NOMA) systems. Firstly, the optimization problem for maximizing sum rate is constructed, with STAR-RIS phase shifts, power allocation and time allocation as optimization parameters. Then the Semi-Definite Programming method (SDP) is used to optimize the phase shifts of these two STAR-RISs. Finally, the power allocation and time allocation are optimized alternately by iterative method. In each iteration, Lagrange dual decomposition method is used to optimize power allocation and function extremum method is used to optimize time allocation. Simulation results show that the sum rate of the dual STAR-RIS-assisted NOMA system is higher than that of the single STAR-RIS-assisted NOMA system.
2024, 46(9): 3544-3552.
doi: 10.11999/JEIT240149
Abstract:
Passive intelligent reflecting surfaces hold great potential in enhancing wireless communication systems and improving physical layer security, but they suffer from significant drawbacks such as “double fading” and partial coverage. Therefore, an active Simultaneously Transmitting And Reflecting Reconfigurable Intelligent Surface (STAR-RIS) is conducted in this paper. Considering the coupling between reflection and transmission phase shifts, a joint optimization problem for maximizing the security energy efficiency of base station beams and active STAR-RIS beams is formulated. To solve the resulting non-convex optimization problem, continuous convex approximation, penalty function method, semi-definite relaxation, and alternating optimization techniques are employed to transform the original problem into a convex one. Additionally, a penalty dual decomposition algorithm is proposed. Simulation results validate the effectiveness of the algorithm proposed in this paper.
Passive intelligent reflecting surfaces hold great potential in enhancing wireless communication systems and improving physical layer security, but they suffer from significant drawbacks such as “double fading” and partial coverage. Therefore, an active Simultaneously Transmitting And Reflecting Reconfigurable Intelligent Surface (STAR-RIS) is conducted in this paper. Considering the coupling between reflection and transmission phase shifts, a joint optimization problem for maximizing the security energy efficiency of base station beams and active STAR-RIS beams is formulated. To solve the resulting non-convex optimization problem, continuous convex approximation, penalty function method, semi-definite relaxation, and alternating optimization techniques are employed to transform the original problem into a convex one. Additionally, a penalty dual decomposition algorithm is proposed. Simulation results validate the effectiveness of the algorithm proposed in this paper.
2024, 46(9): 3553-3562.
doi: 10.11999/JEIT240151
Abstract:
Ubiquitous coverage will become the main form of 6G networks, and complete the deployment in the mountains, hills, deserts and other blind area, to achieve full-area wireless coverage. However, the large-scale deployment of terrestrial base stations in remote areas is extremely difficult. For this reason, combining Unmanned Aerial Vehicle (UAV) communications with Non-Orthogonal Multiple Access (NOMA) technology, an energy-efficient three-dimensional coverage scheme to maximize the energy efficiency of network throughput is proposed in this paper. First, the system model is established and a user pairing algorithm is proposed based on the K-Means algorithm and the Gale-Shapley algorithm. Then, after user pairing is completed, the initial problem is split into two optimization subproblems, which are transformed to convex respectively. Finally, the block coordinate ascent method is used to alternately optimize the UAV trajectory and transmit power to maximize the energy efficiency. Simulation results show that compared with benchmarks, the proposed scheme can significantly improve the throughput energy efficiency of air-ground networks under large-scale wireless coverage.
Ubiquitous coverage will become the main form of 6G networks, and complete the deployment in the mountains, hills, deserts and other blind area, to achieve full-area wireless coverage. However, the large-scale deployment of terrestrial base stations in remote areas is extremely difficult. For this reason, combining Unmanned Aerial Vehicle (UAV) communications with Non-Orthogonal Multiple Access (NOMA) technology, an energy-efficient three-dimensional coverage scheme to maximize the energy efficiency of network throughput is proposed in this paper. First, the system model is established and a user pairing algorithm is proposed based on the K-Means algorithm and the Gale-Shapley algorithm. Then, after user pairing is completed, the initial problem is split into two optimization subproblems, which are transformed to convex respectively. Finally, the block coordinate ascent method is used to alternately optimize the UAV trajectory and transmit power to maximize the energy efficiency. Simulation results show that compared with benchmarks, the proposed scheme can significantly improve the throughput energy efficiency of air-ground networks under large-scale wireless coverage.
2024, 46(9): 3563-3570.
doi: 10.11999/JEIT230116
Abstract:
In order to reduce the cost of the existing Wi-Fi indoor positioning technology algorithm and ensure the positioning accuracy, a Wi-Fi fingerprint matching positioning algorithm uniting Spline interpolation is proposed in this paper. In terms of constructing the signal strength fingerprint database, the construction of a sparse fingerprint database is proposed, which greatly reduces the workload and hardware requirements of data collection. In addition, the combination of hybrid filtering and spline interpolation method is proposed to enrich the sparse fingerprint database. In terms of interpolation of the signal strength fingerprint database, after the same degree of hybrid filtering, compared with the known Inverse Distance Weighting(IDW) interpolation algorithm, the spline interpolation method can accurately fill the database and achieve higher positioning accuracy. In terms of fingerprint matching and positioning, matching algorithms such as K-Nearest Neighbor(KNN) are used to achieve high-precision positioning. Simulation experiments show that the proposed Wi-Fi fingerprint positioning method uniting spline interpolation can ensure high positioning accuracy under the premise of only building a low-cost sparse fingerprint database.
In order to reduce the cost of the existing Wi-Fi indoor positioning technology algorithm and ensure the positioning accuracy, a Wi-Fi fingerprint matching positioning algorithm uniting Spline interpolation is proposed in this paper. In terms of constructing the signal strength fingerprint database, the construction of a sparse fingerprint database is proposed, which greatly reduces the workload and hardware requirements of data collection. In addition, the combination of hybrid filtering and spline interpolation method is proposed to enrich the sparse fingerprint database. In terms of interpolation of the signal strength fingerprint database, after the same degree of hybrid filtering, compared with the known Inverse Distance Weighting(IDW) interpolation algorithm, the spline interpolation method can accurately fill the database and achieve higher positioning accuracy. In terms of fingerprint matching and positioning, matching algorithms such as K-Nearest Neighbor(KNN) are used to achieve high-precision positioning. Simulation experiments show that the proposed Wi-Fi fingerprint positioning method uniting spline interpolation can ensure high positioning accuracy under the premise of only building a low-cost sparse fingerprint database.
2024, 46(9): 3571-3582.
doi: 10.11999/JEIT240112
Abstract:
Considering the spectrum sharing between cellular users and D2D users, and the wireless channel characteristic of urban streets, an Intelligent Reflecting Surface (IRS)-assisted joint beamforming method is proposed. Under the constraints of signal to interference plus noise ratio for D2D link, the parameter including optimal beamforming vectors and phase-shift matrices and D2D transmitting powers are designed with the objective of maximizing cellular user capacity. The nonconvex coupling variable optimization problem is transformed into the convex decoupling variable optimization problem and binary search power allocation by introducing slack variables, and the reflection phase-shift matrices are also optimized with Riemann conjugate gradient algorithms. Simulation results show that the proposed algorithm has perfect convergence and higher user channel capacity comparing with the baseline schemes.
Considering the spectrum sharing between cellular users and D2D users, and the wireless channel characteristic of urban streets, an Intelligent Reflecting Surface (IRS)-assisted joint beamforming method is proposed. Under the constraints of signal to interference plus noise ratio for D2D link, the parameter including optimal beamforming vectors and phase-shift matrices and D2D transmitting powers are designed with the objective of maximizing cellular user capacity. The nonconvex coupling variable optimization problem is transformed into the convex decoupling variable optimization problem and binary search power allocation by introducing slack variables, and the reflection phase-shift matrices are also optimized with Riemann conjugate gradient algorithms. Simulation results show that the proposed algorithm has perfect convergence and higher user channel capacity comparing with the baseline schemes.
2024, 46(9): 3583-3591.
doi: 10.11999/JEIT240203
Abstract:
Considering the phenomena of complex electromagnetic environment, multipath clutter and dense interference signals in complex urban environments, the traditional Unmanned Aerial Vehicle(UAV) detection method extracts the target Doppler information for detection by obtaining echo signals, which is susceptible to environmental impacts and leads to unsatisfactory detection results. A micro-Doppler-assisted formation detection method for UAVs in urban environments is proposed in this paper, which makes full use of micro-motion characteristics to improve detection accuracy. Firstly, parametric modeling characterizes the radar echo micro-Doppler signals of UAV rotor blades in urban complex environments, and detects the micro-Doppler scintillation pulses by using YOLOv5s to effectively extract the positional information. Then, the Pulse Repetition Interval (PRI) transform of the radar signal sorting method is introduced to classify and obtain the number of UAV formations. Finally, K-means algorithm is utilized to verify the accuracy of the UAV formation detection method. The results show that the proposed method has a detection accuracy of more than 90% for seven UAVs at a signal-to-noise ratio of 2 dB, and can be used for UAV formation detection in urban complex environments where there are interfering pulses, multipath effects, and local pulse loss.
Considering the phenomena of complex electromagnetic environment, multipath clutter and dense interference signals in complex urban environments, the traditional Unmanned Aerial Vehicle(UAV) detection method extracts the target Doppler information for detection by obtaining echo signals, which is susceptible to environmental impacts and leads to unsatisfactory detection results. A micro-Doppler-assisted formation detection method for UAVs in urban environments is proposed in this paper, which makes full use of micro-motion characteristics to improve detection accuracy. Firstly, parametric modeling characterizes the radar echo micro-Doppler signals of UAV rotor blades in urban complex environments, and detects the micro-Doppler scintillation pulses by using YOLOv5s to effectively extract the positional information. Then, the Pulse Repetition Interval (PRI) transform of the radar signal sorting method is introduced to classify and obtain the number of UAV formations. Finally, K-means algorithm is utilized to verify the accuracy of the UAV formation detection method. The results show that the proposed method has a detection accuracy of more than 90% for seven UAVs at a signal-to-noise ratio of 2 dB, and can be used for UAV formation detection in urban complex environments where there are interfering pulses, multipath effects, and local pulse loss.
2024, 46(9): 3592-3601.
doi: 10.11999/JEIT231408
Abstract:
The low-frequency sound propagating in the shallow water has the multi-mode characteristic and dispersion effect. The de-dispersion transform of signal frequency spectrum can eliminate the dispersion effect to achieve passive ranging. Focusing on the multi-value problem of the de-dispersion transform of frequency spectrum, a passive ranging method based on the De-Dispersion Transform of Power Spectral Density (PSD-DDT) is proposed. First, the field model KRAKEN is used to calculate the horizontal wavenumbers of each normal mode. Next, given the approximate range of waveguide invariant in the shallow water, the dispersion constant between any two modes is estimated. Then, the power spectral density that retains the modal interference term is subjected to the de-dispersion transform. Finally, the estimated value of the source distance is the ratio of the independent variable corresponding to the maximum amplitude of PSD-DDT to the dispersion constant. In addition, when the waveguide parameters are unknown, PSD-DDT is performed separately on the measured source and the guided source, and the distance is determined by the ratio of the independent variables. This condition does not need to calculate the dispersion constant. The effectiveness of PSD-DDT is verified through numerical simulation and sea trial. The effects of waveguide invariant, mode order, and signal-to-noise ratio on the ranging results are analyzed. Based on the trial data in the Northern Yellow Sea of China, compared with the DDT results, the ranging error of PSD-DDT has decreased by about 49.2%, The relative error within a range of 35 km under the best waveguide invariant is approximately 2.55% with high ranging accuracy.
The low-frequency sound propagating in the shallow water has the multi-mode characteristic and dispersion effect. The de-dispersion transform of signal frequency spectrum can eliminate the dispersion effect to achieve passive ranging. Focusing on the multi-value problem of the de-dispersion transform of frequency spectrum, a passive ranging method based on the De-Dispersion Transform of Power Spectral Density (PSD-DDT) is proposed. First, the field model KRAKEN is used to calculate the horizontal wavenumbers of each normal mode. Next, given the approximate range of waveguide invariant in the shallow water, the dispersion constant between any two modes is estimated. Then, the power spectral density that retains the modal interference term is subjected to the de-dispersion transform. Finally, the estimated value of the source distance is the ratio of the independent variable corresponding to the maximum amplitude of PSD-DDT to the dispersion constant. In addition, when the waveguide parameters are unknown, PSD-DDT is performed separately on the measured source and the guided source, and the distance is determined by the ratio of the independent variables. This condition does not need to calculate the dispersion constant. The effectiveness of PSD-DDT is verified through numerical simulation and sea trial. The effects of waveguide invariant, mode order, and signal-to-noise ratio on the ranging results are analyzed. Based on the trial data in the Northern Yellow Sea of China, compared with the DDT results, the ranging error of PSD-DDT has decreased by about 49.2%, The relative error within a range of 35 km under the best waveguide invariant is approximately 2.55% with high ranging accuracy.
2024, 46(9): 3602-3610.
doi: 10.11999/JEIT231440
Abstract:
Considering mismatched problem between theoretical steering vector and actual steering vector causes false-alarm-rate increase in the process of maritime radar detection, the adaptive mismatched detectors are studied under Compound Gaussian Model (CGM). In order to reject mismatched signal, the fictitious signal orthogonal to theoretical steering vector is introduced in the null hypothesis, and a target detection with mismatched signal is given. The texture component of CGM is represented by generalized inverse distribution, and the Adaptive Beamformer Orthogonal Rejection Test (ABORT) is developed based on two-step Generalized Likelihood Ratio Test (GLRT) and Maximum A Posteriori GLRT (MAP GLRT) criterions respectively. Both the proposed detectors are testified to have Constant False AlaRm (CFAR) characteristics for speckle covariance matrix and target doppler steering vector. Experimental results based on simulated and real measured sea clutter data indicate that the proposed mismatched detectors show preferable target detection performance under the matched steering vector condition and anti-mismatch capability under the mismatched steering vector condition.
Considering mismatched problem between theoretical steering vector and actual steering vector causes false-alarm-rate increase in the process of maritime radar detection, the adaptive mismatched detectors are studied under Compound Gaussian Model (CGM). In order to reject mismatched signal, the fictitious signal orthogonal to theoretical steering vector is introduced in the null hypothesis, and a target detection with mismatched signal is given. The texture component of CGM is represented by generalized inverse distribution, and the Adaptive Beamformer Orthogonal Rejection Test (ABORT) is developed based on two-step Generalized Likelihood Ratio Test (GLRT) and Maximum A Posteriori GLRT (MAP GLRT) criterions respectively. Both the proposed detectors are testified to have Constant False AlaRm (CFAR) characteristics for speckle covariance matrix and target doppler steering vector. Experimental results based on simulated and real measured sea clutter data indicate that the proposed mismatched detectors show preferable target detection performance under the matched steering vector condition and anti-mismatch capability under the mismatched steering vector condition.
2024, 46(9): 3611-3618.
doi: 10.11999/JEIT240123
Abstract:
The traditional radar adaptive target detectors in sea clutter usually assumes that the clutter texture is independent and identically distributed in the range dimension, ignoring the correlation information of the texture in the range dimension. In order to improve the adaptive detection performance of radar targets in sea clutter with texture range correlation, the texture component of compound Gaussian sea clutter is modeled as a lognormal random variable, and then a generalized likelihood ratio test with homogeneous lognormal texture detector is proposed based on the generalized likelihood ratio test. The proposed detector uses the prior distribution of texture and the correlated information of texture range. The simulation and the measured data beging used show that the detection probability of this detector for radar targets in compound Gaussian sea clutter with texture range correlation is higher than that of the existing detectors.
The traditional radar adaptive target detectors in sea clutter usually assumes that the clutter texture is independent and identically distributed in the range dimension, ignoring the correlation information of the texture in the range dimension. In order to improve the adaptive detection performance of radar targets in sea clutter with texture range correlation, the texture component of compound Gaussian sea clutter is modeled as a lognormal random variable, and then a generalized likelihood ratio test with homogeneous lognormal texture detector is proposed based on the generalized likelihood ratio test. The proposed detector uses the prior distribution of texture and the correlated information of texture range. The simulation and the measured data beging used show that the detection probability of this detector for radar targets in compound Gaussian sea clutter with texture range correlation is higher than that of the existing detectors.
2024, 46(9): 3619-3628.
doi: 10.11999/JEIT240130
Abstract:
An innovative aviation target tracking algorithm is presented in this paper, utilizing high-altitude unmanned airship dual photoelectric sensors in conjunction with Unmanned Aerial Vehicle (UAV)-borne two-coordinate radar. The algorithm addresses the challenge of integrating sensor data to accurately track targets when individual sensors lack complete target position information, thus overcoming limitations of traditional point-trace association methods. Initially, a two-level point-trace correlation algorithm based on angle and distance is introduced for multi-sensor measurement association following coordinate system transformation. Subsequently, a line-plane intersection fusion localization algorithm is proposed to determine the initial target track position through techniques such as least squares method, intersection projection, distance nearest point solution, and homologous data compression. Leveraging heterogeneous information from space-based multi-platform reconnaissance, an extended Unscented Kalman Filter (UKF) is designed to track aviation targets by enhancing the traditional UKF. Simulation results demonstrate that this algorithm achieves superior precision in tracking high-speed aerial targets.
An innovative aviation target tracking algorithm is presented in this paper, utilizing high-altitude unmanned airship dual photoelectric sensors in conjunction with Unmanned Aerial Vehicle (UAV)-borne two-coordinate radar. The algorithm addresses the challenge of integrating sensor data to accurately track targets when individual sensors lack complete target position information, thus overcoming limitations of traditional point-trace association methods. Initially, a two-level point-trace correlation algorithm based on angle and distance is introduced for multi-sensor measurement association following coordinate system transformation. Subsequently, a line-plane intersection fusion localization algorithm is proposed to determine the initial target track position through techniques such as least squares method, intersection projection, distance nearest point solution, and homologous data compression. Leveraging heterogeneous information from space-based multi-platform reconnaissance, an extended Unscented Kalman Filter (UKF) is designed to track aviation targets by enhancing the traditional UKF. Simulation results demonstrate that this algorithm achieves superior precision in tracking high-speed aerial targets.
2024, 46(9): 3629-3636.
doi: 10.11999/JEIT240057
Abstract:
Considering the problem of 3D weak multi target detection, a 3-level Parallel-line-coordinate Transformation (PT) Track Before Detect (TBD) algorithm based on the dual accumulation self-feedback optimized is proposed in this paper. By introducing PT into TBD technology, the measurement points are transformed and accumulated sequentially on the normalized radial distance-time, azimuth angle-time and elevation angle-time planes, then the power accumulation are used to feedback the optimized binary accumulation, effectively mitigating the mutual interference between strong targets overwhelming weak targets and formation targets. Simulation results show that when the overall signal-to-clutter ratio reaches 10 dB, the overall detection probability of the proposed algorithm is close to 80%, demonstrating the effectiveness of the algorithm.
Considering the problem of 3D weak multi target detection, a 3-level Parallel-line-coordinate Transformation (PT) Track Before Detect (TBD) algorithm based on the dual accumulation self-feedback optimized is proposed in this paper. By introducing PT into TBD technology, the measurement points are transformed and accumulated sequentially on the normalized radial distance-time, azimuth angle-time and elevation angle-time planes, then the power accumulation are used to feedback the optimized binary accumulation, effectively mitigating the mutual interference between strong targets overwhelming weak targets and formation targets. Simulation results show that when the overall signal-to-clutter ratio reaches 10 dB, the overall detection probability of the proposed algorithm is close to 80%, demonstrating the effectiveness of the algorithm.
2024, 46(9): 3637-3645.
doi: 10.11999/JEIT231428
Abstract:
A dual-band flexible wearable antenna integrated with an Artificial Magnetic Conductor(AMC) is presented. The design operates at 3.5 GHz and 5.8 GHz. The proposed antenna is composed of a dual-band monopole antenna and a dual-band artificial magnetic conductor with a 4×4 array, both of which are printed on flexible materials. The antenna dimensions are 0.70\begin{document}$ {\lambda _0} $\end{document} \begin{document}$ {\lambda _0} $\end{document} \begin{document}$ {\lambda _0} $\end{document} \begin{document}$ {\lambda _0} $\end{document}
A dual-band flexible wearable antenna integrated with an Artificial Magnetic Conductor(AMC) is presented. The design operates at 3.5 GHz and 5.8 GHz. The proposed antenna is composed of a dual-band monopole antenna and a dual-band artificial magnetic conductor with a 4×4 array, both of which are printed on flexible materials. The antenna dimensions are 0.70
A Multi-scale-multi-input Complementation Classification Network for Fast Coding Tree Unit Partition
2024, 46(9): 3646-3653.
doi: 10.11999/JEIT240223
Abstract:
Deep Neural Networks (DNN) have been widely applied to Coding Tree Unit(CTU) partition of intra-mode High Efficiency Video Coding(HEVC) for reducing the HEVC encoding complexity, however, existing DNN-based CTU partition methods always neglect the correlation of features between Coding Units (CU) at different scales and suffer from the accumulation of classification errors. Therefore, in this paper, a Multi-scale-multi-input Complementation Classification Network (MCCN) for faster and more accurate CTU partition is proposed. First, a Multi-scale Multi-input Convolutional Neural Network (MMCNN) is proposed, which builds up the correlation of features between CUs at different scales by fusing multi-scale CU features. Therefore, our MMCNN possess more powerful representation abilities. Second, a Complementary Classification Strategy (CCS) is proposed, in which the final depth prediction results for each CU are determined by combining the results of multi-classification with the results of binary classification and triplex classification with the voting mechanism. The proposed CCS avoids the accumulation of classification errors and achieves more accurate CTU partition. Extensive experiments demonstrate that our MCCN achieves lower HEVC encoding complexity and more accurate CTU partition: reduce the average encoding complexity by 71.49% only at the cost of a 3.18% average Bjøntegaard Delta Bit-Rate(BD-BR). And the average accuracies of 32×32 CU depth prediction and 16×16 CU depth prediction are increased by 0.65%～0.93% and 2.14%～9.27% respectively.
Deep Neural Networks (DNN) have been widely applied to Coding Tree Unit(CTU) partition of intra-mode High Efficiency Video Coding(HEVC) for reducing the HEVC encoding complexity, however, existing DNN-based CTU partition methods always neglect the correlation of features between Coding Units (CU) at different scales and suffer from the accumulation of classification errors. Therefore, in this paper, a Multi-scale-multi-input Complementation Classification Network (MCCN) for faster and more accurate CTU partition is proposed. First, a Multi-scale Multi-input Convolutional Neural Network (MMCNN) is proposed, which builds up the correlation of features between CUs at different scales by fusing multi-scale CU features. Therefore, our MMCNN possess more powerful representation abilities. Second, a Complementary Classification Strategy (CCS) is proposed, in which the final depth prediction results for each CU are determined by combining the results of multi-classification with the results of binary classification and triplex classification with the voting mechanism. The proposed CCS avoids the accumulation of classification errors and achieves more accurate CTU partition. Extensive experiments demonstrate that our MCCN achieves lower HEVC encoding complexity and more accurate CTU partition: reduce the average encoding complexity by 71.49% only at the cost of a 3.18% average Bjøntegaard Delta Bit-Rate(BD-BR). And the average accuracies of 32×32 CU depth prediction and 16×16 CU depth prediction are increased by 0.65%～0.93% and 2.14%～9.27% respectively.
2024, 46(9): 3654-3661.
doi: 10.11999/JEIT231439
Abstract:
There is significant uncertainty in the measurements of active sonar in terms of range-bearing resolution because of the complex underwater environment. In this case, the energy of the target echo may occupy multiple adjacent coordinate grids in the sonar range-bearing energy spectrum. Moreover, the measurement uncertainty mentioned above will cause multiple regional clutter interferences when there exists strong reverberation in the environment. To reduce the bias of state space estimation, Particle Filtering (PF) based tracking methods require a large number of particles to approximate the posterior probability density, resulting in a rapid decrease in real-time tracking performance. A Box-Particle Filtering tracking method based on Interval measurement (IBPF) is proposed to address the above problem. IBPF utilizes a box particle with range-bearing intervals instead of point measurements of active sonar, which greatly reduces the number of particles required for posterior probability density estimation while improving the stability of state estimation, and can further improve computational efficiency. The experiment result shows that the proposed IBPF achieves better tracking performance with higher computational efficiency, which reduces computation time by 18.06% and increases the number of successful tracking frames by 4.29%.
There is significant uncertainty in the measurements of active sonar in terms of range-bearing resolution because of the complex underwater environment. In this case, the energy of the target echo may occupy multiple adjacent coordinate grids in the sonar range-bearing energy spectrum. Moreover, the measurement uncertainty mentioned above will cause multiple regional clutter interferences when there exists strong reverberation in the environment. To reduce the bias of state space estimation, Particle Filtering (PF) based tracking methods require a large number of particles to approximate the posterior probability density, resulting in a rapid decrease in real-time tracking performance. A Box-Particle Filtering tracking method based on Interval measurement (IBPF) is proposed to address the above problem. IBPF utilizes a box particle with range-bearing intervals instead of point measurements of active sonar, which greatly reduces the number of particles required for posterior probability density estimation while improving the stability of state estimation, and can further improve computational efficiency. The experiment result shows that the proposed IBPF achieves better tracking performance with higher computational efficiency, which reduces computation time by 18.06% and increases the number of successful tracking frames by 4.29%.
2024, 46(9): 3662-3671.
doi: 10.11999/JEIT240318
Abstract:
For cross-modal person Re-IDentification (Re-ID) in visible-infrared images, methods using modality conversion and adversarial networks yield associative information between modalities. However, these approaches fall short in effective feature recognition. Thus, a two-stage approach using visual-text matching and graph embedding for enhanced re-identification effectiveness is proposed in this paper. A context-optimized scheme is utilized by the method to construct learnable text templates that generate person descriptions as associative information between modalities. Specifically, in the first stage, unified text descriptions of the same person across different modalities are utilized as prior information, assisting in the reduction of modality differences, based on the Contrastive Language–Image Pre-training (CLIP) model. Meanwhile, in the second stage, a cross-modal constraint framework based on graph embedding is applied, and a modality-adaptive loss function is designed, aiming to improve person recognition accuracy. The method’s efficacy has been confirmed through extensive experiments on the SYSU-MM01 and RegDB datasets, with a Rank-1 accuracy of 64.2% and mean Average Precision (mAP) of 60.2% on SYSU-MM01 being achieved, thereby demonstrating significant improvements in cross-modal person re-identification.
For cross-modal person Re-IDentification (Re-ID) in visible-infrared images, methods using modality conversion and adversarial networks yield associative information between modalities. However, these approaches fall short in effective feature recognition. Thus, a two-stage approach using visual-text matching and graph embedding for enhanced re-identification effectiveness is proposed in this paper. A context-optimized scheme is utilized by the method to construct learnable text templates that generate person descriptions as associative information between modalities. Specifically, in the first stage, unified text descriptions of the same person across different modalities are utilized as prior information, assisting in the reduction of modality differences, based on the Contrastive Language–Image Pre-training (CLIP) model. Meanwhile, in the second stage, a cross-modal constraint framework based on graph embedding is applied, and a modality-adaptive loss function is designed, aiming to improve person recognition accuracy. The method’s efficacy has been confirmed through extensive experiments on the SYSU-MM01 and RegDB datasets, with a Rank-1 accuracy of 64.2% and mean Average Precision (mAP) of 60.2% on SYSU-MM01 being achieved, thereby demonstrating significant improvements in cross-modal person re-identification.
2024, 46(9): 3672-3682.
doi: 10.11999/JEIT240364
Abstract:
The weakly supervised semantic segmentation methods have been widely applied in the analysis of Whole Slide Images (WSI), saving a considerable amount of manual annotation costs. Addressing the issues of pixel instance independence, local inconsistency in segmentation results, and insufficient supervision from image-level labels in Multiple-Instance Learning (MIL) methods for pathological image analysis, a novel end-to-end MIL approach named DASMob-MIL is proposed in this paper. Firstly, to overcome the independence among pixel instances, features are extracted using a local perception network to establish local pixel dependencies, while a Global Information Perception Branch (GIPB) is constructed by cascading cross-attention modules to establish global pixel dependencies. Secondly, a Pixel-Adaptive Refinement (PAR) module is introduced to address the problem of local inconsistency in weakly supervised semantic segmentation results by constructing affinity kernels based on the similarity between multi-scale neighborhood local sparse features. Finally, a Deep Association Supervision (DAS) module is designed to optimize the training process by performing weighted fusion on the segmentation maps generated from multi-stage feature maps. Then, employing a weighted factor-associated loss function to mitigate the impact of insufficient supervision from weakly supervised image-level labels. Compared with other models, the DASMob-MIL model demonstrates advanced segmentation performance on the self-built colorectal cancer dataset YN-CRC and the public weakly supervised histopathology image dataset LUAD-HistoSeg-BC, with a model weight of only 14MB and an F1 score of 89.5% on the YN-CRC dataset, which was 3% higher than that of the advanced Multi-Layer Pseudo-Supervision (MLPS) model. Experimental results indicate that DASMob-MIL achieves pixel-level segmentation utilizing only image-level labels, effectively improving the segmentation performance of weakly supervised histopathological images.
The weakly supervised semantic segmentation methods have been widely applied in the analysis of Whole Slide Images (WSI), saving a considerable amount of manual annotation costs. Addressing the issues of pixel instance independence, local inconsistency in segmentation results, and insufficient supervision from image-level labels in Multiple-Instance Learning (MIL) methods for pathological image analysis, a novel end-to-end MIL approach named DASMob-MIL is proposed in this paper. Firstly, to overcome the independence among pixel instances, features are extracted using a local perception network to establish local pixel dependencies, while a Global Information Perception Branch (GIPB) is constructed by cascading cross-attention modules to establish global pixel dependencies. Secondly, a Pixel-Adaptive Refinement (PAR) module is introduced to address the problem of local inconsistency in weakly supervised semantic segmentation results by constructing affinity kernels based on the similarity between multi-scale neighborhood local sparse features. Finally, a Deep Association Supervision (DAS) module is designed to optimize the training process by performing weighted fusion on the segmentation maps generated from multi-stage feature maps. Then, employing a weighted factor-associated loss function to mitigate the impact of insufficient supervision from weakly supervised image-level labels. Compared with other models, the DASMob-MIL model demonstrates advanced segmentation performance on the self-built colorectal cancer dataset YN-CRC and the public weakly supervised histopathology image dataset LUAD-HistoSeg-BC, with a model weight of only 14MB and an F1 score of 89.5% on the YN-CRC dataset, which was 3% higher than that of the advanced Multi-Layer Pseudo-Supervision (MLPS) model. Experimental results indicate that DASMob-MIL achieves pixel-level segmentation utilizing only image-level labels, effectively improving the segmentation performance of weakly supervised histopathological images.
2024, 46(9): 3683-3692.
doi: 10.11999/JEIT240189
Abstract:
To address challenges such as high complexity in monocular depth estimation networks and low accuracy in regions with weak textures, a Direction-Aware Enhancement-based lightweight self-supervised monocular depth estimation Network (DAEN) is proposed in this paper. Firstly, the Iterative Dilated Convolution module (IDC) is introduced as the core of the encoder to extract correlations among distant pixels. Secondly, the Directional Awareness Enhancement module (DAE) is designed to enhance feature extraction in the vertical direction, providing the depth estimation model with additional depth cues. Furthermore, the problem of detail loss during the decoder upsampling process is addressed through the aggregation of disparity map features. Lastly, the Feature Attention Module (FAM) is employed to connect the encoder and decoder, effectively leveraging global contextual information to resolve adaptability issues in regions with weak textures. Experimental results on the KITTI dataset demonstrate that the proposed method has a model parameter count of only 2.9M, achieving an advanced performance with\begin{document}$ \delta $\end{document}
To address challenges such as high complexity in monocular depth estimation networks and low accuracy in regions with weak textures, a Direction-Aware Enhancement-based lightweight self-supervised monocular depth estimation Network (DAEN) is proposed in this paper. Firstly, the Iterative Dilated Convolution module (IDC) is introduced as the core of the encoder to extract correlations among distant pixels. Secondly, the Directional Awareness Enhancement module (DAE) is designed to enhance feature extraction in the vertical direction, providing the depth estimation model with additional depth cues. Furthermore, the problem of detail loss during the decoder upsampling process is addressed through the aggregation of disparity map features. Lastly, the Feature Attention Module (FAM) is employed to connect the encoder and decoder, effectively leveraging global contextual information to resolve adaptability issues in regions with weak textures. Experimental results on the KITTI dataset demonstrate that the proposed method has a model parameter count of only 2.9M, achieving an advanced performance with
2024, 46(9): 3693-3702.
doi: 10.11999/JEIT240178
Abstract:
The multimodal fusion method can effectively improve the ground object classification accuracy by using the complementary characteristics of different modalities, which has become a research hotspot in various fields in recent years. The existing multimodal fusion methods have been successfully applied to multi-source remote sensing classification tasks oriented to HyperSpectral Image (HSI) and Light Detection And Ranging (LiDAR). However, existing research still faces many challenges, including difficulty in capturing spatial dependencies among irregular ground objects and obtaining discriminative information in multimodal data. To address the above challenges, a Scale Adaptive Fusion Network (SAFN)is proposed in this paper, by integrating the fusion of multimodal, multiscale, and multiview features into a unified framework. First, a dynamic multiscale graph module is proposed to capture the complex spatial dependencies of ground object, enhancing the model’s adaptability to irregular and scale-dissimilar ground object. Second, the complementary properties of LiDAR and HSI are utilized to constrain ground object within the same spatial neighborhood to have similar feature representations, thereby acquiring discriminative remote sensing features. Then, a multimodal spatial-spectral graph fusion module is proposed to establish feature interactions among multimodal, multiscale, and multiview features, providing discriminative fusion features for classification tasks by capturing class-recognition information that can be shared among features. Finally, the fusion features are fed into a classifier to obtain class probability scores for predicting the ground object class. To verify the effectiveness of SAFN, experiments are conducted on three datasets (i.e., Houston, Trento, and MUUFL). The experimental results show that, SAFN achieved state-of-the-art performance in multi-source remote sensing data classification tasks when compared with existing mainstream methods.
The multimodal fusion method can effectively improve the ground object classification accuracy by using the complementary characteristics of different modalities, which has become a research hotspot in various fields in recent years. The existing multimodal fusion methods have been successfully applied to multi-source remote sensing classification tasks oriented to HyperSpectral Image (HSI) and Light Detection And Ranging (LiDAR). However, existing research still faces many challenges, including difficulty in capturing spatial dependencies among irregular ground objects and obtaining discriminative information in multimodal data. To address the above challenges, a Scale Adaptive Fusion Network (SAFN)is proposed in this paper, by integrating the fusion of multimodal, multiscale, and multiview features into a unified framework. First, a dynamic multiscale graph module is proposed to capture the complex spatial dependencies of ground object, enhancing the model’s adaptability to irregular and scale-dissimilar ground object. Second, the complementary properties of LiDAR and HSI are utilized to constrain ground object within the same spatial neighborhood to have similar feature representations, thereby acquiring discriminative remote sensing features. Then, a multimodal spatial-spectral graph fusion module is proposed to establish feature interactions among multimodal, multiscale, and multiview features, providing discriminative fusion features for classification tasks by capturing class-recognition information that can be shared among features. Finally, the fusion features are fed into a classifier to obtain class probability scores for predicting the ground object class. To verify the effectiveness of SAFN, experiments are conducted on three datasets (i.e., Houston, Trento, and MUUFL). The experimental results show that, SAFN achieved state-of-the-art performance in multi-source remote sensing data classification tasks when compared with existing mainstream methods.
2024, 46(9): 3703-3712.
doi: 10.11999/JEIT240277
Abstract:
A novel end-to-end algorithm is proposed to tackle the dependency of Multi-Object Tracking (MOT) algorithm performance on detection accuracy and data association strategies. Concerning detection, the Spatial Residual Feature Pyramid Network (SRFPN) is introduced based on feature pyramid networks to enhance feature fusion and information propagation efficiency. Subsequently, a Global Local Feature Interaction Module (GLFIM) is introduced to balance local details and global contextual information, thereby improving the focus of multi-scale feature outputs and the model’s adaptability to target scale variations. Regarding the association, an Angular Momentum Mechanism (AMM) is introduced to consider target motion direction, thereby enhancing the accuracy of target matching between consecutive frames. Experimental validation on MOT17 and UAVDT datasets demonstrates significant enhancements in both detection and association performance of the proposed tracker, showcasing robustness in complex scenarios such as target occlusion, scale variation, and cluttered backgrounds.
A novel end-to-end algorithm is proposed to tackle the dependency of Multi-Object Tracking (MOT) algorithm performance on detection accuracy and data association strategies. Concerning detection, the Spatial Residual Feature Pyramid Network (SRFPN) is introduced based on feature pyramid networks to enhance feature fusion and information propagation efficiency. Subsequently, a Global Local Feature Interaction Module (GLFIM) is introduced to balance local details and global contextual information, thereby improving the focus of multi-scale feature outputs and the model’s adaptability to target scale variations. Regarding the association, an Angular Momentum Mechanism (AMM) is introduced to consider target motion direction, thereby enhancing the accuracy of target matching between consecutive frames. Experimental validation on MOT17 and UAVDT datasets demonstrates significant enhancements in both detection and association performance of the proposed tracker, showcasing robustness in complex scenarios such as target occlusion, scale variation, and cluttered backgrounds.
2024, 46(9): 3713-3721.
doi: 10.11999/JEIT240025
Abstract:
The misuse of deepfake technology on social networks has raised serious concerns about the authenticity and reliability of visual content. The degradation phenomenon of deepfake videos on social networks has not been adequately considered in existing detection algorithms, resulting in deepfake detection performance being limited by challenging issues such as compression artifacts interference and lack of context-related information. Compression encoding and up-sampling operations in deepfake generation algorithms can leave artifacts on videos, which can result in fine-grained differences between real videos and deepfake videos. The common mechanisms between compression artifacts and deepfake artifacts are analyzed to reveal the structural similarities between them, which provides a reliable theoretical basis for enhancing the robustness of deepfake detection models against compression. Firstly, to address the interference of compression noise on deepfake features, the frequency-domain adaptive notch filter is designed based on the structural similarity of compression artifacts and deepfake artifacts to eliminate the interference of compression artifacts on specific frequency bands. Secondly, the denoising branch based on residual learning is designed to reduce the sensitivity of the deepfake detection model to unknown noise. Additionally, the attention-based feature fusion method is adopted to enhance the discriminative features of deepfakes. Metric learning strategies are adopted to optimize network models, achieving deepfake detection with resistance to compression. Theoretical analysis and experimental results indicate that the detection performance of compressed deepfake videos is significantly enhanced by using the algorithm proposed in this paper. It can be used as a plug-and-play model combined with existing detection methods to enhance their robustness against compression.
The misuse of deepfake technology on social networks has raised serious concerns about the authenticity and reliability of visual content. The degradation phenomenon of deepfake videos on social networks has not been adequately considered in existing detection algorithms, resulting in deepfake detection performance being limited by challenging issues such as compression artifacts interference and lack of context-related information. Compression encoding and up-sampling operations in deepfake generation algorithms can leave artifacts on videos, which can result in fine-grained differences between real videos and deepfake videos. The common mechanisms between compression artifacts and deepfake artifacts are analyzed to reveal the structural similarities between them, which provides a reliable theoretical basis for enhancing the robustness of deepfake detection models against compression. Firstly, to address the interference of compression noise on deepfake features, the frequency-domain adaptive notch filter is designed based on the structural similarity of compression artifacts and deepfake artifacts to eliminate the interference of compression artifacts on specific frequency bands. Secondly, the denoising branch based on residual learning is designed to reduce the sensitivity of the deepfake detection model to unknown noise. Additionally, the attention-based feature fusion method is adopted to enhance the discriminative features of deepfakes. Metric learning strategies are adopted to optimize network models, achieving deepfake detection with resistance to compression. Theoretical analysis and experimental results indicate that the detection performance of compressed deepfake videos is significantly enhanced by using the algorithm proposed in this paper. It can be used as a plug-and-play model combined with existing detection methods to enhance their robustness against compression.
2024, 46(9): 3722-3730.
doi: 10.11999/JEIT240102
Abstract:
Querying present and future traffic velocities of road segments is a routine task in urban intelligence transportation management, and a Vehicle-equipped-Edge Dominant (VED) method is proposed to answer the querying of present and future velocity of urban road segments. The collected data is exchanged with the other mobile sides by every vehicle-equipped mobile side when the mobile side’s speed falls below a given threshold, and the light-weighted present and history velocity indexes are constructed locally to support the querying of present velocity. To train as few models as possible to predict future velocities, a road network is proposed to be partitioned into a set of road-segment clusters based on the segments’ topological morphism and the spatio-temporal space is proposed to be partitioned into a set of model-equivalence classes according to the periodic time windows and road-segment clusters. The similar traffic patterns are exhibited by the road segments in the same model-equivalence class within the given time window. For every model-equivalence class, the federated learning is performed between the mobile sides and the data center to train the Long Short-Term Memories (LSTMs) which are stored at the mobile sides to answer the querying of future velocities of nearby areas. Data is indexed by every mobile side and queries are answered locally, thus the query response latency and possible communication congestion can be avoided. Further, data is stored at the mobile sides, rather than at one data center, so as to prevent the privacy leakage due to security attacks.
Querying present and future traffic velocities of road segments is a routine task in urban intelligence transportation management, and a Vehicle-equipped-Edge Dominant (VED) method is proposed to answer the querying of present and future velocity of urban road segments. The collected data is exchanged with the other mobile sides by every vehicle-equipped mobile side when the mobile side’s speed falls below a given threshold, and the light-weighted present and history velocity indexes are constructed locally to support the querying of present velocity. To train as few models as possible to predict future velocities, a road network is proposed to be partitioned into a set of road-segment clusters based on the segments’ topological morphism and the spatio-temporal space is proposed to be partitioned into a set of model-equivalence classes according to the periodic time windows and road-segment clusters. The similar traffic patterns are exhibited by the road segments in the same model-equivalence class within the given time window. For every model-equivalence class, the federated learning is performed between the mobile sides and the data center to train the Long Short-Term Memories (LSTMs) which are stored at the mobile sides to answer the querying of future velocities of nearby areas. Data is indexed by every mobile side and queries are answered locally, thus the query response latency and possible communication congestion can be avoided. Further, data is stored at the mobile sides, rather than at one data center, so as to prevent the privacy leakage due to security attacks.
2024, 46(9): 3731-3738.
doi: 10.11999/JEIT240218
Abstract:
With the rapid development of deep learning and computer vision technologies, fine-tuning pre-trained models for remote sensing tasks often requires substantial computational resources. To reduce memory requirements and training costs, a method called “Multi-Fusion Adapter (MuFA)” for fine-tuning remote sensing models is proposed in this paper. MuFA introduces a fusion module that combines bottleneck modules with different down sample rates and connects them in parallel with the original vision Transformer model. During training, the parameters of the original vision Transformer model are frozen, and only the MuFA module and classification head are fine-tuned. Experimental results demonstrate that MuFA achieves superior performance on the UCM and NWPU-RESISC45 remote sensing scene classification datasets, surpassing other parameter efficient fine-tuning methods. Therefore, MuFA not only maintains model performance but also reduces resource overhead, making it highly promising for various remote sensing applications.
With the rapid development of deep learning and computer vision technologies, fine-tuning pre-trained models for remote sensing tasks often requires substantial computational resources. To reduce memory requirements and training costs, a method called “Multi-Fusion Adapter (MuFA)” for fine-tuning remote sensing models is proposed in this paper. MuFA introduces a fusion module that combines bottleneck modules with different down sample rates and connects them in parallel with the original vision Transformer model. During training, the parameters of the original vision Transformer model are frozen, and only the MuFA module and classification head are fine-tuned. Experimental results demonstrate that MuFA achieves superior performance on the UCM and NWPU-RESISC45 remote sensing scene classification datasets, surpassing other parameter efficient fine-tuning methods. Therefore, MuFA not only maintains model performance but also reduces resource overhead, making it highly promising for various remote sensing applications.
2024, 46(9): 3739-3748.
doi: 10.11999/JEIT231436
Abstract:
Among various methods to detect the ship targets in Synthetic Aperture Radar (SAR) image, the Constant False Alarm Rate (CFAR) detection algorithm with an adaptive detection threshold is the most important and extensively used one. In order to improve the detection performance for ships in SAR image, various statistical distributions are applied, with an attempt to accurately model the SAR clutter backgrounds, such as Gamma distribution, K distribution, log-normal distribution, G0 distribution and the alpha-stable distribution, etc. In modern radar systems, the use of the CFAR technique is necessary to keep the false alarms at a suitably low rate in an a priori unknown time-varying and spatially nonhomogeneous backgrounds, and to improve the detection probability as much as possible. The clutter background in SAR images is complicated and variable, when the actual clutter background deviates from the assumed statistical distribution, the performance of the parametric CFAR detectors deteriorates, whereas the nonparametric CFAR method exhibits its advantage. In this paper, the Wilcoxon nonparametric CFAR scheme for ship detection in SAR image is proposed and analyzed. By comparison with several typical parametric CFAR schemes on 3 real SAR images of Radarsat-2, ICEYE-X6 and Gaofen-3, the robustness of the Wilcoxon nonparametric detector to maintain a good false alarm performance in these different detection backgrounds is revealed, and its detection performance for the weak ship is improved evidently. Moreover, the detection speed of the Wilcoxon nonparametric detector is fast, and it has the simplicity of hardware implementation.
Among various methods to detect the ship targets in Synthetic Aperture Radar (SAR) image, the Constant False Alarm Rate (CFAR) detection algorithm with an adaptive detection threshold is the most important and extensively used one. In order to improve the detection performance for ships in SAR image, various statistical distributions are applied, with an attempt to accurately model the SAR clutter backgrounds, such as Gamma distribution, K distribution, log-normal distribution, G0 distribution and the alpha-stable distribution, etc. In modern radar systems, the use of the CFAR technique is necessary to keep the false alarms at a suitably low rate in an a priori unknown time-varying and spatially nonhomogeneous backgrounds, and to improve the detection probability as much as possible. The clutter background in SAR images is complicated and variable, when the actual clutter background deviates from the assumed statistical distribution, the performance of the parametric CFAR detectors deteriorates, whereas the nonparametric CFAR method exhibits its advantage. In this paper, the Wilcoxon nonparametric CFAR scheme for ship detection in SAR image is proposed and analyzed. By comparison with several typical parametric CFAR schemes on 3 real SAR images of Radarsat-2, ICEYE-X6 and Gaofen-3, the robustness of the Wilcoxon nonparametric detector to maintain a good false alarm performance in these different detection backgrounds is revealed, and its detection performance for the weak ship is improved evidently. Moreover, the detection speed of the Wilcoxon nonparametric detector is fast, and it has the simplicity of hardware implementation.
2024, 46(9): 3749-3756.
doi: 10.11999/JEIT231047
Abstract:
To address the challenges of poor recognition effect of the infrared substation equipment image caused by multi-target, small target and occlusion target in complex background, an infrared image recognition method for substation equipment based on CenterNet is proposed. By combining the Adaptive Spatial Feature Fusion(ASFF) module and Feature Pyramid Networks (FPN), a feature fusion network with the structure of ASFF+FPN is constructed, and the cross-scale feature fusion capability of the model for multi-target and small target is enhanced, which excludes background information. To improve the feature capturing ability of occluding targets, the global attention mechanism is introduced to the feature fusion network to enhance target saliency. Additionally, depthwise separable convolution is introduced to reduce parameters number and model inference time, and a lightweight model is achieved. Finally, the problem of poor sensitivity to obscured targets is overcame by using the distribution focal loss function, and the convergence speed and recognition accuracy is improved. Tests are performed on a self-built dataset containing seven infrared substation equipment images. Experimental results demonstrate that the proposed algorithm achieves a recognition accuracy of 95.19%, an improvement of 3.55% compared with the original algorithm, while it only has 32.52M model parameters. Furthermore, the method shows significant advantages in recognition accuracy and algorithm complexity, over four main target recognition algorithms.
To address the challenges of poor recognition effect of the infrared substation equipment image caused by multi-target, small target and occlusion target in complex background, an infrared image recognition method for substation equipment based on CenterNet is proposed. By combining the Adaptive Spatial Feature Fusion(ASFF) module and Feature Pyramid Networks (FPN), a feature fusion network with the structure of ASFF+FPN is constructed, and the cross-scale feature fusion capability of the model for multi-target and small target is enhanced, which excludes background information. To improve the feature capturing ability of occluding targets, the global attention mechanism is introduced to the feature fusion network to enhance target saliency. Additionally, depthwise separable convolution is introduced to reduce parameters number and model inference time, and a lightweight model is achieved. Finally, the problem of poor sensitivity to obscured targets is overcame by using the distribution focal loss function, and the convergence speed and recognition accuracy is improved. Tests are performed on a self-built dataset containing seven infrared substation equipment images. Experimental results demonstrate that the proposed algorithm achieves a recognition accuracy of 95.19%, an improvement of 3.55% compared with the original algorithm, while it only has 32.52M model parameters. Furthermore, the method shows significant advantages in recognition accuracy and algorithm complexity, over four main target recognition algorithms.
2024, 46(9): 3757-3762.
doi: 10.11999/JEIT240309
Abstract:
Complementary Sequence Sets (CSS) have ideal aperiodic auto-correlation functions and are widely used in the field of communication and sensing. In order to solve the problem of limited length of complementary sequence sets, two new constructions of binary complementary sequence sets are proposed using concatenation operator and interleaving operator, with the base sequence as the initial sequence. The proposed construction fills the gap in the length of the binary complementary sequence set and solves the public problem proposed by Adhikary and Majhi.
Complementary Sequence Sets (CSS) have ideal aperiodic auto-correlation functions and are widely used in the field of communication and sensing. In order to solve the problem of limited length of complementary sequence sets, two new constructions of binary complementary sequence sets are proposed using concatenation operator and interleaving operator, with the base sequence as the initial sequence. The proposed construction fills the gap in the length of the binary complementary sequence set and solves the public problem proposed by Adhikary and Majhi.
2024, 46(9): 3763-3770.
doi: 10.11999/JEIT240028
Abstract:
The secure sorting problem is derived from the millionaire problem and is a fundamental problem in secure multi-party computation research. Multi-party string sorting is of great significance for the research of database confidential queries and electronic voting sum problems. The existing research on secure sorting problems mostly focuses on private data sorting or two-party string sorting. Efficient multi-party string sorting schemes are still being explored. Based on the improved SM2 homomorphic encryption algorithm and threshold cryptography algorithm, this paper first proposes a confidentiality multi-party single character sorting protocol in a semi honest model, and then designs a weight-based confidentiality single character sorting protocol and a confidentiality multi-party string sorting protocol. Simulation paradigm is used to demonstrate the security of three protocols. The paper conducts performance analysis and simulation experiments on the three protocols. The results show that the performance of the proposed secure multi-party single character sorting protocol and secure multi-party string sorting protocol is significantly improved compared to existing similar schemes.
The secure sorting problem is derived from the millionaire problem and is a fundamental problem in secure multi-party computation research. Multi-party string sorting is of great significance for the research of database confidential queries and electronic voting sum problems. The existing research on secure sorting problems mostly focuses on private data sorting or two-party string sorting. Efficient multi-party string sorting schemes are still being explored. Based on the improved SM2 homomorphic encryption algorithm and threshold cryptography algorithm, this paper first proposes a confidentiality multi-party single character sorting protocol in a semi honest model, and then designs a weight-based confidentiality single character sorting protocol and a confidentiality multi-party string sorting protocol. Simulation paradigm is used to demonstrate the security of three protocols. The paper conducts performance analysis and simulation experiments on the three protocols. The results show that the performance of the proposed secure multi-party single character sorting protocol and secure multi-party string sorting protocol is significantly improved compared to existing similar schemes.
2024, 46(9): 3771-3776.
doi: 10.11999/JEIT231434
Abstract:
The DNA origami nanostructure encapsulates intricate sequence-folding information, presenting a novel avenue for exploiting cryptography with a vast key space. This paper introduces an encryption strategy that fully realizes the structure-based potential of DNA origami. In contrast to previous approach centered on the folding of DNA origami scaffold, an alternative methodology is introduced based on the nonlinear combination characteristics of staple ensembles. This approach aims to achieve a larger key space by exploring the inherent extensive folding diversity of staple. The key space computational model is delineated into three factors: the binding domain mode, cooperative folding, and independence of staples. These three factors respectively account for the intra-chain distribution, inter-chain arrangement diversity, and sequence specificity of staples. The combination of these factors effectively converts the folding diversity of DNA origami in per unit of geometric space into key space. This strategy represents a cryptography rooted in the principles of biomolecular thermodynamics, offering new possibilities for extending the application scenarios of information security.
The DNA origami nanostructure encapsulates intricate sequence-folding information, presenting a novel avenue for exploiting cryptography with a vast key space. This paper introduces an encryption strategy that fully realizes the structure-based potential of DNA origami. In contrast to previous approach centered on the folding of DNA origami scaffold, an alternative methodology is introduced based on the nonlinear combination characteristics of staple ensembles. This approach aims to achieve a larger key space by exploring the inherent extensive folding diversity of staple. The key space computational model is delineated into three factors: the binding domain mode, cooperative folding, and independence of staples. These three factors respectively account for the intra-chain distribution, inter-chain arrangement diversity, and sequence specificity of staples. The combination of these factors effectively converts the folding diversity of DNA origami in per unit of geometric space into key space. This strategy represents a cryptography rooted in the principles of biomolecular thermodynamics, offering new possibilities for extending the application scenarios of information security.
2024, 46(9): 3777-3787.
doi: 10.11999/JEIT240030
Abstract:
In the field of intelligent transportation, real-time and accurate traffic flow prediction has always been the top priority in urban development, which plays a crucial role in improving the operation efficiency of the road network. Most of the existing traffic flow prediction methods are based on machine learning, ignoring cases where the client is unwilling to participate in the prediction task or lies in order to obtain high rewards, resulting in a decline in the accuracy of traffic flow prediction when the model is aggregated. This paper proposes a Traffic Flow Prediction Method Based on Blockchain and Federated Learning (TFPM-BFL) to solve this problem. In this method, the client uses the Long Short-Term Memory (LSTM) model with attention mechanism to make local prediction and improve the prediction accuracy. An incentive mechanism based on credit rating is designed. Local and local credit values are obtained by evaluating the quality of the model uploaded by the client, and rewards are distributed according to the credit rating results, so as to encourage the client to participate in federal learning. Edge Server (ES) uses the model aggregation method based on credit value and compression rate to improve the model aggregation quality. The simulation results show that TFPM-BFL can achieve accurate and timely traffic flow prediction, effectively motivate clients to participate in Federated Learning (FL) tasks while ensuring the privacy of underlying data, and realize high-quality model aggregation.
In the field of intelligent transportation, real-time and accurate traffic flow prediction has always been the top priority in urban development, which plays a crucial role in improving the operation efficiency of the road network. Most of the existing traffic flow prediction methods are based on machine learning, ignoring cases where the client is unwilling to participate in the prediction task or lies in order to obtain high rewards, resulting in a decline in the accuracy of traffic flow prediction when the model is aggregated. This paper proposes a Traffic Flow Prediction Method Based on Blockchain and Federated Learning (TFPM-BFL) to solve this problem. In this method, the client uses the Long Short-Term Memory (LSTM) model with attention mechanism to make local prediction and improve the prediction accuracy. An incentive mechanism based on credit rating is designed. Local and local credit values are obtained by evaluating the quality of the model uploaded by the client, and rewards are distributed according to the credit rating results, so as to encourage the client to participate in federal learning. Edge Server (ES) uses the model aggregation method based on credit value and compression rate to improve the model aggregation quality. The simulation results show that TFPM-BFL can achieve accurate and timely traffic flow prediction, effectively motivate clients to participate in Federated Learning (FL) tasks while ensuring the privacy of underlying data, and realize high-quality model aggregation.
2024, 46(9): 3788-3796.
doi: 10.11999/JEIT240141
Abstract:
The Internet of Vehicles (IoVs) is widely used to obtain information about vehicles and road conditions, which is transmitted in public channels. Hence, the most important requirement is the data security. Because of characters of IoVs, we need to make it keep in a strict delay. Authentication is a common method to solve it. Due to limited resources and delay sensitivity of IoVs, vehicles must complete authentication within appropriate resources cost and delay. However, existing schemes are prone to physical, forgery and collusion attacks, and moreover, they are computationally heavy. Therefore, a lightweight security identity authentication scheme for vehicle-road collaboration is proposed in this paper, which utilizes lightweight Physical Unclonable Function (PUF) as the trust root of entities to resist physical and collusion attacks; Besides, most of computations are offloaded to Road Side Units (RSUs) certified by Trusted Authority (TA) through the vehicle-road-cloud collaboration architecture; In addition, vehicular pseudonym construction and update include Challenge-Response Pairs (CRPs), which are utilized to protect identity and trajectory privacy and expose malicious vehicular identities in identity tracking phase. Furthermore, there are formal and informal security analyses to prove our scheme is secure. Finally, the simulation experiment shows our scheme is more secure and efficient than other schemes in real scenarios.
The Internet of Vehicles (IoVs) is widely used to obtain information about vehicles and road conditions, which is transmitted in public channels. Hence, the most important requirement is the data security. Because of characters of IoVs, we need to make it keep in a strict delay. Authentication is a common method to solve it. Due to limited resources and delay sensitivity of IoVs, vehicles must complete authentication within appropriate resources cost and delay. However, existing schemes are prone to physical, forgery and collusion attacks, and moreover, they are computationally heavy. Therefore, a lightweight security identity authentication scheme for vehicle-road collaboration is proposed in this paper, which utilizes lightweight Physical Unclonable Function (PUF) as the trust root of entities to resist physical and collusion attacks; Besides, most of computations are offloaded to Road Side Units (RSUs) certified by Trusted Authority (TA) through the vehicle-road-cloud collaboration architecture; In addition, vehicular pseudonym construction and update include Challenge-Response Pairs (CRPs), which are utilized to protect identity and trajectory privacy and expose malicious vehicular identities in identity tracking phase. Furthermore, there are formal and informal security analyses to prove our scheme is secure. Finally, the simulation experiment shows our scheme is more secure and efficient than other schemes in real scenarios.
2024, 46(9): 3797-3806.
doi: 10.11999/JEIT240060
Abstract:
The seed-adaptive mutation scheduling strategy is the latest technology in mutation-based fuzzing, which can adaptively adjust the probability distribution of the mutation operators according to the syntax and semantic characteristics of the seed. However, it has two problems: (1) it is unable to adaptively adjust the probability distribution according to the mutation position; (2) The Thompson Sampling algorithm used in the fuzzing scenario is easy to lead to the learned probability distribution close to the average distribution, which leads to the failure of the mutation scheduling strategy. Focusing on the above problems, a position-adaptive mutation scheduling strategy is proposed. This technology establishes the relationship between the mutation position and the mutation operators through a user-defined double-layer multi-armed bandit model, and uses the Upper Confidence Bound algorithm to select the mutation operator, so as to achieve position adaptation and avoid the problem of average distribution. The position-adaptive fuzzer Position-Adaptive Mutation Scheduling Strategy AFL (PAMSSAFL) is implemented based on American Fuzzy Lop (AFL). The comparison results show that the position-adaptive mutation scheduling strategy can improve the bug detection ability and coverage ability of the fuzzer.
The seed-adaptive mutation scheduling strategy is the latest technology in mutation-based fuzzing, which can adaptively adjust the probability distribution of the mutation operators according to the syntax and semantic characteristics of the seed. However, it has two problems: (1) it is unable to adaptively adjust the probability distribution according to the mutation position; (2) The Thompson Sampling algorithm used in the fuzzing scenario is easy to lead to the learned probability distribution close to the average distribution, which leads to the failure of the mutation scheduling strategy. Focusing on the above problems, a position-adaptive mutation scheduling strategy is proposed. This technology establishes the relationship between the mutation position and the mutation operators through a user-defined double-layer multi-armed bandit model, and uses the Upper Confidence Bound algorithm to select the mutation operator, so as to achieve position adaptation and avoid the problem of average distribution. The position-adaptive fuzzer Position-Adaptive Mutation Scheduling Strategy AFL (PAMSSAFL) is implemented based on American Fuzzy Lop (AFL). The comparison results show that the position-adaptive mutation scheduling strategy can improve the bug detection ability and coverage ability of the fuzzer.
2024, 46(9): 3807-3817.
doi: 10.11999/JEIT231099
Abstract:
Always-on intelligent visual sense-computing (Senputing) system has higher requirement on the accuracy and real-time of edge feature extraction on target image, and thus the accompanying hardware energy consumption increases accordingly. Since an analog subtracter can realize visual sensing and edge feature extraction synchronously in analogue domain instead of the traditional digital processing, the overall energy consumption of sensing-storage-computing integrated system can be effectively reduced. But meanwhile, the long calculation time beyond the order of 10–7 s has also become the bottleneck of design of analog subtracter circuits. A novel analogue subtraction circuit structure is proposed in this paper, which consists of two functional circuits in analogue domain: signal sampling and subtraction module. The signal sampling circuit is further composed of an improved bootstrapped sampling switch and a pair of sampling capacitors; The subtraction operation is performed by a novel switched capacitor analog subtraction circuit, which can realize high-speed parallel processing of three subtraction operations in two sampling times. Based on TSMC 180 nm/1.8 V CMOS technology, the design of the whole analog subtraction circuit is implemented. The simulation results show that, The proposed analog subtracter can realize synchronous parallel processing of signal sampling and computation in analogue domain, and the cycle of one parallel processing is only 20 ns, which has high-speed computing capability. The calculated value range of the subtracter is sufficiently wide from –900～900 mV, the relative error is less than 1.65%, the lowest one is only about 0.1%, which proves that the computing accuracy is high; The energy consumption is 25～27.8 pJ, which is in the acceptable medium level. Therefore, the proposed analog subtracter has a significant performance trade-off on speed, precision and energy consumption, and can be effectively applied to high-performance always-on intelligent visual senputing system.
Always-on intelligent visual sense-computing (Senputing) system has higher requirement on the accuracy and real-time of edge feature extraction on target image, and thus the accompanying hardware energy consumption increases accordingly. Since an analog subtracter can realize visual sensing and edge feature extraction synchronously in analogue domain instead of the traditional digital processing, the overall energy consumption of sensing-storage-computing integrated system can be effectively reduced. But meanwhile, the long calculation time beyond the order of 10–7 s has also become the bottleneck of design of analog subtracter circuits. A novel analogue subtraction circuit structure is proposed in this paper, which consists of two functional circuits in analogue domain: signal sampling and subtraction module. The signal sampling circuit is further composed of an improved bootstrapped sampling switch and a pair of sampling capacitors; The subtraction operation is performed by a novel switched capacitor analog subtraction circuit, which can realize high-speed parallel processing of three subtraction operations in two sampling times. Based on TSMC 180 nm/1.8 V CMOS technology, the design of the whole analog subtraction circuit is implemented. The simulation results show that, The proposed analog subtracter can realize synchronous parallel processing of signal sampling and computation in analogue domain, and the cycle of one parallel processing is only 20 ns, which has high-speed computing capability. The calculated value range of the subtracter is sufficiently wide from –900～900 mV, the relative error is less than 1.65%, the lowest one is only about 0.1%, which proves that the computing accuracy is high; The energy consumption is 25～27.8 pJ, which is in the acceptable medium level. Therefore, the proposed analog subtracter has a significant performance trade-off on speed, precision and energy consumption, and can be effectively applied to high-performance always-on intelligent visual senputing system.
2024, 46(9): 3818-3826.
doi: 10.11999/JEIT240010
Abstract:
Memristors have a natural plasticity that enables silicon-based neurons and nano-synapses with similar or the same mechanisms as biological neurons and synapses. Using a memristor as a synapse to couple two heterogeneous memristive cellular neural networks, a memristor-coupled heterogeneous cellular neural network is constructed in this paper. The coupled network contains a space equilibrium set related to the initial value conditions of memristor synapse and subnets, which can exhibit complex dynamic evolution. The multi-stable behaviors of the coupling network, such as stable point, period, chaos, hyperchaos and unbounded oscillation, which depend on the initial value conditions, are revealed by numerical simulation method. In addition, under the control of memristor synapse, two heterogeneous subnets can achieve phase synchronization. Finally, the experimental verification of the circuit is completed based on STM32 MCU hardware platform.
Memristors have a natural plasticity that enables silicon-based neurons and nano-synapses with similar or the same mechanisms as biological neurons and synapses. Using a memristor as a synapse to couple two heterogeneous memristive cellular neural networks, a memristor-coupled heterogeneous cellular neural network is constructed in this paper. The coupled network contains a space equilibrium set related to the initial value conditions of memristor synapse and subnets, which can exhibit complex dynamic evolution. The multi-stable behaviors of the coupling network, such as stable point, period, chaos, hyperchaos and unbounded oscillation, which depend on the initial value conditions, are revealed by numerical simulation method. In addition, under the control of memristor synapse, two heterogeneous subnets can achieve phase synchronization. Finally, the experimental verification of the circuit is completed based on STM32 MCU hardware platform.
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