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2024 Vol. 46, No. 7
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2024, 46(7): 2681-2693.
doi: 10.11999/JEIT230882
Abstract:
Tomographic Synthetic Aperture Radar (TomoSAR) can effectively recover the information of ground objects in steep terrain, and is one of the research hotspots in urban mapping. However, the current public data sets lack the true values of the object models, and cannot quantitatively verify the TomoSAR algorithm. To solve this problem and further promote the development of TomoSAR technology, this paper first proposes an RT-SBRAS (Ray Tracing Based Space Borne Radar Advanced Simulator), which can quickly and stably simulate the spaceborne SAR images of complex buildings compared with previous methods. Based on this, the 1.0 version of the 3D SAR Building Simulation (3DSARBuSim) data set is constructed, which contains the full-link simulation data of eight typical building scenes in dual-band and multi-pass. Finally, Orthogonal Matching Pursuit (OMP) and dual-frequency OMP algorithms are verified on the proposed data set, and the data set can provide clear and accurate quantitative comparison for the algorithms.
Tomographic Synthetic Aperture Radar (TomoSAR) can effectively recover the information of ground objects in steep terrain, and is one of the research hotspots in urban mapping. However, the current public data sets lack the true values of the object models, and cannot quantitatively verify the TomoSAR algorithm. To solve this problem and further promote the development of TomoSAR technology, this paper first proposes an RT-SBRAS (Ray Tracing Based Space Borne Radar Advanced Simulator), which can quickly and stably simulate the spaceborne SAR images of complex buildings compared with previous methods. Based on this, the 1.0 version of the 3D SAR Building Simulation (3DSARBuSim) data set is constructed, which contains the full-link simulation data of eight typical building scenes in dual-band and multi-pass. Finally, Orthogonal Matching Pursuit (OMP) and dual-frequency OMP algorithms are verified on the proposed data set, and the data set can provide clear and accurate quantitative comparison for the algorithms.
2024, 46(7): 2694-2702.
doi: 10.11999/JEIT231005
Abstract:
To solve the problem of low spectrum utilization of multi-node autonomous frequency decision-making in the dynamic electromagnetic countermeasure environment, the research on intelligent cooperative spectrum allocation technology for in complete electromagnetic information is carried out, which improves spectrum utilization through multi-node intelligent collaboration. Firstly, the spectrum allocation problem is modelled as an optimization problem to maximize the frequency-using equipment, and secondly, a resource decision-making algorithm based on the multi-node cooperative diversion experience repetition mechanism (Cooperation- Deep double Q-network, Co-DDQN) is proposed. This algorithm evaluates the historical experience data based on the cooperative diversion function and is trained by a hierarchical experience pool, so that each agent can form a lightweight cooperative decision-making ability under self-observation, and solve the problem of inconsistency between the optimization direction of multi-node decision-making and the overall optimization goal under low-visibility conditions. Besides, a hybrid reward function based on confidence allocation is designed, and each node considers itself when the decision is made, which can reduce the emergence of lazy nodes, explore a better overall action strategy, and further improve the system efficiency. Simulation results show that when the number of nodes is 20, the number of accessible devices of the proposed algorithm outperforms the global greedy algorithm and the genetic algorithm, and the difference with the centralized spectrum allocation algorithm with complete information sharing is within 5%, which is more suitable for cooperative spectrum allocation of low-visibility nodes.
To solve the problem of low spectrum utilization of multi-node autonomous frequency decision-making in the dynamic electromagnetic countermeasure environment, the research on intelligent cooperative spectrum allocation technology for in complete electromagnetic information is carried out, which improves spectrum utilization through multi-node intelligent collaboration. Firstly, the spectrum allocation problem is modelled as an optimization problem to maximize the frequency-using equipment, and secondly, a resource decision-making algorithm based on the multi-node cooperative diversion experience repetition mechanism (Cooperation- Deep double Q-network, Co-DDQN) is proposed. This algorithm evaluates the historical experience data based on the cooperative diversion function and is trained by a hierarchical experience pool, so that each agent can form a lightweight cooperative decision-making ability under self-observation, and solve the problem of inconsistency between the optimization direction of multi-node decision-making and the overall optimization goal under low-visibility conditions. Besides, a hybrid reward function based on confidence allocation is designed, and each node considers itself when the decision is made, which can reduce the emergence of lazy nodes, explore a better overall action strategy, and further improve the system efficiency. Simulation results show that when the number of nodes is 20, the number of accessible devices of the proposed algorithm outperforms the global greedy algorithm and the genetic algorithm, and the difference with the centralized spectrum allocation algorithm with complete information sharing is within 5%, which is more suitable for cooperative spectrum allocation of low-visibility nodes.
2024, 46(7): 2703-2711.
doi: 10.11999/JEIT230999
Abstract:
In the radar target adaptive detection problem, the presence of clutter edges in the auxiliary data will cause a serious decrease in the estimation performance of the Clutter Covariance Matrix (CCM), which greatly affects the target detection performance. In order to solve this problem, a clutter edge detection method is proposed, which can adaptively discriminate the number and position of clutter edges in auxiliary data. Firstly, assuming the presence of clutter edges in the auxiliary data, the model order selection algorithm and the maximum likelihood estimation method are used to complete the clutter parameter estimation, and the clutter edge position is obtained by the cyclic search method. Then, the clutter parameter estimation results are applied to the detection algorithm, and the existence of clutter edges is determined by the generalized likelihood ratio test method. In addition, in order to further improve the robustness of the algorithm under the condition of small samples, the special structure of CCM is introduced as a priori knowledge, and the algorithm is generalized to the situation where CCM is persymmetry, spectrum symmetry and central-symmetry. Both simulation and measured data show that the proposed algorithm can efficiently identify the number and location of clutter edges in radar auxiliary data, and the introduction of prior knowledge can further improve the performance of the algorithm when the amount of auxiliary data is small.
In the radar target adaptive detection problem, the presence of clutter edges in the auxiliary data will cause a serious decrease in the estimation performance of the Clutter Covariance Matrix (CCM), which greatly affects the target detection performance. In order to solve this problem, a clutter edge detection method is proposed, which can adaptively discriminate the number and position of clutter edges in auxiliary data. Firstly, assuming the presence of clutter edges in the auxiliary data, the model order selection algorithm and the maximum likelihood estimation method are used to complete the clutter parameter estimation, and the clutter edge position is obtained by the cyclic search method. Then, the clutter parameter estimation results are applied to the detection algorithm, and the existence of clutter edges is determined by the generalized likelihood ratio test method. In addition, in order to further improve the robustness of the algorithm under the condition of small samples, the special structure of CCM is introduced as a priori knowledge, and the algorithm is generalized to the situation where CCM is persymmetry, spectrum symmetry and central-symmetry. Both simulation and measured data show that the proposed algorithm can efficiently identify the number and location of clutter edges in radar auxiliary data, and the introduction of prior knowledge can further improve the performance of the algorithm when the amount of auxiliary data is small.
2024, 46(7): 2712-2720.
doi: 10.11999/JEIT230887
Abstract:
Due to the complex marine environment, it is difficult for a maritime radar to achieve high-performance detection of slow and small targets on the sea surface. For such targets, the traditional energy-based statistical detection algorithms suffer from serious performance loss. Confronted with this problem, a detection algorithm of small targets based on Deep Graph Infomax framework is proposed to realize unsupervised target anomaly detection in the background of sea clutter. In the traditional neural networks, there is an assumption that the samples are independent and identically distributed, which, however, the high-resolution radar echo does not meet. Therefore, this paper re-models the data from the perspective of graph and constructs the graph topological structure according to the correlation characteristics of the echo. Moreover, this paper puts forward the relative maximum node degree, and combines it with the relative average amplitude and the relative Doppler vector entropy to be the initial representation vectors of the graph nodes. With the graph modeling done, the graph attention network is used as the encoder in the Deep Graph Infomax framework to learn representation vectors. Finally, the anomaly detection algorithm is used to detect the targets, and the false alarm can be controlled. The detection result on the measured datasets shows that the performance of the proposed detector is improved by 9.2% compared to the three-feature detector when using the fast convex hull learning algorithm. Compared to the time-frequency three-feature detector, the performance is improved by 7.9%. When the network outputs a higher-dimensional representation vectors, the performance of the detector using the isolated forest algorithm is improved by 27.4%.
Due to the complex marine environment, it is difficult for a maritime radar to achieve high-performance detection of slow and small targets on the sea surface. For such targets, the traditional energy-based statistical detection algorithms suffer from serious performance loss. Confronted with this problem, a detection algorithm of small targets based on Deep Graph Infomax framework is proposed to realize unsupervised target anomaly detection in the background of sea clutter. In the traditional neural networks, there is an assumption that the samples are independent and identically distributed, which, however, the high-resolution radar echo does not meet. Therefore, this paper re-models the data from the perspective of graph and constructs the graph topological structure according to the correlation characteristics of the echo. Moreover, this paper puts forward the relative maximum node degree, and combines it with the relative average amplitude and the relative Doppler vector entropy to be the initial representation vectors of the graph nodes. With the graph modeling done, the graph attention network is used as the encoder in the Deep Graph Infomax framework to learn representation vectors. Finally, the anomaly detection algorithm is used to detect the targets, and the false alarm can be controlled. The detection result on the measured datasets shows that the performance of the proposed detector is improved by 9.2% compared to the three-feature detector when using the fast convex hull learning algorithm. Compared to the time-frequency three-feature detector, the performance is improved by 7.9%. When the network outputs a higher-dimensional representation vectors, the performance of the detector using the isolated forest algorithm is improved by 27.4%.
2024, 46(7): 2721-2731.
doi: 10.11999/JEIT231012
Abstract:
Most of the existing intelligent algorithms for identifying real and false targets are based on supervised learning and perform poorly under a low signal-to-noise ratio. Considering the above problems, an unsupervised clustering identification method of real and false targets based on frequency response features in multi-Coherent Processing Intervals(CPIs) is proposed by using the variability and uniqueness of the scattering characteristics of real and false targets in multi-CPIs, respectively. Firstly, the real and false targets are windowed and truncated along the fast time dimension in the fast-slow time domain, and the fast-slow time domain frequency response features are extracted to construct a preliminary sample set. Then, the real and false targets are identified by a two-step recognition algorithm composed of an Agglomerative clustering and a feature fusion network. Finally, a multi-CPI joint decision method is proposed to improve the recognition performance and reliability. It is proved by simulation and measured data that the proposed method can effectively identify real targets and multiple active false targets.
Most of the existing intelligent algorithms for identifying real and false targets are based on supervised learning and perform poorly under a low signal-to-noise ratio. Considering the above problems, an unsupervised clustering identification method of real and false targets based on frequency response features in multi-Coherent Processing Intervals(CPIs) is proposed by using the variability and uniqueness of the scattering characteristics of real and false targets in multi-CPIs, respectively. Firstly, the real and false targets are windowed and truncated along the fast time dimension in the fast-slow time domain, and the fast-slow time domain frequency response features are extracted to construct a preliminary sample set. Then, the real and false targets are identified by a two-step recognition algorithm composed of an Agglomerative clustering and a feature fusion network. Finally, a multi-CPI joint decision method is proposed to improve the recognition performance and reliability. It is proved by simulation and measured data that the proposed method can effectively identify real targets and multiple active false targets.
2024, 46(7): 2732-2739.
doi: 10.11999/JEIT230949
Abstract:
In order to solve the problems of time synchronization accuracy degradation of two-way time transfer due to relative motion between nodes in Flying Ad hoc NETwork (FANET), a full-duplex Two-Way Time Transfer (TWTT) method is proposed. Firstly, a system of equations to be solved is constructed according to the full-duplex two-way time transfer procedure, and the synchronization error expression for single full-duplex two-way time transfer is derived. Then, the convergence of iteratively performing full-duplex two-way time transfer with or without frequency offset is analyzed. Finally, the performance of full-duplex two-way time transfer method is compared with traditional two-way time transfer methods by simulation analysis and experimental validation. The simulation and experimental results show that full-duplex two-way time transfer method can achieve the same time synchronization accuracy as the physical layer timestamps under high-speed maneuvering between nodes, and the synchronization accuracy is better than the existing motion compensation methods.
In order to solve the problems of time synchronization accuracy degradation of two-way time transfer due to relative motion between nodes in Flying Ad hoc NETwork (FANET), a full-duplex Two-Way Time Transfer (TWTT) method is proposed. Firstly, a system of equations to be solved is constructed according to the full-duplex two-way time transfer procedure, and the synchronization error expression for single full-duplex two-way time transfer is derived. Then, the convergence of iteratively performing full-duplex two-way time transfer with or without frequency offset is analyzed. Finally, the performance of full-duplex two-way time transfer method is compared with traditional two-way time transfer methods by simulation analysis and experimental validation. The simulation and experimental results show that full-duplex two-way time transfer method can achieve the same time synchronization accuracy as the physical layer timestamps under high-speed maneuvering between nodes, and the synchronization accuracy is better than the existing motion compensation methods.
2024, 46(7): 2740-2747.
doi: 10.11999/JEIT231061
Abstract:
To address the high computational performance demands of delay-sensitive tasks in complex terrains, the collaborative computation offloading scheme for reusable tasks in mobile edge computing with the assistance of Unmanned Aerial Vehicle (UAV) is proposed. Firstly, the minimization of the average total energy consumption is formulated by jointly optimizing user offloading, user transmission power, server assignment on UAV, computation frequencies of users and UAV servers, as well as UAV flight trajectory, while meeting the latency constraints. Secondly, a deep reinforcement learning approach is employed to solve the optimization problem, and a Soft Actor-Critic (SAC) based optimization algorithm is introduced. The SAC algorithm utilizes a maximum entropy policy to encourage exploration that enhances the algorithm’s exploration capabilities and accelerates the training convergence speed. Simulation results demonstrate that the proposed SAC algorithm effectively reduces the average total energy consumption of the system while exhibiting good convergence.
To address the high computational performance demands of delay-sensitive tasks in complex terrains, the collaborative computation offloading scheme for reusable tasks in mobile edge computing with the assistance of Unmanned Aerial Vehicle (UAV) is proposed. Firstly, the minimization of the average total energy consumption is formulated by jointly optimizing user offloading, user transmission power, server assignment on UAV, computation frequencies of users and UAV servers, as well as UAV flight trajectory, while meeting the latency constraints. Secondly, a deep reinforcement learning approach is employed to solve the optimization problem, and a Soft Actor-Critic (SAC) based optimization algorithm is introduced. The SAC algorithm utilizes a maximum entropy policy to encourage exploration that enhances the algorithm’s exploration capabilities and accelerates the training convergence speed. Simulation results demonstrate that the proposed SAC algorithm effectively reduces the average total energy consumption of the system while exhibiting good convergence.
2024, 46(7): 2748-2756.
doi: 10.11999/JEIT230974
Abstract:
Unmanned Aerial Vehicles (UAV) loaded with various payloads can achieve multiple tasks such as sensing, communication, and computing, and are often deployed in fields such as Data Acquisition (DA) and auxiliary computing. However, so far, the vast majority of research has only focused on single function drone resource allocation and trajectory optimization, and the problem of multi task oriented drone resource allocation and trajectory optimization has not been solved yet. Therefore, an allocation strategy for optimizing drone communication network resources is proposed that comprehensively considers drone data acquisition, data broadcasting, and computing task offloading. The aim is to maximize user offloading by jointly optimizing transmission duty cycle, user transmission power, and drone trajectory, while meeting the real-time broadcast of target location data collection. In order to solve the problem of multivariable coupled optimization, an efficient iterative optimization algorithm based on Block Coordinate Descent (BCD) and Successive Convex Approximate (SCA) is proposed. The coupled optimization problem is decomposed into three sub problems for iterative optimization. Finally, a large number of simulation results show that the algorithm outperforms other testing schemes in terms of fairness and total offloading computation.
Unmanned Aerial Vehicles (UAV) loaded with various payloads can achieve multiple tasks such as sensing, communication, and computing, and are often deployed in fields such as Data Acquisition (DA) and auxiliary computing. However, so far, the vast majority of research has only focused on single function drone resource allocation and trajectory optimization, and the problem of multi task oriented drone resource allocation and trajectory optimization has not been solved yet. Therefore, an allocation strategy for optimizing drone communication network resources is proposed that comprehensively considers drone data acquisition, data broadcasting, and computing task offloading. The aim is to maximize user offloading by jointly optimizing transmission duty cycle, user transmission power, and drone trajectory, while meeting the real-time broadcast of target location data collection. In order to solve the problem of multivariable coupled optimization, an efficient iterative optimization algorithm based on Block Coordinate Descent (BCD) and Successive Convex Approximate (SCA) is proposed. The coupled optimization problem is decomposed into three sub problems for iterative optimization. Finally, a large number of simulation results show that the algorithm outperforms other testing schemes in terms of fairness and total offloading computation.
2024, 46(7): 2757-2764.
doi: 10.11999/JEIT230992
Abstract:
To overcome the drawback of poor adaptability of existing unbiased self-synchronous scrambling code recognition algorithms at low Signal-to-Noise Ratios (SNR), a soft-judgement recognition method based on the log conditional likelihood ratio is proposed. Firstly, the linear constraint equations for the pairwise even-vector product of the self-synchronous scrambler of linear grouping codes and the self-synchronous scrambler of convolutional codes are constructed, and then the log likelihood ratio function is introduced, the log conditional likelihood ratio function based on the soft judgment is constructed, and the distribution characteristics of its mean and variance are analyzed. Finally the identification of self-synchronous scrambler of linear grouping codes and self-synchronous scrambler of convolutional codes is accomplished through binary assumption and the derived coresponding judgement threshold value. The simulations show that the proposed algorithm is able to complete the recognition of generating polynomials at low signal-to-noise ratios, and has a good low signal-to-noise adaptation capability. Compared with the recognition method based on solving the cost function, the recognition rate of the algorithm is greatly improved at signal-to-noise ratios below 3 dB, and when the recognition rate is 90%, the proposed algorithm achieves a performance gain of about 3 dB.
To overcome the drawback of poor adaptability of existing unbiased self-synchronous scrambling code recognition algorithms at low Signal-to-Noise Ratios (SNR), a soft-judgement recognition method based on the log conditional likelihood ratio is proposed. Firstly, the linear constraint equations for the pairwise even-vector product of the self-synchronous scrambler of linear grouping codes and the self-synchronous scrambler of convolutional codes are constructed, and then the log likelihood ratio function is introduced, the log conditional likelihood ratio function based on the soft judgment is constructed, and the distribution characteristics of its mean and variance are analyzed. Finally the identification of self-synchronous scrambler of linear grouping codes and self-synchronous scrambler of convolutional codes is accomplished through binary assumption and the derived coresponding judgement threshold value. The simulations show that the proposed algorithm is able to complete the recognition of generating polynomials at low signal-to-noise ratios, and has a good low signal-to-noise adaptation capability. Compared with the recognition method based on solving the cost function, the recognition rate of the algorithm is greatly improved at signal-to-noise ratios below 3 dB, and when the recognition rate is 90%, the proposed algorithm achieves a performance gain of about 3 dB.
2024, 46(7): 2765-2772.
doi: 10.11999/JEIT231187
Abstract:
To overcome the “double path loss” in Intelligent Reflecting Surface (IRS) assisted communication system and further enhance the reliability and spectral efficiency, a Reflection Modulation (RM) system scheme based on grouping of active IRS reflecting elements is proposed. This scheme utilizes the number of active reflecting element groups to transmit additional information. Then the average pairwise error probability of both the symbols transmitted by base station and the number of active reflecting element groups under the maximum likelihood detection algorithm is derived based on the moment generating function, and an upper bound on the theoretical Bit Error Probability (BEP) as well as the achievable data rate of the system are obtained. Simulation results verify the accuracy of the theoretical derivation and demonstrate the superior error performance and spectral efficiency of the proposed scheme.
To overcome the “double path loss” in Intelligent Reflecting Surface (IRS) assisted communication system and further enhance the reliability and spectral efficiency, a Reflection Modulation (RM) system scheme based on grouping of active IRS reflecting elements is proposed. This scheme utilizes the number of active reflecting element groups to transmit additional information. Then the average pairwise error probability of both the symbols transmitted by base station and the number of active reflecting element groups under the maximum likelihood detection algorithm is derived based on the moment generating function, and an upper bound on the theoretical Bit Error Probability (BEP) as well as the achievable data rate of the system are obtained. Simulation results verify the accuracy of the theoretical derivation and demonstrate the superior error performance and spectral efficiency of the proposed scheme.
2024, 46(7): 2773-2782.
doi: 10.11999/JEIT231103
Abstract:
In order to balance the transmission reliability and efficiency of large-scale multi-mode mesh networks in the new power system, a two-stage algorithm is proposed based on reinforcement learning for joint routing selection and resource scheduling in large-scale multi-mode mesh networks, building upon the description and analysis of optimization problems. In the first stage, based on the network topology information and service requirements, a multi shortest path routing algorithm is utilized to generate all the shortest paths. In the second stage, a resource scheduling algorithm based on Multi-Armed Bandit (MAB) is proposed. The algorithm constructs the arms of the MAB based on the obtained set of shortest paths, then calculates the reward according to the service demands, and finally gives the optimal route selection and resource scheduling mode for service transmission. Simulation results show that the proposed algorithm can meet different service transmission requirements, and achieve an efficient balance between the average end-to-end path delay and the average transmission success rate.
In order to balance the transmission reliability and efficiency of large-scale multi-mode mesh networks in the new power system, a two-stage algorithm is proposed based on reinforcement learning for joint routing selection and resource scheduling in large-scale multi-mode mesh networks, building upon the description and analysis of optimization problems. In the first stage, based on the network topology information and service requirements, a multi shortest path routing algorithm is utilized to generate all the shortest paths. In the second stage, a resource scheduling algorithm based on Multi-Armed Bandit (MAB) is proposed. The algorithm constructs the arms of the MAB based on the obtained set of shortest paths, then calculates the reward according to the service demands, and finally gives the optimal route selection and resource scheduling mode for service transmission. Simulation results show that the proposed algorithm can meet different service transmission requirements, and achieve an efficient balance between the average end-to-end path delay and the average transmission success rate.
2024, 46(7): 2783-2792.
doi: 10.11999/JEIT231092
Abstract:
A decentralized, revocable, and privacy-preserving Self-Sovereign Identity (SSI) solution based on blockchain is proposed to address digital identity management challenges for users in the context of the Vehicular Ad hoc NETworks (VANETs). The Road Side Units (RSU) authorized by a Trusted Authority (TA) to form a committee are responsible for user registration, credential issuer and management.The threshold BLS signature and the Practical Byzantine Fault Tolerance (PBFT) consensus mechanism are uesd to create digital identity credentials to resolve the issues resulted in the centralized certification authorities. The combining secret sharing and zero-knowledge proof addresses privacy concerns during identity credential issuance and verification. The cryptographic accumulator is employed to tackle the revocation of user identity credentials in distributed storage scenarios. Finally, our comprehensive security analysis demonstrates the novel scheme can meet the proposed security objectives. The novel solution is implemented on an Ethereum private blockchain by using smart contracts, and experimental results show the reliability, feasibility and effectiveness of our scheme.
A decentralized, revocable, and privacy-preserving Self-Sovereign Identity (SSI) solution based on blockchain is proposed to address digital identity management challenges for users in the context of the Vehicular Ad hoc NETworks (VANETs). The Road Side Units (RSU) authorized by a Trusted Authority (TA) to form a committee are responsible for user registration, credential issuer and management.The threshold BLS signature and the Practical Byzantine Fault Tolerance (PBFT) consensus mechanism are uesd to create digital identity credentials to resolve the issues resulted in the centralized certification authorities. The combining secret sharing and zero-knowledge proof addresses privacy concerns during identity credential issuance and verification. The cryptographic accumulator is employed to tackle the revocation of user identity credentials in distributed storage scenarios. Finally, our comprehensive security analysis demonstrates the novel scheme can meet the proposed security objectives. The novel solution is implemented on an Ethereum private blockchain by using smart contracts, and experimental results show the reliability, feasibility and effectiveness of our scheme.
2024, 46(7): 2793-2802.
doi: 10.11999/JEIT230984
Abstract:
In order to mitigate the problem of inaccurate synchronization sensory information in Digital Twins (DTs) caused by unreliable and delayed transmission in Radio Access Networks (RAN), a sensory information synchronization strategy for DTs based on intelligent hierarchical slicing technology is proposed. The strategy aims to optimize the allocation of wireless resources for slicing and the synchronization of DTs’ sensing information in dual time scales, with the goals of maximizing the satisfaction of sensing information and minimizing the costs associated with slicing reconfiguration and DTs’ synchronization. Firstly, at large time scales, network slicing is employed to provide isolation for DTs with varying Quality of Service (QoS) and resolve deployment challenges; At small time scales, a more flexible wireless resource allocation is utilized to enhance the adaptability of DTs’ sensory information synchronization to dynamic environments. Secondly, in order to optimize the synchronization of DTs’ sensory information at different time scales, a two-layer Deep Reinforcement Learning (DRL) framework is introduced to facilitate efficient network resource interaction, and in the framework the lower-layer control algorithm incorporates the Prioritized Experience Replay (PER) mechanism to accelerate convergence speed. Finally, the effectiveness of the proposed strategy is validated through simulation results.
In order to mitigate the problem of inaccurate synchronization sensory information in Digital Twins (DTs) caused by unreliable and delayed transmission in Radio Access Networks (RAN), a sensory information synchronization strategy for DTs based on intelligent hierarchical slicing technology is proposed. The strategy aims to optimize the allocation of wireless resources for slicing and the synchronization of DTs’ sensing information in dual time scales, with the goals of maximizing the satisfaction of sensing information and minimizing the costs associated with slicing reconfiguration and DTs’ synchronization. Firstly, at large time scales, network slicing is employed to provide isolation for DTs with varying Quality of Service (QoS) and resolve deployment challenges; At small time scales, a more flexible wireless resource allocation is utilized to enhance the adaptability of DTs’ sensory information synchronization to dynamic environments. Secondly, in order to optimize the synchronization of DTs’ sensory information at different time scales, a two-layer Deep Reinforcement Learning (DRL) framework is introduced to facilitate efficient network resource interaction, and in the framework the lower-layer control algorithm incorporates the Prioritized Experience Replay (PER) mechanism to accelerate convergence speed. Finally, the effectiveness of the proposed strategy is validated through simulation results.
2024, 46(7): 2803-2811.
doi: 10.11999/JEIT230935
Abstract:
Considering the problem of blind identification of Unique Words (UW) for Time Division Multiple Access (TDMA) signals in non-cooperative communication, a blind identification algorithm for distributed UW is proposed in this paper. Different from the unique codes recognition algorithm at the bit layer, a unique words recognition algorithm at the waveform layer oriented to the correlation is proposed between different windows of the modulated data for centralised unique words and distributed unique words, respectively. The algorithm takes advantage of the consistency and correlation of the unique words and proceeds in two steps: firstly, the unique words of different burst signals are vertically aligned by eliminating the effects of frequency and phase bias between the different burst signals through differential accumulation, and then the positions and lengths of the unique words are identified by the multilayer differential conjugate fourth order correlation algorithm. The performance of the algorithm is simulated and analysed with different number of bursts, signal-to-noise ratios, and with or without frequency and phase biases, and the effectiveness of the waveform layer identification of unique words is verified, and the algorithm achieves more than 95% of the identification rate at a signal-to-noise ratio of 5dB for both centralized and distributed unique words, which is of certain value for engineering applications.
Considering the problem of blind identification of Unique Words (UW) for Time Division Multiple Access (TDMA) signals in non-cooperative communication, a blind identification algorithm for distributed UW is proposed in this paper. Different from the unique codes recognition algorithm at the bit layer, a unique words recognition algorithm at the waveform layer oriented to the correlation is proposed between different windows of the modulated data for centralised unique words and distributed unique words, respectively. The algorithm takes advantage of the consistency and correlation of the unique words and proceeds in two steps: firstly, the unique words of different burst signals are vertically aligned by eliminating the effects of frequency and phase bias between the different burst signals through differential accumulation, and then the positions and lengths of the unique words are identified by the multilayer differential conjugate fourth order correlation algorithm. The performance of the algorithm is simulated and analysed with different number of bursts, signal-to-noise ratios, and with or without frequency and phase biases, and the effectiveness of the waveform layer identification of unique words is verified, and the algorithm achieves more than 95% of the identification rate at a signal-to-noise ratio of 5dB for both centralized and distributed unique words, which is of certain value for engineering applications.
2024, 46(7): 2812-2820.
doi: 10.11999/JEIT230830
Abstract:
Unmanned Aerial Vehicle (UAV) equipped with Intelligent Reflecting Surface (IRS) can effectively solve the problem of inefficient information and energy transmission between the hybrid access point and nodes in complex wireless scenarios due to obstacle occlusion. A novel framework for aerial IRS-assisted wireless powered communication networks is proposed that exploits the flexibility of aerial IRS to improve the performance of the network. The architecture achieves the transmission of energy and data for each time slot employing the harvest-then-transmit scheme. A multi-variable coupled optimization problem that combines the flight trajectory, node selection association variable, time slot allocation ratio, and the phase is established while satisfying the node energy harvesting threshold. Thus, the block coordinate descent algorithm is utilized to decompose the optimization problem into four sub-problems to be solved separately. Firstly, the closed-form solution for the optimal phase of the intelligent reflecting surface is derived based on the beam alignment theory. Secondly, the non-convex problem is transformed into a convex problem by introducing auxiliary variables and employing a successive convex approximation algorithm. Finally, the solution is iteratively solved utilizing the block coordinate descent algorithm. Simulation results show that the proposed scheme has excellent convergence performance and significantly improve the average throughput.
Unmanned Aerial Vehicle (UAV) equipped with Intelligent Reflecting Surface (IRS) can effectively solve the problem of inefficient information and energy transmission between the hybrid access point and nodes in complex wireless scenarios due to obstacle occlusion. A novel framework for aerial IRS-assisted wireless powered communication networks is proposed that exploits the flexibility of aerial IRS to improve the performance of the network. The architecture achieves the transmission of energy and data for each time slot employing the harvest-then-transmit scheme. A multi-variable coupled optimization problem that combines the flight trajectory, node selection association variable, time slot allocation ratio, and the phase is established while satisfying the node energy harvesting threshold. Thus, the block coordinate descent algorithm is utilized to decompose the optimization problem into four sub-problems to be solved separately. Firstly, the closed-form solution for the optimal phase of the intelligent reflecting surface is derived based on the beam alignment theory. Secondly, the non-convex problem is transformed into a convex problem by introducing auxiliary variables and employing a successive convex approximation algorithm. Finally, the solution is iteratively solved utilizing the block coordinate descent algorithm. Simulation results show that the proposed scheme has excellent convergence performance and significantly improve the average throughput.
2024, 46(7): 2821-2830.
doi: 10.11999/JEIT230976
Abstract:
Federated Learning (FL) emerges as a distributed training method in the Internet of Vehicle (IoV), allowing Connected and Automated Vehicles (CAVs) to train a global model by exchanging models instead of raw data, protecting data privacy. Due to the limitation of model accuracy and communication overhead in FL, in this paper, a Directed Acyclic Graph (DAG) blockchain-based IoV is proposed that comprises a DAG layer and a CAV layer for model sharing and training, respectively. Furthermore, a DAG blockchain-based Asynchronous Federated Mutual distillation Learning (DAFML) algorithm is introduced to improve the model performance, which utilizes a teacher model and a student model to mutual distillation in the local training. Specifically, the teacher model with a professional network could achieve higher model accuracy, while the student model with a lightweight network could reduce the communication overhead in contrast. Moreover, to further improve the model accuracy, the personalized weight based on global epoch and model accuracy is designed to adjust the mutual distillation in the model updating. Simulation results demonstrate that the proposed DAFML algorithm outperforms other benchmarks in terms of the model accuracy and distillation ratio.
Federated Learning (FL) emerges as a distributed training method in the Internet of Vehicle (IoV), allowing Connected and Automated Vehicles (CAVs) to train a global model by exchanging models instead of raw data, protecting data privacy. Due to the limitation of model accuracy and communication overhead in FL, in this paper, a Directed Acyclic Graph (DAG) blockchain-based IoV is proposed that comprises a DAG layer and a CAV layer for model sharing and training, respectively. Furthermore, a DAG blockchain-based Asynchronous Federated Mutual distillation Learning (DAFML) algorithm is introduced to improve the model performance, which utilizes a teacher model and a student model to mutual distillation in the local training. Specifically, the teacher model with a professional network could achieve higher model accuracy, while the student model with a lightweight network could reduce the communication overhead in contrast. Moreover, to further improve the model accuracy, the personalized weight based on global epoch and model accuracy is designed to adjust the mutual distillation in the model updating. Simulation results demonstrate that the proposed DAFML algorithm outperforms other benchmarks in terms of the model accuracy and distillation ratio.
2024, 46(7): 2831-2841.
doi: 10.11999/JEIT231016
Abstract:
The Space-Air-Ground Integrated Network (SAGIN) can effectively meet the communication needs of various service types by improving the resource utilization of the ground network, but ignoring the adaptive ability and robustness of the system and the Quality of Service (QoS) in different users. In response to this problem, a Deep Reinforcement Learning (DRL) Resource allocation algorithm for urban and suburban communications under the SAGIN architecture is proposed in this paper. Based on Reference Signal Reception Power (RSRP) defined in the 3rd Generation Partnership Project (3GPP) standard, considering ground co-frequency interference, and using the time-frequency resources of base stations in different domains as constraints, an optimization problem to maxmize the downlink throughput of system users is constructed. When using the Deep Q-network (DQN) algorithm to solve the optimization problem, a reward function which can comprehensively consider the user’s QoS requirements, system adaptability and system robustness is defined. Considering the service requirements of unmanned vehicles, immersive services and ordinary mobile communication services, the simulation results show that the value of the reward function which represents the performance of the system is increased by 39.1% compared with the greedy algorithm under 2 000 iterations. For the unmanned vehicle services, the average packet loss rate by the DQN algorithm is 38.07% lower than that by the greedy algorithm, and the delay by the DQN algorithm is also 6.05% lower than that by the greedy algorithm.
The Space-Air-Ground Integrated Network (SAGIN) can effectively meet the communication needs of various service types by improving the resource utilization of the ground network, but ignoring the adaptive ability and robustness of the system and the Quality of Service (QoS) in different users. In response to this problem, a Deep Reinforcement Learning (DRL) Resource allocation algorithm for urban and suburban communications under the SAGIN architecture is proposed in this paper. Based on Reference Signal Reception Power (RSRP) defined in the 3rd Generation Partnership Project (3GPP) standard, considering ground co-frequency interference, and using the time-frequency resources of base stations in different domains as constraints, an optimization problem to maxmize the downlink throughput of system users is constructed. When using the Deep Q-network (DQN) algorithm to solve the optimization problem, a reward function which can comprehensively consider the user’s QoS requirements, system adaptability and system robustness is defined. Considering the service requirements of unmanned vehicles, immersive services and ordinary mobile communication services, the simulation results show that the value of the reward function which represents the performance of the system is increased by 39.1% compared with the greedy algorithm under 2 000 iterations. For the unmanned vehicle services, the average packet loss rate by the DQN algorithm is 38.07% lower than that by the greedy algorithm, and the delay by the DQN algorithm is also 6.05% lower than that by the greedy algorithm.
2024, 46(7): 2842-2849.
doi: 10.11999/JEIT230918
Abstract:
In response to the characteristics of heterogeneous node resources and dynamic changes in the network topology in the Internet of Vehicles (IoV), a Two-layer Asynchronous Federated Learning with Two-factor updating (TTAFL) framework is established in this paper. Considering the impact of model version differences and the number of times that vehicles participate in Federated Learning (FL) on server model updates, a model update scheme based on staleness factor and contribution factor is proposed. Furthermore, to avoid the problem of roadside unit switching caused by vehicle mobility during the training process, a node selection scheme considering the residence time is given. Finally, in order to reduce the accuracy loss and system energy consumption, a reinforcement learning method is used to optimize the number of local iterations of FL and the number of local model updates of roadside units. Simulation results show that the proposed algorithm effectively improves the training efficiency and training accuracy of federated learning and reduces the system energy consumption.
In response to the characteristics of heterogeneous node resources and dynamic changes in the network topology in the Internet of Vehicles (IoV), a Two-layer Asynchronous Federated Learning with Two-factor updating (TTAFL) framework is established in this paper. Considering the impact of model version differences and the number of times that vehicles participate in Federated Learning (FL) on server model updates, a model update scheme based on staleness factor and contribution factor is proposed. Furthermore, to avoid the problem of roadside unit switching caused by vehicle mobility during the training process, a node selection scheme considering the residence time is given. Finally, in order to reduce the accuracy loss and system energy consumption, a reinforcement learning method is used to optimize the number of local iterations of FL and the number of local model updates of roadside units. Simulation results show that the proposed algorithm effectively improves the training efficiency and training accuracy of federated learning and reduces the system energy consumption.
2024, 46(7): 2850-2859.
doi: 10.11999/JEIT230898
Abstract:
Considering the problem of unknown and highly heterogeneous user preference in the current edge caching scenario, a joint optimization strategy of user request perceived edge caching and user recommendation is proposed. Firstly, the basic model of Click Through Rate (CTR) prediction is established, and the contrastive learning method is introduced to generate high-quality feature representation, which could better help Factorization Machine(FM) model to predict user preference. Then, based on the predicted user preference, a dynamic recommendation mechanism is designed to reshape the content request probability of different users, thereby affecting cache decision; Finally, a joint optimization problem of edge caching and user recommendation is established with the goal of minimizing the average user content acquisition delay. It is decoupled into edge caching subproblem and user recommendation subproblem, and solved based on the region greedy algorithm and one-to-one exchange matching algorithm, respectively. The convergence optimization results are obtained through iterative update. The results show that compared with the benchmark model, the contrastive learning method has improved Area Under Curve (AUC) and ACCuracy (ACC) by 1.65% and 1.30%, respectively, and the joint optimization algorithm can effectively reduce the average content acquisition latency of users and improve the system cache performance.
Considering the problem of unknown and highly heterogeneous user preference in the current edge caching scenario, a joint optimization strategy of user request perceived edge caching and user recommendation is proposed. Firstly, the basic model of Click Through Rate (CTR) prediction is established, and the contrastive learning method is introduced to generate high-quality feature representation, which could better help Factorization Machine(FM) model to predict user preference. Then, based on the predicted user preference, a dynamic recommendation mechanism is designed to reshape the content request probability of different users, thereby affecting cache decision; Finally, a joint optimization problem of edge caching and user recommendation is established with the goal of minimizing the average user content acquisition delay. It is decoupled into edge caching subproblem and user recommendation subproblem, and solved based on the region greedy algorithm and one-to-one exchange matching algorithm, respectively. The convergence optimization results are obtained through iterative update. The results show that compared with the benchmark model, the contrastive learning method has improved Area Under Curve (AUC) and ACCuracy (ACC) by 1.65% and 1.30%, respectively, and the joint optimization algorithm can effectively reduce the average content acquisition latency of users and improve the system cache performance.
2024, 46(7): 2860-2868.
doi: 10.11999/JEIT230942
Abstract:
Considering the secure communication of the distributed Reconfigurable Intelligent Surfaces (RISs) under imperfect Channel State Information (CSI), a joint optimization problem of the secrecy rate maximization based on the active beamforming, Artificial Noise(AN), and RISs’ phase shifts is formulated. Then an efficient algorithm based on alternating optimization and 1-Dimensional linear search is proposed to solve the non-convex optimization problem. Simulation results demonstrate that, compared with the random phase optimization scheme and the secure transmission without AN scheme, the proposed scheme can achieve a higher secrecy rate. The superiority of the proposed scheme over the other transmission schemes becomes more prominent with the increase of the number of distribution units. The proposed scheme has better robustness than the other transmission schemes to the uncertainty of communication channel in our considered network.
Considering the secure communication of the distributed Reconfigurable Intelligent Surfaces (RISs) under imperfect Channel State Information (CSI), a joint optimization problem of the secrecy rate maximization based on the active beamforming, Artificial Noise(AN), and RISs’ phase shifts is formulated. Then an efficient algorithm based on alternating optimization and 1-Dimensional linear search is proposed to solve the non-convex optimization problem. Simulation results demonstrate that, compared with the random phase optimization scheme and the secure transmission without AN scheme, the proposed scheme can achieve a higher secrecy rate. The superiority of the proposed scheme over the other transmission schemes becomes more prominent with the increase of the number of distribution units. The proposed scheme has better robustness than the other transmission schemes to the uncertainty of communication channel in our considered network.
2024, 46(7): 2869-2878.
doi: 10.11999/JEIT230902
Abstract:
In order to solve the high-dimensional Service Function Chain (SFC) deployment problem of high reliability and low cost in the Network Function Virtualization (NFV) environment, an Improving Service and Reducing Consumption based on Proximal Policy Optimization (PPO-ISRC) is proposed. Firstly, considering the characteristics of the underlying physical server and SFC, the state transition process of the underlying server network is descried, and the deployment of SFC is taken as a Markov Decision Process. Then the reward function is set with the optimization goal of maximizing the service rate and minimizing resource consumption. Finally the PPO method is used to solve the SFC deployment strategy. The results show that compared with the heuristic algorithm First-Fit Dijkstra (FFD) and the Deep Deterministic Policy Gradient (DDPG) algorithm, the proposed algorithm has the characteristics of fast convergence speed and higher stability. Under the requirements of service quality, the deployment cost is reduced and the reliability of network service is improved.
In order to solve the high-dimensional Service Function Chain (SFC) deployment problem of high reliability and low cost in the Network Function Virtualization (NFV) environment, an Improving Service and Reducing Consumption based on Proximal Policy Optimization (PPO-ISRC) is proposed. Firstly, considering the characteristics of the underlying physical server and SFC, the state transition process of the underlying server network is descried, and the deployment of SFC is taken as a Markov Decision Process. Then the reward function is set with the optimization goal of maximizing the service rate and minimizing resource consumption. Finally the PPO method is used to solve the SFC deployment strategy. The results show that compared with the heuristic algorithm First-Fit Dijkstra (FFD) and the Deep Deterministic Policy Gradient (DDPG) algorithm, the proposed algorithm has the characteristics of fast convergence speed and higher stability. Under the requirements of service quality, the deployment cost is reduced and the reliability of network service is improved.
2024, 46(7): 2879-2887.
doi: 10.11999/JEIT230986
Abstract:
In recent years, mounting Mobile Edge Computing (MEC) servers on Unmanned Aerial Vehicle (UAV) to provide services for mobile ground users has been widely researched in academia and industry. However, in malicious jamming environments, how to effectively schedule resources to reduce system delay and energy consumption becomes a key challenge. Therefore, this paper considers a UAV-assisted MEC system under a malicious jammer, where an optimization model is established to minimize the weighted energy consumption and delay by jointly optimizing UAV flight trajectories, resource scheduling, and task allocation. As the optimization problem is difficult to be solved and the malicious jamming behavior is dynamic, a Twin Delayed Deep Deterministic (TD3) policy gradient algorithm is proposed to search for the optimal policy. At the same time, the Prioritized Experience Replay (PER) technique is added to improve the convergence speed and stability of the algorithm, which is highly effective against malicious interference attacks. The simulation results show that the proposed algorithm can effectively reduce the delay and energy consumption, and achieve good convergence and stability compared with other algorithms.
In recent years, mounting Mobile Edge Computing (MEC) servers on Unmanned Aerial Vehicle (UAV) to provide services for mobile ground users has been widely researched in academia and industry. However, in malicious jamming environments, how to effectively schedule resources to reduce system delay and energy consumption becomes a key challenge. Therefore, this paper considers a UAV-assisted MEC system under a malicious jammer, where an optimization model is established to minimize the weighted energy consumption and delay by jointly optimizing UAV flight trajectories, resource scheduling, and task allocation. As the optimization problem is difficult to be solved and the malicious jamming behavior is dynamic, a Twin Delayed Deep Deterministic (TD3) policy gradient algorithm is proposed to search for the optimal policy. At the same time, the Prioritized Experience Replay (PER) technique is added to improve the convergence speed and stability of the algorithm, which is highly effective against malicious interference attacks. The simulation results show that the proposed algorithm can effectively reduce the delay and energy consumption, and achieve good convergence and stability compared with other algorithms.
2024, 46(7): 2888-2897.
doi: 10.11999/JEIT231033
Abstract:
The system energy consumption minimization problem is studied for a data compression based Non-Orthogonal Multiple Access-Mobile Edge Computing (NOMA-MEC) system. Considering the partial compression and offloading schemes and the limited computation capacity at the base station, a system energy consumption minimization optimization problem is formulated by jointly optimizing the users’ data compression and offloading ratios, transmit power, data compression time, etc. In order to solve this problem, closed-form expression of each user’s optimal transmit power is firstly derived. Then the Successive Convex Approximation (SCA) method is used to approximate the non-convex constraints of the formulated problem, and An SCA based efficient iterative algorithm is proposed to solve the formulated problem, obtaining the optimal resource allocation scheme of the system. Finally, the simulation results verify the advantages of the proposed scheme via computer simulations and show that compared with other benchmark schemes, the proposed scheme can effectively reduce the system energy consumption.
The system energy consumption minimization problem is studied for a data compression based Non-Orthogonal Multiple Access-Mobile Edge Computing (NOMA-MEC) system. Considering the partial compression and offloading schemes and the limited computation capacity at the base station, a system energy consumption minimization optimization problem is formulated by jointly optimizing the users’ data compression and offloading ratios, transmit power, data compression time, etc. In order to solve this problem, closed-form expression of each user’s optimal transmit power is firstly derived. Then the Successive Convex Approximation (SCA) method is used to approximate the non-convex constraints of the formulated problem, and An SCA based efficient iterative algorithm is proposed to solve the formulated problem, obtaining the optimal resource allocation scheme of the system. Finally, the simulation results verify the advantages of the proposed scheme via computer simulations and show that compared with other benchmark schemes, the proposed scheme can effectively reduce the system energy consumption.
2024, 46(7): 2898-2907.
doi: 10.11999/JEIT230652
Abstract:
In tasks such as precise guidance and obstacle avoidance navigation based on optical compound eyes, the calibration of optical compound eyes plays a crucial role in achieving high accuracy. The classical Zhang’s calibration method requires each ommatidium of the optical compound eyes to observe a complete chessboard pattern. However, the complexity of the optical compound eye structure makes it difficult to satisfy this requirement in practical applications. In this paper, a joint internal and external parameters calibration algorithm of optical compound eyes based on a random noise plate calibration pattern is proposed. This algorithm utilizes the local information captured by the ommatidia when photographing the random noise calibration pattern, enabling simple and fast calibration for optical compound eyes with arbitrary configurations and numbers of ommatidia. To improve the robustness of the calibration, a multi-threshold matching mechanism is introduced to address the issue of sparse feature point quantity in ommatidial visual fields leading to matching failures. Moreover, an error model for the joint internal and external parameters calibration of optical compound eyes is presented to evaluate the accuracy of the proposed algorithm. Experimental comparisons with Zhang’s calibration method demonstrate the robustness of the proposed algorithm. Furthermore, the high accuracy of the proposed joint calibration algorithm is validated in a physical system of optical compound eyes.
In tasks such as precise guidance and obstacle avoidance navigation based on optical compound eyes, the calibration of optical compound eyes plays a crucial role in achieving high accuracy. The classical Zhang’s calibration method requires each ommatidium of the optical compound eyes to observe a complete chessboard pattern. However, the complexity of the optical compound eye structure makes it difficult to satisfy this requirement in practical applications. In this paper, a joint internal and external parameters calibration algorithm of optical compound eyes based on a random noise plate calibration pattern is proposed. This algorithm utilizes the local information captured by the ommatidia when photographing the random noise calibration pattern, enabling simple and fast calibration for optical compound eyes with arbitrary configurations and numbers of ommatidia. To improve the robustness of the calibration, a multi-threshold matching mechanism is introduced to address the issue of sparse feature point quantity in ommatidial visual fields leading to matching failures. Moreover, an error model for the joint internal and external parameters calibration of optical compound eyes is presented to evaluate the accuracy of the proposed algorithm. Experimental comparisons with Zhang’s calibration method demonstrate the robustness of the proposed algorithm. Furthermore, the high accuracy of the proposed joint calibration algorithm is validated in a physical system of optical compound eyes.
2024, 46(7): 2908-2917.
doi: 10.11999/JEIT230803
Abstract:
Due to the interference and background drift between new and old task parameters, semantic segmentation model based on deep neural networks promotes catastrophic forgetting of old knowledge. Furthermore, information frequently cannot be stored owing to privacy concerns, security concerns, and other issues, which leads to model failure. Therefore, a continual semantic segmentation method based on gating mechanism and replay strategy is proposed. First, without storing old data, generative adversarial network and webpage crawling are used as data sources, the label evaluation module is used to solve the unsupervised problem and the background self-drawing module is used to solve background drift problem. Then, catastrophic forgetting is mitigated by replay strategy; Finally, gated variables are used as a regularization means to increase the sparsity of the module and study the special case of gated variables combined with continual learning replay strategy. Our evaluation results on the Pascal VOC2012 dataset show that in the settings of complex scenario 10-2, Generative Adversarial Networks (GAN) and Web, the performance of the old task after all incremental steps are improved by 3.8% and 3.7% compared with the baseline, and in scenario 10-1, they are improved by 2.7% and 1.3% compared with the baseline, respectively.
Due to the interference and background drift between new and old task parameters, semantic segmentation model based on deep neural networks promotes catastrophic forgetting of old knowledge. Furthermore, information frequently cannot be stored owing to privacy concerns, security concerns, and other issues, which leads to model failure. Therefore, a continual semantic segmentation method based on gating mechanism and replay strategy is proposed. First, without storing old data, generative adversarial network and webpage crawling are used as data sources, the label evaluation module is used to solve the unsupervised problem and the background self-drawing module is used to solve background drift problem. Then, catastrophic forgetting is mitigated by replay strategy; Finally, gated variables are used as a regularization means to increase the sparsity of the module and study the special case of gated variables combined with continual learning replay strategy. Our evaluation results on the Pascal VOC2012 dataset show that in the settings of complex scenario 10-2, Generative Adversarial Networks (GAN) and Web, the performance of the old task after all incremental steps are improved by 3.8% and 3.7% compared with the baseline, and in scenario 10-1, they are improved by 2.7% and 1.3% compared with the baseline, respectively.
2024, 46(7): 2918-2931.
doi: 10.11999/JEIT230719
Abstract:
Salient Object Detection (SOD) aims to recognize and segment visual salient objects in images, which is one of the important research contents in computer vision tasks and related fields. Existing Fully Convolutional Networks (FCNs)-based SOD methods have achieved good performance. However, the types and sizes of salient objects are variable and unfixed in real-world scenes, which makes it still a huge challenge to detect and segment salient objects accurately and completely. For that, in this paper, a novel integrating multiple context and hybrid interaction for SOD task is proposed to efficiently predict salient objects by collaborating Dense Context Information Exploration (DCIE) module and Multi-source Feature Hybrid Interaction (MFHI) module. The DCIE module uses dilated convolution, asymmetric convolution and dense guided connection to progressively capture the strongly correlated multi-scale and multi-receptive field context information, and enhances the expression ability of each initial input feature by aggregating context information. The MFHI module contains diverse feature aggregation operations, which can adaptively interact with complementary information from multi-level features to generate high-quality feature representations for accurately predicting saliency maps. The performance of the proposed method is tested on five public datasets. The performance of the proposed method is tested on five public datasets. Experimental results demonstrate that our method achieves superior prediction performance compared with 19 state-of-the-art SOD methods under different evaluation metrics.
Salient Object Detection (SOD) aims to recognize and segment visual salient objects in images, which is one of the important research contents in computer vision tasks and related fields. Existing Fully Convolutional Networks (FCNs)-based SOD methods have achieved good performance. However, the types and sizes of salient objects are variable and unfixed in real-world scenes, which makes it still a huge challenge to detect and segment salient objects accurately and completely. For that, in this paper, a novel integrating multiple context and hybrid interaction for SOD task is proposed to efficiently predict salient objects by collaborating Dense Context Information Exploration (DCIE) module and Multi-source Feature Hybrid Interaction (MFHI) module. The DCIE module uses dilated convolution, asymmetric convolution and dense guided connection to progressively capture the strongly correlated multi-scale and multi-receptive field context information, and enhances the expression ability of each initial input feature by aggregating context information. The MFHI module contains diverse feature aggregation operations, which can adaptively interact with complementary information from multi-level features to generate high-quality feature representations for accurately predicting saliency maps. The performance of the proposed method is tested on five public datasets. The performance of the proposed method is tested on five public datasets. Experimental results demonstrate that our method achieves superior prediction performance compared with 19 state-of-the-art SOD methods under different evaluation metrics.
2024, 46(7): 2932-2941.
doi: 10.11999/JEIT230801
Abstract:
To address the challenge of aligning multimodal data and improving the slow translation speed in sign language translation, a Transformer Sign Language Translation Non-Autoregression (Trans-SLT-NA) is proposed in this paper, which utilizes a self-attention mechanism. Additionally, it incorporates a contrastive learning loss function to align the multimodal data. By capturing the contextual and interaction information between the input sequence (sign language videos) and the target sequence (text), the proposed model is able to perform sign language translation to natural language in s single step. The effectiveness of the proposed model is evaluated on publicly available datasets, including PHOENIX-2014-T (German), CSL (Chinese) and How2Sign (English). Results demonstrate that the proposed method achieves a significant improvement in translation speed, with a speed boost ranging from 11.6 to 17.6 times compared to autoregressive models, while maintaining comparable performance in terms of BiLingual Evaluation Understudy (BLEU-4) and Recall-Oriented Understudy for Gisting Evaluation (ROUGE) metrics.
To address the challenge of aligning multimodal data and improving the slow translation speed in sign language translation, a Transformer Sign Language Translation Non-Autoregression (Trans-SLT-NA) is proposed in this paper, which utilizes a self-attention mechanism. Additionally, it incorporates a contrastive learning loss function to align the multimodal data. By capturing the contextual and interaction information between the input sequence (sign language videos) and the target sequence (text), the proposed model is able to perform sign language translation to natural language in s single step. The effectiveness of the proposed model is evaluated on publicly available datasets, including PHOENIX-2014-T (German), CSL (Chinese) and How2Sign (English). Results demonstrate that the proposed method achieves a significant improvement in translation speed, with a speed boost ranging from 11.6 to 17.6 times compared to autoregressive models, while maintaining comparable performance in terms of BiLingual Evaluation Understudy (BLEU-4) and Recall-Oriented Understudy for Gisting Evaluation (ROUGE) metrics.
2024, 46(7): 2942-2951.
doi: 10.11999/JEIT230966
Abstract:
A hybrid detector AEM-YOLO based on the adaptive fusion of different scale features is proposed, aiming at the problems of difficult detection of small objects in remote sensing images caused by the high background noise, dense arrangement of small objects, and wide-scale distribution. Firstly, a two-axes k-means clustering algorithm combining width and height information with scale and ratio information is proposed to generate anchor boxes with high matching degrees with remote sensing datasets. Secondly, an adaptive enhance module is designed to address information conflicts caused by direct fusion between different scale features. A lower feature layer is introduced to broadcast small object details along the bottom-up path. By using multi-task learning and scale guidance factor, the recall for objects with a high aspect ratio can be effectively improved. Finally, the experiments on the DIOR dataset show that compared with the original model, the AP of AEM-YOLO is improved by 7.8%, and increased by 5.4%, 7.2%, and 8.6% in small, medium, and large object detection, respectively.
A hybrid detector AEM-YOLO based on the adaptive fusion of different scale features is proposed, aiming at the problems of difficult detection of small objects in remote sensing images caused by the high background noise, dense arrangement of small objects, and wide-scale distribution. Firstly, a two-axes k-means clustering algorithm combining width and height information with scale and ratio information is proposed to generate anchor boxes with high matching degrees with remote sensing datasets. Secondly, an adaptive enhance module is designed to address information conflicts caused by direct fusion between different scale features. A lower feature layer is introduced to broadcast small object details along the bottom-up path. By using multi-task learning and scale guidance factor, the recall for objects with a high aspect ratio can be effectively improved. Finally, the experiments on the DIOR dataset show that compared with the original model, the AP of AEM-YOLO is improved by 7.8%, and increased by 5.4%, 7.2%, and 8.6% in small, medium, and large object detection, respectively.
2024, 46(7): 2952-2960.
doi: 10.11999/JEIT231082
Abstract:
Considering the problem of the non-uniformity of the ramp signal in the large-array CMOS Image Sensors (CIS), a ramp uniformity correction method for CMOS image sensors is proposed in this paper. The correction method is based on error storage and level shift ideas. Storage capacitor that are used to store ramp non-uniformity error are introduced in column readout circuit. According to the stored ramp non-uniformity error, the ramp signal of each column is shifted. So as to ensure the uniformity of the ramp signal. Based on the 55 nm 1P4M CMOS process, this paper has completed the detailed circuit design and comprehensive simulation verification of the proposed method. Under the design conditions that the voltage range of the ramp signal is 1.4 V, the slope of the ramp signal is 71.908 V/ms, the number of pixel area arrays is 8192(H)×8192(V), and a single pixel size is 10 μm, the proposed correction method reduces the ramp non-uniformity error from 7.89mV to 36.8 μV. The Differential NonLinearity (DNL) of the ramp signal is +0.001 3/–0.004 LSB and the Integral NonLinearity (INL) is +0.045/–0.02 LSB. The Column Fixed Pattern Noise(CFPN) is reduced from 1.9% to 0.01%. The proposed ramp uniformity correction method reduces the ramp non-uniformity error by 99.53% on the basis of ensuring the high linearity of the ramp signal, without significantly increasing the chip area and without introducing additional power consumption. It provides a certain theoretical support for the design of high-precision CMOS image sensors.
Considering the problem of the non-uniformity of the ramp signal in the large-array CMOS Image Sensors (CIS), a ramp uniformity correction method for CMOS image sensors is proposed in this paper. The correction method is based on error storage and level shift ideas. Storage capacitor that are used to store ramp non-uniformity error are introduced in column readout circuit. According to the stored ramp non-uniformity error, the ramp signal of each column is shifted. So as to ensure the uniformity of the ramp signal. Based on the 55 nm 1P4M CMOS process, this paper has completed the detailed circuit design and comprehensive simulation verification of the proposed method. Under the design conditions that the voltage range of the ramp signal is 1.4 V, the slope of the ramp signal is 71.908 V/ms, the number of pixel area arrays is 8192(H)×8192(V), and a single pixel size is 10 μm, the proposed correction method reduces the ramp non-uniformity error from 7.89mV to 36.8 μV. The Differential NonLinearity (DNL) of the ramp signal is +0.001 3/–0.004 LSB and the Integral NonLinearity (INL) is +0.045/–0.02 LSB. The Column Fixed Pattern Noise(CFPN) is reduced from 1.9% to 0.01%. The proposed ramp uniformity correction method reduces the ramp non-uniformity error by 99.53% on the basis of ensuring the high linearity of the ramp signal, without significantly increasing the chip area and without introducing additional power consumption. It provides a certain theoretical support for the design of high-precision CMOS image sensors.
2024, 46(7): 2961-2969.
doi: 10.11999/JEIT231067
Abstract:
The integrated scheduling algorithm of flexible equipment network is difficult to reasonably select the relevant processes of processing equipment, which affects the completion time of products. An Integrated Scheduling algorithm for Flexible Equipment Network considering the Same layer after Process (SP-FENIS) is proposed. Firstly, the priority strategy of the reverse order layer is adopted, which assigns each process to the set of processes to be scheduled in the reverse layer. Then, the average reverse-order compact path strategy is proposed to determine the scheduling sequence of the processes to be scheduled in each reverse order layer. Finally, the earliest completion time strategy and equipment idle insertion strategy are proposed. When the earliest completion time of the process on the flexible equipment is the same, the processing time on the flexible equipment and the processing equipment of the same layer after the process are considered, and the processing equipment and processing time of the target process are determined. The example shows that, compared with the existing algorithm, the proposed algorithm can shorten the product completion time.
The integrated scheduling algorithm of flexible equipment network is difficult to reasonably select the relevant processes of processing equipment, which affects the completion time of products. An Integrated Scheduling algorithm for Flexible Equipment Network considering the Same layer after Process (SP-FENIS) is proposed. Firstly, the priority strategy of the reverse order layer is adopted, which assigns each process to the set of processes to be scheduled in the reverse layer. Then, the average reverse-order compact path strategy is proposed to determine the scheduling sequence of the processes to be scheduled in each reverse order layer. Finally, the earliest completion time strategy and equipment idle insertion strategy are proposed. When the earliest completion time of the process on the flexible equipment is the same, the processing time on the flexible equipment and the processing equipment of the same layer after the process are considered, and the processing equipment and processing time of the target process are determined. The example shows that, compared with the existing algorithm, the proposed algorithm can shorten the product completion time.
2024, 46(7): 2970-2980.
doi: 10.11999/JEIT231113
Abstract:
The style transfer method, due to its excellent domain adaptation capability, is widely used to alleviate domain gap of computer vision domain. Currently, stereo matching based on style transfer faces the following challenges: (1) The transformed left and right images need to remain matched; (2) The content and spatial information of the transformed images should remain consistent with the original images. To address these challenges, an Edge Domain Adaptation Stereo matching (EDA-Stereo) method is proposed. First, an Edge-guided Generative Adversarial Network (Edge-GAN) is constructed. By incorporating edge cues and synthetic features through the Spatial Feature Transform (SFT) layer. the Edge-GAN guides the generator to produce pseudo-images that retain the structural features of syntheitic domain images. Second, a warping loss is introduced to guarantee the left image to be reconstructed based on the transformed right image to approximate the original left image, preventing mismatches between the transformed left and right images. Finally, a normal loss based stetreo matching network is proposed to capture more geometric details by characterizing local depth variations, thereby improving matching accuracy. By training on synthetic datasets and comparing with various methods on real datasets, results show the effectiveness in mitigating domain gaps. On the KITTI 2012 and KITTI 2015 datasets, the D1 error is 3.9% and 4.8%, respectively, which is a relative reduction of 37% and 26% compared to the state-of-the-art Domain-invariant Stereo Matching Networks (DSM-Net) method.
The style transfer method, due to its excellent domain adaptation capability, is widely used to alleviate domain gap of computer vision domain. Currently, stereo matching based on style transfer faces the following challenges: (1) The transformed left and right images need to remain matched; (2) The content and spatial information of the transformed images should remain consistent with the original images. To address these challenges, an Edge Domain Adaptation Stereo matching (EDA-Stereo) method is proposed. First, an Edge-guided Generative Adversarial Network (Edge-GAN) is constructed. By incorporating edge cues and synthetic features through the Spatial Feature Transform (SFT) layer. the Edge-GAN guides the generator to produce pseudo-images that retain the structural features of syntheitic domain images. Second, a warping loss is introduced to guarantee the left image to be reconstructed based on the transformed right image to approximate the original left image, preventing mismatches between the transformed left and right images. Finally, a normal loss based stetreo matching network is proposed to capture more geometric details by characterizing local depth variations, thereby improving matching accuracy. By training on synthetic datasets and comparing with various methods on real datasets, results show the effectiveness in mitigating domain gaps. On the KITTI 2012 and KITTI 2015 datasets, the D1 error is 3.9% and 4.8%, respectively, which is a relative reduction of 37% and 26% compared to the state-of-the-art Domain-invariant Stereo Matching Networks (DSM-Net) method.
2024, 46(7): 2981-2993.
doi: 10.11999/JEIT231004
Abstract:
To achieve formation control of multiple snake robots, an error-constrained anti-interference path-following method is proposed in this paper. A highly coupled dynamic frequency compensator is used to adjust the motion speed of each robot to ensure consistency in the position and velocity of the formation members. In dynamic control, the singularity phenomenon of virtual variables is eliminated by the equivalent principle of barrier functions, improving the stability of path following. In addition, predictive values for model uncertainty and external interference are designed to pre-compensate for joint offsets and torque inputs of the robots, further improving the convergence rate and steady-state performance of the following errors. Finally, the Lyapunov theory is used to prove the Uniform Ultimate Boundedness (UUB) of this system. Simulation data demonstrate that the proposed method and control strategy have higher following accuracy compared to other classic methods.
To achieve formation control of multiple snake robots, an error-constrained anti-interference path-following method is proposed in this paper. A highly coupled dynamic frequency compensator is used to adjust the motion speed of each robot to ensure consistency in the position and velocity of the formation members. In dynamic control, the singularity phenomenon of virtual variables is eliminated by the equivalent principle of barrier functions, improving the stability of path following. In addition, predictive values for model uncertainty and external interference are designed to pre-compensate for joint offsets and torque inputs of the robots, further improving the convergence rate and steady-state performance of the following errors. Finally, the Lyapunov theory is used to prove the Uniform Ultimate Boundedness (UUB) of this system. Simulation data demonstrate that the proposed method and control strategy have higher following accuracy compared to other classic methods.
2024, 46(7): 2994-3001.
doi: 10.11999/JEIT230763
Abstract:
The Multivariate Variational Mode Decomposition (MVMD), being an extension of the Variational Mode Decomposition (VMD), inherits the merits of VMD. However, it encounters an issue wherein its decomposition performance relies heavily on two predefined parameters, the number of modes (K) and the penalty factor (\begin{document}$ \alpha $\end{document} \begin{document}$ \alpha $\end{document} \begin{document}$ \alpha $\end{document}
The Multivariate Variational Mode Decomposition (MVMD), being an extension of the Variational Mode Decomposition (VMD), inherits the merits of VMD. However, it encounters an issue wherein its decomposition performance relies heavily on two predefined parameters, the number of modes (K) and the penalty factor (
2024, 46(7): 3002-3009.
doi: 10.11999/JEIT231093
Abstract:
As an ideal designated verifier signature, Chameleon Signature (CS) can solve the problem of signature secondary transmission more subtly by embedding an efficient Chameleon Hash Function (CHF) into the signing algorithm. In addition to non-transferability, CS also should satisfy unforgeability, deniability, non-repudiation for the signer, and so on. To solve the problems that cryptosystems based on the traditional number theory problems, such as the large integer factorization or discrete logarithm cannot resist quantum computing attacks, and the schemes that provably secure in the random oracle model may not be secure in a practical implementation, a lattice-based CS scheme in the standard model is proposed; Furthermore, to solve the problem of requiring a significant local storage to obtain deniability for the signer, a lattice-based CS scheme without local storage in the standard model is proposed, the new scheme completely eliminates the signer’s dependence on the local signature library, and enables the signer to assist an arbitrator to reject a forged signature of any adversary without storing the original message and signature. Particularly, based on the hardness of the small integer solution problem and learning with errors problem, both schemes are proved secure in the standard model.
As an ideal designated verifier signature, Chameleon Signature (CS) can solve the problem of signature secondary transmission more subtly by embedding an efficient Chameleon Hash Function (CHF) into the signing algorithm. In addition to non-transferability, CS also should satisfy unforgeability, deniability, non-repudiation for the signer, and so on. To solve the problems that cryptosystems based on the traditional number theory problems, such as the large integer factorization or discrete logarithm cannot resist quantum computing attacks, and the schemes that provably secure in the random oracle model may not be secure in a practical implementation, a lattice-based CS scheme in the standard model is proposed; Furthermore, to solve the problem of requiring a significant local storage to obtain deniability for the signer, a lattice-based CS scheme without local storage in the standard model is proposed, the new scheme completely eliminates the signer’s dependence on the local signature library, and enables the signer to assist an arbitrator to reject a forged signature of any adversary without storing the original message and signature. Particularly, based on the hardness of the small integer solution problem and learning with errors problem, both schemes are proved secure in the standard model.
2024, 46(7): 3010-3018.
doi: 10.11999/JEIT230923
Abstract:
Federated learning is an emerging distributed machine learning framework that effectively solves the problems of data silos and privacy leakage in traditional machine learning by performing joint modeling training without leaving the user’s private data out of the domain. However, federated learning suffers from the problem of training-lagged clients dragging down the global training speed. Related research has proposed asynchronous federated learning, which allows the users to upload to the server and participate in the aggregation task as soon as they finish updating their models locally, without waiting for the other users. However, asynchronous federated learning also suffers from the inability to recognize malicious models uploaded by malicious users and the problem of leaking user’s privacy. To address these issues, a privacy-preserving Secure Aggregation scheme for asynchronous Federated Learning(SAFL) is designed. The users add perturbations to locally trained models and upload the perturbed models to the server. The server detects and rejects the malicious users through a poisoning detection algorithm to achieve Secure Aggregation(SA). Finally, theoretical analysis and experiments show that in the scenario of asynchronous federated learning, the proposed scheme can effectively detect malicious users while protecting the privacy of users’ local models and reducing the risk of privacy leakage. The proposed scheme has also a significant improvement in the accuracy of the model compared with other schemes.
Federated learning is an emerging distributed machine learning framework that effectively solves the problems of data silos and privacy leakage in traditional machine learning by performing joint modeling training without leaving the user’s private data out of the domain. However, federated learning suffers from the problem of training-lagged clients dragging down the global training speed. Related research has proposed asynchronous federated learning, which allows the users to upload to the server and participate in the aggregation task as soon as they finish updating their models locally, without waiting for the other users. However, asynchronous federated learning also suffers from the inability to recognize malicious models uploaded by malicious users and the problem of leaking user’s privacy. To address these issues, a privacy-preserving Secure Aggregation scheme for asynchronous Federated Learning(SAFL) is designed. The users add perturbations to locally trained models and upload the perturbed models to the server. The server detects and rejects the malicious users through a poisoning detection algorithm to achieve Secure Aggregation(SA). Finally, theoretical analysis and experiments show that in the scenario of asynchronous federated learning, the proposed scheme can effectively detect malicious users while protecting the privacy of users’ local models and reducing the risk of privacy leakage. The proposed scheme has also a significant improvement in the accuracy of the model compared with other schemes.
2024, 46(7): 3019-3025.
doi: 10.11999/JEIT231111
Abstract:
Short Quasi-Cyclic (QC) Low-Density Parity-Check (LDPC) codes without small cycles suitable for an arbitrary row weight (i.e., Row-Weight Universal (RWU)), are of great significance for both theoretical research and engineering application. Existing methods having RWU property and guaranteeing the nonexistence of 4-cycles and 6-cycles, can only offer short QC-LDPC codes for the column weights of 3 and 4. Based on the Greatest Common Divisor (GCD) framework, three new methods are proposed in this paper for the column weights of 5 and 6, which can possess RWU property and at the same time remove all 4-cycles and 6-cycles. Compared with existing methods with RWU property, the code lengths of the novel methods are sharply reduced from the fourth power of row weight to the third power of row weight. Therefore, the new methods can provide short RWU QC-LDPC codes without 4-cycles and 6-cycles for occasions where base codes with large column weights are required, such as composite constructions and advanced optimization pertaining to QC-LDPC codes. Moreover, compared with the search-based symmetric QC-LDPC codes, the new codes need no search, have lower description complexity, and exhibit better decoding performance.
Short Quasi-Cyclic (QC) Low-Density Parity-Check (LDPC) codes without small cycles suitable for an arbitrary row weight (i.e., Row-Weight Universal (RWU)), are of great significance for both theoretical research and engineering application. Existing methods having RWU property and guaranteeing the nonexistence of 4-cycles and 6-cycles, can only offer short QC-LDPC codes for the column weights of 3 and 4. Based on the Greatest Common Divisor (GCD) framework, three new methods are proposed in this paper for the column weights of 5 and 6, which can possess RWU property and at the same time remove all 4-cycles and 6-cycles. Compared with existing methods with RWU property, the code lengths of the novel methods are sharply reduced from the fourth power of row weight to the third power of row weight. Therefore, the new methods can provide short RWU QC-LDPC codes without 4-cycles and 6-cycles for occasions where base codes with large column weights are required, such as composite constructions and advanced optimization pertaining to QC-LDPC codes. Moreover, compared with the search-based symmetric QC-LDPC codes, the new codes need no search, have lower description complexity, and exhibit better decoding performance.
2024, 46(7): 3026-3035.
doi: 10.11999/JEIT231118
Abstract:
In 5G Vehicular to Everything (5G-V2X), service messages are provided to a group of vehicles belonging to a specific region by means of point-to-multipoint transmission. To address security threats and privacy leakage, an authentication and key negotiation scheme is proposed for multicast service message transmission between content providers and vehicles. A certificate-less aggregated signature technique is used to batch-verify all vehicles in the group, and improves the efficiency of authentication requests. Secure key negotiation is realized based on the polynomial key management technique, which makes it impossible for illegal users or the core network to obtain the shared session key. Finally, a dynamic key update mechanism for vehicles in the group is implemented, so that when a vehicle joins or leaves the group, the content provider only needs to send a key update message to update the session key. The proposed scheme can guarantee security requirements such as anonymity, unlinkability, forward and backward security, and resistance to conspiracy attacks, as shown by formal verification tools and further security analysis. The computational efficiency is improved by about 34.2% compared to existing schemes.
In 5G Vehicular to Everything (5G-V2X), service messages are provided to a group of vehicles belonging to a specific region by means of point-to-multipoint transmission. To address security threats and privacy leakage, an authentication and key negotiation scheme is proposed for multicast service message transmission between content providers and vehicles. A certificate-less aggregated signature technique is used to batch-verify all vehicles in the group, and improves the efficiency of authentication requests. Secure key negotiation is realized based on the polynomial key management technique, which makes it impossible for illegal users or the core network to obtain the shared session key. Finally, a dynamic key update mechanism for vehicles in the group is implemented, so that when a vehicle joins or leaves the group, the content provider only needs to send a key update message to update the session key. The proposed scheme can guarantee security requirements such as anonymity, unlinkability, forward and backward security, and resistance to conspiracy attacks, as shown by formal verification tools and further security analysis. The computational efficiency is improved by about 34.2% compared to existing schemes.
2024, 46(7): 3036-3045.
doi: 10.11999/JEIT231074
Abstract:
In order to solve the problem that the travel information of vehicle users in Intelligent Transportation System (ITS) is easy to be illegally stolen and the traffic data stored in the cloud server of transportation system is abused by malicious users, a new Attribute Based Searchable Encryption (ABSE) scheme is proposed in this paper, which has the functions of privacy protection, key aggregation and lightweight calculation. The scheme realizes full privacy protection in key generation stage, access control stage and partial decryption stage. The search keyword is embedded into the access structure, which can realize partial policy hiding and keyword security. Through key aggregation technology, all file identities that meet the search conditions and access policies are aggregated into one aggregate key, which reduces the burden of key storage for users, and further ensures the security of file keys and data. The security analysis shows that the scheme has the advantages of hidden access structure security, keyword ciphertext indistinguishable security and trapdoor indistinguishable security. The theoretical analysis and numerical simulation showed the proposed scheme was efficient and practical in terms of communication and computing overhead.
In order to solve the problem that the travel information of vehicle users in Intelligent Transportation System (ITS) is easy to be illegally stolen and the traffic data stored in the cloud server of transportation system is abused by malicious users, a new Attribute Based Searchable Encryption (ABSE) scheme is proposed in this paper, which has the functions of privacy protection, key aggregation and lightweight calculation. The scheme realizes full privacy protection in key generation stage, access control stage and partial decryption stage. The search keyword is embedded into the access structure, which can realize partial policy hiding and keyword security. Through key aggregation technology, all file identities that meet the search conditions and access policies are aggregated into one aggregate key, which reduces the burden of key storage for users, and further ensures the security of file keys and data. The security analysis shows that the scheme has the advantages of hidden access structure security, keyword ciphertext indistinguishable security and trapdoor indistinguishable security. The theoretical analysis and numerical simulation showed the proposed scheme was efficient and practical in terms of communication and computing overhead.
2024, 46(7): 3046-3053.
doi: 10.11999/JEIT230953
Abstract:
With the growing use of smart devices, the ease of sharing digital media content has been enhanced. Concerns have been raised about unauthorized access, particularly via screen shooting. In this paper, a novel end-to-end watermarking framework is proposed, employing invertible neural networks and inverse gradient attention, to tackle the copyright infringement challenges related to screen content leakage. A single invertible neural network is employed by the proposed method for watermark embedding and extraction, ensuring information integrity during network propagation. Additionally, robustness and visual quality are enhanced by an inverse gradient attention module, which emphasizes pixel values and embeds the watermark in imperceptible areas for better invisibility and model resilience. Model parameters are optimized using the Learnable Perceptual Image Patch Similarity (LPIPS) loss function, minimizing perception differences in watermarked images. The superiority of this approach over existing learning-based screen-shooting resilient watermarking methods in terms of robustness and visual quality is demonstrated by experimental results.
With the growing use of smart devices, the ease of sharing digital media content has been enhanced. Concerns have been raised about unauthorized access, particularly via screen shooting. In this paper, a novel end-to-end watermarking framework is proposed, employing invertible neural networks and inverse gradient attention, to tackle the copyright infringement challenges related to screen content leakage. A single invertible neural network is employed by the proposed method for watermark embedding and extraction, ensuring information integrity during network propagation. Additionally, robustness and visual quality are enhanced by an inverse gradient attention module, which emphasizes pixel values and embeds the watermark in imperceptible areas for better invisibility and model resilience. Model parameters are optimized using the Learnable Perceptual Image Patch Similarity (LPIPS) loss function, minimizing perception differences in watermarked images. The superiority of this approach over existing learning-based screen-shooting resilient watermarking methods in terms of robustness and visual quality is demonstrated by experimental results.
2024, 46(7): 3054-3062.
doi: 10.11999/JEIT231123
Abstract:
Two-Dimensional (2-D) convolution with different kernel sizes enriches the overall performance in computer vision tasks. Currently, there is a lack of an efficient design method of reconfigurable 2-D convolver, which limits the deployment of Convolution Neural Network (CNN) models at the edge. In this paper, a new approach based on multiplication management and triangular numbers decomposition is proposed. The proposed 2-D convolver includes a certain number of Processing Elements (PE) and corresponding control units, where the former is responsible for computing tasks and the latter manages the combination of multiplication operations to achieve different convolution sizes. Specifically, an odd number list is determined based on the application scenario, which represents the supported sizes of the 2-D convolutional kernel. The corresponding triangular number list is obtained using the triangular numbers decomposition method. Then, the total number of PEs is determined based on the triangular number list and computational requirements. Finally, the corresponding control units and the interconnection of PEs are determined by the addition combinations of triangular numbers. The proposed reconfigurable 2-D convolver is designed by Verilog Hardware Description Language (HDL) and implemented by Vivado 2022.2 software on the XCZU7EG board. Compared with similar methods, the proposed 2-D convolver significantly improves the efficiency of multiplication resources, increasing from 20%～50% to 89%, and achieves a throughput of 1 500 MB/s with 514 logic units, thereby demonstrating its wide applicability.
Two-Dimensional (2-D) convolution with different kernel sizes enriches the overall performance in computer vision tasks. Currently, there is a lack of an efficient design method of reconfigurable 2-D convolver, which limits the deployment of Convolution Neural Network (CNN) models at the edge. In this paper, a new approach based on multiplication management and triangular numbers decomposition is proposed. The proposed 2-D convolver includes a certain number of Processing Elements (PE) and corresponding control units, where the former is responsible for computing tasks and the latter manages the combination of multiplication operations to achieve different convolution sizes. Specifically, an odd number list is determined based on the application scenario, which represents the supported sizes of the 2-D convolutional kernel. The corresponding triangular number list is obtained using the triangular numbers decomposition method. Then, the total number of PEs is determined based on the triangular number list and computational requirements. Finally, the corresponding control units and the interconnection of PEs are determined by the addition combinations of triangular numbers. The proposed reconfigurable 2-D convolver is designed by Verilog Hardware Description Language (HDL) and implemented by Vivado 2022.2 software on the XCZU7EG board. Compared with similar methods, the proposed 2-D convolver significantly improves the efficiency of multiplication resources, increasing from 20%～50% to 89%, and achieves a throughput of 1 500 MB/s with 514 logic units, thereby demonstrating its wide applicability.
