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Acta Electronica Sinica, launched in 1962, is the flagship academic journal of the Chinese Institute of Electronics (CIE). The journal publishes cutting-edge research findings in the fields of electronics, information science, and related disciplines.
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- 摘要：To address the collaborative requirements of high-security transmission and high quality of service (QoS) in dispersed computing network under harsh wireless communication environments, this paper proposes a secure communication and resource optimization scheme for intelligent reconfigurable surface (IRS)-assisted dispersed computing network. Firstly, due to the energy limitations of unmanned aerial vehicle (UAV) nodes, this paper studies a novel energy harvesting (EH) scheme. By dividing the IRS passive reflection array in geometric space, some reflection elements are used for information reflection, and some elements are used for EH, so as to realize the cooperation of information transmission and EH. Secondly, the secure sum rate maximization optimization model in IRS-assisted dispersed computing network is formulated. The model jointly optimizes multiple coupling variables such as user transmission power, IRS reflection element phase shift, EH constraint and communication QoS, while improve the overall system security performance and resource utilization efficiency. Since the formulated optimization problem is highly non-convex and the variables are strongly coupled, traditional optimization methods are difficult to directly obtain the global optimal solution. Furthermore, considering the characteristics of dispersed computing network, such as high user mobility, rapidly varying wireless channels, and uncertain environmental states, a robust deep reinforcement learning (DRL)-based dynamic resource optimization algorithm is designed to guarantee QoS in dynamic dispersed computing environments. Simulation results show that the performance of the IRS-assisted dispersed computing network scheme based on robust DRL proposed in this paper not only outperforms existing learning-based solutions but also achieves performance close to that of the exhaustive search method, verifying the effectiveness and superiority of the proposed scheme.
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- 摘要：Physical reservoir computing (PRC), characterized by its low training cost and high parallel efficiency, has emerged as a promising paradigm for temporal signal processing and complex dynamical system modeling. By leveraging the intrinsic nonlinearity and short-term memory dynamics of physical devices, PRC maps temporal inputs into a high-dimensional state space, generating rich reservoir states that enable accurate solving and prediction of complex state equations. With advantages such as readout-layer-only training, low inference power consumption, and few-shot data driven, PRC offers a novel pathway for real-time solving of chaotic differential and partial differential equations on edge hardware. However, effectively co-designing high-order temporal processing algorithms with the intrinsic modulation mechanisms of physical devices remains a key bottleneck hindering practical deployment. Here, we report a highly efficient PRC system based on indium gallium zinc oxide (IGZO) electrolyte transistors, achieving the solving and prediction of time-dependent chaotic differential equations and partial differential equations. Leveraging the low-power characteristics and continuous nonlinear mapping capability of IGZO devices, we designed a lightweight algorithm co-optimizing reservoir training, inference, and equation solving. The electrical response and ion migration dynamics of the IGZO transistors were accurately modeled and characterized, followed by system-level validation on the field programmable gate array (FPGA). Resource evaluation demonstrates that, compared to a conventional fourth-order Runge-Kutta (RK4) implementation, our approach reduces digital signal processor (DSP) and flip-flop utilization by approximately 64% and 50%, respectively, while maintaining equivalent accuracy. In performance benchmarks using the Mackey-Glass equation, Lorenz chaotic sequence, and one-dimensional heat diffusion equation, our system achieves an inference latency of 11 μs and reduces power consumption by 15%, with a normalized root mean square error (NRMSE≈0.04) comparable to the Runge-Kutta method. For the partial differential equation (PDE) task, computation time is reduced to 1/400 of that required by the second-order Runge-Kutta (RK2) method, while maintaining a comparable NRMSE≈0.008. This work not only validates the efficiency and accuracy of IGZO-based PRC in solving general differential equations, but also provides a scalable architectural foundation and practical engineering reference for next-generation lightweight, low-power edge scientific computing and neuromorphic hardware platforms.
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- 摘要：With the rapid development of deep learning technology, convolutional neural networks (CNNs) have demonstrated exceptional performance in image recognition and processing tasks. However, as the network depth increases, the massive transmission of intermediate data imposes tremendous pressure on the on-chip memory and memory access bandwidth of hardware accelerators. The increasingly prominent “memory wall” problem has severely constrained the overall throughput and energy efficiency of the system.To address this issue, existing inter-layer feature data compression methods are mainly divided into two categories. The first category focuses on lightweight hardware implementation: despite low area overhead, their compression ratio is limited by algorithm complexity, making it difficult to effectively alleviate the off-chip bandwidth pressure in high-throughput scenarios. The second category pursues superior compression performance, but incurs excessive hardware area overhead, which is hard to deploy on resource-constrained edge devices.Aiming at the above challenges, this paper proposes a statistic-aware hybrid compression method for CNN inter-layer feature maps, with the core design goal of achieving high compression ratio and low hardware overhead to resolve the difficulty in balancing compression performance and resource consumption. By deeply exploiting the sparsity and distribution characteristics of the data, this method realizes hardware-friendly data coding combined with a hardware-software co-design mechanism of “offline analysis-online compression”. In the offline analysis stage, statistical analysis is performed on the CNN inter-layer feature data to generate the required coding tables and baseline values. In the online compression stage, the feature data are classified into zero-value data and non-zero-value data. For zero-value data, an enhanced zero run-length encoding combined with entropy coding is adopted; for non-zero data, dynamic baseline-delta encoding is applied. This differentiated coding mechanism reduces the hardware area overhead by 58.7% to 72.9% while maintaining a high compression ratio, which solves the problem of high hardware complexity in traditional compression algorithms.We conduct a systematic evaluation of the compression performance of the proposed method under different network structures and data formats, based on compression experiments on inter-layer feature maps of four representative CNNs: AlexNet, VGG16, ResNet34, and MobileNetV2. Experimental results show that, compared with similar studies, the proposed data compression method achieves a maximum improvement of 58.5% in compression ratio under the INT8 quantization format, and a maximum improvement of 36.7% under FP32/FP16 formats. When deploying the VGG16 model on the ALINX AXU5EV target platform, the accelerator based on the proposed data compression method reaches an inference throughput of 242.8 GOPS. Compared with the compression-free baseline architecture, the computing performance and energy efficiency are improved by 41.4% and 27.8%, respectively.The experimental results demonstrate that the proposed method balances the compression ratio and hardware overhead for CNN inter-layer feature map compression, and provides a new solution for the design of CNN accelerators in resource-constrained edge scenarios.
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- 摘要：Passive tracking of uncooperative maneuvering radiation sources by a single unmanned aerial vehicle (UAV) using only received signal strength indicator (RSSI) measurements is of considerable strategic and engineering value for missions such as electronic reconnaissance, spectrum monitoring, and emergency search and rescue. However, existing RSSI localization methods rely heavily on environmental propagation priors and treat parameter estimation, state filtering, and path planning as isolated serial processes, leaving them unable to handle dynamic scenarios in which core propagation parameters such as transmit power and path loss exponent are unknown and the target keeps maneuvering. The inherent low spatial resolution of scalar RSSI from a single channel further intensifies the structural conflict between exploration and exploitation. To address these challenges, this paper proposes an embodied intelligence-based closed-loop tracking method for a single UAV, which adopts structured uncertainty metrics as a unified carrier of cross-layer information flow and deeply couples perception, cognition, and decision-making through bidirectional propagation of uncertainty, forming a cooperative tracking architecture driven by closed-loop information flow. At the perception layer, a parameter prior inference network is built to extract the empirical distribution of propagation parameters from historical spatio-temporal observation sequences, providing data-driven initialization for the subsequent Bayesian inference. At the cognition layer, a dual-chain decoupled inference mechanism based on Rao-Blackwellized conditional decomposition is designed for the joint estimation of unknown propagation parameters and maneuvering states, in which the outer particle filter estimates large-scale fading parameters online while the inner adaptive interacting multiple model filter accurately infers maneuvering states of the target. A particle revival mechanism guided by effective sample size monitoring is further introduced together with a belief inheritance mechanism to prevent parameter resampling from disrupting the continuity of state tracking. At the decision layer, to mitigate the exploration-exploitation conflict noted above, a hierarchical planning strategy fusing information gain with model predictive control is constructed, and a learnable ranking gating network is introduced to perform real-time utility evaluation over candidate strategies including tracking, probing, and conservative options, allowing the UAV to adaptively transition from broad-area exploration to tight escort flying according to belief uncertainty. Simulation results show that the proposed method outperforms the compared baseline approaches across all core metrics under cold-start conditions with completely unknown propagation parameters, and the tracking performance under the mode without manual calibration priors approaches and in certain metrics surpasses the ideal performance of several baselines operating with known parameters, which verifies the adaptability and robustness of the proposed architecture under degraded sensing conditions.
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- 摘要：In dense urban environments, blockage by high-rise buildings, concurrent radiation from multiple wireless systems, and the superposition of multi-source interference make the electromagnetic environment highly time-varying and spatially heterogeneous. Traditional radio maps, which are constructed offline and updated at a low frequency, cannot reflect the real channel state in time. As a result, they are unable to meet the requirements of dynamic spectrum management, wireless resource scheduling, and low-altitude communication support in terms of both accuracy and timeliness. This challenge is more pronounced in complex mission scenarios, where online sensing and rapid decision-making are especially important. To address this issue, this paper proposes an uncertainty-driven active sensing method for UAV-based spectrum mapping. First, the online radio map updating and path planning processes are jointly modeled as a Markov decision process. The state explicitly includes statistical measures of map uncertainty, UAV position, flight time, target distance, and mission progress, so as to improve the policy's ability to represent environmental changes and mission stages. Second, a pre-trained U-Net is employed to reconstruct the SINR radio map online and to output pixel-level uncertainty estimates. The reduction in uncertainty is used to characterize information gain, which guides the UAV to sense high-value unknown regions with priority. Furthermore, a FiLM-D3QN decision network based on Feature-wise Linear Modulation is designed. It uses uncertainty and mission-progress conditions to dynamically modulate intermediate features and value estimation, thereby achieving an adaptive balance between information acquisition and trajectory efficiency. Simulation results show that, in urban scenarios with dense interference, the proposed method effectively reduces map reconstruction error while maintaining an arrival rate above 90%. The RMSE is reduced by about 7.1% compared with the classical IPP method and by about 9.8% compared with the original D3QN. The proposed method also achieves a shorter average episode length. These results verify its effectiveness for online radio map updating and active sensing in complex urban electromagnetic environments.
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- 摘要：Air-sea cross-medium communication is a crucial technology for enabling high-speed air-sea cross-domain interconnection. Owing to its excellent oceanic penetration characteristics, the blue-green light band (450 nm~550 nm) has emerged as an ideal transmission window for underwater wireless optical communication links, providing vital support for high-speed cross-medium communication. However, subjected to the nonlinear coupling effects of the atmosphere, random sea surface waves, and seawater scattering and absorption, the cross-medium channel exhibits significant non-stationarity and strong time-varying characteristics. In such rapidly time-varying channel environments, traditional fixed coding and modulation strategies, which rely on quasi-static channel assumptions, suffer from degraded system error performance due to mismatches with the actual channel conditions. To address this issue, this paper proposes a transmission scheme based on dynamic channel feature learning for air-sea cross-medium communication. By constructing a closed-loop feedback mechanism of "periodic probing—dynamic learning—reconstruction," the coding structure can be dynamically optimized in response to varying sea states. Specifically, the transmitter periodically transmits pre-designed pilot sequences. The receiver utilizes these pilot signals for channel estimation, compiles real-time statistics on the instantaneous characteristics of the current channel, and subsequently generates a polar code construction scheme that optimally matches the prevailing sea conditions. This construction information is then fed back to the transmitter via a feedback link. Based on this information, the transmitter executes code reconstruction and applies adaptive polar coding to the information sequence to be transmitted. This closed-loop feedback process operates periodically, ensuring that the code reconstruction closely tracks the non-stationary variations of the cross-medium time-varying channel, thereby overcoming the mismatch between fixed coding strategies and the actual physical channel. To verify the performance of the proposed system, a cross-medium communication channel model encompassing the atmosphere, the air-sea interface, and the seawater medium was established. Three typical water types—Jerlov IB, Jerlov II, and Jerlov Ⅲ—were selected to simulate oceanic environments with weak to strong absorption and scattering effects, respectively. Simulation results demonstrate that the proposed dynamic channel feature learning strategy can effectively adapt to variations in the seawater medium. Compared to uncoded systems, applying polar coding under the same Jerlov water type yields a significant coding gain, with the bit error rate (BER) markedly reduced. Furthermore, compared to the fixed polar code construction strategy adopted in the 3GPP-5G standard, the proposed dynamic channel feature learning strategy achieves substantial performance improvements across all three Jerlov water conditions. Notably, the performance gain increases as water turbidity rises and channel conditions deteriorate. These results validate that adaptive coded transmission based on dynamic channel feature learning can perceive and adapt to the dynamic changes of the seawater medium, providing a solid theoretical foundation and technical support for the development of highly reliable air-sea cross-medium adaptive communication systems.
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Featured Articles
PAPERS
摘要：With the explosive growth of image data in the field of e-commerce, target image retrieval has become a challenging work in information retrieval research. The existing traditional image retrieval models only rely on a single text description or similar image, which is difficult to accurately capture the user’s retrieval intention, resulting in unsatisfactory retrieval results. In order to solve this problem, this paper proposes a combined retrieval method that fuses image and text features. Swin Transformer (SwinT) is used to extract the multi-layer features of the reference image, and the image and text features are fused at multiple levels, so that the text features can modify the reference image features at multi-level and fine-grained, and get closer to the target image features. Then, the modified image features and the target image features are embedded in a space for similarity measurement, and the batch-based classification loss is used to optimize the retrieval performance. Experimental results on Fashion200k, MIT-States and CSS datasets show that the proposed method improves the performance by 5 percentage points on average compared with the existing mainstream methods.880
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摘要：In unsupervised domain adaptation tasks, the source and target domains usually do not satisfy the independent and identical distribution assumption. In order to generate the usable labels for the target domain, classical domain adaptation methods select the category with the highest prediction probability of the classifier as the pseudo-label of the target sample. Thus, the pseudo-label inevitably contains certain noise information, which may cause negative transfer to the domain adaptation model. In addition, traditional adversarial domain adaptation methods usually consider the global distribution between domains and ignore the category information of samples. How to extract discriminative category-level features in domain adaptation tasks is also an important problem. Therefore, an unsupervised adversarial domain adaptation method is proposed using feature anomaly detection and pseudo-label regression. The target samples of the same class predicted by the classifier are formed into the category subdomain within the target domain. The Gaussian uniform mixture model is used to detect the subdomain samples with abnormal distance from the class mean. The posterior probability of the samples is calculated and the correctness of the sample pseudo-labels in the subdomain is measured, which is used as a loss factor to limit the influence of pseudo-labels on the model in training. Meanwhile, the pseudo-label regression function is used to reduce the difference between the predicted label and the high-confidence pseudo-label of the classifier. The category constraint of the unlabeled target domain is adopted to improve the distinguishability of feature categories. Experimental results show that the average recognition accuracy of the proposed method on datasets Office-31, Image-CLEF, and Office-Home are 90.2%, 89.6%, and 69.5%, respectively, which are all higher than the related popular algorithms.490
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Special Focus: Recipient of CIE Outstanding Doctoral Dissertation
摘要：This paper proposes a contactless human vital-sign sensing method in non-line-of-sight (NLoS) scenarios, which integrates reconfigurable intelligent surfaces (RISs) into wireless human sensing, and achieves NLoS human vital-sign sensing by utilizing the flexible manipulation capability of RISs over electromagnetic waves. Firstly, through a visual-aided mechanism, this paper locates human targets using the deep learning model Yolo-v7. Then, the metasurface optimizes the coding matrix based on the estimated human location, thereby altering the propagation path of sensing signals to achieve human sensing in non-line-of-sight scenarios. Additionally, addressing the shortcomings of traditional variational mode decomposition (VMD) algorithms in human vital-sign sensing, we further propose an improved VMD algorithm to achieve precise estimation of breathing rate and heartbeat rate. Experimental results demonstrate that the visual-aided module can accurately locate the position of human chest, thus enabling precise beamforming controlled by the intelligent metasurface. The proposed filtering-based VMD algorithm can achieve precise estimation of human vital signs, with average estimation errors of 0.6 RPM for respiration and 5.3 BPM for heartbeat, respectively. Furthermore, further analysis also demonstrates the effectiveness and accuracy of the RIS-based sensing scheme for human vital-sign sensing in NLoS scenarios.482
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PAPERS
摘要：The wireless powered communication is a key technology to support energy sustainable wireless communications in the future 6G era. For the first time, this paper proposes a complete prototype design for the large-scale antenna based wireless powered communication system, and realizes this prototype system, which consists of a base station with a 32-transmit-antenna array, a user equipment and a feedback link, on the software defined radio platforms. At the base station, by designing flexible frame structures, this paper originally proposes a dynamic wireless energy transmission scheme to properly balance the required channel estimation accuracy and the wireless energy transmission efficiency. At the user equipment, by applying the practical non-linear energy harvesting and energy consumption models, this paper proposes a novel information-energy double threshold based battery management scheme to meet the requirements of different information and energy transmission. Based on the prompt status feedback from the user equipment, this paper completes a close-loop controlled system framework for adaptive wireless information and energy transmissions. Extensive experiments for both indoor and outdoor scenarios are implemented to verify the good environment adaptation and transmission performance of the wireless powered communication prototype system.333
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摘要：With the rapid development of the fifth generation (5G) mobile communications technology, Internet of vehicles (IoV) has become one of the important scenarios for 5G applications. Due to the urgent requirements of these applications for timely content caching, a dynamic content caching-enabled edge caching scheme is investigated to ensure timeliness, which allows vehicles to obtain cache content with freshness constraints from an edge caching-enabled access point (CAP). Furthermore, based on the idea of collaborative cache, a random model of CAP cache refreshing and delivery is established by probability theory. Meanwhile, considering the metric of hit rate with age of information (AoI) as the quality of service (QoS) index of IoV cache, a system model of caching performance value is developed. To ensure the effectiveness of content caching, two types of vehicular content requests with different AoI requirements are considered in a hierarchical slicing framework. Finally, the optimization problem to maximize the content caching value of cooperative content placement is formulated, which is a multi-objective, multi-dimensional and multi-choice knapsack problem and is proved to be NP-hard. Therefore, the problem is divided into several independent optimization problems, and a maximum incremental algorithm based on the age value of the content is designed to solve it. Simulation results show that the CAP cooperative content placement strategy outperforms other traditional methods.279
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摘要：At present, there are few researches on profile attacks against RSA-CRT implementation. This paper takes modular reduction operation as the research object, and a template attack method against RSA-CRT implementation is proposed. The core of this method is to solve the difficulty to recover the RSA-CRT private key from the Hamming weight of the intermediate value of ciphertext modular reduction. The characteristic of this method is to build a model based on the Hamming weight of the intermediate value derived from modular reduction. The Hamming weight can be obtained by collecting the power traces of chosen ciphertext modular reduction for template matching, and the intermediate value is recovered from the Hamming weight variation, the private key of the RSA-CRT algorithm can be further inferred based on the intermediate value. In addition, the advantage of this method is that ideally, templates based on the intermediate Hamming weight model can be shared, and there is no limit on the number of bits of the intermediate value for modelling, which can be in byte size, 64 bit size, or even the bit size of p. In the actual environment, it can be selected according to the leaked information. Finally, in this paper, the lowest byte of the intermediate value is selected to model to verify the feasibility of this method, and the defense suggestions are also provided.230
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Most Downloaded Articles
- 摘要：Sampling is the bridge between analog source signal and digital signal.With the rapid progress of information technologies,the demands for information are increasing dramatically.So the existing systems are very difficult to meet the challenges of high speed sampling,large volume data transmission and storage.How to acquire information in signal efficiently is an urgent problem in electronic information fields.In recent years,an emerging theory of signal acquirement——compressed sensing (CS) provides a golden opportunity for solving this problem.This paper reviews the theoretical framework and the key technical problems of compressed sensing and introduces the latest developments of signal sparse representation,design of measurement matrix and reconstruction algorithm.Then this paper also reviews several open problems in CS theory and discusses the existing difficult problems.In the end,the application fields of compressed sensing are introduced.
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- 摘要：Compressive Sensing(CS) is a new developed theoretical framework for information acquisition and processing,which is based on matrix analysis,statistical probability theory,topological geometry,optimization and opsearch,functional analysis and so on.The high-dimensional signals can be recovered from the low-dimensional and sub-Nyquist sampling data based on the compressibility of signals.It not only inspires us to survey the linear problem again,but also enriches the optimization approaches for signal recovery to promote the combination of mathematics with engineering application.Nowadays the researches on compressive sensing have developed from the earlier concept understanding,numerical simulation,principle verification,and primary system designation,to the deeper researches on theory,development and application of practical system.In this paper,we introduce the basic idea of compressive sensing,and the development history,current and future challenges.
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- 摘要：Tongue Diagnosis is one of the essential methods of traditional Chinese medical diagnosis.The accuracy of tongue diagnosis can be improved by tongue characterizaton.This paper investigates the use of image analysis techniques for tongue characterization by evaluating visual features obtained from images.Several algorithms are presented for color calibration,tongue area segmentation,feature analysis for the tongue and its coating and quantification of the cracks of the tongue.The clinical application shows the effectiveness of the algorithms.
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- 摘要：Brain-computer interfaces provide a direct communication and control channel for sending messages and instructions from brain to external computers or other electronic devices.Using the non-muscular channel,subjects with severe neuromuscular dysfunction can directly express their thought and manipulate the external devices without using human language and actions.This will greatly enhance the ability of these subjects to manage external event and will improve their living quality.Brain-computer interface technology is an interdisciplinary technology integrating neurology,signal collection,signal processing,pattern recognition and more other relevant techniques.In the present paper,we review the development,the rationale and the methodology of BCI's technology and the major limitations in the BCI's implementation and applications,and then discuss some vital aspects that may be conducted in the future investigations.
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- 摘要：Most existing trust management systems have two main problems,(1) hard to provide security guarantee for web resources since their implements do not comply with Web Service standards;(2) the chief trust engine runs at a low efficiency,cannot fulfill demands of on time security policy in the all long changeable environment.We use java language to design and implement a trust engine oriented to Web Service based on mobile agent to satisfy security demands in the open and dynamic web environment.The main work of trust engine is to verify whether a web resource can be visited by some client.We use mobile agent to do bidirectional trust chain discovery to impro ve the efficiency of the trust engine.
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- 摘要：An accurate automatic personal identification is critical to our highly inter-connected information society.Biometrics refers to automatic identification of an individual based on her/his physiological or behavioral traits.Though biometrics is not an identi-fication panacea,it is beginning to provide very powerful tools for problems requiring positive identification.This paper attempts to summarize important research issues in biometrics.
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