Y. Zhang, H. M. Liu, S. Li, Z. B. Wang, S. J. Fang [references] [full-text] [DOI: 10.13164/re.2025.0001] [Download Citations]
Compact Wideband High-Selectivity Filtering Power Divider Using Four-Coupled-Lines

In the paper, a compact wideband filtering power divider (FPD) with high frequency selectivity is presented, which is merely based on the four-coupled-lines (FCLs) and isolated resistors. Since the FCL with diagonal short-circuited of input port has filtering response, an FPD without adding extra resonators can be easily realized. Further, two types of FCLs are cascaded as multi-mode resonators for bandwidth enhancement, and two resistors are added for isolation improvement. For validation, a 3-dB prototype with a size of 0.4λg × 0.07λg is implemented. Measurements show that the proposed FPD has a fractional bandwidth of more than 80%. Besides, the stopband rejection is over 35 dB with a rectangle coefficient (|BW20dB/BW3dB|) of 1.28, which indicates high frequency selectivity.

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3. GUO, X., LIU, Y. H., WU, W. Wideband unequal filtering power divider with arbitrary constant power ratio and phase difference. IEEE Transactions on Circuits and Systems II: Express Briefs, 2023, vol. 70, no. 2, p. 421-425. DOI: 10.1109/TCSII.2022.3212675
4. ZHANG, S. R., LIU, H. M., CHEN, S. Y., et al. Wideband filtering power divider with unequal power division ratio and all-frequency input absorptive feature. IEEE Transactions on Circuits and Systems II: Express Briefs, 2024, vol. 71, no. 3, p. 1136-1140. DOI: 10.1109/TCSII.2023.3324915
5. ZHU, Y. H., CAI, J., CAO, Y., et al. Compact wideband absorptive filtering power divider with a reused composite T-shape network. IEEE Transactions on Circuits and Systems II: Express Briefs, 2023, vol. 70, no. 3, p. 899-903. DOI: 10.1109/TCSII.2022.3217462
6. ZHANG, Y. F., WU, Y. L., YAN, J., et al. Wideband high selectivity filtering all-frequency absorptive power divider with deep out-of-band suppression. IEEE Transactions on Plasma Science, 2021, vol. 49, no. 7, p. 2099-2106. DOI: 10.1109/TPS.2021.3083780
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Keywords: Filtering power divider (FPD), high selectivity, four-coupled-line (FCL), wideband, miniaturization

J. Wang, Y. Luo, J. Zhang, L. Hou, J. Wang, B. Liu [references] [full-text] [DOI: 10.13164/re.2025.0009] [Download Citations]
A Wide-band High-performance Voltage-controlled Oscillator for 5G IoT Wireless Communication

A low phase noise and power-efficient class-B/C hybrid voltage-controlled oscillator (VCO) is presented for applying to 5G Internet of Things (IoT) wireless communication in this paper. The proposed three sets of switch capacitor array (SCA) are adopted first to widen the bandwidth by dividing the VCO output into eight overlapped frequency bands while maintaining the flexible frequency tuning. Then a multiple bias variable capacitor array (VCA) is designed to realize the fine-grain tuning of output frequency, which also improves the linearity within frequency-voltage tuning, the curvature variation in tunable gain, while minimizes the phase noise and stabilize tuning control on output frequency. After circuit implementation based on 180nm/1.2V CMOS standard process, the post-layout simulation results demonstrate that the proposed VCO achieves a wide frequency output from 4.63 GHz to 5.13 GHz, with consuming a total consumption of 0.19 mW at 1.2 V power supply voltage. The key phase noise is -115.1 dBc/Hz@1MHz on the 4.82 GHz center frequency, and the figure of merit (FoM) value can reach up to -195.6 dBc/Hz, which can surpass the performance to comparable similar class VCO design cases.

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5. LI, P., TIAN, T., PU, et al. A 5.67 8.75 GHz LC VCO with small gain variation for 2.4 GHz band WLAN applications. IEICE Electronics Express, 2021, vol. 18, no. 23, p. 20210387-20210387. DOI: 10.1587/elex.18.20210387
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9. XU, H., YAN, Y., WANG, Y., et al. A low voltage class-D VCO with implicit common-mode resonator implemented in 55 nm CMOS technology. Electronics, 2023, vol. 12, no. 10, p. 1-13. DOI: 10.3390/electronics12102262
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Keywords: LC voltage-controlled oscillator, Internet of Things communication, low phase noise, low power

Y. Wang, Z. H. Guo, K. Sun, H. B. Xiao, W. M. Wang [references] [full-text] [DOI: 10.13164/re.2025.0018] [Download Citations]
Youth Depression Diagnosis Algorithm Based on 3D-WGMobileNet and Transfer Learning

Depression is a common mental illness that not only profoundly infests the psychological state of patients, but also tends to cause damage to the functioning of patients' brain areas. To construct a comprehensive and detailed framework for a supporting diagnostic network that will help physicians make accurate and timely diagnoses when dealing with patients at different stages of depression, a network model based on three-dimensional (3D) weight group MobileNet (3D-WGMobileNet) and transfer learningis proposed. Firstly, fMRI data is preprocessed, and regional homogeneity analysis is used to reduce the dimension of the image. Then, the characteristics of Alzheimer's disease are learned by transfer learning and transferred to the proposed model. Next, the dynamic group convolution was used to construct the expert weight matrix of the convolution kernel, and the sliding window group convolution was used to compress the parameters of the model to improve the expression ability and computing power of the model. By using 5-fold cross-validation, we conducted experiments using data from HCP and REST-meta-MDD. The experiment results show that the proposed model gives a superior performance compared with other state-of-the-art methods, especially on the classification of the healthy group with major depression groups, where the two datasets achieve 88% and 91% accuracy, respectively, which verifies the feasibility and effectiveness of our model.

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Keywords: Depression, functional magnetic resonance imaging, transfer learning, MobileNet, dynamic group convolution

G. Q. Zhang, X. Rao, J. L. Fu, W. W. Hu, J. F. Hu [references] [full-text] [DOI: 10.13164/re.2025.0028] [Download Citations]
Integrated Waveform Design of Radar and Communication Based on Chirp-rate Hopping Modulation

The increasing demand for spectrum resources and the need for reducing the burden of modern electronic combat platforms have prompted the development of integrated radar and communication systems. An integrated radar and communication waveform scheme based on Chirp-rate Hopping Modulation (CrHM) is proposed in this paper to realize target detection and information transmission simultaneously. The CrHM signal can be regarded as the synthesis of multiple sub-pulses, and each sub-pulse is a chirp signal, which is determined by the communication information sequence. The performance of the CrHM signal is analyzed, including the ambiguity function, the principle of demodulation, and the robust properties of demodulation. Subsequently, several simulations are provided to testify the high weak target detection performance of the CrHM signal and robustness of demodulation.

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Keywords: Integrated radar and communication, waveform design, chirp-rate, chirp-rate hopping modulation, weak target detection

R. Ding, L. Cheng, X. Wang, Y. Yao, H. Xu, E. Zhao [references] [full-text] [DOI: 10.13164/re.2025.0037] [Download Citations]
Robust Optimal Operation Method for Active Distribution Networks with Multiple Types of Regulation Resources Considering Source and Load Uncertainties

The increasing prevalence of renewable energy sources and the heightened uncertainty in load demands within active distribution networks (ADNs) have led to more fluctuations in power flow and voltage levels during operational periods. In light of these challenges, this paper proposes a robust optimization framework specifically designed for ADNs, which carefully balances system secu¬rity, economic efficiency, and operational flexibility with multiple types of regulation resources. Firstly, a compre¬hensive regulation methodology is employed to integrate a variety of dispatchable resources. Secondly, the proposed model accounts for the inherent uncertainties related to load demand and the output of renewable energy genera¬tion by using the robust optimization (RO) technique. The proposed robust operational model for ADNs aims to min¬imizing power losses within the network and reducing voltage deviations, thereby improving overall network performance and reliability. Thirdly, the proposed model is linearized and reformulated as a convex optimization problem utilizing second-order cone relaxation techniques, and a relaxed cooperative co-evolution algorithm is im¬plemented to solve it efficiently. Numerical results across various scenarios indicate that, compared to the conven¬tional model without regulation resources, the proposed robust optimization model with multiple types of regulation resources can reduce voltage fluctuations by 89.6% and network losses by 12.9%. The proposed algorithm demon-strates better computational performance compared to conventional methods.

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Keywords: Active distribution networks, renewable energy integration, dispatch resources, source and load uncertainties, second-order cone relaxation techniques

J. Jiang, Q. Luo, Z. Xu, H. Li, C. Gao [references] [full-text] [DOI: 10.13164/re.2025.0050] [Download Citations]
Enhanced Reliability Assessment in Distribution Network Planning via Optimal Double Q Strategy with Explicit Topology-Variable Consideration

In the planning of distribution networks, assessing reliability is essential for enhancing network design and selection. This research introduces a new distribution network planning model that aims to balance economic performance and system reliability through a double Q strategy. The model integrates important reliability assessment metrics with the optimized design of the distribution network's topology. To address the computational difficulties associated with traditional power flow calculations in complex network configurations, this study employs a linearized power flow method, which enhances the model's practicality and adaptability. Additionally, recognizing the discrete decision-making aspects of the planning issue, a mixed-integer linear programming model is developed. By utilizing the adaptive ε-constraint method, the study investigates the global Pareto frontier between reliability and cost, offering valuable decision-making support for planners. Results from case studies demonstrate that the proposed method effectively lowers the overall construction and operational costs of the distribution network, albeit with a minor reduction in system reliability.

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Keywords: Reliability assessment, distribution network planning, double Q strategy, linearized power flow method, adaptive ε-constraint

Z. J. Xu, S. Zhang, Z. W. Liu, J. L. Lin, X. S. Huang, Y. Gong [references] [full-text] [DOI: 10.13164/re.2025.0064] [Download Citations]
Binary Quasi-differential Stochastic Process Keying Modulation Scheme for Covert Communications

Covert communication working at the physical layer provides an important means for ensuring the security of private user data. This work proposes a novel covert communication system based on binary quasi-differential stochastic process keying (BQDSPK). At the transmitter, the polarity of the correlation coefficient of two consecutive stochastic sequences is modulated by one binary covert bit. At the receiver, the correlation between two consecutively received random sequences is computed, and the transmitted covert bit is inferred through a hard decision process. A pseudo-random sequence is introduced to eliminate the transmitted sequences' correlation. The transmitted signal has the same statistical characteristics as the ambient noise to avoid attracting the attention of eavesdroppers. We theoretically demonstrate that the proposed system fully satisfies the requirement of covert communication when the signal-to-noise ratio (SNR) is less than a certain threshold value. In addition, theoretical bit error rate (BER) expressions are derived under additive white Gaussian noise (AWGN) channels and frequency-flat fading channels. The simulation results show that the theoretical BERs are very close to the BERs obtained from the simulations, regardless of which stochastic process is used as the carrier. Specifically, when the number of samples within a bit period is 400, the BER approaches approximately 10^-5 at a SNR of -5 dB under an AWGN channel, which adequately satisfies the communication requirements.

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13. learning. Wireless Networks, 2023, vol. 29, p. 877–899. DOI: 10.1007/s11276-022-03037-2
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Keywords: Physical layer covert communication, stochastic process, pseudo-random sequence, modulation, correlation

H. Yang, M. Liu, J. Zou, R. Xu, J. Huang, P. Geng [references] [full-text] [DOI: 10.13164/re.2025.0079] [Download Citations]
Intelligent Layout and Optimization of EV Charging Stations: Initial Configuration via Enhanced K-Means and Subsequent Refinement through Integrated GCN

This paper proposes an optimization model for the layout of EV charging stations, aiming to ensure a wide and efficient service area to meet the increasing demand for charging. Through an in-depth study of the deployment optimization of EV charging stations, a layout algorithm based on K-Means and simulated annealing is first introduced to determine the optimal locations for new charging stations. Building on this, a layout optimization algorithm utilizing a Residual Attention Graph Convolutional Network (RAGCN) is proposed, which leverages the efficient learning capability of Graph Convolutional Networks (GCN) on graph-structured data to learn and obtain the best layout for charging stations. Finally, the effectiveness of the model is validated in Nanjing, Jiangsu Province. The results show that the optimized layout of charging stations, which added 493 new stations in high-demand areas such as business districts and corporate enterprises, significantly enhances the convenience and utilization rate of charging for EV users. Additionally, sensitivity analysis and ablation experiments based on Points of Interest (POI) data are conducted to evaluate the impact of various POI features on the layout of charging stations and to explore the contribution of different model components to classification performance.

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Keywords: Electric vehicle charging station, points of interest, fused residual network, attention mechanism, graph convolutional neural network

Y. Feng, M. Li, J. Li, Y. Yu [references] [full-text] [DOI: 10.13164/re.2025.0092] [Download Citations]
Edge Cloud Resource Scheduling with Deep Reinforcement Learning

Designing optimal scheduling algorithms for task allocation in edge cloud clusters presents significant challenges due to the constantly changing workloads and service requests in edge cloud data center environments. These challenges stem from the need to manage the vast amounts of information transmitted by IoT devices, as well as the necessity of offloading computational tasks to cloud data centers. To tackle this issue, we propose a novel deep reinforcement learning-based resource allocation method called Decima#, which offers an effective resource optimization solution for edge cloud data centers. We utilize a transformer architecture to capture resource states on directed acyclic graphs (DAGs), accelerating the aggregation speed of the Graph Neural Network (GNN). Moreover, we develop innovative reward functions and concurrent processing mechanisms to minimize training time. Furthermore, we enhance the Proximal Policy Optimization (PPO) algorithm to improve adaptability, increase the accuracy of likelihood ratio estimation, identify a more suitable activation function, and impose constraints on gradient updates. In simulation environments, Decima# reduced the average job duration by 19% compared to the Decima algorithm, while also achieving a 56% increase in training convergence speed. Code has been made available at https://github.com/limengzhaolihai/spark-decimasharp-ppog.

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Keywords: Edge computing, deep reinforcement learning, resource scheduling

T. Yu, L. Liu [references] [full-text] [DOI: 10.13164/re.2025.0109] [Download Citations]
Gridless Azimuth and Polarization Parameter Estimation Based on Uniform Circular COLD Arrays

In order to achieve gridless azimuth and polarization parameter estimation, the polarization atomic norm minimization (P-ANM) algorithm is proposed. The polarization-sensitive uniform circular array (P-UCA) consisting of concentered orthogonal loop and dipole (COLD) antennas is considered in this paper for its stable estimation ability for all 0 degrees ~ 360 degrees range. The proposed method is capable of estimating the parameters with only single snapshot and overcomes the grid mismatch. First, the the mathematical models of signals received by the P-UCA are established and the P-ANM algorithm is applied. Then, The P-UCA is mapped into the virtual uniform linear array based on the Fourier expansion. Subsequently, the dual method is employed to solve the P-ANM model and determine the azimuths of the signals. Ultimately, by reconstructing the signal vectors, the polarization information can be inversely estimated based on the relationship between the electrical and magnetic signals. The simulations demonstrate that the proposed P-ANM algorithm exhibits superior joint estimation ability for all the azimuth and polarization parameters of the signals.

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Keywords: Polarization Sensitive Array (PSA), Uniform Circular Array (UCA), Concentered Orthogonal Loop and Dipole (COLD), Atomic Norm Minimization (ANM)

Y. Shu, Y. Wang, M. Zhang, J. Yang, Y. Wang, J. Wang, Y. Zhang [references] [full-text] [DOI: 10.13164/re.2025.0118] [Download Citations]
Context Aware Multimodal Fusion YOLOv5 Framework for Pedestrian Detection under IoT Environment

Pedestrian detection based on deep networks has become a research hotspot in the field of computer vision. With the rapid development of the Internet of Things (IoT) and autonomous driving technology, the deployment of pedestrian detection models on mobile devices places higher demands on the accuracy and real-time performance of detection. In addition, fully integrating multimodal information can further improve the robustness of the model. To this end, this article proposes a novel multimodal fusion YOLOv5 network for pedestrian detection. Specifically, to improve the performance of multi-scale pedestrian detection, we enhance contextual awareness abilities by embedding the multi-head self-attention (MSA) mechanism and graph convolution operations in the existing YOLOv5 framework. In addition, we can fully explore the real-time advantages of the YOLOv5 framework in pedestrian detection tasks. To improve multimodal information fusion, we introduce the joint cross-attention fusion mechanism to enhance knowledge interaction between different modalities. To validate the effectiveness of the proposed model, we conduct a large number of experiments on two multimodal pedestrian detection datasets. All the results confirm that our proposed model obtains the highest performance in terms of multi-scale pedestrian detection. Moreover, compared to other multimodal deep models, our proposed model still shows superior performance.

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Keywords: Pedestrian detection, IoT, deep learning, multimodal fusion, YOLOv5

C. Xiu, Y. Li [references] [full-text] [DOI: 10.13164/re.2025.0132] [Download Citations]
Adaptive Constant False Alarm Rate Detector Based on Long Short-term Memory Network

To solve the problem of degradation of detection performance of adaptive constant false alarm rate (CFAR) detectors due to low accuracy of environment recognition, an automatic clipping adaptive CFAR detector based on long short-term memory (LSTM) network is proposed. LSTM network is used to recognize the environmental type information contained in radar echo signals, and the appropriate detector is determined based on the recognition results. When there are interferences in both the leading and lagging reference windows, the interferences are clipped, and an ordered statistics CFAR detector is used to detect the target. Simulation results show that the designed adaptive CFAR detector, compared to the variability index CFAR detector, achieves an average improvement of 0.31% in detection probability in homogeneous environment. In the environment with interferences in a single-sided reference window, the average improvement in detection probability is 5.43%. In the environment with interferences in both the leading and lagging reference windows, the average improvement in detection probability is 41.57%. The automatic clipping adaptive CFAR detector based on LSTM network can more accurately recognize background environments and clipping interferences when interferences exist in both the leading and lagging reference windows, so its detection performance can be enhanced.

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Keywords: Constant false alarm rate, adaptive detection, target detection, long short-term memory network

X. Yang, G. Zhang, H. Song [references] [full-text] [DOI: 10.13164/re.2025.0143] [Download Citations]
Radar HRRP recognition based on supervised exponential sparsity preserving projection with small training data size

The echo signals from ships and sea clutter are coherently accumulated. Therefore, it is difficult to capture and distinguish the features within the signals. In addition, due to poor measurement conditions, the radar system can only collect data from a limited number of non-cooperative ships. In this article, a method termed supervised exponential sparsity preserving projection (E-MMC-SPP) is proposed for recognizing ship classes based on high-resolution range profile (HRRP). The method consists of three parts: First, to extract richer features from sea clutter, a maximum margin criterion sparse reconstructive relationship is constructed, which maximally preserves the sparse reconstruction of data and enhances class separability. Second, matrix exponential is utilized to ensure the positive definiteness of the coefficient matrices, thereby addressing the small-sample-size (SSS) problem. Finally, an efficient numerical method is presented for solving the corresponding large-scale matrix exponential eigenvalue problem. Experimental results on measured radar data demonstrate that the proposed method effectively reduces feature dimensionality and enhances target recognition performance with limited training data.

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Keywords: Supervised exponential sparsity preserving projection, high-resolution range profile, ship recognition, small-sample-size (SSS) problem

P. T. P. Nguyen, K. A. Phan, L. Tran [references] [full-text] [DOI: 10.13164/re.2025.0155] [Download Citations]
An Area-Efficient and Low-Latency Analog Content-Addressable Memory Design Using gm/ID Methodology with Memristors

In-memory computing (IMC) is an emerging approach to mitigating the memory bottleneck, a critical issue affecting energy efficiency and latency in modern digital computing. IMC operating in the analog domain can achieve high data density and accelerate signal processing tasks such as neural network training by leveraging nonvolatile memory technologies, specifically resistive switching devices. Conversely, content-addressable memories (CAMs), known for their inherent parallelism and fast digital lookup capabilities, are constrained by their large area and high energy consumption. To address these limitations, analog CAMs, which combine the analog domain with the tunability of memristors, have been proposed to enhance storage density and energy efficiency. In this work, we introduce a novel topology that reduces latency and area by employing the gm/ID design methodology to optimize the sizing of MOS devices. Utilizing the VTEAM model for simulations, our circuit achieves approximately twice the latency reduction compared to the 10T2M design, while occupying up to 66% less area. Additionally, our design exhibits the lowest latency among existing multi-bit and analog CAM approaches, reducing latency by 96%.

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Keywords: In-memory computing, content-addressable memory, analog CAM, memristor, gm/ID design methodology

M. Rahman, W. Wang, J. Wang, Y. Wang [references] [full-text] [DOI: 10.13164/re.2025.0166] [Download Citations]
Artifact Aware Deep Learning with Diffuse Model for MRI Brain Tumor Image Segmentation

Brain tumor segmentation in MRI images is crucial for clinical diagnosis and treatment planning but those scans are usually affected by imaging artifacts which decrease the quality of data and hamper segmentation performance. To address these challenges, this study proposed a unique framework that seamlessly combines artifact correction with segmentation of tumors. The framework features a data preparation module which is able to prepare realistic artifact-contaminated and artifact-free MRI image pairs that have been used for training. It also includes a diffuse model which acts on MRI images and removes the artifacts thus giving high-quality inputs for segmentation. In ad-dition, a modified 3D Convolutional Neural Network (CNN) architecture which integrates attention blocks and squeeze-and-excitation (SE) layers is used to segment the tumor sub-regions, including the enhancing tumor (ET), tumor core (TC), and whole tumor (WT). The framework was evaluated with artifact-corrupted data and clean data and achieved better results regarding the generation of artifact-free data and stable segmentation than the other baseline methods. This method emphasizes the magnitude of imaging artifacts on MRI-based segmentation and facilitates improvement in the clinical workflows. The code is available at https://github.com/Rahman3175/MMR

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Keywords: MRI artifacts, diffuse model, artifact-free images, attention blocks, brain tumor segmentation, 3D CNN