Chinese Journal of Electronics

Energy-Efficient D2D-Aided Dual UAV Data Collection

Huang Qiulei, Wang Wei, Song Zhaohui, Zhao Nan

, Available online , doi: 10.23919/cje.2023.00.271

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Aided by device-to-device (D2D) connections, unmanned aerial vehicle (UAV) can significantly enhance the coverage of wireless communications. In this paper, we consider a data collection system with the assistance of D2D, where two fixed-wing UAVs as aerial base stations cooperatively serve the ground devices. To accommodate more devices, we propose two effective algorithms to establish the multi-hop D2D connections. Then, the user scheduling, UAV trajectory, and transmit power are jointly optimized to maximize the energy efficiency, which is a non-convex problem. Accordingly, we decompose it into three subproblems. The scheduling optimization is first converted into a linear programming. Then, the trajectory design and the transmit power optimization are reformulated as two convex problems by the Dinkelbach method. Finally, an iterative algorithm is proposed to effectively solve the original problem. Simulation results are presented to verify the effectiveness of the proposed scheme.

A 8–26 GHz Passive Mixer with Excellent Port Matching Utilizing Marchand Balun and Capacitor Compensation

Zhang Yi, Zhuang Yuhang, Zhang Hu, Yang Lei, Wang Jing, Zhang Changchun, Guo Yufeng

, Available online , doi: 10.23919/cje.2023.00.178

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In this study, a broadband monolithic microwave integrated circuit (MMIC) double-balanced mixer designed for operation within the frequency range of 8–26 GHz is presented. The design is implemented using a 0.15 μm GaAs process. Traditional Marchand baluns, when applied to wideband mixers, face challenges in simultaneously achieving broad bandwidth and good port matching characteristics. To address this issue, we employ a spiral Marchand balun with a compensation capacitor. This innovative approach not only maintains the mixer’s wide bandwidth but also enhances the matching between the LO and RF ports. Additionally, it significantly simplifies the complexity of designing the matching circuit. The optimization principle of the compensation capacitor is elaborated in detail within this paper. Experimental results demonstrate that, with an LO power of 14 dBm, the conversion loss remains below 8.5 dB, while the voltage standing wave ratio (VSWR) of the LO and IF ports is less than 2 and the VSWR of the RF port is below 2.4. In comparison with existing literature, our designed mixer exhibits a broader bandwidth and lower loss.

A Multi-Granularity Task Scheduling Method for Heterogeneous Computing Resources

Li Han, Xu Chenxi, Zhao Zhuofeng, Liu Mengyuan

, Available online , doi: 10.23919/cje.2023.00.378

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In light of the rapid advancement of technologies related to the Internet of Things (IoT), IoT service platforms have become one of the main solutions for providing intelligent and efficient services in the industrial sector. Scheduling is an effective means to match resources and guarantee quality of service (QoS). However, existing service scheduling models and methods have not fully considered the special needs of new IoT platforms. Therefore, this article summarizes the special requirements of the new IoT platform, including the heterogeneity of IoT service platform resources, complexity and diversity of tasks, as well as considering the demand for low energy consumption and low latency. Constructed a multi-granularity task scheduling model for cloud-edge collaborative environments, which takes the special needs mentioned above into account. Combined with priority experience replay and importance sampling, a task scheduling algorithm priority replay with importance-based method in actor critic (PRIME-AC) based on deep reinforcement learning (DRL) is proposed. The experimental results show that PRIME-AC has better performance in both task execution delay and load balancing than other baselines.

Congestion Control Method for Campus Opportunity Network based on Ant Colony Algorithm

Li Peng, Cao Yumei, Jia Huan, Wang Xiaoming, Wu Xiaojun

, Available online , doi: 10.23919/cje.2024.00.019

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Due to the limited storage resources of portable devices, congestion control has become a hot direction in opportunity networks. To address the issue of heavy loads on certain nodes, which can impact routing efficiency and overall network performance, this paper proposes a load-balancing algorithm based on ant colony optimization (ACO) in a campus environment. The congestion status is represented by the ratio of message drop receptions within a certain period and the occupancy of the cache. Path selection is based on the concentration of pheromones and the pheromones on the path are updated when a data transmission is completed. In the event of congestion, the algorithm prevents a large amount of data from entering the node and unloads the data to other nodes, even if they are not the optimal relay nodes. Experimental results demonstrate that the proposed algorithm effectively improves data transmission success rates, reduces network loads, and decreases the number of packet losses, especially under low latency conditions.

Differential Evolution with Perturbation Estimation Strategy for Multiobjective Optimization

Wang Shuai, Zhou Aimin, Zhang Yi

, Available online , doi: 10.23919/cje.2023.00.322

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In recent years, multiobjective differential evolution (DE) algorithms have gained significant attention due to their effective search capabilities for multiobjective optimization problems (MOPs). The differential mutations of DE operators distinguish them from other generators. However, the efficiency of DE operators heavily relies on the selection of parents used to generate differential perturbation vectors. To address this challenge, this work proposes a novel algorithm, called perturbation estimation strategy based DE algorithm (PESDE), for multiobjective optimization. In PESDE, at each iteration, it utilizes a clustering approach to partition the population, and then constructs a probability model to estimate the distributions of differential perturbation vectors of the solutions within a cluster. Specifically, the differential perturbation vectors of solutions are regarded as trial points in building a probability model in the proposed approach. In this way, perturbation vectors are sampled from the built probability model, and then embedded in the solutions to generate new trial solutions. Empirical experimental studies are conducted to investigate the performance of PESDE by comparing it with five representative multiobjective evolutionary algorithms on several test instances with complicated Pareto set and front shapes. The results have demonstrated the advantages of the proposed algorithm over other approaches.

Word2State: Modeling Word Representations as States with Density Matrices

ZHANG Chenchen, LI Qiuchi, SU Zhan, SONG Dawei

, Available online , doi: 10.23919/cje.2023.00.336

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Polysemy is a common phenomenon in linguistics. Quantum-inspired complex word embeddings based on Semantic Hilbert Space play an important role in natural language processing (NLP), which may accurately define a genuine probability distribution over the word space. However, the existing quantum-inspired works manipulate on the real-valued vectors to compose the complex-valued word embeddings, which lack direct complex-valued pre-trained word representations. Motivated by quantum-inspired complex word embeddings, we propose a complex-valued pre-trained word embedding based on density matrices, called Word2State. Unlike the existing static word embeddings, our proposed model can provide non-linear semantic composition in the form of amplitude and phase, which also defines an authentic probabilistic distribution. We evaluate this model on twelve datasets from the word similarity task and six datasets from the relevant downstream tasks. The experimental results on different tasks demonstrate that our proposed pre-trained word embedding can capture richer semantic information and exhibit greater flexibility in expressing uncertainty.

Persistent-Fault Based Differential Analysis and Applications to Masking and Fault Countermeasures

Zheng Shihui, Zang Shoujin, Xing Ruihao, Zhang Jiayu, Ou Changhai

, Available online , doi: 10.23919/cje.2023.00.381

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A persistent fault analysis (PFA) can break implementations of AES secured by fault attack countermeasures that prevent differential analyses based on transient faults (DFA). When the AES implementation is protected by the higher-order masking countermeasure – RP [1], the number of required ciphertexts increases exponentially with the growth of the number of shares. We present a persistent-fault-based differential analysis (PFDA) against AES implementations. Two error patterns are detected by ciphertext pairs. Namely, only one error occurs at a SubBytes operation in round 10, and only one error occurs at a SubBytes operation in round 9. The latter is used to derive a differential characteristic (DC) for the key recovery, and the former is explored to deduce the input difference of the DC. Thus, the computational complexity is reduced compared to DFA. Encrypting a fixed plaintext many times to tolerate errors is utilized in PFDA against RP countermeasures. The number of required encryptions increases linearly with the growth of the number of shares. The simulation results show that PFDA can break unprotected AES implementations and implementations secured by fault attack countermeasures or the above higher-order masking countermeasures. Compared to other analyses based on persistent fault, the required number of ciphertexts of PFDA is the lowest.

A Millimeter-Wave Sensor and Differential Filter-Paper-Based Measurement Method for Cancer Cell Detections

LE Yi, LIU Hao, SU Guodong, LIU Jun, WANG Xiang, SUN Lingling

, Available online , doi: 10.23919/cje.2024.00.047

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This paper introduces a novel, easily-designed millimeter-wave sensor and an innovative liquid sensing method, both suitable for biological sample detection and cancer cell discrimination. The sensor, composed of coplanar waveguides with load resonators, features a centrally symmetric stepped-impedance resonator that creates a detection region, capable of achieving multiple transmission poles and zeros. This resonator is responsive to the equivalent dielectric constant of the surrounding space, mirroring the electromagnetic properties of the tested sample via the resonant frequency and notch depth. The proposed sensing method uses filter paper to characterize a liquid’s electromagnetic properties by comparing the s-parameters of dry and wet filter paper loaded onto the sensor. This method, an alternative to traditional microfluidic channels, allows all planar microwave/millimeter-wave solid dielectric constant sensors to robustly detect liquid materials. Applied to biomedicine, the design enables the sensor to generate multiple transmission peaks in the 20–60 GHz range, thereby facilitating discrimination of various cancer cell culture media and suspensions. Compared to traditional biochemical methods, this approach significantly reduces cancer detection costs and offers new avenues for label-free, real-time detection.

Multimodal Cross-Attention Mechanism-Based Algorithm for Elderly Behavior Monitoring and Recognition

LIU Hao, FENG Zhiquan, GUO Qingbei

, Available online , doi: 10.23919/cje.2023.00.263

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In contrast to the general population, behavior recognition among the elderly poses increased specificity and difficulty, rendering the reliability and usability aspects of safety monitoring systems for the elderly more challenging. Hence, this study proposes a multi-modal perception-based solution for an elderly safety monitoring recognition system. The proposed approach introduces a recognition algorithm based on multi-modal cross-attention mechanism, innovatively incorporating complex information such as scene context and voice to achieve more accurate behavior recognition. By fusing four modalities, namely image, skeleton, sensor data, and audio, we further enhance the accuracy of recognition. Additionally, we introduce a novel human-robot interaction mode, where the system associates directly recognized intentions with robotic actions without explicit commands, delivering a more natural and efficient elderly assistance paradigm. This mode not only elevates the level of safety monitoring for the elderly but also facilitates a more natural and efficient caregiving approach. Experimental results demonstrate significant improvement in recognition accuracy for 11 typical elderly behaviors compared to existing methods.

Fake Face Detection Based on Fusion of Spatial Texture and High-Frequency Noise

ZHANG Dengyong, QI Feifan, CHEN Jiahao, CHEN Jiaxin, GONG Rongrong, TIAN Yuehong, ZHANG Lebing

, Available online , doi: 10.23919/cje.2023.00.342

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The rapid development of the Internet has led to the widespread dissemination of manipulated facial images, significantly impacting people’s daily lives. With the continuous advancement of Deepfake technology, the generated counterfeit facial images have become increasingly challenging to distinguish. There is an urgent need for a more robust and convincing detection method. Current detection methods mainly operate in the spatial domain and transform the spatial domain into other domains for analysis. With the emergence of Transformers, some researchers have also combined traditional convolutional networks with Transformers for detection. This paper explores the artifacts left by Deepfakes in various domains and, based on this exploration, proposes a detection method that utilizes the steganalysis rich model (SRM) to extract high-frequency noise to complement spatial features. We have designed two main modules to fully leverage the interaction between these two aspects based on traditional convolutional neural networks. The first is the multi-scale mixed feature attention module, which introduces artifacts from high-frequency noise into spatial textures, thereby enhancing the model’s learning of spatial texture features. The second is the multi-scale channel attention module, which reduces the impact of background noise by weighting the features. Our proposed method was experimentally evaluated on mainstream datasets, and a significant amount of experimental results demonstrate the effectiveness of our approach in detecting Deepfake forged faces, outperforming the majority of existing methods.

A Helmet Detection Algorithm Based on Transformers with Deformable Attention Module

CHEN Songle, SUN Hongbo, WU Yuxin, SHANG Lei, RUAN Xiukai

, Available online , doi: 10.23919/cje.2023.00.346

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Wearing a helmet is one of the effective measures to protect workers’ safety. To address the challenges of severe occlusion, multi-scale, and small target issues in helmet detection, this paper proposes a helmet detection algorithm based on deformable attention Transformers. The main contributions of this paper are as follows. A compact end-to-end network architecture for safety helmet detection based on Transformers is proposed. It cancels the computationally intensive Transformer Encoder module in the existing detection transformer (DETR) and uses the Transformer Decoder module directly on the output of feature extraction for query decoding, which effectively improves the efficiency of helmet detection. A novel feature extraction network named DSwin Transformer is proposed. By sparse cross-window attention, it enhances the contextual awareness of multi-scale features extracted by Swin Transformer, and keeps high computational efficiency simultaneously. The proposed method generates the query reference points and query embeddings based on the joint prediction probabilities, and selects an appropriate number of decoding feature maps and sparse sampling points for query decoding, which further enhance the inference capability and processing speed. On the benchmark safety-helmet-wearing-dataset (SHWD), the proposed method achieves the average detection accuracy mAP@0.5 of 95.4% with 133.35G FLOPs and 20 FPS, the state-of-the-art method for safety helmet detection.

Exploring Potential Barrier Estimation Mechanism Based on Quantum Dynamics Framework

TANG Quan, WANG Peng

, Available online , doi: 10.23919/cje.2023.00.293

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Due to the probability characteristics of quantum mechanism, the combination of quantum mechanism and intelligent algorithm has received wide attention. Quantum dynamics theory uses the Schrödinger equation as a quantum dynamics equation. Through three approximation of the objective function, quantum dynamics framework (QDF) is obtained which describes basic iterative operations of optimization algorithms. Based on QDF, this paper proposes a potential barrier estimation (PBE) method which originates from quantum mechanism. With the proposed method, the particle can accept inferior solutions during the sampling process according to a probability which is subject to the quantum tunneling effect, to improve the global search capacity of optimization algorithms. The effectiveness of the proposed method in the ability of escaping local minima was thoroughly investigated through double well function (DWF), and experiments on two benchmark functions sets show that this method significantly improves the optimization performance of high dimensional complex functions. The PBE method is quantized and easily transplanted to other algorithms to achieve high performance in the future.

CMOS Temperature Sensors: From Module Design to System Design

TANG Zhong, YU Xiao-Peng, SHI Zheng, Tan Nianxiong Nick

, Available online , doi: 10.23919/cje.2023.00.425

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In a smart CMOS temperature sensor, the temperature information is converted to an electrical signal, such as voltage, current, or time delay, and then it is digitized by an analog-to-digital converter (ADC). Instead of categorizing sensors according to their sensing elements, this work introduces different CMOS temperature sensors based on their signal processing domains of the readout circuits. To design a suitable sensor for a specific application, two general design methodologies are also introduced with state-of-the-art examples. Depending on the applications, the corresponding types of the sensor and design methodology can be chosen to optimize the performance.

Ultralow Ohmic Contact in Recess-Free Ultrathin Barrier AlGaN/GaN Heterostructures Across a Wide Temperature Range

WANG Yuhao, HUANG Sen, JIANG Qimeng, WANG Xinhua, FAN Jie, YIN Haibo, WEI Ke, ZHENG Yingkui, LIU Xinyu

, Available online , doi: 10.23919/cje.2023.00.309

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‘Ohmic-before-passivation’ process was implemented on ultrathin-barrier (UTB) AlGaN (<6 nm)/GaN heterostructure to further reduce the ohmic contact resistance (R_c). In this process, alloyed Ti/Al/Ni/Au ohmic metal was formed first, followed by AlN/SiN_x passivation contributed to restore two-dimensional electron gas (2DEG) in the access region. Due to the sharp change in the concentration of 2DEG at the metal edge, a reduced transfer length (L_T) consisted with lower R_c are achieved compared to that of ohmic contact on AlGaN (~20 nm)/GaN heterostructure with pre-ohmic recess process. Temperature-dependent current voltage measurements demonstrate that the carrier transport mechanism is dominated by thermionic field emission above 200 K and by field emission below 200 K. The ‘ohmic-before-passivation’ process enables the relative stability of ohmic contacts between 50 K to 475 K and significantly improves the DC characteristics of GaN-MIS-HEMTs, offering a promising means for scaling down and enabling the utilization of low-voltage GaN-based power devices in extreme environmental conditions.

Comparative Analysis of Noise Margin between Pure SET-SET and Hybrid SET-PMOS Inverters

ZHANG Fan, LIU Yi, WANG Yibo, WU Minghu, HU Sheng, DONG Youli

, Available online , doi: 10.23919/cje.2023.00.287

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Single-electron transistor (SET) is considered as one of the promising candidates for future electronic devices due to its advantages of low power consumption and high integration. The comparative analysis of SET-based inverters, especially the noise margin, is carried out. Pure SET-SET and hybrid SET-PMOS inverters are designed for investigation. The effects of SET supply voltage, junction resistance and junction capacitance on noise tolerance and power consumption of inverters are studied. For hybrid SET-PMOS inverters, the noise margin high (NMH) is less than 60 mV under various conditions, which may become the bottleneck of its application. For pure SET-SET inverters, both NMH and NML could reach 300 mV at a supply voltage of 0.8 V. The minimum power consumption of pure SET-SET and hybrid SET-PMOS inverters is 2.85 nW and 58 nW, respectively. The pure SET-SET inverters have greater noise tolerance and lower power consumption, which is more conducive to large-scale integration. When junction capacitance $ C_{\mathrm{J }}$ = 0.0273 aF and junction resistance $ R_{\mathrm{T}} \ge $ 1 M in SET-SET inverters at a supply voltage of 0.8 V, the NMH and NML are not significantly affected by the junction resistance and the noise margin fluctuates at 300 mV.

Antenna Selection Method for Distributed Dual-function Radar Communication in MIMO System

ZHAO Haitao, DING Zhongzheng, WANG Qin, XIA Wenchao, Bo XU, ZHU Hongbo

, Available online , doi: 10.23919/cje.2023.00.270

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Distributed dual-function radar systems are an emerging trend in next-generation wireless systems, offering the possibility of improved parameter estimation for target localization as well as improved communication performance. With sufficient resource allocation, the achievable minimum estimated mean square error (MSE) and maximum total communication rate of localization may exceed the intended performance metrics of the system, which may consume an excessive number of antennas as well as antenna costs. In order to avoid resource wastage, this paper proposes a distributed dual-function radar communication (DFRC) multiple-input multiple-output (MIMO) system capable of performing radar and communication tasks simultaneously. The distributed system achieves the desired MSE performance metrics and communication performance metrics by efficiently selecting a subset of antennas, and minimizing the number of transmitting antennas and receiving antennas used in the system as well as the cost. In this paper, the problem is modeled as a knapsack problem (KP) where the objective is to obtain the maximal MSE performance and the maximal total communication rate performance at the lowest cost, for which we design a heuristic antenna selection algorithm. The designed algorithm is effective in reducing the time complexity as well as reducing the cost of antenna, and minimizing the number of antennas required.

A High-Resolution Calibration method for Time-to-Digital Converter of Lidar

LIU Ruqing, LI Feng, ZHU Jingguo, JIANG Yan, JIANG Chenghao, HU Tao

, Available online , doi: 10.23919/cje.2023.00.237

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High-resolution time-to-digital converter (TDC) finds major applications in light detection and ranging (Lidar) systems as one of the high precision time measuring techniques. In this work, a high-resolution TDC is designed and implemented on a Xilinx FPGA board. For precision time measurements, the proposed TDC uses an internal tapped delay chain written in Verilog. The TDC circuit measurement errors are examined and calibrated following several principles of error reduction techniques to meet the specific demand for the high-precision Lidar range. Experiments have shown that the suggested calibration TDC has higher performance, achieving sub 35 ps resolution. The design is fully customizable and implemented as a set of separate IP cores. This allows for easy implementation and meets the requirements of the present-day pulse Lidar systems.

An Improved YOLOv7-tiny Algorithm for Vehicle and Pedestrian Detection with Occlusion in Autonomous Driving

SU Jian, WANG Fang, ZHUANG Wei

, Available online , doi: 10.23919/cje.2023.00.256

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Future transportation is advancing in the direction of intelligent transportation systems, where an essential part is vehicle and pedestrian detection. Due to the complex urban traffic environment, vehicles and pedestrians in road monitoring have different forms of occlusion problems, resulting in the missed detection of objects. We design an improved YOLOv7-tiny algorithm for vehicle and pedestrian detection under occlusion, with the following four main improvements. In order to locate the object more accurately, 1 × 1 convolution and identity connection are added to the 3 × 3 convolution, and convolution reparameterization is used to enhance the inference speed of the network model. In view of the complex road background and more interference, the coordinate attention was added to the connection part of backbone and neck to enhance the network’s capacity to detect the object and lessen interference from other targets. At the same time, before being sent to the detection head, global attention mechanism is added to improve the accuracy of model detection by capturing three-dimensional features. Considering the issue of imbalanced training samples, we propose focal CIOU loss instead of CIOU loss to become the bounding box regression loss, so that the regression process attention to high-quality anchor boxes. Experiments show that the improved YOLOv7-tiny algorithm achieves 82.2% map@0.5 in PASCAL VOC dataset, which is 2.8% higher than before the improvement. The performance of map@0.5:0.95 is 5.2% better than the previous improvement. The proposed improved algorithm can availably to detect partial occlusion objects.

Imperceptible Audio Watermarking with Local Invariant Points and Adaptive Embedding Strength

WU Shiqiang, GUAN Hu, LIU Jie, ZENG Zhi, HUANG Ying, ZHANG Shuwu

, Available online , doi: 10.23919/cje.2023.00.356

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Audio watermarking is a promising technique for copyright protection of audio data. The existing audio watermarking algorithms cannot satisfy requirements on imperceptibility, embedding capacity, and robustness, especially against desynchronization attacks, such as cropping, jittering, and time-scale modification. This paper proposes a novel audio watermarking algorithm, LIPAS, based on local invariant points and adaptive embedding strength. We consider one feature robust to desynchronization attacks, i.e., local invariant points, and use these invariant points as positional references for the embedding regions. An adaptive embedding strength strategy is proposed to enhance the imperceptibility of the watermark and ensure robustness. The watermarks are embedded into the audio vectors using a polarity adjustment method. The effectiveness, imperceptibility, and robustness of the LIPAS algorithm were demonstrated in the experiments.

14-bit SAR ADC with On-Chip Digital Bubble Sorting Calibration Technology

FAN Hua, CHEN Zhuorui, XU Tongrui, Maloberti Franco, WEI Qi, FENG Quanyuan

, Available online , doi: 10.23919/cje.2023.00.307

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This article designs a 14-bit successive approximation register analog-to-digital converter (SAR ADC). A novel digital bubble sorting calibration method is proposed and applied to eliminate the effect of capacitor mismatch on the linearity of the SAR ADC. To reduce the number of capacitors, a hybrid architecture of a high 8-bit binary-weighted capacitor array and a low 6-bit resistor array is adopted by the digital-to-analog (DAC). The common-mode voltage V_CM-based switching scheme is chosen to reduce the switching energy and area of the DAC. The time-domain comparator is employed to obtain lower power consumption. Sampling is performed through a gate voltage bootstrapped switch to reduce the nonlinear errors introduced when sampling the input signal. Moreover, the SAR logic and the whole calibration is totally implemented on-chip through digital integrated circuit (IC) tools such as Design Compiler, IC compiler, etc. Finally, a prototype is designed and implemented using 0.18 μm Bipolar-complementary metal oxide semiconductor (CMOS)-double-diffused MOS 1.8 V CMOS technology. The measurement results show that the SAR ADC with on-chip bubble sorting calibration method achieves the signal-to-noise-and-distortion ratio of 69.75 dB and the spurious-free dynamic range of 83.77 dB.

IPFA-Net: Important Points Feature Aggregating Net for Point Cloud Classification and Segmentation

WANG Jingya, ZHANG Yu, ZHANG Bin, XIA Jinxiang, WANG Weidong

, Available online , doi: 10.23919/cje.2023.00.065

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This paper focuses on the problems of point cloud deep neural networks in classification and segmentation tasks, including losing important information during down-sampling, ignoring relationships among points when extracting features, and network performance being susceptible to the sparsity of point cloud. To begin with, this paper proposes a farthest point sampling (FPS)-important points sampling (F-IPS) method for down-sampling, which can preserve important information of point clouds and maintain the geometry of input data. Then, the local feature relation aggregating (LFRA) method is proposed for feature extraction, improving the network’s ability to learn contextual information and extract rich local region features. Based on these methods, the important points feature aggregating net (IPFA-Net) is designed for point cloud classification and segmentation tasks. Furthermore, this paper proposes the multi-scale multi-density feature connecting (MMFC) method to reduce the negative impact of point cloud data sparsity on network performance. Finally, the effectiveness of IPFA-Net is demonstrated through experiments on ModelNet40, ShapeNet part, and ScanNet v2 datasets. IPFA-Net is robust to reducing the number of point clouds, with only a 3.3% decrease in accuracy under a 16-fold reduction of point number. In the part segmentation experiments, our method achieves the best segmentation performance for five objects.

Linear Forgery Attacks on the Authenticated Encryption Cipher ACORN-like

LI Yunqiang, CUI Ting

, Available online , doi: 10.23919/cje.2023.00.016

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The authenticated encryption stream cipher ACORN is one of the finalists of the Competition for Authenticated Encryption: Security, Applicability, and Robustness (CAESAR) and is intended for lightweight applications. Because of structural weaknesses in the state update function of ACORN, we can introduce a linear function to analyze conditions and differential trails of the state collision and present a linear method to construct forgery messages under the condition that the key and initialization vector are known or the register state at a certain time is known. The attack method is suitable for three versions of ACORN and may be also extended to any ACORN-like, of which the linear feedback shift register (LFSR) can be replaced by other LFSRs and the feedback function can be replaced by other nonlinear functions. For continuous $ l\ (l > 293) $ bits of new input data, we can construct $2^{l-294}$ forgery messages for any given message of ACORN. Using a standard PC, a concrete forgery message can be constructed almost instantly and the required CPU time and memory are equivalent to the required resources for solving a system of 293 linear equations over the binary field. These attacks in this paper make that the sender and receiver may easily cheat each other, which is not a desirable property for an ideal cipher and casts some doubt on the necessary authentication security requirements of ACORN.

Research on Object Detection and Combination Clustering for Railway Switch Machine Gap Detection

FENG Qingsheng, XIAO Shuai, LIU Wangyang, LI Hong

, Available online , doi: 10.23919/cje.2023.00.268

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Turnouts and switch machines play a crucial role in facilitating train line operations and establishing routes, making them vital for ensuring the safety and efficiency of railway transportation. Through the gap detection system of switch machines, the real-time working status of turnouts and switch machines on railway sites can be quickly known. However, due to the challenging working environment and demanding conversion tasks of switch machines, the current gap detection system has often experienced the issues of fault detection. To address this, this study proposes an automatic gap detection method for railway switch machines based on object detection and combination clustering. Firstly, a lightweight object detection network, specifically the MobileNetV3-YOLOv5s model, is used to accurately locate and extract the focal area. Subsequently, the extracted image undergoes preprocessing and is then fed into a combination clustering algorithm to achieve precise segmentation of the gap area and background, the algorithm consists of simple linear iterative clustering (SLIC), Canopy and kernel fuzzy c-means clustering (KFCM). Finally, the Fisher optimal segmentation criterion is utilized to divide the data sequence of pixel values, determine the classification nodes and calculate the gap size. The experimental results obtained from switch machine gap images captured in various scenes demonstrate that the proposed method is capable of accurately locating focal areas, efficiently completing gap image segmentation with a segmentation accuracy of 93.55%, and swiftly calculating the gap size with a correct rate of 98.57%. Notably, the method achieves precise detection of gap sizes even after slight deflection of the acquisition camera, aligning it more closely with the actual conditions encountered on railway sites.

AOYOLO Algorithm Oriented Vehicle and Pedestrian Detection in Foggy Weather

SU Jian, MAO Shiang, ZHUANG Wei

, Available online , doi: 10.23919/cje.2023.00.280

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In the context of complex foggy environments, the acquired images often suffer from low visibility, high noise, and loss of detailed information. The direct application of general object detection methods fails to achieve satisfactory results. To address these issues, this paper proposes a foggy object detection method based on YOLOv8n, named AOYOLO. The all-in-one dehazing network (AOD-Net), a lightweight defogging network, is employed for data augmentation. Additionally, the ResCNet module is introduced in the backbone to better extract features from low-illumination images. The GACSP module is proposed in the neck to capture multi-scale features and effectively utilize them, thereby generating discriminative features with different scales. The detection head is improved using WiseIoU, which enhances the accuracy of object localization. Experimental evaluations are conducted on the publicly available datasets annotated realworld task-driven testing set (RTTS) and synthetic foggy KITTI dataset. The results demonstrate that the proposed AOYOLO algorithm outperforms the original YOLOv8n algorithm with an average mean average precision improvement of 3.3% and 4.6% on the RTTS and KITTI datasets, respectively. The AOYOLO method effectively enhances the performance of object detection in foggy scenes. Due to its improved performance and stronger robustness, this experimental model provides a new perspective for foggy object detection.

Secure Fine-grained Multi-keyword Ciphertext Search Supporting Cloud-edge-end Collaboration in IoT

ZHENG Kaifa, ZHOU Ziyu, LIU Jianwei, YU Beiyuan

, Available online , doi: 10.23919/cje.2023.00.244

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The massive terminals access the Internet of things (IoT) through edge nodes, bringing forth new security and privacy challenges in ciphertext search and data sharing. Meanwhile, existing ciphertext search schemes often overlook lightweight computing paradigms and pay little attention to the search requirements of multiple data owners (DOs)/data users (DUs). To address these issues, we propose a secure fine-grained multi-keyword ciphertext search scheme with cloud-edge-end collaboration computing (SFMS-CC). This SFMS-CC scheme focuses on the efficiency of end users and employs a cloud-edge-end collaborative computing paradigm, effectively offloading the incremental overhead from terminals and achieving low-cost constant overhead for the first time on the DO/DU side. Furthermore, based on public-key cryptography, a ciphertext search framework supporting multi-keyword is presented systematically. Each user is assigned an exclusive search Tok to enhance the user experience. Additionally, by integrating attribute-based encryption (ABE), a multi-DOs/multi-DUs model is constructed, seamlessly embedding entity private keys and public keys into encryption, search, decryption, and other steps, ensuring high privacy and security of this scheme. Security analysis demonstrates that the SFMS-CC scheme withstands choose plaintext attack (CPA), providing privacy-preserving for outsourced data and user information. Simulation results indicate that the SFMS-CC scheme is efficient and feasible in practice.

Fault Diagnosis for Railway Point Machines Using VMD Multi-scale Permutation Entropy and ReliefF Based on Vibration Signals

SUN Yongkui, CAO Yuan, LI Peng, SU Shuai

, Available online , doi: 10.23919/cje.2023.00.258

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Abstract:
The railway point machine plays an important part in railway systems. It is closely related to the safe operation of trains. Considering the advantages of vibration signals on anti-interference, this paper develops a novel vibration signal-based diagnosis approach for railway point machines. First, variational mode decomposition (VMD) is adopted for data preprocessing, which is verified more effective than empirical mode decomposition. Next, multi-scale permutation entropy is extracted to characterize the fault features from multiple scales. Then ReliefF is utilized for feature selection, which can greatly decrease the feature dimension and improve the diagnosis accuracy. By experiment comparisons, the proposed approach performs best on diagnosis for railway point machines. The diagnosis accuracies on reverse-normal and normal-reverse processes are respectively 100% and 98.29%.

Research on Low-frequency Multi-directional Piezoelectric Energy Harvester with Combined Cantilever Beam

REN Qingying, LIU Yuxuan, WANG Debo

, Available online , doi: 10.23919/cje.2023.00.351

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Abstract:
In order to realize the collection and utilization of low-frequency vibration energy, a multi-directional piezoelectric energy harvester is proposed, which consists of a lower circular arc beam and an upper L-shaped beam. Both the lower and upper beams can achieve multi-directional energy harvesting, and the upper L-shaped beam can also act as a mass block to reduce the resonant frequency. The structure of this energy harvester is optimized. Four different structures are studied with varying combination angles between the upper and lower layers to acquire data related to resonant frequency, vibration shape, stress distribution, open-circuit voltage, and output power. Additionally, the performance of each structure is comprehensively prepared and measured to verify its effectiveness. The optimal structure achieved a resonant frequency of 11 Hz and an output power of 57.1 μW at the optimal load resistance of 201 kΩ. Consequently, this work provides valuable reference for the study of low-frequency vibration energy harvesting technology.

An Improved Z-buffer Accelerated PO Method for EM Scattering from Electrically Large Targets

BAI Jiangfei, YANG Shunchuan, SU Donglin

, Available online , doi: 10.23919/cje.2024.00.025

Abstract(203) HTML (93) PDF(20)

Abstract:
The physical optical (PO) method is widely used to solve the electromagnetic scattering problems involved electrically large structures, in which each surface element is required to determine whether it is blocked by others. It may suffer from the computational efficiency issue through elementwise shadowing testing. In this paper, an efficient Z-buffer based shadowing testing method is proposed to accelerate this procedure. In the proposed method, all triangular facets are first mapped to a grid plane as the Z-buffer method, and for each grid cell, all projected triangles intersecting it are recorded. Then, rigorous shadowing testing is made for all facets recorded in the grid where the centroid of each triangle is projected. It can avoid a large number of redundant operations for pairs of triangles with no occlusion relation, which lead to the same accuracy as the traditional rigorous shadowing testing method with significantly efficiency improvement. Four numerical examples are carried out to validate its accuracy and efficiency.

Study on the Impact of Imbalance between Transmission Lines on Crosstalk: a Novel Perspective of Displacement Current

LU Xiaozhu, SONG Lingnan, XU Hui, SU Donglin

, Available online , doi: 10.23919/cje.2024.00.049

Abstract(254) HTML (122) PDF(19)

Abstract:
This paper systematically studies the impact of imbalances between adjacent lines and effects on crosstalk. A novel perspective of displacement current is introduced to analyze and explain the simulated observations. The imbalances caused by coupling between single-single, single-differential, and differential-differential lines are studied and analyzed by considering the near-field coupling through the generated displacement currents. Measurements are conducted for various cases of coupled adjacent lines. An equivalent model considering the variation of displacement current with geometrical parameters is also proposed, and the corresponding coupling coefficients are extracted based on simulations to characterize the impact of imbalances. The methods and results presented in this paper provide useful guidelines for designing high-speed circuit layouts with closely spaced transmission lines.

An Enhanced Clustering-Based (k, t)-Anonymity Algorithm for Graphs

WANG Yuanyuan, ZHANG Xing, CHU Zhiguang, SHI Wei, LI Xiang

, Available online , doi: 10.23919/cje.2023.00.276

Abstract(199) HTML (98) PDF(8)

Abstract:
As people become increasingly reliant on the Internet, securely storing and publishing private data has become an important issue. In real life, the release of graph data can lead to privacy breaches, which is a highly challenging problem. Although current research has addressed the issue of identity disclosure, there are still two challenges: First, the privacy protection for large-scale datasets is not yet comprehensive; Second, it is difficult to simultaneously protect the privacy of nodes, edges, and attributes in social networks. To address these issues, this paper proposes a ($k$, $t$)-graph anonymity algorithm based on enhanced clustering. The algorithm uses $k$-means++ clustering for $k$-anonymity and $t$-closeness to improve $k$-anonymity. We evaluated the privacy and efficiency of this method on two datasets and achieved good results. This research is of great significance for addressing the problem of privacy breaches that may arise from the publication of graph data.

Reliable and Fair Trustworthiness Evaluation Protocol for Platoon Service Recommendation System

CHENG Hongyuan, LIU Yining, ZHOU Fei, TAN Zhiyuan, ZHANG Xianchao

, Available online , doi: 10.23919/cje.2023.00.012

Abstract(196) HTML (94) PDF(17)

Abstract:
Aiming at the problems of the communication inefficiency and high energy consumption in vehicular networks, the platoon service recommendation systems (PSRS) are presented. Many schemes for evaluating the reputation of platoon head vehicles have been proposed to obtain and recommend reliable platoon head vehicles. These trustworthiness evaluation protocols for PSRS fail to achieve both reliability and fairness. We first provide a reliable trustworthiness evaluation method to ensure that the reputation level of platoon head vehicle can be calculated by cloud service provider (CSP) with the help of key agreement mechanism and truth discovery technology. The semi-trusted entity CSP may maliciously tamper with the reputation level of the platoon head vehicle. We also provide a reputation level confirmation method to ensure the fairness of trustworthiness evaluation. Formal security proof and security analysis are provided to show that our trustworthiness evaluation protocol can achieve the goals of privacy protection, reliability, fairness and resistance to several security attacks. Experiments demonstrate that this protocol can save execution time and achieve reliable and fair trustworthiness evaluation for PSRS.

A Study of Epileptogenic Foci Localization Algorithm Based on Automatic Detection of Comprehensive Feature HFOs and RF-LR

DU Yuxiao, LI Gaoming

, Available online , doi: 10.23919/cje.2023.00.213

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Abstract:
Studies have shown that fast ripples of 250–500 Hz in epileptic Electroencephalography (EEG) signals are more pathological and closer to the epileptogenic focus itself compared to ripples of 80–250 Hz. However, artifacts of fast ripples and high-frequency oscillations (HFOs) are easily confused and difficult to discriminate, and manual visual screening is both time-consuming and unable to avoid subjectivity. To this end, this paper presents a method for localizing epileptogenic foci based on the automatic detection of integrated feature HFOs and Random Forest-logistic regression (RF-LR). In this paper, we first extract multivariate features from the preprocessed epileptic EEG signals, and use the random forest algorithm to filter out three features with high importance, based on which, suspicious leads containing HFOs are identified. Then, wavelet time-frequency maps were used for the primary screening of suspected leads to improve the signal calibration efficiency and further localize HFOs in time and frequency. Finally, a logistic regression model was used to automatically classify and identify ripples and fast ripples in HFOs. The results show that the sensitivity, specificity, and accuracy of the model for detecting ripple are 89.37%, 88.26%, and 90.1%, respectively; the sensitivity, specificity, and accuracy for detecting fast ripple are 94.31%, 94.83%, and 93.46%, respectively. Compared with single features, the multivariate features in this paper more comprehensively characterize the complex epileptic EEG signals and provide more accurate information for the localization of epileptogenic foci. The automatic detection algorithm of HFOs proposed in this paper can analyze a large amount of data in a short time and has a good detection performance, which can help clinicians accurately determine the region of epileptogenic foci.

Confidential Image Super-resolution with Privacy Protection

HAN Yiran, LIU Jianwei, DENG Xin, JING Junpeng, ZHANG Yanting

, Available online , doi: 10.23919/cje.2023.00.034

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Abstract:
Nowadays, people are getting used to upload images to a third-party for post-processing, such as image denoising and super-resolution. This may easily lead to the disclosure of the privacy in the confidential images. One possible solution is to encrypt the image before sending it to the third party, however, the encrypted image can be easily detected by a malicious attacker in the transmission channel. We propose a confidential image super-resolution method namely HSR-Net, which firstly hide the secret image and then super-resolve it in the hidden domain. The HSR-Net is composed of three important modules: image hiding module (IHM), image super-resolution module (ISM), and image revealing module (IRM). The IHM aims to encode secret image and hide it into a cover image to generate the stego image. The stego image looks similar to the cover image but contains the information of the secret image. Then, the third party uses the ISM to perform image super-resolution on the stego image. After that, the user can reveal the super-resolved secret image from the stego image. Our HSR-Net has two advantages. Firstly, it ensures that the third party cannot not directly operate on the secret image to protect the user’s privacy. In addition, due to the similarity between the stego image and cover image, we can reduce the attacker’s suspicion to further improve the image security. The experimental results on various datasets, including DIV2K dataset and Flickr2K dataset. The PSNR of IHM is 38.81dB, the PSNR of ISM is 28.91 dB, and the PSNR of IRM is 23.51 dB, which verify that our HSR-Net is able to achieve image super-resolution and protect user’s privacy simultaneouly.

Priority Encoder Based on DNA Strand Displacement

WANG Fang, ZHANG Xinjian, CHEN Xin, LV Shuying, CHEN Congzhou, SHI Xiaolong

, Available online , doi: 10.23919/cje.2022.00.042

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Abstract:
The slow development of traditional computing has prompted a search for new materials to replace silicon-based computers. Bio-computers, which use molecules as the basis of computation, are highly parallel and information capable, attracting a lot of attention. In this study, we designed a NAND logic gate based on the DNA strand displacement mechanism. Further, we assembled a molecular calculation model, a 4-wire-2-wire priority encoder logic circuit, by cascading the proposed NAND gates. Different concentrations of input DNA chains were added into the system, resulting in corresponding output, through DNA hybridization and strand displacement. Therefore, it achieved the function of a priority encoder. Simulation results verify the effectiveness and accuracy of the molecular NAND logic gate and the priority coding system presented in this study. The unique point of this proposed circuit is that we cascaded only one kind of logic gate, which provides a beneficial exploration for the subsequent development of complex DNA cascade circuits and the realization of the logical coding function of information.

Towards Reliable Configuration Management in Clouds: A Lightweight Consistency Validation Mechanism for Virtual Private Clouds

QIU Yuhang, ZHAO Gongming, XU Hongli, LI Long, HUANG He, HUANG Liusheng

, Available online , doi: 10.23919/cje.2023.00.387

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Abstract:
The Virtual Private Cloud service currently lacks a real-time end-to-end consistency validation mechanism, which prevents tenants from receiving immediate feedback on their requests. Existing solutions consume excessive communication and computational resources in such large-scale cloud environments, and suffer from poor timeliness. To address these issues, we propose a lightweight consistency validation mechanism that includes real-time incremental validation and periodic full-scale validation. The former leverages message layer aggregation to enable tenants to swiftly determine the success of their requests on hosts with minimal communication overhead. The latter utilizes lightweight validation checksums to compare the expected and actual states of hosts locally, while efficiently managing the checksums of various host entries using inverted indexing. This approach enables us to efficiently validate the complete local configurations within the limited memory of hosts. In summary, our proposed mechanism achieves closed-loop implementation for new requests and ensures their long-term effectiveness.

Theoretical Research on a D-Band Traveling Wave Extended Interaction Amplifier

CUI Zhongtao, YUAN Xuesong, XU Xiaotao, CHEN Dongrui, ZU Yifan, Cole Matthew Thomas, CHEN Qingyun, YAN Yang

, Available online , doi: 10.23919/cje.2022.00.345

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Abstract:
A traveling-wave, extended interaction amplifier is herein investigated for use in millimeter-wave and terahertz amplification sources. By placing engineered extended interaction cavities between the traveling wave structures, higher gain is obtained with a shorter high frequency circuit, compared with conventional traveling wave tubes architectures. The bandwidth of the device is significantly increased relative to extended interaction klystrons. A D-band beam wave interaction circuit of 26 mm long has been designed. Particle in cell simulations at 21.5 kV operating voltage, 0.3 A beam current, and 5 mW input power show that the maximum output power reaches 351 W, with a gain of 48.4 dB and 3-dB bandwidth of 1.42 GHz.

Self-Decoupled Square Patch Antenna Arrays by Exciting and Using Mixed Electric/Magnetic Coupling between Adjacent Radiators

LIU Qianwen, ZHU Lei, LU Wenjun

, Available online , doi: 10.23919/cje.2023.00.222

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Abstract:
This article presents and develops a simple decoupling method for the planar square patch antenna arrays by virtue of mixed electric and magnetic coupling property. Since the resonant modes of TM₁₀ and TM₀₁ are a pair of degenerate modes in the square patch radiator which are intrinsically orthogonal, a superposed mode of them can be generated to possess consistent field distributions along all the four sides of the patch by adjusting the feeding position. By employing such superposed mode, the mutual coupling between two horizontally adjacent patch elements will become identical to that between two vertical ones, indicating an expected possibility that the complex 2-D decoupling problem in a large-scale antenna patch array can be effectively facilitated and simplified to a 1-D one. Subsequently, metallic pins and connecting strip are properly loaded in each square patch resonator, such that appropriate electric and magnetic coupling strengths can be readily achieved and thus the mutual coupling can get highly decreased. A 1×2 antenna array with an edge-to-edge separation of 1mm, which corresponding to 0.0117λ₀, is firstly discussed, simulated, and fabricated. The measured results show that the isolation can be highly improved from 4 dB to 17 dB across the entire passband. In final, 1×3, 2×2, and 4×4 antenna array prototypes are constructed and studied for verification of the expansibility and feasibility of the proposed decoupling method to both linear and 2-D antenna arrays.

Model Checking Computation Tree Logic over Multi-Valued Decision Processes and Its Reduction Techniques

LIU Wuniu, WANG Junmei, HE Qing, LI Yongming

, Available online , doi: 10.23919/cje.2021.00.333

Abstract(572) HTML (283) PDF(24)

Abstract:
Model checking computation tree logic based on multi-valued possibility measures has been studied by Li et al. in 2019. However, the previous work did not consider the nondeterministic choices inherent in systems represented by multi-valued Kripke structures (MvKSs). This nondeterminism is crucial for accurate system modeling, decision making, and control capabilities. To address this limitation, we draw inspiration from the generalization of Markov chains to Markov decision processes in probabilistic systems. By integrating nondeterminism into MvKS, we introduce multi-valued decision processes (MvDPs) as a framework for modeling MvKSs with nondeterministic choices. We investigate the problems of model checking over MvDPs. Verifying properties are expressed by using multi-valued computation tree logic based on schedulers. Our primary objective is to leverage fixpoint techniques to determine the maximum and minimum possibilities of the system satisfying temporal properties. This allows us to identify the optimal or worst-case schedulers for decision making or control purposes. We aim to develop reduction techniques that enhance the efficiency of model checking, thereby reducing the associated time complexity. We mathematically demonstrate three reduction techniques that improve model checking performance in most scenarios.

A Fast Algorithm for Computing the Deficiency Number of a Mahjong Hand

YAN Xueqing, LI Yongming, LI Sanjiang

, Available online , doi: 10.23919/cje.2022.00.259

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Abstract:
The tile-based multiplayer game Mahjong is widely played in Asia and has also become increasingly popular worldwide. Face-to-face or online, each player begins with a hand of 13 tiles and players draw and discard tiles in turn until they complete a winning hand. An important notion in Mahjong is the deficiency number (a.k.a. shanten number in Japanese Mahjong) of a hand, which estimates how many tile changes are necessary to complete the hand into a winning hand. The deficiency number plays an essential role in major decision-making tasks such as selecting a tile to discard. This paper proposes a fast algorithm for computing the deficiency number of a Mahjong hand. Compared with the baseline algorithm, the new algorithm is usually 100 times faster and, more importantly, respects the agent’s knowledge about available tiles. The algorithm can be used as a basic procedure in all Mahjong variants by both rule-based and machine learning-based Mahjong AI.

Subspace Clustering via Block-Diagonal Decomposition

FU Zhiqiang, ZHAO Yao, CHANG Dongxia, WANG Yiming

, Available online , doi: 10.23919/cje.2022.00.385

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Abstract:
The subspace clustering has been addressed by learning the block-diagonal self-expressive matrix. This block-diagonal structure heavily affects the accuracy of clustering but is rather challenging to obtain. A novel and effective subspace clustering model, i.e., subspace clustering via block-diagonal decomposition (SCBD), is proposed, which can simultaneously capture the block-diagonal structure and gain the clustering result. In our model, a strict block-diagonal decomposition is introduced to directly pursue the k block-diagonal structure corresponding to k clusters. In this novel decomposition, the self-expressive matrix is decomposed into the block indicator matrix to demonstrate the cluster each sample belongs to. Based on the strict block-diagonal decomposition, the block-diagonal shift is proposed to capture the local intra-cluster structure, which shifts the samples in the same cluster to get smaller distances and results in more discriminative features for clustering. Extensive experimental results on synthetic and real databases demonstrate the superiority of SCBD over other state-of-the-art methods.

Federated Offline Reinforcement Learning with Proximal Policy Evaluation

YUE Sheng, DENG Yongheng, WANG Guanbo, REN Ju, ZHANG Yaoxue

, Available online , doi: 10.23919/cje.2023.00.288

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Abstract:
Offline reinforcement learning (RL) has gathered increasing attention in recent years, which seeks to learn policies from static datasets without active online exploration. However, the existing offline RL approaches often require a large amount of pre-collected data and hence are hardly implemented by a single agent in practice. Inspired by the advancement of federated learning (FL), this paper studies federated offline reinforcement learning (FORL), whereby multiple agents collaboratively carry out offline policy learning with no need to share their raw trajectories. Clearly, a straightforward solution is to simply retrofit the off-the-shelf offline RL methods for FL, whereas such an approach easily overfits individual datasets during local updating, leading to instability and subpar performance. To overcome this challenge, we propose a new FORL algorithm, named model-free (MF)-FORL, that exploits novel “proximal local policy evaluation” to judiciously push up action values beyond local data support, enabling agents to capture the individual information without forgetting the aggregated knowledge. Further, we introduce a model-based variant, MB-FORL, capable of improving the generalization ability and computational efficiency via utilizing a learned dynamics model. We evaluate the proposed algorithms on a suite of complex and high-dimensional offline RL benchmarks, and the results demonstrate significant performance gains over the baselines.

FMR-GNet: Forward Mix-Hop Spatial-Temporal Residual Graph Network for 3D Pose Estimation

YANG Honghong, LIU Hongxi, ZHANG Yumei, WU Xiaojun

, Available online , doi: 10.23919/cje.2022.00.365

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Abstract:
Graph convolutional networks that leverage spatial-temporal information from skeletal data have emerged as a popular approach for 3D human pose estimation. However, comprehensively modeling consistent spatial-temporal dependencies among the body joints remains a challenging task. Current approaches are limited by performing graph convolutions solely on immediate neighbors, deploying separate spatial or temporal modules, and utilizing single-pass feedforward architectures. To solve these limitations, we propose a forward multi-scale residual graph convolutional network (FMR-GNet) for 3D pose estimation from monocular video. First, we introduce a mix-hop spatial-temporal attention graph convolution layer that effectively aggregates neighboring features with learnable weights over large receptive fields. The attention mechanism enables dynamically computing edge weights at each layer. Second, we devise a cross-domain spatial-temporal residual module to fuse multi-scale spatial-temporal convolutional features through residual connections, explicitly modeling interdependencies across spatial and temporal domains. Third, we integrate a forward dense connection block to propagate spatial-temporal representations across network layers, enabling high-level semantic skeleton information to enrich lower-level features. Comprehensive experiments conducted on two challenging 3D human pose estimation benchmarks, namely Human3.6M and MPI-INF-3DHP, demonstrate that the proposed FMR-GNet achieves superior performance, surpassing the most state-of-the-art methods.

Hybrid ITÖ Algorithm for Large-Scale Colored Traveling Salesman Problem

DONG Xueshi

, Available online , doi: 10.23919/cje.2023.00.040

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Abstract:
In the fields of intelligent transportation and multi-task cooperation, many practical problems can be modeled by colored traveling salesman problem (CTSP). When solving large-scale CTSP with a scale of more than 1000 dimensions, their convergence speed and the quality of their solutions are limited. This paper proposes a new hybrid ITÖ (HITÖ) algorithm, which integrates two new strategies, crossover operator and mutation strategy, into the standard ITÖ. In the iteration process of HITÖ, the feasible solution of CTSP is represented by the double chromosome coding, and the random drift and wave operators are used to explore and develop new unknown regions. In this process, the drift operator is executed by the improved crossover operator, and the wave operator is performed by the optimized mutation strategy. Experiments show that HITÖ is superior to the known comparison algorithms in terms of the quality solution.

Knowledge Graph Completion Method of Combining Structural Information with Semantic Information

HU Binhao, ZHANG Jianpeng, CHEN Hongchang

, Available online , doi: 10.23919/cje.2022.00.299

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Abstract:
With the development of knowledge graphs, a series of applications based on knowledge graphs have emerged. The incompleteness of knowledge graphs makes the effect of the downstream applications and affected by the quality of the knowledge graphs. To improve the quality of knowledge graphs, translation-based graph embeddings, such as TransE, learn structural information by representing triples as low-dimensional dense vectors. However, it is difficult to generalize to unseen entities that are not observed during training but appear during testing. The other methods use the powerful representational ability of pre-trained language models to learn entity descriptions and contextual representation of triples. Although they are robust to incompleteness, but they need to calculate the score of all candidate entities for each triple during inference. We consider combining two models to enhance the robustness of unseen entities by semantic information, and prevent combined explosion by reducing inference overhead through structured information. We use a pre-training language model to code triples and learn the semantic information within them, and then use a hyperbolic space-based distance model to learn structural information and integrate the two types of information together. We evaluate our model by performing link prediction experiments in standard datasets. In experiments, our model achieves better performances than state-of-the-art methods on two standard datasets.

A High-Quality and Efficient Bus-Aware Global Router

Liu Genggeng, Wei Ling, Yu Yantao, Xu Ning

, Available online , doi: 10.23919/cje.2023.00.061

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Abstract:
As advanced technology nodes enter the nanometer era, the complexity of integrated circuit (IC) design is increasing, and the proportion of bus in the net is increasing. The bus routing has become a key factor affecting the performance of the chip. In addition, the existing research does not distinguish between buses and non-buses in the complete global routing process, which directly leads to the expansion of bus deviation and the degradation of chip performance. In order to solve these problems, we propose a high-quality and efficient bus-aware global router, which includes the following key strategies: 1) By introducing the routing density graph, we propose a routing model that can simultaneously consider the routability of non-buses and the deviation value of buses. 2) A dynamic routing resource adjustment algorithm is proposed to optimize the bus deviation and wirelength simultaneously, which can effectively reduce the bus deviation. 3) We propose a layer assignment algorithm consider deviation to significantly reduce the bus deviation of the 3D routing solution. 4) A DFS-based algorithm is proposed to obtain multiple routing solutions, from which the routing result with the lowest deviation is selected. Experimental results show that the proposed algorithm can effectively reduce bus deviation with the existing algorithms, so as to obtain high-quality 2D and 3D routing solutions considering bus deviation.

Enhancing Entity Relationship Extraction in Dialogue Texts using Hypergraph and Heterogeneous Graph

ZHANG Shunmiao, ZHENG Siyuan, HUANG Degen, LI Dan

, Available online , doi: 10.23919/cje.2023.00.315

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Abstract:
Dialogue relationship extraction (RE) aims to predict relationships between two entities in dialogue. Current approaches to dialogue relationship extraction grapple with long-distance entity relationships in dialogue data as well as complex entity relationships, such as a single entity with multiple types of connections. To address these issues, this paper presents a novel approach for dialogue relationship extraction termed the hypergraphs and heterogeneous graphs model (HG2G). This model introduces a two-tiered structure, comprising dialogue hypergraphs and dialogue heterogeneous graphs, to address the shortcomings of existing methods. The dialogue hypergraph establishes connections between similar nodes using hyper-edges and utilizes hypergraph convolution to capture multi-level features. Simultaneously, the dialogue heterogeneous graph connects nodes and edges of different types, employing heterogeneous graph convolution to aggregate cross-sentence information. Ultimately, the integrated nodes from both graphs capture the semantic nuances inherent in dialogue. Experimental results on the DialogRE dataset demonstrate that the HG2G model outperforms existing state-of-the-art methods.

The Optimization of Binary Randomized Response Based on Lanke Privacy and Utility Analysis

ZHOU Yihui, WANG Wenli, YAN Jun, WU Zhenqiang, LU Laifeng

, Available online , doi: 10.23919/cje.2023.00.272

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Abstract:
Currently, it has become a consensus to enhance privacy protection. Randomized response (RR) technique, as the mainstream perturbation mechanism for local differential privacy, has been widely studied. However, most of the research in literature managed to modify existing RR schemes and propose new mechanisms with better privacy protection and utility, which are illustrated only by numerical experiments. We study the properties of generalized binary randomized response mechanisms from the perspectives of Lanke privacy and utility. The mathematical expressions of privacy and utility for the binary RR mechanism are given respectively. Moreover, the comparison principle for privacy and utility of any two mechanisms is proved. Finally, the optimization problem of the binary RR mechanism is discussed. Our work is based on a rigorous mathematical proof of privacy and utility for the general binary RR mechanism, and numerical verification illustrates the correctness of the conclusions. It can provide theoretical support for the design of binary RR mechanism and can be applied in data collection, analysis and publishing.

Collaborative Service Provisioning for UAV-assisted Mobile Edge Computing

QU Yuben, WEI Zhenhua, QIN Zhen, WU Tao, MA Jinghao, DAI Haipeng, DONG Chao

, Available online , doi: 10.23919/cje.2021.00.323

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Abstract:
As a way of coping with delay-sensitive and computing-intensive tasks, unmanned aerial vehicle (UAV)-assisted mobile edge computing (MEC) is considered to be a key technology to solving the challenges of terrestrial MEC networks. In this work, we study the problem of collaborative service provisioning for UAV-assisted MEC (CSP). Specifically, taking into account the task latency and other resource constraints, this paper investigates how to minimize the total energy consumption of all terrestrial user equipments (UEs), by jointly optimizing computing resource allocation, task offloading, UAV trajectory, and service placement. The CSP problem is a non-convex mixed integer nonlinear programming (MINLP) problem, owing to the complex coupling of mixed integral variables and non-convexity of CSP. To address CSP, this paper proposes an alternating optimization-based solution with convergence guarantee as follows. We iteratively deal with the joint service placement and task offloading subproblem, and UAV movement trajectory subproblem, by branch and bound (BnB) and successive convex approximation (SCA), respectively, while the closed form of the optimal computation resource allocation can be efficiently obtained. Extensive simulations validate the effectiveness of the proposed algorithm compared to three baselines.

Service Migration Algorithm Based on Markov Decision Process with Multiple Service Types and Multiple System Factors

MA Anhua, PAN Su, ZHOU Weiwei

, Available online , doi: 10.23919/cje.2022.00.128

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Abstract:
This paper proposes a Markov decision process based service migration algorithm to satisfy quality of service (QoS) requirements when the terminals leave the original server. Services were divided into real-time services and non-real-time services, each type of them has different requirements on transmission bandwidth and latency, which were considered in the revenue function. Different values were assigned to the weight coefficients of QoS parameters for different service types in the revenue and cost functions so as to distinguish the differences between the two service types. The overall revenue was used for migration decisions, rather than fixed threshold or instant revenue. The Markov decision process was used to maximize the overall revenue of the system. Simulation results show that the proposed algorithm obtained more revenue compared with the existing works.

Performance Analysis of Spatial Modulation Aided UAV Communication Systems in Cooperative Relay Networks

YU Xiangbin, XIE Mingfeng, LI Ning, PAN Cuimin

, Available online , doi: 10.23919/cje.2021.00.369

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Abstract:
In this paper, by introducing the spatial modulation (SM) scheme into the unmanned aerial vehicle (UAV) relaying system, a SM-aided UAV (SM-UAV) cooperative relay network is presented. The performance of the SM-UAV relay network is investigated over Nakagami-m fading channels, where the UAV remains stationary over a given area. According to the performance analysis, using the amplify-and-forward (AF) protocol, the effective signal-to-noise ratio (SNR) and the corresponding probability density function and moment generating function are, respectively, derived. With these results, the average bit error rate (BER) is further deduced, and resultant approximate closed-form expression is achieved. Based on the approximate BER, we derive the asymptotic BER to characterize the error performance of the system at high SNR. With this asymptotic BER, the diversity gain of the system is derived, and the resulting diversity order is attained. Simulation results illustrate the effectiveness of the performance analysis. Namely, approximate BER has the value close to the simulated one, and asymptotic BER can match the corresponding simulation well at high SNR. Thus, the BER performance of the system can be effectively assessed in theory, and conventional simulation will be avoided. Besides, the impacts of the antenna number, modulation order, fading parameter and UAV position on the system performance are also analyzed. The results indicate that the BER performance is increased with the increases of Nakagami parameter m and/or receive antenna and/or the decrease of modulation order.

Virtual Coupling Trains Based on Multi-agent System Under Communication Delay

QIN Guodong, MENG Xiangxi, WEN Tao, CAI Baigen

, Available online , doi: 10.23919/cje.2022.00.253

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Abstract:
With the rapid development of railway transportation, virtual coupling (VC) has become a popular research topic. VC can greatly reduce tracking distance and increase the line capacity. Under VC control, the train formation control not only considers the behavior and speed adjustment strategy of the leader train but also the communication delays between trains. The quality of data communication between trains is an important aspect of train tracking control. We consider a virtually coupled train set (VCTS) as a multi-agent system. The Luenberger observer is introduced to estimate the real-time state of the train, based on the estimation, the train control consistency protocol is designed to account for communication delays. The stability of the error system is proven by constructing a Lyapunov function. The consistency of the coordinated train control is verified through simulation.

Predicting circRNA-disease Associations by Using Multi-biomolecular Networks Based on Variational Graph Auto-encoder with Attention Mechanism

YANG Jing, LEI Xiujuan, PAN Yi

, Available online , doi: 10.23919/cje.2023.00.344

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Abstract:
CircRNA-disease association (CDA) can provide a new direction for the treatment of diseases. However, traditional biological experiment is time-consuming and expensive, this urges us to propose the reliable computational model to predict the associations between circRNAs and diseases. And there is existing more and more evidence indicates that the combination of multi-biomolecular information can improve the prediction accuracy. We propose a novel computational model for CDA prediction named MBCDA, we collect the multi-biomolecular information including circRNA, disease, miRNA and lncRNA based on 6 databases, and construct three heterogeneous network among them, then the multi-heads graph attention networks (GAT) are applied to these three networks to extract the features of circRNAs and diseases from different views, the obtained features are put into variational graph auto-encoder (VGAE) network to learn the latent distributions of the nodes, a fully connected neural network (FCNN) is adopted to further process the output of VGAE and uses sigmoid function to obtain the predicted probabilities of circRNA-disease pairs. As a result, MBCDA achieved the values of AUC and AUPR under 5-fold cross-validation of 0.893 and 0.887. MBCDA was applied to the analysis of the top-25 predicted associations between circRNAs and diseases, these experimental results show that our proposed MBCDA is a powerful computational model for CDA prediction.

A Lattice-Based Method for Recovering the Unknown Parameters of Truncated Multiple Recursive Generators with Constant

YU Hanbing, ZHENG Qunxiong

, Available online , doi: 10.23919/cje.2022.00.387

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Abstract:
Multiple recursive generators with constant, the high-order extension of linear congruence generators, are an important class of pseudorandom number generators that are widely used in cryptography. The predictability of truncated sequences output by multiple recursive generators with constant that predicts the whole sequences by the truncated high-order bits of the sequences is a cryptographically crucial problem. This paper studies the predictability of truncated multiple recursive generators with constant. Given a few truncated digits of high-order bits output by a multiple recursive generator with constant, we first convert the multiple recursive generator with constant to multiple recursive generator and then adopt the method we proposed recently to recover the modulus, the coefficients, and the differences of initial state. In particular, we give an estimation of the number of truncated digits required for recovering the differences of initial state by using the expected norm of target vector. We prove by exponential sums that the number of truncated digits required for uniquely determining both the initial state and the constant is finite and give an upper bound. Extensive experiments confirm the correctness of our method.

A Local Differential Privacy Hybrid Data Clustering Iterative Algorithm for Edge Computing

ZHOU Yousheng, WANG Zhonghan, LIU Yuanni

, Available online , doi: 10.23919/cje.2023.00.332

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Abstract:
As a new computing method, edge computing not only improves the computing efficiency and processing power of data, but also reduces the transmission delay of data. Due to the wide variety of edge devices and the increasing amount of terminal data, third-party data centers are unable to ensure that user privacy data leaked. To solve these problems, this paper proposes an iterative clustering algorithm local differential privacy iterative aggregation (LDPIA) based on localized differential privacy, which implements local differential privacy (LDP). To address the problem of uncertainty in numerical types of mixed data, random perturbation is applied to the user data at the attribute category level. The server then performs clustering on the perturbed data, and density threshold and disturbance probability are introduced to update the cluster point set iteratively. In addition, a new distance calculation formula is defined in combination with attribute weights to ensure the availability of data. The experimental results show that LDPIA algorithm achieves better privacy protection and availability simultaneously.

Analytical Channel Modeling: From MIMO to Extra Large-Scale MIMO

TIAN Jiachen, HAN Yu, JIN Shi, ZHANG Jun, WANG Jue

, Available online , doi: 10.23919/cje.2023.00.418

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Abstract:
Multiple antenna technologies, from traditional multiple-input multiple-output (MIMO) to massive MIMO and the emerging extra large-scale MIMO, have consistently played a pivotal role in enhancing transmission rates by increasing the number of antennas. To guide the design of transmission strategies, channel models, especially analytical ones, are always significant tools, which can also reveal the performance improvements brought about by multiple antenna technologies. Analytical channel models have enjoyed significant success in traditional MIMO and massive MIMO systems. Nevertheless, due to the extended size of the array in an extra large-scale MIMO system, the distance between the receiver and the transmitter decreases and new channel properties, which did not manifest in massive MIMO systems, begin to kick in. To model the channel tailored for extra large-scale MIMO systems analytically, it is crucial to conduct a comprehensive review of traditional analytical MIMO channel models, which serves as a foundational step in understanding the fundamental characteristics of multi-antenna channels. In this paper, we first provide a survey on the state-of-the-art analytical MIMO channel models from the perspective of spatial correlation and signal propagation. Subsequently, we summarize the new properties of extra large-scale MIMO systems, i.e., near-field properties and spatial non-stationarities, and their influences on analytical channel modeling. Our objective is to elucidate how these novel properties affect the analytical MIMO channel models, and ultimately facilitate the development of precise analytical channel models well-suited to the extra large-scale MIMO systems.

PriChain: Efficient Privacy-preserving Fine-grained Redactable Blockchains in Decentralized Settings

GUO Hongchen, GAN Weilin, ZHAO Mingyang, ZHANG Chuan, WU Tong, ZHU Liehuang, XUE Jingfeng

, Available online , doi: 10.23919/cje.2023.00.305

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Abstract:
Recently, redactable blockchain has been proposed and leveraged in a wide range of real systems for its unique properties of decentralization, traceability, and transparency while ensuring controllable on-chain data redaction. However, the development of redactable blockchain is now obstructed by three limitations, which are data privacy breaches, high communication overhead, and low searching efficiency, respectively. In this paper, we propose PriChain, the first efficient privacy-preserving fine-grained redactable blockchain in decentralized settings. PriChain provides data owners with rights to control who can read and redact on-chain data while maintaining downward compatibility, ensuring the one who can redact will be able to read. Specifically, inspired by the concept of multi-authority attribute-based encryption, we utilize the isomorphism of the access control tree, realizing fine-grained redaction mechanism, downward compatibility, and collusion resistance. With the newly designed structure, PriChain can realize ${\cal{O}}(n) $ communication and storage overhead compared to prior ${\cal{O}}(n^2) $ schemes. Furthermore, we integrate multiple access trees into a tree-based dictionary, optimizing searching efficiency. Theoretical analysis proves that PriChain is secure against the chosen-plaintext attack and has competitive complexity. The experimental evaluations show that PriChain realizes 10× efficiency improvement of searching and 100× lower communication and storage overhead on average compared with existing schemes.

Enhanced Acceleration for Generalized Nonconvex Low-Rank Matrix Learning

ZHANG Hengmin, YANG Jian, DU Wenli, ZHANG Bob, ZHA Zhiyuan, WEN Bihan

, Available online , doi: 10.23919/cje.2023.00.340

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Abstract:
Matrix minimization techniques that employ the nuclear norm have gained recognition for their applicability in tasks like image inpainting, clustering, classification, and reconstruction. However, they come with inherent biases and computational burdens, especially when used to relax the rank function, making them less effective and efficient in real-world scenarios. To address these challenges, our research focuses on generalized nonconvex rank regularization problems in robust matrix completion (RMC), low-rank representation (LRR), and robust matrix regression (RMR). We introduce innovative approaches for effective and efficient low-rank matrix learning, grounded in generalized nonconvex rank relaxations inspired by various substitutes for the $\ell_0$-norm relaxed functions. These relaxations allow us to more accurately capture low-rank structures. Our optimization strategy employs a nonconvex and multi-variable alternating direction method of multipliers (ADMM), backed by rigorous theoretical analysis for complexity and convergence. This algorithm iteratively updates blocks of variables, ensuring efficient convergence. Additionally, we incorporate the randomized singular value decomposition technique and/or other acceleration strategies to enhance the computational efficiency of our approach, particularly for large-scale constrained minimization problems. In conclusion, our experimental results across a variety of image vision-related application tasks unequivocally demonstrate the superiority of our proposed methodologies in terms of both efficacy and efficiency when compared to most other related learning methods.

A Review of Terahertz Solid-state Electronic/Optoelectronic Devices and Communication Systems

LI Wenbo, ZENG Hongxin, HUANG Lin, GONG Sen, CAO Haoyi, WANG Weipeng, WANG Zheng, ZHOU Hongji, LIANG Shixiong, YANG Ziqiang, ZHANG Yaxin

, Available online , doi: 10.23919/cje.2023.00.282

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Abstract:
With the rapid development of modern communication technology, spectrum resources have become non-renewable and precious resources, and the terahertz frequency band has entered people’s vision. Nowadays, terahertz communication technology has become one of the core technologies for future high-capacity and high-rate communication. This paper discusses and analyzes the core technologies related to the field of terahertz communication. We introduce the characteristics, domestic and international comparisons and development trends of the core devices for terahertz communication, and also introduce and discuss the terahertz solid-state frequency mixing communication system, terahertz direct modulation communication system, and terahertz optoelectronic communication system. Finally, we summarize the development of terahertz communication technology and the outlook of future applications.

A Novel Subspace-Based GMM Clustering Ensemble Algorithm for High-dimensional Data

HE Yulin, HE Yingting, ZHAN Zhaowu, PHILIPPE Fournier-Viger, HUANG Joshua Zhexue

, Available online , doi: 10.23919/cje.2023.00.153

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Abstract:
The Gaussian mixture model (GMM) is a classical probability representation model widely used in unsupervised learning. GMM performs poorly on high-dimensional data (HDD) due to the requirement of estimating a large number of parameters with relatively few observations. To address this, the paper proposes a novel subspace-based GMM clustering ensemble (SubGMM-CE) algorithm tailored for HDD. The proposed SubGMM-CE algorithm comprises three key components. First, a series of low-dimensional subspaces are dynamically determined, considering the optimal number of GMM components. The GMM-based clustering algorithm is applied to each subspace to obtain a series of heterogeneous GMM models. These GMM base clustering results are merged using the newly-designed relabeling strategy based on the average shared affiliation probability, generating the final clustering result for high-dimensional unlabeled data. An exhaustive experimental evaluation validates the feasibility, rationality, effectiveness, and robustness to noise of the SubGMM-CE algorithm. Results show that SubGMM-CE achieves higher stability and more accurate clustering results, outperforming nine state-of-the-art clustering algorithms in normalized mutual information, clustering accuracy, and adjusted rand index scores. This demonstrates the viability of the SubGMM-CE algorithm in addressing HDD clustering challenges.

Virtual Coupling Train Cruise Control Based on Finite Time Distributed Control

FAN Ying, CHEN Haiyan, ZHANG Yang

, Available online , doi: 10.23919/cje.2023.00.407

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Abstract:
Virtual coupling is a research hotspot to improve railway transport capacity. Train cruise control is the key to realizing virtual coupling. A virtual coupling train cruise control problem based on agent theory is proposed. A leader-follower model of a virtual coupling train is established, taking into account the dynamic changes in basic resistance and random additional resistance. Information transfer between trains is achieved using wireless communication technology. Based on the finite time distributed multi-agent control theory, a novel virtual coupling train cruise controller was designed based on finite time distributed. The effect of the controller designed in this paper is verified and analyzed through the simulation comparison experiment. Compared with the existing non-finite time controllers, the results show that the proposed controller based on finite time distribution is effective, especially in control precision and convergence speed. The initial train working condition do not have any effect on the convergence rate of the finite time distributed controller.

Magnetic Shutter Mechanical Antenna for Cross-Media Communication

LI Na, SHAN Yuyu, BAO Jianqiang, FENG Hongzhang, ZHANG Yiqun, LIU Guo

, Available online , doi: 10.23919/cje.2023.00.132

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Abstract:
In low-frequency cross-media communication systems, traditional mechanical antennas have problems such as limiting the upper limit of operating frequency due to motor speed, waveform distortion, and limiting transmission rate due to modulation methods. Then, we designed a new magnetic shutter type mechanical antenna. It is designed based on the radiation equation of a rotating magnetic dipole, combined with the principle of relative motion between the magnetic dipole and the high permeability shutter material. By relying on the shutter structure rotation, the magnetic field of the spherical permanent magnet array is intermittently shielded, generating a low-frequency magnetic induction signal that is multiplied by the motor speed.The entire antenna system uses a cross array of spherical permanent magnets with two evenly distributed magnetic poles and a two-dimensional signal modulation method that combines frequency modulation and amplitude modulation, so it has high radiation intensity and transmission rate in the ultra-low frequency band. Experimental results show that when the motor speed is n r/s, the operating frequency of the mechanical antenna can reach 4nHz, and the signal amplitude measured at 5 m is 50 mV, which is about 3.5 nT. Compared with the current mechanical antenna of the same volume, its signal radiation intensity is stronger.

Asynchronous Consensus Algorithm Integrating Dynamic Weight Sharding Strategy

XIONG Ao, ZHANG Wang, SONG Yu, WANG Dong, LI Da, GUO Qinglei, BAI Desheng

, Available online , doi: 10.23919/cje.2023.00.313

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Abstract:
Blockchain technology has broad application prospects in many fields due to its unique characteristics such as decentralization, traceability, and non-tampering, and has become a research hotspot in recent years. As a key component of blockchain technology, the consensus algorithm is one of the important factors affecting blockchain performance. However, many consensus algorithms currently used in consortium chains are based on time assumptions and lack horizontal expansion capabilities. That is to say, the consensus algorithm cannot reach a consensus in an asynchronous network where the receiving time of network packets is uncertain, and its efficiency will decrease as the number of nodes increases, which hinders the large-scale application of the alliance chain. In order to solve the above problems, this paper proposes the DS-Dumbo algorithm, an asynchronous consensus algorithm that integrates dynamic sharding strategies, based on the currently excellent DumboBFT asynchronous consensus algorithm. The main work of this paper revolves around how to fragment and optimize the consensus process. This paper designs a node asynchronous sharding model based on multi-dimensional weights, so that the re-sharding work of each blockchain node can be executed concurrently with the asynchronous consensus algorithm, reducing the conflict between blockchain sharding and asynchronous consensus algorithms. We also designed an intelligent transaction placement strategy, which calculates the relevance score of each transaction for all shards to determine which shard the transaction is processed in order to reduce the number of complex cross-shard transactions. We optimized the execution process of the DumboBFT algorithm, converted its internal synchronous working mode to an asynchronous working mode, and reduced the consumption of consensus work to a certain extent. The experimental evaluation shows that the DS-Dumbo algorithm has higher throughput and lower delay than the DumboBFT algorithm, can increase the throughput with the increase of nodes, and has the ability of horizontal expansion.

Dynamic Sitting Posture Recognition System Using Passive RFID Tags in Internet of Things

SU Jian, CHEN Shijie

, Available online , doi: 10.23919/cje.2023.00.354

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Abstract:
The rapidly developing Internet of things (IoT) technology is gradually being used to monitor people’s unhealthy behaviors. Sedentary and wrong sitting posture are common health issues which can detrimentally impact the physical and psychological health of teenagers. An effective way to promptly rectify improper sitting postures among teenagers is to use equipment to monitor and recognize the alterations of sitting posture. The majority of conventional sitting posture recognition methods rely on cameras or sensors to recognize sitting posture. The employment of cameras will violate user’s privacy, and the utilization of sensors will increase the cost of implementation. A dynamic sitting posture recognition system based on commodity off-the-shelf (COTS) radio frequency identification (RFID) devices is proposed. This system can recognize six common erroneous sitting postures by simply sticking five passive RFID tags on the user’s back. We collect phase and RSSI data of passive RFID tags, then transform them into Doppler shift and RSSI difference data respectively, and finally input them into the established deep residual neural network for the classification of sitting postures. The experiment results show that our system achieves an average recognition accuracy of 99.17% with six sitting postures and is highly robust to different users and different usage environments.

Model Parameter Extraction for InGaN/GaN Multiple Quantum Well-based Solar Cells using Dynamic Programming

SHAN Hengsheng, LI Chengke, LI Xiaoya, LI Minghui, SONG Yifan, MA Shufang, XU Bingshe

, Available online , doi: 10.23919/cje.2023.00.337

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Abstract:
A dynamic programming (DP) algorithm is proposed for parameter extraction of the single-diode model (SDM). Five parameters of SDM are extracted from current-voltage (I-V) curves of InGaN/GaN Multi-quantum wells solar cells (SCs) under AM1.5 standard sunlight conditions, with indium (In) compositions of 7% and 18%. The range of series resistance (Rs) of the device is adaptively selected and its value is randomly determined. After the series resistance and the range of ideal factors are planned the parameters of SDM are iteratively solved using the root mean square error (RMSE) of the I-V curve and the photoelectric conversion efficiency (η). Due to this approach, the proposed algorithm is fast and accurate compared with other conventional algorithms. Additionally, the obtained RMSE value is controlled within 1.2e-5, and the calculated fill factor (FF) and η are consistent with the measured values. This study provides a reference for power optimization of advanced semiconductor photovoltaic cell systems.

Wideband Circularly Polarized Substrate-Integrated Waveguide Aperture-Coupled Metasurface Antenna Array for Millimeter-Wave Applications

LIAN Jiwei, GENG Chun, LU Xue, DING Dazhi

, Available online , doi: 10.23919/cje.2023.00.029

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Abstract:
A wideband circularly polarized (CP) aperture-coupled metasurface antenna operating at millimeter-wave frequency spectrum in substrate-integrated waveguide (SIW) technology is proposed. Such a proposed metasurface antenna is composed of two substrates. The first substrate contains an end-shorted SIW section with a slot etched. The introduced metasurface is printed on the top of the second substrate. The metasurface is comprised of 3 × 3 unit cells, each of which contains two interconnected patches and two parasitic patches. The working mechanism of the proposed metasurface antenna is illustrated in details. The proposed metasurface antenna has wide impedance bandwidth and axial ratio (AR) bandwidth, which are 66.7% and 40%, respectively. Using the proposed metasurface antenna, a 4 × 4 CP metasurface antenna array with an impedance bandwidth of 24%, an AR bandwidth of 30%, and a peak gain of 18.7 dBic in simulation is developed in this paper for millimeter-wave applications.

Dual-Mode Resonant Sectorial Monopole Antenna with Stable Backfire Gain

JI Feiyan, ZHANG Heng, XING Xiuqiong, LU Wenjun, ZHU Lei

, Available online , doi: 10.23919/cje.2023.00.032

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Abstract:
A novel design approach to wideband, dual-mode resonant monopole antenna with stable, enhanced backfire gain is advanced. The sectorial monopole evolves from a linear, 0.75-wavelength electric prototype monopole under wideband dual-mode resonant operation. As theoretically predicted by the two resonant modes ${\mathrm{TE}}_{3/5,1} $ and ${\mathrm{TE}}_{9/5,1} $ within a 150° radiator, the operation principle is revealed at first. As have been numerically demonstrated and experimentally validated at 2.4-GHz band, the designed antenna exhibits a wide impedance bandwidth over 90.1%(i.e., 2.06−5.44 GHz), in which the stable gain bandwidth in the backfire, $ -x $-direction ($ \theta $ = 90°, $ \varphi $ = 180°) with peak value of 3.2 dBi and fluctuation less than 3 dB is up to 45.3% (i.e., 3.74−5.44 GHz). It is concluded that the stable wideband backfire gain frequency response should be owing to the high-order resonant mode in the unique sectorial monopole antennas.

Research on Semantic Communication Based on Joint Control Mechanism of Shallow and Deep Neural Network

XIE Wenwu, XIONG Ming, REN Ziqing, WANG Ji, YANG Zhihe

, Available online , doi: 10.23919/cje.2023.00.278

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Abstract:
With the rapid development of deep learning, various semantic communication models are emerging, but the current semantic communication models still have much room for improvement in the coding layer. For this reason, a joint-residual neural networks (Joint-ResNets) framework based on the joint control of shallow neural networks (SNNs) and deep neural networks (DNNs) is proposed to cope with the problems in semantic communication coding. The framework synergizes SNNs and DNNs based on their shared utility, and uses variable weight $\alpha$ term to control the ratio of SNNs and DNNs to fully utilize the simplicity of SNNs and the richness of DNNs. The article details the construction of the Joint-ResNets framework and its canonical use in classical semantic communication models, and illustrates the control mechanism of the variable weight $\alpha$ term in the Joint-ResNets framework and its importance in balancing the model complexity between SNNs and DNNs. The article takes the task-oriented communication model in the device edge collaborative reasoning system as an example for experimentation and analysis. The experimental validation shows that DNNs and SNNs can be combined in a more effective way to standardize semantic coding, which improves the overall predictive performance, interpretability, and robustness of semantic communication models, and this framework is expected to bring new breakthroughs in the field of semantic communication.

Cooperative Self-Learning: A Framework for Few-Shot Jamming Identification

SHI Yuxin, LU Xinjin, SUN Yifu, AN Kang, LI Yusheng

, Available online , doi: 10.23919/cje.2023.00.229

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Abstract:
Jamming identification is the key objective behind effective anti-jamming methods. Due to the requirement of low-complexity and the condition of few labeled shots for a real jamming identification, it is very challenging to identify jamming patterns with high accuracy. To this end, we first propose a general framework of cooperative jamming identification among multiple nodes. Moreover, we further propose a novel fusion center (FC) aided self-learning scheme, which uses the guidance of the FC to improve the effectiveness of the identification. Simulations show that the proposed framework of the cooperative jamming identification can significantly enhance the average accuracy with low-complexity. It is also demonstrated that the proposed FC aided self-learning scheme has the superior average accuracy compared with other identification schemes, which is very effective especially in the few labeled jamming shots scenarios.

Sparse Homogeneous Learning: A New Approach for Sparse Learning

SHI Jiajie, YANG Zhi, LIU Jiafeng, SHI Hongli

, Available online , doi: 10.23919/cje.2023.00.130

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Abstract:
Many sparse representation problems boil down to address the underdetermined systems of linear equations subject to solution sparsity restriction. Many approaches have been proposed such as sparse Bayesian learning. In order to improve solution sparsity and effectiveness in a more intuitive way, a new approach is proposed, which starts from the general solution of the linear equation system. The general solution is decomposed into the particular and homogeneous solutions, where the homogeneous solution is designed to counteract as many elements of particular solution as possible to make the general solution sparse. First, construct a special system of linear equations to link the homogeneous solution with particular solution, which typically is an inconsistent system. Second, the largest consistent sub-system are extracted from the system so that as many corresponding elements of two solutions as possible cancel each other out. By improving implementation efficiency, the procedure can be accomplished with moderate computational time. The results of extensive experiments for sparse signal recovery and image reconstruction demonstrate the superiority of the proposed approach in terms of sparseness or recovery accuracy with acceptable computational burden.

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