Search

Beamforming technique can effectively improve the spectrum utilization in the multi‐antenna systems, while the dirty‐paper coding (DPC) technique can reduce the inter‐user interference. In this letter, it is aimed to maximize the weighted sum‐rate under the total power constraint in the multiple‐input‐single‐output (MISO) system with the DPC technique. However, the existing methods of beamforming optimization mainly rely on customized iterative algorithms, which have high computational complexity. To address this issue, the beamforming neural network (BFNNet) is devised by utilizing the deep learning technique and the uplink‐downlink duality and exploring the optimal solution structure, which includes the deep neural network module and the signal processing module. Simulation results show that the BFNNet can achieve near‐optimal solutions and significantly reduce computational complexity. [ABSTRACT FROM AUTHOR]

In this paper, a new ultra‐wideband (UWB) bandpass filter (BPF) based on QMR (quintuple‐mode resonator) loaded with quasi‐lumped elements is proposed for wide stopband application. The method of connecting five folded U‐shaped resonators with rectangular patches to construct a QMR was proposed. By adjusting the relative position of the rectangular patches, the frequency band width of the filter can be freely adjusted. Further, quasi‐lumped elements were loaded to the QMR to generate three transmission zeros to enhance stopband performance. The filter designed in this paper can independently adjust both the stopband and the passband, providing greater design flexibility. The passband of the final filter is 3.18–9.43 GHz, and the FBW (fractional relative bandwidth) is 99%. The out‐of‐band rejection is better than −15 dB in the frequency range of 9.6–28.42 GHz. The measurement results show good performance without any tuning, and are in good agreement with the simulation results. [ABSTRACT FROM AUTHOR]

With the development of processes and reduction of transistor size, transistor sensitivity to voltage changes has increased. Traditional SRAM bit cells struggle to function properly at low voltages, and the lengthy write time necessitated by the write conflict problem will inevitably result in write failure. As ultra‐low‐voltage SRAM has emerged as a significant direction of research for SRAM, this paper proposes an ultra‐low‐voltage 9T SRAM bit cell that is conflict‐free. By circumventing write conflicts and enabling rapid writing, the bit cell demonstrates its superiority, particularly at ultra‐low voltages, by eliminating the requirement for peripheral write‐assist circuitry to accomplish chip writing. To assess the performance of the conflict‐free 9T bit cell, simulation experiments are conducted utilizing the 28 nm process model. Simulation results indicate that the 9T bit cell proposed in this paper requires only 66% of the writing time of the traditional 6T cell. This enables the cell to accomplish fast writing and more stable writing performance. [ABSTRACT FROM AUTHOR]

This document is a guest editorial from the journal "Electronics Letters" that discusses the importance of wideband/multiband millimeter-wave antennas for 5G/6G and radar applications. It highlights the increasing demand for higher data rates and system capacity in wireless systems. The editorial presents 16 research papers accepted for publication in a special issue, covering topics such as millimeter-wave antennas for 5G/6G and radar applications, microwave antennas for 5G/6G and radar applications, measurement methods for millimeter-wave antennas, and decoupling methods and other millimeter-wave components. The papers explore innovative designs and technologies to meet the requirements of these applications. The authors of the papers are researchers and professors from various universities and research institutions. [Extracted from the article]

This paper proposes a digital delay locked loop (DLL) with a monotonic delay line (DL). This DLL adopts the calibration mode to reduce the non‐monotonic effects for the coarse‐tuning delay line (CTDL) and the fine‐tuning delay line (FTDL). The calibration mode detects the delay time of the delay unit, the timing resolution of CTDL, to adjust the delay range of the FTDL. Thus, the calibration mode can limit the overlap range of the delay time between the CTDL and the FTDL. The proposed DLL was implemented using a 0.18‐μm CMOS process, and the RMS and the peak‐to‐peak jitters of the DLL were 0.21% and 1.72%, respectively, at 560 MHz. [ABSTRACT FROM AUTHOR]

This paper proposes a digital clock and data strobe aligner for write calibration of dynamic random access memory (DRAM). The edge recognizer determines the input phase skew between a falling edge of clock and a rising edge of data strobe (DQS) signal to maximize setup and hold margins of DQS and clock synchronization. According to experimental results, the proposed circuit achieves the minimum and maximum output skews are −6.9 ps and 7.5 ps when DQS skews of ±227 ps are applied. The chip has been fabricated in 180 nm CMOS technology and the active chip area is 0.091 mm². [ABSTRACT FROM AUTHOR]

This article discusses the importance of information, network, and communication security in today's high-tech society. It highlights the increasing reliance on computer communication networks and the potential risks associated with insecure networks. The article also provides a summary of three papers accepted in a special issue on "Information, Network, and Communications Security." These papers focus on topics such as blockchain, Internet of Things (IoT), and wireless communication networks. Overall, the article emphasizes the importance of reliable security strategies in reducing economic losses and enhancing social stability and national defense security. [Extracted from the article]

The introduction of YOLOv9, the latest version of the you only look once (YOLO) series, has led to its widespread adoption across various scenarios. This paper is the first to apply the YOLOv9 algorithm model to the fracture detection task as computer‐assisted diagnosis to help radiologists and surgeons to interpret X‐ray images. Specifically, this paper trained the model on the GRAZPEDWRI‐DX dataset and extended the training set using data augmentation techniques to improve the model performance. Experimental results demonstrate that compared to the mAP 50–95 of the current state‐of‐the‐art model, the YOLOv9 model increased the value from 42.16% to 43.73%, with an improvement of 3.7%. The implementation code is publicly available at https://github.com/RuiyangJu/YOLOv9‐Fracture‐Detection. [ABSTRACT FROM AUTHOR]

Radar coincidence imaging (RCI) is a high‐resolution radar imaging mode which constructs a temporal spatial stochastic radiation field (TSSRF) and uses the correlation between reference signal and echoes for imaging. The correlation between the reference matrix and the echoes is the main factor affecting the imaging performance. In fact, radar target characteristics cause fluctuations in the radar cross section and variations in the scattering intensity of each resolution element. The latter degrades the correlation between the reference matrix and the echoes, seriously affecting the image reconstruction. This paper designs the waveform based on image entropy under fixed transmitting and receiving arrays. The targets with fluctuating scattering intensity at each resolution element are statistically modelled. Simulation results show this approach can improve the imaging performance and reduce the energy dissipation degree of the target grid. [ABSTRACT FROM AUTHOR]

With the development of distributed generation and power electronics technology, DC microgrids have made certain progress with their advantages of simple structure, economic efficiency, and easy control. Considering factors such as the geographical location of some residential areas, microgrids with fuel cells that can operate independently are also receiving increasing attention. In this paper, an online control method named virtual hydrogen consumption is proposed based on an independent DC microgrid which solves the problem of electro‐hydrogen conversion successfully. By solving the overall consumption function, the analytical solution is further obtained to realize the online control. Based on the results of RT‐LAB platform, the proposed method can maintain the output of fuel cell system in its high efficiency range with a lower volatility. [ABSTRACT FROM AUTHOR]

As the power source of the engine, the Fuel Pump(FP) plays a vital role in the safe operation of the aircraft. Due to the complexity of the working mechanism of Aviation Fuel Pumps (AFP) and the strong correlation between the components, the assessment model cannot reflect the health state of the FPs better, while the initial parameters in the assessment model will affect the assessment effect of the model. Therefore, this paper proposes a health status assessment model that can fully integrate monitoring data. To improve the accuracy of the model parameters, the Random Forest algorithm is used to give the feature weights to make up for the limitation of relying on expert knowledge, and the model parameters are optimized by the Covariance Matrix Adaptive Evolutionary Strategy algorithm, which achieves an accurate assessment of the state. Finally, the AFP test bed was built and the AFP was tested. Compared with other methods, the accuracy of the proposed method in this question reaches 96%, which is greatly superior to other methods and verifies the effectiveness of the proposed method. It also provides an outlook on future research directions for health state assessment. [ABSTRACT FROM AUTHOR]

Under the background of green and low‐carbon transformation of industrial high‐energy‐consuming parks, in order to realize the coordinated development planning of energy and transportation under different peaking capacity requirements, this paper proposes a collaborative optimization model of industrial high‐energy‐consuming parks under green and low‐carbon. Firstly, by studying the energy balance mechanism between industrial load and new energy, an energy topology prior model of energy interaction in the next generation of industrial new energy power grid is constructed. Then, based on the probability characteristics of the equilibrium state of different regions in the high‐energy‐consuming industrial zone, combined with the prior knowledge of industrial load and low‐carbon power system, a collaborative development planning model of high‐energy‐consuming industrial parks is constructed. Finally, the proposed model is simulated. From the simulation results the authors can see the dynamic division model and solution algorithm of energy balance region established in this paper can effectively improve the energy balance ability of power grid, reduce peak shaving demand and use more renewable energy. [ABSTRACT FROM AUTHOR]

A passive, low‐cost, paper‐based intelligent reflecting surface (IRS) is designed to reflect a signal in a desired direction to overcome non‐line‐of‐sight scenarios in indoor environments. The IRS is fabricated using conductive silver ink printed on paper with a specific nanoparticle arrangement, yielding a cost‐effective paper‐based IRS that can easily be mass‐produced. Full‐wave numerical simulation results were consistent with measurement results, demonstrating the IRS's ability to reflect incident waves into a desired nonspecular direction based on the inkjet‐printed design and materials. [ABSTRACT FROM AUTHOR]

Energy information is vulnerable to malicious denial of service (DoS) attacks due to the diversity and openness of the smart grid environment. In order to cope with the above challenges, this paper first proposes to adopt Markov hopping model to describe the random packet loss of measurement due to DoS attacks. Then, based on Holt two‐parameter exponential smoothing and untraced Kalman filtering techniques, a one‐step predictive value compensation method for measurement data loss is proposed, and an improved dynamic untraced particle filtering algorithm based on data fusion compensation strategy is designed. Finally, an IEEE‐30 bus system is used to simulate the proposed dynamic state estimation method, which proves that the proposed method can effectively resist DoS attack. [ABSTRACT FROM AUTHOR]

Visual defect recognition techniques based on deep learning models are crucial for modern industrial quality inspection. The backbone, serving as the primary feature extraction component of the defect recognition model, has not been thoroughly exploited. High‐performance vision transformer (ViT) is less adopted due to high computational complexity and limitations of computational resources and storage hardware in industrial scenarios. This paper presents LSA‐Former, a lightweight transformer architectural backbone that integrates the benefits of convolution and ViT. LSA‐Former proposes a novel self‐attention with linear computational complexity, enabling it to capture local and global semantic features with fewer parameters. LSA‐Former is pre‐trained on ImageNet‐1K and surpasses state‐of‐the‐art methods. LSA‐Former is employed as the backbone for various detectors, evaluated specifically on the PCB defect detection task. The proposed method reduces at least 18M parameters and exceeds the baseline by more than 2.2 mAP. [ABSTRACT FROM AUTHOR]

This paper presents a kernel extreme learning machine (KELM) integrated with the improved whale optimization algorithm (IWOA) to address the power quality disturbance (PQD) issue in microgrids. First, an adaptive variational mode decomposition method is employed to extract PQD signals in microgrids. Then, the IWOA is utilized to optimize the penalty factor and kernel function parameters for the KELM classifier model, thereby enhancing the performance of the classifier. Furthermore, the test results indicate that the proposed IWOA–KELM achieves high classification accuracy and rapid convergence for complex PQD signals.This paper presents a kernel extreme learning machine integrated with the improved whale optimization algorithm to address power quality issues in microgrids resulting from distributed power access. In this work, the adaptive variational mode decomposition method is employed to decompose the complex disturbance signals in microgrids. [ABSTRACT FROM AUTHOR]

To achieve real‐time and effective prediction of industrial site risks, this paper proposes an industrial risk prediction framework for multimodal data based on edge computing. First, the authors gather and annotate industrial risk multimodal data that consist of text descriptions, images, and videos. Then, the authors transfer the data to the edge server, and apply deep learning models such as Bidirectional Encoder Representations from Transformers (BERT), ResNet etc., to extract features and learn representations for text, image, and video data respectively. The authors input the fused feature data into an enhanced long short term memory (LSTM) model and train it on the dataset. Finally, the authors perform the risk prediction based on the collected multimodal data. The experimental results demonstrate that the method proposed in this paper exhibits superior performance, achieving a 1.4% enhancement in predictive accuracy. [ABSTRACT FROM AUTHOR]

In recent years, the internet has not only enhanced the quality of our lives but also made us susceptible to high‐frequency cyber‐attacks on communication networks. Detecting such attacks on network traffic is made possible by intrusion detection systems (IDS). IDSs can be broadly divided into two groups based on the type of detection they provide. According to the established rules, the first signature‐based IDS detects threats. Secondly, anomaly‐based IDS detects abnormal conditions in the network. Various machine and deep learning approaches have been used to detect anomalies in network traffic in the past. To improve the detection of anomalies in network traffic, researchers have compared several machine learning models, such as support vector machines (SVM), logistic regressions (LRs), K‐Nearest Neighbour (KNN), Nave Bayes (NBs), and boosting algorithms. The accuracy, precision, and recall of many studies have been satisfactory to an extent. Therefore, this paper proposes an ensemble learning‐based stacking classifier (ELSC) to achieve a better accuracy rate. In the proposed ELSC algorithm, KNN, NB, LR, and Decision Trees (DT) served as the base classifiers, while SVM served as the meta classifier. Based on a Network Intrusion detection dataset provided by Kaggle.com, ELSC is compared to base classifiers such as KNN, NB, LR, DT, SVM, and Linear Discriminate Analysis. As a result of the simulations, the proposed ELBS stacking classifier was found to outperform the other comparative models and converge with an accuracy of 99.4%. [ABSTRACT FROM AUTHOR]

This paper presents a design of a gyro‐TWT operating in the large‐orbit electron beam mode, with the aim of reducing the working magnetic field while ensuring operational efficiency. The periodic dielectric‐loaded structure, which has been successfully applied in the development of small‐orbit devices, is adopted as the high‐frequency interaction circuit. This paper conducts a comprehensive study and analysis of this structure and achieves stable operation in the Ka‐band after the optimization of the tube. This tube works at second harmonic of electron frequency in the mode of large‐orbit electron beam. The required magnetic field is only 5100 gauss, which can be generated using electromagnetic coils instead of superconducting magnets. The operational parameters include voltage of 75 kV, current of 9A, and velocity spread of 3.5%. Under these conditions, the device presents stable operation, with −3 dB bandwidth of 4.3 GHz, and maximum output power of 165 kW. This result meets the expected requirements for magnetic field and operational efficiency, thus validating the feasibility of practical fabrication of large‐orbit gyro‐TWT with periodic dielectric‐loaded structure. [ABSTRACT FROM AUTHOR]

When ground communication infrastructure within a region cannot be used for some reason, deploying unmanned aerial vehicle (UAV)- mounted base stations is undoubtedly the most effective way to provide communication services. This paper investigates the problem of joint deployment and power allocation of multiple UAVs, where ground terminals (GTs) seek to maximize quality of experience (QoE). In the mixed line-of-sight and non-line-of-sight environment, UAVs need to change position in order to collect channel information until deployment problem is solved. Moreover, only neighboring UAVs communicate with each other, which makes the problem more difficult to solve. In order to solve this problem, game theory is used to model this problem and design a distributed learning algorithm to maximize the QoE of all GTs in the entire system. Simulation results validate the effectiveness of the proposed learning algorithm in improving the QoE fairness and achieving the rapid deployment of UAVs. [ABSTRACT FROM AUTHOR]

Due to the excellent characteristics of Synthetic Aperture Radar (SAR), it has become an important technical means for land and sea target detection applications worldwide. However, the SAR image imaging process is often accompanied by speckle noise, which affects the accuracy of target detection. For this problem, this paper proposes an SAR image reconstruction model based on image denoising and feature extraction. Given a noisy SAR image, at each pixel of the image, the value of the singularity index of a metric defined by the signal gradient norm density is calculated. Then through a hierarchical decomposition of these singular indices, a set of pixels containing the most informative feature set of the image is extracted, and a denoised image is reconstructed using the extracted features. In comparison to traditional filtering and denoising techniques such as non-local filtering, this algorithm achieves remarkable local filtering and denoising effects while preserving the image's texture information. The denoising performance of the proposed method is evaluated through a series of quantitative indicators and visual comparisons. The results demonstrate that this method effectively reduces background clutter, resulting in a higher contrast between the target and the background. [ABSTRACT FROM AUTHOR]

One of the most important challenges in an autonomous and robotics system is the path planning in which the system finds the optimal path from start point to goal point. The traditional path planning algorithms may have large memory requirements which scale with the size and resolution of the configuration space. To address these challenges, this paper introduces a novel path planning algorithm that combines Particle Swarm Optimization and Artificial Potential Field in the form of a path planning algorithm for mobile robots. The biological and physical concepts from Particle Swarm Optimization and Artificial Potential Field algorithms are combined to yield an algorithm which minimizes instances of getting stuck in local minima and generates a smooth but feasible path. The developed method requires memory which scales only with the number of particles and the time taken to reach the goal. This results in a memory‐efficient solution that generates smooth and feasible paths for mobile robots navigating in a 2D space. [ABSTRACT FROM AUTHOR]

The integrated communication and jamming system is proposed by exploiting the diversity gain and flexible deployment of unmanned aerial vehicle (UAV)‐based distributed multiple input multiple output technology, which is expected to gain higher spectral efficiency and system scalability. This paper designs transmit beamforming of multiple UAVs so that they can simultaneously impose jamming signal to jamming targets and communication signal to communication users at the same frequency. The aim is to maximize the total jamming effect of all jamming targets while achieving a certain communication rate of communication users by optimizing the transmit beamforming matrices. Two cases are considered in which the UAV swarm works in clock synchronization and clock asynchronization. Specifically, the problem is formulated into a non‐convex optimization in the synchronous case. A near optimal solution is derived by decoupling the beamforming matrix into multi‐user interference suppression matrix and single‐user beamforming matrix. In the asynchronous case, the transmitter–receiver matching relation needs to consider and multiple optimization variables are highly coupled, penalty factors and intermediate variables to convert the optimization problem to convex programming are introduced. The simulation results show that the proposed beamforming design algorithm coordinates the communication and jamming signal rationally and outperforms the conventional beamforming design algorithms. [ABSTRACT FROM AUTHOR]

Tomographic synthetic aperture radar is an advanced multi‐channel interferometric technique for retrieving 3‐D spatial information. It can be regarded as an inherently sparse reconstruction problem and can be solved using compressive sensing algorithms. However, the performances are limited by the number of acquisitions and suffer from computational burdens in practice. This paper proposes a novel method based on deep learning, which is carried out and optimized in an end‐to‐end manner by the generative adversarial neural networks. The proposed method applies the cascaded U‐Net architectures to achieve the reconstruction of full‐channel synthetic aperture radar images and the refinement of obtained tomographic results, respectively. The proposed network is trained using simulated data and validate the technique on simulated and real data. The tests show promising results with the limited number of acquisitions while reducing the computation time. [ABSTRACT FROM AUTHOR]

Target localization for distributed radars requires precise knowledge of the calibration errors in time, frequency, and space. Previous studies focus on the sensor location error and sensor directional error in space. This paper concentrates on the system errors in time and frequency which directly affect the targets estimation of range and velocity. Based on linear frequency modulation waveform, the impact of the system errors including sample interval, pulse repetition interval, and centre frequency are analysed. An alternating optimization algorithm is proposed to achieve the target localization, velocity estimation and system errors estimation simultaneously. Simulation shows the effectiveness of the algorithm and the influence on localization due to varying number of targets and radars. [ABSTRACT FROM AUTHOR]

This paper presents the decentralized sampled‐data fuzzy observer design technique for nonlinear interconnected systems based on the Takagi–Sugeno fuzzy model. The interconnected system is considered that the subsystems include uncertain interconnections and are not needed to satisfy stable conditions. Based on the continuous‐time Lyapunov functional approach, the observer design technique is proposed to minimize the interconnection bound to attenuation degree ratio and formulated into the linear matrix inequality formats. Finally, by simulation example, it shows the validity and superiority of the proposed observer design technique. [ABSTRACT FROM AUTHOR]

This paper presents experimental verification results for a finite‐sized array of a simple non‐reciprocal metasurface elements. The metasurface elements consists of ferrite loaded metal patches. Experimental results demonstrate nonreciprocity, achieving isolation exceeding 20 dB at 6.4 GHz in a 2×2 array. Additionally, these findings are compared with simulation results for a 4×4 array to validate its practicality in a finite array setting. [ABSTRACT FROM AUTHOR]

This paper proposes an X‐band quasi Class‐F high power amplifier (HPA) monolithic microwave integrated circuit (MMIC) using DynaFET gallium nitride (GaN) high electron mobility transistor (HEMT) modelling. To enhance efficiency, a novel quasi Class‐F output matching circuit is proposed. DynaFET modelling for GaN HEMT is utilized for accurate HPA MMIC design. The proposed quasi Class‐F HPA MMIC, fabricated using ETRI's 0.15 µm GaN process, achieves an output power of 43.5∼44.5 dBm with a power‐added efficiency of 36∼40.7% within the 9.1∼10.3 GHz frequency bandwidth. [ABSTRACT FROM AUTHOR]

In recent years, key generation schemes based on the reciprocity of wireless channels have gained widespread attention in the field of wireless physical layer security. However, due to the openness of wireless channels, eavesdroppers may obtain partial legitimate keys by eavesdropping on the wireless channel. To address this issue, this paper proposes a wireless channel coding scheme that induces an "avalanche" of errors for eavesdroppers, which could maximize eavesdropper's bit error rate. The design of this scheme is based on the significant differences between the eavesdropping channel and the legitimate channel, and the average bit key disagreement ratio of the keys generated by the legitimate channel is around 8%. The scheme selects polar codes with strong error correction capabilities and chooses a coding rate matching the error level of the legitimate channel, forcing the eavesdropping channel's error rate into an "avalanche" state, where the error rate approaches 50%. This paper presents the structure of the encoder and determines the coding rate through simulation. Simulation results demonstrate that this scheme can significantly degrade the error rate of the eavesdropping channel while ensuring the prevention of eavesdroppers from obtaining secret information transmitted through the legitimate channel. [ABSTRACT FROM AUTHOR]

This paper presents an energy‐efficient digital‐to‐analog converter (DAC) switching method with low common‐mode variations for high‐resolution successive approximation register (SAR) analog‐to‐digital converters (ADCs), while enabling to implement resolutions such as 14‐bit as compared to the typical 10‐bit. The proposed switching method enables high resolution by having a nearly constant common‐mode voltage and employing input‐swapping to use the reference voltage (Vref) only in the sampling phase. This method eliminates the need for the third reference voltage during the entire DAC switching steps, which reduces the required number of switches at the bottom plates of the capacitors. The use of lower number of switches not only lowers the DAC control logic complexity, but also results in a faster operation, lower power, and smaller area. When compared to conventional 10‐bit SAR ADCs, the proposed switching method in a 10‐bit implementation reduces the average energy and area by 96.09% and 75%, respectively, while offering high‐resolution implementation options such as 14 bits. [ABSTRACT FROM AUTHOR]

The switching power supply is widely used in the secondary equipment of the power grid. The failure of the switching power supply will cause the protection device to malfunction, leading to a risk of large‐scale power failure. In this paper, a real‐time monitoring method based on only output voltage information is proposed. The experiment shows that this parameter‐reduced approach, relying only on voltage information, outperforms the existing method in detection accuracy. [ABSTRACT FROM AUTHOR]

In this paper, a quasi‐optical resonator with new coupling method and parasitic‐mode‐suppressing in 110–170 GHz is proposed. The coupling of terahertz (THz) quasi‐optical resonator in this paper is realized by PCB substrate processing and pressurized flange. At the same time, the parasitic mode is effectively suppressed by slitting the plane mirror to reduce the influence on the main mode during the test. And the fused quartz sample was tested to verify the feasibility of the system for complex dielectric testing of materials in 110–170 GHz. [ABSTRACT FROM AUTHOR]

In this paper, a 2‐bit digital reflection‐type phase shifter (RTPS) working at 120 GHz is presented. It uses a compact coupled‐lines coupler with low insertion loss and high isolation over a wide bandwidth. The loads are made by a microstrip‐line loaded by three PN jonction separated by an intermediate region I (PIN) diodes whose states are tuned ON/OFF to obtain 90°, 180° and 270° phase shift relative to the reference (0°). Measurement results show root mean square (RMS) phase and amplitude error equal to 10.3° and 1.2 dB, respectively. The maximum insertion loss is equal to 8.6 dB, leading to a figure of merit of 31°/dB. As shown by simulation, by flipping PIN diodes and using negative voltage for biasing, the maximum insertion loss could be reduced to 3.6 dB (figure of merit of 75°/dB) along with a great improvement in RMS phase and amplitude errors, thus showing the potential of the proposed architecture.In this paper, a 2‐bit digital reflection‐type phase shifter working at 120 GHz is presented. It uses a compact coupled‐lines coupler with low insertion loss and high isolation over a wide bandwidth. The loads are made by a microstrip‐line loaded by three PN jonction separated by an intermediate region I (PIN) diodes whose states are tuned ON/OFF to obtain 90°, 180° and 270° phase shift relative to the reference (0°). [ABSTRACT FROM AUTHOR]

A compact high‐sensitivity probe for sensing up tangential E‐field is proposed and measured in this paper. The probe consists of an electric dipole with patches used to induce the tangential E‐field, a resonator made up of open circuit stubs (capacitor) and short circuit stubs (inductor), for achieving a specific resonance, and a compact coupled balun for transforming the differential‐mode voltage into the single‐output voltage and impedance matching. The probe is fabricated on five‐layer dielectric lamina. Compared with the referenced broadband probe, the measured |S12| of the probe is enhanced by 18.81 dB. The proposed probe in this paper is validated with simulation and measurement. [ABSTRACT FROM AUTHOR]

Intrusion Detection System (IDS) is a critical cybersecurity task that involves monitoring network traffic for malicious activity and taking appropriate action to stop it. However, insufficient training data or improperly chosen thresholds often limit the accuracy of such systems, resulting in high false‐positive rates. To improve the accuracy of an IDS, blockchain technology can be used as it provides a secure, decentralized, immutable ledger that can track suspicious activity over time and also identify intrusions globally. In this paper, the authors propose a novel methodology to improve the accuracy of blockchain‐based IDS. The approach combines different intrusion detection algorithms using a blockchain‐integrated architecture. It is based on the fusion principle and weighted votes, which the authors used to determine their results. The authors tested the system on DARPA 99 and MIT‐Lincoln Labs datasets using accuracy and false‐positive rate as their two metrics. The system achieved 92.6% accuracy and 7.4% false‐positive rates, indicating that the proposed system significantly increases the accuracy while reducing the false‐positive rate, opening up new opportunities for the development of highly accurate networks. In this paper, the authors propose a novel methodology to improve the accuracy of blockchain‐based intrusion detection and prevention systems, which is based on combining different intrusion detection algorithms and using a blockchain‐integrated architecture. The authors tested the system on DARPA 99 and MIT‐Lincoln Labs datasets using accuracy and false‐positive rate as their two metrics. The system achieved 92.6% accuracy and 7.4% false‐positive rate, indicating that the proposed system significantly increases the accuracy while reducing the false‐positive rate, opening up new opportunities for the development of highly accurate networks. [ABSTRACT FROM AUTHOR]

This paper proposes a background calibration scheme for the pipelined‐Successive Approximation Register (SAR) Analog‐to‐Digital Converter (ADC) based on the neural network. Due to the non‐linear function fitting capability of the neural network, the linearity of the ADC is improved effectively. However, the hardware complexity of the neural network limits its application and promotion in ADC calibration. Hence, this paper also presents the optimization schemes, including the neuron‐based sharing neural network and the partially binarized with fixed neural network, in terms of calibration architecture and algorithm. A 60 MS/s 14‐bit pipelined‐SAR ADC prototyped in 28‐nm technology is utilized to verify the feasibility of the proposed calibration method. The measurement results show that the proposed calibration greatly enhances the Spurious Free Dynamic Range (SFDR) and Signal‐to‐Noise‐and‐Distortion Ratio (SNDR) from low frequency to Nyquist frequency. Meanwhile, the original calibrator and improved calibrator are synthesized in Synopsys Design Compiler to compare their hardware complexity. Compared with the unoptimized version, the optimized schemes can decrease the logic area and the network weights up to 76% and 52%, with negligible loss in calibration performance. [ABSTRACT FROM AUTHOR]

During terminal guidance, the attack platform is provided with a high‐resolution image of the target area through the application of synthetic aperture radar. Additionally, the stealth trajectory with low observability can significantly impact mission success. This paper considers both the performance of missile‐borne synthetic aperture radar imaging and stealth performance as influencing factors for terminal trajectory optimization, which is modelled as a constrained multi‐objective optimization problem. The application of the pseudospectral method in the solution of optimal control problems has led to the proposal of the hybrid genetic algorithm pseudospectral optimization framework. The problem is decomposed into several single‐objective optimal control problems, which can generate a specific initial population for the genetic algorithm to obtain a set of Pareto‐optimal solutions. Finally, the numerical simulations demonstrate the effectiveness of the proposed optimization approach compared with the benchmark scheme. [ABSTRACT FROM AUTHOR]

Distribution system (DS) communication failures following extreme events often degrade monitoring and control functions, thus preventing the acquisition of complete global DS component state information, on which existing post‐disaster DS restoration methods are based. This paper proposes methods of inferring the states of DS components in the case of incomplete component state information. By using the known DS information, the operating states of unobservable DS branches and buses can be inferred, providing complete information for DS performance restoration before full communication recovery. [ABSTRACT FROM AUTHOR]

In the humanoid robots, waist mechanism is an important structure to realize the whole body coordinated movement. This paper proposes a series–parallel mechanism of waist structure, and its kinematics and dynamics characteristics are studied. Based on the dynamic model, a backstepping sliding mode controller is designed to simulate control performance. Under the continuous disturbance force, the stability and accuracy of the mechanism are verified. Compared with the experiment, the results prove that the waist mechanism has the biomimetic spine movements and desired control performance, which can be applied to life support robots in the future. [ABSTRACT FROM AUTHOR]

Three‐phase ac choppers feature output voltage amplitude controllability and enable more compact system realizations compared to autotransformers. For the practical realization advantageously standard power transistor with unipolar voltage blocking capability such as MOSFETs can be employed as a naturally resulting offset voltage between the grid and the input‐stage starpoint ensures purely positive power transistor voltages. This offset voltage is, however, not strictly defined and may drift to higher voltage values, resulting in high power transistor voltage stresses and finally a potential overvoltage breakdown. Traditionally, the offset voltage drift is prevented by introducing discharge resistors across the input‐stage capacitors which, however, results in substantial ohmic losses. This paper analyzes the offset voltage formation in ac choppers and proposes a novel clamping modulation scheme which ensures a strictly defined and minimum time‐varying offset voltage without need for discharge resistors. Theoretical analyses and circuit simulations are finally experimentally verified with a 400 V (rms, line‐to‐line) 50 Hz grid connected three‐phase buck‐boost ac chopper with 3 kW rated power. [ABSTRACT FROM AUTHOR]

Recently, deep learning‐based stereo matching has achieved great success. However, models trained on the source domain dataset encounter substantial performance degradation when directly tested on an unseen target domain dataset because of neglecting the generalization to out‐of‐distribution (OOD) stereo samples. This paper proposes a stereo sample generation‐based domain generalization network (SGDG‐Net) for stereo matching. Specifically, to expand the distribution span of training samples, OOD stereo samples are generated to assist training. To effectively generate OOD left samples, a style transfer‐based generation mechanism is proposed to transmit perturbations to the source left samples. In addition, to generate the OOD right samples, a disparity‐assisted generation strategy is proposed by using disparity map labels as auxiliary information. Experimental results demonstrate that the proposed SGDG‐Net produces remarkable results on four benchmark datasets. [ABSTRACT FROM AUTHOR]

This paper investigates the design of transmit beamforming in an integrated sensing and communication system, where an unmanned aerial vehicle (UAV) equipped with a uniform linear array (ULA) transmits combined information‐carrying and dedicated sensing signals to perform multi‐user communication and simultaneously sense potential targets. First, the transmit beamforming design is formulated as a series of non‐convex optimisation problems, and then jointly design the information and sensing beamforming to maximise the minimum communication rate under an optimised UAV deployment location, subject to the sensing beampattern gain requirements and the limited transmit power of the UAV. However, due to the application of ULA on the UAV, the formulated problems are highly non‐convex and difficult to solve optimally. To address the aforementioned issue, an efficient algorithm is proposed to find suboptimal yet high‐quality solutions. This is achieved by adopting the successive convex approximation and semidefinite relaxation. The numerical results confirm the effectiveness of the proposed algorithm and demonstrate that the transmit beamforming design achieves a balance between communication and sensing performance. [ABSTRACT FROM AUTHOR]

Integrated Sensing and Communications (ISAC) need to process data streams in high‐speed sensor data acquisition or high‐speed wireless communications. To process the data can require more computing and communication resources, resulting in higher power consumption. Halved‐Phase Only Multiple Input Multiple Output (HPO‐MIMO) communication technology can solve this problem by using low‐power nonlinear detection devices. In ISAC, Channel Estimation (CE) technology can provide key channel characteristics and state information for sensing and collaborative work of perception and communication tasks. However, HPO‐MIMO system cannot realize CE using traditional receiver schemes because of the missing amplitude. In order to solve this problem, two HPO‐MIMO CE schemes based on model‐driven deep learning are proposed in this paper. The proposed schemes include a Densely Residual Network (DRN) and a Inception‐Resnet (IR), which is suitable for the case of sufficient data and insufficient data, respectively. The simulation results show that the performance of DRN based scheme is better than that of IR based scheme when the data amount is sufficient, and the performance of IR based scheme is better when the dataset is small. In addition, the proposed CE schemes work well with a range of antenna sizes and distances. [ABSTRACT FROM AUTHOR]

To improve the speed of mass transfer, a magnetic suspension platform (MSP) is used to transport the display chips. A novel 6‐DOF MSP is proposed in this paper. An improved Halbach array is used to supply the magnetic flux density. Two types of permanent magnets are utilised in the improved Halbach array. The smaller permanent magnet serves to consolidate the magnetic flux density, thereby enhancing the uniformity of the magnetic field. The finite element method is employed to analyse how variations in angle impact both the strength and uniformity of the magnetic flux density. The results show that the magnetisation direction of 45° is the most effective in terms of improving the uniformity and strength of the magnetic density. Finally, a 6‐DOF MSP prototype is manufactured. A magnetic density measurement experiment and a stable suspension experiment for the actuator are conducted. The results indicate an enhancement in the uniformity of the magnetic field within the air gap. Additionally, the maximum suspension positioning accuracy of the actuator is about 750 nm. [ABSTRACT FROM AUTHOR]

Occluded person re‐identification (Re‐ID) is to identify a particular person when the person's body parts are occluded. However, challenges remain in enhancing effective information representation and suppressing background clutter when considering occlusion scenes. This paper proposes a novel attention map‐driven network (AMD‐Net) for occluded person Re‐ID. In AMD‐Net, human parsing labels are introduced to supervise the generation of partial attention maps, while a spatial‐frequency interaction module is suggested to complement the higher‐order semantic information from the frequency domain. Furthermore, a Taylor‐inspired feature filter for mitigating background disturbance and extracting fine‐grained features is proposed. Moreover, a part‐soft triplet loss, which is robust to non‐discriminative body partial features is also designed. Experimental results on Occluded‐Duke, Occluded‐Reid, Market‐1501, and Duke‐MTMC datasets show that this method outperforms existing state‐of‐the‐art methods. The code is available at: https://github.com/ISCLab‐Bistu/SA‐ReID. [ABSTRACT FROM AUTHOR]

Wind power systems (WPs) are complex non‐linear systems with varying parameters affected by environmental changes, including wind speed fluctuations. Extracting maximum power from WPs poses a significant challenge due to these factors. Direct power control (DPC) is a highly effective technique known for its simplicity and ease of implementation. However, it suffers from power ripples caused by the use of hysteresis comparators and switching tables that operate at variable frequencies. To address this issue, this paper presents the robust neural controller (NC) based on DPC, which replaces the switching tables. The Double‐Fed Induction Generator (DFIG) is the chosen generator for the studied WP system due to its advantageous features. The NC‐DPC effectively regulates the exchange of active and reactive powers between the DFIG and the system, maximizing power extraction from the WP system while reducing Total Harmonic Distortion and enhancing overall system quality. The effectiveness of the NC‐DPC is evaluated through MATLAB simulations and further supported by experimental data obtained using the Real‐Time Interface of the dSPACE‐DS1104 Controller card. [ABSTRACT FROM AUTHOR]

With the wide application of vision‐based autonomous driving and mobile robots, the impact of frequent sand‐dust weather on computer vision applications in landlocked countries during spring and autumn has also attracted more and more attention. Although there has been a lot of research on sand‐dust image enhancement, no research has been conducted on how to improve the positioning accuracy of vision‐based autonomous driving or mobile robots in sand‐dust environments, especially because there is currently a lack of dataset to evaluate visual positioning in sand‐dust weather. Therefore, a complete set of visual positioning dataset construction methods in sand‐dust weather is proposed to fill the gap in the evaluation dataset of application fields such as autonomous driving or mobile robot attitude estimation in sand‐dust weather. At the same time, this method is also suitable for the construction of visual positioning dataset under haze and other similar weather. In addition, this paper further demonstrates to readers how to use the converted dust visual positioning dataset to conduct positioning evaluation experiment of automatic driving in sand‐dust weather. [ABSTRACT FROM AUTHOR]

Although radar sensors have been known for their robustness in challenging environmental conditions, they face difficulties in distinguishing between upper structure (US), such as road signs, traffic lights, overpasses, and pedestrian bridges, and forward vehicle (FV). This misclassification can lead to false alarms and unwanted braking events. In this paper, the authors propose a method distinguishing between US and FV at different distances based on change in radar cross section (RCS). The study collects real‐world RCS data from various objects identified as US and FV during road driving scenarios. [ABSTRACT FROM AUTHOR]

A partial energy current‐sharing method for an interleaved LLC resonant converter with a phase shift current balance module (CBM) is presented in this paper. The current sharing is achieved by controlling the phase shift angle of the bridge in the CBM. The proposed method maintains excellent soft‐switching performance, minimizes power losses, and ensures high overall efficiency as only partial energy is processed. This method simplifies current sharing in the resonant converter by using phase shift control, making it easy to implement and extend to multiple phases. A 500‐kHz two‐phase interleaved LLC prototype was built to validate the effectiveness of this method in achieving current balancing. [ABSTRACT FROM AUTHOR]

This paper presents a high‐precision, low‐power delta‐sigma modulator (DSM) designed for healthcare and medical diagnostics. It utilizes a hybrid switching integrator to reduce distortion caused by the non‐linear on‐resistance of switches at a low supply voltage. Leveraging the characteristics of the hybrid switching integrator, a non‐50% duty cycle sampling timing and a corresponding tunable Miller‐compensated operational transconductance amplifier are proposed to reduce resistor thermal noise and meet lower power consumption requirements. The DSM is simulated based on 130‐nm CMOS technology and achieves 93.5‐dB signal‐to‐noise‐plus‐distortion ratio at a 1‐kHz bandwidth while consuming 23.7 µW from a 1.5‐V supply. [ABSTRACT FROM AUTHOR]