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Resolution-aware beam scanning for joint detection and communication in ISAC systems

Science China(Information Sciences) 2025年第5期68卷 250-266页

作者： Yiming XU Dongfang XU Zhanyuan XIE Zheng JIANG Shenghui SONG Derrick Wing Kwan NG Department of Electronic and Computer Engineering The Hong Kong University of Science and Technology China Telecom Research Institute School of Electrical Engineering and Telecommunications University of New South Wales

Beam scanning for joint detection and communication in integrated sensing and communication(ISAC) systems plays a critical role in continuous monitoring and rapid adaptation to dynamic environments. However, the design of sequential scanning beams for target detection with the required sensing resolution has not been tackled in the *** bridge this gap, this paper introduces a resolution-aware beam scanning design. In particular, the transmit information beamformer, the covariance matrix of the dedicated radar signal, and the receive beamformer are jointly optimized to maximize the average sum rate of the system while satisfying the sensing resolution and detection probability requirements.A block coordinate descent(BCD)-based optimization framework is developed to address the non-convex design problem. By exploiting successive convex approximation(SCA), S-procedure, and semidefinite relaxation(SDR), the proposed algorithm is guaranteed to converge to a stationary solution with polynomial time complexity. Simulation results show that the proposed design can efficiently handle the stringent detection requirement and outperform existing antenna-activation-based methods in the literature by exploiting the full degrees of freedom(DoFs) brought by all antennas.

关键词： integrated sensing and communication (ISAC) detection beam scanning resolution beamwidth

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UETT4K Anti-UAV: A Large Scale 4K Benchmark Dataset for Vision-based Drone Detection in High-Resolution Imagery

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IEEE Access 2025年 13卷 73553-73568页

作者： Awan, Mughees Sarwar Zaidi, Syed Azhar Ali Mir, Junaid Dr. A. Q. Khan Institute of Computer Sciences and Information Technology Kahuta Pakistan University of Engineering and Technology Department of Electronics Engineering Taxila47080 Pakistan University of Engineering and Technology Department of Electrical Engineering Taxila47080 Pakistan

In recent years, incidents involving unmanned aerial vehicles (UAVs) have increased significantly, raising concerns over security and privacy, especially concerning civilian and military facilities. Vision-based approaches, especially those employing deep convolutional neural networks (DCNNs), show great promise in addressing the need for an accurate and cost-effective UAV detection system. However, DCNNs rely heavily on extensive and well-labeled datasets, which are essential for achieving high accuracy and effectiveness. For drone detection tasks, a dataset of high-resolution images is especially valuable, as it provides more contextual information for DCNNs, enabling more accurate drone detection. This work presents a new drone detection dataset composed of 4K resolution images named University of engineering and technology Taxila 4K Anti-UAV (UETT4K Anti-UAV). The dataset is created by obtaining real-world videos of different types of drones in diverse environmental and challenging conditions. A custom dataset of 33601 images, manually annotated with hand-labeled bounding boxes, is created from these *** used our dataset to train, validate, and test eight state-of-the-art YOLOv6v3 algorithm models. The test results and qualitative analysis guide selecting the most suitable model for drone detection applications. The proposed extensive 4K dataset offers a valuable resource for effectively training deep learning models to detect drones across diverse conditions accurately. © 2013 IEEE.

关键词： Convolutional neural networks

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Resilient Reliability/Loss-Based Distribution Network Reconfiguration: A Strategy Against FDI Attacks During State Estimation Procedure

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IEEE Transactions on Network Science and engineering 2025年第3期12卷 1994-2006页

作者： Behnam, Reza Baghaee, Hamid Reza Gharehpetian, Gevork B. Ahmadiahangar, Roya Rosin, Argo Amirkabir University of Technology Department of Electrical Engineering Tehran15875-4413 Iran Tarbiat Modares University Faculty of Electrical and Computer Engineering Tehran14115-111 Iran Tallinn University of Technology Tallinn19086 Estonia

One of the intrinsic properties of Distribution networks, resilience, is the ability to resist, adjust, and recover from extreme, high-impact, low-probability events such as earthquakes, floods, hurricanes, thunderstorms, and cyber and physical attacks. Besides, the uncertainty of the network elements has a significant effect on the operation of the distribution system. Operators require methods and planning strategies to improve grid resilience. Distribution network reconfiguration (DNR) enhances reliability and reduces power losses. This paper proposes an application of DNR as a strategy to get a resilient configuration against false data injection (FDI) attack during state estimation (SE) procedure, minimize power losses, and improve the reliability of the distribution network simultaneously. In this paper, a driving training-based optimization (DTBO) method is exploited for DNR to demonstrate the effectiveness of the proposed strategy. The proposed strategy is tested on IEEE 33-bus, 69-bus, and 118-bus systems to reduce FDI attack impact on power measurements, power loss, and energy not supplied (ENS). The proposed DNR is evaluated by offline digital time-domain simulations on the distribution test systems in the MATLAB software environment. The simulations and comparisons of the proposed DNR strategy effectively prove the proposed strategy's effectiveness, accuracy, and authenticity. © 2013 IEEE.

关键词： Power distribution reliability

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Enhancing cyber threat detection with an improved artificial neural network model

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Data Science and Management 2025年第1期8卷 107-115页

作者： Oyinloye, Toluwase Sunday Arowolo, Micheal Olaolu Prasad, Rajesh Department of Computer Science African University of Science and Technology Abuja900107 Nigeria Department of Electrical Engineering and Computer Science University of Missouri ColumbiaMO65201 United States

Identifying cyberattacks that attempt to compromise digital systems is a critical function of intrusion detection systems (IDS). Data labeling difficulties, incorrect conclusions, and vulnerability to malicious data injections are only a few drawbacks of using machine learning algorithms for cybersecurity. To overcome these obstacles, researchers have created several network IDS models, such as the Hidden Naive Bayes Multiclass Classifier and supervised/unsupervised machine learning techniques. This study provides an updated learning strategy for artificial neural network (ANN) to address data categorization problems caused by unbalanced data. Compared to traditional approaches, the augmented ANN's 92% accuracy is a significant improvement owing to the network's increased resilience to disturbances and computational complexity, brought about by the addition of a random weight and standard scaler. Considering the ever-evolving nature of cybersecurity threats, this study introduces a revolutionary intrusion detection method. © 2024 Xi'an Jiaotong University

关键词： Network intrusion

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Implementation of 16-QAM OFDM for TV white space spectrum efficiency gain in AWGN channel

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International Journal of Wireless and Mobile Computing 2025年第3期28卷 245-256页

作者： Azhar, Khola Omar, Muhammad Usman, Muhammad Khan, Saad Saleem Larkin, Stephen Electrical Engineering Department University of Engineering and Technology Punjab Taxila Pakistan Department of Electrical Engineering United Arab Emirates University Al Ain United Arab Emirates Department of Electrical Engineering National University of Computer and Emerging Sciences Islamabad Pakistan Africa New Energies London United Kingdom

Wireless communication has grown tremendously in recent years, impacting nearly every feature of our lives. The increased exigency for wireless broadband services leads to a huge demand for dynamic spectrum access, such as TV White Spaces (TVWS). The possibility of interference increases with the high density of users, reducing the Spectrum Efficiency (SE). SE improvement techniques have been utilised to resolve network congestion issues. This paper presents the Orthogonal Frequency Division Multiplexing (OFDM) wireless communication model with the Quadrature Amplitude Modulation (16-QAM) technique for TVWS SE gain. The performance analysis of the proposed model is based on parameters such as Packet Loss, Bit Error Rate (BER) and Signal-to-Noise Ratio (SNR). The constellation diagrams and signal trajectories are used to evaluate the efficacy of the modulation technique. Zero packet loss is achieved by implementing the 16-QAM OFDM, demonstrating efficient usage of TVWS with extended coverage and almost homogeneous data distributions. Copyright © 2025 Inderscience Enterprises Ltd.

关键词： Signal to noise ratio

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Deep Learning-Based Cloud Security: Innovative Attack Detection and Privacy Focused Key Management

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IEEE Transactions on computers 2025年第6期74卷 1978-1989页

作者： Ahmad, Shahnawaz Arif, Mohd Mehfuz, Shabana Ahmad, Javed Nazim, Mohd Bennett University School of Computer Science Engineering and Technology Greater Noida201310 India Galgotias University School of Computer Science & Engineering Greater Noida203201 India Jamia Millia Islamia Department of Electrical Engineering New Delhi110025 India Sharda University Department of Computer Science and Engineering Sharda School of Engineering and Technology Greater Noida201310 India Noida Institute of Engineering and Technology Department of Computer Science and Engineering School of Computer Science and IT Greater Noida201306 India

Cloud Computing (CC) is widely adopted in sectors like education, healthcare, and banking due to its scalability and cost-effectiveness. However, its internet-based nature exposes it to cyber threats, necessitating advanced security frameworks. Traditional models suffer from high false positives and limited adaptability. To address these challenges, VECGLSTM, an attack detection model integrating Variable Long Short-Term Memory (VLSTM), capsule networks, and the Enhanced Gannet Optimization Algorithm (EGOA), is introduced. This hybrid approach enhances accuracy, reduces false positives, and dynamically adapts to evolving threats. EGOA is employed for its superior optimization capability, ensuring faster convergence and resilience. Additionally, Chaotic Cryptographic Pelican Tunicate Swarm Optimization (CCPTSO) is proposed for privacy-preserving key management. This model combines chaotic cryptographic techniques with the Pelican Tunicate Swarm Optimization Algorithm (PTSOA), leveraging the pelican algorithm’s exploration strength and the tunicate swarm’s exploitation ability for optimal encryption security. Performance evaluation demonstrates 99.675% accuracy, 99.5175% recall, 99.7075% precision, and 99.615% F1-score, along with reduced training (1.79s), encryption (0.986s), and decryption (1.029s) times. This research significantly enhances CC security by providing a scalable, adaptive framework that effectively counters evolving cyber threats while ensuring efficient key management. © 1968-2012 IEEE.

关键词： Cloud security

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Feature Reconstruction Attacks and Countermeasures of DNN Training in Vertical Federated Learning

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IEEE Transactions on Dependable and Secure Computing 2025年第3期22卷 2659-2669页

作者： Ye, Peng Jiang, Zhifeng Wang, Wei Li, Bo Li, Baochun Hong Kong University of Science and Technology Department of Computer Science and Engineering Hong Kong University of Toronto Department of Electrical and Computer Engineering TorontoM5S 1A1 Canada

Federated learning (FL) has increasingly been deployed, in its vertical form, among organizations to facilitate secure collaborative training. In vertical FL (VFL), participants hold disjoint features of the same set of sample instances. The one with labels - the active party, initiates training and interacts with other participants - the passive parties. It remains largely unknown whether and how an active party can extract private feature data owned by passive parties, especially when training deep neural network (DNN) models. This work examines the feature security problem of DNN training in VFL. We consider a DNN model partitioned between active and passive parties, where the passive party holds a subset of the input layer with some features of binary values. Though proved to be NP-hard. we demonstrate that, unless the feature dimension is exceedingly large, it remains feasible, both theoretically and practically, to launch a reconstruction attack with an efficient search-based algorithm that prevails over current feature protection. We propose a novel feature protection scheme by perturbing intermediate results and fabricated input features, which effectively misleads reconstruction attacks towards pre-specified random values. The evaluation shows it sustains feature reconstruction attack in various VFL applications with negligible impact on model performance. © 2004-2012 IEEE.

关键词： Federated learning

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Regression analysis of squeezing-induced hybrid nanofluid flow in Darcy-Forchheimer porous medium

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Applied Mathematics and Mechanics(English Edition) 2025年第1期46卷 193-208页

作者： K.MUHAMMAD M.SARFRAZ Department of Humanities and Sciences School of Electrical Engineering and Computer ScienceNational University of Sciences and TechnologyIslamabad 44000Pakistan Department of Mathematics Quaid-i-Azam UniversityIslamabad 44000Pakistan

This article presents a mathematical model addressing a scenario involving a hybrid nanofluid flow between two infinite parallel *** plate remains stationary,while the other moves downward at a squeezing *** space between these plates contains a Darcy-Forchheimer porous medium.A mixture of water-based fluid with gold(Au)and silicon dioxide(Si O2)nanoparticles is *** contrast to the conventional Fourier's heat flux equation,this study employs the Cattaneo-Christov heat flux equation.A uniform magnetic field is applied perpendicular to the flow direction,invoking magnetohydrodynamic(MHD)***,the model accounts for Joule heating,which is the heat generated when an electric current passes through the *** problem is solved via NDSolve in *** and statistical analyses are conducted to provide insights into the behavior of the nanomaterials between the parallel plates with respect to the flow,energy transport,and skin *** findings of this study have potential applications in enhancing cooling systems and optimizing thermal management *** is observed that the squeezing motion generates additional pressure gradients within the fluid,which enhances the flow rate but reduces the frictional ***,the fluid is pushed more vigorously between the plates,increasing the flow *** the fluid experiences higher flow rates due to the increased squeezing effect,it spends less time in the region between the *** thermal relaxation,however,abruptly changes the temperature,leading to a decrease in the temperature fluctuations.

关键词： convective boundary condition Darcy-Forchheimer medium hybrid nanofuid Joule heating magnetohydrodynamic(MHD) numerical solution squeezing flow regression analysis

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Fully Soft Switched Single Stage Bridgeless Power Factor Correction Converter 16

Fully Soft Switched Single Stage Bridgeless Power Factor Cor...

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16th Annual Power Electronics, Drive Systems, and Technologies Conference, PEDSTC 2025

作者： Mohebbiyan, Saeed Maghsoudi, Mohammad Farzanehfard, Hosein Isfahan University of Technology Department of Electrical and Computer Engineering Isfahan Iran

ISBN: (纸本)9798331533946

This article proposes a high-efficiency single-stage bridge-less PFC converter. The converter main switches operate with fully ZVS switching during both turn-on and turn-off, enabling the converter to function effectively at high frequencies. Due to its operation in DCM, the converter inherently provides power factor correction without the need for an additional control circuit. This paper examines the operation principles of the proposed converter and validates its performance through simulation results. © 2025 IEEE.

关键词： DC-DC converters

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Isolated Soft Switched Resonant Led Driver with Constant Output Current and Wide Input Voltage Range 16

Isolated Soft Switched Resonant Led Driver with Constant Out...

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16th Annual Power Electronics, Drive Systems, and Technologies Conference, PEDSTC 2025

作者： Askari, Shirin Farzanehfard, Hosein Maghsoudi, Mohammad Isfahan University of Technology Department of Electrical and Computer Engineering Isfahan Iran

ISBN: (纸本)9798331533946

This paper proposes an isolated LCC resonant converter (LCC-RC) designed to deliver a constant output current for LED drivers across a wide range of input voltages by integrating a boost front-end stage with an LCC resonant converter. This topology leverages an LCC network to deliver numerous advantages. It integrates the boost filter inductor and transformer magnetizing inductance, ensuring that the primary switches achieve full Zero Voltage Switching (ZVS) and the secondary rectifier diodes experience Zero Current Switching (ZCS) across a wide range of duty cycles. Notably, the output current remains load-independent, making this configuration well-suited for LED applications. In addition, efficiency is improved by functioning near the resonant frequency. The hybrid structure of the proposed converter facilitates the use of a straightforward constant-frequency asymmetrical pulsewidth modulation (APWM) scheme to control the output current. To confirm the theoretical analysis and validate the LED driver functionality, a 50 W experimental prototype of the proposed driver was successfully created in the laboratory. This prototype supports an extensive input voltage range spanning from 18 V to 40 V, enabling it to supply a 500 mA output current to a series of 1 to 30 LEDs. This corresponds to an extensive output voltage capability spanning 3.3-100 V. © 2025 IEEE.

关键词： Zero voltage switching

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