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CNN-Transformer-Based Cooperative Spectrum Sensing in Cognitive Radio Networks

IEEE Wireless Communications Letters 2025年第5期14卷 1576-1580页

作者： Fang, Xiangqian Jin, Ming Guo, Qinghua Jiang, Tao Ningbo University Faculty of Electrical Engineering and Computer Science Ningbo315211 China University of Wollongong School of Electrical Computer and Telecommunications Engineering WollongongNSW2522 Australia

Recently, deep learning (DL)-based data-driven spectrum sensing has attracted much attention due to its powerful capabilities to exploit underlying signal features. Existing DL-based cooperative spectrum sensing (CSS) detectors usually extract the signal features from each secondary user (SU) individually, followed by feature fusion at a central fusion center (FC). However, this cannot extract correlation features across SUs. To address this limitation, this letter proposes a CNN-Transformer neural network to exploit the correlations among the received signals across and within SUs. Leveraging the CNN-Transformer, we propose a posteriori probability ratio detector, which is optimal for the binary hypothesis testing problem under the Neyman-Pearson criterion. Moreover, unlike most DL-based CSS detectors, the proposed detector considers imperfect and independent reporting channels between SUs and the FC, and exploits the temporal correlation of signals among neighboring samples. Numerical results are provided to demonstrate the superior performance of the proposed detector, showing an improvement of approximately 6 dB over traditional model-driven energy detectors. Moreover, the proposed detector improves the detection probability to 0.99 from 0.88 of data-driven detectors under Rician fading channels. © 2012 IEEE.

关键词： Fading channels

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RelaxVR: Cybersickness Reduction in Immersive Virtual Reality through Explainable AI and Large Language Models

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IEEE Access 2025年 13卷 84689-84712页

作者： Kundu, Ripan Kumar Hoque, Khaza Anuarul University of Missouri Department of Electrical Engineering Computer Science ColumbiaMO United States

Virtual reality (VR) systems are susceptible to cybersickness, significantly hindering user immersion. Very recently, researchers introduced explainable artificial intelligence (XAI) enabled methods for detecting and explaining cybersickness features. Since XAI methods can identify the dominant features causing cybersickness, we argue that this knowledge can also guide the dynamic adoption of effective cybersickness reduction strategies, unlike state-of-the-art static approaches that rely on fixed methods. This paper introduces a new dataset MazeSick and RelaxVR, an interactive XAI-guided VR cybersickness reduction framework to predict, explain, and reduce cybersickness. Specifically, we propose an innovative XAI-guided cybersickness reduction engine, which selects the most appropriate reduction technique based on XAI-provided feature importance scores from a cybersickness reduction library of different reduction techniques. We also design an interactive dialogue system powered by large language models, enabling users to engage in the VR simulation via voice commands to understand the reasoning of cybersickness and select effective reduction strategies through natural language interaction. We deployed RelaxVR on a consumer-grade VR headset (e.g., HTC VIVE Pro) and validated it through a user study on cybersickness evaluation, where participants were immersed in a custom-built VR Maze simulation. Our results demonstrate that RelaxVR effectively reduces cybersickness with minimal impact on immersion, and 94% of the users agreed that RelaxVR is highly effective for reducing cybersickness and easier to use through the dialogue engine for selecting effective reduction strategies. © 2013 IEEE.

关键词： Virtual reality

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Multi-Modal Self-Supervised Learning AlgorithmBased Product Recommendation

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IEEE Transactions on Automation science and engineering 2025年 22卷 15371-15380页

作者： Gao, Li Fan, Haiping Chen, Qingkui Yang, Heyu School of Optical-Electrical and Computer Engineering University of Shanghai for Science and Technology Shanghai China

With the rise of e-commerce, personalized recommendation algorithms have received much attention in recent years. Meanwhile, multimodal recommendation algorithms have become the next competitive track of personalized recommendation. The current mainstream recommendation algorithms (e.g., NeuMF, NGCF) rely too much on empirical data and do not effectively utilize multimodal information to model user preferences, while there is the problem of sparsity of user preference data. Secondly, they are still limited to using the same weights to fuse the item features of different modalities, and have the problem of inaccurate recommendation. For this reason, this paper proposes Multi-Modal Self-Supervised Learning Algorithm Based Product Recommendation (MMSLRec). (1) Data augmentation by self-supervised adversarial perturbation generation learning is performed to supplement the missing interaction information of tailed products to solve the common data sparsity problem in product recommendation. (2) A multimodal representation learning module is introduced to capture deep features in image and text modalities using AlexNet and Bert, respectively, in order to avoid the recommendation accuracy degradation problem caused by insufficient data feature learning. (3) Learning the user's perceived strength in different modalities, using the user co-occurrence matrix to fuse the features and make recommendations. Experiments on Amazon-Electronics, Amazon-Clothing, and Amazon-Baby, with different degrees of data sparsity verify the effectiveness of our MMSLRec in solving the data sparsity problem and the multimodal user preference modeling problem, and compared to the state-of-the-art baseline recommendation model on the three datasets, the Recall@20 improved by 9.26%, 3.11% and 2.92%, respectively. © 2004-2012 IEEE.

关键词： Self-supervised learning

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A Deep Learning based Ultra-Lightweight Architecture for Atrial Fibrillation Detection Using Single-Lead ECG Recordings

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IEEE Access 2025年 13卷 86474-86486页

作者： Khan, Abbas Mughal, Muhammad Rizwan Irtaza, Syed Ali Khan, Mohsin Tahir, Madiha Ali, Ahtisham Saeed, Zoraize Ghulam Ishaq Khan Institute of Engineering Science and Technology Pakistan Sultan Qaboos University Electrical and Computer Engineering Department Muscat Oman Institute of Space Technology Electrical Engineering and Computer Science Department Islamabad Pakistan FAST National University of Computer and Emerging Sciences Islamabad Pakistan

In this paper, we have proposed a novel deep-learning model to process electrocardiogram (ECG) signals from single-lead ECG device. This is achieved by using a hybrid of CNN (convolutional neural network) and LSTM (long short-term memory) neural netwrok, where CNN extracts localized features from ECG signals, while the LSTM network extract temporal relationships. Moreover, a filtering-based method is proposed to generate synthetic data to overcome the data imbalance posed by the publicly available PhysioNet/CinC Challenge 2017 dataset. Specifically, three distinct ECG filters: Neurokit, BioSPPy, and EngZeeMod are used to perform data augmentation. As depicted by the simulation results, the proposed model exhibits superior performance with an F1 score of 99.56% providing a gain of 102.25 dB in terms of model size and performance gain of 1.06% outperforming existing state-of-the-art method enabling cost-effective real-time diagnosis with an inference time of 0.48×10-3 secs. Atrial fibrillation (A-Fib), a common cardiac arrhythmia, is linked to serious complications like heart failure and stroke, which early diagnosis could prevent. However, its paroxysmal and often asymptomatic nature complicates detection. In this paper, we propose a novel deep-learning model for single-lead ECG signal analysis, combining a convolutional neural network (CNN) to extract localized features and a long short-term memory (LSTM) network to capture temporal dependencies. To address data imbalance in the PhysioNet/CinC Challenge 2017 dataset, we employ a filtering-based approach with Neurokit, BioSPPy, and EngZeeMod for synthetic data generation. Simulation results show the model achieves a 99.56% F1 score, a 102.25 dB gain in efficiency, a 1.06% performance gain over the state-of-the-art method, and real-time diagnosis with a 0.48×10-3 second inference time. These simulation results confirm the superior performance of the proposed model. © 2013 IEEE.

关键词： Electrocardiography

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Energy- and Area-Efficient CMOS Neuron and Max Pooling Circuit for RRAM-Based CNN Accelerators

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IEEE Access 2025年 13卷 84329-84340页

作者： Kim, Dowon Lee, Byung-Geun Gwangju Institute of Science and Technology School of Electrical Engineering and Computer Science Gwangju61005 Korea Republic of

This study proposes an energy- and area-efficient CMOS neuron and analog max pooling circuit for RRAM-based convolutional neural network (CNN) accelerators. The proposed max pooling circuit implements a 2 × 2 max pooling operation with a simple analog circuit in the current domain. The currentmode max pooling is performed before the current-to-voltage and voltage-to-digital conversions, thus reducing the number of circuits required for the conversions in the conventional CNN accelerators. Consequently, the 2 × 2 pooling circuit reduces the energy and area of the accelerator to a quarter. In addition to the max pooling circuit, the neuron circuit improves energy efficiency. The proposed neuron circuit implements the ReLU activation function along with digital conversion by modifying the operating principle of a conventional single-slope analog-to-digital converter. By implementing the ReLU function and digital conversion, the neuron achieves high energy efficiency by applying a high-speed asynchronous operation. A prototype chip, which included the neuron and current-mode max pooling circuit, was fabricated using a 130 nm standard CMOS process. The measurement results of the chip demonstrate its energy efficiency of 5.5 TOPS/W. Additionally, a simulation for image recognition demonstrates that a CNN accelerator employing the proposed neuron and max pooling circuit achieves 96.2 and 82.7% classification accuracies for the MNIST and Fashion-MNIST datasets, respectively. © 2013 IEEE.

关键词： RRAM

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A Hybrid P&O-Fuzzy-Based Maximum Power Point Tracking (MPPT) Algorithm for Photovoltaic Systems under Partial Shading Conditions

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IEEE Access 2025年 13卷 86046-86056页

作者： Karimi, Hamed Siadatan, Alireza Rezaei-Zare, Afshin Khatam University Department of Electrical Engineering Tehran Iran York University Department of Electrical Engineering and Computer Science Toronto Canada

Photovoltaic arrays receive varying levels of solar radiation due to factors such as shadows created by clouds, surrounding buildings, and other obstructions. Therefore, an effective Maximum Power Point Tracking (MPPT) algorithm should be used to maximize power generation from non-uniform arrays. Most MPPT algorithms do not perform well during partial shadow conditions, as they can become trapped in local maxima and fail to identify the absolute global maximum point (GP). This paper proposes a new MPPT approach that combines Perturb & Observe (P&O) and Fuzzy Logic Controller (FLC) with the Particle Swarm Optimization (PSO) algorithm. The FLC algorithm is then used to maximize search accuracy. The configurations of this system and the study results demonstrate the dynamic and promising performance of the proposed method under various environmental and shading conditions. In addition to its simplicity and ease of implementation, the proposed algorithm exhibits high accuracy in tracking the maximum power point. This has been evidenced through both simulation and laboratory results of the hardware. © 2025 Institute of electrical and Electronics Engineers Inc.. All rights reserved.

关键词： Particle swarm optimization (PSO)

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Adaptive Cooperative Quality Weight Spectrum Sensing for Mitigating Byzantine Attacks in Cognitive Radio

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IEEE Access 2025年 13卷 80930-80940页

作者： Srisomboon, Kanabadee Pipanmekaporn, Luepol Prayote, Akara Lee, Wilaiporn Division of Electrical Engineering Department of Electrical and Computer Engineering Faculty of Engineering Bangkok10800 Thailand King Mongkut’s University of Technology North Bangkok Faculty of Applied Science Department of Computer and Information Science Bangkok10800 Thailand

Spectrum sensing data falsification (SSDF) attack, i.e., Byzantine attack, is one of the critical threats of the cooperative spectrum sensing where the Byzantine attackers (BAs) forward incorrect local sensing results to mislead the fusion center on channel availability decisions. By using traditional voting rule, the cooperative spectrum sensing performance deteriorates significantly due to incorrect local sensing results. Then, reliability weight strategy becomes the popular solution to avoid incorrect sensing results from BAs and unreliable cognitive radio users (CRUs). However, it is very difficult to detect the attackers since they also occasionally provide correct sensing results to the fusion center for concealing the attack objective. Based on existing techniques, the BAs and CRUs may be assigned with low reliability weights or distinguished from the data fusion account. However, it is very difficult to detect the attackers since they also occasionally provide correct sensing results to the fusion center for concealing the attack objective. Then, existing techniques still suffer from BAs and negative impact of unreliable CRUs. In this paper, we propose the adaptive cooperative quality weight algorithm for mitigating the Byzantine attack issue by distinguishing the BAs and CRUs from the data fusion account while selecting only useful CRUs since the number of members in the account is also the important factor for cooperative spectrum sensing. In our proposed algorithm, we adopt a stable preference ordering towards ideal solution (SPOTIS) for determining the reliability of SUs which shows low computational complexity as compared to other reliability weight-based techniques. To achieve high sensing performance, our global decision threshold is adapted according to the reliability of reliable users. From the simulation results, our proposed algorithm significantly improves global detection probability and total error probability compared to the traditional votin

关键词： Sensors Reliability Barium Radio spectrum management Cooperative communication Cognitive radio Data integration Signal to noise ratio Heuristic algorithms Dynamic spectrum access

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A Novel Lightweight Joint Source-Channel Coding Design in Semantic Communications

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IEEE Internet of Things Journal 2025年第11期12卷 18447-18450页

作者： Yu, Xianhua Li, Dong Zhang, Ning Shen, Xuemin Macau University of Science and Technology School of Computer Science and Engineering 999078 China University of Windsor Department of Electrical and Computer Engineering Canada University of Waterloo Department of Electrical and Computer Engineering WaterlooONN2L 3G1 Canada

Semantic communication has emerged as a promising solution to meet the growing demand for efficient data transmission in the information age. Unlike traditional communication methods that focus on transmitting raw data, semantic communication prioritizes preserving the meaning of transmitted information, which significantly reduces the data volume. However, implementing semantic communication systems in resource-constrained environments, such as Internet of Things (IoT) devices, remains challenging due to limited computational resources. In this letter, we propose a novel lightweight deep learning (DL) model, termed the lightweight image compression and reconstruction network (LICRnet). LICRnet leverages depthwise separable convolution (DSC) and a local and non-local mixture (LNLM) block to significantly reduce computational costs. Additionally, the LNLM incorporates a variable window size-based multi-scale attention mechanism (VW-MSA), enabling it to effectively learn from both local detailed features and global high-level meaningful features. Extensive simulations demonstrate that LICRnet significantly reduces computational complexity while maintaining satisfactory image compression and reconstruction performance, making it highly suitable for deployment in resource-constrained environments. © 2014 IEEE.

关键词： Channel coding

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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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Geometric Interpretation of Sensitivity to Structured Uncertainties in Spintronic Networks

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IEEE Control Systems Letters 2025年 9卷 192-197页

作者： O'Neil, S.P. Jonckheere, E.A. Schirmer, S. United States Military Academy Department of Electrical Engineering and Computer Science NY United States University of Southern California Dept of Electrical and Computer Engineering CA United States Swansea University Faculty of Science and Engineering Physics United Kingdom

We present a geometric model of the differential sensitivity of the fidelity error for state transfer in a spintronic network based on the relationship between a set of matrix operators. We show an explicit dependence of the differential sensitivity on the fidelity (error), and we further demonstrate that this dependence does not require a trade-off between the fidelity and sensitivity. We prove that for closed systems, ideal performance in the sense of perfect state transfer is both necessary and sufficient for optimal robustness in terms of vanishing sensitivity. We demonstrate the utility of this geometric interpretation of the sensitivity by applying the model to explain the sensitivity versus fidelity error data in two examples. © 2017 IEEE.

关键词： Sensitivity analysis

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