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A Scalable Security Approach in IoT networks: Smart Contracts and Anomaly-Based IDS for Gateways Using Hardware Accelerators

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IEEE ACCESS 2024年 12卷 159519-159533页

作者： Ngo, Duc-Minh Lightbody, Dominic Temko, Andriy Murphy, Colin C. Popovici, Emanuel Univ Coll Cork Dept Elect & Elect Engn Cork T12K 8AF Ireland

With the widespread integration of new technologies, IoT devices are becoming increasingly diverse and capable of handling highly complex tasks, compared to previous generations. This evolution has led to demands for a comprehensive security approach across multiple layers of an IoT architecture. This work proposes a scalable security solution from the edge to the cloud, combining Blockchain technology and anomaly-based Intrusion Detection Systems (IDSs). Smart contracts provide a transparent environment for registering and managing IoT devices on the cloud. Specifically, the smart contract includes two authorization levels for managing administrators and IoT devices. Besides, anomaly-based IDSs are deployed at Gateways to detect network attacks. We propose using lightweight machine learning models on FPGA hardware acceleration for Gateways. We have simulated the Blockchain network on the Ganache software, demonstrating that the smart contract effectively manages administrators and devices such that only authorized entities can access the system. The FPGA-based Gateway, which contains pre-trained Artificial neural network (ANN) and Convolutional neural network (CNN) detection models from the IoT-23 dataset, has been deployed on the Alveo U280 card. The ANN model has achieved the highest processing speed at 20Gbps. The results indicate that integrating Blockchain and anomaly-based IDS significantly enhances scalable security in IoT networks.

关键词： Blockchains Smart contracts Security Internet of Things Engines Access control distributed ledger Logic gates Training Hardware acceleration network architecture scalable security IoT access control blockchain smart contracts neural networks hardware accelerators NetFPGA

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STATE-AUGMENTED INFORMATION ROUTING IN COMMUNICATION SYSTEMS WITH GRAPH neural networkS 49

STATE-AUGMENTED INFORMATION ROUTING IN COMMUNICATION SYSTEMS...

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49th IEEE International Conference on Acoustics, Speech, and Signal processing (ICASSP)

作者： Das, Sourajit NaderiAlizadeh, Navid Ribeiro, Alejandro Univ Penn Philadelphia PA 19104 USA Duke Univ Durham NC USA

ISBN: (纸本)9798350344868;9798350344851

We consider the problem of routing network packets in a large-scale communication system where the nodes have access to only local information. We formulate this problem as a constrained learning problem, which can be solved using a distributed optimization algorithm. We approach this distributed optimization using a novel state-augmentation (SA) strategy to maximize the aggregate information packets at different source nodes, leveraging dual variables corresponding to flow constraint violations. The construction is based on graph neural networks (GNNs) that employ graph convolutions over the underlying communication network topology. We devise an unsupervised learning algorithm to transform the output of the GNN architecture into optimal routing decisions. The proposed method takes advantage of only the local information available at each node and efficiently routes the desired packets to the destination. We provide numerical results demonstrating the superiority of the proposed method over baseline routing algorithms.

关键词： Routing distributed optimization graph neural networks unsupervised learning Lagrangian duality

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MSSTNet: A multi-stream time-distributed spatio-temporal deep learning model to detect mind wandering from electroencephalogram signals

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COMPUTERS & ELECTRICAL ENGINEERING 2025年 122卷

作者： Pain, Subrata Chatterjee, Subhrasankar Sarma, Monalisa Samanta, Debasis Indian Inst Technol Kharagpur Adv Technol Dev Ctr Kharagpur India Indian Inst Technol Kharagpur Dept Comp Sci & Engn Kharagpur India Indian Inst Technol Kharagpur Subir Chowdhury Sch Qual & Reliabil Kharagpur India

The automated detection of mind-wandering (MW) and associated attention lapses through Electroencephalogram (EEG) signals holds significant potential for practical applications. Traditional handcrafted features have been proven inadequate in capturing the spatially and temporally distributed patterns of MW-EEG signals, limiting the effectiveness of Machine Learning-based detection models. This study proposes a multi-stream spatio-temporal Deep Learning network (MSSTNet) for automated detection of MW episodes. In this approach, EEG data from individual frequency bands, extracted directly from raw EEG signals, are fed into distinct feature extraction blocks embedded within each stream of the MSSTNet architecture. These feature extraction blocks comprise time-distributed Convolutional neural network-Long Short-Term Memory (CNN-LSTM) modules, enabling the model to capture fine-grained spatio-temporal features across different frequency bands. The CNN layers extract spatial and short-term temporal features, and the LSTM units capture the long-term temporal evolution of these features, which is critical for recognizing prolonged MW episodes. The features extracted from each frequency band are subsequently sent to fully connected layers for MW detection. The MSSTNet model is validated on a publicly available MW and focus dataset, achieving a mixed-subject classification accuracy of 95.07%, substantially outperforming baseline models. Furthermore, the intra-subject and cross-subject classification accuracies of 94.48% and 83.13%, respectively, demonstrate the robustness and generalizability of the proposed model. The band-wise analysis reveals that the Beta band exhibits the most pronounced alterations due to MW onset. MSSTNet's capacity to capture subtle spatio-temporal patterns across frequency bands underscores its efficacy as an MW detection framework, with promising scope for broader EEG-based applications.

关键词： Mind wandering Electroencephalogram signal processing Spatio-temporal EEG Deep convolutional-recurrent neural networks Multi-stream models

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Non-separation Method-Based Global Stability Criteria for Takagi-Sugeno Fuzzy Quaternion-Valued BAM Delayed neural networks Using Quaternion-valued Auxiliary Function-Based Integral Inequality

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neural processing LETTERS 2024年第2期56卷 101-101页

作者： Ramalingam, Sriraman Kwon, Oh-Min SRM Inst Sci & Technol Coll Engn & Technol Dept Math Kattankulathur 603203 Tamil Nadu India Chungbuk Natl Univ Sch Elect Engn Cheongju 361763 South Korea

This paper focuses on the global asymptotic stability (GAS) problem for Takagi-Sugeno (T-S) fuzzy quaternion-valued bidirectional associative memory neural networks (QVBAMNNs) with discrete, distributed and leakage delays by using non-separation method. By applying T-S fuzzy model, we first consider a general form of T-S fuzzy QVBAMNNs with time delays. Then, by constructing appropriate Lyapunov-Krasovskii functionals and employing quaternion-valued integral inequalities and homeomorphism theory, several delay-dependent sufficient conditions are obtained to guarantee the existence and GAS of the considered neural networks (NNs). In addition, these theoretical results are presented in the form of quaternion-valued linear matrix inequalities (LMIs), which can be verified numerically using the effective YALMIP toolbox in MATLAB. Finally, two numerical illustrations are presented along with their simulations to demonstrate the validity of the theoretical analysis.

关键词： Quaternion-valued neural network BAM neural network Global asymptotic stability Lyapunov-Krasovskii functional Takagi-Sugeno fuzzy model

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A facial expression recognition algorithm incorporating SVM and explainable residual neural network

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SIGNAL IMAGE AND VIDEO processing 2023年第8期17卷 4245-4254页

作者： Ji, Lipeng Wu, Shilong Gu, Xiaoqing Univ Shanghai Sci & Technol Sch Mech Engn Shanghai 200093 Peoples R China East China Normal Univ Dept Educ Informat Technol Shanghai 200062 Peoples R China

To address the problem that traditional convolutional neural networks cannot classify facial expression image features precisely, an interpretable face expression recognition method combining ResNet18 residual network and support vector machines (SVM) is proposed in the paper. The SVM classifier is used to enhance the matching ability of feature vectors and labels under the expression image feature space, to improve the expression recognition effect of the whole model. The class activation mapping and t-distributed stochastic neighbor embedding methods are used to visualize and interpret facial expression recognition's feature analysis and decision making under the residual neural network. The experimental results and the interpretable visualization analysis show that the algorithm structure can effectively improve the recognition ability of the network. Under the FER2013, JAFFE, and CK+ datasets, it achieved 67.65%, 84.44%, and 96.94% emotional recognition accuracy, respectively, showing a certain generalization ability and superior performance.

关键词： Facial expression recognition Residual neural network SVM Interpretability

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Design and Implementation of an NoC-Based Convolution Architecture With GEMM and Systolic Arrays

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IEEE EMBEDDED SYSTEMS LETTERS 2024年第1期16卷 49-52页

作者： Ortega-Cisneros, S. CINVESTAV Guadalajara Dept Elect Design Zapopan 45017 Mexico

neural networks have been used for a long time for image detection and recognition applications due to their ability and efficiency in complex problem solving. Several researchers have chosen to design and develop hardware accelerators for the convolution layer due to the large computational expense consumed by this layer. For that reason, a system that performs indirect GEMM convolution is implemented in a FPGA in this letter. Thus, the input data is segmented and distributed into acceleration modules in a parallel and distributed manner using the network-on-Chip (NoC) paradigm, and a systolic array (SA) is implemented for the matrix multiplication operation as processing blocks within each NoC Node. Synthesis and performance results show that the implementation of this system presents better results compared to the state of the art in areas, such as acceleration factor, consumption of resources, latency, and operational frequency.

关键词： Routing FPGA GEMM hardware accelerator network on chip systolic array (SA)

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Charbonnier Quasi Hyperbolic Momentum Spline Based Incremental Strategy for Nonlinear distributed Active Noise Control

IEEE OPEN JOURNAL OF SIGNAL PROCESSING

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IEEE OPEN JOURNAL OF SIGNAL processing 2025年 6卷 1-15页

作者： Kranthi, Rajapantula Vasundhara Kar, Asutosh Christensen, Mads Grasboll Natl Inst Technol Dept Elect & Commun Engn Warangal 506004 India Dr BR Ambedkar Natl Inst Technol Jalandhar Dept Elect & Commun Engn Jalandhar 144008 India Aalborg Univ Dept Elect Syst DK-9220 Aalborg Denmark

Noise mitigation proves to be a challenging task for active noise control in the existence of nonlinearities. In such environments, functional link neural network (FLN) and adaptive exponential FLN techniques improve the performance of distributed active noise control systems. Nonlinear spline approaches are well known for their low computational complexity and ability to effectively alleviate noise in nonlinear systems. This paper proposes a new cost function for distributed active noise control (DANC) system which is based on the Charbonnier quasi hyperbolic momentum spline (CQHMS) involving incremental approach. This incremental based CQHMS DANC method employs Charbonnier loss and quasi hyperbolic momentum approach which minimizes gradient variance and local crossover points in order to enhance the convergence and steady-state performance. The technique being proposed demonstrates enhanced performance and achieves accelerated convergence when compared to existing techniques in a range of nonlinear DANC scenarios in lieu of varied nonlinear primary path and nonlinear secondary path conditions.

关键词： Noise Splines (mathematics) Microphones Convergence Adaptive systems Adaptation models Prevention and mitigation Loudspeakers Adaptive filters Signal processing algorithms Charbonnier function nonlinear distributed ANC quasi hyperbolic momentum spline

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Interference Mitigation for Automotive FMCW Radar Based on Contrastive Learning With Dilated Convolution

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IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS 2024年第1期25卷 545-558页

作者： Wang, Jianping Li, Runlong Zhang, Xinqi He, Yuan Delft Univ Technol Fac Elect Engn Math & Comp Sci NL-2628 CD Delft Netherlands Beijing Univ Posts & Telecommun Key Lab Trustworthy Distributed Comp & Serv Beijing 100876 Peoples R China

As one of the crucial sensors for environment sensing, frequency modulated continuous wave (FMCW) radars are widely used in modern vehicles for driving assistance/autonomous driving. However, the limited frequency bandwidth and the increasing number of equipped radar sensors would inevitably cause mutual interference, degrading target detection and producing safety hazards. In this paper, a deep learning-based interference mitigation (IM) approach is proposed for FMCW radars by using the dilated convolution for network construction and a designated contrast learning strategy for training. The dilated convolution enlarges the receptive field of the neural network, and the designated contrastive learning strategy enforces to distinguish better between interferences and desired signals. The results of numerical simulation and experimental data processing show that the dilated convolution-based IM network, compared to the traditional convolution-based ones, can achieve a higher Signal-to-Interference-plus-Noise-Ratio (SINR) and target detection rate. Moreover, the designated contrastive learning strategy enables a better and more stable IM performance without increasing the complexity of the network, which can facilitate faster signal processing.

关键词： Radar Convolution Interference Feature extraction Radar detection Radar signal processing Kernel Automotive radar interference mitigation deep learning dilated convolution contrastive learning

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Synchronization of reaction-diffusion neural networks with distributed delay via quantized boundary control

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INTERNATIONAL JOURNAL OF ADAPTIVE CONTROL AND SIGNAL processing 2023年第5期37卷 1166-1177页

作者： Zhang, Chuan Xiang, Han Zhang, Xianfu Guo, Yingxin Zhang, Hao Qufu Normal Univ Sch Math Sci Qufu 273165 Shandong Peoples R China Shandong Univ Sch Control Sci & Engn Jinan Peoples R China Taiyuan Univ Technol Coll Informat & Comp Taiyuan Peoples R China

This paper investigates the synchronization of reaction-diffusion neural networks (RDNNs) with distributed delay via quantized boundary control. To reduce the communication burden, a novel control strategy combined boundary control and logarithmic quantizer is proposed, and two controllers respectively subject to constant and adaptive coefficients are carried out. Worth mentioning that the adaptive feedback gain is a matrix in this paper rather than a one-dimensional variable in most of the existing literatures. Using the Lyapunov functional, the sufficient conditions for delay-dependent synchronization are obtained through linear matrix inequalities. The effectiveness of the proposed control strategy is illustrated via two examples.

关键词： adaptive control distributed delay quantized boundary control reaction-diffusion neural network

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A Deep neural network Framework for Landslide Susceptibility Mapping by Considering Time-Series Rainfall

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IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING 2024年 17卷 5946-5969页

作者： Gao, Binghai He, Yi Chen, Xueye Chen, Hesheng Yang, Wang Zhang, Lifeng Minist Nat Resources Key Lab Urban Land Resources Monitoring & Simulat Shenzhen 518034 Peoples R China Lanzhou Jiaotong Univ Fac Geomatics Lanzhou 730070 Peoples R China

Landslide susceptibility mapping (LSM) is of great significance in geohazard early warning and prevention. The existing LSM methods mostly used traditional static landslide conditioning factors (LCFs), which only considered the spatial features of single-pixel neighborhoods and could not extract the time-series dynamic features of developing landslides, resulting in low accuracy and insufficient reliability of LSM. To solve this problem, this study proposes to introduce time-series rainfall factors based on the traditional static factors and construct an integrated time-series dynamic neural network (TSDNN) model for LSM. A convolutional neural network (CNN) adding time-distributed convolution and a bidirectional long and short-term memory neural network is utilized to extract time-series rainfall features, and a multiscale convolutional neural network (MSCNN) is utilized to extract static features of the static LCFs. In this study, multicollinearity analysis and geodetector are utilized to analyze the LCFs. Multiple evaluation metrics are utilized to analyze the proposed model performance. The results indicate that the overall accuracy has improved by introducing time-series rainfall factors, and the susceptibility area of actual predicted is more refined. The study indicates that significant advantages of the proposed TSDNN model are over models [CNN, MSCNN, random forest (RF)] when processing combined static and rainfall data. This is notably evident that the accuracy is enhanced by 12.9%, 10.7%, and 11.4% compared to CNN, MSCNN, and RF models in the receiver operating characteristic curve analysis, respectively. Moreover, two typical areas containing three recent landslide events validate the reliability of the proposed TSDNN model. The proposed network model framework for LSM considering time-series rainfall factors can provide new ideas and key technical support for landslide disaster prevention.

关键词： Deep learning (DL) dynamic factors interferometry synthetic aperture radar (InSAR) landslide susceptibility

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