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A Distributed Neural Network Architecture for Dynamic Sensor Selection With Application to Bandwidth-Constrained Body-Sensor Networks

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IEEE Journal of Biomedical and Health Informatics 2025年第5期29卷 3465-3477页

作者： Strypsteen, Thomas Bertrand, Alexander KU Leuven Department of Electrical Engineering STADIUS Center for Dynamical Systems Signal Processing and Data Analytics LeuvenB-3001 Belgium Leuven.AI KU Leuven Institute for AI LeuvenB-3001 Belgium

We propose a dynamic sensor selection approach for deep neural networks (DNNs), which is able to derive an optimal sensor subset selection for each specific input sample instead of a fixed selection for the entire dataset. This dynamic selection is jointly learned with the task model in an end-to-end way, using the Gumbel-Softmax trick to allow the discrete decisions to be learned through standard backpropagation. We then show how we can use this dynamic selection to increase the lifetime of a wireless sensor network (WSN) by imposing constraints on how often each node is allowed to transmit. We further improve performance by including a dynamic spatial filter that makes the task-DNN more robust against the fact that it now needs to be able to handle a multitude of possible node subsets. Finally, we explain how the selection of the optimal channels can be distributed across the different nodes in a WSN. We validate this method on a use case in the context of body-sensor networks, where we use real electroencephalography (EEG) sensor data to emulate an EEG sensor network for motor execution decoding. For this use case, we demonstrate that the distributed algorithm -with only a small amount of cooperation between the nodes- achieves a performance close to the upper bound defined by a fully centralized dynamic selection (maximum absolute decrease of 4% in accuracy). Furthermore, we observe that our dynamic sensor selection framework can achieve large reductions in transmission energy with a limited cost to the task accuracy, validating it as a practical tool for increasing the lifetime of body-sensor networks. © 2013 IEEE.

关键词： Sensor nodes

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Experimental implementation of chaos-based video encryption using visible light communication technology

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Applied Optics 2025年第9期64卷 C101-C118页

作者： Fotso, Sorel Bagio Nono Mbé, Jimmi Hervé Talla Takotue, Jean Ernest Takougang, Armand Cyrille Nzeukou Ndjiongue, Alain Richard Research Unit of Condensed Matter Electronics and Signal Processing Department of Physics University of Dschang P.O. Box 67 Dschang Cameroon Research Unit of Automation and Applied Computer Department of Electrical Engineering IUT-FV Bandjoun University of Dschang P.O. Box: 134 Bandjoun Cameroon Laboratory of Industrial Systems and Environmental Engineering Fotso Victor University Institute of Technology University of Dschang P.O. Box 134 Bandjoun Cameroon School of Electrical and Information Engineering Faculty of Engineering and the Built Environment University of the Witwatersrand Johannesburg South Africa

Simulations are commonly used to develop and evaluate video encryption algorithms. Although these approaches are useful for demonstrating theoretical feasibility and algorithm performance, they neglect practical challenges and real-world communication conditions. This oversight creates a critical gap in evaluating their effectiveness and security in practical applications. In this paper, we describe a novel, to the best of our knowledge, video encryption scheme that employs a modified chaotic Colpitts oscillator, a customized Josephus problem, and ribonucleic acid (RNA) operations, and is implemented in visible light communication (VLC) environment. The modified Colpitts oscillator reveals significant phenomena, the most prominent for this study being chaos. The encryption scheme involves block scrambling and pixel-level permutation employing a customized Josephus problem and a column and row shifting technique. Diffusion is then achieved through a custom Josephus problem-oriented RNA operation combined with basic RNA operations and cipher block chaining (CBC). Based on the results, we found that the customized Josephus problem-based permutation performed more efficiently than traditional methods, whereas the column and row shifting permutation was more robust. By eliminating key sequence coding and item-to-item operations, the proposed RNA operation reduces computational overhead. This video encryption scheme is proven to be effective and resilient at securing video data transmitted over the VLC channel, and it constitutes a significant advancement in areas such as surveillance, medical imaging, and military communication requiring robust secured video data. © 2025 Optica Publishing Group. All rights, including for text and data mining (TDM), Artificial Intelligence (AI) training, and similar technologies, are reserved.

关键词： Medical imaging

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Efficient Fault Detection in Nonlinear Industrial Process: A Reduced Kernel PCA-Based Spectral Clustering Approach

Efficient Fault Detection in Nonlinear Industrial Process: A...

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Artificial Intelligence, Applications, Innovation and Ethics (AI2E), International Conference for

作者： Khadija Attouri Majdi Mansouri Mansour Hajji Kais Bouzrara Laboratory of Advanced Materials and Nanotechnologies Higher Institute of Applied Sciences and Technology of Kasserine Kairouan University Tunisia Department of Electrical and Computer Engineering College of Engineering Sultan Qaboos University Muscat Oman Research Laboratory of Automation Signal Processing and Image National Engineering School of Monastir Tunisia

ISBN: (数字)9798331513733

ISBN: (纸本)9798331513740

The challenges posed by nonlinearities in industrial systems necessitate innovative techniques that outperform the limitations of traditional methods such as principal component analysis (PCA). While Kernel Principal Component Analysis (KPCA) offers a robust solution to handle nonlinear data, its computational requirements are a significant issue, especially for large-sized datasets. In this work, we propose a novel technique, namely, reduced kernel principal component analysis-based spectral clustering (RKPCA SpC ), to monitor and detect faults in the benchmark Tennessee Eastman process. The suggested approach addresses the complexity associated with KPCA by reducing data size during the model training phase. This reduction involves retaining only the principal components, preserving informative features, and selecting pertinent samples without compromising the original data's content. The efficacy of the proposed method is evaluated through key performance metrics, including false alarm rate (FAR), missed detection rate (MDR), detection time delay (DTD), and computation time (CT). Additionally, gained execution time (GET), gained storage space (GSP), and loss function (LF) are considered, providing a comprehensive assessment of the developed paradigms' effectiveness. The results demonstrate the promising capabilities of our proposed scheme.

关键词： Training Measurement Technological innovation Fault detection Delay effects Computational modeling Complexity theory Kernel Monitoring Principal component analysis

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Short-Term Photovoltaic Power Forecasting-Based Kernel Ridge Regression

Short-Term Photovoltaic Power Forecasting-Based Kernel Ridge...

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Artificial Intelligence, Applications, Innovation and Ethics (AI2E), International Conference for

作者： Manel Marweni Majdi Mansouri Mansour Hajji Mohamed Faouzi Mimouni Research Laboratory of Automation Signal Processing and Image National Engineering School of Monastir Tunisia Department of Electrical and Computer Engineering College of Engineering Sultan Qaboos University Muscat Oman Laboratory of Advanced Materials and Nanotechnologies Higher Institute of Applied Sciences and Technology of Kasserine Kairouan University Kasserine Tunisia

ISBN: (数字)9798331513733

ISBN: (纸本)9798331513740

The modern energy landscape is undergoing a seismic change from traditional, finite energy sources and toward cleaner, renewable alternatives. The restrictions faced by traditional sources, which are not only finite but increasingly shrink in the face of burgeoning global energy demands driven by population increase and industrial expansion, are driving this change. Although promising, renewable energy poses complications, particularly the reliance on climatic conditions. An important aspect of addressing these difficulties is effective energy management within distribution systems, which includes forecasting and optimization phases. This research focuses on forecasting using an advanced machine learning (ML) approach. Accurately forecasting renewable energy generation over time is critical for improving energy management. This technique is evaluated using a variety of performance indicators, including Mean Error (ME), Mean Absolute Error (MAE), Root Mean Square Error (RMSE), and the Coefficient of Determination (R 2 ). Empirical studies support the method's usefulness, demonstrating noteworthy performance with low error rates.

关键词： Photovoltaic systems Renewable energy sources Technological innovation Accuracy Machine learning Predictive models Forecasting Kernel Energy management Optimization

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Designing fuzzy logic systems

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IEEE TRANSACTIONS ON CIRCUITS AND systems II-EXPRESS BRIEFS 1997年第11期44卷 885-895页

作者： Mendel, JM Mouzouris, GC the Signal and Image Processing Institute Department of Electrical Engineering-Systems University of Southern California

We present a formulation of a fuzzy logic system (FLS) that can be used to construct nonparametric models of nonlinear processes, given only input-output data. In order to effectively construct such models, we discuss several design methods with different properties and features. We compare and illustrate systems designed with each one of the methods, using an example on the predictive modeling of a nonlinear dynamic (chaotic) system.

关键词： backpropagation method chaotic time series design forecasting fuzzy logic systems one-pass method RLS method

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Neural fuzzy motion estimation and compensation

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IEEE TRANSACTIONS ON signal processing 1997年第10期45卷 2515-2532页

作者： Kim, HM Kosko, B Department of Electrical Engineering—Systems Signal and Image Processing Institute University of Southern California

Neural fuzzy systems can improve motion estimation and compensation for video compression. Motion estimation and compensation are key parts of video compression. They help remove temporal redundancies in images. But most motion estimation algorithms neglect the strong temporal correlations of the motion field. The search windows stay the same through the image sequences and the estimation needs heavy computation. A neural vector quantizer system can use the temporal correlation of the motion field to estimate the motion vectors. First-and second-order statistics of the motion vectors give ellipsoidal search windows. This algorithm reduced the search area and entropy and gave clustered motion fields. Motion-compensated video coding further assumes that each block of pixels moves with uniform translational motion. This often does not hold and can produce block artifacts. We use a neural fuzzy system to compensate for the overlapped block motion. This fuzzy system uses the motion vectors of neighboring blocks to map the prior frame's pixel values to the current pixel value. The neural fuzzy system used 196 rules that came from the prior decoded frame. The fuzzy system learns and updates its rules as it decodes the image. The fuzzy system also improved the compensation accuracy. The Appendix derives both the fuzzy system and the neural learning laws that tune its parameters.

关键词： .pixels compensation motion vectors MOTION ESTIMATION temporal correlation block motion fuzzy systems Neural Fuzzy motion field search windows

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Dynamic non-Singleton fuzzy logic systems for nonlinear modeling

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IEEE TRANSACTIONS ON FUZZY systems 1997年第2期5卷 199-208页

作者： Mouzouris, GC Mendel, JM Signal and Image Processing Institute Department of Electrical Engineering-Systems University of Southern California

We investigate dynamic versions of fuzzy logic systems (FLS's) and, specifically, their non-Singleton generalizations (NSFLS's), and derive a dynamic learning algorithm to train the system parameters, The history-sensitive output of the dynamic systems gives them a significant advantage over static systems in modeling processes of unknown order, This is illustrated through an example in nonlinear dynamic system identification. Since dynamic NSFLS's can be considered to belong to the family of general nonlinear autoregressive moving average (NARMA) models, they are capable of parsimoniously modeling NARMA processes. We study the performance of both dynamic and static FLS's in the predictive modeling of a NARMA process.

关键词： dynamic NSFLS NARMA nonlinear modeling

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Approximate maximum likelihood hyperparameter estimation for Gibbs priors

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IEEE TRANSACTIONS ON image processing 1997年第6期6卷 844-861页

作者： Zhou, ZY Leahy, RM Qi, JY Signal and Image Processing Institute Department of Electrical Engineering-Systems University of Southern California

The parameters of the prior, the hyperparameters, play an important role in Bayesian image estimation, Of particular importance for the case of Gibbs priors is the global hyperparameter, beta, which multiplies the Hamiltonian, Here we consider maximum likelihood (ML) estimation of beta from incomplete data, i.e., problems in which the image, which is drawn from a Gibbs prior, is observed indirectly through some degradation or blurring process, Important applications include image restoration and image reconstruction from projections. Exact ML estimation of beta from incomplete data is intractable for most image processing, Here we present an approximate ML estimator that is computed simultaneously with a maximum a posteriori (MAP) image estimate, The algorithm is based on a mean field approximation technique through which multidimensional Gibbs distributions are approximated by a separable function equal to a product of one-dimensional (1-D) densities, We show how this approach can be used to simplify the ML estimation problem. We also show how the Gibbs-Bogoliubov-Feynman (GBF) bound can be used to optimize the approximation for a restricted class of problems, We present the results of a Monte Carlo study that examines the bias and variance of this estimator when applied to image restoration.

关键词： .Southern California Bayesian approximation mean field blurring image Restoration Gibbs Priors signal and image hyperparameters incomplete data approximated

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MAXIMUM-LIKELIHOOD LOCALIZATION OF SOURCES IN NOISE MODELED AS A STABLE PROCESS

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IEEE TRANSACTIONS ON signal processing 1995年第11期43卷 2700-2713页

作者： TSAKALIDES, P NIKIAS, CL IEEE Signal and Image Processing Institute Department of Electrical Engineering-Systems University of Southern California

This paper introduces a new class of robust beamformers which perform optimally over a wide range of non-Gaussian additive noise environments, The maximum likelihood approach is used to estimate the bearing of multiple sources from a set of snapshots when the additive interference is impulsive in nature. The analysis is based on the assumption that the additive noise can be modeled as a complex symmetric alpha-stable (S alpha S) process. Transform-based approximations of the likelihood estimation are used for the general S alpha S class of distributions while the exact probability density Function is used for the Cauchy case. It is shown that the Cauchy beamformer greatly outperforms the Gaussian beamformer in a wide variety of non-Gaussian noise environments, and performs comparably to the Gaussian beamformer when the additive noise is Gaussian. The Cramer-Rao bound for the estimation error variance is derived for the Cauchy case, and the robustness of the S alpha S beamformers In a wide range of impulsive interference environments is demonstrated via simulation experiments.

关键词： Maximum likelihood estimation Additive noise Multiple signal classification Gaussian noise Noise robustness Interference Estimation error signal processing Direction of arrival estimation Working environment noise

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The robust covariation-based MUSIC (ROC-MUSIC) algorithm for bearing estimation in impulsive noise environments

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IEEE TRANSACTIONS ON signal processing 1996年第7期44卷 1623-1633页

作者： Tsakalides, P Nikias, CL IEEE Signal & Image Processing Institute Department of Electrical Engineering-Systems University of Southern California

This paper presents a new subspace-based method for bearing estimation in the presence of impulsive noise which can be modeled as a complex symmetric alpha-stable (S alpha S) process. We define the covariation matrix of the array sensor outputs and show that eigendecomposition-based methods, such as the MUSIC algorithm, can be applied to the sample covariation matrix to extract the bearing information from the measurements. A consistent estimator for the marginals of the covariation matrix is presented and its asymptotic performance is studied. The improved performance of the proposed source localization method in the presence of a wide range of impulsive noise environments is demonstrated via Monte Carlo experiments.

关键词： Noise robustness Multiple signal classification Direction of arrival estimation Sensor arrays Working environment noise Symmetric matrices Gaussian processes Array signal processing Random processes Computational efficiency

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