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检索条件"主题词=Signal Processing Algorithms"

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A Hybrid TDOA-ANN Framework for Accurate Real-Time Node Localization in Wireless Sensor Network

A Hybrid TDOA-ANN Framework for Accurate Real-Time Node Loca...

引用

Innovations in High Speed Communication and signal processing (IHCSP), International Conference on

作者： Tannmay Gupta S. Yuvaraj Ashu Katyal Jayaprakash B Suruchi Gaurav Dedgaonkar N. V. Balaji Centre of Research Impact and Outcome Chitkara University Rajpura Punjab India Department of ECE Prince Shri Venkateshwara Padmavathy Engineering College Chennai India Chitkara Centre for Research and Development Chitkara University Himachal Pradesh India School of Sciences Computer Science & IT JAIN (Deemed to be University) Bangalore Karnataka India IT Vishwakarma Institute of Technology Pune India Department of Computer Science Karpagam Academy of Higher Education Coimbatore

ISBN: (数字)9798350368949

ISBN: (纸本)9798350368956

A prototype autonomous Wireless Sensor Network node based on a microcontroller for the prognosis and forecasting of fire accidents in multi-store buildings is presented in this thesis. The system has been evaluated for its dependability and resilience, and it reacts precisely in 300 ms. One range-based localization approach that uses the Hyperbolic based least square, Taylor series based least square, and Artificial Neural Network (ANN) methods is the TDOA based localization, which aims to achieve real-time communication of sensed data. Solving the very nonlinear systems of equations with the help of the least square estimator yields the predicted locations of the sensor nodes. The suggested procedures can only be replicated with accurate TDOA measurements. Different methods can be used to calculate TDOA: one uses the hyperbolic based method, which takes into account the difference between two TOAs; another uses the Taylor series based method, which takes into account the difference between round-trip TOA measurements between a sensor node and an anchor node; and yet another uses the TDOA measurement that results from the difference between the times of arrival of Ultrasonic pulses and RF signals transmitted from a sensor to an anchor node simultaneously. The hyperbolic technique outperforms the Taylor series by a little margin and accomplishes precise localization, according to the simulation findings. The ANN technique is both practical and simple to use.

关键词： Location awareness Wireless sensor networks Accuracy Ultrasonic variables measurement Neurons signal processing algorithms Artificial neural networks Taylor series Real-time systems Resilience

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Unsupervised Classification of Polarimetric SAR Images Using Bayesian Nonparametric Model and Markov Random Field

Unsupervised Classification of Polarimetric SAR Images Using...

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European signal processing Conference (EUSIPCO)

作者： Wassim Bdiri Nizar Bouhlel Stéphane Méric Eric Pottier Fathi Kallel Univ Rennes IETR UMR CNRS 6164 Campus de Beaulieu bat 11D Rennes cedex France Enetcom Univ Sfax Sfax Technopark BP 1163 CP 3018 Sfax Tunisia Institut Agro Rennes-Angers Univ Angers INRAE IRHS SFR QuaSaV Angers France INSA Rennes IETR UMR CNRS 6164 Rennes France

ISBN: (数字)9789464593617

ISBN: (纸本)9798331519773

A Bayesian Nonparametric (BNP) approach such as the Dirichlet process mixture model (DPMM) is a potential method for classification tasks. It provides a framework for estimating both the number of components in a mixture model and the parameters of the individual mixture components simultaneously from PolSAR data. The Markov random field (MRF) is commonly used for incorporating spatial correlation of image structure and has been widely applied to the classification of remote sensing images. In this paper, by combining the DPMM model and the MRF framework, a novel unsupervised classification method for multilook Polarimetric Synthetic Aperture Radar (PolSAR) images is developed. The prior distribution of the class label is considered as the product of a sitewise term and an interaction term. The expectation–maximization/maximization of the posterior marginals (EM/MPM) algorithm is used to update the class labels and the parameters of the proposed DPMM-MRF model for high resolution multilook PolSAR images. The performance of the proposed method EM-DPMM-MRF is evaluated with PolSAR images and the preliminary results on classification are satisfactory and are encouraging.

关键词： signal processing algorithms Clustering algorithms Mixture models signal processing Polarimetric synthetic aperture radar Radar polarimetry Classification algorithms Bayes methods Remote sensing Markov random fields

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Early Diagnosis of Parkinson’s Disease Using Machine Learning

Early Diagnosis of Parkinson’s Disease Using Machine Learni...

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International Conference on Communications and signal processing

作者： K Karthikaa J Keerthikumar V S Selvakumar M Madhesh Department of ECE Rajalakshmi Engineering College Chennai India

ISBN: (数字)9798350353068

ISBN: (纸本)9798350353075

Parkinson’s disease presents a frequent challenge in neurological practice due to its complex nature. Diagnosis and management are often intricate as patients exhibit a broad spectrum of motor and non-motor symptoms. Given the clinical nature of diagnosis, distinguishing Parkinson’s disease from other neurological disorders can be particularly demanding due to the overlap of symptoms with various conditions. This proposed method aims to improve diagnostic accuracy and facilitate proactive intervention by combining clinical data with machine learning algorithms. This paper describes the important aspects of the Random Classifier algorithm-based prediction system using the Streamlit framework and then evaluates the system’s performance. Utilizing an extensive dataset from several healthcare facilities, feature engineering methods extract pertinent features, which are subsequently supplied into the Random Forest Classifier for training the model. The model shows high predictive accuracy, sensitivity, specificity, and robust generalization capabilities.

关键词： Training Machine learning algorithms Sensitivity signal processing algorithms Medical services signal processing Feature extraction

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Hardware Trojan Design and Analysis in FPGA an Introductory Exploration

Hardware Trojan Design and Analysis in FPGA an Introductory ...

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signal processing, Computation, Electronics, Power and Telecommunication (IConSCEPT), International Conference on

作者： Aghilan A Maran Ponnambalam Ganesh Kumar Chellamani Electronics and Communication Engineering Amrita Vishwa Vidyapeetham Amrita School of Engineering

ISBN: (数字)9798331540685

ISBN: (纸本)9798331540692

Hardware Trojan (HT) has been a rapidly developing area of interest in the hardware security world. They have impacted the Integrated Chips (IC) industry and increased the standards of testing and detection of malicious hardware. Although a wide range of literature has contributed to enhancing the design of hardware trojans and detection algorithms to identify trojans, scarce academic work is found in the area of hardware trojans in Field Programmable Gated Arrays (FPGAs). This project aims to understand the design and implementation of hardware trojans from an attacker's perspective, encompassing the technique of hardware trojan design, the potential attack vector, and the stealthiness of the hardware trojan. A state-machine-based hardware trojan is designed to attack the Universal Asynchronous Receive Transmitter UART. Firmware updates for a wide range of medical devices happen through serial communication or wireless transfer. Attacking the UART of these devices could corrupt the firmware of the device, which could corrupt the boot loader path, resulting in the malfunction of the device. This poses a very dangerous threat, especially in the medical device field, where less attention is given to security. The paper presents a demonstrative example of a hardware trojan attacking Booth's algorithm.

关键词： Wireless communication Threat modeling Medical devices Transmitters signal processing algorithms Hardware Vectors

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A conjugate gradient method for solving unconstrained optimization problems

A conjugate gradient method for solving unconstrained optimi...

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Intelligent Control, Measurement and signal processing and Intelligent Oil Field (ICMSP), International Conference on

作者： Shuangye Yang Benyu Wang Liangbo Hu Chao Zhang Biao Guo Bintao Yan Hongwei Zhang Haoyang Li Department Of Information Center Beijing Petroleum Machinery Co. Ltd Beijing China Technology & Information Dept. CNPC Baoji Petrcleum Pipe Inoustry Co.Ltd Baoji China Compliance Management Department CNPC Kunlun Manufacturing Co. Ltd Beijing China

ISBN: (数字)9798331516550

ISBN: (纸本)9798331516567

For the conjugate gradient method to solve the unconstrained optimization problem, given a new interval method to obtain the direction parameters, and a new conjugate gradient algorithm is designed. The global convergence of the algorithm under the Armijo step search is also discussed. Making use of MATLAB to programming, and numerical examples’ results show that the new algorithm is more efficient than the FR,PR,HS conjugate gradient algorithm based on Armijo step search.

关键词： Gradient methods Runtime signal processing algorithms signal processing Programming Search problems MATLAB Optimization Convergence Intelligent control

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Unrolled Expectation Maximization for Sparse Radio Interferometric Imaging

Unrolled Expectation Maximization for Sparse Radio Interfero...

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European signal processing Conference (EUSIPCO)

作者： Nawel Arab Yassine Mhiri Isabelle Vin Mohammed Nabil El Korso Pascal Larzabal SATIE ENS Paris-Saclay Université Paris-Saclay Gif-sur-Yvette France MAP5 Université Paris Cité Paris France L2S CentraleSupelec Université Paris-Saclay Gif-sur-Yvette France

ISBN: (数字)9789464593617

ISBN: (纸本)9798331519773

This paper introduces an unrolled Expectation Maximization (EM) algorithm for sparse image reconstruction from radio interferometric measurements in the presence of a compound Gaussian distribution noise. Traditional model-based reconstruction methods, rooted in inference and optimization fields, provide an initial foundation with theoretical guarantees, but their performance is highly linked to the model accuracy and the choice of hyperparameter values. The popularity of supervised machine learning rose over the last decade, yet they faced hurdles related to interpretability and theoretical foundations. The emergence of unrolled algorithms addresses these limitations by combining the strengths of both approaches. We specifically focus on unrolling a regularized EM algorithm as a feedforward neural network with a residual connection. Experimental results showcase improvements over the iterative EM version.

关键词： Machine learning algorithms Accuracy Computational modeling Supervised learning signal processing algorithms Radio interferometry Extraterrestrial measurements Noise measurement Compounds Image reconstruction

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Web Based Legal Text Summarizer

Web Based Legal Text Summarizer

引用

International Conference on signal processing, Communication, Power and Embedded System (SCOPES)

作者： Sowmya Uppala Saradhambica Kesavarapu NV S Shankar P A Sunny Dayal A Sravani CVKK Chaitanya Department of CSE Centurion University of Technology and Management Vizianagaram Andhra Pradesh India Department of AI&DS GIT GITAM Visakhapatnam India Department of Anesthisia Centurion University Vizianagaram Andhra Pradesh India

ISBN: (数字)9798331506452

ISBN: (纸本)9798331506469

The legal industry is currently grappling with the issue of a growing backlog of unresolved cases. This paper aims to address the problem by proposing a web-based tool designed to generate extractive summaries from selected legal documents. These summaries can support decision-making within the judicial process. The primary goal of the tool is to reduce the time and effort legal professionals spend reviewing case files, thereby streamlining workflows. The key objective is to identify cases that can be resolved or settled more efficiently, allowing legal practitioners and courts to focus their attention on more complex matters. To achieve this, the tool must generate comprehensive summaries of key elements within the case documents. Three unsupervised and two supervised learning techniques for summarizing legal texts were investigated during the development process. Of these, the TextRank, an unsupervised learning algorithm, delivered optimal results. Consequently, this method was implemented in the tool to ensure effective and accurate summarization of lengthy legal documents. The system helps streamline the handling of pending case files and assists decision-makers in accelerating their work by summarizing important case details such as evidence and legal sections. It ensures no crucial statements are overlooked, which could be critical for the case. Additionally, a web application was developed to implement the TextRank method, automating the process. The generated summaries are stored in a database for future reference, providing a useful resource for ongoing and future legal matters.

关键词： Industries Embedded systems Law Supervised learning Decision making Text summarization signal processing algorithms signal processing Object recognition Unsupervised learning

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Research on Linear Programming and Grey Wolf Optimization Algorithm in Energy Storage System Configuration

Research on Linear Programming and Grey Wolf Optimization Al...

引用

signal processing, Communications and Engineering Management Conference (ISPCEM), International

作者： Wentao Li Tao Shen College of Automation Nanjing University of Science and Technology Nanjing China

ISBN: (数字)9798331528676

ISBN: (纸本)9798331528683

This paper focuses on the research and application of linear optimization algorithm and grey Wolf optimization algorithm, takes the energy storage system configuration optimization of the park energy system as the research object, and discusses in detail the similarities and differences and advantages of the energy storage system configuration under the three modes of independent operation, joint operation and collaborative configuration of wind storage. Firstly, a linear programming model for optimizing system configuration was established to determine the optimal energy storage power and capacity. In addition, under the consideration of State of Charge (SOC) and other constraints, the grey Wolf optimization algorithm was applied to further optimize the energy storage system configuration of independent parking lots to improve the accuracy and reliability of the optimization results. Secondly, the optimal energy storage configuration of the independent park is analyzed in detail, and the optimal energy storage scheme is deduced through the solution of the model and algorithm, which reflects the superiority of linear programming and grey Wolf optimization algorithm. The final focus is on developing a coordinated future configuration scheme for wind and solar storage. At the same time, the peak and valley electricity factor is introduced into the linear programming model, and the optimized grey Wolf algorithm is used to obtain the optimized energy scheduling and energy storage utilization strategy of the park's power generation and electricity price changes in the next five years.

关键词： Computational modeling signal processing algorithms Microgrids Linear programming Approximation algorithms Reliability engineering Mathematical models Scheduling Optimization Energy storage

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Sparse Bayesian Learning Using Complex t-Prior for Massive Multi-User MIMO Channel Estimation

Sparse Bayesian Learning Using Complex t-Prior for Massive M...

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IEEE Conference on Vehicular Technology (VTC)

作者： Kengo Furuta Takumi Takahashi Hideki Ochiai Graduate School of Engineering Osaka University Suita Japan

ISBN: (数字)9798331517786

ISBN: (纸本)9798331517793

This paper proposes a novel beam-domain channel estimation (CE) algorithm based on sparse Bayesian learning (SBL) using complex t-prior for massive multi-user multiple-input multiple-output (MU-MIMO) systems. Due to the sidelobe leakage and insufficient observation resolution, the equivalent channel after digital beamforming at the receiver does not have a sparse structure strictly consisting of zero/non-zero elements, but has a structure characterized by differences in signal intensity consisting of a large number of small non-zero elements and a few large elements. To fully capture this pseudo-sparse structure, a complex t-distribution with appropriate degrees of freedom (DoF) is incorporated into the SBL algorithm as a hierarchical Bayesian model. This heavy-tailed prior allows for efficient beam-domain CE accounting for small but non-negligible elements, which is verified by the analysis of regularization based on an equivalent optimization problem. Simulation results show that the proposed method significantly outperforms the state-of-the-art (SotA) sparse signal recovery (SSR)-based alternatives in sub-6 GHz wireless communication scenarios.

关键词： Wireless communication Vehicular and wireless technologies Heavily-tailed distribution signal processing algorithms Channel estimation Receivers Bayes methods Optimization signal resolution MIMO communication

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KPCA-CAM: Visual Explainability of Deep Computer Vision Models Using Kernel PCA

KPCA-CAM: Visual Explainability of Deep Computer Vision Mode...

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IEEE Workshop on Multimedia signal processing

作者： Sachin Karmani Thanushon Sivakaran Gaurav Prasad Mehmet Ali Wenbo Yang Sheyang Tang Department of Electrical and Computer Engineering University of Waterloo Waterloo Canada

ISBN: (数字)9798350387254

ISBN: (纸本)9798350387261

Deep learning models often function as black boxes, providing no straightforward reasoning for their predictions. This is particularly true for computer vision models, which process tensors of pixel values to generate outcomes in tasks such as image classification and object detection. To elucidate the reasoning of these models, class activation maps (CAMs) are used to highlight salient regions that influence a model's output. This research introduces KPCA-CAM, a technique designed to enhance the interpretability of Convolutional Neural Networks (CNNs) through improved class activation maps. KPCA-CAM leverages Principal Component Analysis (PCA) with the kernel trick to capture nonlinear relationships within CNN activations more effectively. By mapping data into higher-dimensional spaces with kernel functions and extracting principal components from this transformed hyperplane, KPCA-CAM provides more accurate representations of the underlying data manifold. This enables a deeper understanding of the features influencing CNN decisions. Empirical evaluations on the ILSVRC dataset across different CNN models demonstrate that KPCA-CAM produces more precise activation maps, providing clearer insights into the model's reasoning compared to existing CAM algorithms. This research advances CAM techniques, equipping researchers and practitioners with a powerful tool to gain deeper insights into CNN decision-making processes and overall behaviors.

关键词： Visualization Computer vision Convolution Computational modeling signal processing algorithms Cognition Cams Convolutional neural networks Kernel Principal component analysis

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