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检索条件"主题词=Signal Processing Algorithms"

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Revolutionizing Presence Management with Facial Recognition

Revolutionizing Presence Management with Facial Recognition

引用

International Conference on Communications and signal processing

作者： M. Bommy M.Anjaneya Reddy T. Balaji T. Devesh D.Dilip Kumar Reddy Department of Computer Science & Engineering Madanapalle Institute of Technology & Science Madanapalle Andhra Pradesh India

ISBN: (数字)9798350353068

ISBN: (纸本)9798350353075

The manual attendance monitoring system is laborintensive and difficult to maintain. This article proposes a Facial recognition-based intelligent attendance system that aims to modernize and streamline it. Advanced biometric technology, namely Face Recognition algorithms based on Deep Learning (DL), is utilized in the proposed system. The main training dataset for the Local Binary Patterns Histogram (LBPH) Face Recognizer is composed of human faces. A user-friendly web page is implemented by developing the user interface using the Flask framework. The ability to record attendance data, including timestamps, in a database is a notable improvement to this system Another new feature is the ability to utilize the Fast to SMS website to send parents information on their child’s attendance, behavior, and grades. This enhancement fosters a more educated and engaged student body culture while also increasing productivity and strengthening the channels of communication between parents and schools.

关键词： Training Productivity Face recognition Web pages signal processing algorithms Manuals User interfaces

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Beamforming Optimization: Extended to Bidirectional Integrated Sensing and Communication System

Beamforming Optimization: Extended to Bidirectional Integrat...

引用

European signal processing Conference (EUSIPCO)

作者： Siyang Guan Zehan Liu Rui Duan Jian Zhang Jun Bao School of Information and Communication Engineering University of Electronic Science and Technology of China Institute of Advanced Millimeter-Wave Technology University of Electronic Science and Technology of China

ISBN: (数字)9789464593617

ISBN: (纸本)9798331519773

This paper investigates the joint transmit and receive beamforming of the bidirectional integrated sensing and communication (BD-ISAC) system. Through in-band full-duplex (FD), the BD-ISAC system aims to achieve the coexistence of bidirectional communication and radar detection. An optimization problem is proposed with the object of maximizing the weighted sum of communication rates, subject to the constraints of radar performance and interference suppression. To be more specific, the similarity between BD-ISAC beamforming weights and referential ones with ideal radar performance is constrained, therefore taking radar properties into account while optimizing communication rates. Nulling of interference is also introduced to tackle the challenges of self-interference within a node and mutual interference between nodes. An algorithm based on the weighted minimum mean square error (WMMSE) method is developed to tackle the non-convex problem. Numerical results demonstrate the effectiveness of the proposed beamforming method for BD-ISAC systems.

关键词： Array signal processing System performance signal processing algorithms Radar detection Radar Bidirectional control Transforms Integrated sensing and communication Transceivers Optimization

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A Cross-Attention Emotion Recognition Algorithm Based on Audio and Video Modalities

A Cross-Attention Emotion Recognition Algorithm Based on Aud...

引用

Acoustics, Speech, and signal processing Workshops (ICASSPW), IEEE International Conference on

作者： Xiao Wu Xuan Mu Wen Qi Xiaorui Liu Automation School of Qingdao University Qingdao China School of Future Technology South China University of Technology Guangzhou China Shandong Key Laboratory of Industrial Control Technology Qingdao China

ISBN: (数字)9798350374513

ISBN: (纸本)9798350374520

In recent years, emotion recognition has received significant attention. In this paper, multimodal information, including speech and facial expressions, is adopted to realize human emotion classification. Firstly, we propose a speech recognition model based on the Parallel convolutional module (Pconv), and an expression emotion recognition model based on the improved Inception-ResnetV2 network. The recognize futures of speech and expression will be further fused by using a cross-attention module coordinated with Bidirectional Long Short-Term Memory (BiLSTM). The experimental results organized on CH-SIMS and CMU-MOSI datasets have demonstrated that the proposed algorithm achieves high recognition accuracy. Each component of this model could contribute to performance improvement in the fair way.

关键词： Emotion recognition Recurrent neural networks Convolution Conferences signal processing algorithms Speech recognition Acoustics

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Unrolled Expectation Maximization for Sparse Radio Interferometric Imaging

Unrolled Expectation Maximization for Sparse Radio Interfero...

引用

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

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

引用

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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A Deep Network Solution for DOA Estimation of Adjacent signals with Uncertain Number

A Deep Network Solution for DOA Estimation of Adjacent Signa...

引用

IEEE International Conference on Information Communication and signal processing (ICICSP)

作者： Liyuan Zhu Ke Song Fei Huang Juan Rong Qianrong Lu Shanghai Radio Equipment Research Institute Shanghai China

ISBN: (数字)9798350355895

ISBN: (纸本)9798350355901

This paper introduces a deep neural network algorithm to estimate the direction of arrival (DOA) of nearby signals under unknown source number. Firstly, the covariance matrix is processed by a multi-channel convolutional network to extract the features of each potential signal, and then the on-grid estimates of independent components are obtained. Subsequently, by combining these extracted features and the spectral peak angles from each channel, a deep fully connected network calculates the continuous DOA estimates, meanwhile the number of signals is estimated simultaneously. Finally, the simulations have been carried out to validate the performance of the proposed algorithm with respect to generalization ability in DOA estimation. Moreover, the proposed algorithm could also offer superior resolution than that of the classic MUSIC algorithm, especially for the low signal-to-noise ratio, few snapshots, and adjacent signals.

关键词： Direction-of-arrival estimation Simulation signal processing algorithms Estimation Feature extraction Multiple signal classification Convolutional neural networks Spatial resolution signal resolution signal to noise ratio

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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...

引用

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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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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A conjugate gradient method for solving unconstrained optimization problems

A conjugate gradient method for solving unconstrained optimi...

引用

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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