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检索条件"主题词=Signal Processing Algorithms"

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Performance Analysis of FFT(Fast Fourier Transform) with Respect to Different Multipliers

Performance Analysis of FFT(Fast Fourier Transform) with Res...

引用

Artificial Intelligence and signal processing (AISP), INDIA, International Conference on

作者： Pratikhya Raut P. Yamini K. Jayathi Chowdary K. Nagendra Siva Kumar Department of Electronics and Communication Engineering Velagapudi Ramakrishna Siddhartha Engineering College Deemed to be University Vijayawada India

ISBN: (数字)9798350350654

ISBN: (纸本)9798350350661

The main goal of this work is to implement the Fast Fourier Transform (FFT) using Booth multipliers and Vedic Multipliers, particularly using the Urdhva Triyambakam Sutra for Multiplication. These unique multiplier architectures enhance the efficiency of FFT computations and also to speed up FFT computations. Through implementation we came to know the relative benefits and drawbacks of FFT with Booth multiplier implementations versus FFT with Vedic multiplier implementations. The combination and contrast of these effective methods gives better information regarding how to best optimize FFT calculations for different applications such as advancements in signal processing algorithms that go beyond conventional multiplier structures. The result discusses the comparison of metrics such as time delay, power consumption, utilization of FFT implementations using both Vedic and Booth Multipliers.

关键词： Performance evaluation Power demand Fast Fourier transforms Delay effects signal processing algorithms signal processing Computational efficiency Table lookup Performance analysis Field programmable gate arrays

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Analysis of Decentralized Stochastic Successive Convex Approximation for Composite Non-Convex Problems

Analysis of Decentralized Stochastic Successive Convex Appro...

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IEEE Workshop on Machine Learning for signal processing

作者： Shivangi Dubey Sharma Basil M. Idrees Ketan Rajawat Indian Institute of Technology Kanpur

ISBN: (数字)9798350372250

ISBN: (纸本)9798350372267

This work considers the decentralized successive convex approximation (SCA) method for minimizing stochastic non-convex objectives subject to convex constraints, along with possibly non-smooth convex regularizers. Although SCA has been widely applied in decentralized settings, its stochastic first order (SFO) complexity is unknown, and it is thought to be slower than the centralized momentum-enhanced SCA variants. In this work, we advance the state-of-the-art for SCA methods by proposing an accelerated variant, namely the Decentralized Momentum-based Stochastic SCA (D-MSSCA) and analyze its SFO complexity. The proposed algorithm entails creating a stochastic surrogate of the objective at every iteration, which is minimized at each node separately. Remarkably, the D-MSSCA achieves an SFO complexity of $\mathcal{O}(\epsilon^{-3/2})$ to reach an ∊-stationary point, which is at par with the SFO complexity lower bound for unconstrained stochastic non-convex optimization in centralized setting.

关键词： Machine learning algorithms Conferences signal processing algorithms Machine learning signal processing Cost function Complexity theory Convergence

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Direct Update of Back-Projected Demixing Matrices in Blind Source Separation

Direct Update of Back-Projected Demixing Matrices in Blind S...

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European signal processing Conference (EUSIPCO)

作者： Yui Kuriki Taishi Nakashima Nobutaka Ono Graduate School of Systems Design Tokyo Metropolitan University Tokyo Japan

ISBN: (数字)9789464593617

ISBN: (纸本)9798331519773

Blind source separation (BSS) is a widely used technique for separating mixed signals originating from multiple sources. Traditionally, BSS is formulated as an optimization problem with respect to the demixing matrix. In order to resolve the scale ambiguity inherent in BSS, a post-processing step called “projection back” is employed, yielding a rescaled demixing matrix which we call a “back-projected” matrix. However, incorporating prior information or constraints directly on the back-projected matrix has not been possible, as the optimization problem is defined with respect to the original demixing matrix and the back-projected matrix is obtained only after the optimization and post-processing. In this paper, we address this limitation by reformulating the BSS optimization problem in terms of the back-projected matrices instead of the original matrices. Specifically, we derive simple conditions that back-projected matrices must satisfy and reformulate the BSS problem under these constraints. We propose a novel algorithm for solving the reformulated problem, which is shown to monotonically decrease the objective function while satisfying the constraints. Through experiments, we demonstrate that our reformulation enables the direct imposition of constraints on the back-projected matrices, providing a more flexible and effective approach to BSS with prior information on the rescaled demixing matrix.

关键词： signal processing algorithms Europe Linear programming Blind source separation Vectors Optimization signal resolution

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Research on Vehicle Detection Based on Deep Learning

Research on Vehicle Detection Based on Deep Learning

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signal processing and Intelligent Computing (SPIC), International Conference on

作者： Lizhong Zhu Xiaonian Li Linxi Xie Shenyang Ligong University Shenyang China Dalian Neusoft University of Information Dalian China

ISBN: (数字)9798350368888

ISBN: (纸本)9798350368895

To address the issue of low real-time performance in traditional target detection algorithms for vehicle detection and tracking, low accuracy, and susceptibility to occlusion leading to omission and mixed detection, a deep learning-based vehicle detection algorithm is proposed. The algorithm incorporates a lightweight convolutional attention module (CBAM), improves the trunk-head network and introduces adaptive spatial feature fusion (ASFF) as well as a multi-branch RFB module layer on the basis of the original YOLOv5s network model. After the introduction of this algorithm, remarkable improvements are observed in detection accuracy, real-time performance, and the mitigation of occlusion-related issues like omission and mixing, surpassing the performance of traditional network applications.

关键词： YOLO Deep learning Accuracy Target tracking Convolution Vehicle detection Computational modeling Prevention and mitigation signal processing algorithms Real-time systems

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Real-Time Accurate Altimetry Based on Satellite Reflection signals

Real-Time Accurate Altimetry Based on Satellite Reflection S...

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

作者： Linkun Ma Ning Chang Haitao Xiao Shuo Ma Qinyao Li The Key Laboratory of intelligent Network and Network Security Ministry of Education China School of Information and Communication Engineering Xi'an Jiaotong University Xi'an China

ISBN: (数字)9798350355895

ISBN: (纸本)9798350355901

Altimetry based on Global Navigation Satellite System - Reflection (GNSS-R) is widely used in the field of surveying and mapping. Existing GNSS-R technology focuses on measurement accuracy and neglects real-time measurement. Precision strike bombs need to be detonated at different elevations according to the established mission, which requires a missile-borne detector to realize high-precision and real-time missile elevation measurement. Based on this, a spatial alternating maximum likelihood algorithm based on satellite reflection signals is proposed. The algorithm combines the code phase altimetry, multi-frequency joint processing technology and multi-antenna technology, and finally obtains the real-time missile height by searching and estimating the signal pitch angle, time delay and Doppler frequency deviation parameter in three dimensions of air, time and frequency domains. The simulation results show that the method has good real-time and accuracy in the height measurement of a high-speed flying missile-borne detector.

关键词： Maximum likelihood estimation Maximum likelihood detection Accuracy Satellites Codes signal processing algorithms Detectors Real-time systems Reflection Altimetry

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Secure Beamforming Design Based on Multistatic Cooperative Sensing via IRS

Secure Beamforming Design Based on Multistatic Cooperative S...

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International Conference on Wireless Communications and signal processing (WCSP)

作者： Xianglin Yu Jinlei Xu Xiaoqi Qint Jie Tang Xu Jiang Nan Zhao Dusit Niyato School of Information and Communication Engineering Dalian University of Technology Dalian China State Key Laboratory of Networking and Switching Technology Beijing University of Posts and Telecommunications Beijing China School of Electronic and Information Engineering South China University of Technology Guangzhou China School of Information Science and Engineering Harbin Institute of Technology (Weihai) Weihai China School of Computer Science and Engineering Nanyang Technological University Singapore

ISBN: (数字)9798350390643

ISBN: (纸本)9798350390650

In this paper, we study the multistatic cooperative sensing assisted secure transmission via intelligent reflecting surface (IRS). To be specific, a multistatic cooperative sensing scheme is proposed to obtain the angle of arrival and the localization of the eavesdropping target to achieve the beam alignment accurately. Our goal is to maximize the sum secrecy rate by jointly optimizing the association variables of base station (BS) and users, the BS beamforming and the IRS phase shifts, subject to the requirement of target sensing. Due to the coupling of variables and nonconvex objective function, we decompose it into three subproblems and develop an alternative algorithm to solve them iteratively. The association variables are first obtained by successive convex approximation. Then, the BS transmit beamforming can be derived via the semidefinite relaxation. Finally, an alternating direction method of multiplier is adopted for the IRS phase-shift design. Simulation results indicate that the multistatic cooperative sensing via IRS can enhance the sensing performance and guarantee the security.

关键词： Wireless communication Location awareness Array signal processing Simulation signal processing algorithms Linear programming Sensors Security Surface treatment signal to noise ratio

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PCA Dimensionality Reduction Algorithm Based on Ordered Pair of Normalized Real Numbers

PCA Dimensionality Reduction Algorithm Based on Ordered Pair...

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International Congress on Image and signal processing, BioMedical Engineering and Informatics (CISP-BMEI)

作者： Xiang Zhao Yi Zheng Xiaoqin Pan Lei Zhou School of Computer Science and Technology Southwest University of Science and Technology Engineering Technology Center of Southwest University of Science and Technology School of Mathematics and Physics Southwest University of Science and Technology Sichuan China

ISBN: (数字)9798331507398

ISBN: (纸本)9798331507404

This paper presents a PCA (Principal Component Analysis) data dimensionality reduction algorithm based on OPNs (Ordered Pair of Normalized Real Numbers), referred to as OPNs-PCA. This algorithm aims to improve the dimensionality reduction performance of data. Especially, it shows significant advantages in removing feature redundancy and noise, providing a clearer and more effective data basis for subsequent machine learning or data mining tasks. The algorithm proposed in this paper introduces the concept of OPNs into the PCA algorithm and performs PCA dimensionality reduction operations under the framework of OPNs. Through the characteristic of OPNs' operation in processing feature relationships in non-real number spaces, it can effectively reduce data noise, enhance the processing ability of nonlinear data, and improve the performance of subsequent classification algorithms. We have conducted detailed experimental verification on multiple public data sets. The results show that unlike classic PCA, LLE, KPCA, SPCA and other algorithms that require more principal components to retain sufficient information, OPNs-PCA can achieve a similar or even better level of information retention with fewer principal components, and has a significant improvement in the processing ability of nonlinear data.

关键词： Dimensionality reduction Machine learning algorithms Noise Redundancy signal processing algorithms Classification algorithms Data mining Informatics Principal component analysis Biomedical engineering

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Time Series Pattern Matching Technique for Low-Energy Arrhythmia Classification in Implantable Cardiac Devices

Time Series Pattern Matching Technique for Low-Energy Arrhyt...

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Computing and Machine Intelligence (ICMI), International Conference on

作者： Prianka Sengupta Eugene B. John Dept. of Electrical and Computer Engineering Texas A&M University College Satation TX Dept. of Electrical and Computer Engineering University of Texas at San Antonio San Antonio TX

ISBN: (数字)9798350372977

ISBN: (纸本)9798350372984

Implantable cardiac devices (ICDs) are often used as an effective treatment for arrhythmia. Although these devices have access to a live Electrocardiogram (ECG) stream, currently they do not offer on-device classification of arrhythmia due to the limited computing capability and severe power constraints. In this paper we propose a low-energy computing method for extracting shape-based features from ECG in combination with machine learning techniques for classifying nine different cases of arrhythmia. This is achieved by using a Localized Longest Common Subsequence (LLCS) algorithm which has low computational requirements that allows on-device execution. The proposed method strongly focuses on maintaining minimal energy and computational footprint, in line with the operating constraints of implantable devices. To demonstrate the energy efficiency and low computation load of the proposed method we implement the classification pipeline on a low-power RISC microcontroller and compare its performance with existing classification techniques. The classification accuracy and energy of the proposed method is compared with state-of-the art research in arrhythmia classification.

关键词： Performance evaluation Accuracy Arrhythmia Time series analysis Pipelines signal processing algorithms Implants

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Innovative Wearable EEG Solution for Stress Assessment and Control

Innovative Wearable EEG Solution for Stress Assessment and C...

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Intelligent Systems and Advanced Applications (ICISAA), International Conference on

作者： Aviral Patel Prerna Zagade Shravan Ambudkar Aboli Jadhav Bhavna Ambudkar Department of EnTC Symbiosis Institute of Technology Pune India Department of EnTC Symbiosis Professionals and Skills University Pune India Department of Artificial Intelligence and Machine Learning Symbiosis Institute of Technology Pune India

ISBN: (数字)9798331539948

ISBN: (纸本)9798331539955

In this work, wearable electroencephalogram (EEG) technology is investigated as a cutting-edge stress assessment and treatment method. An increasing number of people require simple, practical methods for monitoring and managing their stress levels because of the increasing prevalence of illnesses associated with stress. Wearable electroencephalography (EEG) devices offer a feasible path for the high temporal resolution monitoring of stress circumstances by permitting real-time, noninvasive brain activity measurements. This abstract examines the current status of wearable EEG technology, emphasizing how advancements in sensor technology, hardware downsizing, and signal processing techniques have made it simpler to integrate EEG monitoring into everyday life. The project also investigates the utility of EEG-based Brain-Computer Interfaces (BCIs) for stress detection and control, as well as the potential of EEG signals as biomarkers for stress-related cognitive processes. The paper also covers the methodological aspects of implementing EEG-based stress assessment protocols, including feature extraction, signal processing algorithms, and machine learning techniques for stress prediction. The end of the abstract discusses the therapeutic applications of wearable EEG devices in stress management therapy and points to potential directions for future study in this rapidly emerging field. Because wearable electroencephalography (EEG) technology offers new insights into the neurophysiological foundation of stress and makes individualized therapies based on individual stress profiles possible, it has the potential to completely transform paradigms for stress measurement and regulation.

关键词： Electric potential Medical treatment signal processing algorithms Transforms Electroencephalography Biomedical monitoring Stress Monitoring signal resolution Stress measurement

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Automated design of Convolutional Neural Network architecture using Gray Wolf Optimization for plant seedlings classification

Automated design of Convolutional Neural Network architectur...

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International Conference on Image and signal processing and their Applications (ISPA)

作者： Lamis Badis Wahiba Aliouat Kenza Bouchiba Department of Computer Sciences Faculty of Sciences and Applied Sciences University of Bouira Algeria Department of Computer Sciences LIM Laboratory Faculty of Sciences and Applied Sciences University of Bouira Algeria

ISBN: (数字)9798350309249

ISBN: (纸本)9798350309256

Convolutional Neural Networks (CNNs) have gained significant popularity in image classification tasks, yet achieving their optimal design remains a challenge due to the vast array of possible layer configurations and associated hyperparameters. Selecting the best CNN model for a given task often demands considerable time investment in training numerous models. To address this issue, we propose an automated method for CNN architecture design, utilizing pretrained models like the Backbone and employing Gray Wolf Optimization. This approach automatically generates and evaluates candidate CNN architectures for classifying plant seedlings. Our objective is to distinguish between weed and crop seedlings. Additionally, we introduce a gray wolf representation to encode CNN architectures and their hyperparameters. Our method combines the strengths of transfer learning from pre-trained models to extract meaningful image features with the optimization capabilities of the Gray Wolf Optimization (GWO) algorithm. By leveraging these techniques, our method achieves exceptional accuracy, surpassing state-of-the-art methods with a validation accuracy of up to 97.83%. This innovative approach offers a transformative tool for enhancing the accuracy of CNN models, tailored specifically to the dataset at hand.

关键词： Training Transfer learning Neural networks signal processing algorithms signal processing Feature extraction Service-oriented architecture

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