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检索条件"主题词=Signal processing algorithms"

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Performance Analysis of VLSI Architecture of Dadda Multiplier Using Divide and Conquer Strategy with Approximate Adders

Performance Analysis of VLSI Architecture of Dadda Multiplie...

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Emerging Trends in Engineering and Medical Sciences (ICETEMS), International Conference on

作者： Bala Sindhuri Kandula Raghumandala Devi Muddala Kamakshi Devi Pilli Gamya Geethika Prodduturi Hitesh P Y N S Vara Prasad Department of E.C.E. S.R.K.R Engineering College (A) Bhimavaram India

ISBN: (数字)9798331529352

ISBN: (纸本)9798331529390

Multiplication plays a pivotal role in diverse fields, including digital signal processing, scientific computing, machine learning, cryptography, computer graphics, control systems, finance, and embedded systems. Optimizing multiplication operations can significantly enhance performance and efficiency across these domains. This paper presents a VLSI architecture for a hardware multiplier module that utilizes the Dadda algorithm in conjunction with the Divide and Conquer strategy, incorporating Approximate Adders. These adders strike a balance between accuracy and resource efficiency, leading to reductions in power consumption and silicon area. Approximate Adders are essential in optimizing power, area, speed, and heat dissipation in digital systems, particularly in applications where absolute precision is not important. This work underscores the potential of integrating approximate computing with efficient multiplication techniques to advance digital circuit design. The architecture is implemented in Verilog, and its functionality is validated using Vivado 2023.2 and Cadence tools.

关键词： Power demand Approximate computing signal processing algorithms Computer architecture Very large scale integration Approximation algorithms Hardware Performance analysis Delays Adders

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Deep Learning Associated with Linear Regression for Heart beat Rate (HBR) and Respiration Rate(RR) Measurement

Deep Learning Associated with Linear Regression for Heart be...

引用

Information Technology, Electronics and Intelligent Communication Systems (ICITEICS), IEEE International Conference on

作者： Valarmathi Ramasamy S. Sugumaran KV Praveen P. Ramani Venkata NagaRaniBandaru St.peter's College of Engineering and Technology Chennai Vishnu Institute of Technology Bhimavaram Andhra Pradesh SRM Institute of Science and Technology Ramapuram Chennai

ISBN: (数字)9798350382693

ISBN: (纸本)9798350382709

The Heart beat Rate (HBR) and the Respiration Rate (RR) are considered to be a dynamic signs and indications of the patient“s health condition. Most probably it is neglected in normal treatment because of the deviation in the modest sensors considered for convenient measurement. The modulations identified in the respiratory tract by Photo Plethysmo Gram (PPG) and electrocardiogram (ECG) are much helpful in deriving the respiration rateand heartbeat rate by the use of different rules and markings followed in signal processing and voice processing techniques. In this work the HBR and RR is analysed by taking the speech signal as input. The Relationship found in between the human voice, respiration rate and heartbeat rate are discussed in this work. In this work a modified deep-learning algorithm is proposed which uses Hybrid Fuzzy based Convolutional neural networks (HFCNN) for acoustic voice recognition and a Hybrid Deep Support vector machine (HDSVM) for classification along with *** experimentation, the percentage of error margin is identified to be better while estimating the various patterns of breathing signs from the audio information. About 92.25% of sensitivity is recorded with 4.22% of error while estimating the RR and HBR. Then for estimating the HBR, the HFCNN-based regression model provides better prediction values with 0.2873 as root mean-squareerror and gives a better classification rate after implementing the HDSVM. The root mean square error is 0.3093 which is better when compared to the other methods. The accuracy of prediction in association with the precision and recall values are identified to be better when compared to the other existing methods.

关键词： Support vector machines Sensitivity Heart beat signal processing algorithms Speech recognition Acoustics Classification algorithms

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PulseSync BP: A Neural Network Framework for Non-Contact Blood Pressure Estimation

PulseSync BP: A Neural Network Framework for Non-Contact Blo...

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Advances in Data Engineering and Intelligent Computing Systems (ADICS), International Conference on

作者： Tanumay Ghosh Omisha Singal Namit Lodh S Suchitra Department of Data Science and Business Systems School of Computing SRM Institute of Science and Technology Kattankulathur Tamil Nadu India

ISBN: (数字)9798350364828

ISBN: (纸本)9798350364835

Using a cutting-edge neural network framework, “PulseSync BP” estimates blood pressure without contact. Using photoplethysmogram (PPG) signals and other physiological data, this novel model uses advanced signal processing. The suggested framework changes blood pressure monitoring by eliminating cuff-based measures. PulseSync BP's RNN architecture is carefully designed for optimal prediction accuracy. The algorithm effectively estimates blood pressure through extensive experimentation, demonstrating its potential for continuous healthcare applications. This framework's capacity to use various physiological cues shows its adaptability and versatility. PulseSync BP allows real-time, non-intrusive blood pressure monitoring, advancing individualized health tracking solutions. Cuffless blood pressure estimate is advanced here, demonstrating how artificial intelligence could improve healthcare accessibility and patient-centric monitoring. PulseSync BP and other revolutionary technologies can transform blood pressure monitoring in healthcare.

关键词： Neural networks signal processing algorithms Estimation signal processing Blood pressure Real-time systems Biomedical monitoring

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Evolution and Recent Trends for the SGD Algorithm: Key Takeaways from an Educational Short Course

Evolution and Recent Trends for the SGD Algorithm: Key Takea...

引用

European signal processing Conference (EUSIPCO)

作者： Paul Rodriguez Department of Electrical Engineering Pontificia Universidad Católica del Perú Lima Peru

ISBN: (数字)9789464593617

ISBN: (纸本)9798331519773

One of the key challenges when designing a ten (10) hours educational short course, entitled “A Hands-on Approach for Implementing Stochastic Optimization algorithms from Scratch”, which was accepted for inclusion at ICASSP'23, was related to addressing “how to introduce the Stochastic Gradient Descent (SGD) algorithm and variants in a consistent, accessible fashion”? From a simplistic perspective, the SGD algorithm is nothing else than the classical gradient descent (GD) algorithm along with a (very) noisy gradient. Nonetheless, arguably, SGD's most influential variants, e.g. AdaGrad, RMSprop and Adam, nor more recent ones (LookAhead, E-Adam, MadGrad, among several others) may not be explained in such superficial terms. Moreover, such variants are usually given as as black-boxes by most deep-learning (DL) libraries (e.g. TensorFlow, PyTorch, etc.). In this article, based on the experience of the aforementioned short-course, I propose to link the SGD algorithm and variants via an “evolutionary path”, in which each SGD variant may be understood as a set of add-on features over the vanilla SGD, resulting in a generalized algorithm along with a “family tree” graph which are both intuitive and useful when implementing a given SGD variant.

关键词： Geometry signal processing algorithms Europe Closed box signal processing Market research Approximation algorithms Libraries Noise measurement Optimization

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Covariance Denoising with Applications to Adaptive Beamforming

Covariance Denoising with Applications to Adaptive Beamformi...

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IEEE International Symposium on Phased Array Systems and Technology

作者： Ruifu Li Danijela Cabric Electrical and Computer Engineering Department University of California Los Angeles

ISBN: (数字)9798350392142

ISBN: (纸本)9798350392159

Adaptive beamforming is of high importance in array signal processing applications that require interference suppression. In practice, minimum variance distortion-less re-sponse (MVDR) is one of the most widely used algorithms for adaptive beamforming. Classical MVDR implementations require processing number of samples larger than the number of elements in the array, whereas existing sample-based methods have high computational complexity. This work introduces computationally efficient sample-based method for MVDR beamforming. Utilizing knowledge of the array manifold, the method first finds a sparse representation of the covariance, which is then used to compute MVDR beamformers. The proposed method has low sample complexity and performs well as long as the number of ele-ments is much greater than number of interfering sources. This feature makes the method a promising candidate for adaptive beamforming with large arrays.

关键词： Phased arrays Array signal processing Sparse approximation Superresolution Noise reduction signal processing algorithms Adaptive arrays Parallel processing Computational complexity signal to noise ratio

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Power User Behavior and Load Forecasting Based on Big Data

Power User Behavior and Load Forecasting Based on Big Data

引用

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

作者： Jihong Song Baohai Li Hui Li State Grid Qinghai Electric Power Company Xining China Tibetan Autonomous Prefecture of Haibei State Grid Qinghai Haibei Power Supply Company China

ISBN: (数字)9798331528676

ISBN: (纸本)9798331528683

With the improvement of power system intelligence and the development of power collection technology, a huge amount of power data is generated. This data includes the number of times of electricity consumption, time of electricity consumption, electricity equipment and other users' electricity consumption behaviours. In this paper, a set of power consumption behavior analysis model based on power big data was constructed, which helped to deeply understand the characteristics and behavioral laws of users' power consumption, and provided important support for the energysaving management and optimization of China's power grid enterprises. Traditional research on electricity consumption behaviour mainly relies on small-sample research and simple statistical analysis, which cannot completely and accurately grasp electricity consumption behaviour. However, the electric power consumption collection and analysis model based on electric power big data, with its large data scale, multiple dimensions, wide coverage and high accuracy, can study users' electricity consumption behaviour from a finer and more comprehensive perspective. In this paper, a user electricity consumption behaviour analysis model based on electricity big data was introduced, and the K-SVD (K-Singular Value Decomposition) algorithm was used to decompose the user's electricity consumption patterns in order to extract the user's electricity consumption characteristics and behavioural patterns. The experimental results showed that the K-SVD algorithm was able to predict the power consumption of users more accurately, and the prediction error was small, usually between 1-2 kWh. Load prediction based on the K-SVD algorithm shows high efficiency and stability in practical applications, providing strong support for energy management and optimization.

关键词： Analytical models Load forecasting Electricity signal processing algorithms Big Data Power system stability Prediction algorithms Data models Socioeconomics Optimization

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Neuromorphic Computing Architectures for EnergyEfficient Edge Devices in Autonomous Vehicles

Neuromorphic Computing Architectures for EnergyEfficient Edg...

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IEEE International Conference on Intelligent Techniques in Control, Optimization and signal processing (INCOS)

作者： Rahul Pradhan Annapurna Gummadi P. Tanusha Gandla Sowmya S. Vaitheeshwari Ponni Valavan M Department of Computer Science and Engineering GLA University Mathura Uttar Pradesh India Department of CSE (Data Science) CVR College of Engineering Hyderabad India Department of Computer Science and Engineering KL University Vaddeswaram Andhra Pradesh India Department of Computer Science and Engineering NRI Institute of Technology Vijayawada Andhra Pradesh India Department of Computer Science Veltech Multitech Dr. Rangarajan Dr. Sakunthala Engineering College Avadi Chennai India Department of AI & DS K. Ramakrishnan College of Engineering Trichy Tamil Nadu India

ISBN: (数字)9798350361186

ISBN: (纸本)9798350361193

Autonomous vehicles hold the promise of significantly reducing road accidents caused by human error, such as distracted driving or impaired judgment. However, the widespread adoption of autonomous vehicles faces challenges, including regulatory frameworks, liability concerns, ethical considerations, and the need for comprehensive infrastructure updates. This research focuses on the development of neuromorphic computing architectures tailored for energyefficient edge devices in autonomous vehicles. The significance of this study lies in addressing the escalating computational demands of real-time data processing in autonomous driving scenarios while mitigating the inherent power constraints of edge computing environments. The conventional approaches in autonomous vehicle computing, often centralized and powerintensive, pose challenges in terms of energy consumption and real-time responsiveness. The proposed neuromorphic computing architectures leverage spiking neural networks and event-driven processing to emulate the brain's efficiency, offering a novel solution to enhance both computational performance and energy efficiency. The research addresses prior issues related to the power-hungry nature of traditional computing paradigms in autonomous vehicles and emphasizes the need for more sustainable and responsive edge computing solutions. The novelty of this research lies in the integration of neuromorphic principles into edge devices, enabling onboard processing that is not only energy-efficient but also well-suited for the sporadic and dynamic nature of real-world driving environments. This study paves the way for advancements in autonomous vehicle technology, promising enhanced energy efficiency, reduced latency, and improved overall system performance.

关键词： Performance evaluation Neuromorphic engineering signal processing algorithms Computer architecture Energy efficiency Real-time systems Computational efficiency

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Application the Deconvolution Deblurring to Nearfield Beamforming for Acoustic Array

Application the Deconvolution Deblurring to Nearfield Beamfo...

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IEEE International Symposium on Intelligent signal processing and Communication Systems (ISPACS)

作者： Bo-Hsien Wu Nien-Hao Chang Department of Systems and Naval Mechatronic Engineering National Cheng Kung University Tainan R.O.C

ISBN: (数字)9798350389210

ISBN: (纸本)9798350389227

In this study, three deconvolution approaches increasing the accuracy of conventional beamforming were reviewed by numerical simulation. One of the approaches is “Deconvolution approach for the mapping of acoustic sources” (DAMAS). It regarded conventional beamforming as a system of linear equations and solved it by Gauss-SeidelMethod (GSM). The other method is “Sparsity constrained deconvolution approaches for acoustic mapping” (SCDAMAS). It assumed the elements of linear equations to be sparsity constrained. Which made it a quadratic convex optimization problem. Solving it by “least absolute shrinkage and selection operator” (LASSO) algorithm. Finally, there is “Covariance matrix fitting” (CMF). It established an optimization problem by sampled cross spectral matrix (CSM). All of them eliminated the energy spread by the sound source location and performed better when frequency increased.

关键词： GSM Deconvolution Array signal processing signal processing algorithms Position measurement Numerical simulation Mathematical models Acoustics Covariance matrices Optimization

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Anti-interference Intelligent Decision-making Technology Based on Deep Reinforcement Learning

Anti-interference Intelligent Decision-making Technology Bas...

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

作者： Shuo Ma Haitao Xiao Qinyao Li Linkun Ma 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

In recent years, with the rapid development of wireless communication technology in the military field and the proliferation of the number of various types of wireless communication devices, the battlefield electromagnetic environment has become more and more complex, which puts forward higher requirements for the anti-interference ability of communication systems. In order to enhance the reliability and adaptability of communication systems in complex electromagnetic environments, this paper combines Deep Q-Learning (DQN) with Long Short-Term Memory (LSTM), and proposes a communication anti-jamming intelligent decision- making algorithm based on DQN-LSTM, which realizes adaptive frequency use. In this method, dynamic s-greedy strategy and LSTM are utilized to improve the convergence and stability of DQN. Simulation results show better anti-jamming performance compared to traditional anti-jamming methods and traditional DQN decision-making algorithms.

关键词： Wireless communication Heuristic algorithms Decision making signal processing algorithms Interference Deep reinforcement learning Stability analysis Electromagnetics Jamming Long short term memory

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Novel Gradient Sparsification Algorithm via Bayesian Inference

Novel Gradient Sparsification Algorithm via Bayesian Inferen...

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IEEE Workshop on Machine Learning for signal processing

作者： Ali Bereyhi Ben Liang Gary Boudreau Ali Afana ECE Department University of Toronto Ericsson Canada Ottawa Canada

ISBN: (数字)9798350372250

ISBN: (纸本)9798350372267

Error accumulation is an essential component of the Top-k sparsification method in distributed gradient descent. It implicitly scales the learning rate and prevents the slow-down of lateral movement, but it can also deteriorate convergence. This paper proposes a novel sparsification algorithm called regularized Top-k (REGTop-k) that controls the learning rate scaling of error accumulation. The algorithm is developed by looking at the gradient sparsification as an inference problem and determining a Bayesian optimal sparsification mask via maximum-a-posteriori estimation. It utilizes past aggregated gradients to evaluate posterior statistics, based on which it prioritizes the local gradient entries. Numerical experiments with ResNet-18 on CIFAR-10 show that at 0.1% sparsification, REGTop-k achieves about 8% higher accuracy than standard Top-k.

关键词： Training Computer aided instruction Machine learning algorithms Distance learning signal processing algorithms Machine learning signal processing Inference algorithms Bayes methods Standards

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