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检索条件"任意字段=Conference on Advanced Signal Processing Algorithms"

共 3500 条记录，以下是271-280 订阅

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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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A Multi-Agent Multi-Environment Mixed Q-Learning for Partially Decentralized Wireless Network Optimization

A Multi-Agent Multi-Environment Mixed Q-Learning for Partial...

引用

International conference on Acoustics, Speech, and signal processing (ICASSP)

作者： Talha Bozkus Urbashi Mitra Department of Electrical and Computer Engineering University of Southern California USA

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

Q-learning is a powerful tool for network control and policy optimization in wireless networks, but it struggles with large state spaces. Recent advancements, like multi-environment mixed Q-learning (MEMQ), improves performance and reduces complexity by integrating multiple Q-learning algorithms across multiple related environments so-called digital cousins. However, MEMQ is designed for centralized single-agent networks and is not suitable for decentralized or multi-agent networks. To address this challenge, we propose a novel multi-agent MEMQ algorithm for partially decentralized wireless networks with multiple mobile transmitters (TXs) and base stations (BSs), where TXs do not have access to each other’s states and actions. In uncoordinated states, TXs act independently to minimize their individual costs. In coordinated states, TXs use a Bayesian approach to estimate the joint state based on local observations and share limited information with leader TX to minimize joint cost. The cost of information sharing scales linearly with the number of TXs and is independent of the joint state-action space size. The proposed scheme is 50% faster than centralized MEMQ with only a 20% increase in average policy error (APE) and is 25% faster than several advanced decentralized Q-learning algorithms with 40% less APE. The convergence of the algorithm is also demonstrated.

关键词： Q-learning Costs Transmitters Wireless networks signal processing algorithms Complexity theory Bayes methods Speech processing Standards Convergence

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Tender Coconut Classification using Decision Tree and Deep Learning Technique

Tender Coconut Classification using Decision Tree and Deep L...

引用

International conference on signal processing and Integrated Networks (SPIN)

作者： Ishani Bhat V Umadevi Nishchitha Jagadeesh Savithri Bhat Rashmi S Shenoy Dept. of Computer Science PES University Bengaluru India Dept. of Computer Science B.MS. College of Engineering Bengaluru India Dept. of Biotechnology B.MS. College of Engineering Bengaluru India

A number of industry sectors such as Food & nutraceuticals, Pharma, Irrigation, Transport and Automobile use coconut and its parts for varied products. As different sectors make use of different type and parts of coconut, an automated separation of coconut Would aid in faster processing. Thus the major issue faced by these sectors is the automated sorting of coconut into different categories such as Non-Tender Coconut, Tender Whole coconut, Tender coconut cut With flesh and Without flesh. In this Work We have trained and tested a Decision Tree and DenseNet model to automatically classify coconut images into various categories. For Decision Tree 93% of accuracy Was obtained While the advanced Deep learning model, DenseNet, gave 100% accuracy. This showed that the Deep Learning models such as DenseNet perform better as compared to conventional machine learning algorithms such as Decision Tree.

关键词： Deep learning Industries Irrigation Machine learning algorithms signal processing algorithms signal processing Classification algorithms

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Proposed Statistical Framework for signal Modulation and processing

Proposed Statistical Framework for Signal Modulation and Pro...

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International conference on Computing and Networking Technology (ICCNT)

作者： Naveen Kumar Rajendran Mandar Krishnarao Mokashi K. Ranjith Singh Abhishek Singla P. Chandrakala Samaksh Goyal Department of Aerospace Engineering School of Engineering and Technology JAIN Bangalore Karnataka Vishwakarma Institute of Information Technology Pune Department of Computer Science Karpagam Academy of Higher Education Coimbatore India Chitkara University Institute of Engineering and Technology Chitkara University Punjab India Department of Electrical and Electronics Engineering Prince Shri Venkateshwara Padmavathy Engineering College Chennai Quantum University Research Quantum University

ISBN: (数字)9798350370249

ISBN: (纸本)9798350370270

This paper proposes a new statistical framework to facilitate sign modulation and processing. The proposed framework models every signal as a combination of different distributions and makes use of advanced statistical inference techniques to perceive the parameters of each issue. additionally, the framework takes into account the temporal correlations between person samples of the sign and uses superior gadget gaining knowledge of techniques to lessen interference from noise, allowing for the a hit transmission of alerts over noisy channels. The proposed framework is designed to lessen the computational value of signal processing and for that reason enhance the transmission price of signals. The overall performance of the proposed framework is evaluated through both simulated and actual-world experiments. Consequences show that the proposed framework considerably improves the sign-to-noise ratio of transmitted indicators and also reduces the computational cost of sign processing.

关键词： Correlation Filtering Noise Modulation signal processing algorithms Estimation Interference Inference algorithms Computational efficiency Noise measurement

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Demonstration of a Real-Time Testbed for D-Band Integrated Sensing and Communication

Demonstration of a Real-Time Testbed for D-Band Integrated S...

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MILCOM, Military Communications conference

作者： Sven Wittig Rodrigo Hernangómez Karen Vardanyan Ramez Askar Amr Haj-Omar Michael Peter Sławomir Stańczak Fraunhofer Heinrich Hertz Institute Berlin Germany NI Austin TX USA Technische Universität Berlin Berlin Germany

ISBN: (数字)9798350374230

ISBN: (纸本)9798350374247

The D-band, spanning 110 GHz to 170 GHz, has emerged as a relevant frequency range for future mobile communications and radar sensing applications, particularly in the context of 6G technologies. This demonstration presents a high-bandwidth, real-time integrated sensing and communication (ISAC) platform operating in the upper D-band at 160 GHz. The platform comprises a software-defined intermediate frequency transceiver and a D-band radio frequency module. Its flexible software design allows for rapid integration of signal processing algorithms. Highlighting its potential for advanced research in ISAC systems, the platforms’s efficacy is showcased through a live demonstration with an algorithm implementing human target tracking and activity classification.

关键词： Military communication Target tracking Software design Software algorithms signal processing algorithms Integrated sensing and communication Real-time systems Transceivers Radar applications Classification algorithms

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Real-Time Wild Animal Detection using YOLOv10 Algorithm

Real-Time Wild Animal Detection using YOLOv10 Algorithm

引用

International conference on Wireless Communications, signal processing and Networking (WiSPNET)

作者： Nishanth I Rakeshkumar R Thenmozhi V Department of Electronics and Communication Engineering Amrita School of Engineering Amrita Vishwa Vidyapeetham Chennai India

ISBN: (数字)9798331524227

ISBN: (纸本)9798331524234

This project utilizes the YOLOv10 algorithm on the NVIDIA Jetson Nano board to create a real-time wild animal detection system. By continuously analyzing video feeds from wildlife monitoring setups, the system accurately and efficiently detects animals across diverse environmental conditions. Leveraging the Jetson Nano’s edge computing capabilities alongside YOLOv10’s advanced object detection, the research presents a cost-effective, low-power solution for automated wildlife monitoring. With its ability to continuously track and identify animals in remote areas, this approach enhances conservation efforts and provides valuable data for biodiversity studies and the protection of endangered species.

关键词： YOLO Wireless communication Animals Biological system modeling Surveillance Wildlife signal processing algorithms Streaming media Real-time systems Environmental management

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A novel method to detect and classify High Impedance Fault in EV integrated distribution system

A novel method to detect and classify High Impedance Fault i...

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2023 IEEE International conference on Power Electronics, Smart Grid, and Renewable Energy, PESGRE 2023

作者： Kumar Joga, S Ramana Saiprakash, Chidurala Diet Eee Department Visakhapatanam Anakapalle India Sr University Electrical and Electronics Engineering Warangal India

ISBN: (纸本)9798350310573

With the growing adoption of electric vehicles (EVs) and the integration of renewable energy sources in power distribution systems, the need to ensure safe and reliable operation becomes paramount. High Impedance Faults (HIFs) are a particular concern due to their elusive nature and potential to cause electrical fires and equipment damage. This abstract presents a study on the diagnosis of HIFs in distribution systems integrated with electric vehicles. The proposed diagnosis method combines advanced monitoring techniques, fault detection algorithms, and machine learning approaches to accurately identify and locate HIFs. By leveraging the data collected from smart meters, phasor measurement units, and EV charging infrastructure, a comprehensive system-level analysis is performed to distinguish HIFs from other faults and normal operation. In this paper, a novel method based on RADWT & SVM is used to detect and classify high impedance faults in electrical distribution system. Rational dilation wavelet transform is used for feature extraction and SVM is used for fault detection and classification. Necessary simulations are carried out in MATLAB and verified with experimental setup. © 2023 IEEE.

关键词： discrete wavelet transform fault classification fault detection machine learning signal processing wavelet

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A Real-Time Multiple Traffic Violation Detection System Using DeepSORT

A Real-Time Multiple Traffic Violation Detection System Usin...

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

作者： Renuka Devi S M A Manasa Karkala Deepti Y. Rakesh Kumar ECE Department G Narayanamma Institute of Technology & Science(for women) Hyderabad TS India ECE Department G Narayanamma Institute of Technology & Science Hyderabad TS India ECE Department Vasavi College of Engineering Hyderabad TS India

ISBN: (数字)9798350350654

ISBN: (纸本)9798350350661

As population growth accelerates, the number of vehicles on the roads increases, leading to rise in road accidents. A significant cause of these accidents is traffic rule violations, such as speeding and wrong-way driving. To address this issue, an advanced real-time system is needed to detect traffic rule violations using CCTV cameras. The proposed traffic signal violation detection system automates the monitoring process, performing tasks such as speed and wrong-way driving detection without human intervention. The system focuses on three primary tasks: vehicle detection, vehicle tracking, and violation detection. Vehicles are first located using the YOLOv3 deep learning method. The Kalman filter combined with DeepSORT is then used to track the detected vehicles, assigning each a unique ID. algorithms for wrong-way driving and speed violations are subsequently applied to identify rule-breaking vehicles. This method ensures high efficiency and accuracy in detecting vehicles that violate traffic rules.

关键词： YOLO Deep learning Accuracy Road accidents Roads signal processing algorithms signal processing Real-time systems Kalman filters Monitoring

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Research Analysis on Current Advances in Parkinson’s Disease Detection Using signal processing and Machine Learning-Based Techniques 12th

Research Analysis on Current Advances in Parkinson’s Diseas...

引用

12th International conference on Soft Computing for Problem Solving, SocProS 2023

作者： Goel, Kshitij Sood, Neetu Saini, Indu Department of Electronics and Communication Engineering Dr. B.R. Ambedkar National Institute of Technology Jalandhar Punjab Jalandhar144011 India

ISBN: (纸本)9789819732913

Parkinson's Disease (PD) is a neurodegenerative condition characterized by the degeneration of dopaminergic neurons in the brain, resulting in both motor and non-motor symptoms. The timely and precise diagnosis of PD is of paramount importance for effective management and treatment strategies. Recent years have witnessed the emergence of diverse diagnostic approaches for PD, encompassing clinical assessments, advanced imaging techniques, and biochemical analyses. This comprehensive technical review aims to present a comprehensive overview of the various methodologies employed for diagnosing Parkinson's disease. Numerous scholarly articles have demonstrated the utilization of Deep Learning Neural Networks for classification purposes, while a select few have developed novel methods and Machine Learning models by incorporating existing Deep Learning techniques, often complemented by various optimization approaches like the Moth Flame Optimizer (MFO) or feature selection techniques to enhance the accuracy and precision of PD prediction. Notably, each article included in this study has harnessed Electrophysiological signals as the primary features of Machine Learning algorithms, showcasing their prominence in the field of PD classification and prediction, particularly within the biomedical domain. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Neurons

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Traffic Sign and Obstacle Detection for Autonomous Vehicle Navigation using Edge Computing

Traffic Sign and Obstacle Detection for Autonomous Vehicle N...

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International conference on Wireless Communications, signal processing and Networking (WiSPNET)

作者： M. Vijayalakshmi M. Bharathwaj D. Anjelin Deva Dharshini Department of IST CEG Anna University Chennai India

ISBN: (数字)9798331524227

ISBN: (纸本)9798331524234

The advancement of autonomous vehicle technology necessitates robust systems for real-time traffic sign recognition and obstacle detection to ensure safe navigation. This paper proposes a comprehensive autonomous vehicle (AV) simulation framework utilizing the Carla simulator, advanced image processing, and state-of-the-art machine learning models to enhance autonomous navigation capabilities. The system initiates by loading a specified virtual map within the Carla simulator and deploying an ego vehicle equipped with a camera sensor to capture environmental images. These images undergo preprocessing techniques, to ensure suitability for analysis. Traffic sign recognition system uses a pre-trained YOLOv7 model, enabling the AV to identify and respond to various traffic signals amidst occlusion, adverse weather, and variations in placement and size. Concurrently, a pre-trained Faster R-CNN model detects obstacles such as vehicles and pedestrians along the vehicle’s path. A dedicated distance estimation module further analyzes these preprocessed images to calculate the relative distances of detected obstacles using depth estimation techniques, providing crucial spatial information. The decision-making algorithm resolves conflicts between recommended actions from traffic sign recognition and obstacle detection modules, prioritizing safety-critical commands to ensure operational safety and efficiency. The models are stored on the network edge, facilitating efficient deployment and real-time processing. This framework demonstrates an effective integration of simulation, image analysis, and decision-making, advancing the reliability and performance of autonomous navigation systems.

关键词： Navigation Heuristic algorithms Decision making signal processing algorithms Cameras Real-time systems Road safety Classification algorithms Autonomous vehicles Edge computing

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