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

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Raspberry Pi-Based Monitoring System for Grid-Connected PV Systems using Deep Learning Technique*

Raspberry Pi-Based Monitoring System for Grid-Connected PV S...

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International conference on Control, Decision and Information Technologies (CoDIT)

作者： Zahra Yahyaoui Wajdi Saadaoui Mansour Hajji Majdi Mansouri Mohamed Nounou Kais Bouzrara Research Unit Advanced Materials and Nanotechnologies Higher Institute of Applied Sciences and Technology of Kasserine Kairouan University Tunisia Department of Electrical and Computer Engineering Program Texas A&M University at Qatar Doha Qatar Research Laboratory of Automation Signal Processing and Image National Engineering School of Monastir Tunisia

ISBN: (数字)9798350373974

ISBN: (纸本)9798350373981

The evolution of embedded systems has demonstrated their reliability as a solution for monitoring and controlling industrial systems, particularly in renewable energy conversion systems like photovoltaic (PV) energy. The increasing adoption of PV systems highlights the critical need for effective fault diagnosis to ensure their reliable operation. In this paper, we present a novel fault diagnosis approach utilizing Long Short-Term Memory (LSTM) networks optimized through Bayesian optimization techniques. Our methodology is implemented on a Raspberry Pi platform, demonstrating the feasibility of deploying sophisticated fault diagnosis algorithms in resource-constrained environments. Through extensive experiments, we demonstrate the effectiveness of our approach to accurately diagnose faults in grid-connected photovoltaic systems, thereby improving the reliability and efficiency of integrated environmental monitoring *** obtained results highlight the potential of combining advanced deep learning techniques with embedded systems to address complex diagnostic challenges, as demonstrated by achieving a 100% accuracy rate.

关键词： Fault diagnosis Deep learning Photovoltaic systems Embedded systems Accuracy Economic indicators Bayes methods Environmental monitoring Long short term memory Optimization

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Bio-signal Acquisition System for Psychological Stress Classification

Bio-Signal Acquisition System for Psychological Stress Class...

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International conference on Information and Communication Technology Convergence (ICTC)

作者： Daris Muhammad Rahman Arvanida Feizal Permana Maria Mahardini Sakanti Cornelia Adristi Win-Jet Luo C. Bambang Dwi Kuncoro Department of Refrigeration Air Conditioning and Energy Engineering National Chin-Yi University of Technology Taichung Taiwan Graduate Institute of Prospective Technology of Electrical Engineering and Computer Science National Chin-Yi University of Technology Taichung Taiwan Graduate Institute of Precision Manufacturing National Chin-Yi University of Technology Taichung Taiwan Department of Electrical Engineering Faculty of Engineering Universitas Indonesia Depok Indonesia

ISBN: (数字)9798350364637

ISBN: (纸本)9798350364644

The increasing prevalence of psychological stress in modern society necessitates the development of effective monitoring and classification systems. This paper presents the design and implementation of the acquisition system for the bio-signal measurement. The proposed system utilizes a combination of sensors and a microcontroller to collect and analyze physiological data indicative of stress levels. The bio-signals data monitored include heart rate, pulse rate, galvanic skin response, body temperature, and acceleration. The data acquisition process is managed by a microcontroller, which processes the raw signals and transmits them to a central processing unit for further analysis. The built system captures the acquired data in real time, offering immediate feedback and long-term monitoring capabilities. The system operates on a small power supply of 5V and 250mA, featuring a modular and portable design for ease of use. This approach aims to offer a cost-effective, non-invasive solution for early detection and management of psychological stress, contributing to improved mental health outcomes. Experimental results demonstrate the system's accuracy and reliability in acquiring and processing bio-signal data. These results represent preliminary research, with future work focusing on integrating advanced data analysis techniques and machine learning algorithms to enhance stress classification and prediction, expanding the system's applicability in clinical and everyday settings.

关键词： Temperature measurement Temperature sensors Heart rate Data analysis Power supplies Microcontrollers Human factors Skin Biomedical monitoring Monitoring

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An Improved Functional Link Architecture for Nonlinear AEC

An Improved Functional Link Architecture for Nonlinear AEC

引用

European signal processing conference (EUSIPCO)

作者： Srikanth Burra Asutosh Kar Mads Græsbøll Christensen Indian Institute of Information Technology Design and Manufacturing Chennai India Department of Architecture Design and Media Technology Audio Analysis Lab Aalborg University Denmark

ISBN: (数字)9789082797091

ISBN: (纸本)9781665467995

In practice, hands-free devices commonly employ low-cost electronic components. Unfortunately, the nonlinear distortion arising from them affects the performance of acoustic echo cancellers. To address this issue, this paper proposes a new adaptive filtering algorithm based on nonlinear acoustic echo cancellation (NAEC) framework for improving its echo cancellation performance. The advanced method employs a novel combination of an adaptive filter based on sub-filter and proportionate adaptation, and presents an enhanced NAEC framework. In addition to that, both convergence and steady-state analysis of the proposed NAEC algorithm are presented. The performance evaluation made in the presence of speech and colored noise inputs has shown an average improvement of 3–4 dB compared to the existing algorithms.

关键词： Performance evaluation Echo cancellers Filtering Nonlinear distortion signal processing algorithms Adaptive filters Nonlinear filters

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Enhancing Liver Cancer Detection with AI-Powered Image processing

Enhancing Liver Cancer Detection with AI-Powered Image Proce...

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Sustainable Expert Systems (ICSES), International conference on

作者： A. Suresh Kumar S. Rajathi Vineetha Vargheese M. Sudha Anju A P. Lokeshkiran Department of Computer Science and Engineering Rathinam Technical Campus Coimbatore Tamil Nadu India Department of Information Technology Karpagam Institute of Technology Coimbatore Tamil Nadu India Department of Electronics and Communication Engineering Rathinam Technical Campus Coimbatore Tamil Nadu India

ISBN: (数字)9798331540364

ISBN: (纸本)9798331540371

This project aims to identify liver tumors using deep learning techniques. The core of our approach is a sophisticated neural network trained on real CT scans of patients with liver tumors. By leveraging advanced signal-processing algorithms and deep learning models, our AI method accurately distinguishes between tumors and healthy liver tissue. Accurate identification of liver tumors on CT images is crucial for planning effective radiation therapy. CT and MRI scans provide detailed images of the internal structure of the body by analyzing tissues. Our primary objective is to develop an AI-driven neural network that can reliably identify liver tumors in these images. Given the variability in liver location among individuals, our approach employs specific techniques to ensure precision. Using U-net technology, our goal is to enhance the speed and accuracy of tumor identification, thereby improving patient access to timely and accurate diagnoses. This project underscores the significant potential of deep learning applications in health care.

关键词： Deep learning Image segmentation Accuracy Computed tomography Neural networks Liver Planning Radiation therapy Object recognition Tumors

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Biomedical Image processing with Improved SPIHT Algorithm and optimized Curvelet Transform Technique 7

Biomedical Image Processing with Improved SPIHT Algorithm an...

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7th International conference on advanced Computing and Communication Systems, ICACCS 2021

作者： Ahmed, L Jubair Anish Fathima, B. Mahaboob, M. Gokulavasan, B. Sri Eshwar College of Engineering Department of Ece Coimbatore India Sri Krishna College of Engineering and Technology Department of Ece Coimbatore India

ISBN: (纸本)9781665405201

The prime idea is to propose an effective compression technique obtaining improved PSNR and better compression ratio (CR) with Curvelet transformation technique and encoding the multi-wavelet parameters using Set Partitioning In Hierarchical Trees (SPIHT) algorithm. Also a comparative study is presented which highlights a dynamic image encoding algorithm against the conventional algorithms such as SVM, EZW SPECK. Both quantitative and qualitative parameters of algorithm are analyzed for bio-medical images. Analysis inferences represents that the proposed technique treats structures and patterns considerably superior than conventional techniques. © 2021 IEEE.

关键词： signal encoding

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Pix2Pix Generative adversarial Networks (GAN) for breast cancer detection

Pix2Pix Generative adversarial Networks (GAN) for breast can...

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International conference on Multimedia, signal processing and Communication Technologies, (IMPACT)

作者： Zainab Aizaz Kavita Khare Afreen Khursheed Aizaz Tirmizi Department of Electronics and Communication Engg. Maulana Azad National Institute of Technology (MANIT) Bhopal India Department of Electronics and Communication Engg. Indian Institute of Information Technology (IIIT) Bhopal India Institute of Advanced Skill Training (IAST) Bhopal India

ISBN: (纸本)9781665476485

Breast cancer remains to be one of the most common cancers throughout the world. Detection of mammographically occult breast cancer is a trivial process due to hidden tumours in women with dense breasts. Data augmentation techniques are in demand due to existing paucity in the dash images that are labeled. In this brief, a model for the detection of mammographically occult breast cancer is developed with pix2pix Generative Adversarial Networks (GAN) as in imperative tool for data augmentation. In this work, GANs are used to construct transformations of original images in the dataset. The U-net based of CNN layers is used to preserve long-range connections in wide neighbourhoods to provide a balanced partitioning. For the cancer detection, this model can be used as an individual layer in a Convolutional neural network (CNN). Upon comparison with transfer learning approach, the proposed approach has higher accuracy for two VGG-16 and Resnet-50 CNNs.

关键词： Machine learning algorithms Image analysis Transfer learning signal processing algorithms signal processing Generative adversarial networks Cancer detection

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Implementation of Genetic Algorithm Optimization based Artificial Neural Network on Raspberry Pi for Fault Diagnosis

Implementation of Genetic Algorithm Optimization based Artif...

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International conference on Control, Decision and Information Technologies (CoDIT)

作者： Amal Hichri Wajdi Saadaoui Mansour Hajji Majdi Mansouri Mohamed Nounou Kais Bouzrara Research Unit Advanced Materials and Nanotechnologies Higher Institute of Applied Sciences and Technology of Kasserine Kairouan University Tunisia Electrical and Computer Engineering Program Texas A&M University at Qatar Qatar Laboratory of Automatic Signal and Image Processing National Engineering School of Monastir University of Monastir Tunisia

ISBN: (数字)9798350373974

ISBN: (纸本)9798350373981

This study presents an embedded system (ES) designed for fault detection and diagnosis in grid-connected photovoltaic (GCPV) systems using transient regime analysis. The primary aim of transient regime analysis is to facilitate real-time decision-making, especially during critical faults. A neural network classifier, incorporating a Genetic Algorithm for automated hyperparameter optimization, is developed for GCPV fault classification. These classifiers are seamlessly integrated into a Raspberry Pi 4 platform for fault diagnosis in GCPV systems. Both simulation and experimental results substantiate the ES's viability for fault diagnosis in the examined GCPV system, achieving high accuracy and enabling prompt decision-making to enhance the reliability and safety of GCPV systems.

关键词： Fault diagnosis Photovoltaic systems Embedded systems Accuracy Fault detection Decision making Artificial neural networks Hyperparameter optimization Transient analysis Genetic algorithms

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advanced Image processing Techniques for the Detection and Monitoring of TV Datacenter System

Advanced Image Processing Techniques for the Detection and M...

引用

International conference on Electrical, Control and Instrumentation Engineering (ICECIE)

作者： Mohamed Fakhreddine Teodora Bakardjieva Galina Momcheva Varna Free University Varna Bulgaria

ISBN: (纸本)9781665480772

TV Datacenter is the primary storage facility where servers for data storage are located. TV Datacenters have exploded in popularity over the last decade, becoming the epicenter of the technical landscape. As the size and ability of the centers grow, the complexity in managing them grows as well. The information and functionality grid are in danger of imploding, with potentially catastrophic consequences. As a result, solutions that can keep pace with the growth of data delivery, as well as Datacenter storage sizes, are needed. The main objective behind this research study is to propose a complete and advanced system for accommodating the enlargement of Datacenters, for enhancing data delivery in Datacenters, and for detection of errors and problems in TV Datacenters. Moreover, the system will also perform advanced and robust monitoring of TV Datacenters. The significance of the study relies on the algorithms that are proposed since these algorithms are completely novel as well as they are highly performant. In addition, these algorithms incorporate new technologies of image processing. The results obtained using the algorithms showed a detection rate obtained of 83.33% which can be considered to be an indicator of the high performance of the proposed detection system. Moreover, according to the results, the minimum time of replacement is zero seconds meaning that the replacement was done automatically after the detection of the corruption in the video. The results also showed that for all videos, the detected number of frames were all successfully replaced. The frame replacement showed 100% efficiency for every video.

关键词： Visualization TV Automation Films signal processing algorithms Media Motion pictures

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Efficient and Robust Automotive Radar Coherent Integration With Range Migration

Efficient and Robust Automotive Radar Coherent Integration W...

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European signal processing conference (EUSIPCO)

作者： Oded Bialer Amnon Jonas Oren Longman General Motors - Advanced Technical Center Israel

ISBN: (数字)9789082797091

ISBN: (纸本)9781665467995

Conventional automotive radar perform range-Doppler coherent integration (stretch processing) under the assumption that the range of each object is constant during the integration interval. This assumption yields an efficient computation algorithm. However, when the object's relative speed is high and/or the coherent integration interval is large, the range migration is significant with respect to the range resolution, and as a result, the detection performance of the conventional range-Doppler coherent integration degrades significantly. The Radon-Fourier Transform (RFT) is the optimal method (in the sense of detection performance) for coherent integration with range migration, however, its complexity is large and may not be practical for implementation. In this paper, we develop a range-Doppler coherent integration algorithm that takes into account the range migration with efficient computation. We utilize the fact that range migration is a function of the Doppler frequency and derive an approximation to the RFT. The proposed algorithm significantly outperforms conventional coherent integration when the object's range migration is significant. Furthermore, it attains the performance of the RFT but with significantly lower complexity.

关键词： Radar measurements signal processing algorithms Transforms Approximation algorithms Doppler radar Computational efficiency Doppler effect

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Target Selection in Multistatic Microwave Breast Imaging Setup Using Dielectric Lens

Target Selection in Multistatic Microwave Breast Imaging Set...

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European conference on Antennas and Propagation, EuCAP

作者： Daniela M. Godinlio Joao M. Felício Carlos A. Fernandes Raquel C. Conceição Instituto de Biofísica e Engenharia Biomédica Faculdade de Ciĉncias da Universidade de Lisboa Lisbon Portugal Centro de Investigação Naval (CINAV) Escola Naval Almada Portugal Instituto de Telecomunicações Instituto Superior Técnico Universidade de Lisboa Lisbon Portugal

Microwave Imaging (MWI) has been studied to aid early breast cancer detection. Current prototypes in more advanced stages of development include both monostatic or multi-static setups. However, multistatic configurations usually include a high number of antennas which consequently require complex and computationally-intensive signal processing algorithms to ensure a good target detection. We previously presented a novel approach using a dielectric lens which reduces the signal processing burden of multistatic setups, while ensuring good spatial resolution. In this paper, we evaluate this novel setup using an anatomically realistic breast phantom and its capability of selecting targets inside the breast. We show a successful detection of the targets using an artefact removal algorithm based on singular value decomposition when the Bessel beam is centered at the target location.

关键词： Microwave antennas signal processing algorithms Phantoms Breast signal processing Dielectrics Spatial resolution

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