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检索条件"主题词=Signal Processing Algorithms"

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AI-Based Bolt Loosening Diagnosis with Deep Learning from Laser Ultrasonic Wave Data

AI-Based Bolt Loosening Diagnosis with Deep Learning from La...

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Big Data & Machine Learning (ICBDML), IEEE International Conference on

作者： Arangarajan M Amar Pal Yadav Ch Veena Sasthi Kumar C Gaurang A. Patel Dillip Narayan Sahu Department of Mechanical Engineering Shri Angelman College of Engineering and Technology Tiruchirappalli Tamil Nadu India CSE (AI) Noida Institute of Engineering and Technology Greater Noida Uttar Pradesh Engineering Hyderabad Dundigal Department of Computer Science Organization - Karpagam Academy of Higher Education Coimbatore Department of Mechanical Engineering Chandubhai S. Patel Institute of Technology Charotar University of Science & Technology Gujarat India Department of MCA Gangadhar Meher University Odisha India

ISBN: (数字)9798350374100

ISBN: (纸本)9798350374117

Deep learning (DL) algorithms for non-destructive evaluation (NDE) are becoming more popular, especially for the use of laser ultrasonic methods to identify and estimate looseness in bolted joints. By examining the connection between the energy lost by the passage of ultrasound waves and the bolt head-plate's genuine contact area, this work advances the field. The study used ultrasonic wave travel imaging to produce a 3D full-field sonar dataset utilizing an acoustic emission camera and a Q-switched Nd: YAG pulsating laser. The research also highlighted how crucial signal processing methods are to improving DL performance for automated looseness estimate.

关键词： Deep learning Ultrasonic imaging Three-dimensional displays Vibrometers signal processing algorithms Sonar signal processing

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A New hybrid deep learning coder for Alzheimer classification

A New hybrid deep learning coder for Alzheimer classificatio...

引用

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

作者： Zoulikha Bebboukha Athmane Zitouni Department of Electrical Engineering Faculty of Science and Technology University of Mohammed Khider Biskra Algeria Vision and Communication Systems Laboratory (VCS) Biskra

ISBN: (数字)9798350309249

ISBN: (纸本)9798350309256

Deep learning algorithms offer distinct advantages over other machine learning methods, enabling the exploitation of advanced techniques to analyze brain MRI scans. In a recent research study, a combination of ResNet-50 and DenseNet-201 convolutional neural network models was utilized to extract crucial features from brain MRI images, which were then fed into a compact classification model. The proposed model demonstrated remarkable results in accurately classifying the stages of Alzheimer’s disease. It outperformed all other approaches using the same dataset and achieved an outstanding accuracy of 99.9 % in categorizing the four cases, affirming the effectiveness and promising potential of this proposed model in the precise diagnosis and classification of Alzheimer’s disease.

关键词： Deep learning Machine learning algorithms Databases Magnetic resonance imaging signal processing algorithms signal processing Feature extraction

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Automatic generation algorithm of power report summary based on machine learning

Automatic generation algorithm of power report summary based...

引用

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

作者： Chao Geng Bengang Wei Sujie Zhang Long Zheng Congjin Du Wenkai Xu Equipment Department State Grid Shanghai Electric Power Company Shanghai China State Grid Shanghai Electric Power Company Shanghai China Jiangsu Titan Wisdom Technology Co. LTD Wuxi China

ISBN: (数字)9798331528676

ISBN: (纸本)9798331528683

This study proposes a novel summary generation algorithm that combines TF-IDF, BERT, and Seq2Seq models and aims to automate summary generation of professional reports in the power industry. Firstly, the TF-IDF algorithm is used to identify key terms and topics in power reports to extract the most representative and informative content segments, and then these segments are deeply semantic analyzed by BERT model to enhance the understanding of expertise in the power domain. Finally, the BERT-encoded vector sequence is input into the Seq2Seq framework to generate a coherent and content-rich summary. This combined algorithm takes advantage of complementary advantages, covers the whole process from keyword extraction to semantic understanding to summary generation, and can deal with complex domain knowledge to ensure the professionalism and accuracy of the summary.

关键词： Productivity Machine learning algorithms Semantics signal processing algorithms Bidirectional control signal processing Transformers Rendering (computer graphics) Encoding Vectors

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Efficient Customer Segmentation Using Silhouette Based K-Means Algorithm

Efficient Customer Segmentation Using Silhouette Based K-Mea...

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

作者： Pavan Karthik Chilla Sunil Kumar Nahak Mudem Veera Venkata Deepika Balivada Sai Rohit Tankala Hemalatha Asish Kumar Dalai Department of Computer Science KL University Vaddeswaram Andhra Pradesh India Department of Department of Computer Science and Engineering NIST University Berhampur Odisha India Department of Electronics and Communication Engineering Vijaya Institute of Technology for Women AP India School of Computer Science and Engineering VIT AP University Amaravati Andhra Pradesh India

ISBN: (数字)9798350350654

ISBN: (纸本)9798350350661

Customer segmentation is now essential for firms seeking to maintain and optimize revenue from consumers. The present research investigates the potential of utilizing behavioral variables such as expenses and income to successfully segment clients, beyond conventional demographic methods. The k-means clustering procedure, which is an unsupervised machine learning methodology, is utilized using the Elbow method and Silhouette score to ascertain the most suitable number of clusters. A Python application, with basic scaling, examines a dataset from a retail mall to visually represent similarities among customers and categorize them into clusters. The project aims to segment customers based on their purchase behavior using the silhouette-based K-means algorithm. The seaborn library will be used for implementation. The objective is to create a resilient consumer segmentation approach that assists organizations in comprehending target markets and enhancing marketing strategies suitably. By employing this algorithm, organizations acquire knowledge about client behavior, facilitating customized marketing campaigns and enhanced income generating.

关键词： Clustering algorithms signal processing algorithms Data visualization Organizations Machine learning signal processing Libraries Data mining Elbow Optimization

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Contrast Enhancement and ROI detection in satellite images Application to the environment and sustainable development

Contrast Enhancement and ROI detection in satellite images A...

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

作者： Aicha Mehidi Malika Mimi Abdallah Mehidi Soraya Zehani Electrical Engineering Department Laboratory of Signals and Systems Faculty of Sciences and Technology University of Abdelhamid Ibn Badis Mostaganem Algeria Laboratory of Signals and Systems Faculty of Sciences and Technology University of Abdelhamid Ibn Badis Mostaganem Algeria Electrical Engineering Department Laboratory of Vision and Communication Systems Faculty of Sciences and Technology University of Biskra

ISBN: (数字)9798350309249

ISBN: (纸本)9798350309256

During the last few years, remote sensing is considerably used for Earth observation for the environment and sustainable development. The temporal classes of satellite images provide better information for monitoring the earth's surface at different scales; therefore these images are becoming a very relevant opportunity of investigating. The numerical analyses of image processing are based on the artificial intelligence (AI) tool rather than the traditional methods. The environmental indicators from these images offer very important statistics, the carried out work of dynamic phenomena, the observation event and interpretation of evolving circumstances like: atmospheric conditions, crises and natural disasters taking the opportunity to be studied, several problems come out of deforestation and monitoring of water resources (water bodies). The purpose of this study is focused on drawing out details from the database used and then to give an offer for a texture analysis strategy. Firstly, using specific development and then developing the existing software in the second hand. This work is to establish an algorithm for the detection of region of interest (water body) which is based on the theory of fuzzy logic and the hypothesis of Fuzzy C-Means.

关键词： Earth Image segmentation Software algorithms signal processing algorithms Satellite images Sustainable development Artificial intelligence

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Cell-Free Integrated Sensing and Communication for UGV Detection Based on Decomposed Back Projection Algorithm

Cell-Free Integrated Sensing and Communication for UGV Detec...

引用

IEEE International Conference on Unmanned Systems (ICUS)

作者： Zhiqiang Yue Yongming Huang Huazhou Hou Dongxuan He School of Information Science and Engineering Southeast University Nanjing China Purple Mountain Laboratories Nanjing China

ISBN: (数字)9798350384185

ISBN: (纸本)9798350384192

This paper considers the localization and velocimetry problem of UGV by integrated sensing and communications in a Sub-6GHz cell-free system. Multiple distributed antennas are used to detect a single target and the echo signals are processed. For the receiver processing algorithm, we develop an improved algorithm suitable for real systems to address the problem that the matched-filter based backprojection algorithm consumes too much time. By de-composing range and velocity, the original algorithm is split into range backprojection and velocity backprojection algorithms respectively, and the effective target is identified by constant alarm false rate (CFAR) algorithm. Simulation results show that the improved algorithm increased the computation speed by at least 100 times. For the real system, better results are achieved by proposed algorithm for localization and velocimetry as well.

关键词： Location awareness Autonomous systems Simulation signal processing algorithms Receivers Integrated sensing and communication Vectors Projection algorithms Antenna arrays

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Artificial Neural Network-Based IoT Device for Predicting the Electromagnetic Compatibility of Medical Equipment: A Case Study of Hospitals in Rwanda

Artificial Neural Network-Based IoT Device for Predicting th...

引用

International Conference on signal processing and Information Security (ICSPIS)

作者： Jean Pierre Nyakuri Amevi Acakpovi Omar Gatera Gerard Rushingabigwi Celestin Twizere Chiedza Hwata African Centre of Excellence in Internet of Things (ACEIoT) University of Rwanda Kigali Rwanda Electrical and Electronics Engineering Department Accra Technical University Accra Ghana East Africa’s Regional Centre of Excellence in Biomedical Engineering and E-Health (CEBE) University of Rwanda Kigali Rwanda

ISBN: (数字)9798350368673

ISBN: (纸本)9798350368680

Electromagnetic radiation (EMR) and electromagnetic compatibility (EMC) issues in medical equipment can lead to inaccurate medical results, and harmful radiation emissions, posing risks to both patients and healthcare providers. Currently, there is a lack of effective prediction mechanisms to detect potential EMR and EMC issues in medical devices, contributing to equipment malfunctions and compromised safety. In this study, we propose an Internet of Things (IoT) device capable of predicting EMR and EMC in medical equipment using a Machine Learning (ML) model based on an Artificial Neural Network (ANN) model. The input parameters for prediction include temperature, aging, initial electromagnetic radiation (IEMR), frequency, and voltage of the device. The ANN model was trained on a dataset collected from four hospitals in Rwanda and its performance was compared with other machine learning algorithms, including Multilinear Regression, Decision Tree, Random Forest, and Support Vector Machine (SVM). The results demonstrated that the ANN model outperformed the other algorithms in prediction accuracy, achieving $99.5 \%$, thus demonstrating its suitability for real-time EMR and EMC prediction in medical devices. The proposed device exhibited reliable performance in a clinical environment, enhancing both the safety and functionality of medical equipment. It can also be employed by medical device manufacturers for testing and predicting the EMR and EMC reliability of their products. These findings highlight the potential of machine learning in improving the safety and accuracy of medical devices.

关键词： Performance evaluation Support vector machines Medical devices signal processing algorithms Artificial neural networks Predictive models Electromagnetic compatibility Prediction algorithms Safety Internet of Things

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Enhanced Real-Time Object Detection on Customized Dataset with YOLOv9 Architecture

Enhanced Real-Time Object Detection on Customized Dataset wi...

引用

Intelligent signal processing and Effective Communication Technologies (INSPECT), IEEE International Conference on

作者： P V Adithiyan M Ranjith Kumar Rahul K Nikhil V Annamalai R Dept. of Computer Science and Engg. Amrita School of Computing Amrita Vishwa Vidyapeetham Chennai India School of Computer Science and Engineering Vellore Institute of Technology Chennai India

ISBN: (数字)9798350379525

ISBN: (纸本)9798350379532

YOLO has developed into a primary real-time object identification platform for applications such as video surveillance systems, autonomous vehicles, and robots. This research proposes an improved real-time object recognition approach based on the YOLOv9 architecture, trained on a football-specific custom dataset. The 255 annotated images in the dataset are separated into training, validation, and test sets with an 80-15-5 split ratio; there are 204 images in the training set, 38 in the validation set, and 13 in the test set. Our gelan-c model achieves remarkable detection performance with 467 layers, 25,414,044 parameters, and 102.5 GFLOPs. Upon validation, Gelan-c shows an aggregate mAP50 of 75.9%, with mAPs ranging from 32.2% (ball) to 93.7 % (goalkeeper) depending on the specified class. The impressive aspect of the Gelan-C is its 31.6 ms picture processing rate, which assures real-time performance. The methodology covers environmental setup, preparation of data, YOLOv9 architecture introduction, and experimental evaluation to demonstrate the model's football-related recognition capabilities. The suggested approach has potential applications in sports event monitoring and evaluation due to its reliability and efficacy in detecting football players, referees, and the ball.

关键词： Training YOLO Systematics signal processing algorithms Streaming media Video surveillance Real-time systems Data models Object recognition Sports

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Improved Target Trajectory Reconstruction in HFSWRs Using a DBSCAN Clustering Algorithm

Improved Target Trajectory Reconstruction in HFSWRs Using a ...

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Mediterranean Conference on Embedded Computing (MECO)

作者： Zoran Lončarević Dragan Golubović Vlatacom Institute Belgrade Serbia

ISBN: (数字)9798350387568

ISBN: (纸本)9798350387575

In this paper we propose the enhanced method for target trajectory reconstruction in High Frequency Surface Wave Radars using DBSCAN clustering algorithm. The goal is to improve the accuracy and reliability of reconstructed trajectories by clustering detections obtained by radar’s high-resolution primary signal processing. Although the paper does not focus directly on the trajectory creation from obtained clusters, it demonstrates how clustering can be successfully applied along known vessel trajectories which are verified by Automatic Identification System data. The experimentally obtained results confirm the validity of the proposed method which has significant potentials for further performance improvement of maritime vessel tracking.

关键词： Surface reconstruction Target tracking Surface waves signal processing algorithms Clustering algorithms signal processing Radar tracking

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EEG-Based Stress Detection Using Random Forest Algorithm

EEG-Based Stress Detection Using Random Forest Algorithm

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International Conference on signal processing, Communication, Power and Embedded System (SCOPES)

作者： Vamaraju Venkata Karthik Nagaraju Devarakonda Paladugu Yasaswini VIT-AP University Andhra Pradesh India

ISBN: (数字)9798331506452

ISBN: (纸本)9798331506469

This paper aims to investigate the efficacy of EEG-based stress detection using a Random Forest classifier during the Stroop Test, a key psychological assessment probing cognitive functions such as processing speed, cognitive flexibility, and attentional capacity. The study involved 10 participants (5 male,5 female), aged 18 to 30, all with normal or corrected-to-normal vision and no documented cognitive impairments. EEG data was recorded using PsychoPy software and standardized Stroop Test stimuli. signal processing, including notch and bandpass filtering, was applied to eliminate noise and emphasize relevant brainwave frequencies. Artifact rejection, notably Independent Component Analysis (ICA), was utilized to remove physiological artifacts, and epoching was used to segment the EEG data for detailed analysis. A Random Forest classifier, trained on the preprocessed EEG data, achieved an accuracy of 83.33% in distinguishing between different cognitive states. These findings highlight the efficacy of combining signal processing and machine learning techniques for cognitive state detection through EEG analysis.

关键词： Machine learning algorithms Noise signal processing algorithms Psychology Human factors Independent component analysis Electroencephalography Software Physiology Random forests

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