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

共 3477 条记录，以下是321-330 订阅

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Cyber Shield An AI-Driven Solution For Identifying Phishing Websites

Cyber Shield An AI-Driven Solution For Identifying Phishing ...

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International conference on Communications and signal processing

作者： V K Ashwinkumar V Loganathan Department of Computer Science and Engineering Saveetha Engineering College Chennai India

ISBN: (数字)9798350353068

ISBN: (纸本)9798350353075

Phishing attacks, a prevalent form of social engineering and cyber threats, exploit human vulnerabilities to deceive internet users into divulging sensitive data for deceptive intentions. Among these, URL-based phishing attacks pose significant risks to individuals and organizations by manipulating trust in URLs to steal data or inject malware. This paper seeks to address this issue by employing machine learning models and deep neural networks trained on a dataset containing both malicious and normal URLs. Extracting relevant features from URL and website content, the models are evaluated and compared based on their predictive performance. This research contributes to enhancing cybersecurity measures against URL-based phishing attacks by leveraging advanced machine learning techniques for detection and prevention.

关键词： Uniform resource locators Support vector machines Phishing Scalability signal processing algorithms Artificial neural networks Organizations

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The Role of Noise signal processing and Feature Extraction in Automotive Fault Diagnosis

The Role of Noise Signal Processing and Feature Extraction i...

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Telecommunications and Power Electronics (TELEPE), International conference on

作者： Yaodong Hao Daliang Chen China Automotive Technology&Research Center Co. Ltd Tianjin China

ISBN: (数字)9798350369212

ISBN: (纸本)9798350380002

In today's technology-driven automotive industry, accurate and efficient fault diagnosis is crucial for maintaining vehicle performance and ensuring road safety. As automotive technology continues to evolve, especially in the areas of electronic control systems and advanced driver assistance systems (ADAS), the requirements for fault diagnosis technologies are also increasing. This study aims to delve into the application and effectiveness of noise signal processing and feature extraction technologies in modern automotive fault diagnosis. By precisely processing and analyzing the noise signals generated during vehicle operation, this study endeavors to extract key features from these signals to more accurately diagnose potential faults. This paper first introduces the basic principles of noise signal processing and feature extraction and their application background in automotive fault diagnosis. It elaborates on how these technologies adapt to the complexity of modern automotive systems and discusses their potential in handling big data and real-time diagnostics. Then, the paper explores different signal processing technologies, such as Fast Fourier Transform (FFT) and wavelet transform, and their effectiveness in extracting features from automotive noise signals. These technologies have shown significant advantages in identifying fault characteristics, distinguishing fault types, and predicting fault development trends. Furthermore, through practical case studies, the paper demonstrates how to use these technologies to diagnose specific automotive faults, such as engine anomalies, braking system problems, and electronic control unit (ECU) faults. These cases not only show the application of the technologies but also emphasize the importance of interdisciplinary knowledge in fault diagnosis, including mechanical engineering, electrical engineering, and computer science. The research finds that combining advanced noise signal processing technologies with precise fea

关键词： Fault diagnosis Industries Wavelet transforms Accuracy Fast Fourier transforms Noise signal processing algorithms Feature extraction Electric vehicles Automotive engineering

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Single-Path High-Resolution Digital Pulse Width Modulator (HRDPWM) without SR Latch

Single-Path High-Resolution Digital Pulse Width Modulator (H...

引用

IEEE Annual Southern Power Electronics conference (SPEC)

作者： Marziyeh Hajiheidari Joel Fushekati Mohammad Emad Bas Vermulst Jeroen Van Duivenbode Department of Electrical Engineering Eindhoven University of Technology Eindhoven The Netherlands

ISBN: (数字)9798350351156

ISBN: (纸本)9798350351163

High-Resolution Digital Pulse Width Modulators (HRDPWMs) increase duty cycle resolution compared to standard pulse-width modulation, offering fine-grained control that is ideal for high-frequency power converters. By employing advanced digital signal processing and innovative circuit design, HRDPWMs reduce quantization error, thereby improving dynamic range and output quality. This paper presents a single-path HRDPWM using delay primitives. Comparisons between the proposed architecture and other solutions demonstrate the method's effectiveness in terms of linearity, simplicity of implementation, and reduced dependence on Placement and Routing (PAR) algorithms, as well as voltage and temperature variations. The proposed single-path HRDPWM architecture and a conventional dual-path HRDPWM method are implemented and compared on an AMD Artix-7 FPGA.

关键词： Temperature distribution Temperature dependence Latches Switching frequency Linearity signal processing algorithms Routing signal resolution Field programmable gate arrays Standards

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Hybridization of Wavelet Decomposition and Machine Learning for Brain Waves based Emotion Recognition

Hybridization of Wavelet Decomposition and Machine Learning ...

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advanced Innovations in Smart Cities (ICAISC), International conference on

作者： Mirna Ali Saeed Mian Qaisar Tamanna Anurulafchar Electrical and Computer Engineering Department Effat University Jeddah Saudi Arabia LINEACT Laboratory CESI Engineering School Villeurbanne France Computer science Department Effat University Jeddah Saudi Arabia

Emotion recognition has sparked the interest of researchers from a variety of disciplines. Studies have demonstrated that brain signals may be utilized to characterize a wide range of emotional states. Electroencephalogram (EEG) measures the cerebral activity. Therefore, by exploiting the EEG signals the emotion states can be determined. In this study the EEG signals undergoes through filtering, segmentation, Wavelet Packet Decomposition (WPD), feature mining, and classification. The machine learning algorithms used for classifications are “Decision Tree” (DT), “Support Vector Machine” (SVM), and K-Nearest Neighbor” (K-NN) algorithms are used for categorization. Their performance is compared for automatically identifying the emotion state. It is determined that the best performer is SVM. It has attained 98.2% accuracy, 97.3% precision, 97.3% recall, 98.7% specificity, 97.3% F1, 97.3% kappa, and 99.3% AUC.

关键词： Emotion recognition Technological innovation Machine learning algorithms Smart cities signal processing algorithms Support vector machine classification signal processing

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Machine Learning Application for Classification of Brain Tumors Through MR Images to Detect Malignancy 4

Machine Learning Application for Classification of Brain Tum...

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4th IEEE International conference on Computing, Communication, and Intelligent Systems, ICCCIS 2023

作者： Chanda, Kunal Tiwari, Jay Verdhan Banik, Souvik Ahmed, Washef Advanced Signal Processing Group Centre for Development of Advanced Computing Kolkata India Medical Electronics Ministry of Electronics and Information Technology New Delhi India

ISBN: (纸本)9798350306118

In the present paper classification of Brain Tumours through MRI imaging as Fine, Medium and Coarse textures is proposed. The other methods of classification of brain tumours that are frequently used are based on size, shape and spread of the tumour. The importance of classifying tumours based on texture is that there is correlation between malignancy of the tumour and the texture of the tumour. The other methods normally don't give this information. In this paper relation between texture of the tumour and it's malignancy has been explored. Four methods of feature extraction have been used, in order to obtain texture information of the tumour. They are, Gray Level Co-occurrence Matrix (GLCM), Gradient Matrix (GM), Laplacian Of Gaussian (LoG) and the Run-Length Matrix (RLM). Once the features are extracted, they are passed to Decision Tree classification algorithm. The classification algorithms classified the extracted features as having Fine, Medium or Coarse textures. The result obtained showed correlation between texture of the tumour and its malignancy. If the texture is predicted as Fine, then it is most likely a non-malignant, or benign tumour, and its diagnosis is most likely going to give a Complete response. A Medium texture would most likely give a Partial response, and is not very malignant. This is important, since knowing this information can benefit the patient and clinicians. This improves the diagnostic confidence of the clinicians and allows them to diagnose and report multiple patients that results in lower cost of the examination as well as access to diagnostics to people living in remote areas. © 2023 IEEE.

关键词： Decision trees

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advanced Comparative Analysis of Time-Frequency signal processing Techniques and Machine Learning Models for Optimized Biomedical Classification

Advanced Comparative Analysis of Time-Frequency Signal Proce...

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2025 International conference on Intelligent Control, Computing and Communications, IC3 2025

作者： Reddy, Uma Lakhanpal, Sorabh Singh, Jay Meena, Talari Shakir, Sarah Abbas Sakthi, G. Mallireddy, Lohitha New Horizon College of Engineering Department of Artificial Intelligence and Machine Learning Bangalore India Lovely Professional University Phagwara India Gl Bajaj Institute of Technology and Management Dept. of Eee U.P. Gr. Noida India Mlr Institute of Technology Department of CSE-AI&ML Telangana Hyderabad India College of Medical Technology The Islamic University Radiology Techniques Department Najaf Iraq Galgotias university Department of Electrical Electronics & Communication Engineering Uttar Pradesh Gerater Noida203201 India Griet Department of Cse Telangana Hyderabad India

ISBN: (纸本)9798331527495

This research aims at comparing the performance of TF analysis in classifying biomedical signals, with special reference to ECG and EEG signals using PhysioNet database as the major source of data. Fourier Transform and Wavelet Transform, are used to extract the feature from signals for the time-frequency analysis. These features are then used for classification using the algorithms like Random Forest and Neural Networks. All computations are performed with SciPy, NumPy, and Scikit-learn libraries. The study assesses the methods considering the classification performance, computational cost, and interpretability toward determining which approaches offer the best solution for the biomedical signal classification. It needs to be mentioned that the presented results are aimed at helping researchers and practitioners in choosing proper methods for biomedical applications of ECG and EEG signals, focused on the PhysioNet database. © 2025 IEEE.

关键词： Wavelet decomposition

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Brain Controlled Robotic Car Using Mind Wave

Brain Controlled Robotic Car Using Mind Wave

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Multimedia processing, Communication & Information Technology (MPCIT), International conference on

作者： Shwetha B Sathyanarayana S V Yuvaraj K E Sujith S Sahana M Soppin Sujal G Chinchnekar Dept. of E& CE JNNCE. Shivamogga India Affiliated to VTU Belagavi

ISBN: (数字)9798350375466

ISBN: (纸本)9798350375473

In this paper, Mind Wave technology based brain-controlled robotic car is implemented. The objective is to create an innovative human machine interface, enabling users to control the cars movements seamlessly through their brain signals and facial movement. The Mind Wave headset, equipped with EEG sensors, captures and interprets the users brainwave pattern, translating them into specific commands for the robotic car. The system integrates real time signal processing algorithms to extract relevant features from the EEG data, mapping them to predefined control commands for the robotic car. A comprehension control architecture is established to facilitate smooth navigation, including forward, backward, left, and right movements Additionally, advanced features such as speed modulation and obstacle avoidance are implemented to enhance the user experience and ensure safety. Furthermore, the work explores the challenges and limitations of the proposed brain-controlled robotic car system, addressing issues related to signal noise,user training,and the adaptability of the *** directions for improvement and expansion of the system are discussed, aiming to refine the interface, increase responsiveness, and broken the range of supported commands.

关键词： Adaptation models signal processing algorithms Robot sensing systems Chatbots Brain modeling Electroencephalography Sensors Safety Automobiles Robots

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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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An Enhanced Car Parking Detection Using YOLOv8 Deep Learning Capabilities

An Enhanced Car Parking Detection Using YOLOv8 Deep Learning...

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Electronics, Communication and signal processing (ICECSP), International conference on

作者： Vishnu Shankar Vibhav Singh Vibhash Choudhary Sachin Agrawal Bhaskar Awadhiya Electronics & Communication Engineering National Institute of Technology Delhi

ISBN: (数字)9798350364590

ISBN: (纸本)9798350375381

This research paper introduces a novel approach for car parking slot detection using YOLOv8, an advanced object detection algorithm renowned for its state-of-the-art performance. The objective of this study is to address the increasing demand for efficient parking management in urban areas, where optimizing parking space utilization is essential to alleviate traffic congestion. The proposed model efficiently uses the power of YOLOv8's single-shot detection. It uses anchor boxes to automatically identify and classify vacant and occupied parking slots in real time. By utilizing YOLOv8's deep learning capabilities, the proposed method achieved impressive accuracy and efficiency in car parking slot detection. The simulation results showcase the system's effectiveness, providing valuable insights for urban mobility planning and management.

关键词： Deep learning Accuracy Simulation Urban areas signal processing algorithms Object detection signal processing Car parking Real-time systems Traffic congestion

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EXTENSION FOR HANDWRITTEN CHARACTER RECOGNITION USING RNN-GRU ALGORITHM

EXTENSION FOR HANDWRITTEN CHARACTER RECOGNITION USING RNN-GR...

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signal processing, Computation, Electronics, Power and Telecommunication (IConSCEPT), International conference on

作者： VS Moulishwar P Karthikevan V Mahendra Kumar A Srihariharan Department of Computer science and Engineering Sri Manakula Vinayagar Engineering College Puducherry India

ISBN: (数字)9798331540685

ISBN: (纸本)9798331540692

Recognizing handwritten characters is a complex task in pattern recognition and computer vision, demanding advanced techniques to enable computers to discern and transform handwritten or printed characters into a digital format. The integration of the GRU algorithm within the RNN-CNN hybrid methodology marks a significant advancement in handwritten character recognition. By transitioning from a character-by-character to a line-by-line recognition approach, the system aims to improve the conversion of handwritten text into digital formats. GRU enhances context awareness by predicting subsequent letters in incomplete words, thereby facilitating more accurate recognition. Leveraging the IAM dataset not only provides ample training data but also aids in error detection and correction, enhancing the overall accuracy and reliability of the system. This innovative approach underscores a commitment to advancing OCR capabilities beyond isolated characters and words, toward more comprehensive and context-aware text recognition, thereby paving the way for more effective digitization of handwritten documents.

关键词： Handwriting recognition Accuracy Text recognition signal processing algorithms Training data Transforms signal processing

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