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

共 3499 条记录，以下是381-390 订阅

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Recursive Estimation of Statistical Metrics over Large Datasets or Real-Time signals

Recursive Estimation of Statistical Metrics over Large Datas...

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Annual conference of Industrial Electronics Society

作者： Luis Ibarra Angel Romero-Laguna Renato Galluzzi Institute of Advanced Materials for Sustainable Manufacturing Tecnologico de Monterrey Mexico City Mexico School of Engineering and Sciences Tecnologico de Monterrey Mexico City Mexico

ISBN: (数字)9781665464543

ISBN: (纸本)9781665464550

Today, digital instrumentation and the generation and analysis of data dominate the global economic landscape. Although there is no doubt that processing such data is crucial, certain statistical metrics still rely on conventional techniques that are not computationally efficient. This study introduces a median "filter" utilizing adaptive histograms (AH) that functions recursively without the requirement of storing and sorting all data. The introduced method can be utilized to estimate statistical measures for very large and expanding datasets. Alternatively, a windowed AH is also proposed to achieve online signal processing without arbitrarily discarding past values and addressing asymmetric (skewed) noise. It was subsequently shown that the method is feasible for implementation on a digital device, with its execution time assessed and its performance compared against a widely-used filtering alternative. The results showed that AHs are time-effective, accurate, and robust against uneven sampling and loss of data packets. This highlights the enabled possibility of computing statistical metrics online or over large datasets.

关键词： Histograms Data analysis Filtering Noise signal processing algorithms Personal digital devices Filtering algorithms Digital filters Standards Testing

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Mean-Field Aided QMIX: A Scalable and Flexible Q-Learning Approach for Large-Scale Agent Groups

Mean-Field Aided QMIX: A Scalable and Flexible Q-Learning Ap...

引用

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

作者： Enze Zhang Huaze Tang Xiao-Ping Zhang Wenbo Ding Shenzhen Ubiquitous Data Enabling Key Lab Shenzhen International Graduate School Tsinghua University China RISC-V International Open Source Laboratory Shenzhen China

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

Value decomposition methods are effective for multi-agent reinforcement learning (MARL), with QMIX being one of the most advanced. However, it struggles with scalability and flexibility in large-scale agent systems. The introduction of mean-field theory into MARL provides a potential solution to both scalability and flexibility challenges. In this paper, we propose Mean-Field Aided QMIX (MF-QMIX), a novel algorithm that addresses these challenges by incorporating mean-field theory into the QMIX framework. MF-QMIX significantly reduces the computational complexity from O(n) in QMIX to O(1), making it highly scalable and suitable for large-scale environments with many agents. Additionally, MF-QMIX is flexible, allowing the trained model to be applied across various group sizes without retraining, thus accommodating dynamic changes in the number of agents. Our extensive experiments demonstrate that MF-QMIX outperforms existing methods, both in computational efficiency and adaptability, across cooperative and competitive scenarios. This establishes MF-QMIX as a scalable and flexible solution for large-scale MARL problems.

关键词： Deep learning Q-learning Scalability Computational modeling signal processing algorithms signal processing Computational efficiency Computational complexity Speech processing Multi-agent systems

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Identification of Voltage Transients and Notches in a Distribution System Using True RMS and Naïve Bayes Classifier

Identification of Voltage Transients and Notches in a Distri...

引用

signal processing and Advance Research in Computing (SPARC), International conference on

作者： Surat Pyari Varsha Khair Rajat Kumar Raj Kumar EE Department DEI Agra Uttar Pradesh India EIE Department SLIET Longowal Punjab India

ISBN: (数字)9798350385205

ISBN: (纸本)9798350385212

Frequent utilization of non-linear loads and power electronics devices (semiconductor devices) cause adverse power quality (PQ) disturbances due to this both utility and end users struggle for better power quality. Earlier diagnosis of PQ was time-consuming, currently, advanced monitoring devices and techniques are involved. In this paper, the Naïve Bayes Gaussian classifier and true RMS are discussed for the accurate identification of transients and notches. True RMS calculates the square of the instantaneousness value of input and then takes the averaging of signal and displays the square root of instantaneous value. True RMS perfectly monitors sinusoidal and non-sinusoidal waveforms. Using true RMS waveforms, feature extraction has been made for single-stage disturbances. Provided features further process with Naïve Bays Gaussian classifier. The Naïve Bayes algorithm assumes that one feature is independent of the occurrence of another by evaluating the mean and standard deviation for the training period of the set. The training has been performed on 40 data sets of each disturbance. The proposed artificial intelligence (AI) classifier provides $99.2 \%$ of testing accuracy in the identification of aforementioned disturbances. Thus, Naïve Bayes algorithm recommends adequately classifying single-stage notches and transients.

关键词： Training Accuracy Power quality signal processing algorithms Feature extraction Bayes methods Classification algorithms Artificial intelligence Transient analysis Testing

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Deep learning-driven phase calibration methodology for enhanced beamforming in integrated optical phased array

Deep learning-driven phase calibration methodology for enhan...

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IEEE International conference on Group IV Photonics

作者： Jae-Yong Kim Junhyeong Kim Jinhyeong Yoon Hamza Kurt School of Electrical Engineering Korea Advanced Institute of Science & Technology (KAIST) Daejeon Republic of Korea

ISBN: (数字)9798350394047

ISBN: (纸本)9798350394054

We introduce a novel approach leveraging a deep neural network architecture for precise phase calibration in a 64-channel integrated optical phased array. Experimental results demonstrate its high predictive accuracy and phase calibration performance, which are comparable to conventional optimization-based algorithms across the multiple beam directions.

关键词： Phased arrays Integrated optics Array signal processing Optical computing Optical fiber networks Silicon photonics Prediction algorithms

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Smart Solutions for Utilizing advanced Machine Learning for Robust Credit Card Fraud Detection

Smart Solutions for Utilizing Advanced Machine Learning for ...

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

作者： Shanvi Chauhan Kanwarpartap Singh Gill Rahul Chauhan Hemant Singh Pokhariya Chitkara University Institute of Engineering and Technology Chitkara University Punjab India Computer Science & Engineering Graphic Era Hill University Dehradun Uttarakhand India Computer Science & Engineering Graphic Era Deemed to be University Dehradun Uttarakhand India

ISBN: (数字)9798350364590

ISBN: (纸本)9798350375381

This research primarily aims to shed light on the serious issue of credit card fraud, which has become much worse with the advent of internet shopping and, more specifically, the present COVID-19 epidemic. Developing a machine learning system capable of distinguishing between legitimate and fraudulent credit card transactions is the primary objective of this project, which aims to decrease an annual loss of ∃24 billion. Using data such as transformed numerical characteristics after PCA analyses, transaction time and amount, and Logistic Regression, Decision Tree Classifier, and K-Nearest Neighbours approaches are employed in the research study. The accuracy rates of these algorithms are demonstrated by the cross-validation score, ROC AUC score, and F1 score within the context of fraud detection. Improving the models’ accuracy and resilience is as simple as using statistical tests like ANOVA when selecting features. To improve the detection of fraudulent behaviour and to accurately compare the results of various algorithms, balanced datasets are essential, as this shows. There was a 91% F1 score, 92.35% ROC AUC, and 98.01% crossvalidation accuracy rate for fraud detection in the logistic regression model. Alternatively, a decision tree classifier's fraud detection cross-validation score was 96.67%, ROC AUC was 91.36%, and F1 was 90%. When it came to detecting fraud, K-Nearest Neighbours performed exceptionally well with scores of 97.63% for ROC AUC, 99.34% for cross-validation, and 97% for F1.

关键词： Logistic regression Accuracy Machine learning algorithms signal processing algorithms Machine learning Credit cards Fraud Classification algorithms Decision trees Resilience

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Low-Power B5G Communication Technology Based on Digital Beamforming and DOA Estimation

Low-Power B5G Communication Technology Based on Digital Beam...

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signal, Information and Data processing (ICSIDP), IEEE International conference on

作者： Haboob Tayebelasmaa Peng Chen School of Information Science and Engineering Southeast University Nanjing China State Key Laboratory of Millimeter Waves Southeast University Nanjing China

ISBN: (数字)9798331515669

ISBN: (纸本)9798331515676

This study explores advancements in Beyond 5G (B5G) communication systems, focusing on improving signal quality, system efficiency, and energy management. We assess the impact of Digital Beamforming (DBF), Direction of Arrival (DOA) estimation, and Deep Learning (DL) algorithms across three configurations: without advanced technology, with DBF and DOA, and with DBF, DOA, and DL integration. Numerical simulations confirm that incorporating DL significantly enhances signal integrity and energy efficiency, particularly in long-range scenarios. The proposed DL model achieves competitive performance metrics, with MSE, MAE, and RMSE values all below 0.1, indicating its effectiveness in optimizing spectral and energy efficiency. These findings highlight the potential of DL in real-time beamforming and underscore its role in the future development of B5G networks.

关键词： Deep learning Wireless communication Direction-of-arrival estimation Array signal processing 5G mobile communication Estimation Energy efficiency Mathematical models Numerical models signal to noise ratio

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Research on the Development and Application of Offline Simulation Software for Electric Vehicle Active Sound Design

Research on the Development and Application of Offline Simul...

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Cloud Computing, Big Data Applications and Software Engineering (CBASE), International conference on

作者： Songze Du Yushu Qian Liping Xie Zhien Lin Tao Huang Automotive Engineering Institute Guangzhou Automobile Group (GAC) Guangzhou China Hubei Key Laboratory of Advanced Technology for Automotive Components Wuhan University of Technology Wuhan China College of Mechanical: Engineering & Automation Fuzhou University Fuzhou China

ISBN: (数字)9798331506582

ISBN: (纸本)9798331506599

In recent years, the rise of electric vehicles (EVs) has brought Active Sound Design (ASD) to the forefront as a crucial methodology for enhancing both acoustic quality and the overall driving experience. To address the challenges associated with sound quality design in electric vehicles, an offline simulation software for Active Sound Design (ASD) systems has been developed using the MATLAB platform in this paper, where the vehicle dynamics model and three distinct sound synthesis algorithms have been incorporated into the software, and the formulation and modification of ASD system control strategies can be implemented. The simulations of vehicle operating conditions can be performed, the acoustic performance of the ASD system can be evaluated, and control strategies can be modified expeditiously based on the software developed in this paper. Moreover, the software supports integration with a Digital signal processing (DSP) hardware platform, facilitating a seamless closed-loop from sound design to real vehicle application, which contribute to substantially enhance the efficiency of ASD system development. The practical application effect of the software developed are further demonstrated on the electric SUV model, and the results show that the sound of the real vehicle test is the same as that of the software design, which proves its effectiveness and practicability in the active sound design of the vehicle.

关键词： Software design Software algorithms signal processing algorithms Digital signal processing Electric vehicles Software Mathematical models Acoustics Vehicle dynamics Software engineering

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Construction of a signal-class Experimental Platform Based on Python

Construction of a Signal-class Experimental Platform Based o...

引用

Computer Technologies (ICCTech), International conference on

作者： Ying Lu Niankun Ming Fei Dai Mingyang Wang School of Big Data and Intelligent Engineering Southwest Forestry University Kunming China

ISBN: (数字)9798350395419

ISBN: (纸本)9798350395426

There are many advanced concepts and abstractions involved in signal-class courses, which require complex mathematical formulas to be deduced and proven. The derivation process of these formulas is difficult to follow, and there is a challenge in correlating them to the experimental results, making it difficult for students to understand. In response to the non-open-source nature of Matlab, Python, as a free and open-source programming language, has powerful features and rich third-party library resources, supporting various signal processing methods and algorithms, showing unique advantages and effects. Currently, existing experimental simulation platforms for signal courses are often designed for a certain type of course, such as Digital signal processing, Communication Principle, signals and Systems, etc. These platforms only provide limited cases and experiments, and the course content they cover is relatively single and independent, lacking the connection and integration of knowledge points between different courses. To improve this situation, this article chose PyQt5 as the development tool library, with its excellent performance and rich resources, to design and develop an experimental simulation platform for signal-class courses. This platform closely follows the teaching syllabus and divides the platform into multiple modules according to the division of teaching content. Each module contains corresponding course knowledge points and experimental simulations. The platform also has various teaching service functions such as online self-study and teaching guidance, which can help teachers make teaching more vivid and interesting, and can also help students better understand abstract theories and concepts. The platform has been put into teaching and learning in our college and other brotherly universities, which receiving positive feedback and praise from teachers and students.

关键词： Simulation Instruments Education signal processing algorithms Digital signal processing Libraries Python

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Overview of Machine Learning for Bioengineering EEG signal processing

Overview of Machine Learning for Bioengineering EEG Signal P...

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Intelligent Cyber Physical Systems and Internet of Things (ICoICI), International conference on

作者： Siddhi Dhande Aahash Kamble Swapnil Gundewar Naathi Naresh Babu Praveen Kumar Department of Computer Science and Medical Engineering Datta Meghe Institute of Higher Education and Research Wardha India Department of Artificial Intellegence and Data Science Datta Meghe institute of Higher Education and Research(DU) Wardha Maharashtra Department of Artificial Intellegence and Machine Learning Datta Meghe institute of Higher Education and Research Wardha India

ISBN: (数字)9798331540661

ISBN: (纸本)9798331540678

Electroencephalography (EEG) is a well-established method in neuroscience and bioengineering that offers valuable information about brain function. Recent technological advancements have led to more complex EEG recordings, which necessitate more advanced analysis techniques. Machine learning has emerged as an asset in EEG signal processing, enabling novel approaches to comprehension and utilization of brain activity. This article provides an in-depth analysis of the intersection of machine learning and EEG signal processing in bioengineering applications. This text presents a comprehensive overview of EEG data analysis techniques, focusing on the key steps of acquisition, preprocessing, and feature extraction. It highlights the challenges and strategies used to extract valuable information from raw EEG recordings. Additionally, it surveys various machine learning algorithms, including classic and modern deep learning methods, demonstrating their effectiveness in analyzing EEG signals and opening new frontiers in the field of bioengineering. This paper explores the growing connection between machine learning and EEG signal processing, examining how they work together to enhance healthcare, neurotechnology, and our knowledge of the brain. By studying existing research and cutting-edge developments, this study intends to focus on this synergistic relationship and its importance in these fields.

关键词： Neurological diseases Deep learning Technological innovation Machine learning algorithms Medical services Feature extraction Electroencephalography Recording Neurotechnology Biomedical engineering

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Design Space Exploration at Frame-Level for Joint Decoding Energy and Quality Optimization in VVC

Design Space Exploration at Frame-Level for Joint Decoding E...

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European signal processing conference (EUSIPCO)

作者： Teresa Stürzenhofäcker Matthias Kränzler Christian Herglotz André Kaup Chair of Multimedia Communications and Signal Processing Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU) Erlangen Germany

ISBN: (数字)9789464593617

ISBN: (纸本)9798331519773

In the pursuit of a reduced energy demand of VVC decoders, it was found that the coding tool configuration has a substantial influence on the bit rate efficiency and the decoding energy demand. The advanced Design Space Exploration algorithm as proposed in the literature, can derive coding tool configurations that provide optimal trade-offs between rate and energy efficiency. Yet, some trade-off points in the design space cannot be reached with the state-of-the-art methodology, which defines coding tools for an entire bitstream. This work proposes a novel, granular adjustment of the coding tool usage in VVC. Consequently, the optimization algorithm is adjusted to explore coding tool configurations that operate on frame-level. Moreover, new optimization criteria are introduced to focus the search on specific bit rates. As a result, coding tool configurations are obtained which yield so far inaccessible trade-offs between bit rate efficiency and decoding energy demand for VVC-coded sequences. The proposed methodology extends the design space and enhances the continuity of the Pareto front.

关键词： Design methodology Bit rate signal processing algorithms Europe signal processing Encoding Energy efficiency Decoding Space exploration Optimization

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