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检索条件"任意字段=6th International Conference on Signal Processing and Machine Learning, SPML 2023"

共 763 条记录，以下是41-50 订阅

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RIME-SVM for Electromagnetic Radiation signal Identification in Coal Mining Environment 6

RIME-SVM for Electromagnetic Radiation Signal Identification...

引用

6th IEEE international conference on Power, Intelligent Computing and Systems, ICPICS 2024

作者： Zhu, Pengxue Huang, Shiqi Liu, Le Zhang, Zheng China West Normal University Nanchong China

ISBN: (纸本)9798350374315

In this study, an innovative signal classification and recognition system is proposed for the early warning needs of disasters such as impact ground pressure in coal mining. the RIME-SVM model is introduced, which combines the Rime Optimization Algorithm and Support Vector machine to optimize the classifier parameter configuration and significantly improve the classification performance. Experiments have demonstrated that the model has excellent signal classification ability in industrial environments, which is especially suitable for the identification of electromagnetic radiation signals in coal mine environments. Compared with the traditional method, RIME-SVM significantly improves the classification accuracy, demonstrates the efficiency and accuracy of processing complex industrial signal data, and brings important progress to the field of coal mine safety monitoring technology. © 2024 IEEE.

关键词： Coal mines

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HLS-Based FPGA Implementation of Multi-Label learning for Radar signal Recognition 6

HLS-Based FPGA Implementation of Multi-Label Learning for Ra...

引用

6th international conference on Information Communication and signal processing, ICICSP 2023

作者： Guo, Lei Pan, Wen Zhao, Jian Li, Hongbo Beijing Institute of Space Long March Vehicle Beijing China School of Electronics and Information Engineering Harbin Institute of Technology Harbin China

ISBN: (纸本)9798350339994

In modern Electronic Support (ES) systems, the Radar signal Recognition (RSR) component necessitates real-time processing of overlapping signals. In comparison to CPUs and GPUs, Field-Programmable Gate Arrays (FPGAs) exhibit enhanced energy efficiency and low-latency streaming capabilities, rendering them better suited for energy-sensitive or real-time machine learning endeavors. this paper presents a proposed system that leverages multi-label deep learning technology to autonomously recognize overlapping signals. the system is deployed on the FPGA platform, and optimization of the multi-label learning inference network is achieved through High-Level Synthesis (HLS) and loop pipeline techniques. the implemented solution comprises an energy-efficient HLS-based FPGA convolutional multi-label accelerator specifically designed for RSR applications, utilizing the Virtex-7 platform. the system operates at a frequency of 150MHz, resulting in a 35.2% increase in throughput and a significant 90.3 % reduction in power consumption compared to GPU-based platform. © 2023 IEEE.

关键词： electronic support FPGA high-level synthesis multi-label learning radar signal recognition

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Application and Development of Oil Pipeline Magnetic Flux Leakage Detection Based on machine learning Algorithm 6

Application and Development of Oil Pipeline Magnetic Flux Le...

引用

6th international conference on Intelligent Control, Measurement and signal processing, ICMSP 2024

作者： Wang, Zheng Tang, Yi Yu, Jianbin Liu, Bing Wu, Yaxin Hebei University of Engineering Science Digital Intelligent Industry College Shijiazhuang China Vocational University Aviation College of Hunan Automotive Engineering Zhuzhou China Hebei X-Into Technology Co. Ltd Shijiazhuang China

ISBN: (纸本)9798331516550

In order to evaluate the condition of oil pipeline, the main nondestructive testing method currently used is magnetic flux leakage testing technology. the magnetic flux leakage data of pipeline are collected by magnetic flux leakage detector, and the magnetic flux leakage signal curve is drawn according to the data, and then the variation characteristics of the curve are judged manually to judge the defects on pipeline components. this method has great error and low efficiency. With the rapid development of artificial intelligence technology, many magnetic flux leakage detection technologies based on machine learning algorithms have been proposed, which can realize more efficient and accurate intelligent detection. In this paper, the intelligent methods of pipeline magnetic flux leakage detection are summarized. At first, the basic principle of magnetic flux leakage detection is briefly introduced, and then the detection methods combining multiple physical fields and the methods using machine learning in pipeline magnetic flux leakage detection are emphasized in detail. Finally, the summary is made, and the problems that the intelligent methods still face are discussed. © 2024 IEEE.

关键词： Magnetic leakage

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A Hybrid machine learning Framework to Improve Parkinson's Disease Prediction Accuracy 6

A Hybrid Machine Learning Framework to Improve Parkinson's D...

引用

6th international conference on signal processing and Information Security, ICSPIS 2023

作者： Bediya, Ronak Ravikumar, R.N. Mishra, Krishnanand Kandoi, Khushi Singh, Siddhant Gautam Kumar Singh, Sushil Marwadi University Department of Computer Engineering Rajkot India

ISBN: (纸本)9798350329599

Parkinson's Disease (PD) is one of the second most prevalent neurodegenerative ailment worldwide, behind Alzheimer's disease, according to the World Health Organization (WHO). PD affects both men and women equally and tends to become more prevalent as people age. the prevalence of PD in those over 65 ranges from 2% to 3%. In this research we have used Parkinson's Dataset from UCI repository and employed machine learning and Deep learning algorithms. the research study suggests a hybrid approach for predicting PD that combines Artificial Neural Networks (ANN), Recurrent Neural Networks (RNN), and Decision Trees (DT). It performs better than individual models and offers guidance for selecting a course of therapy. When we evaluated the accuracy of different models, the findings showed that the (ANN) alone could only reach 84% accuracy whereas the combination of ANN and RNN could obtain 85% accuracy. the hybrid algorithm's accuracy was further boosted to 88% with the use of Decision Trees (DT). these findings highlight the value of utilizing a variety of machine learning techniques to enhance Parkinson's disease prediction in order to uncover pertinent characteristics, capture temporal dependencies, and enhance understanding for clinical decision-making to enhance the quality of life over a growing population. © 2023 IEEE.

关键词： Recurrent neural networks

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Analog Features Extractor for Ultra-Low Power Embedded AI Listening and Keyword Spotting 6

Analog Features Extractor for Ultra-Low Power Embedded AI Li...

引用

6th international conference on AI Circuits and Systems (AICAS)

作者： Marzetti, Sebastian Gies, Valentin Barchasz, Valentin Barthelemy, Herve Glotin, Herve Aix Marseille Univ Univ Toulon IM2NP CNRSUMR 7334 Marseille France Aix Marseille Univ Univ Toulon CNRS LISDYNI Marseille France Univ Toulon & Var Ctr Intelligence Artificielle Acoust Nat Toulon France

ISBN: (纸本)9798350383638;9798350383645

In this paper, a novel ultra-low power audio system is designed and tested on keyword spotting (KWS). It implements mixed (analog-digital) processing and allows always-on detection with an average power consumption of < 77 mu W, detecting one word every 30 seconds. Combining mixed signal processing, it achieves 88% classification accuracy between 8 classes. It is based on an always-on Analog Features Extractor (AFE), which provides spectral information, and main processor wakes-up only when necessary for minimal power consumption. then, this information is sampled by a low frequency ADC to compose a spectrogram processed with a Convolutional Neural Network (CNN) for classification. It can be used for long term environmental monitoring or intelligent Internet of things (IoT) using small batteries such as a single CR2032 coin cell to reach up to 1 year of autonomy.

关键词： Ultra Low-Power Keyword Spotting signal processing Embedded System Mixed signal processing Analog Digital Embedded Artificial Intelligence machine learning Voice Detection Long Term Monitoring Soundscape Monitoring

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Research on Gene Identification Algorithms Based on signal processing Techniques 6

Research on Gene Identification Algorithms Based on Signal P...

引用

6th international conference on Artificial Intelligence and Computer Applications, ICAICA 2024

作者： Shi, Chongwei University of Michigan Oral and Maxillofacial Surgery MI48109 United States

ISBN: (纸本)9798350377255

With the rapid advancements in genomics and bioinformatics, gene identification has assumed a pivotal role in contemporary biological research. Gene identification involves pinpointing regions within DNA sequences that encode proteins or functional RNAs, which is of profound significance for understanding the fundamental mechanisms of life, exploring the genetic underpinnings of diseases, and drug development. While traditional experimental biology methods offer reliable tools, they are often time-consuming and costly. Consequently, the application of signal processing techniques for gene identification has emerged as a focal point of current research. signal processing, with its formidable analytical capabilities when dealing with vast amounts of data, is particularly well-suited for high-dimensional and complex data such as DNA sequences. By converting DNA sequences into numerical signals and employing spectral analysis methods like Fourier and wavelet transforms, intrinsic features of gene structures can be unveiled. Moreover, integrating machine learning algorithms such as Support Vector machines (SVM) further enhances the accuracy of gene identification. signal processing methods provide robust support for the efficient and automated processing of complex genomic data from both prokaryotic and eukaryotic organisms. © 2024 IEEE.

关键词： Wavelet transforms

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Research on Pedestrian Passenger Flow Intelligent Transportation System Based on Computer Big Data 6

Research on Pedestrian Passenger Flow Intelligent Transporta...

引用

2023 IEEE 6th international conference on Information Systems and Computer Aided Education, ICISCAE 2023

作者： Han, Zhifang Liaoning University of Technology Liaoning Jinzhou121000 China

ISBN: (纸本)9798350313444

this paper proposes a statistical analysis method of bus passenger flow based on machine learning model. the system includes data collection, data processing, data analysis and statistics and information processing modules. the system realizes the functions of passenger flow data from investigation input, processing adjustment, statistical analysis, result release and query. By establishing a traffic network analysis method based on machine learning, the transfer situation, passenger flow density and dynamic distribution characteristics of urban public transport are analyzed. Visually display and quantitatively analyze the changing characteristics of each time period of the day. In order to minimize the complexity of passenger data processing and passenger index calculation, a corresponding model is established to forecast passengers. the simulation verifies that this scheme can effectively improve the operation speed, efficiency and throughput of the system. this algorithm can reduce the cost of the system and ensure the security of the system. © 2023 IEEE.

关键词： Computer data processing system intelligent transportation machine learning passenger flow pedestrians

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Illness Diagnosis Based on Ayurveda (Vat, Pitta, Kapha Dosha) Using machine learning 6

Illness Diagnosis Based on Ayurveda (Vat, Pitta, Kapha Dosha...

引用

6th IEEE international conference on Advances in Science and Technology, ICAST 2023

作者： Deshmukh, Manjusha Khemchandani, Maahi Saraswati College of Engineering Navi Mumbai India

ISBN: (纸本)9798350359817

Ayurveda's eight clinical limbs, including pulse analysis, are crucial for early disease diagnosis. the pulse (nadi) is the most crucial, and imbalances in vata, pitta, and kapha doshas can identify illness. this paper aims to analyze the relationship between Nadi Pariksha and pulse signal from a peripheral pulse analyzer. the research aims to develop pulse sensing and analysis methods using modern technologies to support Ayurvedic physicians. Prakruti, a balance in these doshas, influences physiological and psychological traits.. © 2023 IEEE.

关键词： machine learning

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Accurate Stereo Matching Based on 3D Labels in Driving Scenes 23

Accurate Stereo Matching Based on 3D Labels in Driving Scene...

引用

6th international conference on signal processing and machine learning, spml 2023

作者： Xu, Zhengkang Zhang, Xiaoshi Xie, Qiwei Li, Jie Long, Qian Aerospace Information Research Institute Chinese Academy of Sciences China Beijing University of Technology China Tianjin University of Science and Technology China

ISBN: (纸本)9798400707575

We present a novel global stereo matching approach aimed at achieving accurate and smooth 3D label disparity estimation based on LocalExp. this innovative scheme can effectively assign an appropriate 3D label to each pixel from an infinite label space and generate a continuous disparity map with subpixel accuracy. To address the persistent challenge of weak texture, our method adopts a novel approach by utilizing calculated initial labels rather than random labels, which yields enhanced disparity maps. the process involves employing grid cells of the same size as the original image pair, allowing the reduced image pair to capture more detailed information in local regions. After enlarging the disparity map of reduced image pair and replacing the random initial labels with the refined ones, we achieve superior results in our method. Experimental results demonstrate the high matching accuracy of our method in driving scenes, and its generalizability is verified. © 2023 ACM.

关键词： Stereo image processing

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Trainable Pre-processing Approach for Efficient Neural Network-based 4K-QAM Soft Demodulator 6

Trainable Pre-Processing Approach for Efficient Neural Netwo...

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6th international conference on Computer Communication and the Internet (ICCCI)

作者： Triamwichanon, Donlaporn Muangkammuen, Patinya Suthisopapan, Puripong Khon Kaen Univ Dept Elect Engn Fac Engn Khon Kaen Thailand

ISBN: (纸本)9798350351774;9798350351767

the practicality of the densely packed and throughput boosting modulation scheme, 4K-QAM, is obstructed by its ultra-high complexity soft demodulator that is essential for generating log-likelihood ratios (LLR) as an input for modern error correction decoders. A pre-processing technique that facilitates efficient training for neural network-based 4K-QAM soft demodulator, which inherently possesses very low computational complexity, is proposed in this paper. the results clearly demonstrate that such an alternative 4K-QAM demodulation can achieve an identical bit error rate (BER) performance with substantial computation complexity reduction compared with the conventional optimal soft demodulator.

关键词： 4K-QAM soft demodulator soft bit LLR machine learning neural networks

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