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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581-590 订阅

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Digital Filter Performance Based on Squared Error

Digital Filter Performance Based on Squared Error

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2021 International conference on advanced Computer Applications, ACA 2021

作者： Alsahlanee, Abbas T University of Thi-Qar Thi-Qar Iraq

ISBN: (纸本)9781665435031

A digital filter with infinite impulse response (IIR) was designed using the hybrid optimization methodology. The algorithm used is a dynamic-static topology of particle swarm optimization (DS-PSO) algorithm. The subject of non-linear mean square error (SE) optimization between the original and required filter responses in the passband, stopband, and transition band while tolerating permitted ripples in those bands was studied. Experimental results indicate a significant decrease in SE rates of passband and stopband with used technique, where SE values of digital IIR filter with DS-PSO were better than they were with evolutionary algorithms like DI-PSO (dynamic inertia PSO) and MDI-PSO (modified dynamic inertia PSO), at rates from about 50%-90% and 10%-90%, respectively, depending on filter's order. As a result, as compared to evolutionary algorithms, the suggested method creates greater fidelity coefficients and stable digital filters. © 2021 IEEE.

关键词： signal processing

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InfaSafe: A Comprehensive, Non-invasive Infant Monitoring System

InfaSafe: A Comprehensive, Non-invasive Infant Monitoring Sy...

引用

IEEE International conference on Electro-Information Technology

作者： Lukas Klicker Alex Maliwat Joanna Findura Xinrui Yu Mikhail Gromov Jafar Saniie Department of Electrical and Computer Engineering Embedded Computing and Signal Processing (ECASP) Research Laboratory Illinois Institute of Technology Chicago IL USA

ISBN: (数字)9798350330649

ISBN: (纸本)9798350330656

InfaSafe emerges as a novel approach to infant health monitoring, uniquely positioned at the convergence of advanced artificial intelligence and edge computing. This system is designed not as a definitive solution but as an advanced platform for comprehensive data archiving, offering valuable insights into the complex and elusive nature of Sudden Unexpected Infant Death (SUID). InfaSafe utilizes AI algorithms for real-time pose estimation, breathing surveillance, and cry analysis, all within an edge computing framework that facilitates prompt and efficient data handling. This paper explores the development and capabilities of InfaSafe, underscoring its role in providing crucial, real-time insights and alerts for caregivers and its potential to contribute significantly to our understanding of neonatal health and SUID. The focus is on leveraging technological advancements to gather comprehensive data, which can be instrumental in shaping future research and interventions in neonatal care.

关键词： Temperature sensors Temperature measurement Pediatrics Temperature distribution Prototypes Real-time systems Thermal analysis

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Early Diabetes prediction with optimal feature selection using ML based Prediction Framework

Early Diabetes prediction with optimal feature selection usi...

引用

International conference on signal processing and Communication (ICSPC)

作者： G. R. Ashisha X. Anitha Mary H. Mohamed Ashif I. Karthikeyan J. Roshan Electronics and Instrumentation Engineering Karunya Institute of Technology and Sciences Coimbatore India Robotics Engineering Karunya Institute of Technology and Sciences Coimbatore India Artificial Intelligence and Data Science Karunya Institute of Technology and Sciences Coimbatore India

The notable developments in healthcare sciences and biotechnology have led to a significant increase in information technology, for instance, clinical data and high efficiency genetic information, produced from intensive Electronic Medical data. The use of data mining and machine learning techniques are essential in the biomedical sciences to distinctly transform all accessible health records into useful knowledge. Early detection of diseases like diabetes is crucial as the growing number of people with diabetes is rising rapidly. According to an International Diabetes Federation report, the number of diabetic cases globally is expected to reach 642 million by 2040. This growing disease requires a lot of attention. Machine learning has quickly advanced, and many facets of medical health have benefited from its use. Due to their capacity for prediction, machine learning algorithms are presently significant in the healthcare industry. The major goal of this work is to design a model that can more accurately predict patients’ diabetes. The research work analyzed the hospital clinical examination data obtained from Sree Guru Hospital, Kanyakumari district, TamilNadu. To improve the accuracy of the system, imbalanced data in the real time dataset were eliminated using oversampling technique. Extra Trees classifier model, and Random Forest (RF) was used to predict diabetes. The result of this work shows that prediction of diabetes with the Extra Trees classifier had the highest accuracy of 98%. Finally, this work effectively determines the pervasiveness and detection of diabetes.

关键词： Machine learning algorithms Hospitals Transforms Predictive models signal processing Real-time systems Diabetes

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Developing License Plate Recognition System Using advanced SCRFD and LPRNet

Developing License Plate Recognition System Using Advanced S...

引用

IEEE International conference on Research, Innovation and Vision for the Future

作者： Phat Nguyen Huu Nghia Duong Tan Thien Pham Ngoc Thi Thu Trang Pham Thanh Le Hai Tien Dzung Nguyen Quang Tran Minh Hanoi University of Science and Technology Hanoi Vietnam Ho Chi Minh City University of Technology (HCMUT) Ho Chi Minh City Vietnam Vietnam National University Ho Chi Minh City (VNU-HCM) Ho Chi Minh City Vietnam

ISBN: (数字)9798331505073

ISBN: (纸本)9798331505080

The paper focuses on solving three main problems in license plate recognition in intelligent traffic. The first is to detect and classify vehicles right at the red light waiting area. The second is to detect license plates from cars detected from problem one. Finally, detect the character on the license plate. We have selected the SCRFD model - a well-known model for face detection for the first two problems of vehicle detection and license plate detection. This paper modified this vehicle and license plate detection model based on key points. The model is trained with vehicle image data on Vietnamese streets to improve the performance of vehicle and license plate detection. The results show that the SCRFD model has an accuracy of 99.48% with a processing speed of 17.075 ms, which is suitable for real-time requirements and achieves good results even in the case of heavy traffic. We use the LPRNet model to simulate character recognition for the third problem of character detection. As a result, the system could recognize with 95% accuracy and a processing speed of 3 ms. This shows that the system is feasible when applied in practice.

关键词： Accuracy signal processing algorithms signal processing Feature extraction Real-time systems Character recognition Face detection License plate recognition Monitoring Accidents

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CPO-VMD Method for Denoising sEMG signals

CPO-VMD Method for Denoising sEMG Signals

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Pervasive and Intelligent Computing (PICom), IEEE conference on

作者： Jingwen Liu Juan Zhao Seiichi Kawata Feng Wang Jinhua She School of Automation China University of Geosciences Wuhan China Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Wuhan China Engineering Research Center of Intelligent Technology for Geo-Exploration Ministry of Education Wuhan China School of Engineering Tokyo University of Technology Hachioji Tokyo Japan

ISBN: (数字)9798331522742

ISBN: (纸本)9798331522759

In the field of biomedical engineering, surface electromyography (sEMG) is a key tool for monitoring muscle activity and is widely used in various fields such as human- computer interfaces, muscle fatigue assessment, and rehabilitation training. However, sEMG signals are often affected by power line interference and other noise sources during the acquisition process, which may mask useful information. In this study, a new method combining the variational mode decomposition (VMD) and the crown porcupine optimization (CPO) algorithm, named CPO-VMD, is proposed, aiming to optimize the VMD parameters to improve the denoising effect of sEMG signals. By automatically adjusting the key parameters of the VMD through the intelligent algorithm, this method solves the problem of difficult parameter selection, furthermore, enhancing the denoising efficiency. In this paper, a simulated sEMG signal is constructed and the VMD parameters are optimized by CPO. The experimental results show that the method effectively improves the quality of sEMG signals and provides a more accurate idea for signal processing in muscle fatigue assessment and rehabilitation applications.

关键词： Training Noise reduction signal processing algorithms Interference Muscles Fatigue Optimization Surface treatment Monitoring signal to noise ratio

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Research on Emotion Recognition Method Based on Deep Learning

Research on Emotion Recognition Method Based on Deep Learnin...

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Computer Information Science and Application Technology (CISAT), International conference on

作者： Binbin Zhang Ke Cheng Xiaoyang Ni Institute of Computer Science Jiangsu University of Science & Technology Zhenjiang Jiangsu China

ISBN: (数字)9798350375107

ISBN: (纸本)9798350375114

Emotions express human’s attitude towards external things and play an important role in social life. Automatic emotion recognition technology plays an important role in the field of human-computer interaction. Facial expression analysis is the most intuitive, convenient and effective method for human to perceive emotions. However, fuzzy facial information in large scenes may lead to decreased recognition accuracy, and gait information containing emotional features is less limited by distance to a certain extent [1] . In view of the above situation, this paper integrates the gait and facial information and conducts in-depth research on the emotion recognition method based on advanced deep learning algorithm to furtherly improve the recognition accuracy. Aiming at the issue of the relatively limited sample quantity in the gait dataset, from the perspective of spatial graph convolution in the field of graph signal processing, a Shift spatio-temporal graph convolution gait emotion recognition network based on the self-attention mechanism is put forward. This effectively mitigates the drawback that the lightweight Shift spatio-temporal graph convolution network disregards the inherent connections among joints and significantly enhances the accuracy of gait emotion recognition.

关键词： Deep learning Human computer interaction Emotion recognition Information science Accuracy Convolution Face recognition signal processing algorithms Robustness Mobile handsets

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Envelope Spectrum Analysis of Noisy signal with Spectral Kurtosis to Diagnose Bearing Defect

Envelope Spectrum Analysis of Noisy Signal with Spectral Kur...

引用

International conference on Recent Advances in Design, Materials and Manufacturing, ICRADMM 2020

作者： Tomar, Arvind Singh Jayaswal, Pratesh Department of Mechanical Engineering Madhav Institute of Technology and Science MP Gwalior India

ISBN: (纸本)9789811640827

Rolling element bearing generates a complex vibration due to geometrical flaws, surface irregularities during production, faulty bearing used, and error in the associated component. These vibration signals are usually covered with background noise. This research paper has shown the extraction of fault feature frequency when the raw vibration signal is immersed with heavy noise and this can be achieved by applying kurtosis, spectral kurtosis, and bandpass filter before the envelope spectrum. Besides, this article has revealed several methods that can extract several frequency spectrum characteristics. In this paper, the vibration signal case study is discussed, and this vibration signal was immersed with heavy background noise. The envelope spectrum analysis fails to isolate the fault function from this signal and needs some advanced strategy to solve this kind of situation. Many researchers used the de-noising process as a pre-filter signal before enveloping. Also, de-noising has some drawbacks, like the loss of original features. The noise degrades the performance of signal-processing algorithms. Applying kurtosis, spectral kurtosis, and bandpass filter before the envelope spectrum is done to overcome these drawbacks. These frequency spectrum characteristics more efficiently serve a bearing health. For bearing diagnostics, envelope analysis is a useful vibration analysis tool. The frequency-domain characteristics allow for a quick assessment of a machine's health without the need for extensive diagnostics. The intermittent impacts of a defected bearing were collected using envelope analysis from the modulated signal. When the vibration signal is comparatively low in energy and "embedded" within additional vibration from the related part, it is still rational. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Frequency domain REB Spectral kurtosis Vibration analysis

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Smart Touchless Control with Credit-Card Sized Radar Sensor and Microcomputer

Smart Touchless Control with Credit-Card Sized Radar Sensor ...

引用

IEEE Global conference on Consumer Electronics (GCCE)

作者： Philip Hann Yung Lee Yilong Lu School of Electrical and Electronic Engineering Nanyang Technological University Singapore Republic of Singapore

ISBN: (纸本)9781665492331

A smart, low-cost, stand-alone, and reliable touchless human-computer interaction (HCI) using a tiny 60 GHz radar sensor and a small Raspberry Pi microcomputer with advanced radar signal processing and machine learning (ML), is proposed and implemented. Three feature extraction techniques and various ML algorithms are explored and compared. Smart gesture classification is realized by an ensemble ML model with 3 non-deep and deep learning (DL) algorithms and the majority voting scheme to improve classification accuracy. Both radar signal processing and ML algorithms are implemented in Python language in a Raspberry Pi microcomputer with an optimization by TensorFlow Lite, which reduces DL CPU time by 120 times. The proposed HCI is also integrated with a menu driven use case, which could be easily adapted to many other gesture control applications for home and industrial appliances.

关键词： Human computer interaction Home appliances Machine learning algorithms signal processing algorithms Radar Microcomputers Radar signal processing

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Research on R-wave recognition method for ECG signal in the optimized firing process

Research on R-wave recognition method for ECG signal in the ...

引用

Computers, Information processing and advanced Education (CIPAE), 2020 International conference on

作者： Jingjie Qiu Fadong Zhao Ling Lu Hongwei Zhuang Engineering University of PAP Xi'an China

ISBN: (纸本)9781665468138

R wave, as an important reference for determining each band of ECG signal, is the premise of ECG automatic analysis. In view of the problem that the preprocessing process of most R-wave recognition algorithms affects the recognition accuracy, a new R-wave recognition method based on the combination of wavelet transform and adaptive threshold method is proposed, which improves the stability and accuracy of R-wave recognition. Firstly, the ECG signal is decomposed based on wavelet, and the 2 3 rd layer wavelet signal is obtained. Then the sliding window width and initial threshold are set. The threshold is adjusted according to the signal situation and the window width is increased to quickly identify the $R$ wave. Finally, the simulation results show that the algorithm is effective in R-wave recognition of heart rate variability ECG signal during shooting.

关键词： Wavelet transforms Computers Firing Simulation Education Interference Information processing

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A Systematic Literature on Recent Advancement in Deep Learning to Diagnose Obstructive Sleep Apnea

A Systematic Literature on Recent Advancement in Deep Learni...

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International conference on advanced Computing and Communication Systems (ICACCS)

作者： Farhana Bano Arif Mohammad Abdul GITAM Deemed to be University CVR College of Engineering Hyderabad India GITAM Deemed to be University Hyderabad India

ISBN: (数字)9798350384369

ISBN: (纸本)9798350384376

Sleep apnea is a sleep-related condition characterized by the swelling or relaxation of throat muscles, causing a blockage in the upper airways. This obstruction interrupts normal breathing patterns during sleep. The current gold standard polysomnography (PSG) test for detecting sleep apnea is expensive, inconvenient, and lacks widespread availability for the general population. This underscores the need for more user-friendly and readily available solutions to diagnose sleep apnea. Collaborative efforts between Deep learning practitioners and healthcare professionals are essential to ensure that the developed techniques align with established clinical standards and contribute effectively to early diagnosis, prediction, and management of sleep apnea. This paper delves into recent studies on intelligent sleep apnea detection mechanisms. We emphasize the background of sleep apnea and the evolution of medical understanding and technological advancement. We demonstrated the diagnosis of sleep apnea through experts like medical professionals. We delve into recent studies on intelligent sleep apnea detection mechanisms using numerous deep-learning techniques and methodologies. In the transition, our attention is directed toward recent research that underscores the significance of integrating health-related features through classifier training.

关键词： Deep learning Training Adaptation models Accuracy Transfer learning signal processing algorithms Prediction algorithms Feature extraction Sleep apnea Medical diagnostic imaging

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