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检索条件"主题词=Signal Processing Algorithms"

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Impact of Aromatherapy Diffusion on Healthcare Monitoring

Impact of Aromatherapy Diffusion on Healthcare Monitoring

引用

International Symposium in Sensing and Instrumentation in IoT Era (ISSI)

作者： Gonçalo Ribeiro Octavian Postolache ISCTE-Instituto Universitário de Lisboa and Instituto de Telecomunicaáões Lisbon Portugal

ISBN: (数字)9798350378689

ISBN: (纸本)9798350378696

Aromatherapy, recognized for its therapeutic benefits in stress reduction and relaxation enhancement, uses heat diffusers to disperse aromatic compounds, creating an environment where the aroma mixes with the vapors. These study aims to categorize aromas based on their effects and contributes to understanding aromatherapy’s impacts on healthcare. By systematically exploring the effects of specific aromas on physiological parameters, this research seeks to optimize aromatherapy practices and expand its therapeutic applications in healthcare settings. This study provides insights into how aromatherapy can be tailored to enhance well-being and support holistic approaches to health management. Participants were exposed to each aroma in sessions structured to include baseline (neutral phase) measurements followed by aroma exposure phases. Physiological parameters including heart rate, heart rate variability, respiratory rate, blood oxygen saturation, and galvanic skin response were continuously monitored using a multichannel sensing system based on photoplethysmography signal processing algorithms and galvanic skin response acquisition. Participants categorized each aroma as stimulating or relaxing and rated its intensity on a scale from 1 to 3. The study presented good results in terms of validating the effects of the different aromas, as well as correlating these effects with variations in physiological parameters, validating aromatherapy’s potential in healthcare and promoting its effective use for enhancing overall well-being.

关键词： Correlation Phase measurement signal processing algorithms Medical services Photoplethysmography Skin Sensors Biomedical monitoring Monitoring Stress

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Advanced Ensemble Machine Learning for ECG Arrhythmia Classification

Advanced Ensemble Machine Learning for ECG Arrhythmia Classi...

引用

International Conference on Advanced Technologies for Communications, ATC

作者： Duc Khuat van Minh Tuan Nguyen Posts and Telecommunications Institute of Technology Ha Noi Viet Nam

ISBN: (数字)9798350353983

ISBN: (纸本)9798350353990

Early diagnosis and classification of cardiac arrhyth-mias from electrocardiogram (ECG) recordings play a crucial role in smart healthcare systems for monitoring the health of individuals with heart conditions. However, the nonlinear nature and low amplitude of ECG recordings pose challenges to accurate classification, and the performance of traditional machine learning (ML) classifiers remains uncertain due to poorly modeled correlations between learning parameters. Ad-dressing these limitations, this study introduces an automated cardiac arrhythmia classification framework, leveraging effective signal processing techniques including median filtering, Butter-worth (BW) filtering, Ensemble Empirical Mode Decomposition (EEMD), and Hilbert Transform (HT). Subsequently, optimized input parameters for ML classifiers are determined using the Marine Predator Algorithm (MPA) on the training set, coupled with cross-validation (CV) to identify optimal parameters for each model. Finally, the proposed Random Forest (RF) model demonstrates high classification performance on the test dataset with optimized input hyperparameters, achieving an average accuracy of 99.90%, precision of 99.74%, recall of 99.73%, and Fl score of 99.74%. This proposed method outperforms in terms of accuracy across all classes, achieving 99.74 %, 99.92 %, 99.89 %, 99.98%, and 99.94% for classes normal (N), supraventricular ectopic (S), ventricular ectopic (V), fusion (F), and unknown (Q), respectively. These results highlight the potential of this approach as a robust clinical tool, particularly in developing countries.

关键词： Radio frequency Accuracy Correlation Arrhythmia Noise signal processing algorithms Medical services Electrocardiography Recording Optimization

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Efficient Color Image Denoising using DWT-based Noise Estimation and Adaptive Wiener Filter

Efficient Color Image Denoising using DWT-based Noise Estima...

引用

International Conference on Imaging, signal processing and Communication (ICISPC)

作者： Yuan-Kang Lee Jian- Jiun Ding Graduate Institute of Communication Engineering National Taiwan University Taipei Taiwan

ISBN: (数字)9798350367157

ISBN: (纸本)9798350367164

The majority of the existing denoising algorithms either suffer from suboptimal denoising performance, making them prone to failure in specific scenarios, or demand significant computational resources, thereby substantially reducing their practical applicability. For real-world noisy color images, the noise level varies across the R, G, and B channels. Hence, the paramount challenge of real-world color image denoising is how to achieve the most accurate image noise estimation in different channels and execute denoising with efficiency. Our proposed image denoising method integrates an improved Discrete Wavelet Transform (DWT)-based image noise estimation method with the enhanced adaptive Wiener filter. Experimental results shown that our proposed algorithm not only maintains excellent denoising performance but also exhibits the fastest computational speed compared to other existing state-of-the-art algorithms.

关键词： Industries Wiener filters Noise signal processing algorithms Estimation Color Robustness

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Security and Threats in Aviation: Cryptographic Based Network Security System

Security and Threats in Aviation: Cryptographic Based Networ...

引用

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

作者： S Harsha Vardhan Mahesh Kumar Jha Raveesh Hegde Monika Singh P Rubini Amar Choudhary Electronics and Communication Engg. CMR Institute of Technology Bengaluru India Computer Science and Engg. SoET CMR University Bengaluru India Electronics and Communication Engg. ACED Alliance University Bengaluru India

ISBN: (数字)9798350385205

ISBN: (纸本)9798350385212

In present era, aviation communication technologies provides safe, effective, and efficient transfer of data between aircraft and facilities on the ground. Cryptography is essential to maintain the confidentiality, integrity, and legitimacy of data transfer in aviation systems. The safe key exchange and authentication that are essential components of aviation communication security are expertly ensured algorithms. Advanced Encryption Standard (AES), Rivest- Shamir- Adleman (RSA), Blowfish, Fernet, and Elliptic curve cryptog- raphy (ECC) are a few of the symmetric and asymmetric encryption method explored for aviation system. In this paper, the development and application of AES and Fernet encryption algorithms, specifically designed for safe client- server communication for the aviation industry sector, are the mainly implemented and performance analyzed. The public key is used for encryption, while the private key is used for decryption. The effectiveness of these algorithms in safeguarding data transfer between a client and server within an airline network is put to the test with great rigor.

关键词： Technological innovation Heuristic algorithms signal processing algorithms Network security Elliptic curve cryptography Data transfer Encryption Cryptography Servers Standards

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Research on Fault Diagnosis Model Method of AGC Hydraulic System Based on Transfer Learning

Research on Fault Diagnosis Model Method of AGC Hydraulic Sy...

引用

signal processing and Intelligent Computing (SPIC), International Conference on

作者： Ji Zang College of Automation and Electrical Engineering Shengyang Ligong University Shenyang China

ISBN: (数字)9798350368888

ISBN: (纸本)9798350368895

The research focuses on the AGC hydraulic system of a cold rolling mill in a certain steel ***-We propose a model that combines transfer learning and CNN for fault diagnosis of AGC hydraulic systems. This model effectively solves the diagnostic difficulties caused by data scarcity. By using simulated fault data as the source domain and real fault data as the target domain, we utilize Hilbert to reduce data differences. Subsequently, CNN learned the fault features in the source domain, and then fine tuned the target domain data to construct a transfer learning fault diagnosis model, which was successfully applied to the fault diagnosis of AGC hydraulic systems.

关键词： Fault diagnosis Training Computational modeling Transfer learning signal processing algorithms Hydraulic systems signal processing Data models Convolutional neural networks Steel

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Camera-Based Remote Heart Rate Monitoring Using Photoplethysmography

Camera-Based Remote Heart Rate Monitoring Using Photoplethys...

引用

International Conference on signal processing and Information Security (ICSPIS)

作者： Asmaa Hosni Hamad Sultan Alaryani Sultan Abdallah Almaqbali Khameis Rashed Alsereidi Nasser Saeed Alkaabi Neethu Mohan Mohammed Atef Electrical and Communication Engineering Department United Arab Emirates University Al Ain United Arab Emirates

ISBN: (数字)9798350368673

ISBN: (纸本)9798350368680

Ensuring individuals’ well-being through health monitoring is vital for safety and overall health. Monitoring vital signs, such as heart rate, is essential for understanding physiological conditions in diverse environments. Traditional methods often involve cumbersome and invasive equipment, making them impractical in confined or remote settings. As a result, there is growing interest in non-invasive, remote monitoring technologies that can track vital signs in real-time without compromising comfort or activities. This paper introduces a system utilizing camera-based photoplethysmography (PPG) technology for remote monitoring of vital signs. The system extracts and analyzes PPG signals from video footage captured by cameras, relying on advanced signal processing algorithms to accurately extract these signals despite challenges like motion artifacts and low signal-to-noise ratios. It employs techniques such as motion artifact removal, adaptive filtering, and real-time peak detection algorithms to improve PPG signal quality and reliability. The study demonstrates the system’s capabilities in non-contact physiological monitoring and underscores its potential benefits in various settings. This research highlights the system’s ability to extract critical health information using cameras, emphasizing its significance in maintaining individuals’ health and well-being.

关键词： Heart rate signal processing algorithms Cameras Photoplethysmography Real-time systems Reliability Biomedical monitoring Remote monitoring signal to noise ratio Motion artifacts

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A Detection Tool Damage Framework Based on Improved GMM and Cross-Correlation Method

A Detection Tool Damage Framework Based on Improved GMM and ...

引用

Prognostics and System Health Management Conference (PHM-Qingdao)

作者： Biao Ma Sen Li Dexin Chen Yue Zhang Zhihua Song Ming Zhao School of Mechanical Engineering Xi'an Jiaotong University Xi'an China

ISBN: (数字)9798350354010

ISBN: (纸本)9798350354027

As the core structure of the machine tool, cutting tools' health status monitoring has the vital meaning for machining process of advanced CNC. Besides of the tool cutting process, workpiece replacement and other processes are also interspersed in the process of automatic machining. However, traditional signal processing methods only can be used for continuous signals. When facing the actual signal, it is not possible to adaptively lock the effective processing. The redundancy of acquisition signal limits the application of intelligent diagnosis method in machine tools. In this paper, a tool damage monitoring method based on improved Gaussian mixture model (GMM) and cross-correlation algorithm is proposed. Firstly, an improved Gaussian mixture model is used to adaptively intercept the effective machining process, then, the cross-correlation method is used to determine the consistency between machining processes and to determine the health state of the tool. Finally, the effectiveness of the method is verified by real engineering data, and the tool state health monitoring is realized.

关键词： Smoothing methods Redundancy signal processing algorithms Clustering algorithms Machining signal processing Robustness Machine tools Monitoring Gaussian mixture model

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Scatterer Model Assisted Altitude Estimation in FDA Radar

Scatterer Model Assisted Altitude Estimation in FDA Radar

引用

IEEE International Conference on Information Communication and signal processing (ICICSP)

作者： Yihuan Lin Yu-Hang Xiao Lei Huang State Key Laboratory of Radio Frequency Heterogeneous Integration (Shenzhen University) Shenzhen China

ISBN: (数字)9798350355895

ISBN: (纸本)9798350355901

In meter-wave radar altitude measurement, inad-equate ground absorption easily leads to significant multipath effects. Traditional processing methodologies, when deployed without considering the implications of multipath phenomena, often incur substantial performance degradation. To mitigate these issues, this study employs the scatterer model, a technique widely utilized in object localization, to effectively counteract multipath distortions. It is worth noting that the scatterer model, typically reliant on the presence of at least two targets or scatter-ers-or known positions of a solitary target or scatterer-faces implementation challenges within radar systems due to these stringent prerequisites. To bridge this gap, we utilize Frequency-Diverse Array (FDA) radar for the initial estimation of path delays and angles, followed by a refinement step employing the scatter model. We integrated insights from previous literature on the scatterer model to devise a non-iterative target altitude estimation algorithm. This algorithm is based on the Best Linear Unbiased Estimator and is combined with FDA radar, by using he angles of arrival (AOA) and time differences of arrival (TDOA) to achieve efficient altitude estimation without iterative processes. This validation underscores the feasibility of integrating FDA radar in conjunction with scatterer models to circumvent the limitations of traditional radar processing in altitude estimation.

关键词： Location awareness Analytical models Radar measurements Estimation signal processing algorithms Scattering Radar Radar scattering Numerical models Multiple signal classification

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Assessment of Damage Degree in High-Rise Building Structures Based on Continuous Wavelet Transform and Improved ResNet18

Assessment of Damage Degree in High-Rise Building Structures...

引用

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

作者： Zhi Zheng Jiaxin Zhang You Dong Department of Civil and Environmental Engineering The Hong Kong Polytechnic University Hong Kong China

ISBN: (数字)9798331515669

ISBN: (纸本)9798331515676

Vibration-based Structural Health Monitoring (SHM) has garnered significant attention due to its ability to monitor health state in real-time. Current applications of SHM mainly focus on the occurrence of damage but are unable to assess the severity of the structural damage. Achieving damage severity assessment through multi-channel signal processing has become a key focus of current research. To address this issue, we propose an improved TriAxial-ResNet18 architecture, which is capable of processing acceleration signals in the three directions simultaneously, thereby enabling comprehensive damage assessment. In this study, finite element modelling (FEM) is utilized to emulate various damage states in the investigated high-rise building, generating signals under different health states. Time-frequency feature imaging based on continuous wavelet transform (CWT) is employed to visualize the vibration signals, converting the raw signals into two-dimensional images for training and validating the deep learning model. Experimental results indicate that the TriAxial-ResNet18 model achieves high classification accuracy in distinguishing various damage levels across different monitoring points. By integrating simulation technology with deep learning methods, this research provides an economical and efficient solution for SHM in high-rise buildings.

关键词： Deep learning Training Accuracy Continuous wavelet transforms Architecture Buildings signal processing algorithms Data models Finite element analysis Monitoring

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Scaling mmWave MU-MIMO: Information-Theoretic Guidance Using Real-World Data

Scaling mmWave MU-MIMO: Information-Theoretic Guidance Using...

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Asilomar Conference on signals, Systems & Computers

作者： Canan Cebeci Oveys Delafrooz Noroozi Upamanyu Madhow ECE Dept. University of California Santa Barbara CA U.S.A.

ISBN: (数字)9798350354058

ISBN: (纸本)9798350354065

Advances in millimeter wave (mmWave) radio frequency integrated circuits (RFICs) potentially enable the realization of one RF chain per antenna for arrays with hundreds of antenna elements, opening up the possibility of fully digital beamforming for truly massive multiuser (MU-)MIMO. In order to scale adaptive multiuser detection to such regimes, an attractive option, explored in several recent studies, is to reduce complexity via “beamspace” techniques which exploit the sparsity of mmWave channels. In this paper, we derive information-theoretic benchmarks using measured mmWave channels, comparing the capacity with ideal channel knowledge without complexity constraints, and several related benchmarks, with that attainable with low-complexity linear adaptive multiuser detection strategies in beamspace. We hope that the resulting insights regarding the price paid for various levels of simplification in processing will guide performance/complexity tradeoffs in the design of scalable signal processing algorithms.

关键词： Multiuser detection Dimensionality reduction Training Spectral efficiency signal processing algorithms Benchmark testing Radiofrequency integrated circuits Millimeter wave communication Information theory Antenna arrays

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