Search Results - signal data processing and classification

BLE Signal Processing and Machine Learning for Indoor Behavior Classification by Yi-Shiun Lee, Yong-Yi Fanjiang, Chi-Huang Hung, Yung-Shiang Huang

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Electromyography Signal Acquisition, Filtering, and Data Analysis for Exoskeleton Development by Jung-Hoon Sul, Lasitha Piyathilaka, Diluka Moratuwage, Sanura Dunu Arachchige, Amal Jayawardena, Gayan Kahandawa, D. M. G. Preethichandra

“…Electromyography (EMG) has emerged as a vital tool in the development of wearable robotic exoskeletons, enabling intuitive and responsive control by capturing neuromuscular signals. This review presents a comprehensive analysis of the EMG signal processing pipeline tailored to exoskeleton applications, spanning signal acquisition, noise mitigation, data preprocessing, feature extraction, and control strategies. …”
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Comparing acoustic representations for deep learning-based classification of underwater acoustic signals: A case study on orca (Orcinus orca) vocalizations by Fabio Frazao, Ruth Joy, Michael Dowd

“…Passive acoustic monitoring of marine mammal vocalizations often relies on automated detectors to process large quantities of data. Many automated systems use spectrograms as a way to represent acoustic information, including those built on deep artificial neural networks (DNNs). …”
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Comprehensive Performance Comparison of Signal Processing Features in Machine Learning Classification of Alcohol Intoxication on Small Gait Datasets by Muxi Qi, Samuel Chibuoyim Uche, Emmanuel Agu

“…This study evaluates 27 signal processing features handcrafted from accelerometer gait data across five domains: time, frequency, wavelet, statistical, and information-theoretic. …”
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Classification of fetal abnormalities based on CTG signal by Baghdad Science Journal

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Low-Memory-Footprint CNN-Based Biomedical Signal Processing for Wearable Devices by Zahra Kokhazad, Dimitrios Gkountelos, Milad Kokhazadeh, Charalampos Bournas, Georgios Keramidas, Vasilios Kelefouras

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Experimental statistical analysis of radar signals reflected from weather hazards by E. S. Boyarenko, E. A. Bolelov, O. V. Vasiliev, S. S. Korotkov

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AI-Based Classification of Pediatric Breath Sounds: Toward a Tool for Early Respiratory Screening by Lichuan Liu, Wei Li, Beth Moxley

“…Breath sound detection and segmentation were performed using digital signal processing techniques. Multiple features—including Mel–Frequency Cepstral Coefficients (MFCCs), Linear Prediction Coefficients (LPCs), Linear Prediction Cepstral Coefficients (LPCCs), spectral entropy, and Dynamic Linear Prediction Coefficients (DLPCs)—were extracted to capture both time and frequency characteristics. …”
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A reliable approach for identifying acute lymphoblastic leukemia in microscopic imaging by Mimosette Makem, Levente Tamas, Lucian Bușoniu

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Substantiation of source data on the parametric algorithms for the classification of weather hazards by O. V. Vasiliev, E. S. Boyarenko, K. I. Galaeva

“…These criteria are cumbersome and complicate the process of automating the WH classification. In this case, there is a natural desire to generalize the criteria and optimize them in accordance with the theory of distinguishing statistical hypotheses. …”
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Enhancing bathymetric LiDAR by applying fractal dimensions to signal processing by J. Rhomberg-Kauert, L. Dammert, G. Mandlburger

“…This introduces an independent measure, which is calculated prior to the signal processing step. The advantage of this initial classification is that the echo pulse extraction could be further improved without need for human supervision, as the correlation between the number of echo pulses and the fractal dimension hints towards a measure of estimating the number of echo pulses within a recorded full-waveform. …”
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Data-Driven Modeling and Enhancement of Surface Quality in Milling Based on Sound Signals by Paschalis Charalampous

“…The present study introduces an AI (Artificial Intelligence) framework for surface roughness assessment in milling operations through sound signal processing. As industrial demands escalate for in-process quality control solutions, the proposed system leverages audio data to estimate surface finish states without interrupting production. …”
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Enhancing sleep stage classification with ballistocardiogram signals: feature selection using attention mechanism and XGBoost by Chao Luo, Banteng Liu, Jiayu Chai, Zhijian Teng

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Machine Learning Ship Classifiers for Signals from Passive Sonars by Allyson A. da Silva, Lisandro Lovisolo, Tadeu N. Ferreira

“…The accurate automatic classification of underwater acoustic signals from passive SoNaR is vital for naval operational readiness, enabling timely vessel identification and real-time maritime surveillance. …”
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Mitigating the Impact of Electrode Shift on Classification Performance in Electromyography Applications Using Sliding-Window Normalization by Taichi Tanaka, Isao Nambu, Yasuhiro Wada

“…In a previous study, while transfer learning narrowed the classification performance gap to −1% in an eight-class scenario under electrode shift, they imposed the burden of additional data collection and re-training. …”
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Ultrafast Time-Stretch Optical Coherence Tomography Using Reservoir Computing for Fourier-Free Signal Processing by Weiqing Liao, Tianxiang Luan, Yuanli Yue, Chao Wang

“…This classification-based approach simplifies the data processing pipeline. …”
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Knee Osteoarthritis Detection and Classification Using Autoencoders and Extreme Learning Machines by Jarrar Amjad, Muhammad Zaheer Sajid, Ammar Amjad, Muhammad Fareed Hamid, Ayman Youssef, Muhammad Irfan Sharif

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Multi-label classification with deep learning techniques applied to the B-Scan images of GPR by El Karakhi, Soukayna, Reineix, Alain, Guiffaut, Christophe

“…With the emergence of deep neural networks and with a learning phase on a large number of Bscan, it becomes possible to extract almost instantaneously the characteristics of GPR radar data. In this study, a multi-label classification (MLC) model based on transfer learning and data augmentation has been developed to generate multiple information elements on the same image and to realize classification. …”
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Comparative analysis of audio-MAE and MAE-AST models for real-time audio classification by Lesia Mochurad

“…Real-time audio classification is a complex process that requires systems to be highly accurate and reduce latency in signal processing. …”
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Complex-Valued CNN-Based Defect Reconstruction of Carbon Steel from Eddy Current Signals by Bing Chen, Tengwei Yu

“…Notably, this approach processes the complete complex-valued signal without relying on prior structural parameters or baseline data, thereby achieving substantial improvements in both defect visualization and classification performance. …”
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