Search Results - signal data processing and classification

Machine-Learning-Based Classification of Electronic Devices Using an IoT Smart Meter by Paulo Eugênio da Costa Filho, Leonardo Augusto de Aquino Marques, Israel da S. Felix de Lima, Ewerton Leandro de Sousa, Márcio Eduardo Kreutz, Augusto V. Neto, Eduardo Nogueira Cunha, Dario Vieira

“…The experimental results emphasize the importance of data preprocessing—especially normalization—in optimizing model performance, revealing distinct behavior between MLP and KNN models depending on the platform. …”
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Advancing cardiac diagnostics: high-accuracy arrhythmia classification with the EGOLF-net model by Deepika Tenepalli, T. M. Navamani

“…The distinctive aspect of EGOLF-Net involves using Enhanced Gray Wolf Optimization to select optimal features, which are then processed by LSTM layers to capture temporal dependencies in the ECG data effectively.Results and Discussion The model achieved an accuracy of 99.61%, demonstrating the potential of EGOLF-Net as a highly reliable tool for classifying arrhythmias, significantly advancing the capabilities of cardiology diagnostic systems. …”
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Multistage Training and Fusion Method for Imbalanced Multimodal UAV Remote Sensing Classification by Shihao Wang, Zhengwei Xu, Yun Lin

“…In remote sensing applications, autonomous aerial vehicles (AAVs) overcome the limitations of single-sensor approaches by integrating multiple sensors and fusing cross-modal data, significantly improving target classification accuracy. …”
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Application of stochastic methods, wavelet transformations and support vectors for the study of electroencephalogram signals by Veronika V. Tolmanova, Denis A. Andrikov

“…When comparing these methods, the specific data type and task should be considered: wavelet transformation is ideal for signal processing, stochastic methods are used for random processes, and SVM excels in classification tasks. …”
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Impact of Microphytobenthos Photosynthesis on the Characteristics of the Echo Signal from Baltic Sandy Sediments by Natalia GORSKA, Ewa KOWALSKA-DUDA, Jacek MARSZAL, Jan Henryk SCHMIDT, Zygmunt KLUSEK

“…The understanding the influence of biological processes on the characteristics of the signals backscattered by the sea floor is crucial in the development of the hydroacoustical benthic habitat classification techniques. …”
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The impact of cross-validation choices on pBCI classification metrics: lessons for transparent reporting by Felix Schroeder, Stephen Fairclough, Frederic Dehais, Matthew Richins

“…Neuroadaptive technologies are a type of passive Brain-computer interface (pBCI) that aim to incorporate implicit user-state information into human-machine interactions by monitoring neurophysiological signals. Evaluating machine learning and signal processing approaches represents a core aspect of research into neuroadaptive technologies. …”
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Classification of Complex Power Quality Disturbances Based on Lissajous Trajectory and Lightweight DenseNet by Xi Zhang, Jianyong Zheng, Fei Mei, Huiyu Miao

“…To achieve a rapid response and highly accurate classification of power quality disturbances (PQDs), this paper proposes an efficient classification algorithm for PQDs based on Lissajous trajectory (LT) and a lightweight DenseNet, which utilizes the concept of Lissajous curves to construct an ideal reference signal and combines it with the original PQD signal to synthesize a feature trajectory with a distinctive shape. …”
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Bearing Fault Detection and Classification Based on Temporal Convolutions and LSTM Network in Induction Machine by Mohammad Hoseintabar Marzebali, Saeed Hasani Borzadaran, Hoda Mashayekhi, Valiollah Mashayekhi

“…The presented method does not need any preprocessing or predetermined signal transformation, and uses the raw time-series sensor data. …”
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Characterizing Breast Tumor Heterogeneity Through IVIM-DWI Parameters and Signal Decay Analysis by Si-Wa Chan, Chun-An Lin, Yen-Chieh Ouyang, Guan-Yuan Chen, Chein-I Chang, Chin-Yao Lin, Chih-Chiang Hung, Chih-Yean Lum, Kuo-Chung Wang, Ming-Cheng Liu

“…The methodology involved several steps: acquisition of multi-b-value IVIM-DWI images, image pre-processing, including correction for motion and intensity inhomogeneity, treating the multi-b-value data as hyperspectral image stacks, applying hyperspectral techniques like band expansion, and evaluating three tumor detection methods: kernel-based constrained energy minimization (KCEM), iterative KCEM (I-KCEM), and deep neural networks (DNNs). …”
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Motion Classification Based on Harmonic Micro-Doppler Signatures Using a Convolutional Neural Network by Cory Hilton, Sheng Huang, Steve Bush, Faiz Sherman, Matt Barker, Aditya Deshpande, Steve Willeke, Jeffrey A. Nanzer

“…A 7-layer convolutional neural network (CNN) multi-class classifier was developed that obtained a real time classification accuracy of 94.24<inline-formula><tex-math notation="LaTeX">$\%$</tex-math></inline-formula>, with a response time of 2 seconds per sample, and with a data processing latency of less than 0.5 seconds.…”
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Characterization and Automated Classification of Underwater Acoustic Environments in the Western Black Sea Using Machine Learning Techniques by Maria Emanuela Mihailov

“…This paper addresses this by presenting a multi-faceted framework using passive acoustic monitoring (PAM) data from fixed locations (50–100 m depth). Acoustic data are processed using advanced signal processing (broadband Sound Pressure Level (SPL), Power Spectral Density (PSD)) for feature extraction (Mel-spectrograms for deep learning; PSD statistical moments for classical/unsupervised ML). …”
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Convolution neural network and 77 ​GHz millimeter wave radar based intelligent liquid classification system by Jiayu Chen, Xinhuai Wang, Yin Xu, Ye Peng, Wen Wang, Junyan Xiang, Qihang Xu

“…The data are collected by the AWR1843 radar platform and processed by the neural network on the host PC in real-time. …”
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Enhanced Coronary Artery Disease Classification Through Feature Engineering and One-Dimensional Convolutional Neural Network by Atitaya Phoemsuk, Vahid Abolghasemi

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Classification of Arrhythmias Using a Pre-trained Deep Learning Model with Binary Images of Segmented ECG by H. Solieman, S. Sali

“…This article studies an arrhythmia classification based on binary images of surface and orthogonal ECG signals. …”
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Research on Data-driven PET-CT Gating Imaging Method by XU Shidong1, 2, 3, 4, SUN Xiaoli1, 3, 4, , LIU Shuangquan1, 3, 4, ZHENG Yushuang1, 3, 4, LI Mohan1, 3, 4, WEI Cunfeng1, 2, 3, 4

“…The PD-PCA method can process the acquired partial data during the data acquisition process, resulting in more accurate heartbeat signals, improves the accuracy of cardiac gating grouping. …”

Advances and Emerging Trends in UAV Sensor Data Fusion Technology by Xiong Liukun, Zheng Ningning

“…This paper systematically reviews the breakthrough progress of multi-source data fusion technology: the space-time fusion architecture based on the Kalman filter (UKF) reduces the dynamic attitude estimation error by 18%, and the GPU accelerated particle filtering scheme realizes 20 Hz real-time processing in real-time. …”
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Sleep Stage Classification Using Variational Mode Decomposition and Wrapper-Based Feature Selection From the Single Channel EEG by Vipin Prakash Yadav, M. A. Aswathy, Sahebgoud Hanamantray Karaddi, Sana Pavankumar Reddy, G. Pradeep Reddy, Aman Kumar

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An intelligent protection scheme based on support vector machine for fault detection in microgrid using transient signals in protection scheme by Tiwari Shankarshan Prasad

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Analysis of the influence of selected audio pre-processing stages on accuracy of speaker language recognition by Olesia Barkovska, Anton Havrashenko

“…Conclusions: In the course of the work, the best sequence of stages of pre-processing audio data was selected for use in further training of the neural network for different ways to convert signals into features. …”
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Analysis of the influence of selected audio pre-processing stages on accuracy of speaker language recognition by Олеся Барковська, Антон Гаврашенко

“…Conclusions: In the course of the work, the best sequence of stages of pre-processing audio data was selected for use in further training of the neural network for different ways to convert signals into features. …”
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