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Journal articles on the topic 'Signal segmentation'

Author: Grafiati

Published: 30 May 2022

Last updated: 31 July 2025

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1

Beasley, Ryan A. "Semiautonomous Medical Image Segmentation Using Seeded Cellular Automaton Plus Edge Detector." ISRN Signal Processing 2012 (May 17, 2012): 1–9. http://dx.doi.org/10.5402/2012/914232.

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Segmentations of medical images are required in a number of medical applications such as quantitative analyses and patient-specific orthotics, yet accurate segmentation without significant user attention remains a challenge. This work presents a novel segmentation algorithm combining the region-growing Seeded Cellular Automata with a boundary term based on an edge-detected image. Both single processor and parallel processor implementations are developed and the algorithm is shown to be suitable for quick segmentations (2.2 s for voxel brain MRI) and interactive supervision (2–220 Hz). Furtherm

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Kaushik, Jamuna, and Abhishek Misal. "Segmentation of Phonocardiograms Signal." International Journal of Engineering Research and Advanced Technology 4, no. 7 (2018): 1–10. http://dx.doi.org/10.31695/ijerat.2018.3284.

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Jellema, Renger H., Shaji Krishnan, Margriet M. W. B. Hendriks, Bas Muilwijk, and Jack T. W. E. Vogels. "Deconvolution using signal segmentation." Chemometrics and Intelligent Laboratory Systems 104, no. 1 (2010): 132–39. http://dx.doi.org/10.1016/j.chemolab.2010.07.007.

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Huang, Lin, Jianjun Yan, Shiyu Cai, Rui Guo, Haixia Yan, and Yiqin Wang. "Automated Segmentation of the Systolic and Diastolic Phases in Wrist Pulse Signal Using Long Short-Term Memory Network." BioMed Research International 2022 (August 21, 2022): 1–9. http://dx.doi.org/10.1155/2022/2766321.

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Purpose. Single-period segmentation is one of the important steps in time-domain analysis of pulse signals, which is the basis of time-domain feature extraction. The existing single-period segmentation methods have the disadvantages of generalization, reliability, and robustness. Method. This paper proposed a period segmentation method of pulse signals based on long short-term memory (LSTM) network. The preprocessing was performed to remove noises and baseline drift of pulse signals. Thus, LabelMe was used to label each period of the pulse signals into two parts according to the location of th

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Greibus, Mindaugas, and Laimutis Telksnys. "Rule Based Speech Signal Segmentation." Journal of Telecommunications and Information Technology, no. 4 (June 27, 2023): 37–43. http://dx.doi.org/10.26636/jtit.2010.4.1094.

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This paper presents the automated speech signal segmentation problem. Segmentation algorithms based on energetic threshold showed good results only in noise-free environments. With higher noise level automatic threshold calculation becomes complicated task. Rule based postprocessing of segments can give more stable results. Off-line, on-line and extrema types of rules are reviewed. An extrema-type segmentation algorithm is proposed. This algorithm is enhanced bya rule base to extract higher energy level segments from noise. This algorithm can work well with energy like features. The experiment

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Zhang, Zhike, Shuixin Zhang, and Hongyu Feng. "Extraction and Visualization of Ocular Blood Vessels in 3D Medical Images Based on Geometric Transformation Algorithm." Journal of Healthcare Engineering 2021 (February 28, 2021): 1–13. http://dx.doi.org/10.1155/2021/5573381.

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Data extraction and visualization of 3D medical images of ocular blood vessels are performed by geometric transformation algorithm, which first performs random resonance response in a global sense to achieve detection of high-contrast coarse blood vessels and then redefines the input signal as a local image shielding the global detection result to achieve enhanced detection of low-contrast microfine vessels and complete multilevel random resonance segmentation detection. Finally, a random resonance detection method for fundus vessels based on scale decomposition is proposed, in which the image

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Kucharczyk, Daniel, Agnieszka Wyłomańska, Jakub Obuchowski, Radosław Zimroz, and Maciej Madziarz. "Stochastic Modelling as a Tool for Seismic Signals Segmentation." Shock and Vibration 2016 (2016): 1–13. http://dx.doi.org/10.1155/2016/8453426.

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In order to model nonstationary real-world processes one can find appropriate theoretical model with properties following the analyzed data. However in this case many trajectories of the analyzed process are required. Alternatively, one can extract parts of the signal that have homogenous structure via segmentation. The proper segmentation can lead to extraction of important features of analyzed phenomena that cannot be described without the segmentation. There is no one universal method that can be applied for all of the phenomena; thus novel methods should be invented for specific cases. The

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El rai, Marwa Chendeb, Muna Darweesh, and Mina Al-Saad. "Semi-Supervised Segmentation of Echocardiography Videos Using Graph Signal Processing." Electronics 11, no. 21 (2022): 3462. http://dx.doi.org/10.3390/electronics11213462.

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Machine learning and computer vision algorithms can provide a precise and automated interpretation of medical videos. The segmentation of the left ventricle of echocardiography videos plays an essential role in cardiology for carrying out clinical cardiac diagnosis and monitoring the patient’s condition. Most of the developed deep learning algorithms for video segmentation require an enormous amount of labeled data to generate accurate results. Thus, there is a need to develop new semi-supervised segmentation methods due to the scarcity and costly labeled data. In recent research, semi-supervi

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Nguyen, Philon, Thanh An Nguyen, and Yong Zeng. "Segmentation of design protocol using EEG." Artificial Intelligence for Engineering Design, Analysis and Manufacturing 33, no. 1 (2018): 11–23. http://dx.doi.org/10.1017/s0890060417000622.

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AbstractDesign protocol data analysis methods form a well-known set of techniques used by design researchers to further understand the conceptual design process. Verbal protocols are a popular technique used to analyze design activities. However, verbal protocols are known to have some limitations. A recurring problem in design protocol analysis is to segment and code protocol data into logical and semantic units. This is usually a manual step and little work has been done on fully automated segmentation techniques. Physiological signals such as electroencephalograms (EEG) can provide assistan

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Mao, Yongjiang, Wenjuan Ren, Xipeng Li, Zhanpeng Yang, and Wei Cao. "Sep-RefineNet: A Deinterleaving Method for Radar Signals Based on Semantic Segmentation." Applied Sciences 13, no. 4 (2023): 2726. http://dx.doi.org/10.3390/app13042726.

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With the progress of signal processing technology and the emergence of new system radars, the space electromagnetic environment becomes more and more complex, which puts forward higher requirements for the deinterleaving method of radar signals. Traditional signal deinterleaving algorithms rely heavily on manual experience threshold and have poor robustness. To address this problem, we designed an intelligent radar signal deinterleaving algorithm that was completed by encoding the frequency characteristic matrix and semantic segmentation network, named Sep-RefineNet. The frequency characterist

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Pak, A., A. Zhumageldikyzy, and N. Ermekova. "A REVIEW: METHODS OF AUTOMATIC SPEECH SEGMENTATION." Herald of Kazakh-British technical university 18, no. 3 (2021): 89–94. http://dx.doi.org/10.55452/1998-6688-2021-18-3-89-94.

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Segmentation is a process of dividing a speech signal into the basic units of language. Segmentation of the speech signals is one of the most important tasks in automatic speech processing systems. This paper proposes a review of methods of automatic speech segmentation. Moreover, methods of wavelet and Hilbert- Huang transformations and techniques based on hidden Markov models are considered.

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Mei, Jincheng, Chuang Li, Yu Cao, Xuyang Wang, and Ze Liu. "Radar signal sorting based on image semantic segmentation." Journal of Physics: Conference Series 2807, no. 1 (2024): 012036. http://dx.doi.org/10.1088/1742-6596/2807/1/012036.

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Abstract This paper explores the application of image semantic segmentation networks in radar signal sorting. Unlike traditional methods, it does not depend on predefined parameters or prior information, so it can show enhanced adaptability in dealing with a more complex electromagnetic environment. Firstly, the pulse descriptor PDW of collected signals is encoded into corresponding sequence images, and then U-net is used to train pulse sequence images. The trained model can be applied with high accuracy in radar signal sorting. At the same time, an attention mechanism is introduced into the U

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Liu, Zhaoting, Xiaodong Zhuang, and Nikos Mastorakis. "Speech Segmentation Based on the Computation of Local Signal Manifold Dimension." WSEAS TRANSACTIONS ON SYSTEMS 22 (May 12, 2023): 416–22. http://dx.doi.org/10.37394/23202.2023.22.44.

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A new computational method of unvoiced and voiced speech segmentation is proposed from the perspective of local linear manifold analysis of speech signals. It is based on the estimation of the dimension of short-time linear subspace. The subspace dimensional characteristics of the single phoneme signal are studied. The local signal vector set is analyzed by using the PCA algorithm to estimate the dimension of the data matrix formed by framing. The local PCA is used to analyze the speech signal to achieve the segmentation of unvoiced and voiced pronunciation. Simulation experiments prove the ef

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Vrbancic, Grega, Iztok Jr Fister, and Vili Podgorelec. "Automatic Detection of Heartbeats in Heart Sound Signals Using Deep Convolutional Neural Networks." Elektronika ir Elektrotechnika 25, no. 3 (2019): 71–76. http://dx.doi.org/10.5755/j01.eie.25.3.23680.

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The analysis of non-stationary signals commonly includes the signal segmentation process, dividing such signals into smaller time series, which are considered stationary and thus easier to process. Most commonly, the methods for signal segmentation utilize complex filtering, transformation and feature extraction techniques together with various kinds of classifiers, which especially in the field of biomedical signals, do not perform very well and are generally prone to poor performance when dealing with signals obtained in highly variable environments. In order to address these problems, we de

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Hu, Zhongshuo, Jianwei Yang, Dechen Yao, Jinhai Wang, and Yongliang Bai. "Subway Gearbox Fault Diagnosis Algorithm Based on Adaptive Spline Impact Suppression." Entropy 23, no. 6 (2021): 660. http://dx.doi.org/10.3390/e23060660.

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In the signal processing of real subway vehicles, impacts between wheelsets and rail joint gaps have significant negative effects on the spectrum. This introduces great difficulties for the fault diagnosis of gearboxes. To solve this problem, this paper proposes an adaptive time-domain signal segmentation method that envelopes the original signal using a cubic spline interpolation. The peak values of the rail joint gap impacts are extracted to realize the adaptive segmentation of gearbox fault signals when the vehicle was moving at a uniform speed. A long-time and unsteady signal affected by w

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Moghaddamjoo, A. "Constraint optimum well-log signal segmentation." IEEE Transactions on Geoscience and Remote Sensing 27, no. 5 (1989): 633–41. http://dx.doi.org/10.1109/tgrs.1989.35947.

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Hubert, Paulo, Linilson Padovese, and Julio Stern. "A Sequential Algorithm for Signal Segmentation." Entropy 20, no. 1 (2018): 55. http://dx.doi.org/10.3390/e20010055.

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Penny, William D., and Stephen J. Roberts. "Dynamic Models for Nonstationary Signal Segmentation." Computers and Biomedical Research 32, no. 6 (1999): 483–502. http://dx.doi.org/10.1006/cbmr.1999.1511.

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Peleshko, Dmytro, Yuriy Pelekh, Mariya Rashkevych, Yuriy Ivanov, and Iryna Verbenko. "AUTOMATIC INITIAL SEGMENTATION OF SPEECH SIGNAL BASED ON SYMMETRIC MATRIX OF DISTANCES." INTERNATIONAL JOURNAL OF COMPUTERS & TECHNOLOGY 13, no. 9 (2014): 4782–91. http://dx.doi.org/10.24297/ijct.v13i9.2344.

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The most common issue of a speech signals analisys and artificial intelligence systems development is determining of temporal and frequency charactristics. Thatâ€™s because any undetermined signal is defined as a nonlinear object. But it is always possible to select time interval from the signals with a given discretization period. Such an interval is called quasistationary interval.The quasistationary interval in combination with the speech signal quality characteristics can be used to build a parametric model of the speech signal. As a result, such a model will be very helpful for solving di

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Chen, Tao, Yu Lei, Limin Guo, and Boyi Yang. "MRCS-Net: Multi-Radar Clustering Segmentation Networks for Full-Pulse Sequences." Remote Sensing 17, no. 9 (2025): 1538. https://doi.org/10.3390/rs17091538.

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To facilitate the full-pulse sequence received by a radar reconnaissance receiver, this study proposed a clustering segmentation method for radar signals. Owing to the influence of the complex electromagnetic environment, the probability of the occurrence of time–frequency overlapping of signals increases, and the demand for signal localization and classification becomes higher. However, most existing studies have only classified and identified individual pulse signals and lack the ability to analyze signals for full pulses. This study proposed a multi-radar cluster-based segmentation network

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Wang, Lanyun, Bin Song, Hao Wang, et al. "The Role of MRI in the Diagnosis of Papillary Thyroid Carcinoma with Hobnail Features: A Case-Control Study." Journal of Medical Imaging and Health Informatics 10, no. 3 (2020): 552–59. http://dx.doi.org/10.1166/jmihi.2020.2983.

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Purpose—To evaluate whether Magnetic Resonance Imaging (MRI) preoperatively could be used as a potentially routine tool to recognize papillary thyroid carcinoma with hobnail features (HPTC). Methods—This retrospective study included 97 patients with confirmed papillary thyroid carcinoma (PTC) who underwent thyroid MRI examination one week before surgery between January 2014 and June 2017. The differences in age, sex, tumor size, each MRI feature (lesion shape; signal heterogeneity on T1WI, T2WI and diffusion weighted imaging (DWI); significantly high signal on T2WI, segmentation linear low sig

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Bielozorova, Yana. "A Method of Increasing the Accuracy of Segmentation of a Speech Signal Based on Its Fractal Characteristics." Information Theories and Applications 29, no. 2 (2022): 159–86. http://dx.doi.org/10.54521/ijita29-02-p03.

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The paper considers the analysis of approaches to the segmentation of the speech signal into vocalized and not vocalized fragments. The necessity of improving the accuracy of segmentation due to a more accurate description of the process of speech signal representation is shown. A rational method for calculating the fractal dimension to increase the accuracy of the speech signal segmentation process has been determined.

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Bershov, Vyacheslav, and Oleksandr Zhuchenko. "ADAPTIVE METHOD OF FORMING COMPLEX SIGNALS ENSEMBLES BASED ON MULTI-LEVEL RECURRENT TIME-FREQUENCY SEGMENT MODELING." Science-based technologies 63, no. 3 (2024): 257–64. http://dx.doi.org/10.18372/2310-5461.63.18953.

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The article investigates the implementation of an adaptive method for forming ensembles of complex signals based on multilevel recurrent time-frequency segmentation. It addresses the key challenges faced by cognitive wireless networks operating in dynamic radio frequency environments with high levels of interference, necessitating rapid adaptation to changes in the spectral characteristics of signals. The study substantiates the need for adaptive filters and specific transformations to enhance signal processing quality, particularly in environments with high variability in frequency characteri

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L., M. Merlin Livingston, and Mary Cynthia S. "Region of Interest Prediction using Segmentation." International Journal of Engineering and Advanced Technology (IJEAT) 9, no. 5 (2020): 613–17. https://doi.org/10.35940/ijeat.D8786.069520.

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Segmentation separates an image into different sections badsed on the desire of the user. Segmentation will be carried out in an image, until the region of interest (ROI) of an object is extracted. Segmentation reliability predicts the progress of the various segmentation techniques. In this paper, various segmentation methods are proposed and quality of segmentation is verified by using quality metrics like Mean Squared Error (MSE),Signal to Noise Ratio (SNR), Peak- Signal to Noise Ratio (PSNR), Edge Preservation Index (EPI) and Structural Similarity Index Metric (SSIM).

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Kumar, Surinder, Sumika Chauhan, Govind Vashishtha, Sunil Kumar, and Rajesh Kumar. "Fault Feature Extraction Using L-Kurtosis and Minimum Entropy-Based Signal Demodulation." Applied Sciences 14, no. 18 (2024): 8342. http://dx.doi.org/10.3390/app14188342.

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The health of mechanical components can be assessed by analyzing the vibration and acoustic signals they produce. These signals contain valuable information about the component’s condition, often encoded within specific frequency bands. However, extracting this information is challenging due to noise contamination from various sources. Narrow-band amplitude demodulation presents a robust technique for isolating fault-related information within the signal. This work proposes a novel approach based on cluster-based segmentation for demodulating the signal and extracting the frequency band of int

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Zheng, Xingjian, Bo Wang, and Yongqi Ge. "A Segmentation Model of ECU Excitation Signal Based on Characteristic Parameters." Sensors 21, no. 12 (2021): 4165. http://dx.doi.org/10.3390/s21124165.

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According to the basic structure and working principle of the excitation signal sensors of a diesel engine electronic control unit (ECU), a segmentation model of an ECU excitation signal based on characteristic parameters (ESCP-SM) is proposed. In the ESCP-SM, the ECU excitation signal is divided into several parts, and each part has its characteristic parameters model. By using the same global parameters and strictly controlling each part’s proportional parameters, the ESCP-SM can achieve signal alignment and dynamic frequency modulation. Based on the simulation experiment, spectrum analysis

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Rosa, Renan Garcia, Bruno Pereira Barella, Iago Garcia Vargas, José Ricardo Tarpani, Hans-Georg Herrmann, and Henrique Fernandes. "Advanced Thermal Imaging Processing and Deep Learning Integration for Enhanced Defect Detection in Carbon Fiber-Reinforced Polymer Laminates." Materials 18, no. 7 (2025): 1448. https://doi.org/10.3390/ma18071448.

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Carbon fiber-reinforced polymer (CFRP) laminates are widely used in aerospace, automotive, and infrastructure industries due to their high strength-to-weight ratio. However, defect detection in CFRP remains challenging, particularly in low signal-to-noise ratio (SNR) conditions. Conventional segmentation methods often struggle with noise interference and signal variations, leading to reduced detection accuracy. In this study, we evaluate the impact of thermal image preprocessing on improving defect segmentation in CFRP laminates inspected via pulsed thermography. Polynomial approximations and

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Weiser, Sydney C., Brian R. Mullen, Desiderio Ascencio, and James B. Ackman. "Data-driven segmentation of cortical calcium dynamics." PLOS Computational Biology 19, no. 5 (2023): e1011085. http://dx.doi.org/10.1371/journal.pcbi.1011085.

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Demixing signals in transcranial videos of neuronal calcium flux across the cerebral hemispheres is a key step before mapping features of cortical organization. Here we demonstrate that independent component analysis can optimally recover neural signal content in widefield recordings of neuronal cortical calcium dynamics captured at a minimum sampling rate of 1.5×106 pixels per one-hundred millisecond frame for seventeen minutes with a magnification ratio of 1:1. We show that a set of spatial and temporal metrics obtained from the components can be used to build a random forest classifier, whi

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Ben Nasr, Mohamed Chiheb, Sofia Ben Jebara, Samuel Otis, Bessam Abdulrazak, and Neila Mezghani. "A Spectral-Based Approach for BCG Signal Content Classification." Sensors 21, no. 3 (2021): 1020. http://dx.doi.org/10.3390/s21031020.

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This paper has two objectives: the first is to generate two binary flags to indicate useful frames permitting the measurement of cardiac and respiratory rates from Ballistocardiogram (BCG) signals—in fact, human body activities during measurements can disturb the BCG signal content, leading to difficulties in vital sign measurement; the second objective is to achieve refined BCG signal segmentation according to these activities. The proposed framework makes use of two approaches: an unsupervised classification based on the Gaussian Mixture Model (GMM) and a supervised classification based on K

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Chen, Baojia, Kaiwen Li, and Yipeng Guo. "Effective Denoising of Multi-Source Partial Discharge Signals via an Improved Power Spectrum Segmentation Method Based on Normalized Spectral Kurtosis." Sensors 25, no. 12 (2025): 3798. https://doi.org/10.3390/s25123798.

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In the field of partial discharge (PD) analysis, traditional methods typically employ single-source PD signal-processing techniques. However, these approaches exhibit significant limitations when applied to transformers with relatively complex structures. To overcome these limitations and achieve precise characterization of composite PD signatures, this study proposes an improved power spectrum segmentation method (IPSK) based on spectral kurtosis. Firstly, normalized power spectral kurtosis is used to select the appropriate parameters. Then, through the improved power spectrum segmentation me

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Li, Ting, Tian Shuang Qiu, and Hong Tang. "A Heart Sound Segmentation Method Based on Cyclostationary Envelope." Applied Mechanics and Materials 347-350 (August 2013): 2280–83. http://dx.doi.org/10.4028/www.scientific.net/amm.347-350.2280.

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Segmentation is generally an important prior process to automatic analysis of heart sounds. The proposed algorithm achieved this in separated cycles. First, the cyclostationary envelopes of heart sound signals are calculated. Second, a threshold is chosen to separate S1 and S2. The segmentation is automatic, and robust to noise. It can identify S1 and S2 correctly even under the circumstance of noise and interference. No reference signal is needed for this segmentation. It was tested by the heart sounds of 20 subjects including 15 normal and 5 abnormal in various clinical cases (715 cycles in

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Aspuru, Javier, Alberto Ochoa-Brust, Ramón Félix, et al. "Segmentation of the ECG Signal by Means of a Linear Regression Algorithm." Sensors 19, no. 4 (2019): 775. http://dx.doi.org/10.3390/s19040775.

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The monitoring and processing of electrocardiogram (ECG) beats have been actively studied in recent years: new lines of research have even been developed to analyze ECG signals using mobile devices. Considering these trends, we proposed a simple and low computing cost algorithm to process and analyze an ECG signal. Our approach is based on the use of linear regression to segment the signal, with the goal of detecting the R point of the ECG wave and later, to separate the signal in periods for detecting P, Q, S, and T peaks. After pre-processing of ECG signal to reduce the noise, the algorithm

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Lynn, Htet Myet, Pankoo Kim, and Sung Bum Pan. "Data Independent Acquisition Based Bi-Directional Deep Networks for Biometric ECG Authentication." Applied Sciences 11, no. 3 (2021): 1125. http://dx.doi.org/10.3390/app11031125.

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In this report, the study of non-fiducial based approaches for Electrocardiogram(ECG) biometric authentication is examined, and several excessive techniques are proposed to perform comparative experiments for evaluating the best possible approach for all the classification tasks. Non-fiducial methods are designed to extract the discriminative information of a signal without annotating fiducial points. However, this process requires peak detection to identify a heartbeat signal. Based on recent studies that usually rely on heartbeat segmentation, QRS detection is required, and the process can b

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Fearnhead, P. "Exact Bayesian curve fitting and signal segmentation." IEEE Transactions on Signal Processing 53, no. 6 (2005): 2160–66. http://dx.doi.org/10.1109/tsp.2005.847844.

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Bombiński, Sebastian, Krzysztof Błażejak, Mirosław Nejman, and Krzysztof Jemielniak. "Sensor Signal Segmentation for Tool Condition Monitoring." Procedia CIRP 46 (2016): 155–60. http://dx.doi.org/10.1016/j.procir.2016.03.203.

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Pikoulis, Erion-Vasilis, and Emmanouil Z. Psarakis. "Automatic Seismic Signal Detection via Record Segmentation." IEEE Transactions on Geoscience and Remote Sensing 53, no. 7 (2015): 3870–2884. http://dx.doi.org/10.1109/tgrs.2014.2386255.

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Grzesiek, Aleksandra, Karolina Gąsior, Agnieszka Wyłomańska, and Radosław Zimroz. "Divergence-Based Segmentation Algorithm for Heavy-Tailed Acoustic Signals with Time-Varying Characteristics." Sensors 21, no. 24 (2021): 8487. http://dx.doi.org/10.3390/s21248487.

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Many real-world systems change their parameters during the operation. Thus, before the analysis of the data, there is a need to divide the raw signal into parts that can be considered as homogeneous segments. In this paper, we propose a segmentation procedure that can be applied for the signal with time-varying characteristics. Moreover, we assume that the examined signal exhibits impulsive behavior, thus it corresponds to the so-called heavy-tailed class of distributions. Due to the specific behavior of the data, classical algorithms known from the literature cannot be used directly in the se

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Gowda, Vanajakshi Puttaswamy, Mathivanan Murugavelu, and Senthil Kumaran Thangamuthu. "Continuous kannada speech segmentation and speech recognition based on threshold using MFCC And VQ." International Journal of Electrical and Computer Engineering (IJECE) 9, no. 6 (2019): 4684. http://dx.doi.org/10.11591/ijece.v9i6.pp4684-4695.

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<p><span>Continuous speech segmentation and its recognition is playing important role in natural language processing. Continuous context based Kannada speech segmentation depends on context, grammer and semantics rules present in the kannada language. The significant feature extraction of kannada speech signal for recognition system is quite exciting for researchers. In this paper proposed method is divided into two parts. First part of the method is continuous kannada speech signal segmentation with respect to the context based is carried out by computing average short term energy

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Vanajakshi, Puttaswamy Gowda, Murugavelu Mathivanan, and Thangamuthu SenthilKumaran. "Continuous kannada speech segmentation and speech recognition based on threshold using MFCC and VQ." International Journal of Electrical and Computer Engineering (IJECE) 9, no. 6 (2019): 4684–95. https://doi.org/10.11591/ijece.v9i6.pp4684-4695.

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Continuous speech segmentation and its recognition is playing important role in natural language processing. Continuous context based Kannada speech segmentation depends on context, grammer and semantics rules present in the kannada language. The significant feature extraction of kannada speech signal for recognition system is quite exciting for researchers. In this paper proposed method is divided into two parts. First part of the method is continuous kannada speech signal segmentation with respect to the context based is carried out by computing average short term energy and its spectral cen

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Zheng, Yalin, and Ke Chen. "A Hierarchical Algorithm for Multiphase Texture Image Segmentation." ISRN Signal Processing 2012 (July 26, 2012): 1–11. http://dx.doi.org/10.5402/2012/781653.

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Image segmentation is a fundamental task for many computer vision and image processing applications. There exist many useful and reliable models for two-phase segmentation. However, the multiphase segmentation is a more challenging problem than two phase segmentation, mainly due to strong dependence on initialization of solutions. In this paper we propose a reliable hierarchical algorithm for multiphase texture image segmentation by making full use of two-phase texture models in a fuzzy membership framework. Application of the new algorithm to the synthetic and real medical imaging data demons

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Wang, Xun, Lisheng Wang, Jianjun Yang, and Xiaoya Feng. "Accurate 3D Reconstruction of White Matter Hyperintensities Based on Attention-Unet." Computational and Mathematical Methods in Medicine 2022 (March 23, 2022): 1–7. http://dx.doi.org/10.1155/2022/3812509.

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White matter hyperintensities (WMH), also known as white matter osteoporosis, have been clinically proven to be associated with cognitive decline, the risk of cerebral infarction, and dementia. The existing computer automatic measurement technology for the segmentation of patients’ WMH does not have a good visualization and quantitative analysis. In this work, the author proposed a new WMH quantitative analysis and 3D reconstruction method for 3D reconstruction of high signal in white matter. At first, the author using ResUnet achieves the high signal segmentation of white matter and adds the

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Li, Wenya, Lang He, Zhengying Li, and Yuan Wan. "Detection of Track Bed Defects Based on Fibre Optic Sensor Signals and an Improved Hidden Markov Model." Electronics 13, no. 13 (2024): 2504. http://dx.doi.org/10.3390/electronics13132504.

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Railway track bed defects affect the normal operation of trains and pose great safety risks. In order to detect such issues early, we developed a railway track bed defect detection method which uses optical fibre sensors and an improved HMM (hidden Markov model) to detect the signals collected by a DAS (distributed acoustic sensing) system. First, by analysing the physical process of train operation and determining the number of hidden states, a waveform segmentation method based on average amplitude was used to solve the problem of unequal signal lengths. Second, an adaptive power spectrum en

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ARI, SAMIT, and GOUTAM SAHA. "ON A ROBUST ALGORITHM FOR HEART SOUND SEGMENTATION." Journal of Mechanics in Medicine and Biology 07, no. 02 (2007): 129–50. http://dx.doi.org/10.1142/s0219519407002200.

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The detection of heart diseases from heart sound signals needs an efficient segmentation algorithm to properly identify the location of the first and second heart sounds. This in turn helps in characterizing murmurs present in the cardiac cycles and the pathological condition by providing an appropriate time reference. The work presented here needs only the average heart rate as discrete auxiliary information that can be easily provided, unlike most of the methods which require the electrocardiography (ECG) signal as a continuous auxiliary signal in a complex setup. The algorithm was tested on

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Wang, Dan Dan, Yu Zhou, Qing Wei Ye, and Xiao Dong Wang. "The Spectrum Segmentation Algorithm of Multimode Vibration Signal Based on Spectral Clustering." Applied Mechanics and Materials 121-126 (October 2011): 2372–76. http://dx.doi.org/10.4028/www.scientific.net/amm.121-126.2372.

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The mode peaks in frequency domain of vibration signal are strongly interfered by strong noise, causing the inaccuracy mode parameters. According to this situation, this paper comes up with the thought of mode-peak segmentation based on the spectral clustering algorithm. First, according to the concept of wave packet, the amplitude-frequency of vibration signal is divided into wave packets. Taking each wave packet as a sample of clustering algorithm, the spectral clustering algorithm is used to classify these wave packets. The amplitude-frequency curve of a mode peak becomes a big wave packet

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Tseng, Kuo-Kun, Chao Wang, Yu-Feng Huang, Guan-Rong Chen, Kai-Leung Yung, and Wai-Hung Ip. "Cross-Domain Transfer Learning for PCG Diagnosis Algorithm." Biosensors 11, no. 4 (2021): 127. http://dx.doi.org/10.3390/bios11040127.

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Cardiechema is a way to reflect cardiovascular disease where the doctor uses a stethoscope to help determine the heart condition with a sound map. In this paper, phonocardiogram (PCG) is used as a diagnostic signal, and a deep learning diagnostic framework is proposed. By improving the architecture and modules, a new transfer learning and boosting architecture is mainly employed. In addition, a segmentation method is designed to improve on the existing signal segmentation methods, such as R wave to R wave interval segmentation and fixed segmentation. For the evaluation, the final diagnostic ar

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Kajor, Marcin, Dariusz Kucharski, Justyna Grochala, and Jolanta E. Loster. "New Methods for the Acoustic-Signal Segmentation of the Temporomandibular Joint." Journal of Clinical Medicine 11, no. 10 (2022): 2706. http://dx.doi.org/10.3390/jcm11102706.

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(1) Background: The stethoscope is one of the main accessory tools in the diagnosis of temporomandibular joint disorders (TMD). However, the clinical auscultation of the masticatory system still lacks computer-aided support, which would decrease the time needed for each diagnosis. This can be achieved with digital signal processing and classification algorithms. The segmentation of acoustic signals is usually the first step in many sound processing methodologies. We postulate that it is possible to implement the automatic segmentation of the acoustic signals of the temporomandibular joint (TMJ

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Ahmad, Mostafa, Ali Ahmed, Hasan Hashim, Mohammed Farsi, and Nader Mahmoud. "Enhancing Heart Disease Diagnosis Using ECG Signal Reconstruction and Deep Transfer Learning Classification with Optional SVM Integration." Diagnostics 15, no. 12 (2025): 1501. https://doi.org/10.3390/diagnostics15121501.

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Background/Objectives: Accurate and efficient diagnosis of heart disease through electrocardiogram (ECG) analysis remains a critical challenge in clinical practice due to noise interference, morphological variability, and the complexity of overlapping cardiac signals. Methods: This study presents a comprehensive deep learning (DL) framework that integrates advanced ECG signal segmentation with transfer learning-based classification, aimed at improving diagnostic performance. The proposed ECG segmentation algorithm introduces a distinct and original approach compared to prior research by integr

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GRAZIANO, F., G. LENZI, and P. SALVANESCHI. "REJECTION OF SEISMIC FALSE TRIGGERS BY STATISTICAL SIGNAL SEGMENTATION." International Journal of Pattern Recognition and Artificial Intelligence 04, no. 01 (1990): 57–63. http://dx.doi.org/10.1142/s0218001490000058.

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Microseismic networks, equipped with digital recorders triggered by STA/LTA threshold, may be affected by a high number of false triggers. These cause wastage of computing resources (cpu time and mass storage) and is unacceptable in managing data from many networks. The aim of this study is to test the capability to reject false triggers by statistical segmentation (as proposed by Chen). The sample set includes 580 readable microquakes and 2,310 other signals including spurious noises and unreadable quakes. Signals are partitioned into fixed length sections. For every couple of contiguous sect

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Rashkevych, Yu, D. Peleshko, I. Pelekh, and I. Izonіn. "Speech signal marking on the base of local magnitude and invariant segmentation." Mathematical Modeling and Computing 1, no. 2 (2014): 234–44. http://dx.doi.org/10.23939/mmc2014.02.234.

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The paper suggests a new watermarking scheme based on invariant method of segmentation and the use of local magnitude for marking speech signals. The watermark is embedded in the chosen form at peaks with the spectrum magnitude of each nonoverlapping frame of audio signal.

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Hong, Duan, and Yang Luo. "A Method of Hand Signal Segmentation Based on YCbCr Space and Background Difference." Applied Mechanics and Materials 380-384 (August 2013): 4112–15. http://dx.doi.org/10.4028/www.scientific.net/amm.380-384.4112.

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This paper proposes a method of segmentation of hand signal, based on the skin color model in YCbCr space and background subtraction under complex background. This paper discussed the reasonable threshold selection of Cb, Cr in the skin color model and the segmentation selection of skin area combining the background segmentation. Finally, an inequality of hands outline feature is proposed to complete the division processing of the palm part of the area of skin. Experiments show the accurate segmentation of gesture under a complex static background.

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