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Journal articles on the topic 'Signals approach'

Author: Grafiati

Published: 3 June 2025

Last updated: 1 August 2025

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1

Davis, Daniel J., and John H. Challis. "Vertical Ground Reaction Force Estimation From Benchmark Nonstationary Kinematic Data." Journal of Applied Biomechanics 37, no. 3 (2021): 272–76. http://dx.doi.org/10.1123/jab.2020-0237.

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Time-differentiating kinematic signals from optical motion capture amplifies the inherent noise content of those signals. Commonly, biomechanists address this problem by applying a Butterworth filter with the same cutoff frequency to all noisy displacement signals prior to differentiation. Nonstationary signals, those with time-varying frequency content, are widespread in biomechanics (eg, those containing an impact) and may necessitate a different filtering approach. A recently introduced signal filtering approach wherein signals are divided into sections based on their energy content and the

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Kawala-Janik, Aleksandra, Michał Podpora, and Mariusz Konieczny. "Innovative approach in signal processing of electromyography signals." Journal of Combat Sports and Martial Arts 5, no. 2 (2014): 101–12. http://dx.doi.org/10.5604/20815735.1141984.

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Granados-Ruiz, Jackeline, David Asael Gutiérrez-Hernández, Carlos Lino-Ramírez, et al. "METHODOLOGICAL APPROACH FOR EXTRACTION OF CHARACTERISTICS OF BIOLOGICAL SIGNALS." COMPUSOFT: An International Journal of Advanced Computer Technology 08, no. 02 (2019): 3011–20. https://doi.org/10.5281/zenodo.14811307.

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Generally, signal processing is applied to a set of data that is derived from the sampling of an acquired signal. This treatment is carried out with the help of a computer that in turn executes a series of logical and mathematical operations. The treatment of signals is linked to other techniques and scientific disciplines. Some of the applications of the signal treatments may be in the form of processing of audio signals, treatment of digital images, digital communications and biological signals. In this case, the treatment was applied to biological signals such as ECG (Electrocardiogram sign

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Zhang, Haowen, and Qiuze Yu. "Photonic Approach to Multi-band Dual-chirp Microwave Waveform Generation with Quadruple Bandwidth." Advances in Engineering Technology Research 9, no. 1 (2024): 762. http://dx.doi.org/10.56028/aetr.9.1.762.2024.

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We propose a scheme for generating a microwave waveform with dual-band, dual-chirp, linearly chirped characteristics and quadruple chirp bandwidth. In this scheme, we employ two cascaded Mach-Zehnder modulators (MZMs), with each modulated by a microwave signal and a linearly frequency modulated (LFM) signal. This modulation technique extends the LFM signal to multiple frequency bands and enhances its bandwidth. By properly adjusting the microwave signal's frequency and the LFM's carrier frequency, we can intelligently combine the up-chirp and down-chirp signals obtained after heterodyne beatin

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Kharouf, Husni, Donald J. Lund, Alexandra Krallman, and Chris Pullig. "A signaling theory approach to relationship recovery." European Journal of Marketing 54, no. 9 (2020): 2139–70. http://dx.doi.org/10.1108/ejm-10-2019-0751.

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Purpose Drawing on signaling theory, the purpose of this study is to investigate the effects of the strength and framing of firm signals sent to repair relationships following relationship violations. Design/methodology/approach Three 2 × 2 scenario-based experiments (total n = 527) manipulate signal strength × violation type (Study 1); signal frame × violation type (Study 2); and signal strength × brand familiarity (Study 3) to examine their dynamic impacts on relationship recovery efforts. Findings Stronger signals are more effective at relationship repair and are especially important follow

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Ponomarchuk, Sergey, Vladimir Kurkin, Nikolay Ilyin, and Maksim Penzin. "HF radio path modeling by waveguide approach." Solar-Terrestrial Physics 10, no. 2 (2024): 93–101. http://dx.doi.org/10.12737/stp-102202409.

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We present a scheme for modeling HF radio signal characteristics along paths of different lengths, which is based on the waveguide approach — the normal mode method. We use a representation of the recorded signal field in the form of Green function products of the angular operator, excitation coefficients, and reception coefficients of individual normal modes. Algorithms have been developed for calculating distance-frequency, frequency-angular, and amplitude characteristics of signals in large spatial regions through analysis and numerical summation of normal mode series. We have implemented a

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Thulasiram Varma, K., and Rangababu Peesapati. "Accelerated Curve Fitting Approach for Denoised NQR Signal Parameter Estimation." Electronics ETF 28, no. 1 (2024): 22–29. http://dx.doi.org/10.53314/els2428022v.

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Signal parameters such as amplitude, frequency, decay constant, and phase play an important role in identifying the signal’s origin in many real-life applications. Standard gradient-based curve-fitting approaches are computationally complex to implement on hardware because of large matrix multiplication and partial derivatives. The current work demonstrates a feasible approach to extract parameters from exponential decay Nuclear Quadrupole Resonance (NQR) signals using derivative-free optimization approaches on a PYNQ-Z2 FPGA board. The hardware implementation results indicate 0.12 seconds is

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Zeng, Hai, Ning Zeng, Jin Han, and Yan Ding. "Engine Fault Detection Approach Based on Angle Domain Signal Model." Journal of Physics: Conference Series 2068, no. 1 (2021): 012034. http://dx.doi.org/10.1088/1742-6596/2068/1/012034.

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Abstract Engine vibration signals include strong noise and non-stationary signals. By the time domain signal processing approach, it is hard to extract the failure features of engine vibration signals, so it is hard to identify engine failures. For improving the success rate of engine failure detection, an engine angle domain vibration signal model is established and an engine fault detection approach based on the signal model is proposed. The angle domain signal model reveals the modulation feature of the engine angular signal. The engine fault diagnosis approach based on the angle domain sig

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Grimaldi, Claudio, and Geoffrey W. Marcy. "Bayesian approach to SETI." Proceedings of the National Academy of Sciences 115, no. 42 (2018): E9755—E9764. http://dx.doi.org/10.1073/pnas.1808578115.

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The search for technosignatures from hypothetical galactic civilizations is going through a new phase of intense activity. For the first time, a significant fraction of the vast search space is expected to be sampled in the foreseeable future, potentially bringing informative data about the abundance of detectable extraterrestrial civilizations or the lack thereof. Starting from the current state of ignorance about the galactic population of nonnatural electromagnetic signals, we formulate a Bayesian statistical model to infer the mean number of radio signals crossing Earth, assuming either no

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Senthilkumar, Dr P., 2Asha.V, Dr G. Ramesh, M.Muthukumar, and Dr Amairullah Khan Lodhi. "ANAUDIO DENOISING APPROACH USING HYBRID MODIFIED FAST WAVELET TRANSFORM METHOD." International Journal Of Trendy Research In Engineering And Technology 06, no. 06 (2022): 17–22. http://dx.doi.org/10.54473/ijtret.2022.6604.

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Noise includes all unwelcome ambient sounds. If a sound is perceived as delightful music or annoying noise, it will be perceived as having the same decibel level. The fundamental drawback of noise in an audio signal is that it degrades the signal's quality during transmission over the communication system. Real-time audio signals are gathered for the present study using a microphone. The additive white Gaussian noise of AWGN is blended with a genuine audio source. The Median, Finite Impulse Response filter with Wavelet Transform approach is a hybrid filter for denoising audio signals that inco

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MOHAMMED, TAG ELSIR AWAD ELSOUFI. "CUMULANT APPROACH FOR MODULATION RECOGNITION." IJIERT - International Journal of Innovations in Engineering Research and Technology 4, no. 3 (2017): 18–23. https://doi.org/10.5281/zenodo.1461227.

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<strong><strong> </strong>In this paper, cumulant approach is used in MQAM modulated signal recognition. This method uses the characteristics o f higher - order cumulants and higher - order statistics to identify different kinds of MQAM signals. We focus on the modulation identification using statistical properties of M QAM signals. The reason of using this method is the ability of higher - order statistics to reflect the distribution characteristics of the constellation diagram. T here are many benefits of using this method. This method needs only small amount of computations. It can eff

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Chen, Zhi Xin, Hai Tao Sun, and Shi Kun Xie. "An Approach to Monitoring of Gearbox Based on HHT Spectrum." Applied Mechanics and Materials 299 (February 2013): 117–20. http://dx.doi.org/10.4028/www.scientific.net/amm.299.117.

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Hilbert-Huang Transform (HHT) Spectrum is analyzed, and is compared with Wigner-Ville distribution(WVD). And it is proposed a method that is “band pass filtering --signal purification and reconstruction --HHT spectrum analyses” method to carry on the state recognition and fault diagnosis of the mechanical equipment. The compared signals include: the signals of harmonic, amplitude modulation and frequency modulation, and the mixed signals that are composed of these single signal, then it is pointed out: HHT Spectrum have higher frequency resolution than WVD, and it has overcome the latter influ

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Salih, Thair A. "New approach to eliminate noise attendants ECG signal corrupted." International Journal Artificial Intelligent and Informatics 2, no. 2 (2022): 67–78. http://dx.doi.org/10.33292/ijarlit.v2i2.36.

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ECG signals have been largely utilized in cardiac pathology to detect heart ailments. ECG signals are commonly corrupted by disparate artifacts such as power line obtrusion exterior electromagnetic domain intervention, noise due to random object motions, breathing motions, and electromyography noise and these should be eliminated before diagnosis.This article explains the creation and performance of three types of band-stop filters based on FPAA technology to accurately estimate an ECG corrupted signal. The characteristics of these filters are improved and their parameters are calculated to mi

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Singh, Omkar, and Ramesh Kumar Sunkaria. "A Unified Approach for Heart Rate Estimation from Electrocardiogram and Arterial Blood Pressure Pulses." Advanced Science, Engineering and Medicine 12, no. 5 (2020): 588–92. http://dx.doi.org/10.1166/asem.2020.2556.

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The objective of this manuscript is to propose a unique methodology for heart rate estimation derived from Electrocardiogram (ECG) or arterial blood pressure (abp) signal. This methodology relies on the identification of a signal's fundamental frequency by use of empirical wavelet analysis, followed by peak identification within windows based on pseudo-periodic assumption. The proposed methodology is based on the concept that the most of the cardiovascular signals are quasi-periodic in nature. The proposed technique estimates the fundamental frequency of the signal from its corresponding Fouri

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Park, Do-Hyun, Min-Wook Jeon, Da-Min Shin, and Hyoung-Nam Kim. "LPI Radar Detection Based on Deep Learning Approach with Periodic Autocorrelation Function." Sensors 23, no. 20 (2023): 8564. http://dx.doi.org/10.3390/s23208564.

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In electronic warfare systems, detecting low-probability-of-intercept (LPI) radar signals poses a significant challenge due to the signal power being lower than the noise power. Techniques using statistical or deep learning models have been proposed for detecting low-power signals. However, as these methods overlook the inherent characteristics of radar signals, they possess limitations in radar signal detection performance. We introduce a deep learning-based detection model that capitalizes on the periodicity characteristic of radar signals. The periodic autocorrelation function (PACF) is an

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Kim, Youngjoo, Jongkwon Choi, Wooyoung Hong, and Keunhwa Lee. "Deep learning approach to generate propeller cavitation noise." Journal of the Acoustical Society of America 154, no. 4_supplement (2023): A341. http://dx.doi.org/10.1121/10.0023736.

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In underwater acoustic simulators, propeller cavitation noise has traditionally been modeled as a modulated broadband signal. This study aims to enhance the realism of these simulators by employing deep learning to generate propeller cavitation noise. The training data were collected from the modeled propeller under various pressure conditions in the Samsung Cavitation Tunnel. We have utilized a variant of Generative Adversarial Networks (GANs), wherein both the generator and discriminator are designed with a recursive structure. To assess the advantages of our data-based approach, we analyze

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Tan, Xiaofeng, Xihai Li, Hongru Li, Xiaoniu Zeng, Tianyou Liu, and Shengjie Luo. "A Deep Learning Approach for Distant Infrasound Signals Classification." Sensors 25, no. 7 (2025): 2058. https://doi.org/10.3390/s25072058.

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Infrasound signal classification represents a critical challenge that demands immediate attention. Feature extraction stands as the core concept for enhancing classification accuracy in infrasound signal processing. However, existing feature extraction methodologies fail to meet the requirements for long-distance detection scenarios. To address these limitations, this study proposes a novel classification framework based on the spatiotemporal characteristics of infrasound signals. The proposed framework incorporates advanced signal processing techniques, signal enhancement algorithms, and deep

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Dhal, Chandan, and Akshat Wahi. "Psycho-physiological Training Approach for Amputee Rehabilitation." Biomedical Instrumentation & Technology 49, no. 2 (2015): 138–43. http://dx.doi.org/10.2345/0899-8205-49.2.138.

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Electromyography (EMG) signals are very noisy and difficult to acquire. Conventional techniques involve amplification and filtering through analog circuits, which makes the system very unstable. The surface EMG signals lie in the frequency range of 6Hz to 600Hz, and the dominant range is between the ranges from 20Hz to 150Hz.1 Our project aimed to analyze an EMG signal effectively over its complete frequency range. To remove these defects, we designed what we think is an easy, effective, and reliable signal processing technique. We did spectrum analysis, so as to perform all the processing suc

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Song, Bo, Sheng-Lin Li, Mian Tan, and Qing-Hui Ren. "A Fast Imbalanced Binary Classification Approach to NLOS Identification in UWB Positioning." Mathematical Problems in Engineering 2018 (December 2, 2018): 1–8. http://dx.doi.org/10.1155/2018/1580147.

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Non-line-of-sight (NLOS) propagation is an important factor affecting the positioning accuracy of ultra-wide band (UWB). In order to mitigate the NLOS ranging error caused by various obstacles in UWB ranging process, some scholars have applied machine learning methods such as support vector machine and support vector data description to the identification NLOS signals for mitigation NLOS error in recent years. Therefore, the identification of NLOS signals is of great significance in UWB positioning. The traditional machine learning method is based on the assumption that the number of samples o

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Al-Rawi, Muaayed F., and Muhanned F. Al-Rawi. "Novel approach in measurement instrument based on computer." International Review of Applied Sciences and Engineering 12, no. 2 (2021): 147–56. http://dx.doi.org/10.1556/1848.2021.00214.

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AbstractMost applications in engineering use a data acquisition device hooked up to a personal computer for data processing. Finding less costly, easily accessible and reliable devices will make personal computer (PC) based data acquisition systems less difficult. A soundcard may be used as such a device for it is standard in almost every PC. It can also process any voltage signal within its limits. This paper proposes a way to enable the PC to be used as an oscilloscope. A voltage signal is acquired via the soundcard LINE IN port. The maximum and minimum input signal amplitude requirements fo

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Rajesh, Naga. "A Segmental Approach with SWT Technique for Denoising the EOG Signal." Modelling and Simulation in Engineering 2015 (2015): 1–7. http://dx.doi.org/10.1155/2015/612843.

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The Electrooculogram (EOG) signal is often contaminated with artifacts and power-line while recording. It is very much essential to denoise the EOG signal for quality diagnosis. The present study deals with denoising of noisy EOG signals using Stationary Wavelet Transformation (SWT) technique by two different approaches, namely, increasing segments of the EOG signal and different equal segments of the EOG signal. For performing the segmental denoising analysis, an EOG signal is simulated and added with controlled noise powers of 5 dB, 10 dB, 15 dB, 20 dB, and 25 dB so as to obtain five differe

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Ponomarchuk, Sergey, Vladimir Kurkin, Nikolay Ilyin, and Maksim Penzin. "HF radio path modeling by waveguide approach." Solnechno-Zemnaya Fizika 10, no. 2 (2024): 99–108. http://dx.doi.org/10.12737/szf-102202409.

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We present a scheme for modeling HF radio signal characteristics along paths of different lengths, which is based on the waveguide approach — the normal mode method. We use a representation of the recorded signal field in the form of Green function products of the angular operator, excitation coefficients, and reception coefficients of individual normal modes. Algorithms have been developed for calculating distance-frequency, frequency-angular, and amplitude characteristics of signals in large spatial regions through analysis and numerical summation of normal mode series. We have implemented a

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WANG, XIAO FAN, and ZHI QUAN WANG. "A ROBUST DEMODULATION APPROACH TO COMMUNICATIONS USING CHAOTIC SIGNALS." International Journal of Bifurcation and Chaos 13, no. 01 (2003): 227–31. http://dx.doi.org/10.1142/s0218127403006479.

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In this Letter, a new demodulation approach to communication using chaotic signals is presented. Information signal is modulated by applying it as an input to a chaotic system. At the receiver, a synchronous subsystem is constructed for estimates on the states of the chaotic system. The key innovation in our communication system is a new robust filter that recovers the information signal from the scalar transmitted signal.

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Aijun, Hu, Lin Jianfeng, Sun Shangfei, and Xiang Ling. "A Novel Approach of Impulsive Signal Extraction for Early Fault Detection of Rolling Element Bearing." Shock and Vibration 2017 (2017): 1–11. http://dx.doi.org/10.1155/2017/9375491.

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The fault signals of rolling element bearing are often characterized by the presence of periodic impulses, which are modulated high-frequency harmonic components. The features of early fault in rolling bearing are very weak, which are often masked by background noise. The impulsiveness of the vibration signal has affected the identification of characteristic frequency for the early fault detection of the bearing. In this paper, a novel approach based on morphological operators is presented for impulsive signal extraction of early fault in rolling element bearing. The combination Top-Hat (CTH)

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Ibrahim, Ayad Assad, Ikhlas Mahmoud Farhan, and Mohammed Ehasn Safi. "A nonlinearities inverse distance weighting spatial interpolation approach applied to the surface electromyography signal." International Journal of Electrical and Computer Engineering (IJECE) 12, no. 2 (2022): 1530. http://dx.doi.org/10.11591/ijece.v12i2.pp1530-1539.

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Spatial interpolation of a surface electromyography (sEMG) signal from a set of signals recorded from a multi-electrode array is a challenge in biomedical signal processing. Consequently, it could be useful to increase the electrodes' density in detecting the skeletal muscles' motor units under detection's vacancy. This paper used two types of spatial interpolation methods for estimation: Inverse distance weighted (IDW) and Kriging. Furthermore, a new technique is proposed using a modified nonlinearity formula based on IDW. A set of EMG signals recorded from the noninvasive multi-electrode gri

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Ayad, Assad Ibrahim, Mahmoud Farhan Ikhlas, and Ehasn Safi Mohammed. "A nonlinearities inverse distance weighting spatial interpolation approach applied to the surface electromyography signal." International Journal of Electrical and Computer Engineering (IJECE) 12, no. 2 (2022): 1530–39. https://doi.org/10.11591/ijece.v12i2.pp1530-1539.

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Spatial interpolation of a surface electromyography (sEMG) signal from a set of signals recorded from a multi-electrode array is a challenge in biomedical signal processing. Consequently, it could be useful to increase the electrodes' density in detecting the skeletal muscles' motor units under detection's vacancy. This paper used two types of spatial interpolation methods for estimation: Inverse distance weighted (IDW) and Kriging. Furthermore, a new technique is proposed using a modified nonlinearity formula based on IDW. A set of EMG signals recorded from the noninvasive multi-e

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Siddiqi, Abul Hasan, and Hulya Kodal Sevindir. "A Wavelet-based Energetic Approach for the Analysis of Electroencephalogram." Sultan Qaboos University Journal for Science [SQUJS] 17, no. 2 (2012): 232. http://dx.doi.org/10.24200/squjs.vol17iss2pp232-244.

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Electroencephalography (EEG) is the recording of electrical activity along the scalp produced by the firing of neurons within the brain. The main application of EEG is in the case of epilepsy, as epileptic activity can create clear abnormalities on a standard EEG study. EEG signals, like many biomedical signals, are highly non-stationary by their nature. Wavelet analysis has found a prominent position in the investigation of biomedical signals for its ability to analyze such signals, in particular EEG signals. Wavelet transform is capable of separating the signal energy among different frequen

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Astaiza Hoyos, Evelio, Pablo Emilio Jojoa Gómez, and Héctor Fabio Bermúdez Orozco. "Compressive sensing: A methodological approach to an efficient signal processing." DYNA 82, no. 192 (2015): 203–10. http://dx.doi.org/10.15446/dyna.v82n192.45512.

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Compressive Sensing (CS) is a new paradigm for signal acquisition and processing, which integrates sampling, compression, dimensionality reduction and optimization, which has caught the attention of a many researchers; SC allows the reconstruction of dispersed signals in a given domain from a set of measurements could be described as incomplete, due to that the rate at which the signal is sampled is much smaller than Nyquist's rate. This article presents an approach to address methodological issues in the field of processing signals from the perspective of SC.

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Deshmukh, Anand B., and Dr Sanjay V. Dudul. "An Innovative Approach for Denoising of the Digital Video Stream." Journal of University of Shanghai for Science and Technology 23, no. 07 (2021): 342–51. http://dx.doi.org/10.51201/jusst/21/06480.

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Everyday tones of video signals are generated, transmitted, and analyzed. The video contents are created for educational purposes, entertainment purposes, surveillance purposes, medical imaging purposes, weather forecasting, satellite imaging, and many other significant places. During the different phases of video content preparation, transmission, and analysis some unwanted signals get interfered with the true contents. Particularly, the medical imaging signals, since they are weak signals, are more prone to unwanted interferences. Such unwanted interference of the noise signals makes it diff

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Zhang, Dengyong, Shanshan Wang, Feng Li, et al. "An ECG Signal De-Noising Approach Based on Wavelet Energy and Sub-Band Smoothing Filter." Applied Sciences 9, no. 22 (2019): 4968. http://dx.doi.org/10.3390/app9224968.

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Electrocardiographic (ECG) signal is essential to diagnose and analyse cardiac disease. However, ECG signals are susceptible to be contaminated with various noises, which affect the application value of ECG signals. In this paper, we propose an ECG signal de-noising method using wavelet energy and a sub-band smoothing filter. Unlike the traditional wavelet threshold de-noising method, which carries out threshold processing for all wavelet coefficients, the wavelet coefficients that require threshold de-noising are selected according to the wavelet energy and other wavelet coefficients remain u

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Stanković, Srdjan, Ljubiša Stanković, and Irena Orović. "Compressive Sensing Approach in the Hermite Transform Domain." Mathematical Problems in Engineering 2015 (2015): 1–9. http://dx.doi.org/10.1155/2015/286590.

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Compressive sensing has attracted significant interest of researchers providing an alternative way to sample and reconstruct the signals. This approach allows us to recover the entire signal from just a small set of random samples, whenever the signal is sparse in certain transform domain. Therefore, exploring the possibilities of using different transform basis is an important task, needed to extend the field of compressive sensing applications. In this paper, a compressive sensing approach based on the Hermite transform is proposed. The Hermite transform by itself provides compressed signal

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Heravi, Mohamad Amin Younessi, Morteza Pishghadam, Hosnieh Raoufian, and Akram Gazerani. "RECURRENCE QUANTIFICATION ANALYSIS OF ELECTROOCULOGRAPHY SIGNAL TO A CONTROL QUESTION TEST: A NEW APPROACH FOR THE DETECTION OF DECEPTION." Biomedical Engineering: Applications, Basis and Communications 32, no. 04 (2020): 2050029. http://dx.doi.org/10.4015/s1016237220500295.

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This study aimed to evaluate a lie-detection system by nonlinear analysis of electrooculography (EOG) signals in the polygraph test. The physiological signals such as photoplethysmography signal, electrodermal response, respiratory changes as well as EOG signal were recorded based on a Control Question Test (CQT). Three psychophysiological signals were evaluated based on the extracted features in the seven-position numerical scoring. The dynamics of EOG signals in subjects that had a total negative score were analyzed by recurrence quantification analysis (RQA). The six values of RQA were calc

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Yang, Yanli. "A Signal Theoretic Approach for Envelope Analysis of Real-Valued Signals." IEEE Access 5 (2017): 5623–30. http://dx.doi.org/10.1109/access.2017.2688467.

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Simpson, D. M., A. De Stefano, R. Allen, and M. E. Lutman. "Demystifying Biomedical Signals: A student centred approach to learning signal processing." Medical Engineering & Physics 27, no. 7 (2005): 583–89. http://dx.doi.org/10.1016/j.medengphy.2004.11.011.

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Abdallah, Hanaa A., and Souham Meshoul. "A Multilayered Audio Signal Encryption Approach for Secure Voice Communication." Electronics 12, no. 1 (2022): 2. http://dx.doi.org/10.3390/electronics12010002.

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In this paper, multilayer cryptosystems for encrypting audio communications are proposed. These cryptosystems combine audio signals with other active concealing signals, such as speech signals, by continuously fusing the audio signal with a speech signal without silent periods. The goal of these cryptosystems is to prevent unauthorized parties from listening to encrypted audio communications. Preprocessing is performed on both the speech signal and the audio signal before they are combined, as this is necessary to get the signals ready for fusion. Instead of encoding and decoding methods, the

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Li, Ji, Huiqiang Zhang, Jianping Ou, and Wei Wang. "A Radar Signal Recognition Approach via IIF-Net Deep Learning Models." Computational Intelligence and Neuroscience 2020 (August 28, 2020): 1–8. http://dx.doi.org/10.1155/2020/8858588.

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In the increasingly complex electromagnetic environment of modern battlefields, how to quickly and accurately identify radar signals is a hotspot in the field of electronic countermeasures. In this paper, USRP N210, USRP-LW N210, and other general software radio peripherals are used to simulate the transmitting and receiving process of radar signals, and a total of 8 radar signals, namely, Barker, Frank, chaotic, P1, P2, P3, P4, and OFDM, are produced. The signal obtains time-frequency images (TFIs) through the Choi–Williams distribution function (CWD). According to the characteristics of the

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Adeluyi, Olufemi, Miguel A. Risco-Castillo, María Liz Crespo, Andres Cicuttin, and Jeong-A. Lee. "A Computerized Bioinspired Methodology for Lightweight and Reliable Neural Telemetry." Sensors 20, no. 22 (2020): 6461. http://dx.doi.org/10.3390/s20226461.

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Personalized health monitoring of neural signals usually results in a very large dataset, the processing and transmission of which require considerable energy, storage, and processing time. We present bioinspired electroceptive compressive sensing (BeCoS) as an approach for minimizing these penalties. It is a lightweight and reliable approach for the compression and transmission of neural signals inspired by active electroceptive sensing used by weakly electric fish. It uses a signature signal and a sensed pseudo-sparse differential signal to transmit and reconstruct the signals remotely. We h

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Xu, Xiaowen, Ying Liang, Pei He, and Junliang Yang. "Adaptive Motion Artifact Reduction Based on Empirical Wavelet Transform and Wavelet Thresholding for the Non-Contact ECG Monitoring Systems." Sensors 19, no. 13 (2019): 2916. http://dx.doi.org/10.3390/s19132916.

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Electrocardiogram (ECG) signals are crucial for determining the health status of the human heart. A clean ECG signal is critical in analysis and diagnosis of heart diseases. However, ECG signals are often contaminated by motion artifact noise in the non-contact ECG monitoring systems. In this paper, an ECG motion artifact removal approach based on empirical wavelet transform (EWT) and wavelet thresholding (WT) is proposed. This method consists of five steps, namely, spectrum preprocessing, spectrum segmentation, EWT decomposition, wavelet threshold denoising, and EWT reconstruction. The propos

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Alhudhaif, Adi. "A novel multi-class imbalanced EEG signals classification based on the adaptive synthetic sampling (ADASYN) approach." PeerJ Computer Science 7 (May 14, 2021): e523. http://dx.doi.org/10.7717/peerj-cs.523.

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Background Brain signals (EEG—Electroencephalography) are a gold standard frequently used in epilepsy prediction. It is crucial to predict epilepsy, which is common in the community. Early diagnosis is essential to reduce the treatment process of the disease and to keep the process healthier. Methods In this study, a five-classes dataset was used: EEG signals from different individuals, healthy EEG signals from tumor document, EEG signal with epilepsy, EEG signal with eyes closed, and EEG signal with eyes open. Four different methods have been proposed to classify five classes of EEG signals.

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Jurdana, Vedran. "Deep Neural Networks for Estimating Regularization Parameter in Sparse Time–Frequency Reconstruction." Technologies 12, no. 12 (2024): 251. https://doi.org/10.3390/technologies12120251.

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Time–frequency distributions (TFDs) are crucial for analyzing non-stationary signals. Compressive sensing (CS) in the ambiguity domain offers an approach for TFD reconstruction with high performance, but selecting the optimal regularization parameter for various signals remains challenging. Traditional methods for parameter selection, including manual and experimental approaches, as well as existing optimization procedures, can be imprecise and time-consuming. This study introduces a novel approach using deep neural networks (DNNs) to predict regularization parameters based on Wigner–Ville dis

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Al-Oudatallah, Jihad, Fariz Abboud, Mazen Khoury, and Hassan Ibrahim. "Overlapping Signal Separation Method Using Superresolution Technique Based on Experimental Echo Shape." Advances in Acoustics and Vibration 2017 (June 11, 2017): 1–9. http://dx.doi.org/10.1155/2017/7132038.

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Overlapping signals separation is a difficult problem, where time windowing is unable to separate signals overlapping in time and frequency domain filtering is unable to separate signals with overlapping spectra. In this work, a simulation under MATLAB is implemented to illustrate the concept of overlapping signals. We propose an approach for resolving overlapping signals based on Fourier transform and inverse Fourier transform. The proposed approach is tested under MATLAB, and the simulation results validate the effectiveness and the accuracy of the proposed approach. The approach is develope

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Konstantaras, A., M. R. Varley, F. Vallianatos, G. Collins, and P. Holifield. "A neuro-fuzzy approach to the reliable recognition of electric earthquake precursors." Natural Hazards and Earth System Sciences 4, no. 5/6 (2004): 641–46. http://dx.doi.org/10.5194/nhess-4-641-2004.

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Abstract. Electric Earthquake Precursor (EEP) recognition is essentially a problem of weak signal detection. An EEP signal, according to the theory of propagating cracks, is usually a very weak electric potential anomaly appearing on the Earth's electric field prior to an earthquake, often unobservable within the electric background, which is significantly stronger and embedded in noise. Furthermore, EEP signals vary in terms of duration and size making reliable recognition even more difficult. An average model for EEP signals has been identified based on a time function describing the evoluti

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Song, GuangDong, JiuLong Cheng, BinXin Hu, et al. "Determination of First Arrival Wave Type of Microseismic Signals and Approach to Wave Velocity Correction." Shock and Vibration 2021 (March 1, 2021): 1–11. http://dx.doi.org/10.1155/2021/6631374.

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Given the complex environment experienced in working mines, the vibration waves produced by processes such as rock fracture in deep formations usually show interference effects when monitored due to other signals, the so-called “clutter” in the signal, which are interfered with the clutter. At the same time, owing to the influence of system noise, the first arrival time and the arrival time difference values of the signals obtained cannot easily be determined accurately. The propagation model for the microseismic signals experienced and the discrimination method used to determine the first arr

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Martinek, Radek, Martina Ladrova, Michaela Sidikova, et al. "Advanced Bioelectrical Signal Processing Methods: Past, Present, and Future Approach—Part III: Other Biosignals." Sensors 21, no. 18 (2021): 6064. http://dx.doi.org/10.3390/s21186064.

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Analysis of biomedical signals is a very challenging task involving implementation of various advanced signal processing methods. This area is rapidly developing. This paper is a Part III paper, where the most popular and efficient digital signal processing methods are presented. This paper covers the following bioelectrical signals and their processing methods: electromyography (EMG), electroneurography (ENG), electrogastrography (EGG), electrooculography (EOG), electroretinography (ERG), and electrohysterography (EHG).

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Živanović, Dragan, Milan Simić, Dragan Denić, and Živko Kokolanski. "SCRIPT FILES APPROACH IN THE POWER QUALITY EVENTS GENERATION." Facta Universitatis, Series: Automatic Control and Robotics 17, no. 2 (2018): 93. http://dx.doi.org/10.22190/fuacr1802093z.

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Generation of typical power quality (PQ) events, based on Script files and virtual instrumentation, is presented in this paper. Such approach provides definition and generation of three-phase voltage signals with various PQ events defined according to relevant international quality standards. Using of Script files enables easy and flexible generation of long-time voltage signals with complex PQ disturbances, according to the predefined test algorithms and scenarios. Detailed front panels and block diagrams of developed virtual instrument for signal generation are described in the paper. As spe

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Yu, Jian Ming, and Ze Zhang. "Research on Feature Extraction for Ultrasonic Echo Signal Based on EEMD Approach." Applied Mechanics and Materials 321-324 (June 2013): 1311–16. http://dx.doi.org/10.4028/www.scientific.net/amm.321-324.1311.

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The bonding quality of composite materials have a critical influence on the quality of the product in modern industry, while the current technology can only make judgments on bonding and de-bonding instead of quantitative evaluation of different de-bonding degrees. We present HHT method to extract features of echo signals used for quantitative recognition of bonding quality of thin plates. For the non-stationary characteristic of the ultrasonic echo signal, empirical mode decomposition(EMD) and ensemble empirical mode decomposition(EEMD) are put forward to decompose the signal and calculate it

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Li, Shuaibing, Lilong Dou, Hongwei Li, Zongying Li, and Yongqiang Kang. "An Innovative Electromechanical Joint Approach for Contact Pair Fault Diagnosis of Oil-Immersed On-Load Tap Changer." Electronics 12, no. 17 (2023): 3573. http://dx.doi.org/10.3390/electronics12173573.

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This paper presents a novel fault diagnosis method for oil-immersed on-load tap changers (OLTC) to address the issue of limited diagnostic accuracy. The proposed method combines the analysis of mechanical vibration signals and high-frequency current signals from the contact pair, aiming to improve the precision of fault diagnosis. To begin with, an experimental platform was used to simulate the OLTC contact, enabling the collection of mechanical vibration signals and high-frequency current signals under different operational states. These signals underwent wavelet packet transform for denoisin

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Ali, Mohd Basri, Shahrum Abdullah, M. Zaki Nuawi, M. M. Padzi, and K. A. Zakaria. "Experimental Analysis of an Instrumented Charpy Impact Using Signal Processing Approach." Advanced Materials Research 197-198 (February 2011): 1621–25. http://dx.doi.org/10.4028/www.scientific.net/amr.197-198.1621.

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The dynamic responses of the standard charpy impact machine are experimentally studied using the relevant data acquisition system in order to obtain the impact response. For this reason, strain gauges were connected to the data acquisition set and it was then attached to the charpy striker for the signal collection. Aluminium 6061 and low carbon steel 1050 were used for extracting strain responses on the striker during the testing. In this work, the power spectrum density (PSD) approach was then used for the energy based observation and a signal was converted from the time domain to the freque

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Li, Gen, and Jason J. Jung. "Maximum Marginal Approach on EEG Signal Preprocessing for Emotion Detection." Applied Sciences 10, no. 21 (2020): 7677. http://dx.doi.org/10.3390/app10217677.

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Emotion detection is an important research issue in electroencephalogram (EEG). Signal preprocessing and feature selection are parts of feature engineering, which determines the performance of emotion detection and reduces the training time of the deep learning models. To select the efficient features for emotion detection, we propose a maximum marginal approach on EEG signal preprocessing. The approach selects the least similar segments between two EEG signals as features that can represent the difference between EEG signals caused by emotions. The method defines a signal similarity described

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Yang, Menghuan, Hong Wu, Qiqi Wang, Yingxin Zhao, and Zhiyang Liu. "A BeiDou Signal Acquisition Approach Using Variable Length Data Accumulation Based on Signal Delay and Multiplication." Sensors 20, no. 5 (2020): 1309. http://dx.doi.org/10.3390/s20051309.

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The secondary modulation with the Neumann-Hoffman code increases the possibility of bit sign transition. Unlike other GNSS signals, there is no pilot component for synchronization in BeiDou B1/B3 signals, which increases the complexity in acquisition. A previous study has shown that the delay and multiplication (DAM) method is able to eliminate the bit sign transition problem, but it only applies to pretty strong signals. In this paper, a DAM-based BeiDou signal acquisition approach, called variable length data accumulation (VLDA), is proposed to acquire weak satellite signals. Firstly, the pe

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