Time Frequency Signal Analysis Research Papers (original) (raw)

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th International Conference on Signal and Image Processing (Signal 2022) is a forum for
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Power quality disturbances present noteworthy ramifications on electricity consumers, which can affect manufacturing process, causing malfunction of equipment and inducing economic losses. Thus, an automated system is required to identify and classify the signals for diagnosis purposes. The development of power quality disturbances detection and classification system using linear time-frequency distribution (TFD) technique which is spectrogram is presented in this paper. The TFD is used to represent the signals in time-frequency representation (TFR), hence it is handy for analyzing power quality disturbances. The signal parameters such as instantaneous of RMS voltage, RMS fundamental voltage, total waveform distortion (TWD), total harmonic distortion (THD) and total non-harmonic distortion (TnHD) are estimated from the TFR to identify the characteristic of the signals. The signal characteristics are then served as the input for signal classifier to classify power quality disturbance...

- by Mohd H A T T A Jopri
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- Engineering, Signal Processing, Power Quality, Digital Signal Processing
- by Boualem Boashash
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- Time Frequency Signal Analysis

This book is a result of author's thirty-three years of experience in teaching and research in signal processing.The book will guide you from a review of continuous-time signals and systems, through the world of digital signal processing, up to some of the most advanced theory and techniques in adaptive systems, time-frequency analysis, and sparse signal processing. It provides simple examples and explanations for each, including the most complex transform, method, algorithm or approach presented in the book. The most sophisticated results in signal processing theory are illustrated on simple numerical examples. The book is written for students learning digital signal processing and for engineers and researchers refreshing their knowledge in this area. The selected topics are intended for advanced courses and for preparing the reader to solve problems in some of the state of art areas in signal processing.

A comparison of different signal processing techniques is presented for fault detection in rolling element bearings. The vibration signals of a rotating machine with normal and defective bearings are processed in time, frequency and time-frequency domains. The features obtained from the original and processed signals are used for detection of bearing condition. The roles of different signal processing techniques and parameters on the effectiveness of bearing fault detection are investigated. The procedure is illustrated using the experimental vibration data of a rotating machine.

- by Boualem Boashash
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- Signal Processing, Signal Analysis, Heart rate variability, Spectrogram
- by Abdul Rahim Abdullah
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- Power Quality, Pattern Recognition, Support Vector Machines, Neural Networks
- by Boualem Boashash
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- Efficient Algorithm for ECG Coding, Analytic Signal, FIR filter, Real Time

SUMMARY Objectives: The study of intracerebral electroencephalography (EEG) seizure-onset patterns is crucial to accurately define the epileptogenic zone and guide successful surgical resection. It also raises important pathophysiologic issues concerning mechanisms of seizure generation. Until now, several seizure-onset patterns have been described using distinct recording methods (subdural, depth electrode), mostly in temporal lobe epilepsies or with heterogeneous neocortical lesions. Methods: We analyzed data from a cohort of 53 consecutive patients explored by stereoelectroencephalography (SEEG) and with pathologically confirmed malforma-tion of cortical development (MCD; including focal cortical dysplasia [FCD] and neu-rodevelopmental tumors [NDTs]). Results: We identified six seizure-onset patterns using visual and time-frequency analysis: low-voltage fast activity (LVFA); preictal spiking followed by LVFA; burst of poly-spikes followed by LVFA; slow wave/DC shift followed by LVFA; theta/alpha sharp waves; and rhythmic spikes/spike-waves. We found a high prevalence of patterns that included LVFA (83%), indicating nevertheless that LVFA is not a constant characteristic of seizure onset. An association between seizure-onset patterns and histologic types was found (p = 001). The more prevalent patterns were as follows: (1) in FCD type I LVFA (23.1%) and slow wave/baseline shift followed by LVFA (15.4%); (2) in FCD type II burst of polyspikes followed by LVFA (31%), LVFA (27.6%), and preictal spiking followed by LVFA (27.6%); (3) in NDT, LVFA (54.5%). We found that a seizure-onset pattern that included LVFA was associated with favorable postsurgical outcome, but the completeness of the EZ resection was the sole independent predictive variable. Significance: Six different seizure-onset patterns can be described in FCD and NDT. Better postsurgical outcome is associated with patterns that incorporate LVFA.

- by Fabrice Bartolomei
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- Time Frequency Signal Analysis, Epilepsy Surgery, SEEG, seizure onset patterns
- by Abdul Sha
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- Power Quality, Pattern Recognition, Support Vector Machines, Neural Networks
- by Osvaldo Anibal Rosso
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- Mathematical Physics, Quantum Physics, Information Theory, Wavelet Analysis

The paper presents a new method for detecting EEG spikes. The method is based on the time-frequency distribution of the signal. As spikes are short time broadband events, they are represented as ridges in the time-frequency domain. In this domain, the high instantaneous energy of spikes allows them to be distinguishable from the background. To detect spikes, the time-frequency distribution of the signal of interest is first enhanced to attenuate the noise.

- by M. Mesbah
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- Computer Science, Signal Processing, Speech Acoustics, Signal Analysis
- by Domingo Rodriguez
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- Everyday Life, Time Frequency Signal Analysis, Spectrum, Real Time Application

This paper shows the design procedures of a virtual system (VI) that is based on LabVIEW software with the aid of National Instruments (NI) Data Acquisition Devices (DAQ). The function of the system developed in this paper comprises data acquisition and generation operations. In addition, it also involves the implementation of several signal processing and analysis techniques such as Fast Fourier Transform (FFT) and Infinite Impulse Response (IIR) filters. The application of the proposed VI can be expanded according to the user requirements. For example, it can be used in the area of vibration monitoring, signal processing and analysis. This may include field dynamic balancing or fault detection and diagnosis. In this work, a measurement system, consisting of an integrated circuit piezoelectric (ICP) accelerometer and data acquisition device (DAQ) was prepared and assembled for the experiments. A shaker was used to produce a periodic vibration signal and hence simulates the most common vibration fault signatures experienced by rotating machines. A program in LabVIEW was designed to collect and analyse the simulated vibration signals in both time and frequency domains.

- by Mohammed Almaged
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- Signal Processing, LabVIEW, Virtual Instruments, Time Frequency Signal Analysis

Fibre optic Distributed Acoustic Sensor (DAS) is a particular sensor which offers the possibility of measuring at thousands of points simultaneously only using, as sensing mean, the normal optical fibre. In this thesis an implementation of this distributed sensing system is presented, also exploiting the so called ϕ-OTDR. With this technology is possible to sense both static and dynamic events such as time varying-signals, vibrations, crack of the bridges

- by Osvaldo Anibal Rosso
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- Mathematical Physics, Quantum Physics, Time Series, Nonlinear dynamics
- by Erol Başar
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- Neuroscience, Cognitive Science, Electroencephalography, Entropy
- by Rajiv Kapoor
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- Power Quality, Pattern Recognition, Parameter estimation, Time Frequency Analysis

A parallel restoration procedure obtained through a splitting of the signal into multiple signals by the paired transform is described. The set of frequency-points is divided by disjoint subsets, and on each of these subsets, the linear filtration is performed separately. The method of optimal Wiener filtration of the noisy signal is considered. In such splitting, the optimal filter is defined as a set of sub filters applied on the splitting-signals. Two new models of filtration are described. In the first model, the traditional filtration is reduced to the processing separately the splitting-signals by the shifted discrete Fourier transforms (DFTs). In the second model, the not shifted DFTs are used over the splitting-signals and sub filters are applied. Such simplified model for splitting the filtration allows for saving 2ܰ − ‫ݎ(4‬ + 1) operations of complex multiplication, for the signals of length ܰ = ‫,ݎ^2‬ ‫ݎ‬ > 2. .

The principal aim of a spectral observer is twofold: the reconstruction of a signal of time via state estimation and the decomposition of such a signal into the frequencies that make it up. A spectral observer can be catalogued as an online algorithm for time-frequency analysis because is a method that can compute on the fly the Fourier transform (FT) of a signal, without having the entire signal available from the start. In this regard, this paper presents a novel spectral observer with an adjustable constant gain for reconstructing a given signal by means of the recursive identification of the coefficients of a Fourier series. The reconstruction or estimation of a signal in the context of this work means to find the coefficients of a linear combination of sines a cosines that fits a signal such that it can be reproduced. The design procedure of the spectral observer is presented along with the following applications: (1) the reconstruction of a simple periodical signal, (2) the approximation of both a square and a triangular signal, (3) the edge detection in signals by using the Fourier coefficients, (4) the fitting of the historical Bitcoin market data from 1 December 2014 to 8 January 2018 and (5) the estimation of a input force acting upon a Duffing oscillator. To round out this paper, we present a detailed discussion about the results of the applications as well as a comparative analysis of the proposed spectral observer vis-à-vis the Short Time Fourier Transform (STFT), which is a well-known method for time-frequency analysis.

- by LIZETH TORRES and +2
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- Signal Processing, Spectral Methods, Fourier and Wavelet Processing, Spectral Music
- by Ljubisa Stankovic
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- Harmonic Analysis, Signal Processing, Multidisciplinary, Speech Processing

The Multisensor Time–FrequencySignal Processing (MTFSP) Matlab package is an analysis tool for multichannel non-stationary signals collected from an array of sensors. By combining array signal processing for non-stationary signals and multichannel high resolution time–frequency methods, MTFSP enables applications such as cross-channel causality relationships, automated component separation and direction of arrival estimation, using multisensor time–frequency distributions (MTFDs). MTFSP can address old and new applications such as: abnormality detection in biomedical signals, source localization in wireless communications or condition monitoring and fault detection in industrial plants. It allows e.g. the reproduction of the results presented in Boashash and Aïssa-El-Bey (in press) [2].

- by Juliana Yordanova and +1
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- Neuroscience, Cognitive Science, Electroencephalography, Entropy

Signal detection techniques based on time-frequency signal analysis with the Wigner-Ville distribution (WVD) and the cross Wigner-Ville distribution (XWVD) are presented. These techniques are shown to provide high-resolution signal characterization in a time-frequency space, and good noise rejection performance. This type of detection is applied to the signaturing, detection, and classification of specific machine sounds: the individual cylinder firings of a marine engine. For this task, a four-step procedure has been ...