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1
Mika, Dariusz, Grzegorz Budzik, and Jerzy Józwik. "Single Channel Source Separation with ICA-Based Time-Frequency Decomposition." Sensors 20, no. 7 (April 3, 2020): 2019. http://dx.doi.org/10.3390/s20072019.
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This paper relates to the separation of single channel source signals from a single mixed signal by means of independent component analysis (ICA). The proposed idea lies in a time-frequency representation of the mixed signal and the use of ICA on spectral rows corresponding to different time intervals. In our approach, in order to reconstruct true sources, we proposed a novelty idea of grouping statistically independent time-frequency domain (TFD) components of the mixed signal obtained by ICA. The TFD components are grouped by hierarchical clustering and k-mean partitional clustering. The distance between TFD components is measured with the classical Euclidean distance and the β distance of Gaussian distribution introduced by as. In addition, the TFD components are grouped by minimizing the negentropy of reconstructed constituent signals. The proposed method was used to separate source signals from single audio mixes of two- and three-component signals. The separation was performed using algorithms written by the authors in Matlab. The quality of obtained separation results was evaluated by perceptual tests. The tests showed that the automated separation requires qualitative information about time-frequency characteristics of constituent signals. The best separation results were obtained with the use of the β distance of Gaussian distribution, a distance measure based on the knowledge of the statistical nature of spectra of original constituent signals of the mixed signal.
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Wang, Yuqi, Jun Wang, Xingxing Jiang, Weiguo Huang, Juanjuan Shi, and Zhongkui Zhu. "Varying-parameter time-frequency manifold for gearbox fault diagnosis." Journal of Physics: Conference Series 2184, no. 1 (March 1, 2022): 012008. http://dx.doi.org/10.1088/1742-6596/2184/1/012008.
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Abstract The vibration signals of a faulty gearbox are non-stationary and contaminated by heavy background noise. Time-frequency transform is able to present the non-stationary fault impulsive features in the time-frequency distribution (TFD). However, the time-frequency fault information is still contaminated by the noise. This paper proposes a varying-parameter time-frequency manifold (VPTFM) method with the aim to remove the noise in the TFD for accurate identification of gearbox fault. First, a high-dimensional TFD is constructed by performing short- time Fourier transform (STFT) using some variable window lengths. Then, local tangent space alignment (LTSA) algorithm is carried out on the high-dimensional TFD to extract the manifold of the fault impulsive features with two dimensions, in which Rényi entropy is employed to select the proper neighborhood size for the LTSA by evaluating the first dimensional manifold. Afterwards, a threshold is designed by exploring the characteristics of the amplitudes of the manifold at two dimensions to adaptively remove the noise survived in the first dimensional manifold. Finally, the amplitudes at the frequency possessing the largest energy in the denoised manifold are taken out for spectrum analysis to identify the fault characteristic frequency. The enhanced performance of the proposed method in extraction of fault impulses and removal of background noise is validated by a gearbox experimental vibration signal measuring when the gear has a wearing fault.
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Wang, S. C., J. Han, Jian Feng Li, and Zhi Nong Li. "Adaptive Signal Analysis Based on Radial Parabola Kernel." Applied Mechanics and Materials 10-12 (December 2007): 737–41. http://dx.doi.org/10.4028/www.scientific.net/amm.10-12.737.
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Because of the deficiency of fixed kernel in bilinear time-frequency distribution (TFD), i.e. for each mapping, the resulting time-frequency representation is satisfactory only for a limited class of signals, a new adaptive kernel function named the radial parabola kernel (RPK), is proposed. The RPK can adopt the optimizing method to filter cross-terms adaptively according to the signal distribution, obtain good time-frequency resolution, and offer improved TFD for a large class of signals. Compared with traditional fixed -kernel functions, such as Wigner-Ville distribution, Choi-Willams distribution and Cone-kernel distribution, the superiority of the RPK function is obvious. At last, the RPK function is applied to the analysis of vibration signals of bearing, and the result proves the RPK function an effective method in analyzing signals.
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Habban, M. F., M. Manap, A. R. Abdullah, M. H. Jopri, and T. Sutikno. "An Evaluation of Linear Time Frequency Distribution Analysis for VSI Switch Faults Identification." International Journal of Power Electronics and Drive Systems (IJPEDS) 8, no. 1 (March 1, 2017): 1. http://dx.doi.org/10.11591/ijpeds.v8.i1.pp1-9.
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This paper present an evaluation of linear time frequency distribution analysis for voltage source inverter system (VSI). Power electronic now are highly demand in industrial such as manufacturing, industrial process and semiconductor because of the reliability and sustainability. However, the phenomenon that happened in switch fault has become a critical issue in the development of advanced. This causes problems that occur study on fault switch at voltage source inverter (VSI) must be identified more closely so that problems like this can be prevented. The TFD which is STFT and S-transform method are analyzed the switch fault of VSI. To identify the VSI switches fault, the parameter of fault signal such as instantaneous of average current, RMS current, RMS fundamental current, total waveform distortion, total harmonic distortion and total non-harmonic distortion can be estimated from TFD. The analysis information are useful especially for industrial application in the process for identify the switch fault detection. Then the accuracy of both method, which mean STFT and S-transform are identified by the lowest value of mean absolute percentage error (MAPE). In addition, the S-transform gives a better accuracy compare with STFT and it can be implement for fault detection system.
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Manap, Mustafa, Abdul Rahim Abdullah, Srete Nikolovski, Tole Sutikno, and Mohd Hatta Jopri. "An improved smooth-windowed Wigner-Ville distribution analysis for voltage variation signal." International Journal of Electrical and Computer Engineering (IJECE) 10, no. 5 (October 1, 2020): 4982. http://dx.doi.org/10.11591/ijece.v10i5.pp4982-4991.
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This paper outlines research conducted using bilinear time-frequency distribution (TFD), a smooth-windowed wigner-ville distribution (SWWVD) used to represent time-varying signals in time-frequency representation (TFR). Good time and frequency resolutions offer superiority in SWWVD to analyze voltage variation signals that consist of variations in magnitude. The separable kernel parameters are estimated from the signal in order to get an accurate TFR. The TFR for various kernel parameters is compared by a set of performance measures. The evaluation shows that different kernel settings are required for different signal parameters. Verification of the TFD that operated at optimal kernel parameters is then conducted. SWWVD exhibits a good performance of TFR which gives high peak-to-side lobe ratio (PSLR) and signal-to-cross-terms ratio (SCR) accompanied by low main-lobe width (MLW) and absolute percentage error (APE). This proved that the technique is appropriate for voltage variation signal analysis and it essential for development in an advanced embedded system.
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Ma, Ding, Li Hua Shi, Shang Chen Fu, and Hong Fu Cao. "Localization of Lamb Wave Scattering Source Based on Time-Frequency Analysis." Applied Mechanics and Materials 281 (January 2013): 276–81. http://dx.doi.org/10.4028/www.scientific.net/amm.281.276.
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Considering the influence of Lamb wave dispersion on the precision of damage detection, a new detection method of scattering source based on time-frequency curves and ellipse localization method is proposed. Empirical mode decomposition(EMD) is used to decompose the scattering signal into finite narrowband signals, and a modified continuous wavelet transform(CWT) is further used to get the time-frequency distribution(TFD) of the detected signal, and the arriving time of different frequency component is estimated based on TFD. A series of location results can be obtained from different frequency components using ellipse localization method. The damage position can finally be estimated by synthesizing localization results at different frequencies. Experiments on aluminum plate are conducted to demonstrate the efficiency of the proposed method. EMD-CWT analysis can get precise time-frequency curves in highly dispersive low frequency band of A0 mode. The damage location results is more accurate and the influence from occasional factors can be suppressed by using the synthesized method.
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Ponce de León, Jesús, José Ramón Beltrán, and Fernando Beltrán. "Instantaneous frequency estimation and representation of the audio signal through Complex Wavelet Additive Synthesis." International Journal of Wavelets, Multiresolution and Information Processing 12, no. 03 (May 2014): 1450030. http://dx.doi.org/10.1142/s0219691314500301.
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In this work, an improvement of the Complex Wavelet Additive Synthesis (CWAS) algorithm is presented. This algorithm is based on a discrete version of the Complex Continuous Wavelet Transform (CCWT) which analyzes the input signal in a frame-to-frame approach and under variable frequency resolution per octave. After summarizing several Time-Frequency Distributions (TFD), concretely the standard Short Time Fourier Transform (STFT), the Pseudo Wigner–Ville Distribution (PWVD), reassignment and complex wavelets, a comparative study of the accuracy in the instantaneous frequency (IF) estimation is shown. The comparative study includes three different signal processing tools (based on the summarized TFD): the Time-Frequency Toolbox (TFTB) of François Auger, the High Resolution Spectrographic Routines (HRSR) of Sean Fulop and the proposed CWAS algorithm. A set of eight synthetic signals have been analyzed using six different methods: the regular STFT spectrogram, the PWVD, their corresponding reassigned versions, the Nelson crossed spectrum method and finally the Complex Continuous Wavelet Transform (CCWT). Finally, two- and three-dimensional Time-Frequency representations of the IF provided by the CWAS algorithm are presented.
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Park, Gi Young, C. K. Lee, Jung Taek Kim, K. C. Kwon, and Sang J. Lee. "Design of a Time-Frequency Distribution for Vibration Monitoring under Corrosions in the Pipe." Key Engineering Materials 321-323 (October 2006): 1257–61. http://dx.doi.org/10.4028/www.scientific.net/kem.321-323.1257.
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To monitor the wear and degradation on a pipe by corrosion during a plant operation, the vibration signals were measured by an accelerometer and analyzed by several analysis techniques. From the conventional methods, it was difficult to identify the wear and degradation on the pipe. And hence, the time-frequency distribution (TFD) and the adaptive cone-kernel distribution (ACKD) devised for reducing the interfering cross-terms are applied to the acquired data. They can provide the distinguishing peak patterns between the normal and corrosion signals.
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Jopri, M. H., A. R. Abdullah, T. Sutikno, M. Manap, M. R. Ab Ghani, and M. R. Yusoff. "A Critical Review of Time-frequency Distribution Analysis for Detection and Classification of Harmonic Signal in Power Distribution System." International Journal of Electrical and Computer Engineering (IJECE) 8, no. 6 (December 1, 2018): 4603. http://dx.doi.org/10.11591/ijece.v8i6.pp4603-4618.
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<p>This paper presents a critical review of time-frequency distributions (TFDs) analysis for detection and classification of harmonic signal. 100 unique harmonic signals comprise of numerous characteristic are detected and classified by using spectrogram, Gabor transform and S-transform. The rulebased classifier and the threshold settings of the analysis are according to the IEEE Standard 1159 2009. The best TFD for harmonic signals detection and classification is selected through performance analysis with regards to the accuracy, computational complexity and memory size that been used during the analysis.</p>
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Mousa, Allam, and Rashid Saleem. "Using Reduced Interference Distribution to Analyze Abnormal Cardiac Signal." Journal of Electrical Engineering 62, no. 3 (May 1, 2011): 168–72. http://dx.doi.org/10.2478/v10187-011-0028-9.
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Using Reduced Interference Distribution to Analyze Abnormal Cardiac SignalDue to the non-stationary, multicomponent nature of biomedical signals, the use of time-frequency analysis can be inevitable for these signals. The choice and selection of the proper Time-Frequency Distribution (TFD) that can reveal the exact multicomponent structure of biological signals is vital in many applications, including the diagnosis of medical abnormalities. In this paper, the instantaneous frequency techniques using two distribution functions are applied for analysis of biological signals. These distributions are the Wigner-Ville Distribution and the Bessel Distribution. The simulation performed on normaland abnormal cardiac signals show that the Bessel Distribution can clearly detect the QRS complexes. However, Wigner-Ville Distribution was able to detect the QRS complexes in the normal signa, but fails to detect these complexes in the abnormal cardiac signal.
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KARTHICK, P. A., G. VENUGOPAL, and S. RAMAKRISHNAN. "ANALYSIS OF SURFACE EMG SIGNALS UNDER FATIGUE AND NON-FATIGUE CONDITIONS USING B-DISTRIBUTION BASED QUADRATIC TIME FREQUENCY DISTRIBUTION." Journal of Mechanics in Medicine and Biology 15, no. 02 (April 2015): 1540028. http://dx.doi.org/10.1142/s021951941540028x.
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In this paper, an attempt has been made to analyze surface electromyography (sEMG) signals under non-fatigue and fatigue conditions using time-frequency based features. The sEMG signals are recorded from biceps brachii muscle of 50 healthy volunteers under well-defined protocol. The pre-processed signals are divided into six equal epochs. The first and last segments are considered as non-fatigue and fatigue zones respectively. Further, these signals are subjected to B-distribution based quadratic time-frequency distribution (TFD). Time frequency based features such as instantaneous median frequency (IMDF) and instantaneous mean frequency (IMNF) are extracted. The expression of spectral entropy is modified to obtain instantaneous spectral entropy (ISPEn) from the time-frequency spectrum. The results show that all the extracted features are distinct in both conditions. It is also observed that the values of all features are higher in non-fatigue zone compared to fatigue condition. It appears that this method is useful in analysing various neuromuscular conditions using sEMG signals.
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Ahmad, Ashraf Adamu, A. S. Saliu, Abel E. Airoboman, U. M. Mahmud, and S. L. Abdullahi. "Identification of Radar Signals Based on Time-Frequency Agility using Short-Time Fourier Transform." Journal of Advances in Science and Engineering 1, no. 2 (August 14, 2018): 1–8. http://dx.doi.org/10.37121/jase.v1i2.18.
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With modern advances in radar technologies and increased complexity in aerial battle, there is need for knowledge acquisition on the abilities and operating characteristics of intercepted hostile systems. The required knowledge obtained through advanced signal processing is necessary for either real time-warning or in order to determine Electronic Order of Battle (EOB) of these systems. An algorithm was therefore developed in this paper based on a joint Time-Frequency Distribution (TFD) in order to identify the time-frequency agility of radar signals based on its changing pulse characteristics. The joint TFD used in this paper was the square magnitude of the Short-Time Fourier Transform (STFT), where power and frequency obtained at instants of time from its Time-Frequency Representation (TFR) was used to estimate the time and frequency parameters of the radar signals respectively. Identification was thereafter done through classification of the signals using a rule-based classifier formed from the estimated time and frequency parameters. The signals considered in this paper were the simple pulsed, pulse repetition interval modulated, frequency hopping and the agile pulsed radar signals, which represent cases of various forms of agility associated with modern radar technologies. Classification accuracy was verified using the Monte Carlo simulation performed at various ranges of Signal-to-Noise Ratios (SNRs) in the presence of noise modelled by the Additive White Gaussian Noise (AWGN). Results obtained showed identification accuracy of 99% irrespective of the signal at a minimum SNR of 0dB where signal and noise power were the same. The obtained minimum SNR at this classification accuracy showed that the developed algorithm can be deployed practically in the electronic warfare field for accurate agility classification of airborne radar signals.
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Zhou, Zibo, Zhihui Wang, Binbin Wang, Saiqiang Xia, and Jianwei Liu. "Clutter Suppression and Rotor Blade Feature Extraction of a Helicopter Based on Time–Frequency Flash Shifts in a Passive Bistatic Radar." Atmosphere 13, no. 8 (August 1, 2022): 1214. http://dx.doi.org/10.3390/atmos13081214.
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This paper presents a passive bistatic radar (PBR) configuration using a global navigation satellite system as an illuminator of opportunity for the rotor blade feature extraction of a helicopter. Aiming at the strong fixed clutter in the surveillance channel of the PBR, a novel iteration clutter elimination method-based singular-value decomposition approach is proposed. Instead of the range elimination method used in the classic extended cancellation algorithm, the proposed clutter elimination method distinguishes the clutter using the largest singular value and by remove this value. At the same time, the fuselage echo of the hovering helicopter can also be suppressed along with the ground clutter, then the rotor echo of this can be obtained. In the micro-motion feature extraction, the mathematic principle of the flash generation process in the time–frequency distribution (TFD) is derived first. Next, the phase compensation method is applied to achieve the time–frequency flash shift in the TFD. After this, the center frequencies of the standard flashes in the TFD are compared with the standard frequency dictionary. The mean l1 norm is utilized to estimate the feature parameters of the helicopter rotor. In the experiments, the scattering point model and the physical optics facet model demonstrate that the proposed method can obtain more accurate parameter estimation results than some classic algorithms.
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Chicherin, I. V., B. A. Fedosenkov, and D. M. Dubinkin. "Monitoring the current trajectories of autonomous heavy platforms moving along the quarry routes of mining enterprises." Mining Industry Journal (Gornay Promishlennost), no. 5/2021 (November 12, 2021): 76–83. http://dx.doi.org/10.30686/1609-9192-2021-5-76-83.
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In order to obtain information about the generated current trajectories (CT) of unmanned mining dump trucks, in the software and hardware complexes of the computer-aided dispatching system (in the external control subsystem and the autonomous control subsystem) installed on-board of an (AHP), one-dimensional (scalar) continuous signals (hereinafter converted into discrete digital ones) with a time-dependent instantaneous frequency, the so-called chirp signals, are put in accordance with the current trajectories of the AHP. This approach makes it possible to continuously monitor and manage the dynamics of current AHP trajectories with a high degree of efficiency. Note that for the purpose of information-rich and semantically transparent representation of information about the current state of the AHP CT, the chirp signals of the CT are converted into multidimensional Cohen’s class time-frequency wavelet distributions. The Wigner-Ville distribution (hereinafter referred to as the Wigner distribution) is selected as a working tool for performing computational procedures in the hardware / software module. This distribution is based on the Gabor basis wavelet functions and the wavelet matching pursuit algorithm. The choice of Gabor wavelets as the main ones is explained by their sinusoidal-like shape, since they are sinusoidal signals modulated by the Gauss window. On the other hand, the analyzed 1D-signals indicating the current position of the AHP on the route are also sinusoidal-like. This makes it possible to approximate current signals with high accuracy based on their comparison with the wavelet functions selected from the redundant wavelet dictionary. This approximation is adaptive, since it is performed on separate local fragments of the signal analyzed depending on approximating wavelets. This is the essence of the wavelet matching pursuit algorithm. The resulting wavelet series is then transformed into the Wigner time-frequency distribution, which is used to form a corresponding CT. As an example, reconstructions of time-frequency distributions (TFD) are given, corresponding to the deviation of a certain CT to the left (the trajectory signal decreases exponentially) and to the right (the CT-signal increases) from the nominal axial trajectory (NAT). The calculated scalar signal and its TFD for the AHP CT deviating to the left from NAT are also presented. In addition, on the basis of theoretical explanations the calculated linear-increasing TFD is demonstrated, corresponding to the CT-deviation to the right from NAT, and the time invariant stationary TFD characterizing the movement of AHP along the NAT line. In conclusion, based on the results obtained, it is concluded that the most appropriate ways to monitor the current trajectories of AHP movement and procedures for processing the corresponding signals are the operations implemented in computer-aided subsystems of external and autonomous control and based on such concepts as the Cohen’s class wavelet distributions, Gabor redundant dictionary of wavelet functions, the wavelet matching pursuit algorithm, and the representation of technological chirp-signals, as well as frequency-stationary signals about the current AHP trajectories represented in the wavelet medium. In this connection, the authors concluded that the procedures realizing the current monitoring of AHP movement on open pit mine routes and implementing the process of analyzing a relevant dynamic change in current trajectories, described in the article and embedded in software and hardware autonomous and external control subsystems of “Smart quarry” are adequate for performing required functions. The introduction of the principles of computer-aided controlling the unmanned mining vehicles allows you to optimize labor costs for the operation of mining equipment, reduce the cost of current work, and attract highly qualified specialists for the development and operation of innovative transport equipment.
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Jopri, M. H., A. R. Abdullah, M. Manap, M. R. Yusoff, T. Sutikno, and M. F. Habban. "An Improved Detection and Classification Technique of Harmonic Signals in Power Distribution by Utilizing Spectrogram." International Journal of Electrical and Computer Engineering (IJECE) 7, no. 1 (February 1, 2017): 12. http://dx.doi.org/10.11591/ijece.v7i1.pp12-20.
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This paper introduces an improved detection and classification technique of harmonic signals in power distribution using time-frequency distribution (TFD) analysis which is spectrogram. The spectrogram is an appropriate approach to signify signals in jointly time-frequency domain and known as time frequency representation (TFR). The spectral information of signals can be observed and estimated plainly from TFR due to identify the characteristics of the signals. Based on rule-based classifier and the threshold settings that referred to IEEE Standard 1159 2009, the detection and classification of harmonic signals for 100 unique signals consist of various characteristic of harmonics are carried out successfully. The accuracy of proposed method is examined by using MAPE and the result show that the technique provides high accuracy. In addition, spectrogram also gives 100 percent correct classification of harmonic signals. It is proven that the proposed method is accurate, fast and cost efficient for detecting and classifying harmonic signals in distribution system.
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Mohamad Basir, Muhammad Sufyan Safwan. "Window Optimisation of Power Quality Signal Detection using Gabor Transform." ASM Science Journal 14 (April 2, 2021): 1–10. http://dx.doi.org/10.32802/asmscj.2020.596.
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This paper presents power quality analysis on different signal characteristics, namely instantaneous sag, momentary sag, temporary sag, instantaneous swell, momentary swell, and temporary swell. Power quality signals were analyzed using linear time-frequency distribution (TFD) namely short-time Fourier transform (STFT) and proposed Gabor transform (GT), and the best technique for power quality detection was determined based on the performance analysis of varied window length. Optimum window length for different signal characteristics which are effective and reliable for developing real-time monitoring system was employed using MATLAB. From a time-frequency representation (TFR) results based on STFT and GT, parameters such as instantaneous RMS voltage, VRMS(t) and instantaneous total waveform distortion, VTWD(t) had been extracted. In finding the best technique for power quality, the TFDs had been compared in terms of accuracy, memory and computational complexity of the analysis. Based on the performance analysis conducted, GT was able to compute with high accuracy with 94% averagely as well as low memory size by 6% compared to STFT. Hence, GT is considered as the best TFD, and recommended for low cost PQ monitoring system.
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Seninete, Sara, Mansour Abed, Azeddine Bendiabdellah, Malika Mimi, Adel Belouchrani, Abdelaziz Ould Ali, and Bilal Djamal Eddine Cherif. "On the Use of High-resolution Time-frequency Distribution Based on a Polynomial Compact Support Kernel for Fault Detection in a Two-level Inverter." Periodica Polytechnica Electrical Engineering and Computer Science 64, no. 4 (August 31, 2020): 352–65. http://dx.doi.org/10.3311/ppee.15469.
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Quadratic Time-Frequency Distributions (TFDs) become a standard tool in many fields producing nonstationary signatures. However, these representations suffer from two drawbacks: First, bad time-frequency localization of the signal's autoterms due to the unavoidable crossterms generated by the bilinear form of these distributions. This results on bad estimation of the Instantaneous Frequency (IF) laws and decreases, in our case, the ability to precisely decide the existence of a motor fault. Secondly, the TFD's parameterization is not always straightforward. This paper deals with faults' detection in two-level inverter feeding induction motors, in particular open-circuit Insulated Gate Bipolar Transistor (IGBT) faults. For this purpose, we propose the use of a recent high-resolution TFD, referred as PCBD for Polynomial Cheriet-Belouchrani Distribution. The latter is adjusted using only a single integer that is automatically optimized using the Stankovic concentration measure, otherwise, no external windows are needed to perform the highest time-frequency resolution. The performance of the PCBD is compared to the best-known quadratic representations using a test bench. Experimental results show that the frequency components characterizing open-circuit faults are best detected using the PCBD thanks to its ability to suppress interferences while maintaining the signal's proper terms.
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H. Jopri, M., A. R. Abdullah, M. Manap, T. Sutikno, and M. R. Ab Ghani. "An Identification of Multiple Harmonic Sources in a Distribution System by Using Spectrogram." Bulletin of Electrical Engineering and Informatics 7, no. 2 (June 1, 2018): 244–56. http://dx.doi.org/10.11591/eei.v7i2.1188.
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The identification of multiple harmonic sources (MHS) is vital to identify the root causes and the mitigation technique for a harmonic disturbance. This paper introduces an identification technique of MHS in a power distribution system by using a time-frequency distribution (TFD) analysis known as a spectrogram. The spectrogram has advantages in term of its accuracy, a less complex algorithm, and use of low memory size compared to previous methods such as probabilistic and harmonic power flow direction. The identification of MHS is based on the significant relationship of spectral impedances, which are the fundamental impedance (Z1) and harmonic impedance (Zh) that estimate the time-frequency representation (TFR). To verify the performance of the proposed method, an IEEE test feeder with several different harmonic producing loads is simulated. It is shown that the suggested method is excellent with 100% correct identification of MHS. The method is accurate, fast and cost-efficient in the identification of MHS in power distribution arrangement.
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Sabeti, Malihe, Ehsan Moradi, Mahsa Taghavi, Mokhtar Mohammadi, and Reza Boostani. "Time-Frequency Distribution Analysis for Electroencephalogram Signals of Patients With Schizophrenia and Normal Participants." International Clinical Neuroscience Journal 9, no. 1 (February 20, 2022): e11-e11. http://dx.doi.org/10.34172/icnj.2022.11.
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Background: Psychiatrists diagnose schizophrenia based on clinical symptoms such as disordered thinking, delusions, hallucinations, and severe distortion of daily functions. However, some of these symptoms are common with other mental illnesses such as bipolar mood disorder. Therefore, quantitative assessment of schizophrenia by analyzing a physiological-based data such as the electroencephalogram (EEG) signal is of interest. In this study, we analyze the spectrum and time-frequency distribution (TFD) of EEG signals to understand how schizophrenia affects these signals. Methods: In this regard, EEG signals of 20 patients with schizophrenia and 20 age-matched participants (control group) were investigated. Several features including spectral flux, spectral flatness, spectral entropy, time-frequency (TF)-flux, TF-flatness, and TF-entropy were extracted from the EEG signals. Results: Spectral flux (1.5388±0.0038 and 1.5497±0.0058 for the control and case groups, respectively, P=0.0000), spectral entropy (0.8526±0.0386 and 0.9018±0.0428 for the control and case groups, respectively, P=0.0004), spectral roll-off (0.3896±0.0434 and 0.4245±0.0410 for the control and case groups, respectively, P=0.0129), spectral flatness (0.1401±0.0063 and 0.1467±0.0077 for the control and case groups, respectively, P=0.0055), TF-flux (1.2675±0.1806 and 1.5284±0.2057 for the control and case groups, respectively, P=0.0001) and TF-flatness (0.9980±0.0000 and 0.9981±0.0000 for the control and case groups, respectively, P=0.0000) values in patients with schizophrenia were significantly greater than the control group in most EEG channels. This prominent irregularity may be caused by decreasing the synchronization of neurons in the frontal lobe. Conclusion: Spectral and time frequency distribution analysis of EEG signals can be used as quantitative indexes for neurodynamic investigation in schizophrenia.
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KOSTYUK, Svetlana, Ivan CHICHERIN, Boris FEDOSENKOV, and Dmitry DUBINKIN. "MONITORING OF THE DYNAMIC STATE OF AUTONOMOUS HEAVY PLATFORMS ON THE QUARRY ROUTES OF MINING ENTERPRISES." Sustainable Development of Mountain Territories 12, no. 4 (December 30, 2020): 600–608. http://dx.doi.org/10.21177/1998-4502-2020-12-4-600-608.
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Purpose of work. The article presents the results of theoretical research and developments obtained at the Kuzbass state technical university on the implementation of current monitoring and bringing about signal processing procedures for the dynamic state of autonomous heavy platforms (AHP) on open pit mine routes. In order to obtain information about the generated current trajectories (CT) of unmanned mining dump trucks, in the software and hardware complexes of the computer-aided dispatching system (in the external control subsystem – ECSS and the autonomous control subsystem – ACSS) installed on-board of an AHP, one-dimensional (scalar) continuous signals (hereinafter converted into discrete digital ones) with a time-dependent instantaneous frequency, the so-called chirp signals, are put in accordance with the current trajectories of the AHP. Research methods. This approach makes it possible to continuously monitor and manage the dynamics of current ATP trajectories with a high degree of efficiency. Note that for the purpose of information-rich and semantically transparent representation of information about the current state of the AHP CT, the chirp signals of the CT are converted into multidimensional Cohen’s class time-frequency wavelet distributions. The Wigner-Ville distribution (hereinafter referred to as the Wigner distribution) is selected as a working tool for performing computational procedures in the hardware / software module. This distribution is based on the Gabor basis wavelet functions and the wavelet matching pursuit algorithm. The choice of Gabor wavelets as the main ones is explained by their sinusoidal-like shape, since they are sinusoidal signals modulated by the Gauss window. On the other hand, the analyzed 1D-signals indicating the current position of the AHP on the route are also sinusoidal-like. This makes it possible to approximate current signals with high accuracy based on their comparison with the wavelet functions selected from the redundant wavelet dictionary. This approximation is adaptive, since it is performed on separate local fragments of the signal analyzed depending on approximating wavelets. This is the essence of the wavelet matching pursuit algorithm. The resulting wavelet series is then transformed into the Wigner time-frequency distribution, which is used to form a corresponding CT. Research results. As an example, reconstructions of time-frequency distributions (TFD) are given, corresponding to the deviation of a certain CT to the left (the trajectory signal decreases exponentially) and to the right (the CT-signal increases) from the nominal axial trajectory (NAT). The calculated scalar signal and its TFD for the AHP CT deviating to the left from NAT are also presented. In addition, on the basis of theoretical explanations the calculated linear-increasing TFD is demonstrated, corresponding to the CT-deviation to the right from NAT, and the time invariant stationary TFD characterizing the movement of AHP along the NAT line. Сonclusion. Based on the results obtained, it is concluded that the most appropriate ways to monitor the current trajectories of AHP movement and procedures for processing the corresponding signals are the operations implemented in computer-aided subsystems of external and autonomous control and based on such concepts as the Cohen’s class wavelet distributions, Gabor redundant dictionary of wavelet functions, the wavelet matching pursuit algorithm, and the representation of technological chirp-signals, as well as frequency-stationary signals about the current AHP trajectories represented in the wavelet medium. In this connection, the authors concluded that the procedures realizing the current monitoring of AHP movement on open pit mine routes and implementing the process of analyzing a relevant dynamic change in current trajectories, described in the article and embedded in software and hardware autonomous and external control subsystems of "Smart opencast mine” are adequate for performing required functions. The introduction of the principles of computer-aided controlling the unmanned mining vehicles allows you to optimize labor costs for the operation of mining equipment, reduce the cost of current work, and attract highly qualified specialists for the development and operation of innovative transport equipment. The implementation of such prospects in mountainous regions (of a country) makes it possible to diversify the range of labor resources and, in general, contribute to the sustainable social and economic development of mountain territories.
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Abdullah, Abdul Rahim, N. A. Abidullah, N. H. Shamsudin, N. H. H. Ahmad, and M. H. Jopri. "Performance Verification of Power Quality Signals Classification System." Applied Mechanics and Materials 752-753 (April 2015): 1158–63. http://dx.doi.org/10.4028/www.scientific.net/amm.752-753.1158.
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Power quality has become a greater concern nowadays. The increasing number of power electronics equipment contributes to the poor quality of electrical power supply. The power quality signals will affect manufacturing process, malfunction of equipment and economic losses. This paper presents the verification analysis of power quality signals classification system. The developed system is based on linear time-frequency distribution (TFD) which is spectrogram that represents the signals jointly in time-frequency representation (TFR). The TFD is very appropriate to analyze power quality signals that have magnitude and frequency variations. Parameters of the signal such as root mean square (RMS) and fundamental RMS, total waveform distortion (TWD), total harmonic distortion (THD) and total non-harmonic distortion (TnHD) of voltage signal are estimated from the TFR to identify the characteristics of the signal. Then, the signal characteristics are used as input for signal classifier to classify power quality signals. In addition, standard power line measurements are also calculated from voltage and current such as RMS and fundamental RMS voltage and current, real power, apparent power, reactive power, frequency and power factor. The power quality signals focused are swell, sag, interruption, harmonic, interharmonic, and transient based on IEEE Std. 1159-2009. The power quality analysis has been tested using a set of data and the results show that, the spectrogram gives high accuracy measurement of signal characteristics. However, the system offers lower accuracy compare to simulation due to the limitation of the system.
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Jopri, M. H., A. R. Abdullah, M. Manap, M. R. Yusoff, T. Sutikno, and M. F. Habban. "An Improved of Multiple Harmonic Sources Identification in Distribution System with Inverter Loads by Using Spectrogram." International Journal of Power Electronics and Drive Systems (IJPEDS) 7, no. 4 (December 1, 2016): 1355. http://dx.doi.org/10.11591/ijpeds.v7.i4.pp1355-1365.
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This paper introduces an improved of multiple harmonic sources identification that been produced by inverter loads in power system using time-frequency distribution (TFD) analysis which is spectrogram. The spectrogram is a very applicable method to represent signals in time-frequency representation (TFR) and the main advantages of spectrogram are the accuracy, speed of the algorithm and use low memory size such that it can be computed rapidly. The identification of multiple harmonic sources is based on the significant relationship of spectral impedances which are the fundamental impedance (Z1) and harmonic impedance (Zh) that extracted from TFR. To verify the accuracy of the proposed method, MATLAB simulations carried out several unique cases with different harmonic producing loads on IEEE 4-bus test feeder cases. It is proven that the proposed method is superior with 100% correct identification of multiple harmonic sources. It is envisioned that the method is very accurate, fast and cost efficient to localize harmonic sources in distribution system.
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Chicherin, Ivan V., Boris A. Fedosenkov, Ilia S. Syrkin, Vladimir Iu Sadovets, and Dmitrii M. Dubinkin. "Using a wavelet medium for computer-aided controlling the movement of unmanned vehicles along quarry routes." Izvestiya vysshikh uchebnykh zavedenii. Gornyi zhurnal 1 (March 30, 2021): 103–12. http://dx.doi.org/10.21440/0536-1028-2021-2-103-112.
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Introduction. It is established that the most effective tool for monitoring and controlling the dynamics of current trajectories (CT) of unmanned vehicles (UMV) when moving along opencast mine routes in open pit mining is the wavelet transforms technique. Methodology. A detailed analysis of the procedures related to the technology of converting 1D-current trajectory signals (CT-signals) into a multidimensional medium of time-frequency distributions (TFD) is carried out. The Wigner distribution is selected as a working distribution for processing CT-signals. This distribution is considered from the point of view of its ability to represent one-dimensional CT-signals of UMV in an information-intensive and functionally transparent format of specific TFDs. Research results and analysis. On the example of curved routes, the nature of the so-called forward and reverse transients of CT-signals of UMV, formed in the subsystems of external and autonomous control (ECSS and ACSS) of unmanned vehicles, is considered. Mathematical tools are described for wavelet transformations: Gabor wavelet functions, the wavelet matching pursuit algorithm (MP-algorithm), and Cohen’s class time-frequency wavelet distributions. Conclusion. The procedures of processing the trajectory signals with using the means mentioned above make it possible to implement effectively the functions of controlling the UMV movement along current trajectories formed by the system on opencast mine routes.
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Jing Wei, Too, Abdul Rahim Bin Abdullah, Norhashimah Binti Mohd Saad, Nursabillilah Binti Mohd Ali, and Tengku Nor Shuhada Binti Tengku Zawawi. "Featureless EMG pattern recognition based on convolutional neural network." Indonesian Journal of Electrical Engineering and Computer Science 14, no. 3 (June 1, 2019): 1291. http://dx.doi.org/10.11591/ijeecs.v14.i3.pp1291-1297.
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In this paper, the performance of featureless EMG pattern recognition in classifying hand and wrist movements are presented. The time-frequency distribution (TFD), spectrogram is employed to transform the raw EMG signals into time-frequency representation (TFR). The TFRs or spectrogram images are then directly fed into convolutional neural network (CNN) for classification. Two CNN models are proposed to learn the features automatically from the images without the need of manual feature extraction. The performance of CNN with different number of convolutional layers is examined. The proposed CNN models are evaluated using the EMG data from 10 intact and 11 amputee subjects through the publicly access NinaPro database. Our results show that CNN classifier offered the best mean classification accuracy of 88.04% in recognizing hand and wrist movements.
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Han, Lixun, and Cunqian Feng. "Micro-Doppler-Based Space Target Recognition with a One-Dimensional Parallel Network." International Journal of Antennas and Propagation 2020 (October 5, 2020): 1–10. http://dx.doi.org/10.1155/2020/8013802.
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Space target identification is key to missile defense. Micromotion, as an inherent attribute of the target, can be used as the theoretical basis for target recognition. Meanwhile, time-varying micro-Doppler (m-D) frequency shifts induce frequency modulations on the target echo, which can be referred to as the m-D effect. m-D features are widely used in space target recognition as it can reflect the physical attributes of the space targets. However, the traditional recognition method requires human participation, which often leads to misjudgment. In this paper, an intelligent recognition method for space target micromotion is proposed. First, accurate and suitable models of warhead and decoy are derived, and then the m-D formulae are offered. Moreover, we present a deep-learning (DL) model composed of a one-dimensional parallel structure and long short-term memory (LSTM). Then, we utilize this DL model to recognize time-frequency distribution (TFD) of different targets. Finally, simulations are performed to validate the effectiveness of the proposed method.
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Razzaq, Huda Saleem, and Zahir M. Hussain. "Instantaneous Frequency Estimation of FM Signals under Gaussian and Symmetric α-Stable Noise: Deep Learning versus Time–Frequency Analysis." Information 14, no. 1 (December 28, 2022): 18. http://dx.doi.org/10.3390/info14010018.
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Deep learning (DL) and machine learning (ML) are widely used in many fields but rarely used in the frequency estimation (FE) and slope estimation (SE) of signals. Frequency and slope estimation for frequency-modulated (FM) and single-tone sinusoidal signals are essential in various applications, such as wireless communications, sound navigation and ranging (SONAR), and radio detection and ranging (RADAR) measurements. This work proposed a novel frequency estimation technique for instantaneous linear FM (LFM) sinusoidal wave using deep learning. Deep neural networks (DNN) and convolutional neural networks (CNN) are classes of artificial neural networks (ANNs) used for the frequency and slope estimation for LFM signals under additive white Gaussian noise (AWGN) and additive symmetric alpha stable noise (SαSN). DNN is composed of input, output, and two hidden layers, where several nodes in the first and second hidden layers are 25 and 8, respectively. CNN is the content input layer; many hidden layers include convolution, batch normalization, ReLU, max pooling, fully connected, and dropout. The output layer consists of a fully connected softmax and classification layers. SαS distributions are impulsive noise disturbances found in many communication environments such as marine systems, their distribution lacks a closed-form probability density function (PDF), except for specific cases, and infinite second-order statistics, hence geometric SNR (GSNR) is used in this work to determine the effect of noise in a mixture of Gaussian and SαS noise processes. DNN is a machine learning classifier with few layers for reducing FE and SE complexity. CNN is a deep learning classifier, designed with many layers, and proved to be more accurate than DNN when dealing with big data and finding optimal features. Simulation results show that SαS noise can be much more harmful to the FE and SE of FM signals than Gaussian noise. DL and ML can significantly reduce FE complexity, memory cost, and power consumption as compared to the classical FE based on time–frequency analysis, which are important requirements for many systems, such as some Internet of Things (IoT) sensor applications. After training CNN for frequency and slope estimation of LFM signals, the performance of CNN (in terms of accuracy) can give good results at very low signal-to-noise ratios where time–frequency distribution (TFD) fails, giving more than 20 dB difference in the GSNR working range as compared to the classical spectrogram-based estimation, and over 15 dB difference with Viterbi-based estimate.
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Kasim, Rizanaliah, Abdul Rahim Abdullah, Nur Asmiza Selamat, N. A. Abidullah, and Tengku Nor Shuhadah Tengku Zawawi. "Lead Acid Battery Analysis Using Spectogram ." Applied Mechanics and Materials 785 (August 2015): 692–96. http://dx.doi.org/10.4028/www.scientific.net/amm.785.692.
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Renewable energy is an alternative option that can be substituted for future energy demand. Many type of battery are used in commerce to propel portable power and this makes the task of selecting the right battery type is crucial. This paper presents the analysis of voltage charging and discharging for lead acid battery using time-frequency distribution (TFD) which is spectrogram. Spectogram technique is used to represent the signals in the time-frequency representation (TFR). The parameter of a signal such as instantaneous root mean square (RMS) voltage, direct current voltage (VDC) and alternating current voltage (VAC) are estimated from the TFR to identify the signal characteristics. This analysis, focus on lead-acid battery with nominal battery voltage of 6 and 12V and storage capacity from 5 until 50Ah. The battery is a model using MATLAB/SIMULINK and the results show that spectrogram technique is capable to identify and determine the signal characteristic of Lead Acid battery.
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Liang, Lin, Lei Shan, Fei Liu, Ben Niu, and Guanghua Xu. "Sparse Envelope Spectra for Feature Extraction of Bearing Faults Based on NMF." Applied Sciences 9, no. 4 (February 21, 2019): 755. http://dx.doi.org/10.3390/app9040755.
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Periodic impulses and the oscillation response signal are the vital feature indicators of rolling bearing faults. However, finding the suitable feature frequency band is usually difficult due to the interferences of other components and multiple resonance regions. According to the characteristics of non-negative matrix factorization (NMF) on a spectrogram, the feature extraction method from a sparse envelope spectrum for rolling bearing faults is proposed in this paper. On the basis of the time–frequency distribution (TFD) of the periodic transient oscillations, the basic matrix can be interpreted as the spectral bases, and the time weight matrix corresponding to spectral bases can be extracted by NMF. Because the bases and the weights have a one-to-one correspondence, the frequency band filtering with the basic component and the time domain envelope of the weight vector are calculated respectively. Then, the sparse envelope spectrum can be derived by the inner product of the above results. The effectiveness of the proposed method is verified by simulations and experiments. Compared with band-pass filtering and spectral kurtosis methods, and considering the time weights and corresponding the spectral bases for the periodic transient oscillations, the weak fault-rated feature can be enhanced in the sparse spectrum, while other components and noise are weakened. Therefore, the proposed method can reduce the requirement of selecting frequency band filtering.
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Rahim Abdullah, Abdul, Nur Hafizah Tul Huda Ahmad, N. A. Abidullah, N. H. Shamsudin, and M. H. Jopri. "Performance Evaluation of Real Power Quality Disturbances Analysis Using S-Transform." Applied Mechanics and Materials 752-753 (April 2015): 1343–48. http://dx.doi.org/10.4028/www.scientific.net/amm.752-753.1343.
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Power quality is main issue because of the impact to electricity suppliers, equipments, manufacturers and user.To solve the power quality problem, an analysis of power quality disturbances is required to identify and rectify any failures on power system. Most of researchers apply fourier transform in power quality analysis, however the ability of fourier transform is limited to spectral information extraction that can be applied on stationary disturbances. Thus, time-frequency analysis is introduced for analyzing the power quality distubances because of the limitation of fourier transform. This paper presents the analysis of real power quality disturbances using S-transform. This time-frequency distribution (TFD) is presented to analyze power quality disturbances in time-frequency representation (TFR). From the TFR, parameters of the disturbances such as instantaneous of root mean square (RMS), fundamental RMS, total harmonic distortion (THD), total nonharmonic distortion (TnHD) and total waveform distortion (TWD) of the disturbances are estimated. The experimental of three phase voltage inverter and starting motor are conducted in laboratory to record the real power quality disturbances. The disturbances are recorded via data logger system which is mplemented using LabVIEW while the analysis is done using Matlab in offline condition. The results show that S-transform gives good performance in identifying, detecting and analyzing the real power quality disturbances, effectively.
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Ahmad, Ashraf Adamu, Abdullahi Daniyan, and David Ocholi Gabriel. "Selection of window for inter-pulse analysis of simple pulsed radar signal using the short time Fourier transform." International Journal of Engineering & Technology 4, no. 4 (November 17, 2015): 531. http://dx.doi.org/10.14419/ijet.v4i4.5139.
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The electronic intelligence (ELINT) system is used by the military to detect, extract information and classify incoming radar signals. This work utilizes short time Fourier transform (STFT) - time frequency distribution (TFD) for inter-pulse analysis of the radar signal in order to estimate basic radar signal time parameters (pulse width and pulse repetition period). Four well-known windows functions of different and unique characteristics were used for the localization of STFT to determine their various effects on the analysis. The window functions are Hamming, Hanning, Bartlett and Blackman window functions. Monte Carlo simulation is carried out to determine the performance of the signal analysis in presence of additive white Gaussian noise (AWGN). Results show that the lower the transition of main lobe width and higher the peak side lobe, the better the performance of the window function irrespective of time parameter being estimated. This is because 100 percent probability of correct estimation is achieved at signal to noise ratio of about -2dB for Bartlett, 4dB for both Hamming and Hanning, and 9dB for Blackman.
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Lin, Lin, Jia Jin Qi, Nan Tian Huang, and Shi Guang Luo. "Time-Frequency Analysis Methods for Power Quality Disturbances Feature Representation." Key Engineering Materials 439-440 (June 2010): 298–303. http://dx.doi.org/10.4028/www.scientific.net/kem.439-440.298.
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Power quality (PQ) analysis is the foundation of power system automation. The premise of power quality analysis is feature representation of power quality events. Time-frequency analysis (TFA) is very suitable for nonstationary signals analysis. The TFA of a PQ signal is to determine the energy distribution along the frequency axis at each time instant. This paper provides a status report of feature representation for PQ events by TFA methods, including short time Fourier transform (STFT), wavelet transform (WT) and S-transform (ST), overview the basic TFA theories for PQ analysis and compare the effectiveness of different TFA methodology. The expectation is that further research and applications of these TFA algorithms will flourish for PQ feature representation in the near future. The analysis direction and emphasis of studying are also put forward.
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Wang, Hui Qi, and Wangyong Lv. "FrFT Angle Division Multiple Access with Optimal Time-Frequency-Angle Resource Distribution." Applied Mechanics and Materials 519-520 (February 2014): 1012–15. http://dx.doi.org/10.4028/www.scientific.net/amm.519-520.1012.
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In this paper, optimal time-frequency-angle (TFA) resource distribution is proposed. It is achieved by fractional Fourier transform (FrFT) angle division multiple access (ADMA), and multiple data streams can be transmitted in the same frequency and time slot. Comparing with conventional time-frequency (TF) resource based orthogonal frequency division multiplexing (OFDM) system, the exponential base at each sub-channel is replaced by a set of chirp bases, which keep mutually approximately orthogonal. Each base station (BS) can support more move stations (MSs) or cell throughput. Simulations show the essential advantages in TFA resource distribution and system spectrum efficiency.
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Liu, Lifeng, Meng Guan, Xiangtao Zhang, Yanhui Zhu, Huaxing Lv, and Chang Meng. "Frequency information extraction based on time-frequency ridges for characterizing thin sand bodies." Journal of Geophysics and Engineering 19, no. 2 (April 2022): 167–77. http://dx.doi.org/10.1093/jge/gxac008.
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Abstract Time-frequency analysis (TFA) is a widely used reservoir prediction technology. During the identification of sand bodies using TFA, abnormally high values in the high-frequency portion of the spectrum usually indicate the presence of thin sand bodies. However, owing to the complexity of the depositional environment, the spatial distribution of thin sand bodies is often variable, and multiple phases of sand bodies—superimposed on each other—can occur with uneven thicknesses. A frequency information extraction technique based on time-frequency ridges (TFRs) is proposed in this study to identify stable TFRs in the time-frequency spectrum of a rearranged smooth pseudo-Wigner–Ville distribution with high energy aggregation, as well as to extract the main- and high-frequency energy contents relative to them. To enhance the ability of these energy components to detect thin sand bodies, a frequency-energy weighting factor is computed. By performing the aforementioned steps, information about each frequency band can be obtained and a comprehensive depiction of sand bodies with different thicknesses achieved. It is concluded that by using the proposed method thin sand bodies such as small riverbeds can be effectively characterized using seismic data.
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Bačnar, David, Nicoletta Saulig, Irena Petrijevčanin Vuksanović, and Jonatan Lerga. "Entropy-Based Concentration and Instantaneous Frequency of TFDs from Cohen’s, Affine, and Reassigned Classes." Sensors 22, no. 10 (May 13, 2022): 3727. http://dx.doi.org/10.3390/s22103727.
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This paper explores three groups of time–frequency distributions: the Cohen’s, affine, and reassigned classes of time–frequency representations (TFRs). This study provides detailed insight into the theory behind the selected TFRs belonging to these classes. Extensive numerical simulations were performed with examples that illustrate the behavior of the analyzed TFR classes in the joint time–frequency domain. The methods were applied both on synthetic and real-life non-stationary signals. The obtained results were assessed with respect to time–frequency concentration (measured by the Rényi entropy), instantaneous frequency (IF) estimation accuracy, cross-term presence in the TFRs, and the computational cost of the TFRs. This study gives valuable insight into the advantages and limitations of the analyzed TFRs and assists in selecting the proper distribution when analyzing given non-stationary signals in the time–frequency domain.
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Manap, M., Nur Sumayyah Ahmad, Abdul Rahim Abdullah, and Norhazilina Bahari. "Comparison of Open and Short-Circuit Switches Faults Voltage Source Inverter (VSI) Analysis Using Time-Frequency Distributions." Applied Mechanics and Materials 752-753 (April 2015): 1164–69. http://dx.doi.org/10.4028/www.scientific.net/amm.752-753.1164.
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Voltage source inverter (VSI) plays an important roles in electrical drive systems. Consistently, expose to hash environmental condition, the lifespan of the electronic component such as insulated-gate bipolar transistor (IGBT) may shorten and many faults related to the inverter especially switches can be occur. The present of VSI switches faults causing equipment failure and increased the cost of manufacturing process. Therefore, faults detection analysis is mandatory to identify the VSI switches faults. This paper presents the analysis of VSI switches faults using time-frequency distributions (TFDs) which are short times Fourier transform (STFT) and spectrogram. From time-frequency representation (TFR) obtained by using the TFDs, parameters of the faults signal are estimated such as instantaneous of average, root mean square (RMS), fundamental, Total Waveform Distortion (TWD), Total Harmonics Distortion (THD) and Total non-Harmonic Distortion (TnHD) of current signals. Then, based on the characteristics of the faults calculated from the signal parameters, VSI switches faults can be detected and identified. The performance of TFD for the faults analysis is also demonstrated to obtain the best TFD for switches faults detection and identification system. The results show that, STFT is the best technique to classify and identify VSI switches faults and can be implemented for automated system.
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Al-Fahoum, Amjed S., and Ausilah A. Al-Fraihat. "Methods of EEG Signal Features Extraction Using Linear Analysis in Frequency and Time-Frequency Domains." ISRN Neuroscience 2014 (February 13, 2014): 1–7. http://dx.doi.org/10.1155/2014/730218.
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Technically, a feature represents a distinguishing property, a recognizable measurement, and a functional component obtained from a section of a pattern. Extracted features are meant to minimize the loss of important information embedded in the signal. In addition, they also simplify the amount of resources needed to describe a huge set of data accurately. This is necessary to minimize the complexity of implementation, to reduce the cost of information processing, and to cancel the potential need to compress the information. More recently, a variety of methods have been widely used to extract the features from EEG signals, among these methods are time frequency distributions (TFD), fast fourier transform (FFT), eigenvector methods (EM), wavelet transform (WT), and auto regressive method (ARM), and so on. In general, the analysis of EEG signal has been the subject of several studies, because of its ability to yield an objective mode of recording brain stimulation which is widely used in brain-computer interface researches with application in medical diagnosis and rehabilitation engineering. The purposes of this paper, therefore, shall be discussing some conventional methods of EEG feature extraction methods, comparing their performances for specific task, and finally, recommending the most suitable method for feature extraction based on performance.
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Hou, Yating, Liming Wang, Xiuli Luo, and Xingcheng Han. "Local maximum synchrosqueezes form scaling-basis chirplet transform." PLOS ONE 17, no. 11 (November 29, 2022): e0278223. http://dx.doi.org/10.1371/journal.pone.0278223.
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In recent years, time-frequency analysis (TFA) methods have received widespread attention and undergone rapid development. However, traditional TFA methods cannot achieve the desired effect when dealing with nonstationary signals. Therefore, this study proposes a new TFA method called the local maximum synchrosqueezing scaling-basis chirplet transform (LMSBCT), which is a further improvement of the scaling-basis chirplet transform (SBCT) with energy rearrangement in frequency and can be viewed as a good combination of SBCT and local maximum synchrosqueezing transform. A better concentration in terms of the time-frequency energy and a more accurate instantaneous frequency trajectory can be achieved using LMSBCT. The time-frequency distribution of strong frequency-modulated signals and multicomponent signals can be handled well, even for signals with close signal frequencies and low signal-to-noise ratios. Numerical simulations and real experiments were conducted to prove the superiority of the proposed method over traditional methods.
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Mohseni Saravi, M., A. A. Safdari, and A. Malekian. "Intensity-Duration-Frequency and spatial analysis of droughts using the Standardized Precipitation Index." Hydrology and Earth System Sciences Discussions 6, no. 2 (March 2, 2009): 1347–83. http://dx.doi.org/10.5194/hessd-6-1347-2009.
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Abstract. Precipitation deficit and its daily, seasonal and annual oscillations are inherent characteristics of Iran's climate. Droughts are generally characterized by a prolonged and abnormal moisture deficiency. In drought studies it is important to characterize the start and end of a drought as well as its intensity, duration, frequency and magnitude. The objective of this study was to analyze drought characteristics and to develop drought maps in the Karoon river basin, Iran. The Standardized Precipitation Index (SPI) was used in drought analysis based on the data for meteorological stations located inside or adjacent to the study area and three time scales including the 3-, 6- and 12-month SPI were evaluated. After determining the dry and wet periods, historical characteristics of droughts were identified and spatial distribution maps of droughts were plotted using GIS. Based on frequency distributions, drought durations and magnitudes were computed corresponding to 5, 10, 20, 50 and 100-year return periods. The Time scale-Duration-Frequency (TDF) and Time scale-Magnitude-Frequency (TMF) relationships were also developed, which constitute an essential tool for water resource design purposes. Drought spatial distribution maps show that extreme conditions dominate the southeastern regions of the basin. The efficiency of the SPI is determined by monitoring the drought of 1999.
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Huang, Yucheng, Xiaodong Zheng, Yanting Duan, and Yi Luan. "Robust time-frequency analysis of seismic data using general linear chirplet transform." GEOPHYSICS 83, no. 3 (May 1, 2018): V197—V214. http://dx.doi.org/10.1190/geo2017-0145.1.
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Time-frequency analysis (TFA) has been widely used in seismic processing and interpretation. A good time-frequency representation can preferably characterize geologic spatial distribution and detect hydrocarbon reservoir anomalies. This paper applies a robust seismic TFA method based on the general linear chirplet transform (GLCT). The GLCT method is an extended form of LCT, which is a unifying framework encompassing the short time Fourier transform (STFT) and the continuous wavelet transform (CWT) using the chirplet atom as the kernel function instead of the sinusoidal wave or wavelets. By rotating the chirplet atom at each time-frequency point, GLCT method could adaptively choose the best atom to fit the local time-frequency feature of seismic signals. The algorithm follows such a simple logic and produces a broadband time-frequency spectrum free of cross-term interference, resulting in good performance characterizing the instantaneous spectral variations. Synthetic data analysis demonstrates that the GLCT method is able to reach a higher energy concentration in the time-frequency plane than conventional methods. Robustness analysis indicates that GLCT produces more stable results that outperform not only STFT, CWT, but also high-resolution methods such as the synchrosqueezing transform and complete ensemble empirical mode decomposition in the case of noisy data. The application to field data illustrates that the isofrequency attributes extracted by GLCT through spectral decomposition could effectively image subtle stratigraphic structures of the subsurface paleotopography and highlight the frequency anomalies associated with hydrocarbons. Sometimes, these anomalies might be otherwise inundated in the background noise. Our method can be a validation tool for seismic facies interpretation improvement and direct hydrocarbon indication in practice.
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Ahrenholz, P., D. Falkenhagen, and H. Klinkmann. "A Simplified Procedure to Compute Dialysis Time and Frequency by Means of Urea Kinetics." International Journal of Artificial Organs 11, no. 5 (September 1988): 366–72. http://dx.doi.org/10.1177/039139888801100511.
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A simplified urea model is presented based on the concept of the time-averaged deviation (TAD) of the blood urea concentration and the introduction of an effective urea generation rate. The increase in the interdialytic blood urea concentration Δc is specific for the individual patient and includes the urea generation rate, distribution volume and residual kidney clearance. By measuring Δc of the largest interdialytic interval of the week the treatment frequency and duration can be calculated. Even for larger residual clearances Kr ≤ 5 ml/min this calculated treatment time does not differ by more than 5 min from the result of the exact urea kinetics. In vivo estimation of the urea clearances versus blood flow for the dialyzer types used is necessary for the application of urea modelling in clinical practice.
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Li, Teng, Zhijie Jiao, Lina Wang, and Yong Mu. "A Method of DC Arc Detection in All-Electric Aircraft." Energies 13, no. 16 (August 13, 2020): 4190. http://dx.doi.org/10.3390/en13164190.
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Arc faults in an aircraft’s power distribution system (PDS) often leads to cable and equipment damage, which seriously threatens the personal safety of the passengers and pilots. An accurate and real-time arc fault detection method is needed for the Solid-State Power Controller (SSPC), which is a key protection equipment in a PDS. In this paper, a new arc detection method is proposed based on the improved LeNet5 Convolutional Neural Network (CNN) model after a Time–Frequency Analysis (TFA) of the DC currents was obtained, which makes the arc detection more real-time. The CNN is proposed to detect the DC arc fault for its advantage in recognizing more time–frequency joint details in the signals; the new structure also combines the adaptive and multidimensional advantages of the TFA and image intelligent recognition. It is confirmed by experimental data that the combined TFA–CNN can distinguish arc faults accurately when the whole training database has been repeatedly trained 3 to 5 times. For the TFA, two kinds of methods were compared, the Short-Time Fourier Transform (STFT) and Discrete Wavelet Transform (DWT). The results show that DWT is more suitable for DC arc fault detection. The experimental results demonstrated the effectiveness of the proposed method.
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Ding, Yi, Weiwei Fan, Zijing Zhang, Feng Zhou, and Bingbing Lu. "Radio Frequency Interference Mitigation for Synthetic Aperture Radar Based on the Time-Frequency Constraint Joint Low-Rank and Sparsity Properties." Remote Sensing 14, no. 3 (February 7, 2022): 775. http://dx.doi.org/10.3390/rs14030775.
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Synthetic aperture radar (SAR) is susceptible to radio frequency interference (RFI), which becomes especially commonplace in the increasingly complex electromagnetic environments. RFI severely detracts from SAR imaging quality, which hinders image interpretation. Therefore, some RFI mitigation algorithms have been introduced based on the partial features of RFI, but the RFI reconstruction models in these algorithms are rough and can be improved further. This paper proposes two algorithms for accurately modeling the structural properties of RFI and target echo signal (TES). Firstly, an RFI mitigation algorithm joining the low-rank characteristic and dual-sparsity property (LRDS) is proposed. In this algorithm, RFI is treated as a low-rank and sparse matrix, and the sparse matrix assumption is made for TES in the time–frequency (TF) domain. Compared with the traditional low-rank and sparse models, it can achieve better RFI mitigation performance with less signal loss and accelerated algorithm convergence. Secondly, the other RFI mitigation algorithm, named as TFC-LRS, is proposed to further reduce the signal loss. The TF constraint concept, in lieu of the special sparsity, is introduced in this algorithm to describe the structural distribution of RFI because of its aggregation characteristic in the TF spectrogram. Finally, the effectiveness, superiority, and robustness of the proposed algorithms are verified by RFI mitigation experiments on the simulated and measured SAR datasets.
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Chmel, A., V. Smirnov, and A. Panov. "Interplay between linear, dissipative and permanently critical mechanical processes in Arctic sea ice." Cryosphere Discussions 4, no. 3 (August 25, 2010): 1433–48. http://dx.doi.org/10.5194/tcd-4-1433-2010.
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Abstract. Mechanical processes in the Arctic ice pack result in fragmented sea ice cover, the regular geometry of which could be described in main features in terms of the conventional mechanics. However, the size distribution of sea ice floes does not exhibit the random (poissonian-like) statistics and follows the power law typical for self-similar (fractal) structures. The analysis of ice floe oscillations in the frequency range specific for cracking, shearing and stick-slip motion evidences the self-organized dynamics of sea ice fracturing, which manifests itself in scaling distributions of both the discrete energy discharges in fracture events and the recurrence times between that one. So determined space-time-energy self-similarity characterises the ice pack as the non-equilibrium, nonlinear thermodynamic system where the synergic relations are established through conventional long propagating wave/oscillations. The presented experimental data were collected at the Russian ice-research camp "North Pole 35" drifting on the Arctic ice pack in 2008.
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Delgado-Arredondo, Paulo Antonio, Arturo Garcia-Perez, Daniel Morinigo-Sotelo, Roque Alfredo Osornio-Rios, Juan Gabriel Avina-Cervantes, Horacio Rostro-Gonzalez, and Rene de Jesus Romero-Troncoso. "Comparative Study of Time-Frequency Decomposition Techniques for Fault Detection in Induction Motors Using Vibration Analysis during Startup Transient." Shock and Vibration 2015 (2015): 1–14. http://dx.doi.org/10.1155/2015/708034.
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Induction motors are critical components for most industries and the condition monitoring has become necessary to detect faults. There are several techniques for fault diagnosis of induction motors and analyzing the startup transient vibration signals is not as widely used as other techniques like motor current signature analysis. Vibration analysis gives a fault diagnosis focused on the location of spectral components associated with faults. Therefore, this paper presents a comparative study of different time-frequency analysis methodologies that can be used for detecting faults in induction motors analyzing vibration signals during the startup transient. The studied methodologies are the time-frequency distribution of Gabor (TFDG), the time-frequency Morlet scalogram (TFMS), multiple signal classification (MUSIC), and fast Fourier transform (FFT). The analyzed vibration signals are one broken rotor bar, two broken bars, unbalance, and bearing defects. The obtained results have shown the feasibility of detecting faults in induction motors using the time-frequency spectral analysis applied to vibration signals, and the proposed methodology is applicable when it does not have current signals and only has vibration signals. Also, the methodology has applications in motors that are not fed directly to the supply line, in such cases the analysis of current signals is not recommended due to poor current signal quality.
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Hyacinth, Hyacinth I., Beatrice E. Gee, Jenifer H. Voeks, Robert J. Adams, and Jacqueline Hibbert. "High Frequency of RBC Transfusions in the STOP Study Was Associated with Reduction in Serum Biomarkers of Neurodegeneration, Vascular Remodeling and Inflammation." Blood 120, no. 21 (November 16, 2012): 244. http://dx.doi.org/10.1182/blood.v120.21.244.244.
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Abstract Abstract 244 Stroke is a major cause of morbidity and mortality among children with sickle cell anemia (SCA). Children with SCA at risk for stroke can be identified by transcranial Doppler (TCD) ultrasound screening for abnormally high cerebral artery blood flow velocity and strokes can be prevented by chronic packed red blood cell (RBC) transfusion. However, the mechanisms that lead to cerebral vasculopathy and stroke in SCA and that explain the beneficial effects of chronic RBC transfusions in stroke prevention are poorly understood. We have previously shown that pre-treatment serum levels of brain derived neurotropic factor (BDNF) and platelet derived growth factor (PDGF) subtypes, biomarkers of cerebral ischemia and arterial remodeling, were associated with both high TCD velocity and development of stroke. We hypothesized that frequency of RBC transfusion would be associated with altered serum levels of neurodegenerative, inflammatory and angiogenic markers in SCA children with high TCD velocity and tested this hypothesis by assaying levels of these markers in post-STOP serum samples. Frozen serum samples drawn one year after subject's exit from the STOP clinical trial phase were utilized. Given the positive trial results, all but 9 subjects had been on chronic transfusion regimen for at least one year at the time of sample collection. Eighty samples were assayed using multiplex antibody immobilized beads (Millipore Corp, Billerica, MA). The mean fluorescent intensity was measured using the Milliplex xMAP system powered by Luminex (Bio-Rad, Hercules, CA). Ten biologically related neurodegenerative, inflammatory and angiogenic biomarkers were tested. The total number (frequency) of RBC transfusions recorded over the study period (4 years) for each participant was categorized into High (≥ 40), Moderate (20 – 39) or Low (< 20) frequency of transfusion. Median distribution with 10 – 90th percentile of the levels of biomarkers and TCD velocity were expressed using box-plots and the differences in median distribution between groups based on frequency of transfusion was estimated using Kruskal-Wallis test. A principal component analysis (PCA) loading plot was used to demonstrate the biological relationships between the biomarkers, taking into consideration linear correlations and spatial relationships between them. There were no significant differences in the hematological and anthropometric measures between groups. Overall, our result showed that low transfusion frequency was associated with high serum levels of biomarkers and vice versa, despite no significant difference in hemoglobin level between groups. The high frequency transfusion group had lower serum levels of BDNF (p = 0.02), sVCAM-1 (p < 0.001), PDGF-AA (p < 0.001), CCL5 (p < 0.01), tPAI-1 (p < 0.01) and NCAM (p < 0.01) levels compared with the low frequency transfusion group (figure 1 a – e). Although not shown in the figures, the same pattern was observed with TCD velocity which was lower (160, 115.7 – 204.9 cm/s) in the low compared with the high (195, 154 – 272 cm/s) frequency transfusion group. In addition, the medium frequency transfusion group had significantly lower serum sVCAM-1 (p < 0.01) compared with the low frequency transfusion group and higher PDGF-AA (p < 0.01) compared with the high frequency transfusion group. A PCA loading plot (figure 2) shows clustering of the biomarkers that are most closely biologically related, these are also the biomarkers that were significantly affected by the frequency of transfusion. Red blood cell transfusions in the STOP study were associated with reduced serum levels of biomarkers of angiogenesis (PDGF-AA and sVCAM-1), cerebral ischemia/neuronal survival adaptation (BDNF and NCAM) and inflammation (RANTES/CCL5), and this effect was most pronounced in the group with the highest frequency of transfusions (equivalent to most chronic transfusion regimen). This suggests that the protective effects of chronic RBC transfusions on stroke development in children with SCA may be attributable to improved cerebral perfusion, reduced inflammation and down-regulation of hypoxia-induced angiogenic responses that promote arterial remodeling. One or more in this group of biologically-related and relevant markers may be useful for monitoring children with SCA receiving stroke prevention therapies and for designing treatment targets. Disclosures: No relevant conflicts of interest to declare.
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Colombatti, Raffaela, Soundrie Padayachee, Corrina Macmahon, Sukhleen Momi, Claire Jane Hemmaway, Maddalena Casale, and Baba Inusa. "Cerebral Blood Flow-Velocity Is Associated with Increased Leukocyte Count and Systolic Blood Pressure in HbSS but Not HbSC." Blood 126, no. 23 (December 3, 2015): 989. http://dx.doi.org/10.1182/blood.v126.23.989.989.
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Abstract BACKGROUND Sickle Cell Disease (SCD) is the most frequent severe genetic disease worldwide. Its frequency is rising in European countries, including Italy and Ireland. In Europe Sickle SC (HbSC) is the second most common form of SCD after sickle cell anaemia (HbSS/HbSB°) and accounts for 25-30% of cases. Neurological events are among the most frequent and disabling complications in children with SCD with an important impact on quality of life, health and educational system costs (DeBaun et al., 2012). Overt and silent stroke are reported in in HbSC disease, although to a lesser extent. Studies suggest that the life-time risk of stroke in HbSC is 2-3% (Deane et al., 2008). Stroke Prevention is limited to only for HbSS/HbSB° but not HbSC. CBF-V as measured by TCD ranges of velocities in the Middle Cerebral Artery (MCA) and in the distal Internal Carotid Artery (dICA) used to stratify patients with HbSS/HbSB° in risk categories might be inappropriate for HbSC patients. Unlike North America, in Europe HbSC phenotype is more common; we therefore set out in this three country European study (SCATES) to describe the pattern of CBF-velocity and also compare the findings with HbSS patients attending the same facilities:. Hypothesis: Aims is to determine mean reference values of TCD velocities in MCA and dICA in a European prospective cohort of children with HbSC in comparison with a cohort of HbSS. Main objectives To assess the pattern of cerebral flow velocity distribution in HbSC and compare with HbSS in three European countries To test if the impact of clinical and hematological factors on Cerebral blood flow velocity in HbSC and HbSS in order to make recommendation for screening HbSC. METHODS Following a formal evaluation and validation of the competency of the screening centres to perform TCD in sickle cell disease; consecutive patients were recruited into the prospective observational study. TCD was performed with imaging TCD (Philps, other makes) by certified TCD operators. The data were entered on-line (Study Trax) and downloaded for statistical analysis using STATA 10.0 (Stata-Corp LP, College Station, Texas). Before regression analyses were performed, all variables that did not have a normal distribution (BP diastolic, albumin creatinine ratio (ACR), lactate dehydrogenase (LDH) and bilirubin) were transformed to achieve a normal distribution. All variables were standardised to allow for clear interpretation. Univariable regression analyses were performed to examine the influence of laboratory and haematological parameters on maximum TCD velocity from the MCA or TICA, in each SCD type subgroup. As TCD velocity had a non-parametric distribution the 95% confidence intervals of TCD velocity for HbSC subgroup was determined using the bootstrap method (resampling with replacement from our data set), with 5,000 replications(Singh & Xie, 2010) RESULTS At recruitment, the participants' age ranged from 2 to 16 years with a mean age of 8.11 (sd ±4.07). Overall, 224 (76.12%) children had HbSS phenotype, and 61 (18.21%) children had HbSC phenotype. Mean values for haematological parameters for HbSS and HbSC subgroups: Mean Hb g/dl (std) was 8.32(1.16) for HbSS and 10.93(0.91) for HbSC; Albumin: creatinine ratio as 12.39 (3.92) and 8.32 (2.68) for HbSS and HbSC respectively. HbSS as expected show inverse relationship between CBF-V and hemoglobin (n = 224; beta = -7.90; 95%CI = -11.9 to -3.89; p = 0.00014); positive correlation with systolic blood pressure increase (beta=11.03; 95% 3.10 to 18.995 and P=0.008); and total leukocyte count (n-120; beta 1.50; 95% CI 0.38-2.63; P=0.009). However, there was no correlation between TCD and any parameter in the HbSC group. Discussion and Conclusion From this pan-European patient cohort with a substantial proportion of patients we show that the CBF-V in HbSC does not follow a normal distribution pattern and appears entirely unrelated to clinical or hemolytic markers as observed with HbSS. Higher systolic blood pressure has been reported as risk factor for the development of silent cerebral infarct in HbSS (DeBaun et al., 2014). To the best of our knowledge this is the first time systolic and increased leukocyte as a marker of thrombosis (Marchetti & Falanga, 2007) is a risk factor in SCD. CBF-V. The fact there is relationship with these markers in HbSC suggests the lack of benefit for this measurement. Disclosures No relevant conflicts of interest to declare.
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Cheng, L., M. Yaeger, A. Viglione, E. Coopersmith, S. Ye, and M. Sivapalan. "Exploring the physical controls of regional patterns of flow duration curves – Part 1: Insights from statistical analyses." Hydrology and Earth System Sciences 16, no. 11 (November 26, 2012): 4435–46. http://dx.doi.org/10.5194/hess-16-4435-2012.
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Abstract. The flow duration curve (FDC) is a classical method used to graphically represent the relationship between the frequency and magnitude of streamflow. In this sense it represents a compact signature of temporal runoff variability that can also be used to diagnose catchment rainfall-runoff responses, including similarity and differences between catchments. This paper is aimed at extracting regional patterns of the FDCs from observed daily flow data and elucidating the physical controls underlying these patterns, as a way to aid towards their regionalization and predictions in ungauged basins. The FDCs of total runoff (TFDC) using multi-decadal streamflow records for 197 catchments across the continental United States are separated into the FDCs of two runoff components, i.e., fast flow (FFDC) and slow flow (SFDC). In order to compactly display these regional patterns, the 3-parameter mixed gamma distribution is employed to characterize the shapes of the normalized FDCs (i.e., TFDC, FFDC and SFDC) over the entire data record. This is repeated to also characterize the between-year variability of "annual" FDCs for 8 representative catchments chosen across a climate gradient. Results show that the mixed gamma distribution can adequately capture the shapes of the FDCs and their variation between catchments and also between years. Comparison between the between-catchment and between-year variability of the FDCs revealed significant space-time symmetry. Possible relationships between the parameters of the fitted mixed gamma distribution and catchment climatic and physiographic characteristics are explored in order to decipher and point to the underlying physical controls. The baseflow index (a surrogate for the collective impact of geology, soils, topography and vegetation, as well as climate) is found to be the dominant control on the shapes of the normalized TFDC and SFDC, whereas the product of maximum daily precipitation and the fraction of non-rainy days was found to control the shape of the FFDC. These relationships, arising from the separation of total runoff into its two components, provide a potential physical basis for regionalization of FDCs, as well as providing a conceptual framework for developing deeper process-based understanding of the FDCs.
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Cheng, L., M. Yaeger, A. Viglione, E. Coopersmith, S. Ye, and M. Sivapalan. "Exploring the physical controls of regional patterns of flow duration curves – Part 1: Insights from statistical analyses." Hydrology and Earth System Sciences Discussions 9, no. 6 (June 6, 2012): 7001–34. http://dx.doi.org/10.5194/hessd-9-7001-2012.
Full textAbstract:
Abstract. The Flow Duration Curve (FDC) is a classical method used to graphically represent the relationship between the frequency and magnitude of streamflow. In this sense it represents a compact signature of temporal runoff variability that can also be used to diagnose catchment rainfall-runoff responses, including similarity and differences between catchments. This paper is aimed at extracting regional patterns of the FDCs from observed daily flow data and elucidating the physical controls underlying these patterns, as a way to aid towards their regionalization and predictions in ungauged basins. The FDCs of total runoff (TFDC) using multi-decadal streamflow records for 197 catchments across the continental United States are separated into the FDCs of two runoff components, i.e., fast flow (FFDC) and slow flow (SFDC). In order to compactly display these regional patterns the 3-parameter mixed gamma distribution is employed to characterize the shapes of the normalized FDCs (i.e., TFDC, FFDC and SFDC) over the entire data record. This is repeated to also characterize the between-year variability of "annual" FDCs for 8 representative catchments chosen across a climate gradient. Results show that the mixed gamma distribution can adequately capture the shapes of the FDCs and their variation between catchments and also between years. Comparison between the between-catchment and between-year variability of the FDCs revealed significant space-time symmetry. Possible relationships between the parameters of the fitted mixed gamma distribution and catchment climatic and physiographic characteristics are explored in order to decipher and point to the underlying physical controls. The baseflow index (a surrogate for the collective impact of geology, soils, topology and vegetation, as well as climate) is found to be the dominant control on the shapes of the normalized TFDC and SFDC, whereas the product of maximum daily precipitation and the fraction of non-rainy days was found to control the shape of the FFDC. These relationships, arising from the separation of total runoff into its two components, provide a potential physical basis for regionalization of FDCs, as well as providing a conceptual framework for developing deeper process-based understanding of the FDCs.
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Ishola, Titilope, and Charles T. Quinn. "Transcranial Doppler Peak Systolic Velocities Overestimate The Risk Of Stroke In Sickle Cell Anemia." Blood 122, no. 21 (November 15, 2013): 2240. http://dx.doi.org/10.1182/blood.v122.21.2240.2240.
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Abstract Background Children with sickle cell anemia (SCA) have a high risk of stroke that can be estimated by transcranial Doppler ultrasonography (TCD). The gold standard TCD measurement to determine the risk of primary stroke is the time-averaged mean of the maximum velocity (TAMMV) in specific intracranial arteries. Peak systolic velocity (PSV), a different TCD measurement, has been proposed as an alternative method for risk stratification, especially for imaging TCD (TCDi) techniques (Jones et al. 2005). Although PSV has been little studied for this purpose, some centers use PSV in addition to TAMMV for the classification of risk of stroke by TCD for clinical care. A systematic bias in the classification of risk of stroke by PSV (higher or lower compared to TAMMV) would substantially affect medical resources and patient outcomes. Objective Describe the test performance characteristics of PSV compared to TAMMV for the classification of risk of stroke in children with SCA and determine the suitability of PSV for classification of TCDs in clinical practice. Methods We studied all patients in our center with homozygous HbSS, sickle-β0-thalassemia, or sickle-Hb D disease (all genotypes referred to here as SCA) who were ≥2 years of age and had a clinical TCD (all by TCDi technique) between 1998-2013. For each patient, the single most recent TCD performed >30 days from a transfusion was used, except for patients receiving chronic transfusions for whom the TCD before the initiation of chronic transfusions was used. Patients were included only once in this analysis. The highest TAMMV and PSV were recorded for the distal internal carotid artery (DICA), bifurcation and middle cerebral artery on the right and left sides of the brain. The TAMMV in each vessel was classified using modified STOP velocity criteria for TCDi: <155, normal; 155-184, conditional; ≥185 abnormal. The PSV in each vessel was classified using criteria proposed by Jones et al.: <200, normal; 200-249, conditional; ≥250 abnormal. Two overall TCD classifications, using either TAMMV or PSV, were made according to standard STOP methods by considering the worst (most abnormal) classification of any vessel as the overall classification. The primary outcome was the multi-level agreement between overall TCD classification based on TAMMV and PSV as measured by the kappa statistic. Kappa was also calculated for individual vessels to determine if agreement differed by anatomic site. Coefficients of determination (r2) were calculated using Pearson correlation. Results We studied 120 patients with SCA [mean age 12.0 years ± 6.0 (S.D.); 51.1% male]. Fifty-nine (49%) had at least one prior transfusion that was given a mean of 588 (median 387) days before the TCD (none ≤40 days). The distribution of overall TCD classification by simultaneous TAMMV and PSV is shown in the Figure (Panel A). The distribution of classifications by TAMMV, compared to PSV, better approximates the expected distribution in a screening population. Classifications by PSV were skewed higher, giving more conditional and abnormal results, when compared to classification by TAMMV (P<0.0001). Kappa was 0.488 (P<0.001) for overall TCD classification, indicating only moderate agreement between the TAMMV and PSV methods. Agreement between TAMMV and PSV classification was highest (“substantial”) in the right and left DICA (k: 0.657–0.717, P<0.001). Agreement was only moderate in all other vessels (k: 0.428–0.539, P<0.001). Compared to TAMMV, use of PSV resulted in misclassification of 28% of overall TCD interpretations (Figure, Panel B); 32 studies (27%) were up-coded (27 normal to conditional; 5 conditional to abnormal). Only 1 study was down-coded (abnormal to conditional). Considering TAMMV and PSV as continuous variables, TAMMV accounted for 84.2-90.2% (r2) of the variation in PSV across different vessels; so, approximately 10-15% of the variability in PSV is not explained by TAMMV. Conclusions The use of PSV (rather than TAMMV) to classify TCDs overestimates the risk of stroke for almost one-third of children with SCA. This systematic bias will unnecessarily increase anxiety, the frequency of follow-up testing, and use of chronic transfusions for primary stroke prophylaxis. PSV should not be used for primary classification of TCDs in clinical practice. Jones A et al. Can peak systolic velocities be used for prediction of stroke in sickle cell anemia? Pediatr Radiol. 2005;35:66-72. Disclosures: No relevant conflicts of interest to declare.
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Islam, Md Riadul, M. A. H. Akhand, Md Abdus Samad Kamal, and Kou Yamada. "Recognition of Emotion with Intensity from Speech Signal Using 3D Transformed Feature and Deep Learning." Electronics 11, no. 15 (July 28, 2022): 2362. http://dx.doi.org/10.3390/electronics11152362.
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Speech Emotion Recognition (SER), the extraction of emotional features with the appropriate classification from speech signals, has recently received attention for its emerging social applications. Emotional intensity (e.g., Normal, Strong) for a particular emotional expression (e.g., Sad, Angry) has a crucial influence on social activities. A person with intense sadness or anger may fall into severe disruptive action, eventually triggering a suicidal or devastating act. However, existing Deep Learning (DL)-based SER models only consider the categorization of emotion, ignoring the respective emotional intensity, despite its utmost importance. In this study, a novel scheme for Recognition of Emotion with Intensity from Speech (REIS) is developed using the DL model by integrating three speech signal transformation methods, namely Mel-frequency Cepstral Coefficient (MFCC), Short-time Fourier Transform (STFT), and Chroma STFT. The integrated 3D form of transformed features from three individual methods is fed into the DL model. Moreover, under the proposed REIS, both the single and cascaded frameworks with DL models are investigated. A DL model consists of a 3D Convolutional Neural Network (CNN), Time Distribution Flatten (TDF) layer, and Bidirectional Long Short-term Memory (Bi-LSTM) network. The 3D CNN block extracts convolved features from 3D transformed speech features. The convolved features were flattened through the TDF layer and fed into Bi-LSTM to classify emotion with intensity in a single DL framework. The 3D transformed feature is first classified into emotion categories in the cascaded DL framework using a DL model. Then, using a different DL model, the intensity level of the identified categories is determined. The proposed REIS has been evaluated on the Ryerson Audio-Visual Database of Emotional Speech and Song (RAVDESS) benchmark dataset, and the cascaded DL framework is found to be better than the single DL framework. The proposed REIS method has shown remarkable recognition accuracy, outperforming related existing methods.