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This study introduces the Synchro-Reassigning Transform (SRT), a cutting-edge method developed for high-resolution time-frequency analysis of power quality (PQ) disturbances. The research assesses the performance of SRT in comparison to other established techniques such as Short-Time Fourier Transform (STFT), Wavelet Transform (WT), Synchro-Squeezing Transform (SST), and Synchro-Extracting Transform (SET). Specifically, the study focuses on the identification and categorization of various PQ issues, including voltage sags, swells, interruptions, harmonics, and inter-harmonics. The analysis reveals that SRT excels in detecting harmonics and inter-harmonics, providing much clearer and more detailed time-frequency representations by minimizing cross-term interference, which is a limitation often observed in other methods. The results highlight SRT's ability to offer more accurate and reliable signal interpretations, leading to enhanced precision in PQ analysis. Overall, this technique represents a significant advancement in power quality monitoring, delivering greater reliability and improving the accuracy of PQ disturbance detection. [ABSTRACT FROM AUTHOR]

Modulated signal recognition is difficult but essential for applications like cognitive radio, intelligent communication, radio supervision, and electronic countermeasure. Current modulation recognition models lack comprehensiveness and typicality of various signals and primarily rely on artificial feature extraction. In this study, a convolutional neural network (CNN)-based method for modulated signal recognition is proposed. The proposed method converts modulation recognition into image identification. To increase the acuity of CNN for learning time-frequency features, channel attention and spatial attention are further introduced based on the fused features. Eight different types of modulated signals, including Rect, LFM, Barker, GFSK, CPFSK, B-FM, DSB-AM, and SSB-AM, are used in the experiments. The recognition rate of the proposed model is greater than 85% when the SNR (signal-to-noise ratio) is greater than -10dB, and it ranges from 92% to 98% when the SNR is 0dB. The recognition rate of the proposed method outperforms the two other comparison methods, CNN without an attention mechanism and LSTM. [ABSTRACT FROM AUTHOR]

Laser Metal Deposition (LMD) is an additive manufacturing process that enables the production of complex structures on existing parts. To reduce the use of costly and energy-intensive produced materials like titanium, these structures can be selectively deposited on favorable but differing substrate materials. However due to differing material characteristics, this results in a high defect potential. Extensive process parameter development and online process monitoring helps to minimize the impact of defect formation. Monitoring of airborne acoustic process emissions can be used to identify critical defects early during the process and gain information about process stability. Two approaches for in-process time-frequency monitoring of the acquired acoustic data were compared within this work: The short time Fast Fourier Transformation and the Continuous Wavelet Transformation. Performance criteria based on detected distinct defect events were defined to evaluate both approaches and define specifications for an Acoustic Emission In-Process Monitoring system. [ABSTRACT FROM AUTHOR]

Parkinson's disease (PD) is the most common neurological disorder that typically affects elderly people. In the earlier stage of disease, it has been seen that 90% of the patients develop voice disorders namely hypokinetic dysarthria. As time passes, the severity of PD increases, and patients have difficulty performing different speech tasks. During the progression of the disease, due to less control of articulatory organs such as the tongue, jaw, and lips, the quality of speech signals deteriorates. Periodic medical evaluations are very important for PD patients; however, having access to a medical appointment with a neurologist is a privilege in most countries. Considering that the speech recording process is inexpensive and very easy to do, we want to explore in this paper the suitability of mapping information of the dysarthria level into the neurological state of patients and vice versa. Three levels of severity are considered in a multiclass framework using time–frequency (TF) features and random-forest along with an Error-Correcting Output Code (ECOC) approach. The multiclass classification task based on dysarthria level is performed using the TF features with words and diadochokinetic (DDK) speech tasks. The developed model shows an unweighted average recall (UAR) of 68.49% with the DDK task /pakata/ based on m-FDA level, and 48.8% with the word /petaka/ based on the UPDRS level using the Random Forest classifier. With the aim, to evaluate the neurological states using the dysarthria level, the developed models are used to predict the MDS-UPDRS-III level of patients. The highest matching accuracy of 32% with the word /petaka/ is achieved. Similarly, the multiclass classification framework based on MDS-UPDRS-III is applied to predict the dysarthria level of patients. In this case, the highest matching accuracy of 18% was obtained with the DDK tasks /pataka/. [ABSTRACT FROM AUTHOR]

Epilepsy is the most common form of neurological disease. The electroencephalogram (EEG) is the primary tool in the observation of epilepsy. The detection and prediction of seizures in EEG signals require multi-domain analysis, one of which is the time domain combined with other approaches for feature extraction. In this study, a method for detecting seizures in epileptic EEG is proposed using analysis of the distribution of the signal spectrum in the time range t. The EEG signal, which includes normal, inter-ictal, and ictal, is transformed into the time-frequency domain using the Short-Time Fourier Transform (STFT). To find the highest detection accuracy, simulations were carried out on varying window length, overlap, and FFT points. The frequency distribution and first-order statistics were then calculated as feature vectors for the classification process. A support vector machine was employed to evaluate the proposed method. The simulation results showed the highest accuracy of 92.3% using 25-20-512 STFT and quadratic SVM. The proposed method in this study is expected to be a basis for detecting and predicting seizures in long-term EEG recordings or real-time EEG monitoring of epilepsy patients. [ABSTRACT FROM AUTHOR]

Epilepsy is a chronic disease caused by sudden abnormal discharges of neurons which lead to transient brain disfunction. For those patients who need surgery, the recognition of the epileptic foci is one of the most important steps. In this paper, a combination of EEG signals processed by Short-time Fourier Transform and processed by Continuous Wavelet Transform is introduced. A deep learning model with an accuracy of 91.3% trained by Convolution Neural Network has been obtained. The experiment uses a methodology that has not been widely tested so far, and it also demonstrated the practical values by its relatively small time consumption and computing power requirements meanwhile ensuring high accuracy. Furthermore, it proves the feasibility of improving accuracy by combining different feature extraction methods. [ABSTRACT FROM AUTHOR]

This paper proposes two wideband spectrum sensing approaches: (i) method A, the cumulative sum (CUSUM) algorithm with short-time Fourier transform, taking advantage of the time-frequency analysis for wideband spectrum. (ii)method B, the quickest spectrum sensing with short-time Fourier transform and compressed sensing, shortening the time of perception and improving the speed of spectrum access or exit. Moreover, method B can take advantage of the sparsity of wideband signals, sampling in the sub-Nyquist rate, and it is more suitable for wideband spectrum sensing. Simulation results show that method A significantly outperforms the single serial CUSUM detection for small SNRs, while method B is substantially better than the block detection based spectrum sensing in small probability of the false alarm. [ABSTRACT FROM AUTHOR]

This work introduces the development of two software routines for Short-Time Fourier Transform (STFT) and Inverse Short-Time Fourier Transform (ISTFT) along with know-how about their practical implementation. The proposed algorithms and the corresponding novel Matlab® functions form a conjugated analysis-synthesis pair and assist the timefrequency analysis, processing, resynthesis and visualization of real-world non-stationary signals. In the paper, background information is given about the mathematical framework of the STFT, ISTFT and the perfect reconstruction condition. Further, the proposed algorithms and routines are described, based on DFT, IDFT and the overlap-add (OLA) synthesis approach. Comprehensive notes and recommendations are given concerning the applied aspects of their usage. Finally, examples are provided to confirm the consistency of the algorithms and routines, including analysis, spectral modification and resynthesis of non-stationary signals, along with example for the outperformance of the proposed analysis function in comparison with the corresponding in-built Matlab® function. [ABSTRACT FROM AUTHOR]

Abstract Monitoring of vital signs using the photoplethysmography (PPG) signal is desirable for the development of home-based healthcare systems in the aspect of feasibility, mobility, comfort, and cost-effectiveness of the PPG device. In this paper, a new technique based on the variational mode decomposition (VMD) for estimating heart rate (HR) from the PPG signal is proposed. The VMD decomposes an input PPG signal into a number of modes or sub-signals. Afterward, the modes which are dominantly influenced by the HR information are selected and further processed for extracting HR of the patient. The proposed scheme is validated over a large number of recordings acquired from three independent databases, namely the Capnobase, MIMIC, and University of Queens Vital Sign (UQVS). Experiments are performed over different data length segments of the PPG recordings. Using the data length of 30 s, the proposed technique outperformed the existing techniques by achieving the lower median (1st quartile, 3rd quartile) values of root mean square error (RMSE) as 0.23 (0.19, 0.31) beats per minute (bpm), 0.41 (0.31, 0.56) bpm and 1.1 (0.9, 1.22) bpm for the Capnobase, MIMIC, and UQVS datasets, respectively. Since the shorter data length is more suitable for the clinical applications, the proposed technique also provided satisfactory agreement between the derived and reference HR values for the shorter data length segments. Performance results over three independent datasets suggest that the proposed technique can provide accurate and reliable HR information using the PPG signal recorded from the patients suffering from dissimilar problems. [ABSTRACT FROM AUTHOR]

• Diagnosing dysphagia from the patient's voice by building a multibranch model. • Non-invasive, fast, and provides accurate results that are part with the existing diagnosis method. • Built a multibranch model that can make the most of the information that can distinguish dysphagia patients. • Increase model performance and reduce variance using protocol ensemble. Patients with dysphagia show changes in articulation and voice quality, and recent studies using machine learning models have been employed to help in the classification. This study aimed to apply a novel deep learning method using only the patient's voice to classify normal controls from dysphagia patients and determine whether this new deep learning method may help provide a rapid and accurate means to supplement the existing clinical methods in dysphagia screening and assessment. Voice samples from 299 healthy controls and 290 patients with post-stroke dysphagia; who performed four simple phonation tasks; were obtained in a prospective manner at a university-affiliated hospital using a smart digital device. Deep learning methods were employed as follows: firstly, a spectrogram is obtained through the short time Fourier transform (STFT) and Mel-frequency cepstral coefficients (MFCC) on a sound signal, respectively. Secondly, the STFT and MFCC spectrograms obtained for each protocol are fed to each multibranch model. Finally, during the test, each model is ensembled in a soft voting method to distinguish normal and dysphagia classes. Five evaluation metrics are used to evaluate the performance of the model: AUC, Sensitivity, Specificity, Positive predictive value (PPV), and Negative predictive value (NPV). Among the performance metrics, sensitivity and specificity levels are compared with the existing diagnostic tools. The ensemble model incorporating all four tasks showed an AUC of 0.950 ± 0.004, with sensitivity and specificity levels as high as 94.7% and 77.9%, respectively. The novel deep learning model proposed in this paper shows promising performance levels in dysphagia classification. Our results show that the ensemble method used in this study may be utilized as a convenient and rapid digital biomarker of dysphagia in a non-invasive and automated manner. [ABSTRACT FROM AUTHOR]

Direction of arrival (DOA) estimation is an important problem in array signal processing. An effective multiple signal classification (MUSIC) method based on the short-time Fourier transform (STFT) and forward/ backward spatial smoothing (FBSS) techniques for the DOA estimation problem of multiple time-frequency (t-f joint LFM sources is addressed. Previous work in the area e. g. STFT-MUSIC algorithm cannot resolve the t-f completely or largely joint sources because they can only select the single-source t-f points. The proposed method constructs the spatial t-f distributions (STFDs) by selecting the multiple-source t-f points and uses the FBSS techniques to solve the problem of rank loss. In this way, the STFTFBSS- MUSIC algorithm can resolve the t-f largely joint or completely joint LFM sources. In addition, the proposed algorithm also owns pretty low computational complexity when resolving multiple LFM sources because it can reduce the times of the feature decomposition and spectrum search. The performance of the proposed method is compared with that of the existing t-f based MUSIC algorithms through computer simulations and the results show its good performance. [ABSTRACT FROM AUTHOR]

The future wireless communication will come up with a strict requirement on high spectral efficiency, developing novel algorithms for spectrum sensing with deep sensing capability will be more challenging. However, traditional expert feature-based spectrum sensing algorithms are lack of sufficient capability of self-learning and adaptability to unknown environments and complex cognitive tasks. To address this problem, we propose to build up a deep learning network to learn short time-frequency transformation (STFT), a basic entity of traditional spectrum sensing algorithms. Spectrum sensing based on the learning to STFT network is supposed to automatically extract features for communication signals and makes decisions for complex cognitive tasks meanwhile. The feasibility and performances of the designed learning network are verified by classifying signal modulation types in deep spectrum sensing applications. [ABSTRACT FROM AUTHOR]

The people who have speech and language problems and have undergone surgery, trauma, etc. need the help of the speech language pathologists to improve their speech process. The pathologists many a times find it difficult to analyze the progress of the patients. So, in order to objectively evaluate the patient a system has been developed which can help the speech pathologists in analysis of the patient. A special hardware is developed for glottal vibration acquisition which is an MFCC based speech recognizer. This speech therapy module can take inputs either from the glottal vibrations or speech. All the signal processing has been done in MATLAB environment. Further, this module can extend and be used to train children to improve their vocabulary, to learn alphabets, numbers, etc. [ABSTRACT FROM AUTHOR]

As a non-invasive approach to monitor heart rate (HR), the photoplethysmography (PPG) signal provides a simple and accurate measurement in silence. However, difficulties are found when dealing with signals monitored during physical activities, because complex noise shares a very close frequency bin with HR. Traditional analogue filters can hardly derive HR in this situation. This paper is aimed to establish a joint framework for HR monitoring from PPG signals during physical activities. A combination of Short-time Fourier Transform (STFT) and spectral analysis was adopted as the principal part, with a medium filter as assisted. The time-frequency resolution in low frequency is enhanced by the size-fixed window function in STFT. Based on 12 datasets sampled at 25 Hz and recorded during different physical activities, the HR data derived via the proposed algorithm was analysed. The average absolute estimation error was 1.06 beats/min and the standard deviation was 0.69 beats/min. Compared with the true HR via ECG, the cross-correlation average was 0.9917 beats/min and the standard deviation was 0.0141 beats/min. Therefore, the proposed framework is proved reliable for HR monitoring from PPG during physical activities of high intensity. It can be applied to smart wearable devices for fitness tracking and health information tracking. [ABSTRACT FROM AUTHOR]

The contribution deals about determination of vibration source and mechanical system by using Short Time Fourier Transformation. To understand the degradation mechanisms in a machine would be required to perform dynamic analysis to identify the source of vibration and their interactions. To excitation of measured object we used run-up and shut down expected rotational excitation source in the system. After performing the measurement has been carried out with determining the interaction between the natural frequencies of the system and excitation frequency. Since it was present for more than one excitation source, the effect of the first was characterized by the independent variable and the second excitation source as the measurement parameter. Based on the measurements can also be adjusted by controlling operating modes of the excitation source. [ABSTRACT FROM AUTHOR]

The article deals with analysis of possible applications easing by Short Time Fourier transformation, in determining the natural frequencies and damage to the monitored equipment. We have focused on the problem when the maximum speed of the equipment at start-up is limited due to the excitation of resonances around this maximum speed. If we are concerned about the frequency region near resonance and above this, and when we do not want to risk damage to the equipment when passing through resonance, then the solution can be complicated. Our solution is based on the use of the excitatory properties of harmonic frequencies generated by the system to build a facility in the desired frequency range. The procedure was applied to solve reducing vibration of the structure in the steel mills and the extension of the interval of possible use of frequencies in production. [ABSTRACT FROM AUTHOR]

Hand gesture-based systems are one of the most effective technological advances and continue to develop with improvements in the field of human–computer interaction. Surface electromyography (sEMG) is utilized as a popular input data for gesture classification with elevated accuracy and advanced control capability. This paper presents a comparative hand gesture classification approach using time–frequency (TF) images of the spontaneous sEMG signals and the transfer learning method. 4-channel sEMG signals are collected from 30 subjects performing 7 specific hand gestures. After the required pre-processing, segmentation, and windowing steps, three TF analysis methods, namely Short-Time Fourier Transform (STFT), Continuous Wavelet Transform (CWT), and Hilbert-Huang Transform (HHT), are applied to EMG signals to obtain TF images. Spectrograms from STFT, scalograms from CWT, and Hilbert-Huang spectra (HHS) from HHT obtained from multi-channel sEMG data are separately fused. TF images are then utilized to extract distinct features using seven state-of-the-art, pre-trained Convolutional Neural Network (CNN) architectures and classify seven hand gestures. Two different robust cross-validation strategies are conducted to evaluate the proposed method; stratified k-fold cross-validation (SKCV) and leave-one-subject-out cross-validation (LOOCV). We also investigate the effect of window size and the combination of Intrinsic Mode Functions (IMFs) on classification performance. The results demonstrated that the HHT utilizing IMFs obtained by Empirical Mode Decomposition (EMD) provided improved TF resolution and better results than STFT and CWT in the classification of sEMG signals. Finally, the best average accuracies (93.75% for SKCV) and (94.41% for LOOCV) are obtained by the HHT method with the pre-trained ResNet-50 model. [ABSTRACT FROM AUTHOR]

Highlights: [•] This definition of heart rate variability frequency bands is controversial because of possible changes in the frequency boundaries. [•] We propose the constrained Gaussian modeling (CGM) method that dynamically models the power spectrum as a two Gaussian shapes mixture. [•] A greater sensitivity was observed with the CGM method in the case of transitory cardiac events. [•] The CGM method showed a better score in the case of a classification purpose. [ABSTRACT FROM AUTHOR]

Abstract: In this study, the short time Fourier transform, continuous wavelet transform (CWT) and S-transform have been used for spectral analysis of the carotid and femoral arteries Doppler signal. Each of these methods can represent the temporal evolution of Doppler spectra know as the sonograms. Time-frequency analysis by S-transform presents a linear resolution that surpasses the problem of Fourier Transform by a slipping window (STFT) of fixed length and also corrects phase concept in the wavelet transform for the analysis of non-stationary signals. This transform provides a very suitable space for extracting features and the localization of discriminating information in time and frequency in Doppler ultrasonic signals. The sonograms have been then used to compare the methods in terms of their frequency resolution and effects in determining the stenosis of carotid and femoral arteries. [Copyright &y& Elsevier]

Abstract: An instantaneous frequency (IF) estimation algorithm in a high noise environment is proposed based on singular value decomposition (SVD) and Wigner Ville distribution (WVD).The signal is demodulated with the estimated phase to make the time frequency representations (TFRs) parallel to time axis, the noise-reduced signal is obtain by SVD, and IF is estimated by the peak detection of WVD. Simulation result shows that the proposed method is not sensitive to the phase function, and has a better noise robustness. [Copyright &y& Elsevier]