Short-time Fourier Transform Analysis Research Articles

Detection of Wheeze Sounds in Respiratory Disorders: A Deep Learning Approach

Respiratory disorders, including chronic obstructive pulmonary disease (COPD) and asthma, are major causes of death globally. Early diagnosis of these conditions is essential for effective treatment. Auscultation of the lungs is the traditional diagnostic method, which has drawbacks such as subjectivity and susceptibility to environmental interference. To overcome these limitations, this study presents a novel approach for wheeze detection using deep learning methods. This approach includes the usage of artificial data created by employing the open ICBHI dataset with the aim of improvement in generalization of learning models. Spectrograms that were obtained as the output of the Short-Time Fourier Transform analysis were employed in feature extraction. Two labeling approaches were used for model comparison. The first approach involved labeling after wheezing occurred, and the second approach assigned labels directly to the time steps where wheezing patterns are seen. Wheeze event detection was performed by constructing four RNN-based models (CNN-LSTM, CNN-GRU, CNN-BiLSTM, and CNN-BiGRU). It was observed that labeling wheeze events directly resulted in more precise detection, with exceptional performance exhibited by the CNN-BiLSTM model. This approach demonstrates the potential for improving respiratory disorders diagnosis and hence leading to improved patient care.

Improved STFT analysis using time-frequency masking for chatter detection in the milling process

In the field of machining, the purposeful increase in cutting depth and rotation speed can potentially cause chatter vibration. Chatter lowers surface quality, tool and spindle life, and produces excessive noise, which limits the productivity of the machining process. In this paper, an improved Short-Time Fourier Transform (STFT) involving time–frequency masking and adaptive thresholding is proposed. Initially, hammering tests were undertaken to ascertain the inherent natural frequency of the cutting system. Subsequently, milling tests were conducted across varied conditions utilizing an accelerometer sensor and a microphone (capturing sound signals) to acquire the requisite experimental data. A 3-level Variational Mode Decomposition (VMD) was used for pre-processing due to its inherentcapabilities for noise reduction. The chatter threshold is established by averaging the total intrinsic mode function (IMF) energy levels that have been selected. To demonstrate the efficacy of the proposed method, root mean square (RMS) and skewness were calculated as chatter indicators. The improved STFT time–frequency representation (TFR) was compared to the conventional TFR. Notably, both indicators demonstrate effectiveness and chatter sensitivity, while the improved STFT offers greater time–frequency resolution than its conventional counterpart and can be successfully used for monitoring the milling process.

Short-Time Fourier Transform Analysis of Current Charge/Discharge Response of Lithium-Sulfur Batteries

Measurements acquired on batteries in the form of time signals such as voltage-time and capacity-time to assess their cyclability performance can be supplemented by examining their frequency-domain response. This allows one to determine the global characteristics of the signals and the battery, but not the local behavior, which is very important for determining for example battery fading. In this study we examine the short-time Fourier transform for time-frequency deconstruction of galvanostatic charge/discharge signals of lithium-sulfur batteries, taken as an example. The results displayed in terms of spectrograms show how the frequency content of such signals (e.g. charge and voltage time series) evolve with the lifetime of the batteries allowing the detection of critical changes in the response that may lead to fading and eventually default.

Optical Time-Mapped Spectrograms (I): From the Time-Lens Fourier Transformer to the Talbot-Based Design

The short-time Fourier transform (STFT), or spectrogram (SP), is the prime method for joint time-frequency signal analysis and processing. Real-time implementation of this powerful tool over instantaneous bandwidths above the GHz range remains challenging. We propose here a universal analog optical processing approach to obtain the STFT of a high-speed temporal waveform (typically, a microwave signal) in a continuous and real-time manner, and with no gaps in the signal acquisition and analysis. The proposed method is based on a photonics time-mapped Fourier transformer, involving temporal modulation of the signal under test (SUT) with a chirped optical pulse (i.e., a time lens) followed by group-velocity dispersion, in which consecutive, overlapping chirped pulses (or time lenses) are utilized for realization of a continuous and gap-free STFT analysis. We derive the design conditions and performance trade-offs of this general scheme, especially concerning its instantaneous bandwidth, time-frequency resolutions and processing speed (number of FTs per second). Moreover, we also show that a previous, simpler time-mapped STFT design based on Talbot effects, in which the SUT is directly sampled with unchirped pulses before dispersion, can be interpreted and evaluated as a particular case of the time-lens STFT scheme introduced here. This suggests a way of implementing an array of overlapping time lenses by simply sampling the SUT with a suitable periodic train of short pulses. The time-lens STFT scheme offers a remarkable versatility to tailor the specifications of the obtained SP, whereas the Talbot design is particularly interesting for SP analysis with enhanced time resolution and ultrahigh processing speed. Our findings are validated through numerical simulations. The proposed method would enable performing the STFT of signals over instantaneous bandwidths above a few tens of GHz, with MHz frequency resolutions and processing speeds exceeding hundreds of millions of FTs per second using realistic fiber-optics technologies.

Machine learning at the edge for AI-enabled multiplexed pathogen detection

Multiplexed detection of biomarkers in real-time is crucial for sensitive and accurate diagnosis at the point of use. This scenario poses tremendous challenges for detection and identification of signals of varying shape and quality at the edge of the signal-to-noise limit. Here, we demonstrate a robust target identification scheme that utilizes a Deep Neural Network (DNN) for multiplex detection of single particles and molecular biomarkers. The model combines fast wavelet particle detection with Short-Time Fourier Transform analysis, followed by DNN identification on an AI-specific edge device (Google Coral Dev board). The approach is validated using multi-spot optical excitation of Klebsiella Pneumoniae bacterial nucleic acids flowing through an optofluidic waveguide chip that produces fluorescence signals of varying amplitude, duration, and quality. Amplification-free 3× multiplexing in real-time is demonstrated with excellent specificity, sensitivity, and a classification accuracy of 99.8%. These results show that a minimalistic DNN design optimized for mobile devices provides a robust framework for accurate pathogen detection using compact, low-cost diagnostic devices.

Highly pixelated, untethered tactile interfaces for an ultra-flexible on-skin telehaptic system

Realizing highly immersive tactile interactions requires a skin-integrated, untethered, high-definition tactile transducer devices that can record and generate tactile stimuli. However, the rigid and bulky form factor, and insufficient resolution of existing actuators are hindering the reproduction of sophisticated tactile sensations and immersive user experiences. Here, we demonstrate an ultra-flexible tactile interface with high spatial resolution of 1.8 mm for telehaptic communication on human skin. Dual mechanism sensors and sub-mm scale piezoceramic actuators are designed to record and generate the static and dynamic pressures in a wide frequency range (1 Hz to 1 kHz). Moreover, actuators are integrated on ultra-flexible substrate with chessboard pattern to minimize stress during mechanical deformations. Finally, remote transmissions of various tactile stimuli, such as shapes, textures, and vibration patterns were demonstrated by the telehaptic system with low latency (<1.55 ms) and high fidelity as proven by the short-time Fourier-transform analysis.

Stator Winding Fault Detection of Permanent Magnet Synchronous Motors Based on the Short-Time Fourier Transform

Abstract In modern drive systems, the high-efficient permanent magnet synchronous motors (PMSMs) have become one of the most substantial components. Nevertheless, such machines are exposed to various types of faults. Hence, on-line condition monitoring and fault diagnosis of PMSMs have become necessary. One of the most common PMSM faults is the stator winding fault. Due to the destructive character of this failure, it is necessary to use fault diagnostic methods that allow fault detection at its early stage. The article presents the results of experimental studies obtained from fast Fourier transform (FFT) and short-time Fourier transform (STFT) analyses of the stator phase current, stator phase current envelope and stator phase current space vector module. The superiority of the proposed method over the classical approach based on the stator current analysis using FFT is highlighted. The proposed solution is experimentally verified under various motor operating conditions. The application of STFT analysis discussed so far in the literature has been limited to the fault diagnosis of induction motors and the narrow range of the analysed motor operating conditions. Moreover, there are no works in the field of motor diagnostics dealing with STFT analysis for stator windings based on the stator current envelope and the stator current space vector module.

Study of the impact of clicks and murmurs on cardiac sounds S1 and S2 through bispectral analysis

Abstract This paper presents a study of the impact of clicks, and murmurs on cardiac sound S1, and S2, and the measure of severity degree through synchronization degree between frequencies, using bispectral analysis. The algorithm is applied on three groups of Phonocardiogram (PCG) signal: group A represents PCG signals having a morphology similar to that of the normal PCG signal without click or murmur, group B represents PCG signals with a click (reduced murmur), and group C represent PCG signals with murmurs. The proposed algorithm permits us to evaluate and quantify the relationship between the two sounds S1 and S2 on one hand and between the two sounds, click and murmur on the other hand. The obtained results show that the clicks and murmurs can affect both the heart sounds, and vice versa. This study shows that the heart works in perfect harmony and that the frequencies of sounds S1, S2, clicks, and murmurs are not accidentally generated; but they are generated by the same generator system. It might also suggest that one of the obtained frequencies causes the others. The proposed algorithm permits us also to determine the synchronization degree. It shows high values in group C; indicating high severity degrees, low values for group B, and zero in group A. The algorithm is compared to Short-Time Fourier Transform (STFT) and continuous wavelet transform (CWT) analysis. Although the STFT can provide correctly the time, it can’t distinguish between the internal components of sounds S1 and S2, which are successfully determined by CWT, which, in turn, cannot find the relationship between them. The algorithm was also evaluated and compared to the energetic ratio. the obtained results show very satisfactory results and very good discrimination between the three groups. We can conclude that the three algorithms (STFT, CWT, and bispectral analysis) are complementary to facilitate a good approach and to better understand the cardiac sounds

STFT Analysis of Broadband Oscillation in VSC-HVDC Connected to Offshore Wind Farms

The transmission technology of offshore windfarms (OWFs) through VSC-HVDC system is developing rapidly. The impact factor of broadband oscillation is complex, it often occurs in VSC-HVDC and direct drive permanent magnet synchronous generator (DDPMSG). In this paper, based on short time fourier transform (STFT), factors that cause broadband oscillation are studied. Firstly, the primary structure of DDPMSG and VSC-HVDC is established. Then the correctness of small signal method (SSM) is verified. Based on eigenvalue analysis, instability caused by controlling parameter is analysed. Finally, time-frequency characteristic of oscillation waveforms in PSCAD is extracted using STFT, asynchronous characteristics of broadband oscillation is verified. It is of guiding significance, for the developing technology of offshore wind farm integrated into grid through VSC-HVDC system.

STFT와 DMD를 이용한 GH₂/GO₂ 로켓 연소기의 연소불안정성 분석

The post-processing analysis tools, Short Time Fourier Transform (STFT) and Dynamic Mode Decomposition (DMD) were applied to analyze dynamic characteristics of the GH₂/GO₂ rocket model combustor. The STFT analysis was performed with pressure data in the chamber to exhibit the appearance of frequency components at instant time. The STFT results indicate resonant frequencies of signal through their amplitudes. DMD analysis was conducted with the planar pressure data to extract the resonant frequencies and damping coefficients of acoustic modes. The damping coefficient is a parameter to evaluate the stability of a certain pressure oscillation in a dynamic system. By combining the results of STFT and DMD, the appearance of the resonant frequencies causing unstable combustion can be determined.

An Effective Detection Method of Voltage and Frequency Fluctuations Based on a Combination of TEO/DESA and STFT Analysis

Nowadays, it is a very essential issue to detect and recognize power quality (PQ) disturbances as soon as possible, because it can give very important information by which various kinds of power quality mitigation devices can respond for each phenomenon quickly and to capture the interested waveform parts instantaneously, and so on. Also, it can be widely used as a trigger or reference signal for data mining to determine the existence of anomalies in the power system. We have studied some applications of Teager Energy Operator (TEO) and its adjunctive techniques known as Discrete Energy Operation Algorithm (DESA) to classify and detect the waveform distortions such as harmonics, inter-harmonics and frequency variation and the voltage synchronization. This paper is written to propose an effective detection method of voltage and frequency fluctuation based on TEO/DESA combined with Short-Time Fourier Transform (STFT), which is designed to overcome the drawback of the former study, a combined TEO and STFT method.

Bearing Fault Diagnosis of a PWM Inverter Fed-Induction Motor Using an Improved Short Time Fourier Transform

Induction motor diagnosis using the Power Spectral Density (PSD) estimation based on the Fourier Transform calculation has been widely used as an analysis method for its simplicity and low computation time. However, the use of PSD is not recommended for processing non stationary signals (case of variable speed applications) and therefore the analysis with PSD is not reliable. To overcome this handicap, the Short Time Fourier Transform (STFT) is proposed in this paper; giving additional information on changes of the frequencies over time for stator current signal analysis. Furthermore, the use of a new approach called Maxima’s Location Algorithm is also proposed. This later will be associated with the STFT analysis to show only those harmonics with useful information on existing faults. This approach will be used in the diagnosis of bearing faults of a PWM inverter-fed induction motor operating at variable speed. Several experimental results in the transient state are carried out firstly to validate the results and secondly to illustrate the merits and effectiveness of the combined STFT/MLA proposed approach.

EEG Processing in Internet of Medical Things Using Non-Harmonic Analysis: Application and Evolution for SSVEP Responses

In recent years, the Internet of Things has been applied in many fields with rapid development, such as software, sensors, and medical and healthcare. In the case of medical and healthcare, extensive research has focused on the development of brain-computer interface systems, particularly those utilizing steady-state visual-evoked potentials (SSVEPs). However, the conventional short-time Fourier transform (STFT) analysis is associated with the low-frequency resolution because of the length of the analysis window, resulting in sidelobe artifacts. In this paper, we utilized the non-harmonic analysis (NHA), which does not depend on the length of the analysis window, to analyze the continuous changes in and determine the classification accuracy of SSVEPs. Moreover, our experiments utilized the gray-scale images, allowing for the presentation of the stimulus as a sinusoidal pattern and reducing the effect of frequency distortion associated with the refresh rate of the liquid-crystal display. Our findings indicated that NHA resulted in exponential improvements in time-frequency resolution when compared with the STFT analysis. As the accuracy of NHA was high, our results suggest that this method is effective for examining SSVEPs and changes in brain waves during experiments conducted using liquid-crystal displays.

GNSS Code Multipath Short-time Fourier Transform Analysis

Global Navigation Satellite System pseudorange multipath errors cannot be analyzed with the Fourier transform as they are non-stationary; thus, other tools must be used. The objective of this paper is to investigate short-time Fourier transform (STFT) performance in detecting Global Positioning System code multipath and analyzing its frequency content. Multipath estimation was achieved by using the code-minuscarrier and the pseudorange multipath observable. Two real datasets were analyzed. A first experiment was carried out using data provided by the Jiufeng (China) International GNSS Service station. The theoretical multipath time-frequency signature was identified by analyzing time series provided by a simulator under the same conditions of the analyzed station. The spectrograms calculated on the real data and those calculated on the simulated data show the same pattern, assessing the effectiveness of the STFT performance. Based on these results, the STFT analysis was applied to a second dataset collected in a strong multipath scenario in Naples (Italy), allowing identification of the multipath sources.

Correlation Between Fuel Lubricity and Vibration Signals Obtained in Ball-disc Analysis Using Fourier Transform

During the contact between metallic surfaces in mechanical systems, some dynamic characteristics present significant changes as the vibration signal patterns. The acquisition and characterization of these signals, by the use of Fourier Transform (FT), are non-intrusive tools in the evaluation of lubricity. This study aimed to evaluate dynamically the fuels lubricity (diesel S50, biodiesel and its blends) by the reciprocating sliding of a ball against a flat disc (AISI 52100 steel). After that, the vibration signals were evaluated by statistical test and FFT (Fast Fourier Transform) and STFT (Short-Time Fourier Transform) analyses and verified their associations with parameters supplied by ASTM D6079. The results present a correlation between lubricity, frequency spectrum and the parameters of profiles from the worn disc surface. In addition, the evolution of wear scars presented an influence of the biodiesel content in these fuels.

Blind compensation of interchannel sampling frequency mismatch for ad hoc microphone array based on maximum likelihood estimation

In this paper, we propose a novel method for the blind compensation of drift for the asynchronous recording of an ad hoc microphone array. Digital signals simultaneously observed by different recording devices have drift of the time differences between the observation channels because of the sampling frequency mismatch among the devices. On the basis of a model in which the time difference is constant within each short time frame but varies in proportion to the central time of the frame, the effect of the sampling frequency mismatch can be compensated in the short-time Fourier transform (STFT) domain by a linear phase shift. By assuming that the sources are motionless and have stationary amplitudes, the observation is regarded as being stationary when drift does not occur. Thus, we formulate a likelihood to evaluate the stationarity in the STFT domain to evaluate the compensation of drift. The maximum likelihood estimation is obtained effectively by a golden section search. Using the estimated parameters, we compensate the drift by STFT analysis with a noninteger frame shift. The effectiveness of the proposed blind drift compensation method is evaluated in an experiment in which artificial drift is generated.

Fourier and Wavelet Spectral Analysis of EMG Signals in 1-km Cycling Time-Trial

Frequency domain analyses in electromyographic (EMG) signals are frequently applied to assess muscle fatigue and similar variables. Moreover, Fourier-based approaches are typically used for investigating these procedures. Nonetheless, Fourier analysis assumes the signal as stationary which is unlikely during dynamic contractions. As an alternative method, wavelet-based treatments do not assume this pattern and may be considered as more appropriate for joint time-frequency domain analysis. Based on the previous statements, the purpose of the present study was to compare the application of Short-Time Fourier Transform (STFT) and Continuous Wavelet Transform (CWT) to assess muscle fatigue in dynamic exercise of a 1-km of cycling (time-trial condition). The results of this study indicated that CWT and STFT analyses have provided similar fatigue estimates (slope) (p> 0.05). However, CWT application represents lesser dispersion (pp> 0.05) according to different methods, it is important to note that these responses seem to show greater values for CWT compared to STFT for 2 superficial muscles. Thereby, we are capable of considering CWT as a reliable and useful method to take into consideration when non-stationary or oscillating exercise models are evaluated.

Post-exercise heart rate variability recovery: a time-frequency analysis

Objective Most studies investigating the eff ects of non-pharmacological interventions, such as physical training (PT), on cardiac autonomic control, assessed the HRV only in resting conditions. Recently, a new time-frequency mathematical approach based on the short-time Fourier transform (STFT) method has been validated for the assessment of HRV in non-stationary conditions such as the immediate post-exercise period. The aim of this study was to evaluate the eff ects of the PT on post-exercise cardiac autonomic control using the time-frequency STFT analysis of the HRV.Methods Twenty-one healthy male volunteers participated in this study. The subjects were initially evaluated for their physical exercise/sport practice and allocated to groups of low physical training (LowPT, n = 13) or high physical training (HighPT, n = 8). The post-exercise HRV was assessed by the STFT method, which provides the analysis of dynamic changes in the power of the low- and high-frequency spectral components (LF and HF, respectively) of the HRV during the whole recovery period.Results Greater LF (from the min 5 to 10) and HF (from the min 6 to 10) in the post-exercise period in the HighPT compared to the LowPT group (P < 0.05) was observed.Conclusion These results indicate that exercise training exerts benefi cial eff ects on post-exercise cardiac autonomic control.

Time–frequency analysis for concrete dam safety control: Correlation between the daily variation of structural response and air temperature

The purpose of this research work is to identify the effect of the daily variation of the air temperature on the structural response of a concrete dam. It is intended to obtain a better knowledge about structural behaviour of concrete dams. In current day to day activities, quantitative interpretation models are used for the assessment of structural dam behaviour of concrete dams. Most models ignore the temperature effect of a wave with a daily variation. However, in dams with automated data acquisition systems this daily effect can be used to anticipate the detection of abnormal behaviour.In this paper, the Short Time Fourier Transform analysis of the residuals is used, obtained by the quantitative interpretation models and measurement data, to identify the signature that the daily variation of the air temperature has on the structural behaviour of a concrete dam. A case study is presented based on the analysis of a horizontal displacement measured on a pendulum, in the Alto Lindoso concrete dam. As a result, the relation between magnitudes of daily variations of the horizontal displacement analysed and the air temperature was defined. The relation obtained can be used to assess if there is alterations or not in the dam response to short period loads corresponding to the daily variations analysed.

Singular points detection using fingerprint orientation field reliability

Singular Points detection is the most challenging and important process in biometrics fingerprint verification and identification systems. Singular points are used for fingerprint classification, fingerprint matching and fingerprint alignment. This paper overcomes problems of the previous methods of miss-deducting or deducting spurious singular points. We propose a novel algorithm for singular point detection based on the fingerprint orientation field reliability. The algorithm starts by enhancing the fingerprint image using the short time Fourier Transform analysis (STFT), followed by calculating the orientation field reliability and locating the singular points. Experimental results have proven that the proposed algorithm locates singular points in a fingerprint image with high accuracy and can even locate the secondary core and delta if they exist. Key words: Biometrics, security, fingerprint image, singular points, reliability.