Time-frequency Matrix Research Articles

Time–frequency domain machine learning for detection of epilepsy using wearable EEG sensor signals recorded during physical activities

Epilepsy is a neurological ailment in which there is a disturbance in the nerve cell activity of the brain, causing recurrent seizures. The electroencephalogram (EEG) signal is widely used as a diagnostic modality to detect epilepsy ailment. The automated detection of epilepsy using wearable EEG sensor data recorded during various physical activities is interesting for continuous monitoring of brain health. This paper proposes a time–frequency (TF) domain machine learning (ML) approach for the automated detection of epilepsy using wearable sensor-based EEG signals. The Gaussian window-based Stockwell transform (GWST) is employed to evaluate the TF matrix from the EEG signal. The features such as the L1-norm and the Shannon entropy are extracted from the TF matrix of the EEG signal. The ML and deep learning (DL) models are employed to detect epilepsy using the TF domain features of EEG signals. The publicly available database containing wearable sensor-based EEG signals recorded from the subjects while performing different physical activities is used to evaluate the performance of the proposed approach. The results show that the random forest (RF) classifier coupled with GWST domain features of EEG signals has obtained an overall accuracy value of 90.74% for detecting epilepsy with hold-out validation using the EEG signals from different physical activity cases. For the 10-fold cross-validation (CV) case, the GWST domain features of EEG signal and multi-layer long short-term memory (LSTM) classifier have produced the average accuracy value of 74.44%. For jogging, running, and idle sitting activities, the GWST-based TF domain entropy features coupled with the multi-layer LSTM model have obtained accuracy values of 82.72%, 82.41%, and 87.30%, respectively. The proposed approach has achieved higher classification accuracy than existing methods to detect epilepsy using wearable sensor-based EEG signals using a 10-fold CV strategy. The suggested approach is compared with existing methods to classify seizure and seizure-free classes using resting-state EEG signals.

Time-frequency analysis of speech signals using the Stockwell transform for the detection of upper respiratory tract infection

The acoustic properties of speech demonstrate modifications in the presence of different health states. Biomedical engineering has great promise for creating non-invasive diagnostic processes that use speech as a biomarker. The use of speech indications to screen for upper respiratory tract infections (URTIs), such as the common cold, may have potential advantages in terms of limiting transmission. In this study, we have employed the Stockwell transform -based time-frequency (TF) analysis of speech signals for URTI detection. The Stockwell transform is applied on speech signals to derive their TF representation. Using a TF matrix, the various statistics of magnitude and phase are calculated and used as features for classifying speech of healthy speakers and speakers with URTI. The URTIC database is employed for evaluating the proposed features. The utilization of an ensemble of support vector machines (SVM) is proposed as a classification approach to address the issue of class imbalance. The results show that the proposed method produces comparable outcomes to state-of-the-art approaches. The proposed features obtain 66.53% and 64.65% UARs on the development and test partitions of the URTIC database.

Fault location method for new distribution networks based on waveform matching of time–frequency travelling waves

AbstractSome existing fault location methods for distribution networks rely too much on the local wave head information of the time or frequency domain signals, making it difficult to adapt to the increasingly complex structure and operating conditions of the distribution network after the new energy access. For improvement, the travelling wave (TW) time and frequency ranges that can effectively avoid waveform distortion caused by new energy access are analysed. The one‐to‐one matching relationship between the TW waveforms in these ranges and the fault positions is revealed. A fault TW time–frequency matrix with specific time and frequency windows is constructed, and a new distribution network fault location method is proposed based on the matching technique of waveform features, which realises the accurate fault location by exploring the proportionality between the cumulative trend of the matrix energy amplitude deviation and the fault point position. Simulation test results show that the proposed method is not affected by the complex structure of distribution networks such as new energy access and overhead‐cable line mixing on fault location and flexibly transforms the fault location problem into a time–frequency TW waveform matching problem, which improves the accuracy and robustness of the fault location for new distribution networks to a degree.

Location Method of Single-Phase to Ground Fault in Distribution Network Based on Time-Frequency Matrix Analysis of Traveling Wave

Location Method of Single-Phase to Ground Fault in Distribution Network Based on Time-Frequency Matrix Analysis of Traveling Wave

A novel method for measuring roll angle

The precise measurement of the spin speed of a high-speed autonomous unmanned aerial vehicle (HSA-UAV) is a key element in mastering flight stability, and the measurement of roll angle is the key to determining the accuracy of navigation control systems. We put forward a novel method for measuring roll angle. This method starts from the time–frequency domain analysis of the output signal of the geomagnetic sensor, extracts the time–frequency ridge of the time–frequency matrix (TFM) to obtain the spin speed of the HSA-UAV, and reconstructs the output signal of the geomagnetic sensor. It can calculate the roll angle when the calibration parameters of the geomagnetic sensor are unknown and have good engineering practical value. In addition, we also propose an improved nonlinear short-time Fourier transform with high-frequency resolution and a forward penalty dynamic path ridge-extraction method with frequency jump suppression to extract instantaneous frequency in the TFM.

A fault location scheme based on composite traveling wave with transients in adjacent fault-free lines

As the speedy expansion of power gird, the traveling wave fault locator in substation may not always connect to newly added transmission lines in time, which reduces the effectiveness of fault locator. In this paper, an adjacent fault-free line based fault location scheme for newly additional line is proposed. Since the traveling wave from adjacent fault-free line results in severe difficulty for identification of traveling waves from faulty line, the time–frequency matrix is performed to screen traveling waves. Firstly, in order to remove the influence of waves from remote terminals of adjacent fault-free line, the current refraction coefficient is theoretically analyzed. Due to the negative refraction coefficient, traveling waves from remote terminals of adjacent fault-free line can be reduced by the proposed composite traveling wave. Secondly, the time–frequency matrices of composite traveling wave and original traveling wave is obtained by S-transform. Then, these matrices are applied to form ratio matrix which thoroughly eliminated waves from remote terminals of fault-free lines. Moreover, the robustness of the proposed fault location scheme is thoroughly validated by the simulation of PSCAD/EMTDC. Finally, the proposed fault location method has the great accuracy in simulation tests and field data test.

Changes in oscillatory patterns of microstate sequence in patients with first-episode psychosis

We aimed to utilize chaos game representation (CGR) for the investigation of microstate sequences and explore its potential as neurobiomarkers for psychiatric disorders. We applied our proposed method to a public dataset including 82 patients with first-episode psychosis (FEP) and 61 control subjects. Two time series were constructed: one using the microstate spacing distance in CGR and the other using complex numbers representing the microstate coordinates in CGR. Power spectral features of both time series and frequency matrix CGR (FCGR) were compared between groups and employed in a machine learning application. The four canonical microstates (A, B, C, and D) were identified using both shared and separate templates. Our results showed the microstate oscillatory pattern exhibited alterations in the FEP group. Using oscillatory features improved machine learning performance compared with classical features and FCGR. This study opens up new avenues for exploring the use of CGR in analyzing EEG microstate sequences. Features derived from microstate sequence CGR offer fine-grained neurobiomarkers for psychiatric disorders.

Implementation of Adaptive Partial Discharge Denoising in Resource-Limited Embedded Systems via Efficient Time-Frequency Matrix Factorization

Implementation of Adaptive Partial Discharge Denoising in Resource-Limited Embedded Systems via Efficient Time-Frequency Matrix Factorization

Two-Stream Networks for COPERT Correction Model with Time-Frequency Features Fusion

Emission factors serve as a valuable tool for quantifying the release of pollutants from road vehicles and predicting emissions within a specific time or area. In order to overcome the limitation of the computer program to calculate emissions from the road transport (COPERT) model in directly obtaining precise emission factors from on-board diagnostic (OBD) data, we propose a novel two-stream network that combines time-series features and time-frequency features to enhance the accuracy of the COPERT model. Firstly, for the instantaneous emission factors of NOx from multiple driving segments provided by heavy-duty diesel vehicles in actual driving, we select the monitored attributes with a high correlation to the emission factor of NOx considering the data scale and employing Spearman rank correlation analysis to obtain the final dataset composed of them and emission factors. Subsequently, we construct an information matrix to capture the impact of past data on emission factors. Each attribute of the time series is then converted into a time-frequency matrix using the continuous wavelet transform. These individual time-frequency matrices are combined to create a multi-channel time-frequency matrix, which represents the historical information. Finally, the historical information matrix and the time-frequency matrix are inputted into a two-stream parallel model that consists of ResNet50 and a convolutional block attention module. This model effectively integrates time-series features and time-frequency features, thereby enhancing the representation of emission characteristics. The reliability and accuracy of the proposed method were validated through a comparative analysis with existing mainstream models.

On-line defect recognition of MIG lap welding for stainless steel sheet based on weld image and CMT voltage: Feature fusion and attention weights visualization

At present, sensor recognition technology has become a research hotspot in welding process monitoring. However, most of the existing defect recognition methods are based on single-sensor information, which hinders the further improvement of recognition accuracy and weakens the anti-interference ability under complex conditions. To overcome the shortcomings of single-sensor recognition technology, multi-source information fusion technology can exploit the advantages of different types of sensors to achieve more comprehensive and accurate monitoring of the welding process. However, given the process of fusion of different information, the feature mining mechanism of the deep learning model is still under-explained, and the direct complementarity of the different information remains unclear. In this paper, we propose a hybrid model that uses the fusion of molten pool image and voltage signals to quickly and accurately identify three types of defects in lap MIG welding: burn-through, weld bias, and uneven width. Firstly, a synchronized trigger device is used to acquire molten pool images and weld voltage information, and the voltage data is sliced and matched with the molten pool image data. Secondly, the Short-Time Fourier Transform (STFT) is applied to explore the frequency variation components in the voltage data, and the resulting time-frequency matrix is transformed into a time spectrogram. Finally, the molten pool image and the time spectrogram are simultaneously fed into the hybrid CNN model for online monitoring of welding defect states. The test results show that the average accuracy of the hybrid CNN model in detecting five different welding states reaches 98.87 %, which is significantly higher than three other models based on single sensor information. The visualization of the confusion matrix and Grad-CAM shows that the hybrid CNN model based on multi-source heterogeneous information has a higher accuracy for detecting the transition states that are difficult to detect in welding, and has a more reasonable mechanism for allocating attention, so it can achieve a more accurate and balanced recognition effect in the whole welding manufacturing process. These studies can provide guidance for quality monitoring of welding processes based on multi-source sensor technology, which can be extended to the field of arc additive monitoring.

Chirplet transform based time frequency analysis of speech signal for automated speech emotion recognition

Nowadays, the recognition of emotion using the speech signal has gained popularity because of its vast number of applications in different fields like medicine, online marketing, online search engines, the education system, criminal investigations, traffic collisions, etc. Many researchers have adopted different methodologies to improve emotion classification accuracy using speech signals. In our study, time–frequency (TF) analysis-based features were used to analyze the emotion classification performance. We used a novel TF analysis method called the chirplet transform (CT) to find the TF matrix of the speech signal. We then calculated the proposed TF-based permutation entropy (TFPE) feature using the TF matrix of the speech signal. To reduce the feature dimension and select the most informative emotional feature, we employed the genetic algorithm (GA) feature selection method. Then, the selected TFPE features are used as input to machine learning classifiers such as SVM, RF, DT, and KNN to classify the emotions in the speech signal. We obtained classification accuracy of 77.2%, 69.57%, 68.78%, 56.9%, and 99.1% for the EMO-DB, EMOVO, SAVEE, IEMOCAP, and TESS datasets without the GA feature selection method. The emotion classification accuracy increased to 85.6%, 78.33%, 77.76%, 63.15%, and 100% with the GA feature selection method. We compared our results with other methods and found that our method performed better in emotion classification than the state-of-the-art methods.

Fourier Synchrosqueezing Transform-ICA-EMD Framework Based EOG-Biometric Sustainable and Continuous Authentication via Voluntary Eye Blinking Activities.

In recent years, limited works on EOG (electrooculography)-based biometric authentication systems have been carried out with eye movements or eye blinking activities in the current literature. EOGs have permanent and unique traits that can separate one individual from another. In this work, we have investigated FSST (Fourier Synchrosqueezing Transform)-ICA (Independent Component Analysis)-EMD (Empirical Mode Decomposition) robust framework-based EOG-biometric authentication (one-versus-others verification) performances using ensembled RNN (Recurrent Neural Network) deep models voluntary eye blinkings movements. FSST is implemented to provide accurate and dense temporal-spatial properties of EOGs on the state-of-the-art time-frequency matrix. ICA is a powerful statistical tool to decompose multiple recording electrodes. Finally, EMD is deployed to isolate EOG signals from the EEGs collected from the scalp. As our best knowledge, this is the first research attempt to explore the success of the FSST-ICA-EMD framework on EOG-biometric authentication generated via voluntary eye blinking activities in the limited EOG-related biometric literature. According to the promising results, improved and high recognition accuracies (ACC/Accuracy: ≥99.99% and AUC/Area under the Curve: 0.99) have been achieved in addition to the high TAR (true acceptance rate) scores (≥98%) and low FAR (false acceptance rate) scores (≤3.33%) in seven individuals. On the other hand, authentication and monitoring for online users/students are becoming essential and important tasks due to the increase of the digital world (e-learning, e-banking, or e-government systems) and the COVID-19 pandemic. Especially in order to ensure reliable access, a highly scalable and affordable approach for authenticating the examinee without cheating or monitoring high-data-size video streaming is required in e-learning platforms and online education strategies. Hence, this work may present an approach that offers a sustainable, continuous, and reliable EOG-biometric authentication of digital applications, including e-learning platforms for users/students.

Satellite navigation method based on high-speed frequency hopping signal

Global navigation satellite system has been widely used, but it is vulnerable to jamming. In military satellite communications, frequency hopping (FH) signal is usually used for anti-jamming communications. If the FH signal can be used in satellite navigation, the anti-jamming ability of satellite navigation can be improved. Although a recently proposed time-frequency matrix ranging method (TFMR) can use FH signals to realize pseudorange measurement, it cannot transmit navigation messages using the ranging signal which is crucial for satellite navigation. In this article, we propose dual-tone binary frequency shift keying-based TFMR (DBFSK-TFMR). DBFSK-TFMR designs an extended time-frequency matrix (ETFM) and its generation algorithm, which can use the frequency differences in different dual-tone signals in ETFM to modulate data and eliminate the negative impact of data modulation on pseudorange measurement. Using ETFM, DBFSK-TFMR not only realizes the navigation message transmission but also ensures the precision and unambiguous measurement range of pseudorange measurement. DBFSK-TFMR can be used as an integrated solution for anti-jamming communication and navigation based on FH signals. Simulation results show that DBFSK-TFMR has almost the same ranging performance as TFMR.

Color design of modern architectural interior space based on environmental psychology

Abstract Due to the diversified development trend of modern architectural design, the functional design and application of houses are being presented in the interior space color layout of houses with its many advantages of interactivity, comprehensiveness, multi-functionality, and personalization. With this as the starting point, this paper analyzes the psychological perception of survey respondents on the same spatial color through big data, converts the perception into EEG signal for decoding, and after the decoding process of EEG data pre-processing, feature extraction, feature identification, and classification, calculates the significance of each element in the time-frequency matrix, which can get a homo-dimensional mask matrix. Then the conditional random field model is established on the random field theory to get the parameters of the model. Finally, the parameters of the model are obtained by maximizing the following entropy function, which is brought into the Lagrangian operator to obtain the pairwise Lagrangian operator. Finally, the EEG signal is decoded to realize the self-control training of color perception under different mentalities. The experimental results showed that by performing the intervention test on self-control and color insight, the mean value of the total self-control score in training was 61.99±11.45, and the intervention effect had stability. Therefore, improving self-control ability and forming correct tendency psychological perception plays a vital role in the color design of architectural space.

The self-control training method of vocal performance teaching in a new media environment

Abstract Due to the diverse development trend of modern media, new media arts and applications are being presented in the field of vocal performance teaching with its many advantages of interactivity, immediacy, sharing, comprehensiveness, versatility, community, and personalization. In this paper, by decoding the EEG signal, through the decoding process of EEG data pre-processing, feature extraction, feature identification, and classification, and calculating the significance of each element in the time-frequency matrix, an iso-dimensional mask matrix can be obtained. Then the conditional random field model is established on the random field theory to get the parameters of the model. Finally, the parameters of the model are obtained by maximizing the following entropy function, which is brought into the Lagrangian operator to obtain the pairwise Lagrangian operator. Finally, the EEG signal is decoded to realize the self-control training of vocal performance teaching in the new media environment. The experimental results show that by conducting the intervention test on self-control and vocal performance insight, the mean value of the total self-control score in self-control training is 61.99±11.45, and the intervention effect has stability. Therefore, improving self-control, forming correct expressions and forms, and enriching emotions are important for vocal performance.

Time-frequency analysis of speech signal using Chirplet transform for automatic diagnosis of Parkinson's disease.

Parkinson's disease (PD) is the second most prevalent neurodegenerative disorder in the world after Alzheimer's disease. Early diagnosing PD is challenging as it evolved slowly, and its symptoms eventuate gradually. Recent studies have demonstrated that changes in speech may be utilized as an excellent biomarker for the early diagnosis of PD. In this study, we have proposed a Chirplet transform (CT) based novel approach for diagnosing PD using speech signals. We employed CT to get the time-frequency matrix (TFM) of each speech recording, and we extracted time-frequency based entropy (TFE) features from the TFM. The statistical analysis demonstrates that the TFE features reflect the changes in speech that occurs in the speech due to PD, hence can be used for classifying the PD and healthy control (HC) individuals. The effectiveness of the proposed framework is validated using the vowels and words from the PC-GITA database. The genetic algorithm is utilized to select the optimum features subset, while a support vector machine (SVM), decision tree (DT), K-Nearest Neighbor (KNN), and Naïve Bayes (NB) classifiers are employed for classification. The TFE features outperform the breathiness and Mel frequency cepstral coefficients (MFCC) features. The SVM classifier is most effective compared to other machine-learning classifiers. The highest classification accuracy rates of 98% and 99% are achieved using the vowel /a/ and word /atleta/, respectively. The results reveal that the proposed CT-based entropy features effectively diagnose PD using the speech of a person.

Damage quantification in composite laminates based on sparsity-difference estimation of the normal time–frequency representation

Damage index (DI) is of vital importance for the health monitoring of composite structures. However, most of the existing DI construction methods cannot reveal the rich information hidden in Lamb waves, such as the time-varying characteristics of the signal amplitude and phase, and thus cannot well characterize the evolutionary trend of damage. To address this limitation, this study proposes a novel DI construction method for damage identification in composite structures. The normal time–frequency transform is first applied to obtain the unbiased time–frequency distribution of the received Lamb wave signal, and then the singular value decomposition method is used to obtain the singular value sequence of the two-dimensional time–frequency matrix to characterize its matrix sparsity. Next, the dynamic time warping method is used to measure the distance between singular value sequences of the current and baseline signals. A quantitative DI is finally defined using these distances. Two experimental studies are utilized to validate the effectiveness of the proposed method. Results show that the proposed method can reveal the rich information hidden in the Lamb wave signals and has better trend-matching performance in fitting the actual damage propagation trend than traditional methods.

A pilot protection method based on the similarity of initial current traveling wave time-frequency matrix for VSC-HVDC grids

A pilot protection method based on the similarity of initial current traveling wave time-frequency matrix for VSC-HVDC grids

A probability model with Variational Bayesian Inference for the complex interference suppression in the acoustic array measurement

The microphone array is widely used in acoustics as a non-contact measurement tool, which can obtain multi-dimensional information about the sound source, such as spatial, time, and frequency. The microphone array is not always used in an ideal anechoic chamber environment, making the sound source signal contaminated with the background interference. The separation of the sound source signal from the complex background interference is very challenging, especially when arrays are used in wind tunnel measurements. A probability model on the time–frequency matrix is constructed in this paper to address this issue. The background interference is constructed by the Gaussian mixture model to fit its complex probability distributions adaptively. The sound source signal is constructed as a low-rank model according to its correlation characteristics on the microphones. The distributions of parameters involved in the low-rank and Gaussian mixture model are estimated through variational Bayesian inference, which can realize the separation of the sound source signal from the complex background interference. The performance of the proposed method is evaluated by the numerical simulation and the DLR closed wind tunnel experimental. The robustness and the effectiveness of extracting the sound source signal from the complex background interference are also verified.

High impedance fault detection in distribution network based on S-transform and average singular entropy

When a high impedance fault (HIF) occurs in a distribution network, the detection efficiency of traditional protection devices is strongly limited by the weak fault information. In this study, a method based on S-transform (ST) and average singular entropy (ASE) is proposed to identify HIFs. First, a wavelet packet transform (WPT) was applied to extract the feature frequency band. Thereafter, the ST was investigated in each half cycle. Afterwards, the obtained time-frequency matrix was denoised by singular value decomposition (SVD), followed by the calculation of the ASE index. Finally, an appropriate threshold was selected to detect the HIFs. The advantages of this method are the ability of fine band division, adaptive time-frequency transformation, and quantitative expression of signal complexity. The performance of the proposed method was verified by simulated and field data, and further analysis revealed that it could still achieve good results under different conditions.