The use of radar technology in the field of human activity recognition (HAR) has garnered considerable interest due to its notable benefits in terms of accuracy, resilience, and safeguarding of privacy. As the back-scattered radar returns are composed of multiple components & non-stationary in behavior, the time-frequency analysis like Wigner-Ville distribution (WVD) can play a significant role, but the presence of cross-terms limits the suitability of WVD. This study introduces a time segmentation and frequency windowing-based novel strategy for cross-terms free WVD of multi-component non-stationary signals. The effectiveness of the proposed method is validated on synthetic signals which outperforms the existing related methods for different performance measures. The proposed TFR is also applied for HAR from the received signals of frequency-modulated continuous-wave (FMCW) radar. A novel analysis of extracting slow-time signatures from the received signal captured by FMCW radar is developed and the proposed technique is utilized to generate cross-term free TFRs from these signatures for each activity. Threshold-based time windowing followed by 2-D segmented features extraction is performed on the TFRs for activity recognition system development. The optimization of ensemble classifier yields an impressive classification accuracy rate of 99.51%, surpassing the performances achieved by previous methodologies on the same dataset.

The fluctuations in the stock market serve as an indicator of societal and economic progress. To enhance the forecasting of financial time series data, this paper introduces a novel hybrid model that integrates successive variational mode decomposition (SVMD) with a long short‐term memory (LSTM) network. SVMD is used as a data‐decomposition technique to facilitate the division of complex original series into its decomposed intrinsic mode functions in order to reduce nonstationarity. This approach has been analyzed for both synthetic signals and financial time series data. LSTM is utilized to analyze each signal and take into consideration the influence of historical data in order to enhance prediction accuracy. The final predicted results are derived by reconstructing each predicted time series. To evaluate the model, financial time‐series data from global stock market have been selected for analysis, i.e., the Hong Kong Hang Seng Index (HSI), Stock Exchange Sensitive Index (SENSEX), Standard and Poor’s 500 (S&P500), and North America West Texas Intermediate (WTI). The proposed model has shown improved prediction results based on the performance metrics when compared with single models and other existing hybrid models.

Surface electromyography (sEMG)-based automated grasp recognition (AGR) has emerged as a vital technology in the field of automatic control, human-machine interfaces, prosthetics, virtual reality (VR), etc. Grasp recognition comes under the category of hand gesture recognition (HGR). However, due to its unique characteristics, traits, and advanced applications, such as a robotic hand with 3-D-grasping capability, gaming, etc., it differs from other hand gestures in the case of force estimation and degree of freedom (DOF). This article provides a comprehensive review of available state-of-the-art methodologies for sEMG-based AGR. The review covers sensing modalities, datasets focusing on different grasps types (including power, precision, cylindrical, spherical, tripod, lateral, hook, and palmar grasps), sEMG acquisition systems, pre-processing techniques, multiresolution analysis (MRA), feature extraction process, and identification systems focusing on machine learning (ML), deep neural networks (DNNs), and model-based approaches. This review provides a detailed year-by-year chronological analysis and comparison of grasp recognition techniques with specific focus on number of subjects and types of grasps. Furthermore, some open research issues have been pointed out from the reviewed literature, and possible future prospects for these challenges have also been presented. Finally, several industry domains that can incorporate sEMG-based AGR systems in future are also discussed.

This paper presents a 2D extension of the successive variational mode decomposition (2D-SVMD) method, which decomposes 2D signals into band-limited intrinsic mode functions without requiring prior knowledge of the number of modes. The proposed 2D-SVMD addresses the limitations of existing 2D variational mode decomposition (2D-VMD) and 2D variational mode decomposition with total variation (2D-TV-VMD) methods, which need initialization of the number of modes and can produce redundant or mixed modes if the number is not accurately specified. 2D-SVMD decomposes in a sequential manner which enhances the convergence rate of the decomposition process and also mitigates the extraction of extraneous modes, collectively resulting in reduced computational overhead. The proposed 2D-SVMD is also evaluated on synthetic and real-world images and compared to 2D-VMD and 2D-TV-VMD, demonstrating its superior performance in achieving natural decomposition of 2D signals.

Early age identification and prompt intervention play a crucial role in mitigating the severity of neurodevelopmental disorders in children. Traditional diagnostic approaches can be lengthy, but there is growing research potential in using electroencephalogram (EEG) signals to detect attention deficit hyperactivity disorder (ADHD) and intellectual developmental disorder (IDD). By recording the electrical activity of the brain, EEG has emerged as a promising technique for the early identification of these disorders. This research proposes a novel integrated method for identifying multiple neurodevelopmental disorders from the EEG signals of children. The approach combines successive multivariate variational mode decomposition (SMVMD) for analyzing multi-component non-stationary signals and a machine learning (ML)-based classifier, addressing the issue of inconsistent numbers of extracted features by introducing an energy-based feature integration approach. By integrating enhanced features from SMVMD with a K-nearest Neighbor (KNN) classifier, the unified approach successfully detects two separate neurodevelopmental disorders from normal subjects. The proposed method demonstrates perfect classification scores in detecting IDD under three different scenarios and achieves 99.17% accuracy in classifying ADHD subjects from normal subjects. Evaluation against different ML-based classifiers confirms the effectiveness of the proposed feature extraction algorithm and highlights its superior performance compared to recent methods published on similar datasets.