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Sign language (SL) is used as primary mode of communication by individuals who experience deafness and speech disorders. However, SL creates an inordinate communication barrier as most people are not acquainted with it. To solve this problem, many technological solutions using wearable devices, video, and depth cameras have been put forth. The ubiquitous nature of cameras in contemporary devices has resulted in the emergence of sign language recognition (SLR) using video sequence as a viable and unobtrusive substitute. Nonetheless, the utilization of SLR methods based on visual features, commonly known as appearance-based methods, presents notable computational complexities. In response to these challenges, this study introduces an accurate and computationally efficient pose-based approach for SLR. Our proposed approach comprises three key stages: pose extraction, handcrafted feature generation, and feature space mapping and recognition. Initially, an efficient off-the-shelf pose extraction algorithm is employed to extract pose information of various body parts of a subject captured in a video. Then, a multi-input stream has been generated using handcrafted features, i.e., joints, bone lengths, and bone angles. Finally, an efficient and lightweight residual graph convolutional network (ResGCN) along with a novel part attention mechanism, is proposed to encode body's spatial and temporal information in a compact feature space and recognize the signs performed. In addition to enabling effective learning during model training and offering cutting-edge accuracy, the proposed model significantly reduces computational complexity. Our proposed method is assessed on five challenging SL datasets, WLASL-100, WLASL-300, WLASL-1000, LSA-64, and MINDS-Libras, achieving state-of-the-art (SOTA) accuracies of 83.33 %, 72.90 %, 64.92 %, 100± 0 %, and 96.70± 1.07 %, respectively. Compared to previous approaches, we achieve superior performance while incurring a lower computational cost. [ABSTRACT FROM AUTHOR]

Photovoltaic (PV) system generates energy that varies with the variation in environmental conditions such as temperature and solar radiation. To cope up with the ever increasing demand of energy, the PV system must operate at maximum power point (MPP), which changes with load as well as weather conditions. This paper proposes a nonlinear backstepping controller to harvest maximum power from a PV array using DC-DC buck converter. A regression plane is formulated after collecting the data of the PV array from its characteristic curves to provide the reference voltage to track MPP. Asymptotic stability of the system is proved using Lyapunov stability criteria. The simulation results validate the rapid tracking and efficient performance of the controller. For further validation of the results, it also provides a comparison of the proposed controller with conventional perturb and observe (P&O) and fuzzy logic-based controller (FLBC) under abrupt changes in environmental conditions. [ABSTRACT FROM AUTHOR]