A converter with high voltage gain is generally necessary for interfacing the photovoltaic (PV) systems with grid. However, more semiconductor components are needed to obtain a higher voltage gain, which results in increased losses. This article proposes a novel non-isolated cubic boost (NNICB) DC-DC converter for high-voltage PV applications with a wide voltage gain at a lower duty ratio. Compared to traditional high-gain DC-DC converter, the NNICB converter counters the drawbacks of increased component count and high voltage stress. The NNICB DC-DC converter has a continuous source current for PV applications with low-voltage stress across the diodes and switches. A detailed steady-state analysis of the NNICB topology is carried out for the ideal and non-ideal models, and their corresponding voltage gain equations are computed. Furthermore, the analysis is performed using MATLAB/Simulink and is validated using a 230 W laboratory prototype. The experimental results show that the efficiency of the proposed NNICB topology is 94.42% with a voltage gain of 10.5 at 45% duty ratio. This proves the superior performance of the proposed novel converter in comparison with the existing topologies.

The squat is a widely performed exercise essential for maintaining physical fitness and overall well-being. It involves a series of movements in which an individual lowers their torso by bending the knees, and then returns to the starting position. Incorrect squat execution can lead to physical injuries, highlighting the importance of expert guidance. However, many individuals cannot afford professional training due to high costs, necessitating an automated workout analysis system. Existing vision-based squat analysis models face challenges related to occlusion and environmental factors. To address these limitations, we propose a method that utilizes squat videos captured by a stereo camera. The MediaPipe algorithm extracts and tracks keypoints from video frames. Detected keypoints are then used to classify squat types and evaluate squat performance using a Bi-CGRU model. Additionally, we introduce a regression-based approach to predict the correct squat posture. A deep learning-based regressor, trained on a dataset of squat videos, estimates the correct joint angles. When an improper squat is detected, the system provides feedback by presenting the correct squat form to the participant. Experimental results demonstrate that the Bi-CGRU classification model achieves 96.1% accuracy, while the pose correction regression model attains 99.0% accuracy using mean squared error loss. Both qualitative and quantitative evaluations indicate that our approach outperforms state-of-the-art methods utilizing stereo video input. The system helps users to refine their squat technique and reduce the risk of injury by offering real-time posture correction based on biomechanics guidance.

Under partial shading conditions, photovoltaic (PV) systems exhibit multiple peaks in their power-voltage (P-V) characteristics. Under these conditions, it is essential to extract maximum energy from the PV system. Several global maximum power point tracking (GMPPT) algorithms presented in the literature, track the global peak using different methods. It is imperative to track the global peak with minimal convergence time. This paper proposes a novel algorithm to track the global peak using voltage and current perturbation. The new GMPPT algorithm operates in a current perturbation or voltage perturbation mode, based on the value of a control variable. In either mode, the proposed technique generates reference current or reference voltage, for navigating the operating point to GMPP location. The proposed algorithm is compared with two GMPPT algorithms, namely, modified maximum power trapezium (M-MPT) and high-performance GMPPT algorithm. The simulation studies are performed in MATLAB and is validated using laboratory prototype, with dSPACE 1202 controller for implementing GMPPT algorithms. Simulation and experimental results show that new technique exhibits superior performance in terms of tracking time. Also, the energy efficiency is improved by 40% while using the proposed GMPPT algorithm for the irradiance profiles considered in this paper compared to other two techniques.

The power – voltage (P-V) characteristics of photovoltaic (PV) systems exhibit multiple power peaks under partially shaded conditions. Several global maximum power point tracking (GMPPT) algorithms in the literature recognize the irradiance change, only after the convergence of operating point to global peak, or use additional hardware to call GMPPT subroutine at definite time intervals to detect any insolation change, and thus track the global peak. However, during fast changing partial shading conditions, these methods are less effective, as they do not detect any irradiance change during the tracking phase of any shading pattern. This paper proposes a novel modified current control approach that uses current as a parameter to detect the insolation change during the tracking phase and track the global peak under fast changing partial shading conditions without any additional hardware. The proposed technique improves the tracking efficiency by as much as 39%, thus proving to be effective under fast-changing partial shading conditions. The superior tracking performance of the proposed algorithm over the existing techniques in terms of its tracking efficiency, dynamic tracking capability, tracking speed, and convergence to the global peak is demonstrated with extensive simulations using MATLAB/Simulink and experimental results.

Near Field Communication (NFC) has emerged as a pivotal wireless technology for short-range data exchange between devices, including smartphones and wearables. Leveraging Radio Frequency (RF) signals and robust authentication, NFC ensures secure communication in proximity. Mutual authentication (MA) holds paramount importance for establishing trust between device pairs. A prime example of NFC technology is Radio Frequency Identification (RFID), wherein seamless communication occurs between a reader and a tag. However, security and privacy concerns are critical factors in RFID systems, necessitating effective authentication protocols. This paper introduces an efficient and secure MA mechanism for RFID devices, using lightweight block ciphers (LBCs). Specifically, the extended tiny encryption algorithm (XTEA) and the hummingbird algorithm (HBA) using the cipher block chaining (CBC) mode is designed. These tailored algorithms establish a secure communication channel between RFID tags and readers. Notably, the proposed RFID-MA using XTEA and HBA occupies less than 2% of the chip area, encompassing slices and lookup tables (LUTs), and achieves authentication with execution times of 11.6 μs and 1.34μs, respectively. In comparison to the RFID-MA approach employing the XTEA methodology, the proposed algorithm reduces the chip area overhead by 24.6%, total power consumption by 3.5%, and execution time by 88.36%. The proposed algorithm is implemented using Verilog hardware description language (HDL) within the Xilinx environment and is realized on a Artix-7 field programmable gate array (FPGA) platform, utilizing the ModelSim simulator. The proposed research extensively presents simulation and synthesis results while considering the critical hardware constraints such as time, power, and area. Furthermore, this study has also conducted a comprehensive performance analysis, comparing essential metrics with existing LBC and authentication methodologies, showcasing substantial improvements.