Autism spectrum disorder (ASD) is a developmental disability related to neurological variations, affecting children's social communication, behavior, learning, and attention. These children often struggle with movement, as they require external support to move their hands and legs. A hand assistive device has been designed to help these children, providing them with movement exercises and physiotherapy. This device also uses EMG (electromyography) sensors to diagnose neuromuscular activities in autistic children. This device is essential for their overall well-being and development.

The application of IoT and machine learning in gas detection systems for sewage offers an innovative approach to real-time gas detection and enhances environmental safety in industrial and urban environments. This system uses advanced technologies such as the Internet of Things (IoT), machine learning algorithms, and high-performance sensors like the MQ135 and the DHT11 to achieve the best results in the gas concentration measurement and collection. It identifies abnormalities and determines which emission control measures are most effective for specific release points and similar situations. The design of the device includes sensor nodes that are primarily responsible for data collection and a central microcontroller (MCU) that operates a machine learning algorithm for efficient anomaly detection and predictive maintenance. The system uses the IoT connection to regularly send data to cloud platform (Blynk), enabling real time monitoring of gas levels and environmental conditions. The system generates a visual image of the captured data that can be accessed online. Distinctive attributes like accurate gas detection, continuous monitoring, predictive maintenance, remote assessment, and comprehensive data visualization, all contribute to smart decision-making for environmental safety.

The proposed work introduces an improved PID controller for boosting operation in solar energy-battery systems, leveraging model-based controller (MBC) techniques. In general, boost converters are crucial for managing and optimizing power flow in renewable energy systems. While PID controllers are commonly used in boosting applications due to their simplicity, they often face challenges in adapting to the dynamic and nonlinear characteristics of solar energy systems. This can result in suboptimal performance and efficiency. Hence, a model-based controller with manual tuning is proposed to improve the stability and efficiency by systematically tuning controller gain parameters based on its system model. Extensive simulation studies conducted using MATLAB/Simulink shows that the proposed controller outperforms the conventional PID controller under various operating conditions, such as fluctuations in solar irradiance and changes in battery state of charge. Further, the experimental tests conducted on a prototype setup validates the feasibility and ability of the model-based controller to accurately regulate the solar panel output and efficiently charge the battery, which leads to enhanced performance. This work offers a promising solution for enhancing energy conversion efficiency and facilitates the effective integration of renewable energy sources into the power grid.

The electric vehicle's most crucial component is its battery, which provides the vehicle with power. A key element of electric vehicles (EVs) is the battery management system (BMS), which ensures the safe and efficient functioning of the battery pack. Previous research on electric vehicles has identified some drawbacks, such as lengthy charging times and the need for different charging methods depending on battery capacity and temperature. In the proposed work, the battery's state of charge and remaining capacity will be estimated by measuring the voltage and current with the use of a current sensor and temperature monitor. The novelty of the work lies in its ability to increase the range of electric vehicles. This is achieved through higher energy densities in high-voltage batteries, which allow for longer driving distances between charges. Additionally, faster charging systems can handle higher charging power levels, resulting in quicker charging times. These improvements in performance are made possible by the use of high-voltage batteries, which provide the necessary power for greater peak speeds and improved acceleration. As electric mobility becomes more widespread, the ability to accurately predict charging times based on layout becomes crucial in building user confidence, optimizing energy grid management, and promoting the widespread adoption of electric vehicles.

This study discusses advanced control strategies for voltage and frequency regulation in smart grids, particularly in the integration of renewable energy sources and electrification. These strategies, including Model Predictive Control (MPC), adaptive control, optimal control, robust control, and distributed control, aim to optimize control actions while adhering to system constraints. Case studies show their effectiveness in high photovoltaic penetration, wind power integration, and microgrid operation. However, challenges persist, such as managing uncertainties and coordinating multiple controllers in decentralized power systems. The study acknowledges ongoing research and development in this field, emphasizing the potential for enhancing voltage and frequency regulation in smart grids.

A DC-DC boost converter is used for providing stabilized output to the variation in R-load. In this research, a suitable controller which can be practically realized for DC-DC boost converter has been designed. Moreover, an optimization technique that possesses simple implementation, better convergence quality and enhanced computational ability has been developed. The PID controller parameters are tuned optimally using various optimization algorithms for enhancing the dynamic response of the converter in the presence of R-load. Tuning the PID controller parameters using QBGA algorithms enhances the efficiency of about 80% with R-load. Furthermore, the technique's effectiveness is demonstrated in terms of settling time, rise time, overshoot, peak time, integral absolute error, integral time absolute value error, and integral square error.

Electricity generation using renewable energy resources rather than fossil fuels reduces gobar gas emissions from the power sector and prevents pollution. The best alternative to conventional energy sources is solar photovoltaic (PV) generation; however, it has some significant disadvantages, including high initial costs, poor photoconversion efficiency, and climatic dependencies. In order to overcome this, PPT techniques are used. The projected work provides a solar power system which tracks the maximum power point using Artificial Neural Network and Perturb and Observe algorithm. Additionally, it adds a new setup for the system that links the photovoltaic array as well as the battery to the system inverter’s DC-link through a single DC-DC converter that can act as both charge controller and Maximum Power Point Tracking (MPPT) device at the same time. When solar energy production exceeds demand, the excess energy is stored in batteries and provided during periods of very low irradiance. The DC voltage is converted into AC voltage using an inverter. The benefit of this technique is that it has greater tracking accuracy. For validation, the proposed model has been analyzed by using MATLAB/Simulink.