This work explores the optimum tensile strength and hardness of AISI 1018 low carbon steel plate welded joint using an E7018 electrode. The effect of metal metal arc welding process parameters namely; welding current and welding travel speed on AISI 1018 low carbon steel samples. The optimum performance of weld joints has been assessed based on the ultimate tensile strength and hardness of welded joints considering the welding current and travel speed variation. Taguchi-based L4 orthogonal array has been considered for the design of the experiment. The welding parameters on Tensile strength and Hardness of AISI 1018 low carbon steel plate welded joints were evaluated. The results show that there was no significant effect in current variation from 80A to 100A on the Ultimate Tensile strength and hardness of AISI 1018 low carbon steel plate with an average UTS and hardness of 434MPa and 122, respectively. However, it seemed that the welding travel speed of 20 to 21 mm/s, slightly affected the ultimate tensile strength and the hardness.

This research addresses the significant challenge of unreliable wireless communication, which hinders the performance of ZigBee-based wireless sensor networks (WSNs) in precision agriculture. A dual-slope log-distance path loss model was developed to accurately predict signal propagation complexities in dense vegetative environments for improved wireless communication. The study was conducted on a cassava farm in Ondo State, Nigeria, characterized by vegetation heights of 1.8 meters, making it an ideal site for investigation. A systematic methodology was employed, incorporating radio frequency measurements in both line-of-sight and non-line-of-sight conditions. This involved deploying two XBee S2C modules operating at 2.4 GHz, with one designated as a coordinator and the other as a router. The collection of Received Signal Strength Indicator (RSSI) and throughput data occurred at 5 meter intervals, with variations in the router's orientation. Results revealed a maximum communication range of 70 meters under non-line-of-sight conditions, compared to 140 meters in line-of-sight scenarios, where the path loss exponent was determined to be 1.78. The path loss exponents for the cassava fields were found to be 2.55 and 4.25. The developed dual-slope path loss model showed a strong fit to additional empirical data from a separate cassava farm location, achieving a Mean Absolute Percentage Error (MAPE) of 3.30 % and an R-squared value of 0.94. Hence, this model offers a comprehensive framework for characterizing radio wave propagation in agricultural environments, enhancing data transmission reliability and energy efficiency in smart farming applications.

The Advanced Encryption Standard (AES) is widely regarded as a robust encryption algorithm, ensuring secure communication and data protection. However, physical vulnerabilities such as side-channel attacks (SCAs) pose a significant threat to its implementations. This paper investigates various types of SCAs, including power analysis and electromagnetic analysis, and explores countermeasures like masking techniques to enhance AES resilience. The study includes an implementation of AES in Vivado using Verilog and a detailed analysis of masked and unmasked designs to validate the effectiveness of proposed countermeasures.

This paper presents an adaptive traffic light control system designed to enhance traffic flow efficiency using real-time vehicle count data. Implemented in Verilog HDL, the system dynamically adjusts signal timings based on vehicle density in each lane, prioritizing heavily congested lanes to reduce delays and improve throughput. The design employs logical circuit modeling and synthesis for optimal performance. By minimizing congestion and accommodating varying traffic conditions, the proposed system demonstrates significant potential for real-world applications in urban traffic management and intelligent transportation systems.

Advances in biomedical engineering and Artificial Intelligence (AI) have resulted in the creation of smart bandages that can monitor wounds in real-time and treat them automatically. These intelligent systems combine ultra-low-power machine learning models, wireless communication, and smart sensors to monitor wound parameters like pH, temperature, and moisture continuously. Deep learning algorithms such as Efficient-Net and YOLOv8s-cls allow for precise wound classification and predictive analysis, while biocompatible materials such as chitosan-infused bio-patches allow for natural healing. Remote monitoring and on-demand drug delivery are also provided by the bandages, and they are hence ideal for telemedicine and chronic wound care. Though integration challenges, cost issues, and privacy concerns regarding data pose challenges, these technologies present a scalable, energy-efficient, and patient-friendly solution to wound care. Through the convergence of diagnostics, treatment, and connectivity, Artificial Intelligence (AI)-enabled smart bandages are a promising move towards more individualized, effective, and accessible healthcare interventions. This paper gives a comprehensive review of the technologies, applications, and future directions of Artificial Intelligence (AI)-powered smart bandages.

Brain hemorrhage is a critical condition that needs quick and precise response and diagnosis for timely treatment. Traditional methods like CT and MRI scans depend on expert interpretation, which can be time-consuming and prone to errors. This study introduces an automated framework with deep learning to detect and classify brain hemorrhages. By utilizing convolutional neural networks (CNNs), the system recognizes important features in medical images and classifies hemorrhages into types such as intracerebral, subarachnoid, subdural, and epidural. Trained and tested on brain scan datasets, the framework depicts the potential of deep learning to deliver quick and accurate diagnoses, avoiding delays and enhancing patient outcomes significantly.

This project introduces a cutting-edge solar-powered rover designed to revolutionize environmental analysis and boost agricultural productivity. Featuring advanced monitoring and control systems, the rover incorporates high-performance solar panels, a sophisticated battery management system, and adaptable operational modes to maintain optimal performance in varying conditions. Outfitted with precise sensors for analyzing water quality (pH, turbidity, TDS), soil moisture, and weather metrics (temperature, humidity), it provides an extensive suite of environmental monitoring capabilities, complemented by real-time data recording. A sleek, user-centric web dashboard and mobile app facilitate effortless interaction, offering customizable interfaces, remote management, and Bluetooth connectivity. The integration of machine learning enhances predictive analytics and dynamic decision-making, while autonomous navigation with obstacle avoidance ensures smooth traversal across challenging terrains. This pioneering solution redefines agricultural efficiency, fostering sustainable practices through intelligent automation and actionable insights.

This paper presents a Discrete Wavelet Transform (DWT)-based approach for medical image fusion, implemented using Verilog HDL and verified with Xilinx ISE. The methodology involves fusing CT and MRI images using the Haar wavelet transform and validating the results through simulation and synthesis. Tools such as MATLAB are used for cross-verification. The objective is to enhance diagnostic accuracy and support medical imaging applications through efficient hardware design.