The Terahertz (THz) spectrum is the next frontier for efficient imaging applications and high-bandwidth wireless communication. A high-powered signal is imperative for the improvement of image resolution. The SiGe HBTs (heterojunction bipolar transistors) low output power level is one of the fundamental difficulties in the development of systems at high frequency and hence the importance of amplification at THz frequency range. This research is about designing, modeling, and simulating a 3-stage, 4-way power combined solid state PA (SSPA). The 3-stage design performance was optimized using a transmission line whose values were chosen optimally to ensure low loss. A single unit of the SSPA contains three stages and by using a splitter and combiner, 4 units of the SSPA were combined to give the desired output power. Simulations were performed using ADS Keysight and a gain of 30dB, saturation power out of 18.847dBm, and PAE (PAE) of 5.7% was achieved. This is a 28.8% increase in gain, an 11.36% increase in PAE, and a 3.3 % increase in saturation power compared to state-of-the-art results.

The livestock sector is an essential component of the global economy. Farmers are losing interest in this profession as animals suffer from a variety of bad health conditions, unpredictable fatal illnesses. The temperature and humidity of the farm have a stronger impact on the health of the cattle. Monitoring the health of dairy cattle and the environmental state of farms can assist to tackle these concerns. In this research, we describe a system that allows farmers to monitor livestock health metrics including body temperature as well as environmental data like temperature, humidity, light levels, quality of air, and dampness. The device will automatically maintain optimum environmental conditions for the livestock in addition to monitoring environmental parameters. Our suggested method would assure adequate water supply to the cattle in order to increase milk production supply. Three ESP32 microcontrollers are utilized as clients in this system to sense different health and environmental aspects, and an ESP32 microcontroller is used as a server to wirelessly link the three ESP32 clients. Through a web application, health and environmental factors may be accessed on the internet. It may also be accessed remotely on a mobile phone. This revolution in advanced technological farm automation will help to improve productivity by reducing the need for human intervention. Finally, the proposed solution would assist in boosting productivity while saving the farmer's time and effort.

Breast cancer is a leading cause of mortality among women worldwide, particularly in developing countries. Accurate and early diagnosis is crucial to improve patient outcomes. This study compares the performance of three supervised machine learning algorithms—Support Vector Machine (SVM), K-Nearest Neighbor (KNN), and Random Forest (RF)—in classifying breast cancer cases using the Breast Cancer Wisconsin dataset. The dataset consists of 569 instances with 33 features, categorized into malignant and benign classes. Each method was evaluated based on its classification accuracy. The results show that Random Forest achieved the highest accuracy at 94.07%, outperforming SVM with 90.06% and KNN with 90.00%. The findings suggest that Random Forest provides the most reliable performance for breast cancer classification within the scope of this dataset. This study highlights the importance of selecting an appropriate algorithm to enhance diagnostic precision and recommends Random Forest as an effective method for similar classification tasks.

This project presents an architecture for approximate matching in Content Addressable Memory (CAM) systems, which are essential for search-intensive applications such as networking and genomic analysis. Traditional CAM designs often suffer from high power consumption and reduced performance. To address these challenges, this work proposes a low complexity sensing scheme that integrates transmission gate logic and a modified inverter architecture using the SAPON technique. The primary objective is to improve power efficiency and write ability in CAM systems. By incorporating the SAPON technique, the design significantly reduces power consumption, enhancing energy efficiency while maintaining high-speed functionality. Transmission gate logic improves write ability, facilitating smoother data operations, particularly in applications requiring approximate matching. The proposed design is thoroughly validated through extensive simulation using the GPDK 45nm library in Cadence Virtuoso. The results show substantial reductions in power consumption and delay, alongside improvements in performance. The optimized CAM architecture demonstrates high tolerance for mismatches, making it ideal for applications such as DNA sequencing and network routing. This CAM design provides a scalable and energy efficient solution for modern computing environments, where performance and low power consumption are critical. Overall, this design offers a reliable and energy-efficient solution for accelerating search operations in data-driven fields, positioning it as an advancement in content-addressable memory technology.

For environmental sustainability and energy security, renewable sources must be incorporated into sustainable energy solutions. Machine learning (ML) techniques are explored in this study to optimize the adoption of renewable energy sources in Bangladesh. Specifically, it proposes a three-phase methodology: (1) forecasting demand for nonrenewable energy, (2) predicting renewable energy availability and costs, and (3) analyzing potential savings and environmental benefits. Utilizing decision trees and random forests, this study presents a comparative analysis of energy demand and cost predictions, contributing to a data-driven framework for energy transition. The results indicate that strategic adoption of renewable energy can mitigate Bangladesh’s electricity shortages while reducing dependency on fossil fuels. Machine learning plays a crucial role in energy optimization by accurately forecasting energy demand and availability, allowing for better resource allocation. It helps identify patterns and trends in energy consumption, enabling more efficient integration of renewable sources. By using techniques like decision trees and random forests, machine learning models can optimize energy production and distribution, ultimately leading to more sustainable and cost-effective energy systems.The findings provide policymakers and energy planners with insights to enhance sustainability efforts.