The mushroom farming industry is on the rise and there is a growing demand for high-quality, sustainable production methods. However, mushroom cultivation can be challenging for growers, as it requires careful environmental control and can be labour-intensive. This paper presents the results and analysis of a mushroom classification system, designed to accurately classify two types of mushrooms: Shiitake and oyster mushrooms. The system utilizes advanced machine learning algorithms and an extensive dataset to achieve accurate classification results. Technology replaces human inspection with computer vision, providing farmers with a quick and precise tool for classifying various mushroom species. The augmented and labelled mushroom dataset is divided into three distinct subsets: validation, testing and training. In this study, 80% of the data is allocated for training, while 20% is reserved for testing. The outcomes have proven the efficacy of You Only Look Once version 8 (YOLOv8) model, with an astounding accuracy of over 90%. This study suggests that the YOLOv8 model has the potential to be an accurate method for distinguishing different varieties of mushrooms. Farmers can significantly reduce the time and effort they previously expended on manual inspections by utilizing this approach.

The advancement of mobile communications has rapidly expanded in recent years, with the invention of transmitting and receiving system technology. The use of multiple antennas, also known as Multiple-Input Multiple-Output (MIMO) technology, is a key feature in modern wireless communication systems such as 5G. MIMO utilizes multiple antennas at both the transmitter and receiver to improve the system's performance in terms of data rate, link reliability, and spectral efficiency. In this paper, a block coding technique, was deployed to enhance the reliability of data transmission and introducing redundancy and error correction capabilities. These techniques involve encoding data into blocks and adding redundancy bits, which allow the receiver to detect and correct errors that may occur during transmission. The encoding technique, called the Asymmetric Arithmetic Coder (AAC), and it is supported in 5G system to provide high speed signal transmission in multiple users’ environment. For retrieving a smooth signal at the receiver, a filter was introduced to improve its efficiency of the wireless link. However, the existing filter in the system, caused the increasing of the peak-to-average-power-ratio (PAPR) at the transmitter system. Thus, the block coding technique was implemented to increase the efficiency and reduced the power ratio. The most common block coding technique for wireless communication is AAC technique. The AAC technique was founded to reduce the power ratio and further improve the signal diversity. Recently, for the application of 5G, the AAC was enhanced by adding the number of symbol and improved the PAPR. It is called the Enhancement Asymmetric Arithmetic Coding (EAAC) technique. The EAAC contributes to the lower value of the PAPR. With the use of EAAC as the block coding technique, the transmitter is said to support the system and thus improved the PAPR measurements by 5.5%. Lastly, the reduction of PAPR contributes to minimize the distortion and enhanced spectral efficiency for wireless communication system.

A preamplifier is a device that produces sound based on the musician’s requirements and expectations. The existing commercial preamplifiers are usually made in

Meat is considered one of the most nutritious animal food products as it contains carbohydrates, proteins, lipids, vitamins, and minerals. The level of freshness in meat plays a significant role in determining its quality for consumption. It is therefore crucial to maintain meat quality to ensure that consumers receive high-grade meat. Traditionally, meat quality assessment has been done visually by comparing actual meat with reference photographs of each meat class. However, this subjective and time-consuming process has its limitations. To overcome these limitations, an automated image processing-based system is required to detect and assess meat quality. In this project, a convolutional neural network (CNN) approach will be implemented to detect the freshness of meat. The meat samples used in the project will be categorized into three types: fresh, half-spoiled, and spoiled. The objective of this project is to develop an image-processing method that analyzes digital pictures to determine the quality of meat. The system's implementation aims to achieve consistent and objective measurements; as different human examiners may yield different results. This will be accomplished by developing an image classification system that utilizes the CNN algorithm along with image processing techniques. By utilizing the developed model, it can be concluded that the method is effectively applied in the classification of meat freshness with an accuracy rate of 80%.