Aquaculture is an agricultural technology that combines aquaculture (fish farming activities) with hydroponic activities (planting crops without soil media) in one circulation. The most important element in aquaculture is the existence of fish, plants, and bacteria. These three elements form a mutually beneficial relationship or symbiotic mutualism. The main purpose of the aquaculture system is to maintain water quality and reduce ammonia levels from the water so that it can be utilized by other organisms. In addition, aquaculture can also save space and can produce two types of human food simultaneously, plants and livestock. Agricultural technology design with Aquaculture also uses the concept of Internet of Things (IoT) as information from sensors and sensors of value generator is accessible through applications installed on smartphones from anywhere with an Internet connection. Development of monitoring of aquaponic ecosystems with IoT systems was developed using a program using micro-controls to control temperature, humidity, pH levels and water pumps. There are some improvements made to this project.

Nowadays, everything is made simpler with information and communication technological advancements. It is preferable to track and monitor using devices rather than perform it manually. This resulted in the rapid growth of Internet of Things (IoT) technology and relevant markets. Low cost IoT products has made access to IoT much easier and desirable. These low cost IoT devices and related technologies are widely used in areas such as educational, transportation, tracking, inventory management and many more. The use of Arduino and RFID in the inventory management system lacks in some areas including hardware limitations. In conjunction to the limitation of using an Arduino and RFID technology, this project aims to develop an IoT based inventory management system that incorporates the uses of a NodeMCU and a load cell. In comparison of the NodeMCU to an Arduino, the NodeMCU stands out with the built in Wi-Fi module with much higher processor and additional properties of it being much smaller. While the use of a load cell is much more convenient as to suit all kinds of inventory management needs compared to the use of RFID that suits better to larger scale businesses with larger inventory and massive stocks. Towards the end, this project is expected to ease inventory management by the implementation of IoT with IoT Based Inventory Management System using Load Cell and NodeMCU. The project will generate the inventory count and automatically stores data in the cloud platform. These data can be accessed with internet connection. The project also alerts users when the inventory is low or high in balance. The output of the project is that the project’s working prototype was successfully developed. Overall, the project is a success as all the objectives of the project was successfully achieved.

A low cost, with high performance, reduced graphene oxide (RGO) Ultra-wide Band (UWB) array sensor is presented to be applied with a technique of confocal radar-based microwave imaging to recognize a tumor in a human brain. RGO is used to form its patches on a Taconic substrate. The sensor functioned in a range of 1.2 to 10.8 GHz under UWB frequency. The sensor demonstrates high gain of 5.2 to 14.5 dB, with the small size of 90 mm × 45 mm², which can be easily integrated into microwave imaging systems and allow the best functionality. Moreover, the novel UWB RGO array sensor is established as a detector with a phantom of the human head. The layers’ structure represents liquid-imitating tissues that consist of skin, fat, skull, and brain. The sensor will scan nine different points to cover the whole one-sided head phantom to obtain equally distributed reflected signals under two different situations, namely the existence and absence of the tumor. In order to accurately detect the tumor by producing sharper and clearer microwave image, the Matrix Laboratory software is used to improve the microwave imaging algorithm (delay and sum) including summing the imaging algorithm and recording the scattering parameters. The existence of a tumor will produce images with an error that is lower than 2 cm.

Rice bran is a by-product of the rice milling process. It contains a high concentration of protein. Rice brans are frequently utilized as feed cattle, fertilizer, and fuel. However, their application as human nutrition supplements is uncommon, and the necessary process for this purpose is yet to be established, including the drying process. This study aims to evaluate the effect of the spray-drying parameters, the inlet temperature, inlet flowrate, and inlet air flowrate, on rice bran protein (RBP) powder and optimize it using response surface methodology (RSM). A thermal water-based extraction method was utilized prior to the drying process. The correlation between the spray-drying parameters, i.e., the inlet temperature (120 to 210 °C), feed flowrate (5 to 55%), and air flowrate (246 to 670 L/h), and the RBP yield were investigated. The quality of the RBP powder was evaluated based on acid amino profiling in the mixture through de novo peptide sequencing. The optimized operating conditions for the maximum yield of RBP powder (25.7 g RBP/100 g RRB) are 178 °C, feed flowrate of 25%, and air flowrate of 450 L/h. The main peptides that contribute to RBP powder protein are globulin and glutelin; meanwhile, prolamin is believed to degrade during the drying process. The process also produced protein sugar, helping to produce fine particles powder without the drying agent.

The number of road accidents in Malaysia shows a steady increment from 2010 to 2019, reported by the Ministry of Transport Malaysia. This project aims to develop a system to prevent an accident by detecting aggressive driving. If an accident occurred, this system would send an alert for an immediate response, which is crucial to reduce the fatality rate. An accelerometer is utilized to detect aggressive driving and accident events. The method to detect aggressive driving is by determining an abrupt change in acceleration. For accident detection, the vehicle tilt angle and acceleration are monitored. An ESP32 SIM800L microcontroller processes the inputs and alert a web-based cloud service and a set phone number by Short Message Service (SMS). The microcontroller is used due to the embedded Global System for Mobile Communications (GSM) and other wireless communication modules. The small form factor gives an advantage in terms of mounting location flexibility. The alert contains the type of event, time, and location. This report contains the development of the proposed system, which includes the simulation for the system circuit and motion simulation. Accident detection, falls, SMS alerts and online alerts are consistently successful, while aggressive driving detection is inconsistent. Live tracking does not directly work during these detections. In conclusion, this project successfully detects accidents and sends alerts via SMS and internet using a Subscriber Identity Module (SIM) card.

Background and Objective: Cervical cancer remains one of the leading causes of death among women worldwide, particularly in regions with limited access to early screening. Pap smear screening is the primary tool for early detection, but manual interpretation is labor-intensive, subjective, and prone to inconsistency and misdiagnosis. Accurate segmentation of cervical cell nuclei is essential for automated analysis but is often hampered by overlapping cells, poor contrast, and staining variability. This research aims to develop an improved algorithm for accurate cervical nucleus segmentation to support automated Pap smear analysis. Method: The proposed method involves a combination of adaptive gamma correction for contrast enhancement, followed by Otsu thresholding for segmentation. Post-processing is performed using adaptive morphological operations to refine the results. The system is evaluated using standard image quality assessment metrics and validated against ground truth annotations. Result: The results show a significant improvement in segmentation performance over conventional methods. The proposed algorithm achieved a Precision of 0.9965, an F-measure of 97.29%, and an Accuracy of 98.39%. The PSNR value of 16.62 indicates enhanced image clarity after preprocessing. The method also improved sensitivity, leading to better identification of nuclei boundaries. Advanced preprocessing techniques, including edge-preserving filters and multi-Otsu thresholding, contributed to more accurate cell separation. The segmentation method proved effective across varying cell overlaps and staining conditions. Comparative evaluations with traditional clustering methods confirmed its superior performance. Conclusions: The proposed algorithm delivers robust and accurate segmentation of cervical cell nuclei, addressing common challenges in Pap smear image analysis. It provides a consistent framework for automated screening tools. This work enhances diagnostic reliability in cervical cancer screening and offers a foundation for broader applications in medical image analysis.