Mohamad Farid Misnan’s research while affiliated with MARA University of Technology and other places

Automated colour sorting, aided by mobile robots, is widely prevalent in the current manufacturing industry. Obstacles, such as fluctuating light conditions and camera angles, frequently hinder this procedure. Creating a colour sorting robot is a complex and time-consuming task, especially due to the vulnerability of the RGB colour space to detection errors in extreme brightness or darkness. In response to these concerns, we introduce a mobile robot that operates on the robot operating system (ROS) platform and incorporates OpenCV. This robot employs the hue, saturation, and value (HSV) colour space model for its image processing capabilities in recognising the colours and Welzl’s algorithm for the ball’s diameter estimation. The robot’s performance was assessed across various luminous fluxes and camera tilt angles. It demonstrated exceptional performance at 64 lm and a tilt angle of 40 degrees, achieving an average accuracy of 87.5% for detecting the colour of the ball, and 81.25% for determining its location based on colour. For the ball’s diameter estimation, it was found that the best estimation was received at 64 lm and 30 degrees, with both 96.32%.

Dynamic load analysis of Ultra-High-Performance Concrete (UHPC) beams is crucial, given the material's widespread use in bridges, enabling engineering feats like 100 meter single-span bridges. Structural vibration monitoring aids in evaluating a structure's ability to withstand dynamic loads, employing finite element (FE) model analysis for verification and enhancement. This study utilizes ANSYS for finite element modelling (FEM) and modal analysis, assessing the UHPC beam's structural integrity. An undamaged UHPC beam model validates dynamic properties, reducing disparities between analytical and experimental results. Modal properties of the first cracked and damaged UHPC beam are updated to represent actual conditions. Vibration analysis reveals inherent vibration modes, frequencies, and forms. Structural stiffness analysis verifies the relationship between stiffness and dynamic qualities. Experimental data updates the UHPC beam model, establishing a connection between structural stiffness and natural frequency under various conditions. In conclusion, ANSYS was employed for FEM, modal analysis, and parameterization verification, revealing the importance of accurate UHPC feature identification and meshing size. Discrepancies highlight the need for experimental tests, reducing differences between FEM and empirical findings. The numerical analysis in ANSYS underscores the correlation between structural stiffness and natural frequency, enabling precise structural health monitoring for UHPC beam damage or deterioration identification.

The research is led to the development of a smart sensory network adaptive traffic light to optimise traffic flow and reduce congestion on Jalan Persisiran Seri Alam in Pasir Gudang. With variable time management, this system can reduce the time spent at traffic junctions and carbon emitted by vehicles, thus supporting the SDG 11 for low carbon smart city. By integrating the dynamic duration of each traffic signal on each dense pathway, this system can help to reduce traffic congestion, fuel consumption, and CO2 emissions. It can also ensure that traffic is managed in a more efficient manner, thus improving the quality of life for the people of Pasir Gudang. The success of this system will be a major step towards achieving the goal of a low carbon smart city, as it will help to reduce air pollution, noise pollution, and improve road safety. Additionally, it can help to improve the efficiency of traffic flow, leading to better traffic management and reduced congestion.

Aquaponics is one of the agricultural production technological advancements that should be publicized. Climate change, population expansion, water scarcity, soil degradation, and food security are just a few of the world's most pressing issues. Aquaponics, which is a closed-loop system that combines hydroponics and aquaculture, may be able to deal with issues like climate change, population expansion, water scarcity, soil degradation, and food security. Thus, this paper aims to design and construct an aquaponics system that combines fish farming with plant growth. The system used a variety of sensors, including temperature and humidity sensors, ultrasonic sensors, and pH sensors, as well as microcontrollers like Arduino, to monitor and manage the water quality, plant humidity, and other variables. When the sensor identifies any abnormal circumstance, early warnings in the form of SMS and push notifications are immediately given to the user to ensure a healthy growing environment for fish and plants. The Arduino Development Environment (IDE) software is used to write a programme that connects the microcontroller to various sensors and other devices. pH sensors, temperature and humidity sensors, ultrasonic sensors, liquid crystal displays (LCDs), and GSM circuits are all built and connected to the system. A GSM notification message is sent to a mobile phone when the pH, temperature, and ultrasonic sensor findings are out of range. The data from this system's monitoring reveals the values that have been taken on a daily basis. The graph demonstrated that the plant's growth is increasing every day.

Water quality is one of the most highly debated issues worldwide at the moment. Inadequate water supplies affect human health, hinder food production, and degrade the environment. Using contemporary technology to analyze pollution statistics can help solve pollution issues. One option is to take advantage of advancements in intelligent data processing to conduct hydrological parameter analysis. To perform conclusive water quality studies, a lot of data is necessary. Unfilled data (information gaps) in the long-term hydrological data set may be due to equipment faults, collection schedule delays, or the data collection officer’s absence. The lack of hydrological data skews its interpretation. Therefore, interpolation is used to recreate and fill missing hydrological data. From 2012 to 2017, the Klang River’s biochemical oxygen demand (BOD) in Selangor, Malaysia, was sampled. This study examined three methods of interpolation for their effectiveness using the MATLAB software: piecewise cubic hermite interpolating polynomial (PCHIP), cubic Spline data interpolation (Spline), and modified Akima partitioned cubic hermite interpolation (Makima). The accuracy is assessed using root mean square error (RMSE). All interpolation algorithms offer excellent results with low RMSE. However, PCHIP delivers the best match between interpolated and original data.

Water is a critical component of life on this planet, as humans, animals, and plants all rely on water supplies for survival. Regrettably, human activity has contaminated water sources. As a means of remedying this situation, each river should have an intelligent system that continuously monitors the water quality index at each water source. However, testing and establishing a realistic acceptability level for all water quality and control parameters takes time. Some of the data on the parameter’s values were also not immediately available. To address this problem, an estimation based on past samples and readings was applied. The process also implemented an exemplary computer system-based method known as an artificial neural network (ANN) Which has evolved into a promising and versatile tool for data prediction over the last few years. This article analyses and compares a host of ANN techniques that have been researched or used in the past to determine water quality. According to this study’s review of available literature, there are two types of ANN computation engines: Feedforward network systems and recurrent network systems that can be used for the purposes of determining water quality. Each method has advantages and disadvantages which were studied and this study hypothesises that with right techniques will be able to accurately predict and account for river water quality.