Sukhairi Sudin
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Preferred name
Sukhairi Sudin
Official Name
Sukhairi, Sudin
Alternative Name
Sudin, S.
Main Affiliation
Scopus Author ID
56572705200
Researcher ID
GFW-2221-2022

Research Output

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Cloud-based System for University Laboratories Air Monitoring

2020-09-21 , Abu Hassan Abdullah , Sukhairi Sudin , Mustafa M.H. , Fathinul Syahir Ahmad Sa'ad , Khairul Azwan Ismail , Muhammad Aizat Abu Bakar , Mohamed Elshaikh Elobaid Said Ahmed , Abdul Ghapar Ahmad , Zahari Awang Ahmad , Sara Yasina Yusuf

Indoor air such as house, shopping complex, hospital, university, office and hotel should be monitor for human safety and wellbeing. These closed areas are prone to harmful air pollutants i.e. allergens, smoke, mold, particles radon and hazardous gas. Laboratories in university are special room in which workers (student, technician, teaching/research assistants, researcher and lecturer) conduct their works and experiment. The activities and the environment will generate specific air pollutant which concentration depending to their parameters. Anyone in the environment that exposure to these pollutants may affect safety and health issue. This paper proposes a study of development of a cloud-based electronic nose system for university laboratories air monitoring. The system consists of DSP33-based electronic nose (e-nose) as nodes which measure main indoor air pollutant along with two thermal comfort variables, temperature and relative humidity. The e-noses are placed at five different laboratories for acquiring data in real time. The data will be sent to a web server and the cloud-based system will process, analyse using Neuro-Fuzzy classifier and display on a website in real time. The system will monitor the laboratories air pollutants and thermal comfort by predict the pollutant concentration and dispersion in the area i.e. Air Pollution Index (API). In case of air hazard safety (e.g., gas spills detection and pollution monitoring), the system will alert the security by activate an alarm and through e-mail. The website will display the API of the area in real-time. Results show that the system performance is good and can be used to monitor the air pollutant in the university laboratories.

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Hand-held shelf life decay detector for non-destructive fruits quality assessment

2024 , Nurul Syahirah Khalid , Nordiana Shariffudiin , Shazmin Aniza Abdul Shukor , Humairah Mansor , Masturah Tunnur Mohamad Talib , Sukhairi Sudin , Ismail I. Ibrahim , Mohd Zamri Hasan , Siti Marhainis Othman , N.D.N Dalila , M.Thaqif B.N Ashimi

Perishable food such as fruits have a limited shelf life and can quickly degrade if not properly stored. One method for detecting decay in these foods is the use of ethylene gas. Ethylene is a naturally occurring hormone that is released by fruits as they ripen. By measuring the levels of ethylene in the storage area, it is possible to detect when fruits and vegetables are starting to degrade. This information can then be used to act, such as removing spoiled produce and adjusting storage conditions, to extend the shelf life of the remaining products. By utilizing ethylene gas for early detection of decay, it is possible to improve food safety and reduce food waste. The project aims to utilized ethylene gas from perishable food such as fruits before decay. This project proposed portable or hand-held detection ethylene gas by including temperature and humidity. The sensor will be measuring the level of ethylene gas, temperature and humidity. Next, machine learning method; K-Nearest Neighbour(KNN) were used to evaluate the accuracy of the proposed system. This project, a hand-held decay detector for perishable food products is believed can help to prevent food waste by detecting early signs of spoilage in fruits.

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Rssi-based for device-free localization using deep learning technique

2020-06-01 , Abdul Syafiq Abdull Sukor , Latifah Munirah Kamarudin , Ammar Zakaria , Norasmadi Abdul Rahim , Sukhairi Sudin , Hiromitsu Nishizaki

Device-free localization (DFL) has become a hot topic in the paradigm of the Internet of Things. Traditional localization methods are focused on locating users with attached wearable devices. This involves privacy concerns and physical discomfort especially to users that need to wear and activate those devices daily. DFL makes use of the received signal strength indicator (RSSI) to characterize the user’s location based on their influence on wireless signals. Existing work utilizes statistical features extracted from wireless signals. However, some features may not perform well in different environments. They need to be manually designed for a specific application. Thus, data processing is an important step towards producing robust input data for the classification process. This paper presents experimental procedures using the deep learning approach to automatically learn discriminative features and classify the user’s location. Extensive experiments performed in an indoor laboratory environment demonstrate that the approach can achieve 84.2% accuracy compared to the other basic machine learning algorithms.

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Development of a Common Waste Combustion System for Generating Electricity at Remote Area

2023-01-01 , Zaini A.S.A. , Sukhairi Sudin , Haris F.A. , Nurul Syahirah Khalid , Fathinul Syahir Ahmad Sa'ad , Muhamad Khairul Ali Hassan , Ibrahim I.I.

Malaysia's daily amount of municipal solid waste (MSW) has rapidly increased. This causes the landfills number to increase due to inadequate waste management systems. Apart from that, Malaysia depends on non-renewable resources for electricity generation which could have a significant effect on the environment. Therefore, this study proposed to reduce landfills in Malaysia in a proper way and supply electricity using municipal solid waste as a renewable resource. In this study, the combustion of municipal solid waste (MSW) produces steam, which will rotate a turbine that is connected to a dynamo. Then, the energized dynamo will supply electricity to appliances including a direct current motor. The motor shaft then rotates the dynamo shaft in the pulley system which causes the electricity to flow in a closed loop. In this system, a pressurized container is crucial to produce sufficient steam. Based on the experimental setup, it was observed that continuous electricity was successfully achieved by looping the system using a pulley on the dynamo and motor.

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Automated Negative Lightning Return Strokes Characterization Using Brute-Force Search Algorithm

2022-04-01 , Haris F.A. , Kadir M.Z.A.A. , Sukhairi Sudin , Jasni J. , Johari D. , Zaini N.H.

Over the years, many studies have been conducted to measure, analyze, and characterize the lightning electric field waveform for a better conception of the lightning phenomenon. Moreover, the characterization mainly on the negative return strokes also significantly contributed to the development of the lightning detection system. Those studies mostly performed the characterization using a conventional method based on manual observations. Nevertheless, this method could compromise the accuracy of data analysis due to human error. Moreover, a longer processing time would be required to analyze data, especially for larger sample sizes. Hence, this study proposed the development of an automated negative lightning return strokes characterization using a brute-force search algorithm. A total of 170 lightning electric field waveforms were characterized automatically using the proposed algorithm. The manual and automated data were compared by evaluating their percentage difference, arithmetic mean (AM), and standard deviation (SD). The statistical analysis showed a good agreement between the manual and automated data with a percentage difference of 1.19% to 4.82%. The results showed that the proposed algorithm could provide an efficient structure and procedure by reducing the processing time and minimizing human error. Non-uniformity among users during negative lightning return strokes characterization can also be eliminated.

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Publication

Development of Cloud-based Electronic Nose for University Laboratories Air Monitoring

2020-12-18 , Abu Hassan Abdullah , Muhammad Aizat Abu Bakar , Saad F.S.A. , Sukhairi Sudin , Zahari Awang Ahmad , Ismail K.A. , Mohamed Elshaikh Elobaid Said Ahmed

Indoor air in area such as house, shopping complex, hospital, university, office and hotel should be monitor for human safety and wellbeing. These closed areas are prone to harmful air pollutants i.e. allergens, smoke, mold, particles, radon and hazardous gas. Laboratories in university are special room in which workers (student, technician, teaching/research assistants, researcher and lecturer) conduct their works and experiments. These activities and the environment will generate air pollutants which concentration depending on their parameters. Anyone in the environment that exposure to these pollutants may have safety and health issue. This paper propose a study of development of a cloud-based electronic nose system for university laboratories air monitoring. The system consists of five dsPIC33-based electronic nose (e-nose) as node which measure main indoor air pollutants along with two thermal comfort variables, i.e. temperature and relative humidity. The nodes are placed at five different laboratories for acquiring air pollutants data in real time. The data will be sent to a web server and the cloud-based system will process, analyse and display by a website in real time. The system will monitor the laboratories main air pollutants and thermal comfort by forecast the contaminants concentration and dispersion in the area. In case of air hazard safety (e.g., gas spills detection and pollution monitoring), the system will alert the security by activate an alarm and through e-mail. The website will display the Air Pollution Index (API) of the area in real-time. Results show that the system performance is good and can be used to monitor the air pollution in the university laboratories.

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Publication

Aquaculture monitoring system using multi-layer perceptron neural network and adaptive neuro fuzzy inference system

2024-01-01 , Abu Hassan Abdullah , Sukhairi Sudin , Saad F.S.A. , Muhamad Khairul Ali Hassan , Muhammad Imran Ahmad , bin Abdul Khalid K.A.

The water quality is the most important parameter for aquatic species health and growth. The condition is very critical and is essential to monitor continuously. Poor water quality will affect health, growth and ability of the animal to survive. These also affected their harvesting yields based on the amount and size of the animal. The main water parameters such dissolved oxygen (DO), pH, temperature, salinity and turbidity are monitored and control for good water quality. The data were acquired by the developed instrument and send wirelessly through GPRS/GSM module to cloud-based database. The data were retrieved and the water quality is predicted using fuzzy logic and multi-layer perceptron. MATLAB software was used for the model which is developed based on Mamdani fuzzy interface system. The membership functions of fuzzy were generated, as well as the simulation and analysis of the water quality system. Results show that the performance of fuzzy method can improve system performance in monitoring the water quality. This system also provides alert signals to farmers based on specific limit value for the water quality parameters. This will help the breeders to make certain adjustment to ensure suitable water quality for the aquaculture system.

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Cycling performance prediction based on cadence analysis by using multiple regression

2021-12-01 , Sukhairi Sudin , Aziz Naim Abdul Aziz , Fathinul Syahir Ahmad Sa'ad , Nurul Syahirah Khalid , Ismail Ishaq Ibrahim

This project examined the influence of the cadence, speed, heart rate and power towards the cycling performance by using Garmin Edge 1000.Any change in cadence will affect the speed, heart rate and power of the novice cyclist and the changes pattern will be observed through mobile devices installed with Garmin Connect application. Every results will be recorded for the next task which analysis the collected data by using machine learning algorithm which is Regression analysis. Regression analysis is a statistical method for modelling the connection between one or more independent variables and a dependent (target) variable. Regression analysis is required to answer these types of prediction problems in machine learning. Regression is a supervised learning technique that aids in the discovery of variable correlations and allows for the prediction of a continuous output variable based on one or more predictor variables. A total of forty days' worth of events were captured in the dataset. Cadence act as dependent variable, (y) while speed, heart rate and power act as independent variable, (x) in prediction of the cycling performance. Simple linear regression is defined as linear regression with only one input variable (x). When there are several input variables, the linear regression is referred to as multiple linear regression. The research uses a linear regression technique to predict cycling performance based on cadence analysis. The linear regression algorithm reveals a linear relationship between a dependent (y) variable and one or more independent (y) variables, thus the name. Because linear regression reveals a linear relationship, it determines how the value of the dependent variable changes as the value of the independent variable changes. This analysis use the Mean Squared Error (MSE) expense function for Linear Regression, which is the average of squared errors between expected and real values. Value of R squared had been recorded in this project. A low R-squared value means that the independent variable is not describing any of the difference in the dependent variable-regardless of variable importance, this is letting know that the defined independent variable, although meaningful, is not responsible for much of the variance in the dependent variable's mean. By using multiple regression, the value of R-squared in this project is acceptable because over than 0.7 and as known this project based on human behaviour and usually the R-squared value hardly to have more than 0.3 if involve human factor but in this project the R-squared is acceptable.

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Development of aquaculture water quality real-time monitoring using multi-sensory system and internet of things

2021-12-01 , Abu Hassan Abdullah , Fathinul Syahir Ahmad Sa'ad , Sukhairi Sudin , Zahari Awang Ahmad , Ahmad I. , Norlida Abu Bakar , Shafie Omar , Sulaiman S.F. , Mohd Hishamuddin Che Mat , Nurul Afidah Umoruddin , Johari B.H.

Water quality is an important parameter for the health and growth of aquatic species in aquaculture farming system. The threshold values of the water main parameters should be monitored continuously. Contaminated aquaculture water will affect the health, growth and ability of animals to survive. In addition, it will also affect the harvesting yields based on the number and size of the animals. To overcome this problem, the main water parameters, namely temperature, pH, Dissolved Oxygen and Electrical Conductivity are monitored in real-time using a multi-sensory system and the internet of things. Data is acquired by a developed instrument and transmitted wirelessly via a GPRS/GSM module to a web server database. The data obtained are analyzed and monitored through the website and in real-time. Therefore, corrective action could be taken immediately for contaminated water, indicated by water parameters out of range. The system also provides an early signal to farmers based on a specific range of water quality parameters values. This will help farmers make adjustments to ensure appropriate water quality for the aquaculture system.

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Intelligent irrigation system using rain water harvesting system and fuzzy interface system

2021-12 , Azremi Abdullah Al-Hadi , Sukhairi Sudin , Ahmad Z.A , Fathinul Syahir Ahmad Sa'ad , I Ahmad , Fadhilnor Abdullah , Wan Mohd Faizal Wan Nik , A. Deraman , N. M Maliki , S R S Kamaruzaman , S R Romle

Shortage of water has become a predominant problem all over the world as water plays an important role in agriculture, domestic and industry. In certain parts of the world, farmers face problems watering their crops especially during the dry season. Limited water resources with low efficiency greatly affect crop growth. Therefore, this study proposes an intelligent irrigation system using Rain Water Harvesting (RWH) and Fuzzy Interface System (FIS) for crops watering process. The RWH is a system that collects, centralises and stores rainwater, while the FIS uses temperature and soil moisture sensors to determine the time and amount needed for the watering process. Thus, the intelligent irrigation system will ensure the process of watering the crops to be efficient. The results of this study show that FIS can analyse temperature and soil moisture data, which improves the efficiency of crops watering process and the use of RWH will make it sustainable. The developed project is currently operating at the Institute of Sustainable Agro Technology, i.e. a university-owned agricultural research institute.

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