Abu Hassan Abdullah
Thumbnail Image
Preferred name
Abu Hassan Abdullah
Official Name
Abdullah, Abu Hassan
Main Affiliation
Web Site
Scopus Author ID
26029734700

Research Output

Filters

23
Reset filters

Settings
Now showing 1 - 10 of 23
Thumbnail Image
Publication

Human breathing assessment using Electromyography signal of respiratory muscles

2017-04-05 , Ahmad Nasrul Norali , Abu Hassan Abdullah , Zulkifli Zakaria , Norasmadi Abdul Rahim , Sathees Kumar Nataraj

Breathing is one of the human physiological activities that catch the interest of researchers especially in the area of medical diagnosis and human physiological performance. Mostly, the measurement and data are in form of pressure and volume variables of air intake and outflow. However, using airflow pressure and volume require installment of certain sensor usually on subject's mouth which could discomfort the subject. Another possible method for assessing the breathing pattern is through human respiratory muscles, which are via electromyography signal. In this paper, experiment is done on acquiring the electromyography signal from four respiratory muscles namely sternocleidomastoid, scalene, intercostal muscle and diaphragm with subjects performing four different breathing tasks. Analysis-of-variance test has been done on the Electromyography (EMG) feature data of the four muscles for the four breathing tasks. Results of ANOVA analysis, show that the p-values has a significant different in the four breathing tasks for each muscle.

View
Thumbnail Image
Publication

Development of portable, application specific electronic nose for agriculture

2014 , Abu Hassan Abdullah

Research groups around the world are working to develop electronic nose systems that are able mimicking the functions and operations of the human nose. The instrument is used to identify and classify different types of odour or smell. The instrument will complement the existing odour assessment techniques; human sensory panels and Gas Chromatography Mass Spectrum (GC-MS) analysis which require long training time and detailed operating procedures. However most of the generic instruments are of laboratories type which are costly and may not operate efficiently for every possible application. The instruments’ broad non-specific sensor arrays’ will limit the detection capabilities. The existing portable instruments in the market are still lacking in reliability, data processing capabilities and quite costly. Therefore, the purpose of this research is to develop a portable Application Specific Electronic Nose (ASEN) to improve their capabilities. The developed instrument uses specific selected sensor arrays which were identified based on experiment and key volatile compounds of the target odorant. Humidity and temperature sensor are also being included in the instrument to measure the environmental condition. The instrument utilises multivariate statistical analysis (PCA, LDA and KNN) and Artificial Neural Network (ANN) as well as an embedded ANN classification algorithm for the data processing. This will increase the instrument’s capability while the portability will minimise the size, cost and operational complexity. A commercial instrument (Cyranose C320 from Smith Detection) is used to evaluate the performance of the instrument. The instrument was successfully developed, tested and calibrated odour samples with variable concentrations. The instrument provides a feasible alternative for non-destructive testing system for the odour samples. The results revealed that the developed instrument is able to identify, discriminate and classify the odour samples with an acceptable percentage of accuracy. This will contribute significantly to acquiring a new and alternative method of using the instrument for agriculture applications i.e., plant disease detection, food quality assurance and poultry farm malodour monitoring. The future works include the development of specific sensors for the application and simplified the training process i.e., performs on-line ANN training by the instrument itself.

View
Thumbnail Image
Publication

Fuzzy logic based prediction of micronutrients demand for harumanis mango growth cycles

2021-12-01 , Wan Mohd Nooriman Wan Yahya , Abu Hassan Abdullah , Norasmadi Abdul Rahim , Erdy Sulino Mohd Muslim Tan

Harumanis is a famous green eating mango cultivar that has been commercially cultivated in Malaysia's state of Perlis. A variety of nutrients are found in soil, all of which are necessary for plant growth. Micronutrients such as Nitrogen (N), Phosphorus (P), and Potassium (K) are essential for Harumanis mango (Mangifera Indica) to growth. The importance of soil fertility in achieving high plant productivity and quality cannot be overstated. It should be used in a moderate amount and in a balanced manner. Predicting appropriate nutrients and the right timing to satisfy the tree's demands is critical. The aim of this study is to create a fuzzy logic-based system to analyse the results of soil tests for nitrogen (N), phosphorus (P), and potassium (K) in the Harumanis mango orchard. The interpreted data are used to estimate N-P-K nutrient levels and indicate the optimal fertilizer solution and application timing for each Harumanis growth stages. The system utilizes Fuzzy Logic Control (FLC) to predict the nutrients demand for Harumanis mango growth. Results shows the system able to calculate and predict values of required N-P-K fertilizer for optimal growth. Thus, assist farmers in predicting the proper amount of N-P-K to apply to Harumanis mango soil.

View
Thumbnail Image
Publication

Multiple-criteria decision analysis for effect of shoot growth at difference combination nutrient fertilizer NPK for Harumanis mango

2023 , Erdy Sulino Mohd Muslim Tan , Norasmadi Abdul Rahim , Marni Azira Markom , Abu Hassan Abdullah , Allan Melvin Andrew , Aimi Salihah Abdul Nasir

It is vital to have the correct fertiliser arrangement for effective tree development, fruit yield, and essential fruit quality. The amount of fertiliser suggested with adequate nutrition will be maintained in the soil to supply the needs of the trees as they grow throughout the various growth stages. This study evaluated the effect of different combinations of Nitrogen(N), Phosphorus(P), and Potassium(K) on the vegetative flush physiology of Harumanis mango (Mangifera Indica. L). Single and combinations of N (511g), P(511g), and K(255g) fertilisers were used, which were N, P, NP, and NPK throughout May 2021. The results revealed that the minimum number of mature green leaves and a higher number of healthy panicles were observed in the NPK-treated plants. Moreover, NPK treatment showed the lowest malformation intensity percentage compared to other fertiliser treatments. The data were analysed to obtain the best regrowth pattern of shoots using Multiple-Criteria Decision Analysis (MCDA) techniques. The results on the pattern of regrowth after pruning when federalised with NPK fertiliser showed that the maximum percentage of total vegetative flush was 87.5% and the remaining 12.5% did not reach a satisfactory level according to the MCDA analysis.

View
Thumbnail Image
Publication

Improved classification of orthosiphon stamineus by data fusion of electronic nose and tongue sensors

2010 , Ammar Zakaria , Ali Yeon Md Shakaff , Abdul Hamid Adom , Mohd Noor Ahmad , Masnan, Maz Jamilah , Abdul Hallis Abdul Aziz , Nazifah Ahmad Fikri , Abu Hassan Abdullah , Latifah Munirah Kamarudin

An improved classification of Orthosiphon stamineus using a data fusion technique is presented. Five different commercial sources along with freshly prepared samples were discriminated using an electronic nose (e-nose) and an electronic tongue (e-tongue). Samples from the different commercial brands were evaluated by the e-tongue and then followed by the e-nose. Applying Principal Component Analysis (PCA) separately on the respective e-tongue and e-nose data, only five distinct groups were projected. However, by employing a low level data fusion technique, six distinct groupings were achieved. Hence, this technique can enhance the ability of PCA to analyze the complex samples of Orthosiphon stamineus. Linear Discriminant Analysis (LDA) was then used to further validate and classify the samples. It was found that the LDA performance was also improved when the responses from the e-nose and e-tongue were fused together.

View
Thumbnail Image
Publication

Electronic Nose Testing for Confined Space Application Utilizes Principal Component Analysis and Support Vector Machine

2020-12-18 , Muhammad Aizat Abu Bakar , Abu Hassan Abdullah , Wan Azani Wan Mustafa , Zol Bahri Razali , Syahrul Affandi Saidi , Mohamed Mydin Hj M.Abdul Kader , Aman M.N.S.B.S.

A confined space has a limited space for entry and exit but it is large enough for workers to enter and perform work inside. It is not designed for continuous occupancy because it can contribute atmospheric hazards accidents that threaten the worker safety and industry progress. In this work, we reported the testing an instrument to assist workers for atmosphere testing during pre-entry. An electronic nose (e-nose) using specific sensor arrays is the integration between hardware and software that able to sense different concentrations of gases in an air sample using pattern recognition techniques. The instrument utilizes multivariate statistical analysis which is Principal Component Analysis (PCA) for discriminate the different concentrations of gases and the Support Vector Machine (SVM) to classify the acquired data from the air sample. The instrument was successfully tested using diesel, gasoline, petrol and thinner. The results show that the instrument able to discriminate an air sample using PCA with total variation for 99.94%, while the classifier success rate for SVM indicates at 98.21% for train performance and 95.83% for test performance. This will contribute significantly to acquiring a new and alternative method of using the instrument for monitoring the atmospheric hazards in confined space to ensure the safety of workers during work progress in a confined space.

View
Thumbnail Image
Publication

Nutrient Requirements and Growth Response of Harumanis Mango (Mangiferaindica L.) during Vegetative Shoot Growth Stages: A Mitscherlich Law Analysis

2023-01-01 , Erdy Sulino Mohd Muslim Tan , Markom M.A.B. , Allan Melvin Andrew , Abu Hassan Abdullah , Norasmadi Abdul Rahim , Fathinul Syahir Ahmad Sa'ad , Imaduddin Helmi Wan Nordin , Abidin M.A.Z. , Jamil S.H.F.S.A. , Yogesh C.

This study investigates the nutrient requirements of Harumanis mango (Mangifera indica L) during different vegetative shoot growth stages by analyzing the soil nutrient test value-relative growth relationships. The research utilizes the Mitscherlich Law to model the response of mango yield in relation to varying nutrient levels. The data came from experimental plots, and the results show the asymptotic behavior of mango yield for three essential nutrients: nitrogen (N), phosphorus (P), and potassium (K). For vegetative shoot growth1, the asymptotic yield was estimated at 665.5 with a decline rate of -3.39 concerning N, -2.17 concerning P, and -1.35 concerning K. The coefficient of determination (R2) was 0.934, indicating a high goodness of fit for the model. Similar trends were observed for vegetative shoot growth2 and 3, where the asymptotic yields and nutrient decline rates varied accordingly. This study provides crucial insights into Harumanis mango nutrient needs across growth stages, aiding orchard management for sustainable yields. Applying the Mitscherlich Law enhances our understanding of how nutrients affect mango growth. These findings support targeted fertilization, boosting productivity and orchard efficiency. Future research can explore more growth factors and long-term nutrient impacts.

View
Thumbnail Image
Publication

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.

View
Thumbnail Image
Publication

A biomimetic sensor for the classification of honeys of different floral origin and the detection of adulteration

2011-08 , Ammar Zakaria , Ali Yeon Md Shakaff , Masnan, Maz Jamilah , Norazian Subari , Nazifah Ahmad Fikri , Abdul Hamid Adom , Mohd Noor Ahmad , Mahmad Nor Jaafar , Latifah Munirah Kamarudin , Abdul Hallis Abdul Aziz , Abu Hassan Abdullah , Supri A. Ghani

The major compounds in honey are carbohydrates such as monosaccharides and disaccharides. The same compounds are found in cane-sugar concentrates. Unfortunately when sugar concentrate is added to honey, laboratory assessments are found to be ineffective in detecting this adulteration. Unlike tracing heavy metals in honey, sugar adulterated honey is much trickier and harder to detect, and traditionally it has been very challenging to come up with a suitable method to prove the presence of adulterants in honey products. This paper proposes a combination of array sensing and multi-modality sensor fusion that can effectively discriminate the samples not only based on the compounds present in the sample but also mimic the way humans perceive flavours and aromas. Conversely, analytical instruments are based on chemical separations which may alter the properties of the volatiles or flavours of a particular honey. The present work is focused on classifying 18 samples of different honeys, sugar syrups and adulterated samples using data fusion of electronic nose (e-nose) and electronic tongue (e-tongue) measurements. Each group of samples was evaluated separately by the e-nose and e-tongue. Principal Component Analysis (PCA) and Linear Discriminant Analysis (LDA) were able to separately discriminate monofloral honey from sugar syrup, and polyfloral honey from sugar and adulterated samples using the e-nose and e-tongue. The e-nose was observed to give better separation compared to e-tongue assessment, particularly when LDA was applied. However, when all samples were combined in one classification analysis, neither PCA nor LDA were able to discriminate between honeys of different floral origins, sugar syrup and adulterated samples. By applying a sensor fusion technique, the classification for the 18 different samples was improved. Significant improvement was observed using PCA, while LDA not only improved the discrimination but also gave better classification. An improvement in performance was also observed using a Probabilistic Neural Network classifier when the e-nose and e-tongue data were fused.

View
Thumbnail Image
Publication

Analysis of Soil Nutrient (NPK) Test Value - Relative yield Relationship for Harumanis Mango using Modification Arcsine-Log Calibration Curve.

2023-01-01 , Erdy Sulino Mohd Muslim Tan , Markom M.A.B. , Allan Melvin Andrew , Abu Hassan Abdullah , Norasmadi Abdul Rahim , Fathinul Syahir Ahmad Sa'ad , Imaduddin Helmi Wan Nordin , Abidin M.A.Z. , Jamil S.H.F.S.A. , Yogesh C.K.

The cultivation of Harumanis mango (Mangifera indica) is of significant agricultural importance, especially in tropical regions like Malaysia, where it is renowned for its exceptional taste and quality. Maximizing mango yield and maintaining fruit quality are vital aspects of successful cultivation, relying on optimal soil nutrient management, particularly nitrogen (N), phosphorus (P), and potassium (K). In this research, the soil nutrient test value - relative yield relationship for Harumanis mango is investigated using a modification arcsine-log calibration curve. Traditional linear calibration curves may not fully capture the nonlinearities observed in crop responses, potentially leading to inaccurate nutrient requirements for optimal yield. By employing the innovative modification arcsine-log calibration curve, a more precise and robust relationship between soil nutrient test values and relative mango yield is established. Soil samples are collected from mango orchards, and NPK levels are measured using standardized laboratory techniques, alongside corresponding relative mango yields. This study advances precision agriculture by offering precise soil nutrient recommendations for mango farmers. Utilizing calibrated curves improves mango yield, minimizes nutrient waste, and encourages sustainable farming. In conclusion, the modified arcsine-log calibration curve reveals vital insights for optimal Harumanis mango production, benefiting the industry and sustainability.

View