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

22
Reset filters

Settings
Now showing 1 - 10 of 22
  • 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.
  • 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.
  • 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.
  • Publication
    Integration of asset tracking system through trilateration method as detection mechanism
    ( 2019)
    M A Fadzilla
    ;
    Shahriman Abu Bakar
    ;
    Azizi Harun
    ;
    Mohd Sani Mohamad Hashim
    ;
    Zuradzman Mohamad Razlan
    ;
    Wan Khairunizam Wan Ahmad
    ;
    Z. Ibrahim
    ;
    Abu Hassan Abdullah
    ;
    J.S.C Turner
    ;
    K.A.A Kassim
    ;
    M.S.A Khalid
    ;
    Z. Jawi
    ;
    M.H.M Isa
    ;
    Rizalafande Che Ismail
    Demands for localization system has been growing rapidly in the last several years both for an outdoor and indoor area. In conjunction with this, the capability and reliability of this system to precisely locate and track objects of interest for the indoor area has catered researchers and study on how to do so. One of the major ideas on making it more advance is by incorporating the use of wireless devices into the system. There are numbers of issues that could interrupt the efficiency and success of the system. One of the main problems is the signal loss mainly caused by the attenuation of the signal as they propagate through from the transmitter to the receiver. These attenuations are mostly due to the surface types the signal are traveling on and the objects that are in the Line of Sight in between the transmitter and receiver. In order to ensure the most reliable and efficient wireless connection between transmitter and receiver, a propagation study on the signal is needed for us to analyze and find the best way to trade off the signal attenuation based on the environment surrounding the system. By doing so, a thorough system that has models that can work efficiently even if we are to consider the attenuation factors. The system consists of nodes installed inside the research institute that acts as both transmitter and receivers. The transmitter and receiver will then process the signal that will then determine their location. The receiver is connected to the laptop in order to get a real-time reading so that we will be able to locate the transmitter. A networked of nodes are installed inside the research institute for experiment and the layout of the research is conferred for future references. Data from the experiment are then analyzed and a model for the signal propagation alongside the research institute is created. This model will be able to apprehend the signal attenuation despite the surrounding environment such as furniture and walls. A completed asset tracking system with models of signal attenuation will be built in the future for a more efficient signal transmission.
  • 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
    ;
    Mohamed Elshaikh Elobaid Said Ahmed
    ;
    Ismail K.A.
    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.
  • 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.
  • 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.
  • Publication
    Development of Ripeness Indicator for Quality Assessment of Harumanis Mango by using Image Processing Technique
    ( 2020-12-18)
    Mohd Nazri Abu Bakar
    ;
    Abu Hassan Abdullah
    ;
    Norasmadi Abdul Rahim
    ;
    Haniza Yazid
    ;
    Fathinul Syahir Ahmad Sa'ad
    ;
    Ahmad K.
    Visual appearance is the main source of information that can be used for quality assessment of mango. In this study, a non-destructive ripeness level estimation for mango of the cultivar Harumanis based on digital image analysis was employed. The changing peel and flesh colour of mango is strongly correlated to ripeness that can be measured as a sensual quality parameter. This measurement of ripeness level has been determined by image analysis technique which needs to attribute external and internal colour feature from image segmentation Multilevel thresholding technique is proposed for colour image segmentation to extract the mango region from the background which every channel of five colour spaces have been applied. Colour analysis technique and Total soluble solids (TSS) is used to compare and evaluate for the prediction. The optimal results were obtained that a∗ channel from L∗a∗b colour space has given more logical and better performance of prediction which is more than 92% accuracy.
  • 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.
      1  24
  • Publication
    Improved maturity and ripeness classifications of magnifera indica cv. Harumanis mangoes through sensor fusion of an electronic nose and acoustic sensor
    ( 2012)
    Ammar Zakaria
    ;
    Ali Yeon Md Shakaff
    ;
    Masnan, Maz Jamilah
    ;
    Fathinul Syahir Ahmad Sa'ad
    ;
    Abdul Hamid Adom
    ;
    Mohd Noor Ahmad
    ;
    Mahmad Nor Jaafar
    ;
    Abu Hassan Abdullah
    ;
    Latifah Munirah Kamarudin
    In recent years, there have been a number of reported studies on the use of non-destructive techniques to evaluate and determine mango maturity and ripeness levels. However, most of these reported works were conducted using single-modality sensing systems, either using an electronic nose, acoustics or other non-destructive measurements. This paper presents the work on the classification of mangoes (Magnifera Indica cv. Harumanis) maturity and ripeness levels using fusion of the data of an electronic nose and an acoustic sensor. Three groups of samples each from two different harvesting times (week 7 and week 8) were evaluated by the e-nose and then followed by the acoustic sensor. Principal Component Analysis (PCA) and Linear Discriminant Analysis (LDA) were able to discriminate the mango harvested at week 7 and week 8 based solely on the aroma and volatile gases released from the mangoes. However, when six different groups of different maturity and ripeness levels were combined in one classification analysis, both PCA and LDA were unable to discriminate the age difference of the Harumanis mangoes. Instead of six different groups, only four were observed using the LDA, while PCA showed only two distinct groups. By applying a low level data fusion technique on the e-nose and acoustic data, the classification for maturity and ripeness levels using LDA was improved. However, no significant improvement was observed using PCA with data fusion technique. Further work using a hybrid LDA-Competitive Learning Neural Network was performed to validate the fusion technique and classify the samples. It was found that the LDA-CLNN was also improved significantly when data fusion was applied.
      7  16