Abu Hassan Abdullah, Associate Prof. Ir. Ts. Dr.This page provides access to scholarly publication by UniMAP Faculty members and researchershttp://dspace.unimap.edu.my:80/xmlui/handle/123456789/330302024-03-29T02:19:35Z2024-03-29T02:19:35ZBraitenberg swarm vehicles for odour plume tracking in laminar airflowSyed Muhammad Mamduh, Syed ZakariaKamarulzaman, KamarudinShaharil, Mad SaadAli Yeon, Md Shakaff, Prof. Dr.Ammar, ZakariaAbu Hassan, Abdullah, Dr.http://dspace.unimap.edu.my:80/xmlui/handle/123456789/341982014-04-30T08:10:45Z2013-04-01T00:00:00ZBraitenberg swarm vehicles for odour plume tracking in laminar airflow
Syed Muhammad Mamduh, Syed Zakaria; Kamarulzaman, Kamarudin; Shaharil, Mad Saad; Ali Yeon, Md Shakaff, Prof. Dr.; Ammar, Zakaria; Abu Hassan, Abdullah, Dr.
This paper presents an algorithm to trace an odour plume using swarm robots in laminar airflow. The algorithm proposed here aims to bridge the gap between single and multiple element systems by mimicking and enhancing biologically derived strategies for odor plume tracking. Simulations were carried out on Webots to verify the potential of the algorithm. A simple gas sensor model was introduced to mimic the response of a real metal oxide sensor in the simulation. A gas sensor model was introduced based on the response of metal oxide sensor (MOS) to closely mimic and provide real environment condition. Different weightage configurations of the gas sensor, kg and wind sensor, kw are compared to find its effects on the performance and behavior of the purposed algorithm. It was found that robots separated from the swarm can still perform the plume tracking task. Also, multiple entity systems show an increase in performance compared to single entity robots.
Proceedings of the IEEE Symposium on Computers and Informatics 2013 (ISCI 2013) at Langkawi, Malaysia on 7 April 2013 through 9 April 2013
2013-04-01T00:00:00ZMethod to convert Kinect's 3D depth data to a 2D map for indoor SLAMKamarulzaman, KamarudinSyed Muhammad Mamduh, Syed ZakariaAli Yeon, Md Shakaff, Prof. Dr.Shaharil, Mad SaadAmmar, ZakariaLatifah Munirah, Kamarudin, Dr.Abu Hassan, Abdullah, Dr.http://dspace.unimap.edu.my:80/xmlui/handle/123456789/341932015-07-14T01:31:47Z2013-03-01T00:00:00ZMethod to convert Kinect's 3D depth data to a 2D map for indoor SLAM
Kamarulzaman, Kamarudin; Syed Muhammad Mamduh, Syed Zakaria; Ali Yeon, Md Shakaff, Prof. Dr.; Shaharil, Mad Saad; Ammar, Zakaria; Latifah Munirah, Kamarudin, Dr.; Abu Hassan, Abdullah, Dr.
Mobile robotics has been strongly linked to localization and mapping especially for navigation purpose. A robot needs a sensor to see objects around it, avoid them and also map the surrounding area. The use of 1D and 2D proximity sensors such as ultrasonic sensor, sonar and laser range finder for area mapping is believed to be less effective since they do not provide information in Y or Z (horizontal and vertical) direction. The robot may miss an object due to its shape and position; thus increasing the risk of collision as well as inaccurate map. In this paper, a 3D visual device particularly Microsoft Kinect was used to perform area mapping. The 3D depth data from the device's depth sensor was retrieved and converted into 2D map using the presented method. A Graphical User Interface (GUI) was also implemented on the base station to depict the real-time map. It was found that the method applied has successfully mapped the potentially missing objects when using 1D or 2D sensor. The convincing results shown in this paper suggest that the Kinect is suitable for indoor SLAM application given that the device's limitations are solved.
Proceeding of The 9th International Colloquium on Signal Processing and its Applications 2013 (CSPA 2013) at Kuala Lumpur, Malaysia on 8 March 2013 through 10 March 2013
2013-03-01T00:00:00ZImproved Maturity and Ripeness Classifications of Magnifera Indica cv. Harumanis Mangoes through Sensor Fusion of an Electronic Nose and Acoustic SensorAmmar, ZakariaAli Yeon, Md Shakaff, Prof. Dr.Maz Jamilah, MasnanFathinul Syahir, Ahmad SaadAbdul Hamid, Adom, Prof. Dr.Mohd Noor, Ahmad, Prof. Dr.Mahmad Nor, Jaafar, Assoc. Prof. Dr.Abu Hassan, AbdullahLatifah Munirah, Kamarudinhttp://dspace.unimap.edu.my:80/xmlui/handle/123456789/254422013-07-23T07:44:40Z2012-05-10T00:00:00ZImproved Maturity and Ripeness Classifications of Magnifera Indica cv. Harumanis Mangoes through Sensor Fusion of an Electronic Nose and Acoustic Sensor
Ammar, Zakaria; Ali Yeon, Md Shakaff, Prof. Dr.; Maz Jamilah, Masnan; Fathinul Syahir, Ahmad Saad; Abdul Hamid, Adom, Prof. Dr.; Mohd Noor, Ahmad, Prof. Dr.; Mahmad Nor, Jaafar, Assoc. Prof. Dr.; 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.
Link to publisher’s homepage at http://www.mdpi.com
2012-05-10T00:00:00ZThe optimum embedded controller for handheld electronic noseAbu Hassan, AbdullahAbdul Hamid, Adom, Prof. Madya Dr.Ali Yeon, Md Shakaff, Prof. Dr.Mohd Noor, Ahmad, Prof. Dr.Ammar, ZakariaFathinul Syahir Ahmad, Sa'adhttp://dspace.unimap.edu.my:80/xmlui/handle/123456789/205812012-08-09T01:43:13Z2012-02-27T00:00:00ZThe optimum embedded controller for handheld electronic nose
Abu Hassan, Abdullah; Abdul Hamid, Adom, Prof. Madya Dr.; Ali Yeon, Md Shakaff, Prof. Dr.; Mohd Noor, Ahmad, Prof. Dr.; Ammar, Zakaria; Fathinul Syahir Ahmad, Sa'ad
Electronic nose (e-nose) is a non-destructive
intelligent instrument that mimics human olfactory system to
detect, discriminate and classify odour. The instrument have vast
potential applications includes food quality assurance, plant
disease and malodour monitoring. The increases of the
instrument potential applications have attracted many research
groups to developed a cost-effective system with simple operating
procedure. Recent developments in embedded technology have
made possible for low cost integration of powerful embedded
system for a small device. This paper discusses the selection of
optimum embedded controller for the development of a handheld
e-nose. The selected controller should enable the instrument
to operate effectively. The developed instrument is using off-theshelf
components i.e. metal oxide sensor, microcontroller and
signal conditioning circuit. The instrument offer rapid response,
versatility and novelty in the detection of sample odour. The data
processing is using multivariate statistical analysis i.e. principal
component analysis (PCA), Hierarchical Cluster Analysis (HCA)
and Linear Discriminate Analysis (LDA). The developed
instrument is tested to discriminate the basic aromatic smell.
Initial results show that the instrument is able to discriminate the
samples based on their odour chemical fingerprint profile. The
multivariate statistical analysis (PCA, HCA and LDA) plot show
that the samples are grouping into different cluster.
International Conference on Man Machine Systems (ICoMMS 2012) organized by School of Mechatronic Engineering, co-organized by The Institute of Engineer, Malaysia (IEM) and Society of Engineering Education Malaysia, 27th - 28th February 2012 at Bayview Beach Resort, Penang, Malaysia.
2012-02-27T00:00:00Z