Ammar Zakaria

A hybrid sensing approach for pure and adulterated honey classification.

This paper presents a comparison between data from single modality and fusion methods to classify Tualang honey as pure or adulterated using Linear Discriminant Analysis (LDA) and Principal Component Analysis (PCA) statistical classification approaches. Ten different brands of certified pure Tualang honey were obtained throughout peninsular Malaysia and Sumatera, Indonesia. Various concentrations of two types of sugar solution (beet and cane sugar) were used in this investigation to create honey samples of 20%, 40%, 60% and 80% adulteration concentrations. Honey data extracted from an electronic nose (e-nose) and Fourier Transform Infrared Spectroscopy (FTIR) were gathered, analyzed and compared based on fusion methods. Visual observation of classification plots revealed that the PCA approach able to distinct pure and adulterated honey samples better than the LDA technique. Overall, the validated classification results based on FTIR data (88.0%) gave higher classification accuracy than e-nose data (76.5%) using the LDA technique. Honey classification based on normalized low-level and intermediate-level FTIR and e-nose fusion data scored classification accuracies of 92.2% and 88.7%, respectively using the Stepwise LDA method. The results suggested that pure and adulterated honey samples were better classified using FTIR and e-nose fusion data than single modality data.

Bio-inspired Vision Fusion for Quality Assessment of Harumanis Mangoes

The perceived quality of fruits, such as mangoes, is greatly dependent on many parameters such as ripeness, aroma, firmness, shape, size, and is influenced by other factors such as harvesting time. Unfortunately, a manual fruit grading has several drawbacks such as subjectivity, tediousness and inconsistency. By automating the procedure, as well as developing new classification technique, it may solve these problems. This paper presents the novel work on the bio-inspired multi-modality sensing system for classification and quality assessment of mangoes cv. Harumanis Mango using charge coupled device (CCD) camera and Infrared (IR) camera. A Fourier-based shape separation method was developed from CCD camera images to grade mango by its shape and able to correctly classify 100%. Colour intensity from infrared image was used to distinguish and classify the level of maturity and ripeness of the fruits. The finding shows 92% correct classification of maturity levels by using infrared vision.

Internet of things: Sensor to sensor communication

Rapidly growing Internet of Things (IoTs) concept have given rise to the concern regarding inter-communicability of sensor nodes practicing a multitude of standard and proprietary wireless communication protocols. A multi wireless communication protocol transceiver can facilitate sensor node to sensor node communication for a better quality of service and decision making in the IoTs environment. In this project, a multi wireless communication protocol receiver is designed and tested in a smart building monitoring system that collects and analyzes ambient data. The key objective of this project is to bridge the communication gap between sensor nodes especially in terms of wireless communication protocol. In overall, this project has successfully demonstrated a smart receiver concept that allows multi-channel communication between the sensor nodes with Zigbee, Bluetooth and WiFi communication protocols.

Cross-sensitivity of metal oxide gas sensor to ambient temperature and humidity: Effects on gas distribution mapping

Metal oxide gas sensors have been widely used in robotics application to perform remote and mobile gas sensing. However, previous researches have indicated that this type of sensor technology is cross-sensitive to environmental temperature and humidity. This paper therefore investigates the effects of these two factors towards gas distribution mapping and gas source localization domains. A mobile robot equipped with TGS2600 gas sensor was deployed to build gas distribution maps of indoor environment, where the temperature and humidity varies. The results from the trials in environment with and without gas source indicated that there is a strong relation between the fluctuation of the mean and variance map with respect to the variations in the temperature and humidity maps.

Assessment on ground-level nitrogen dioxide (NO2) and ammonia (NH3) at secondary forest of Mata Ayer and Kangar, Perlis

The current ground-level concentrations of nitrogen dioxide (NO2) and ammonia (NH3) within forests in Perlis are unknown and hardly investigated. The continual infrastructure development of Perlis and human activities may have played a major role in contributing to the decline of air quality in Perlis. Nitrogen-based trace gases may cause environmental effects while they are airborne or deposited on the ground. Due to the uncertainty of nitrogen trace gases level, this study was conducted to investigate the NO2 and NH3 concentrations within Mata Ayer secondary forest and Kangar. A portable gas monitor-sensor (Aeroqual Series 500) was used to assess the ground-level NO2 and NH3 concentrations, ambient air temperature, and relative humidity. The measurements were conducted in June 2015 between 9:30 am to 4:30 pm. The average NO2 and NH3 concentrations were 0.062 ppm and 0.040 ppm at the secondary forest of Mata Ayer and were found lower than Kangar (0.069 ppm and 0.125 ppm). The ambient air temperature and ...

Feature extraction techniques using multivariate analysis for identification of lung cancer volatile organic compounds

In this experiment, three different cell cultures (A549, WI38VA13 and MCF7) and blank medium (without cells) as a control were used. The electronic nose (E-Nose) was used to sniff the headspace of cultured cells and the data were recorded. After data pre-processing, two different features were extracted by taking into consideration of both steady state and the transient information. The extracted data are then being processed by multivariate analysis, Linear Discriminant Analysis (LDA) to provide visualization of the clustering vector information in multi-sensor space. The Probabilistic Neural Network (PNN) classifier was used to test the performance of the E-Nose on determining the volatile organic compounds (VOCs) of lung cancer cell line. The LDA data projection was able to differentiate between the lung cancer cell samples and other samples (breast cancer, normal cell and blank medium) effectively. The features extracted from the steady state response reached 100% of classification rate while the tran...