Search

Mahalanobis distance Δ2 values are commonly in the range of 0 to +∞ where higher values represent greater distance between class means or points. The increase in Mahalanobis distance is unbounded as the distance multiply. To certain extend, the unbounded distance values pose difficulties in the evaluation and decision for instance in the sensors closeness test. This paper proposes an approach to [0, 1] bounded Mahalanobis distance Δ2 that enable researcher to easily perform sensors closeness test. The experimental data of four different types of rice based on three different electronic nose sensors namely InSniff, PEN3, and Cyranose320 were analyzed and sensor closeness test seems successfully performed within the [0, 1] bound.

Indoor air may be polluted by various types of pollutants which may come from cleaning products, construction activities, perfumes, cigarette smoke, water-damaged building materials and outdoor pollutants. Although these gases are usually safe for humans, they could be hazardous if their amount exceeded certain limits of exposure for human health. A sophisticated indoor air quality (IAQ) monitoring system which could classify the specific type of pollutants is very helpful. This study proposes an enhanced indoor air quality monitoring system (IAQMS) which could recognize the pollutants by utilizing supervised machine learning algorithms: multilayer perceptron (MLP), K-nearest neighbour (KNN) and linear discrimination analysis (LDA). Five sources of indoor air pollutants have been tested: ambient air, combustion activity, presence of chemicals, presence of fragrances and presence of food and beverages. The results showed that the three algorithms successfully classify the five sources of indoor air pollution (IAP) with a classification rate of up to 100 percent. An MLP classifier with a model structure of 9-3-5 has been chosen to be embedded into the IAQMS. The system has also been tested with all sources of IAP presented together. The result shows that the system is able to classify when single and two mixed sources are presented together. However, when more than two sources of IAP are presented at the same period, the system will classify the sources as ‘unknown’, because the system cannot recognize the input of the new pattern.

Heart rate variability (HRV) extracted from human physiological turn to be important data as indicators of human health. Originally, HRV calculated and monitored from Electrocardiography (ECG) signal. This research proved that photoplethysmography (PPG) as a simple non-invasive method can measure HRV in motion condition. PPG used Light Photo Sensor (LPS) to capture light reflectance intensity on the finger. The signal is sampled, filtered, processed and sent wirelessly through ZigBee protocol. The system applied on glove to make it wearable and easy to wear. Experimental results show that the system functioning properly with HRV received, displayed and recorded by the base station.

Lack of effective tools to diagnose lung cancer at an early stage has caused high mortality in cancer patients especially in lung cancer patients. Electronic nose (E-Nose) technology is believed to offer non-invasive, rapid and reliable analytic approach by measuring the odour released from cancer to assist medical diagnosis. In this work, using a commercial E-nose (Cyranose-320), we aimed to detect the volatile organic compounds (VOCs) emitted by different types of cancerous cells. The lung cancer cell (A549) and breast cancer cell (MCF-7) were used for this study. Both cells were cultured using Dulbecco's Modified Eagle's Medium (DMEM) with 10% of Fetal Bovine Serum (FBS) and incubated for three days. The static headspace of cell cultures and blank medium were directly sniffed by Cyranose-320. The preliminary results from this study showed that, the E-nose is able to detect and distinguish the presence of VOCs in cancerous cells with accuracy of 100% using LDA. To this end, the VOCs emitted from cancerous cells can potentially used as biomarker.

The three different culture media namely blood agar, Mueller Hinton and MacConkey were used in this study to identify and classify the causative bacteria on diabetic foot infection using electronic nose (E-nose). All the samples were taken from the clinical specimens using standard swabbing technique. E-nose consisting an array of 32 conducting polymer sensors was used to detect volatile organic compounds (VOCs) released by the bacteria in the infected areas. The VOC profiles of three bacterial groups from three genera namely Escherichia coli (ECOLI), Staphylococcus aureus (SAU) and Pseudomonas aeruginosa (PAE) were characterized using statistical classification technique called Linear Discriminant Analysis (LDA) to differentiate between different agars used with individual bacteria species which accounted for all the data. Although these methods are still fundamental, there is an increasing shift toward molecular diagnostics of bacteria. This investigation showed that the E-nose was able to correctly classify different bacterial species in all three culture media with up to 90% accuracy.

Agriculture is one of the main sources that contribute to the economic development in the country. However, diseases that attack the crops have given a little impact to the agricultural production. Generally, plant pathologist has the difficulties to detect the symptoms that relate to the plant disease. The plant usually gets infected that are caused by different plant pathogens such as bacteria, fungus and virus that attack the plant. Therefore, some method of solution needs to be uncounted in order to provide faster solution in detecting this disease. In order to overcome this problem, the current study proposes detection of plant disease using an electronic nose (e-nose) called PEN3 e-nose. This type of e-nose will detect the volatile organic compounds that are produced by two types of plants which are chilies and papaya. The data that have been collected from PEN3 e-nose are processed using principal component analysis (PCA) and linear discriminant analysis (LDA) methods. In order to validate the detection result, the bacteria sample will be stained based on Gram staining procedure in order to observe the structure and colony of the bacteria cells using a digital microscope. Thus, this study shows that the PEN3 e-nose is able to detect the presence of plant pathogenic bacteria on both chilli and papaya plants.

The existing clinical diagnostics for lung cancer are mostly based on physics, biochemical and imaging techniques. The use of electronic nose (E-nose) system to detect volatile organic compounds (VOCs) in lung cancer cells or exhaled air breath of a patient is expected to be able to classify different volatile components leading to the diagnosis of lung cancer at an early stage. In this preliminary study, a commercialized E-nose consists of an array of 32 conducting polymer sensors (Cyranose 320) was used to detect and discriminate the VOCs emitted from cancer cells which is A549 (lung cancer cell line) between MCF7 (breast cancer cell line). Blank medium was used to obtain controlled value. The VOC profiles of each sample were characterized using a classification algorithm called k-Nearest Neighbors (KNN) to test and benchmark the performance of Enose in identifying VOCs of lung cancer from different cancer cell lines. The E-nose with KNN classifier was able to classify the VOCs of lung cancer cell with over 90% successful accuracy in 30 seconds. This study can conclude that e-nose is capable to rapidly discriminate volatile organic compounds of cancerous cells which generated during cell growth.

Traditionally, the variety of animal species are accessed using conventional method that is normally costly, consumed a lot of time and the most important missing element is that it does not equipped with any permanent recording device. In order to replace this conventional method, an automated sound recording device is developed. This device has the capability to record the animal vocalization and has the power system to be used in remote area. This paper presents the work of sound analyzers for acoustic and health monitoring of the forest. The sound analyzer is important for the researcher as it can be used for further analysis such as frequency of animal vocalization and pattern recognition. The developed system uses the LabVIEW (Laboratory Virtual Instrument Engineering Workbench) platform.

Current lifestyles promote the development and advancement in wireless technologies, especially in Wireless Sensor Networks (WSN) due to its several benefits. WSN offers a low cost, low data rate, flexible routing, longer lifetime, and low-energy consumption suitable for unmanned and long term monitoring. Among huge WSN applications, some key applications are smart houses, environmental monitoring, military applications, and other monitoring applications. As a result, ubiquitous increase in the number of wireless devices occupying the 2.4GHz frequency band. This causes a dense wireless connection followed by interference problem to WSN in the 2.4GHz frequency band. WSN is most affected by the interference issue because it has a lower data rate and transmission power compared to WLAN. Despite efforts made by researchers, to the author's knowledge, the interference issue is still a major problem in wireless networks. This paper aims to review the coexistence and interference issues of existing wireless technologies in the 2.4GHz Industrial, Scientific and Medical (ISM) band.

Vegetable oils from different type of sources may have a distinctive aroma and flavour. This work explored the ability of combining PEN3 and UV-Vis Spectrophotometer for aroma and volatiles analysis. Nine types of vegetable oils were characterized and classified into three categories based on aroma and volatiles absorption characteristics which are fresh, heated and used cooking oil. The results of PCA analysis showed a good separation among three groups of vegetable cooking oil. Data set from both PEN3 (e-nose) and UV-Vis Spectroscopy was subjected to Linear Discriminant Analysis. Our results propose that discriminant analysis provides a rapid, efficient and accurate study for multi-class classification difficulties. LDA is capable to provide 100.0% correct classification of original grouped cases. However, only 85.4% of un-known grouped cases are correctly classified.

Harumanis or its scientific name as Mangifera indica is a popular mango in Malaysia due to its unique aroma and taste, despite its expensive price. The high demand for this mango and its potential in export has been the reason why this tropical fruit being a national agenda for the Malaysian government to classify it as the specialty fruit from Perlis (smallest state in Malaysia). As the sole university in Perlis, University of Malaysia Perlis (UniMAP) has taken the initiative to develop greenhouse specifically for Harumanis mango. To support this, a real-time greenhouse monitoring system has been proposed. The system was developed based on Wireless Sensor Networks technology which consists of three parts: sensing module, radio communication module and gateway module. This system is able to provide real time monitoring of the important factors in plant growth such as the carbon dioxide, temperature, humidity level in the greenhouse. The performance result shows that the temperature inside the greenhouse is slightly higher compared to the open field; which meets the crop requirements. At night, the greenhouse microclimate drops and equilibrates to the surrounding temperature and humidity. This condition ensures good flowering and fruiting of sweet and juicy mangoes.

Current studies document the effectiveness of multi-sensing technique implementation to mimic or to complement human senses. This work demonstrated the successful application of multi-sensing techniques such electronic tongue (e-tongue), electronic nose (e-nose) and Fourier Transform Infrared Spectroscopy (FTIR). The fusion of these modalities enhance the classification of pure Tualang honey using Linear Discriminant Analysis (LDA), Probabilistic Neural Network (PNN), Support Vector Machine (SVM) and k-Nearest Neighbour (KNN). KNN and PNN are able to classify between pure and adulterated honey samples, outperform LDA and SVM. By performing data fusion, SVM and LDA classifier can achieved more than 80% accuracy, while KNN and PNN obtained greater precision, up to 96% correct classification. The findings confirmed that, multi-sensing technique; either KNN or PNN was significantly superior compared to SVM and LDA classification methods. Thus, both analyses are able to discriminate between pure and adulterated honey.

This paper models the signal strength measurements at 2.4 GHz in indoor environment. The received signal strength indicator (RSSI) measurement is used to investigate the wireless network coverage in a real office environment where obstacles such as furniture are present. From this experiment, a mapping is created to determine the suitable positions for a short range sensor nodes deployment for sensing humidity, temperature, human movement, etc. The purpose is to evaluate the suitable area for WSN deployments using RF signal and to minimize the number of sensor nodes required for data gathering and monitoring applications. The result shows that through adequate planning of WSN nodes, good radio coverage and efficient monitoring can be achieved for greener building.

Indoor air quality nowadays playing an important role to make sure the environment is safe for human health. The air quality can be monitored using good sensitivity sensors for gas detection and data collection. High sensitivity sensors can be improved by optimize data acquisition system to collect and transfer the data. I2C interface buses that use two wires to transfer and receive any data communication which reduce the usage of the wires. This project developed to study the ability of the Anthurium plant to absorb the ammonia gas and to develop a fast data acquisition system for data monitoring based on I2C interfaces.

Ultrasonic sensor is one of the most cost-effective sensor used to obtain range information and obstacle avoidance. Due to its simplicity, this sensor is widely used in mobile robot applications to acquire environment features and mapping. Although the sensor can track a still or moving target, it does not provide information on the shape and pattern of the detected object. This paper proposes and highlights a low cost method using an array of ultrasonic sensors to be embedded on multiple robots for wall features extraction. Instead of using a single sensor, multiple sensors are used to increase the accuracy and improve coverage on the field of view of the sensor. More information can be extracted such as bearing angle of walls and possibly the shape of an object. A multiple pulse transmit and instantaneous multiple echo receive approach is implemented. The experimental results prove that this method is able to extract different type of wall features, accurately.

Aquaculture is an important to national food security. Productivity of aquaculture farms hinges on water quality. Lack of appropriate instrumentation for measurement of water quality is a hindrance to the industry. This experiment proposed and verify the application of e-nose and e-tongue for water quality parameters for shrimp farming. Results indicated it has the potential but required additional analytical techniques. Thus, by using sensor array technologies, e-nose and e-tongue has been employed in classification of different type of water that has been used in aquaculture farming. E-nose consists of 10 metal oxide sensors meanwhile e-tongue consists of 13 working electrodes and one reference electrode. Linear Discriminant Analysis (LDA) was used as data classifier. The e-nose and e-tongue was able to classify different type of water with the accuracy up to 95%. These results show the potential use of e-nose and e-tongue to classify the different type of water used in aquaculture industry.

Copper oxide gas sensor was prepared on silicon wafer by sputtering of copper target at different oxygen flow rate of 0, 4, 8 and 16 sccm using RF magnetron sputtering technique. Argon flow rate, RF power, working pressure and substrate bias voltage were fixed at 50 sccm, 400 W, 22.5 mTorr and −40 V, respectively. The effect of varying the oxygen flow rate towards the time response of the copper oxide gas sensor was investigated. In addition, the influence of the copper oxide thin films microstructure and I-V characteristic was also considered. Based on the result, copper oxide gas sensor fabricated at 8 sccm of oxygen flow rate provide a better response of 0.024V/s compare to those fabricated at 0, 4 and 16 sccm.

Indoor air quality is a rising issue these days. People are becoming are of the importance of indoor air quality. This paper further advances the technology for indoor air quality surveillance by adding an index feature to it. The index feature, accompanied with real-time monitoring system is very useful in giving the real-time alert to the users on the conditions of the current indoor air quality. This index is very handy because human being's ability to detect the changes in the air quality is limited while the changes in the air quality may be rapid. The index helps the users to recognize the conditions of the air instantly by giving four signals, good, normal, unhealthy and hazardous. The index is developed based on the index used in the outside environment but by emphasizing on the internal air quality elements.

This paper presents the estimation of purity of honey using Artificial Neural Network (ANN). ANN method was used to automate the decision of estimation, replacing the manual human approximate method. A total of 21 honey purity samples from various concentrations of pure honey, adulterated honey and pure sugar were collected. The data were collected using electronic nose (E-nose) and Fourier Transform Infrared (FTIR). Fusion method was applied in this work by combining the variable from both E-nose and FTIR to produce new sets of data. This data were used as input parameters for ANN learning. The results show that ANN was able to estimate the concentration of honey purity in adulterated honey solution with less error using fusion data as compared to single modality data.

This paper is focused on the development of wireless measured kinematics specifically for cycling. The aims of this study are to create sensory system with portability, reliability and based on the wireless system. The adjustable crank is novel type of prototype crank that design to maximize the minimum torque at bottom dead center (BDC) and top dead center (TDC) where the crank can be set for ±10° maximum with addition of 5° back and forth from inertial 0° TDC point. The system will measure the power output during cycling to evaluate the cyclist performance. In order to measure power output, the angle displacement (kinematic) and force measurement (kinetic) are needed. The inertial measurement unit (IMU) combination of accelerometers and gyrometers was used to measure the angle and angular velocity of the crank. The system has been validated using a visual system to compare the output provided by IMU. The RMS error value between motion capture camera and IMU for crank angle was 0.480 ± 0.325°. The RMS error value for normalized angular velocity was 0.743 ± 0.911 %. The wireless-based system will aid to reduce the wiring complexity and user-friendly portable measuring system. The wireless communication using Zigbee protocol with two Xbee devices point-to-point will be used to transfer the information to the computer controlled system. The enhancement of this system can be used for coaches for cycling monitoring system to improve cyclist coordination, strategy, and technique.

Logarithmic number system (LNS) has been a trend in digital signal processing (DSP) for recent years, particularly digital image processing. LNS was been implemented in DSP processors and even as an enhancer tool in image improvements. The selection of LNS is due to the ease of operation for multiplication, division, square and square-root which been replaced by fixed-point addition, subtraction, and left- and right-shift operations, respectively. Current researches have found that LNS is possible to be a competitor and a potential replacement of floating point (FP) system in near future, as it provides comparable strength towards the latter. The other arithmetic operations, which is the addition and subtraction, however, have to face great challenges in implementations as it requires complex procedures and circuitry. As LNS potential to be a substitution of FP, there is a significant need for LNS to improve the performance. Hence, this paper outlines the evolution of the LNS architecture design and highlights the potential areas for further improvements. These enhancements will then be implemented and verified in a specific digital image processing system.

Vegetable oil of various types and sources may produce different aroma. This study explores the application of electronic nose for aroma analysis, mainly for characterization and classification of different types of vegetable oils. The electronic nose system comprises of metal oxide semiconductor (MOS) sensors which is used to extract the fingerprint of volatile compounds that is present in the samples. In this study, a linear discriminant analysis (LDA) was applied for electronic nose data processing and recognition. Analysis of the LDA showed that the percentage performance for the training group was 100%, while for test group was 93.6% and 85.4% validated. Based on the results obtained, the electronic nose was able to classify the aroma of different types of vegetable oil.

Foot infections may lead to serious complications if failed to detect at an early stage; especially for diabetic patients. It is necessary to develop an easy and reliable method to identify and classify the causative bacteria from the wound to assist health care practitioners. Therefore, this study proposed an alternative to the conventional technique by using an electronic nose with 32 matrices of non-specific conducting polymer sensors known as Cyranose320. A novel odour detection method is developed and targeted for microbial bacteria causing infection on diabetic foot using direct injection of static headspace. The bacteria are obtained from the clinical specimens by swabbing technique and isolated in a blood agar medium to verify the performance of the bacterial specialized medium. Various classification algorithm techniques proved that each bacteria produce certain characteristic of odour and can be used as a surrogate bio-marker. Thus, preliminary results from this study show that the electronic nose is able to identify and classify the presence of causative bacteria with high success rate of over 90% in diabetic foot infection.

The development of electronic nose (e-nose) and electronic tongue (e-tongue) to recognize simple or complex solutions has been a great success to overcome the drawbacks of conventional analytical instrument and human organoleptic profiling panels. The fusion of these sensors is believed to be able to assess flavours. To grab the advantage of the promising achievements and its broad prospect, this research paper focuses on the discrimination of herbal tea flavour. Multiple analyses based on low level data fusion (LLDF) and intermediate level data fusion (ILDF) in assessing herbal drink from different manufacturers were demonstrated in this research. Classification using Linear Discriminant Analysis (LDA), Support Vector Machine (SVM), Probabilistic Neural Network (PNN) and k-Nearest Neighbour (KNN) analysis were illustrated. Experimental results show better classification was achieved for fusion data using the KNN and LDA with zero error rate. The findings demonstrate that the combination of e-nose and e-tongue with either LDA or KNN as the classification methods are suitable for discrimination of herbal tea flavour.

Determination of burning smell is important because it can help in early fire detection and prevention. In this paper, a household burning smell classification system for early fire detection application has been proposed using Probabilistic Neural Network (PNN) and PCA analysis. The experiments were performed on recorded smell samples from combustion of ten different commonly available household, including candle, joss sticks, air freshener, mosquito coil, newspaper, card board, plastic materials, Styrofoam and wood. All the experiments were done in a test chamber with humidity and temperature sensors. Portable Electronic Nose (PEN3) from Airsense Analytics is used as the measurement device. The smell source is placed 0.3m from the PEN3 and the time-series signal is measured for two minutes. The odour metrics is modelled using Probabilistic Neural Network. It is found that the average classification accuracy for the model is 99.62%.

This paper proposes the use of a wireless sensor network (WSN) as a passive human behavior monitoring system for enabling intelligent green building. The proposed application required further investigation and study of the effect of human movement on wireless signals at 2.4GHz. It is important to understand the significant effect caused by human movement on the Received Signal Strength Indicator (RSSI). Several experiments are conducted using WSN mote from MEMSIC to obtain wireless attenuation models based on the number of people and movement speed. Prior to the experiments, the co-existence of different systems in the 2.4GHz frequency band is measured to select unoccupied IEEE802.15.4 channel to prevent co-channel interferences. Results show that the presence of people moving in indoor significantly affects the RSSI and the attenuation varies with the number of people and their movement speed. The attenuation model of human movement in indoor environment has enabled the use of existing WSN in the building to detect the presence of people and act as a passive sensor for human movement to enable effective lighting and air-conditioning control system.

Cupric Oxide (CuO) is one of the p-type metal oxide semiconductors that are suitable for use in gas sensing device. The copper oxide thin film was prepared on silicon wafer by sputtering of pure copper target at difference deposition time of 5 min, 10 min and 15 min using RF magnetron sputtering technique. Argon flow rate, oxygen flow rate, RF power and working pressure were fixed at 50 sccm, 8 sccm, 400 W and 22.5 mTorr, respectively. The influence of the deposition time towards the surface morphology, topography and thickness has been investigated. SEM and AFM analysis showed that the grain size of the films increases with the increase of deposition time. However, the surface structure of the films remains the same. In addition, it is noticed that when the deposition time increased, the surface of the films becomes rougher. The deposition rate was approximately 29 nm/min, and it was evaluated from the film thickness deposited at several times.

Olfactory systems are a device that is able to replicate the human olfactory sense. By using Interdigitated Electrode (IDE) structure, a sensor array for detecting mango volatile utilizing molecular imprinted polymer (MIP) as the sensing material was fabricated. Each sensor will be responsible for detecting different volatiles emitted from mango. The novel MIP sensory system able detects gases selectively through an increase in electrical capacitance when the targeted mango gases are adsorbed on the sensor's surface. Each sensor will be first be calibrated by identifying the baseline value prior inserting the mango in the chamber. Once the mango is inserted, the capacitance shift on each sensor will help to determine different mango volatiles being emitted by mango and absorb by each of the sensor. The capacitance shift will be then calculated by an interface circuit to determine the level of mango maturity ranging from non ripe, semi ripe or ripe. By having an array of such sensors, with each IDE tuned to different unique volatiles, a robust and accurate fruit maturity determination can be performed. These studies provide an interesting and alternative solution for agriculture sector to improve the quality of harvest.

Zinc oxide (ZnO) is one of the metal oxide semiconductors suitable for use in optoelectronic devices and a potential material for the future solar cell applications. In this works, ZnO films were deposited on silicon and glass substrate by reactive rf magnetron sputtering using a solid Zn target. The influence of the oxygen flow rate and the working pressure on the zinc oxide films microstructure were studied. The deposition power was fixed at 200 W. Crystalline structures, morphology characteristics of ZnO films were investigated by X-ray diffraction (XRD), field emission scanning electron microscope (FE-SEM), respectively. The XRD results showed that the ZnO (002) peak was dominant when the oxygen flow ratio was above 5%. The ZnO (102) was observed at 5 mTorr of deposition pressure but almost unseen at 10 mTorr of deposition pressure. On the other hand, the surface morphology of ZnO thin film was varied with the oxygen partial pressure.

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.

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.

The field of measurement technology in the sensors domain is rapidly changing due to the availability of statistical tools to handle many variables simultaneously. The phenomenon has led to a change in the approach of generating dataset from sensors. Nowadays, multiple sensors, or more specifically multi sensor data fusion (MSDF) are more favourable than a single sensor due to significant advantages over single source data and has better presentation of real cases. MSDF is an evolving technique related to the problem for combining data systematically from one or multiple (and possibly diverse) sensors in order to make inferences about a physical event, activity or situation. Mitchell (2007) defined MSDF as the theory, techniques, and tools which are used for combining sensor data, or data derived from sensory data into a common representational format. The definition also includes multiple measurements produced at different time instants by a single sensor as described by (Smith & Erickson, 1991).

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.

Yield Prediction is an essential task to be achieved in order to implement effective forward marketing. Forward marketing is a contract that will be signed between supplier and client based on the amount of delivery and the price of delivery in future. To be able to sign such a contract the supplier should be very confident that the yield could be achieved. The yield sustainability is a challenging process in agriculture. Mango cultivar Harumanis is one of the best table tropical fruit due to its aroma and sweetness. Despite its overwhelming local demand in Malaysia and also internationally, the fruit supply never meets the demand. The flowering phase is identified as an important stage as plant reproductive physiology. Currently, Harumanis mango flowering only happens once a year that restricts the yield. In this paper, data mining is used to quantify the climatic effects on Harumanis mango yield to enable yield prediction.

Swift lets are birds contained within the four genera Aerodramus, Hydrochous, Schoutedenapus and Collocalia. To date, the bird nest grading is based on weight, shape and size. The inspection and grading for raw edible bird nest were performed visually by expert panels. This conventional method is relying more on human judgments. A Fourier-based shape separation (FD) method was developed from Charge Couple Device (CCD) image data to grade bird nest by its shape and size. FD was able to differentiate different shape such as oval and 'v' shaped depending on the swift let species and geographical origin. The Wilks' lambda analysis was invoked to transform and compress the data set comprising of large number of interconnected variables to a reduced set of variates. It can be further used to differentiate bird nest from different geographical origin. Overall, the vision system was able to correctly classify 100% of the V and Oval shaped and 81.3% for each grade in oval shape of the bird nest.

Producing chili is a daunting task as the plant is exposed to the attacks from various micro-organisms and bacterial diseases and pests. The symptoms of the attacks are usually distinguished through the leaves, stems or fruit inspection. This paper discusses the effective way used in performing early detection of chili disease through leaf features inspection. Leaf image is captured and processed to determine the health status of each plant. Currently the chemicals are applied to the plants periodically without considering the requirement of each plant. This technique will ensure that the chemicals only applied when the plants are detected to be effected with the diseases. The image processing techniques are used to perform hundreds of chili disease images. The plant chili disease detection through leaf image and data processing techniques is very useful and inexpensive system especially for assisting farmers in monitoring the big plantation area.

Electronic Nose (e-nose) is an intelligent instrument that is able to classify different types of odours. The e-nose applications include food quality assurance, fragrance industry, medical diagnosis, environmental monitoring, agricultural industry and homeland security. The current enose design trend are portable, small size, low power consumption, high processing power using embedded controller and easy to operate to enable it to perform the designed tasks effectively. This paper deals with the design issues of a hand-held e-nose based on sensor selection and optimum embedded controller capabilities. A summary of proposed hardware and software solutions are provided with emphasis on data processing. The data processing utilizes multivariate statistical analysis i.e. Principal Component Analysis (PCA), Hierarchical Cluster Analysis (HCA) and Linear Discriminate Analysis (LDA). The developed instrument was tested to discriminate the Ganoderma boninense fruiting body (basidiocarp). Initial results show that the instrument is able to discriminate the samples based on their odour chemical fingerprint profile.

To provide reliable and adequate network coverage whilst minimizing the cost of wireless sensor network (WSN) deployments, detailed knowledge of wireless signal propagation within the specific environments is required. There are many WSN devices on the market that have been developed using proprietary systems and therefore have different performances, although implementing similar standards. This paper presents a comparative performance measurement and analysis of three types of WSN devices evaluated for application in a mixed-crop farm. The results show that the Xbee-PRO maintains very strong RSSI values in open field measurements that are sometime 15 dBm higher than those obtained from the IRIS and Microchip motes. Overall, two important factors that influence WSN node performances are antenna height and the type of antenna used. Whip omni-directional antenna has been shown to double the range of the WSN node compared to a patch antenna. Results also show that the log-distance propagation model is a more flexible model that can be used to model a variety of channels, although it lacks standard global parameter values.

Limonene is a type of terpene hydrocarbons commonly produced by plants and known to possess a strong smell of oranges. Electronic nose (e-nose) is an artificial olfaction device that is able to replicate the human olfactory sense. By using Interdigitated Electrode (IDE) structure, an enose sensor for detecting Limonene volatile by using molecular imprinted polymer (MIP) was fabricated. The MIP membrane contained the methacrylic acid, which formed specific cavities originated by target molecule. The ingredients for MIP were then polymerized on the surface of IDE with PET as substrate which is low in cost. The sensing property was examined in gas phase for evaluate the sensor sensitivity and selectivity. The IDE MIP sensor is capable to detect Limonene contained gas. The sensor characteristics were strongly influenced by the composition ratio of crosslinker, functional monomer and template molecule. The remained molecule on MIP can be removed by immersing thus the sensor can be used repeatedly. By determining Limonene volatile released during pre-matured until matured period, one could use this as a data point to determine certain fruit maturity. Apart from monitoring fruit maturity, this sensor can also be use for those who have respiratory allergy towards Limonene where most reported cases of irritation have involved long-term industrial exposure to the pure compound, e.g. during degreasing or the preparation of paints.

This paper presents the findings in developing a decision support in assessing brackish water quality using an electronic taste sensing system made up of sensor array and signal processing tools. Seven ionic sensors and precision centigrade temperature sensor fused together as an array. Developed graphical user interface displays real time measurement and pattern based prediction. Results of periodic test on prepared brackish water solutions showed that the system differentiates samples of varying salinity (0.01 to 0.5 Mol of NaCl per liter), supporting the system usage as salinity detection instrument in aquafarms. Incorporation of temperature sensor increases system stability to temperature drift. System robustness showed that three ionic sensors sufficiently differentiate sixdecade dilution of brackish solution.

For deployment of Wireless Sensor Network in precision farming to be successful, a study of the performance of wireless systems from a network coverage and link quality perspectives must be conducted to ensure a reliable, optimized and cost effective deployment of the network. This paper reports on sets of measurements conducted in a potential precision farming site where there are mixed types of crops which would affect signal strength and link quality of WSNs. We observed insignificant differences in signal strength between antenna heights of 0.5 m and 1m for a flat open grass field. However a significant reduction in signal strength was observed in the presence of thick vegetation on the farmland which limited coverage to only 10 m range. In addition the range of WSN coverage is also reduced to about 16 m for measurements in a greenhouse with grape trellis canopy. This is contrary to enhancements of received signal strength by lateral waves propagating along the canopy reported by other researchers [8]. The paper also provides a comparison in network coverage for the same type of vegetation in greenhouse and outdoor environment.

Mango cultivar Harumanis is one of the best table tropical fruit due to its aroma and sweetness. Despite its overwhelming local demand in Malaysia and also internationally, the fruit supply never meets the demand. The flowering phase is identified as an important stage as plant reproductive physiology. Currently, Harumanis mango flowering only happens once a year and this restricts the yield of this particular mango. In this paper, a model is introduced that address the a biotic factors that induce flowering in mango. Next the model is implemented in agent-based decision support system. The system could be used to support the farmers in decision making, monitoring and controlling the mango trees environment in greenhouse. The greenhouse environment controlled to suit the requirements that are condusive for the Harumanis mango trees flower initiation.

Biometric identification provides a reliable and secure user authentication for the increased security requirements. The deployment of advanced computer vision methods for biometric identification system in an embedded platform are severely constrained in their limited processing capabilities, limited memory, limited power source, and algorithms for biometric identification that provide sufficient accuracy tend to be computationally expensive, leading to unacceptable authentication times. This paper describes the design and preliminary implementation of an embedded system for biometric identification based on iris detection (BIOI2D). Single Board Computer itself is portable and can be used for various purposes such as network based identification system on human face, robot vision platform and embedded web server. In this paper, hardware and software design as well as the experimental result are presented.

We present the design of an efficient multiplication unit. This multiplier architecture is based on radix 4 booth multiplier. In order to improve his architecture, we have made 2 enhancements. The first is to modify the Wen-Chang's modified booth encoder (MBE) since it is the fastest scheme to generate a partial product. However, when implementing this MBE with the simplified sign extension (SSE) method, the multiplication's output is incorrect. The 2nd part is to improve the delay in the 4:2 compressor circuit. The redesigned 4:2 compressor reduced the delay of the carry signal. This modification has been made by rearranging the Boolean equation of the carry signal. This architecture has been designed using Quartus II. The Gajski rule has been adopted in order to estimate the delay and size of the circuit. The total transistor count for this new multiplier is being a slightly bigger. This is due to the new MBE which is uses more transistor. However in performance speed, this efficiency multiplier is quite good. The propagation delay is reduced by about 2% - 7% from other designers.

The design of an Inphase-Quadrature (I/Q) demodulator with low current consumption is described. The I/Q demodulator was designed for wideband code division multiple access (W-CDMA) communication system. SiGe 0.35 mum BiCMOS technology with fT = 45 GHz, fmax = 60 GHz and noise figure = 0.8 dB at 2 GHz was used for the design. A top-down and bottom-up approach was used in designing the I/Q demodulator. CMOS devices were primarily used in the biasing circuits for power consumption reduction while SiGe bipolar devices were used in signal path circuits. The design contained 2 mixers, a local oscillator (LO) divider, negative resistance circuit, buffer amplifiers and bias/control circuits. The I/Q demodulator circuits were then layout and had gone through the post-layout simulation to check performance with the parasitic elements which were introduced from the layout. A design prototype was measured and was found to be agreed with the simulation results. The prototype worked in the region of 190 MHz input frequency and 0-35 MHz output frequency. The prototype had also met the specifications for the W-CDMA hand phone receiver. It achieves EVM of 2.9%. It is also achieved gain ripple of 0.12 dB, gain mismatch of 0.1 dB and phase mismatch of 1.25deg. Finally, the design which is consisted of mixers, frequency dividers, VCO buffer amplifiers and bias/control circuits consumes 4 mA with 3 V power supply.