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The next stage performance evaluation of an athlete can be predicted by implementing Artificial Intelligence technique. In track cycling event, coach and sports physician are concerned with the per...

Gas distribution mapping (GDM) learns models of the spatial distribution of gas concentrations across 2D/3D environments, among others, for the purpose of localizing gas sources. GDM requires run-t ...

Closed circuit television cameras (CCTV) are widely used in monitoring. Most rely on human operators and controllers for detection and information gathering, e.g. the whether there is a person in the monitored area and how many people may be present. This paper presents an adaptive crowd counting system based on two algorithms that estimate the density of each pixel in each frame and use it as a basis for counting people. One algorithm uses scale-invariant feature transform (SIFT) features and clustering to represent pixels of frames (SIFT algorithm) and the other uses features from accelerated segment test (FAST) corner points with SIFT features (SIFT-FAST algorithm). Each algorithm is designed using a novel combination of pixel-wise, motion-region, grid map, background segmentation using Gaussian mixture model (GMM) and edge detection based on Canny algorithm. The Mall and University of California (UCSD) datasets have been used to evaluate the proposed algorithms. Results show that their average accuracies and processing times for each dataset are similar but their accuracies at frame level are different. A fusion technique is proposed and used to increase accuracy by combining the result of the algorithms at frame level. The mean deviation error and the mean absolute error for the two proposed algorithms are less than 0.1 and 3.1, respectively, for the Mall dataset and less than 0.07 and 1.9, respectively, for UCSD dataset.

Cuprous oxide and cupric oxide thin films were deposited on silicon wafer with additional substrate bias voltage using radio-frequency magnetron sputtering of a Cu target with Ar+O2 discharge plasma. Optical emission spectroscopy was employed to monitor the intensity of atomic Cu and O emission lines at various substrate bias voltages and oxygen flow ratios. Thin film transition from cuprous oxide to cupric oxide phase was observed by X-ray diffraction analysis when the oxygen flow ratio increased. This transition was not influenced by the substrate bias voltage. Optical emission spectroscopy showed a real time monitoring results of the transition from cuprous to cupric oxide thin films. The transition was observed at a critical O2 flow ratio of 7%. The results proposed a tightly control of reactive Cu sputtering through a closed loop and real-time monitoring system for precise copper oxide thin films deposition.

The gas chromatography mass spectrometry (GCMS) with combination of Solid Phase Micro-extraction (SPME) was used to study the volatile organic compounds (VOCs) which emitted by the in-vitro cultured human cells and compared with documented volatile biomarker of lung cancer. For this purpose, the lung cancer cell (A549) and non-cancerous lung cell (WI38VA13) were cultured in identical growth medium, concurrently. The VOCs in the headspace of the cell cultures and the blank growth media (reference sample) were collected directly from the culture flask using SPME for 15minutes. The results show that two different volatile metabolites were screened out between A549 cells and Wi38VA13 cells. A549 cell found to emit 2 noticeable VOC which are decane and heneicosane. While for WI38VA13, the VOCs released were 1-Heptanol and heptadecane. The acquired VOCs were compared with the previous studies. The findings in this work conclude that the specific VOC of cells can be act as their odour signature and can be used to provide non-invasively screening of lung cancer using gas array sensor devices.

Assessing the smell of burning is vital, as it can help to further detect and prevent early fire. In this paper, an early stage fire detection algorithm has been introduced using Fuzzy k- Nearest Neighbour (FkNN). The tests were made on normally available seven fire sources and three building structure resources. All the test samples were scorched in a vacuum oven at various temperature points, pushed out using vacuum pumps to be sniffed by the electronic nose. The experiments were done in a confined room with monitored temperature and humidity level. Time domain data were sampled. Prior to be given to the classifier, the smellprints were normalised and the features were extracted. Experimental classification results show that the integration of fuzzy logic into conventional kNN has enhanced the accuracy of the classifier and gave excellent consistency, nonetheless of humidity and temperature disparity, baseline sensor errors, the diverse emission concentration range and varied scorching temperature levels. The average classification precision for the classification system is 96.15%.

Identification of burning smell is crucial because enables early fire recognition and avoidance. Based on this study, an early stage fire detection algorithm is offered via Probabilistic Neural Network (PNN). Experiments were conducted on seven generally accessible flammable materials and three building construction materials. All the materials were scorched in a vacuum oven at various temperature points, pushed out using vacuum pumps to be sniffed by the electronic nose. The experiments were done in a confined room with monitored temperature and humidity level. Standardised feature extractions of the smell print data were carried out prior to subjection of detection categorization. These aspects signify the odour pointers within the time frame. Experimental categorization outcomes indicate that the tuning of spread factor in PNN classifier has enhanced the precision of classification and delivered excellent reliability, irrespective of humidity variation and ambient temperature, the various gas concentration levels, exposure towards different heating temperature range and baseline sensor drift. The mean classification accuracy for this classification model has been identified at 94.18%.

This chapter overviews the issue of multicollinearity in electronic nose (e-nose) classification and investigates some analytical solutions to deal with the problem. Multicollinearity effect may harm classification analysis from producing good parameters estimate during the construction of the classification rule. The common approach to deal with multicollinearity is feature extraction. However, the criterion used in extracting the raw features based on variances may not be appropriate for the ultimate goal of classification accuracy. Alternatively, feature selection method would be advisable as it chooses only valuable features. Two distance-based criteria in determining the right features for classification purposes, Wilk's Lambda and bounded Mahalanobis distance, are applied. Classification with features determined by bounded Mahalanobis distance statistically performs better than Wilk's Lambda. This chapter suggests that classification of e-nose with feature selection is a good choice to limit the cost of experiments and maintain good classification performance.

The lack of information on ground truth gas dispersion and experiment verification information has impeded the development of mobile olfaction systems, especially for real-world conditions. In this paper, an integrated testbed for mobile gas sensing experiments is presented. The integrated 3 m × 6 m testbed was built to provide real-time ground truth information for mobile olfaction system development. The testbed consists of a 72-gas-sensor array, namely Large Gas Sensor Array (LGSA), a localization system based on cameras and a wireless communication backbone for robot communication and integration into the testbed system. Furthermore, the data collected from the testbed may be streamed into a simulation environment to expedite development. Calibration results using ethanol have shown that using a large number of gas sensor in the LGSA is feasible and can produce coherent signals when exposed to the same concentrations. The results have shown that the testbed was able to capture the time varying characteristics and the variability of gas plume in a 2 h experiment thus providing time dependent ground truth concentration maps. The authors have demonstrated the ability of the mobile olfaction testbed to monitor, verify and thus, provide insight to gas distribution mapping experiment.

Atomic force microscope (AFM) is a useful tool to capture the two- and three-dimensional image of height and size of nanostructured thin film. It operate by measuring the forces between a sharp tip and surface of the measured sample. In addition, AFM is equipped with powerful software for image processing to interpret experimental results in detail. For example, by using the height and scanning length parameters of measured sample, average roughness and root mean square roughness can be evaluated. In the present works, the effect of image flattening process toward the surface roughness and surface fluctuations of metal oxide thin films will be presented. Set of samples were prepared by magnetron sputtering deposition and sol-gel coating techniques. In gas sensor industries using metal oxide thin film, surface roughness of metal oxide thin films are very important in order to improve the sensitivity and respond time of gas sensor. Therefore, optimization of thin film deposition and characterization are very important. The correlation between the three-dimensional image and thin film deposition and image processing parameters will also be presented.

The ability to self-energize wireless sensor node promote the popularity of energy harvesting technique especially by using ambient vibration as the source of energy. In addition, the successful integration of the energy harvesting element on the same wafer as a wireless sensor node will promote the production in the MEMS scale and will reduce the overall cost of production. The usage of the cantilever structure as the transducer for converting mechanical energy (vibration) due to deflection of cantilever into the electrical energy is possible by depositing piezoelectric material on the cantilever. The usage of cantilever provide the simplest way for fabrication in the MEMS scale and also provide the ability to achieve low natural frequency. This paper present the work done on the simulation of the cantilever structure with the top end and back etch proof mass towards achieving low natural frequency in the MEMS scale by using IntelliSuite software.

BACKGROUND: Volatile organic compounds (VOCs) emitted from exhaled breath from human bodies have been proven to be a useful source of information for early lung cancer diagnosis. To date, there are still arguable information on the production and origin of significant VOCs of cancer cells. Thus, this study aims to conduct in-vitro experiments involving related cell lines to verify the capability of VOCs in providing information of the cells.METHOD: The performances of e-nose technology with different statistical methods to determine the best classifier were conducted and discussed. The gas sensor study has been complemented using solid phase micro-extraction-gas chromatography mass spectrometry. For this purpose, the lung cancer cells (A549 and Calu-3) and control cell lines, breast cancer cell (MCF7) and non-cancerous lung cell (WI38VA13) were cultured in growth medium.RESULTS: This study successfully provided a list of possible volatile organic compounds that can be specific biomarkers for lung cancer, even at the 24th hour of cell growth. Also, the Linear Discriminant Analysis-based One versus All-Support Vector Machine classifier, is able to produce high performance in distinguishing lung cancer from breast cancer cells and normal lung cells.CONCLUSION: The findings in this work conclude that the specific VOC released from the cancer cells can act as the odour signature and potentially to be used as non-invasive screening of lung cancer using gas array sensor devices.

In this study, an early fire detection algorithm has been proposed based on low cost array sensing system, utilizing gas sensors, dust particles and ambient sensors such as temperature and humidity sensor. The odor or smell-print emanated from various fire sources and building construction materials at early stage are measured. For this purpose, odor profile data from five common fire sources and three common building construction materials were used to develop the classification model. Normalized feature extractions of the smell print data were performed before subjected to prediction classifier. These features represent the odor signals in the time domain. The obtained features undergo the proposed multi-stage feature selection technique and lastly, further reduced by Principal Component Analysis (PCA), a dimension reduction technique. The hybrid PCA-PNN based approach has been applied on different datasets from in-house developed system and the portable electronic nose unit. Experimental classification results show that the dimension reduction process performed by PCA has improved the classification accuracy and provided high reliability, regardless of ambient temperature and humidity variation, baseline sensor drift, the different gas concentration level and exposure towards different heating temperature range., Comment: electronic nose; gas sensors; fire detection; feature selection; feature fusion; Artificial intelligence, machine learning, neural networks, remote sensing, decision support

There are various sources influencing indoor air quality (IAQ) which could emit dangerous gases such as carbon monoxide (CO), carbon dioxide (CO2), ozone (O3) and particulate matter. These gases are usually safe for us to breathe in if they are emitted in safe quantity but if the amount of these gases exceeded the safe level, they might be hazardous to human being especially children and people with asthmatic problem. Therefore, a smart indoor air quality monitoring system (IAQMS) is needed that able to tell the occupants about which sources that trigger the indoor air pollution. In this project, an IAQMS that able to classify sources influencing IAQ has been developed. This IAQMS applies a classification method based on Probabilistic Neural Network (PNN). It is used to classify the sources of indoor air pollution based on five conditions: ambient air, human activity, presence of chemical products, presence of food and beverage, and presence of fragrance. In order to get good and best classification accur...

In this paper, index for indoor air quality (also known as IAQI) and thermal comfort index (TCI) have been developed. The IAQI was actually modified from previous outdoor air quality index (AQI) designed by the United States Environmental Protection Agency (US EPA). In order to measure the index, a real-time monitoring system to monitor indoor air quality level was developed. The proposed system consists of three parts: sensor module cloud, base station and service-oriented client. The sensor module cloud (SMC) contains collections of sensor modules that measures the air quality data and transmit the captured data to base station through wireless. Each sensor modules includes an integrated sensor array that can measure indoor air parameters like Carbon Dioxide, Carbon Monoxide, Ozone, Nitrogen Dioxide, Oxygen, Volatile Organic Compound and Particulate Matter. Temperature and humidity were also being measured in order to determine comfort condition in indoor environment. The result from several experiments...

The unpredictable nature of gas dispersion is a well-known issue in mobile olfaction. As roboticists tend to depend on simulations and try to recreate environmental conditions in such simulations, an accurate representation of the gas plume is needed. Current model based simulations may not be able to capture the time-varying and unpredictable nature of gas distribution accurately. This paper presents the real-time gas distribution dataset collected in a mobile olfaction testbed which captures the time varying nature of a gas plume.

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...

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.

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 ...

This works reports the electrical performance of reduced graphene oxide (RGO)/Molecular imprinted polymer (MIP)- organic thin film transistor (OTFT) for amino-acid detection, serine. These biomimetic sensors consider MIP as man-tailored biomimetic recognition sites that play an important role in signal transduction. MIP provides recognition sites compatible with serine molecules was developed by dispersing serine with methylacrylate acid (MAA) as functional monomer and Ethylene glycol dimethylacrylate (EGDMA) as cross-linker. The imprinted polymeric were mixed with reduced graphene oxide to produced sensing layer for the sensor. RGO-MIP layer was introduced between source and drain of OTFT via spin coating as a detecting layer for serine molecules. RGO was introduced into MIP, to allow a highly conductive sensing material thus enhanced selectivity and sensitivity of the sensor. By analyzing the electrical performance of the sensors, the performances of OTFT sensor enhanced with RGO/MIP interlayer and OTFT...

Characterization and calibration of gas sensor is a complex problem due to the dynamic behavior of gases and the limitations of current technology. This article reports a flexible, robust, and autonomous integrated system that is able to perform characterization on metal oxide-based gas sensors in dynamic environments. The system controls the concentration and flow of the relevant gases into the gas chamber and simultaneously measuring the sensor response. This feature allows the characterization of the sensor under continuous dynamic flow of gases similar to conditions on a robot or flow pipes. Several experiments have been performed on the system using hydrogen sulfide. The results provide information on the general characteristics of the sensor as well as its sensitivity. The noise levels were studied with different reference voltages. Overall, the results verify that the system is reliable and able to produce repeatable measurements.