Monitoring urban growth and land use land cover change in Al Ain, UAE using remote sensing and GIS techniques

Urbanization and industrialization cause a serious land degradation problem, including an increased pressure on natural resources such as deforestation, rise in temperature and management of water resources. The Urban Heat Island (UHI) effects of urbanization are widely acknowledged. Increase of impervious surface is a surrogate measure of urbanization and their effects on local hydrology is well reported in literature. This study investigates the spatial-temporal dynamics of land use and land cover changes in Al Ain, UAE, from 2006 to 2016. The Landsat images of two different periods, i.e., Landsat ETM of 2006 and Landsat 8 for 2016 were acquired from earth explorer site. Semi-supervised known as the hybrid classification method was used for image classification. The change detection was carried out through post-classification techniques. The study area was categorized into five major classes. These are agriculture, gardens, urban, sandy areas and mixed urban/sandy areas. It was observed that agricultural and urban land increases from 42,560 ha to 45,950 ha (8%) and 8150 ha to 9105 ha (12%), respectively. Consequently, the natural sandy area was reduced. It was also found that the urban area was expanded dramatically in the west and southwest directions. The outcomes of this study would help concerning authorities for a sustainable land and water resources management in the Al Ain region

Real-time early infectious outbreak detection systems using emerging technologies

The use of emerging technologies ( such as RFID - Radio Frequency Identification and remote sensing) can be employed to reduce health care costs and also to facilitate the automatic streamlining of infectious disease outbreak detection and monitoring processes in local health departments. It can assist medical practitioners with fast and accurate diagnosis and treatments. In this paper we outline the design and application of a real-time RFID and sensor-base Early Infectious (e.g., cholera) Outbreak Detection and Monitoring (IODM) system for health care.<br /

Microparticle image processing and field profile optimisation for automated Lab-On-Chip magnetophoretic analytical systems

The work described in this thesis, concerns developments to analytical microfluidic Lab-On-Chip platform originally developed by Prof Pamme's research group at the University of Hull. This work aims to move away from traditional laboratory analysis system towards a more effective system design which is fully automated and therefore potentially deployable in applications such as point of care medical diagnosis. The microfluidic chip platform comprises an external permanent magnet and chip with multiple parallel reagent streams through which magnetic micro-particles pass in sequence. These streams may include particles, analyte, fluorescent labels and wash solutions; together they facilitate an on-chip multi-step analytical procedure. Analyte concentration is measured via florescent intensity of the exiting micro-particles. This has previously been experimentally proven for more than one analytical procedure. The work described here has addressed a couple of issues which needed improvement, specifically optimizing the magnetic field and automating the measurement process. These topics are related by the fact that an optimal field will reduce anomalies such as aggregated particles which may degrade automated measurements.For this system, the optimal magnetic field is homogeneous gradient of sufficient strength to pull the particles across the width of the device during fluid transit of its length. To optimise the magnetic field, COMSOL (a Multiphysics simulation program) was used to evaluate a number of multiple magnet configurations and demonstrate an improved field profile. The simulation approach was validated against experimental data for the original single-magnet design.To analyse the results automatically, a software tool has been developed using C++ which takes image files generated during an experiment and outputs a calibration curve or specific measurement result. The process involves detection of the particles (using image segmentation) and object tracking. The intensity measurement follows the same procedure as the original manual approach, facilitating comparison, but also includes analysis of particle motion behaviour to allow automatic rejection of data from anomalous particles (e.g. stuck particles). For image segmentation a novel texture based technique called Temporal- Adaptive Median Binary Pattern (T-AMBP) combining with Three Frame Difference method to model the background for representing the foreground was proposed. This proposed approached is based on previously developed Adaptive Median Binary Pattern (AMBP) and Gaussian Mixture Model (GMM) approach for image segmentation. The proposed method successfully detects micro-particles even when they have very low fluorescent intensity, while most of the previous approaches failed and is more robust to noise and artefacts. For tracking the micro-particles, we proposed a novel algorithm called "Hybrid Meanshift", which combines Meanshift, Histogram of oriented gradients (HOG) matching and optical flow techniques. Kalman filter was also combined with it to make the tracking robust.The processing of an experimental data set for generating a calibration curve, getting effectively the same results in less than 5 minutes was demonstrated, without needing experimental experience, compared with at least 2 hours work by an experienced experimenter using the manual approach

Betelvine (Piper betle L.): A potential source for oral care

Piper betle L. (betelvine) is a valuable crop that is widely used as masticatory and with a long past history of varied traditional uses. Betelvine possesses numerous phytochemicals with important pharmacological attributes.&nbsp; Active molecules such as Fluoride, Eugenol, Hydroxylchavicol, Chlorogenic acid etc. present in betelvine with potent antibacterial, antifungal as well as anti-carcinogenic properties signify tremendous prospective of the plant for the formulation of natural product based drugs for maintaining hygiene and cure of diseases in the oral cavity. &nbsp

Comment on "Four-body charge transfer processes in proton--helium collisions"

We found, within the plane-wave first Born approximation (PWFBA), that the proton-helium fully differential cross section (FDCS) for transfer excitation agrees well with the experimental one at the proton energy Ep = 300 keV and small scattering angles both in shape and in magnitude. This result is in a contradiction with that obtained in [1].Comment: 4 pages, 2 figure

Spam filtering using ML algorithms

Spam is commonly defined as unsolicited email messages, and the goal of spam categorization is to distinguish between spam and legitimate email messages. Spam used to be considered a mere nuisance, but due to the abundant amounts of spam being sent today, it has progressed from being a nuisance to becoming a major problem. Spam filtering is able to control the problem in a variety of ways. Many researches in spam filtering has been centred on the more sophisticated classifier-related issues. Currently,&nbsp; machine learning for spam classification is an important research issue at present. Support Vector Machines (SVMs) are a new learning method and achieve substantial improvements over the currently preferred methods, and behave robustly whilst tackling a variety of different learning tasks. Due to its high dimensional input, fewer irrelevant features and high accuracy, the&nbsp; SVMs are more important to researchers for categorizing spam. This paper explores and identifies the use of different learning algorithms for classifying spam and legitimate messages from e-mail. A comparative analysis among the filtering techniques has also been presented in this paper.<br /

Secure connectivity model in wireless sensor networks (WSN) using first order Reed-Muller codes

In this paper, we suggest the idea of separately treating the connectivity and communication model of a Wireless Sensor Network (WSN). We then propose a novel connectivity model for a WSN using first order Reed-Muller Codes. While the model has a hierarchical structure, we have shown that it works equally well for a Distributed WSN. Though one can use any communication model, we prefer to use the communication model suggested by Ruj and Roy [1] for all computations and results in our work. Two suitable secure (symmetric) cryptosystems can then be applied for the two different models, connectivity and communication respectively. By doing so we have shown how resiliency and scalability are appreciably improved as compared to Ruj and Roy [1].<br /