Design and Development of Activity Attendance Monitoring System based on RFID (Peer Review)

Attending activities organized by the university or institution is one of the important criteria that must be fulfilled by students for multiple purposes. Whether it is by attending classes, or any other activities, the main concern is focused on the process of recording students’ attendance. The use of a paperbased manual system to record students’ attendance is still being widely used due to the lack of an e-management system. These approaches have a lot of disadvantages due to the nature of the paper which is a fragile material - also an expensive cost to procure and produce. This paper, relying on Radio Frequency Identification (RFID), designed and developed an electronic system known as Activity Attendance Monitoring System (AAMS) that utilizes readily available resource – student card, as the student identification when attending an activity. Results from the validation, execution and continuous test suggest that AAMS can be effectively implemented to monitor and record student’s attendance. The main contribution of the study is the design and development model that capable of monitoring students’ activity attendance in university activities context. Developers and researcher in the area can adopt the proposed design and development model in formulating a similar system in managing activity attendance

River segmentation using satellite image contextual information and Bayesian classifier

Satellite-based remote sensing imaging can provide continuous snapshots of the Earth’s surface over long periods. River extraction from remote sensing images is useful for the comprehensive study of dynamic changes of rivers over large areas. This paper presents a new method of extracting rivers by using training samples based on the mathematical morphology, Bayesian classifier and a dynamic alteration filter. The use of a training map from erosion morphology helps to extract the non-predictive river’s curves in the image. The algorithm has two phases: creating the profile to separate river area via evaluated morphological erosion and dilation, namely, a training map; and improving the river’s image segmentation using the Bayesian rule algorithm in which two consecutive filters swipe false positive (non-water area) along the image. The proposed algorithm was tested on the Kuala Terengganu district, Malaysia, an area that includes a river, a bridge, dam and a fair amount of vegetation. The results were compared with two standard methods based on visual perception and on peak signal-to-noise ratio, respectively. The novelty of this approach is the definition of the contextual information filtering technique, which provides an accurate extraction of river segmentation from satellite images

Oligodontia is Caused by Mutation in LTBP3, the Gene Encoding Latent TGF-β Binding Protein 3

We have identified a consanguineous Pakistani family where oligodontia is inherited along with short stature in an autosomal-recessive fashion. Increased bone density was present in the spine and at the base of the skull. Using high-density single-nucleotide polymorphism microarrays for homozygosity mapping, we identified a 28 Mb homozygous stretch shared between affected individuals on chromosome 11q13. Screening selected candidate genes within this region, we identified a homozygous nonsense mutation, Y774X, within LTBP3, the gene for the latent TGF-β binding protein 3, an extracellular matrix protein believed to be required for osteoclast function