Description: This paper discusses anchor nodes placement for effective passive localization. Abstract: In many applications, the exact location of the sensor nodes is unknown after deployment. Localization is a process used to locate sensor nodes' positional coordinates, which is vital information. The localization is generally assisted by anchor nodes that are also sensor nodes but with known locations. Anchor nodes generally are expensive and need to be optimally placed for effective localization. Passive localization is one of the localization techniques where the sensor nodes silently listen to the global events like thunder sounds, seismic waves, lighting, etc. According to previous studies, the ideal location to place anchor nodes was on the perimeter of the sensor network. This may not be the case in passive localization, since the function of anchor nodes here is different than the anchor nodes used in other localization systems. The authors do extensive studies on positioning anchor nodes for effective localization. Several simulations are run in dense and sparse networks for proper positioning of anchor nodes. The authors show that, for effective passive localization, the optimal placement of the anchor nodes is at the center of the network in such a way that no three anchor nodes ...

Description: This poster discusses applications of logic flowcharting with a focus in autonomous robotic operations. The focus of this research project was to determine interactivity between flowcharting algorithms and programming of various robotic platforms.

Description: This report discusses applications of wireless sensors in monitoring Indoor Air Quality in the classroom environment. Abstract: The focus of this research project was to investigate Indoor Air Quality monitoring technologies, government regulations and policies, and best practices to improve IAQ while minimizing the adverse effect of poor IAQ, specifically in the classroom environment. The investigation involved two parts: development of a cost effective indoor air quality prototype sensor unit and the deployment of the unit to monitor 5 different indoor locations. The data from the sample monitoring locations will then be compiled and analyzed. In addition, researching the literature was instrumental in establishing the parameters for testing the environment and conducting experiments. This provided valuable experiences which will be shared with both district teachers and students.

Description: This report discusses aquatic sensors and telemetric monitoring of water quality in an artificial stream. Abstract: This research report covers the use of a wireless sensor network (WSN) using the ZigBee protocol to remotely monitor an artificial aquatic ecosystem. Field tests were conducted at the University of North Texas Water Research Facility to compare the accuracy of a high-end standard YSI multi probe system to a cost efficient lab developed sensor cluster, which would be used in the classroom to bring more real-world experiences in engineering to students. Measurements were recorded every 10 seconds for pH, dissolved oxygen, and temperature for a period of 24 hours. Comparison results show a 10 percent degree of variability in dissolved oxygen possibly due to the sensitivity of the DO sensors themselves. On the other hand, temperature and pH measured less than a 5 percent error.

Description: This article discusses classifying genes to the correct Gene Ontology Slim term in Saccharomyces cerevisiae using neighbouring genes with classification learning. Abstract: Background: There is increasing evidence that gene location and surrounding genes influence the functionality of genes in the eukaryotic genome. Knowing the Gene Ontology Slim terms associated with a gene gives the authors insight into a gene's functionality by informing the authors how its gene product behaves in a cellular context using three different ontologies: molecular function, biological process, and cellular component. In this study, the authors analyzed if they could classify a gene in Saccharomyces cerevisiae to its correct Gene Ontology Slim term using information about its location in the genome and information from its nearest-neighbouring genes using classification learning. Results: The authors performed experiments to establish that the MultiBoostAB algorithm using the J48 classifier could correctly classify Gene Ontology Slim terms of a gene given information regarding the gene's location and information from its nearest-neighbouring genes for training. Different neighbourhood sizes were examined to determine how many nearest neighbours should be included around each gene to provide better classification rules. The authors' results show that by just incorporating neighbour information from each gene's two-nearest neighbours, the ...