GPSense: an algorithmic framework for intelligent sensing at node level in WSN

We proposed a new Genetic Programming algorithm termed GPSense for use in wireless sensor networks (WSN). Existing algorithms for pattern recognition and data mining in WSNs work offline, i.e. they query the WSN nodes, bring data to the base-station and the mining is done at the base-station. This increases the latency of decision making, enhances decision communication costs and generally leads to non-local decisions. It is generally believed that this paradigm is more powerefficient since sensor nodes are significantly constrained in terms of computing power (processor speed, low-power batteries, limited-memory). We believe that a distributed data mining approach can be evolved where small-footprint mining algorithms can be developed to work on the sensor-nodes thereby improving the current state-of-the-art. GPSense is the first of such in-network data mining frameworks and has the following desirable characteristics: It is designed to work as a distributed algorithm that co-ordinates and exchanges genetic material with collaborating nodes. It is aware of the resource constraints at node level with its footprint being consistently smaller than that of the sensor node\u27s processing capabilities. Its localized nature - enables the entire WSN to make decisions at the nodelevel instead of aggregating results from long-running continuous queries to the root node for eventual filtering. In this thesis we describe the GPSense framework and demonstrate its utility based on the results we obtained on a test-bed WSN

Depth and Size Limits for the Visibility of Veins Using the VeinViewer Imaging System

Administration of fluids or medication and blood draw procedures require the nurse or the phlebotomist to access the veins in patients at hospitals or phlebotomy centers. It is important to minimize the discomfort associated with sticking needles in the patient more than once and most often, necessary to find an appropriate vein within few minutes. However, problems involved in accessing veins in pediatric and obese patients make it very difficult to perform a successful stick in a short time. The VeinViewer Imaging System is an infrared imaging device that provides the nurses and phlebotomists a means for locating veins in the very first attempt and within a few seconds. A camera captures an image of the veins illuminated by infrared light and a contrast-enhanced image of the veins is projected back onto the patient’s skin in real-time using a projector, after being processed by a computer. Each vein in the VeinViewer image appears with different contrast against the background skin. To evaluate the performance of the device, a thorough investigation of the properties of the vein affecting its contrast can be of immense value. The goal of this research is to determine quantitatively the effect of physical properties of veins such as depth and diameter on its visibility in the VeinViewer image. The results of this study can be interpreted to understand the biological phenomena influencing the quality of the VeinViewer image. An extension of this study may lead to advancement in the hardware or software which potentially will benefit the phlebotomists and physicians

Yoga and Dental Health: A Review

Yoga is a holistic healing process which provides overall balance to the human body. Yoga has also proved its worth in prevention and management of various oral-facial conditions by stimulating and relaxing various bodily systems which leads to the decrease in the inflammation in the body. The complete information about yoga in dental health had been collected from various journals for the time period of 1997-2018.Conditions such as oral lichen planus, MPDS, xerostomia, aphthous ulcers, bruxism and burning mouth syndrome have been effectively managed by yog

ELEARNING

Web based eLearning Management allow instructors and students to share instructional materials, make class announcements, submit and return course assignments, and communicate with each other online. Technology is evolving in every field, in which is one of the most important in the field of teaching is eLearning Management. We can monitor the individual performance of each student using eLearning Management because one person cannot accurately evaluate the performance of all students. eLearning Management will make it easier to increase individual competence using the student monitoring process. eLearning Management can easily bring in the best educated instructors in the world to teach everyone. We give separate roles and responsibilities for each console and will use the following three consoles of eLearning Management. 1. Administrator, 2. Instructor and 3. Learners. Administrator can control user [i.e. instructor and learner] and courses information. Instructor can manage the content and monitoring the student progress. Learner can view the courses and learn everything. To execute the eLearning Management Website data is stored through MSSQL and the front end is implemented through HTML5, CSS3, Bootstrap and jQuery. We are using the .Net core on Visual Studio using c# Language

Addressing corner detection issues for machine vision based UAV aerial refueling

The need for developing autonomous aerial refueling capabilities for an Unmanned Aerial Vehicle (UAV) has risen out of the growing importance of UAVs in military and non-military applications. The AAR capabilities would improve the range and the loiter time capabilities of UAVs. A number of AAR techniques have been proposed, based on GPS based measurements and Machine Vision based measurements. The GPS based measurements suffer from distorted data in the wake of the tanker. The MV based techniques proposed the use of optical markers which---when detected---were used to determine relative orientation and position of the tanker and the UAV. The drawback of the MV based techniques is the assumption that all the optical markers are always visible and functional. This research effort proposes an alternative approach where the pose estimation does not depend on optical markers but on Feature Extraction methods. The thesis describes the results of the analysis of specific \u27corner detection\u27 algorithms within a Machine Vision---based approach for the problem of Aerial Refueling for Unmanned Aerial Vehicles. Specifically, the performances of the SUSAN and the Harris corner detection algorithms have been compared. Special emphasis was placed on evaluating their accuracy, the required computational effort, and the robustness of both methods to different sources of noise. Closed loop simulations were performed using a detailed SimulinkRTM -based simulation environment to reproduce docking maneuvers, using the US Air Force refueling boom