Energy-aware Scheduling of Surveillance in Wireless Multimedia Sensor Networks

Wireless sensor networks involve a large number of sensor nodes with limited energy supply, which impacts the behavior of their application. In wireless multimedia sensor networks, sensor nodes are equipped with audio and visual information collection modules. Multimedia contents are ubiquitously retrieved in surveillance applications. To solve the energy problems during target surveillance with wireless multimedia sensor networks, an energy-aware sensor scheduling method is proposed in this paper. Sensor nodes which acquire acoustic signals are deployed randomly in the sensing fields. Target localization is based on the signal energy feature provided by multiple sensor nodes, employing particle swarm optimization (PSO). During the target surveillance procedure, sensor nodes are adaptively grouped in a totally distributed manner. Specially, the target motion information is extracted by a forecasting algorithm, which is based on the hidden Markov model (HMM). The forecasting results are utilized to awaken sensor node in the vicinity of future target position. According to the two properties, signal energy feature and residual energy, the sensor nodes decide whether to participate in target detection separately with a fuzzy control approach. Meanwhile, the local routing scheme of data transmission towards the observer is discussed. Experimental results demonstrate the efficiency of energy-aware scheduling of surveillance in wireless multimedia sensor network, where significant energy saving is achieved by the sensor awakening approach and data transmission paths are calculated with low computational complexity

AN INVESTIGATION OF VELOPHARYNGEAL CLOSURE WITH LINEAR REGRESSION

Cleft lip and palate is a common birth defect in the United States. Children diagnosed with this abnormality face difficulties during feeding, hearing and speech. Surgical methods exist to repair the cleft lip and palate but often require subsequent surgeries as children are unable to gain full speech capabilities as they tend to develop hypernasal speech due to velopharyngeal inadequacy. Investigating velopharyngeal closure can help speech pathologists, surgeons and related professionals understand the effect of velopharyngeal anatomy on velopharyngeal function. In order to accomplish this, several studies have used two dimensional and three dimensional modeling to visualize the velum. Very few attempts have been made to track the velum and plot its movement against time. Image segmentation has been used widely for various purposes. However, its proficiency in tracking the velum is questionable at the moment. Two image segmentation methods, EdgeTrak and the Hidden Markov Model, are reviewed in this report. EdgeTrak, a software developed at the Video/Image Modeling and Synthesis Laboratory, has been proven to track the surface of a human tongue during speech production. An attempt was made to similarly track the velum during speech production using EdgeTrak but the results were disappointing. Also, synchronized audio mapping using the Hidden Markov Model was only partially successful. This report describes the challenges image segmentation faces with regards to tracking the velum.M.S

Planning under uncertainty for dynamic collision avoidance

Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2011.This electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections.Cataloged from student submitted PDF version of thesis.Includes bibliographical references (p. 157-169).We approach dynamic collision avoidance problem from the perspective of designing collision avoidance systems for unmanned aerial vehicles. Before unmanned aircraft can fly safely in civil airspace, robust airborne collision avoidance systems must be developed. Instead of hand-crafting a collision avoidance algorithm for every combination of sensor and aircraft configurations, we investigate automatic generation of collision avoidance algorithms given models of aircraft dynamics, sensor performance, and intruder behavior. We first formulate the problem within the Partially Observable Markov Decision Process (POMDP) framework, and use generic MDP/POMDP solvers offline to compute vertical-only avoidance strategies that optimize a cost function to balance flight-plan deviation with risk of collision. We then describe a second framework that performs online planning and allows for 3-D escape maneuvers by starting with possibly dangerous initial flight plans and improving them iteratively. Experimental results with four different sensor modalities and a parametric aircraft performance model demonstrate the suitability of both approaches.by Selim Temizer.Ph.D