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Pristley Sathyaraj, S.
- Study on Acoustic Source Localization in Wireless Sensor Networks using Centralized and Distributed Algorithms
Authors
1 Department of Electrical and Electroics Engineering, Mepco Schlenk Engineering College, Sivakasi, TN-626 005, IN
Source
Wireless Communication, Vol 1, No 5 (2009), Pagination: 216-224Abstract
In recent years, wireless sensor networks have become an evolving technology that has a wide range of potential applications. The emergence of miniature low-power devices that integrate micro-sensing and actuation with onboard processing and wireless communication capabilities has stimulated great interests in developing wireless sensor network Acoustic source localization is a research topic with many application areas, such as voice enhancement, intruder detection, sniper localization, and automatic tracking of speakers in an e-conferencing environment. Utilization of wireless sensor networks for acoustic source localization can provide good coverage, which not only enhances the accuracy, but also increases the robustness of the overall system. In this paper, we gave an overview of the acoustic source localization based on wireless sensor networks. We also briefly survey a large and growing body of sensor localization algorithms. This paper is intended to emphasize the basic statistical signal processing background necessary to understand the state-of-the-art and to make progress in the new and largely open areas of sensor network localization research.we analyzed the characteristics of acoustic source localization in wireless sensor networks. Then, we classified the existing typical acoustic source localization algorithms in wireless sensor networks into centralized algorithms and distributed algorithms. At the same time, we have introduced the algorithms and discussed both merits and drawbacks of the algorithms. Finally, we pointed out the design issues of related middleware services.
Keywords
Acoustic Source System, Automatic Tracking, EM Algorithm, Centralized Algorithms and Distributed Algorithms.
- Analysis of Elt Image of the Lungs by Fuzzy Black Box Back Propagation Intelligent Technique
Authors
1 Department of Electrical and Electronics Engineering, Mepco Schlenk Engineering College, Sivakasi – 626 005, Tamil Nadu, IN
2 Department of Electrical and Electronics Engineering, Mepco Schlenk Engineering College, Sivakasi–626 005, IN
Source
Digital Image Processing, Vol 1, No 2 (2009), Pagination: 68-72Abstract
Electrical Impedance Tomography (EIT) is a functional maging method that is being developed for bedside use in critical care medicine. Aiming at improving the chest anatomical resolution of EIT images, we have developed a fuzzy Black Box back propagation Technique (BBT) based on EIT’s high temporal resolution and the functional information contained in the pulmonary perfusion and ventilation signals. EIT data from an experimental model were collected during normal ventilation and apnea while an injection of hypertonic saline was used as a reference. The fuzzy model was elaborated in three parts: a modeling of the heart, a pulmonary map from ventilation images and a pulmonary map from perfusion images. Image segmentation was performed using a threshold method and a ventilation/perfusion map was generated using Intelligent Black box Back Propagation Technique. EIT images treated by the fuzzy model were compared with the hypertonic saline injection method and CT-scan images, presenting good results in both qualitative (the image obtained by the model was very similar to that of the CT-scan)and quantitative (the ROC curve provided an area equal to 0.97) point of view. Undoubtedly, these results represent an important step in the EIT images area, since they open the possibility of developing EIT-based bedside clinical methods, which are not available nowadays. These achievements could serve as the base to develop EIT diagnosis system for some life-threatening diseases commonly found in critical care medicine.