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a novel approach has been developed for detecting and classifying foraging calls of two mysticete species in passive acoustic recordings. This automated detector/classifier applies a computer-vision based technique, a pattern recognition method, to detect the foraging calls and remove ambient noise effects. The detected calls were then classified as blue whale D-calls [1] or fin whale 40-Hz calls...
In this paper, we proposed a self-adaptive wireless indoor localization system for device diversity. The major feature is that our system can incrementally collect RSS data for device calibration without stopping the localization service. The system has two phases. In the localization phase, we used relative radio signal strength (RSS) features as inputs to overcome device diversity and train classifiers...
Recently, the techniques for indoor localization have become more and more important and play a critical role in many mobile applications. Among them, the fingerprint-based indoor localization system has been recognized as a possible right way toward success. However, some challenges still remain. One issue should be addressed is the device diversity problem, where different devices would receive...
In this paper, we proposed a region-based approach for indoor localization based on the fingerprint of radio signal strength (RSS). Unlike conventional fingerprint-based methods that based on point information to infer user's location, we used region information for indoor localization. Our results show that the proposed system has better positioning accuracy and more robustness to signal noise and...
Message Passing Interfaces (MPI) plays an important role in parallel computing. Many parallel applications are implemented as MPI programs. The existing methods of bug detection for MPI programs have the shortage of providing both input and non-determinism coverage, leading to missed bugs. In this paper, we employ symbolic execution to ensure the input coverage, and propose an on-the-fly schedule...
This paper presents the design of an on-bed rehabilitation exercise monitoring system that utilizes a high density sensor bedsheet to evaluate active range of motion exercises. We propose and develop a novel framework to analyze the progression of pressure image sequences using manifold learning. The image sequences are reduced to a low dimensional subspace that can be measured against expected prior...
This paper presents the design of an on-bed rehabilitation exercise monitoring system that utilizes a high density sensor bedsheet to evaluate active range of motion exercises. We propose and develop a novel framework to analyze the progression of pressure image sequences using manifold learning. The image sequences are reduced to a low dimensional subspace that can be measured against expected prior...
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