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In this paper, an underwater acoustic sensor fault detection method is proposed that determines whether or not each sensor of multi-channel line array hydrophones malfunctions for passive sonar systems. To this end, the proposed method first measures a short-time root mean square (RMS) value of input signal for each channel. Then, it analyzes the RMS difference between the adjacent channels. In addition, the crossing rate of RMS values (RMSCR) is computed for each channel, and then the average value of RMSCR over all the channels is obtained. Some faulty sensors are identified by comparing the RMS difference with a threshold, and others by comparing the ratio between RMSCR of each of them and the average value of RMSCR with a threshold. In order to evaluate the performance of the proposed method, the precision of detecting fault sensors is measured. Consequently, it is shown that the proposed method works well in underwater environments with average RMS of −18.6 and −9.7 dB.

Image of mobile camera is easily blurred during the exposure time by hand motion. To reduce hand motion blur of image, optical image stabilization technologies are widely used. They need additional mechanism and fine alignment of optical components and make the size of system bulky. In the mobile devices, however, using minimum number of optical and mechanical component is important. In this paper, we propose noble image restoration algorithm using upscaled image and recalculated deconvolution filter from the point spread function of optical system not to use any additional mechanical or optical components. Upscaled and restored image represents higher image resolution than the reference image which is not upscaled.