In active sonar systems, the detection of echo from targets can deteriorate due to reverberation. Detection becomes more difficult if targets have low-Doppler frequency and are located near the reverberation band, especially in an environment with low signal-to-reverberation ratio. In this paper, we propose an algorithm for the reverberation suppression of continuous wave signals using non-negative matrix factorization. To extract the target echo signal mixed with reverberations, the bases for the target echo and the reverberation are independently defined, and different constraints are applied for their corresponding estimation. We also derive constraints on temporal continuity and temporal length to estimate bases for the target echo. Experiments using simulated reverberations are performed to evaluate the proposed algorithm, and the results show an enhancement in the signal-to-noise ratio by 6-15 dB, as well as in the detection probability at several signal-to-reverberation ratios. Moreover, an experiment is conducted using reverberation measured from an ocean, and the results show that the proposed algorithm can effectively suppress reverberation and enhance detection performance in practical settings.
Due to the recent explosive growth of location-aware services based on mobile devices, predicting the next places of a user is of increasing importance to enable proactive information services. In this paper, we introduce a data-driven framework that aims to predict the user’s next places using his/her past visiting patterns analyzed from mobile device logs. Specifically, the notion of the spatiotemporal-periodic (STP) pattern is proposed to capture the visits with spatiotemporal periodicity by focusing on a detail level of location for each individual. Subsequently, we present algorithms that extract the STP patterns from a user’s past visiting behaviors and predict the next places based on the patterns. The experiment results obtained by using a real-world dataset show that the proposed methods are more effective in predicting the user’s next places than the previous approaches considered in most cases.
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