Sorry, you need to enable JavaScript to visit this website.

This paper investigates time-of-arrival (TOA) source node self-positioning with unknown clock skews in wireless sensor networks. For the source-to-anchor direction, source node clock skew does not affect the localization performance. When synchronized anchor nodes simultaneously transmit signals to a source node,the source node clock skew will degrade the localization performance.

The method reported here realizes an inaudible echo-hiding based speech watermarking by using sparse subspace clustering (SSC). Speech signal is first analyzed with SSC to obtain its sparse and low-rank components. Watermarks are embedded as the echoes of the sparse component for robust extraction. Self-compensated echoes consisting of two independent echo kernels are designed to have similar delay offsets but opposite amplitudes. A one-bit watermark is embedded by separately performing the echo kernels on the sparse and low-rank components.

End-to-end encryption challenges mobile network operators to assess the quality of the HTTP Adaptive Streaming (HAS), where the quality assessment is coarse-grained, e.g., detecting if there exist stalling during the whole playback. Targeting on this issue, this paper proposes an attention-based hybrid RNN-HMM model, which integrates HMM with attention mechanism to predict the player states. The model is trained and evaluated based on the download speed and player state sequences of encrypted video sessions collected from YouTube.

While sensors have been widely used in various applications, an essential current trend of research consists of collecting and fusing the information that comes from many sensors. Among these researches, combing the position information together with sensor data is particularly popular and prevalent, such as wireless sensor network localization. In this paper, on the contrary, we would like to concentrate on a unique mobile sensor. Our goal is to unveil the multidimensional information entangled within a stream of one-dimensional data. This task is called FRI Sensing.