The problem of super-resolution time delay estimation in multipath environments is addressed in this paper. Two cases, active and passive systems, are considered. The time delay estimation is first converted into a sinusoidal parameter estimation problem. Then the sinusoidal parameters are estimated by generalizing the Multiple Signal Classification (MUSIC) algorithm for single-experiment data. The proposed method, referred to as the MUSIC-type algorithm, approximates the Cramer-Rao bound (CRB) in terms of the mean square errors (MSEs) for different signal-to-noise ratios (SNRs) and separations of multipath components. Simulation results show that the MUSIC-type algorithm performs better than the classical correlation approach and the conventional MUSIC method for the closely spaced components in multipath environments.

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In this paper, we apply reinforcement learning, a significant area of machine learning, to formulate an optimal self-learning strategy to interact in an unknown and dynamically variable underwater channel. The dynamic and volatile nature of the underwater channel environment makes it impossible to employ pre-knowledge. In order to select the optimal parameters to transfer data packets, by using reinforcement learning, this problem could be resolved, and better throughput could be achieved without any environmental pre-information. The slow sound velocity in an underwater scenario, means that the delay of transmitting packet acknowledgement back to sender from the receiver is material, deteriorating the convergence speed of the reinforcement learning algorithm. As reinforcement learning requires a timely acknowledgement feedback from the receiver, in this paper, we combine a juggling-like ARQ (Automatic Repeat Request) mechanism with reinforcement learning to minimize the long-delayed reward feedback problem. The simulation is accomplished by OPNET.

Sparse representation based classification (SRC) as an efficient method has high recognition rate in many pattern recognition applications. Unfortunately, the original SRC method generally requires rigid alignment in classification. In this paper, the feature-based SRC method is addressed by using the PCA-SIFT and SPP-SIFT descriptors, respectively. The presented methods are not only efficient for alignment-free in face and vehicle recognition, but also robust for the image illumination variation, rescaling and affine transform, when the image processing is moved from pixel-domain into the feature-domain and sparse-domain, i.e. PCA-SIFT and SPP-SIFT descriptors. Experimental results show the presented methods in this paper have higher recognition rate, more robustness. In addition, PCA-SIFT-SRC has lower computational complexity than MKD-SRC and SRC in the above scenarios.

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