SUMMARYNetwork coding (NC) is considered a new paradigm for distributed networks. However, NC has an all-or-nothing property. In this paper, we propose a sparse recovery approach using sparse sensing matrix to solve the NC all-or-nothing problem over a finite field. The effectiveness of the proposed approach is evaluated based on a sensor network.
Single-image super-resolution technology has been widely studied in various applications to improve the quality and resolution of degraded images acquired from noise-sensitive low-resolution sensors. As most studies on single-image super-resolution focused on the development of deep learning networks operating on high-performance GPUs, this study proposed an efficient and lightweight super-resolution network that enables real-time performance on mobile devices. To replace the relatively slow element-wise addition layer on mobile devices, we introduced a skip connection layer by directly concatenating a lowresolution input image with an intermediate feature map. In addition, we introduced weighted clipping to reduce the quantization errors commonly encountered during float-to-int8 model conversion. Moreover, a reparameterization method was selectively applied without increasing the cost in terms of inference time and number of parameters. Based on the contributions, the proposed network has been recognized as the best solution in Mobile AI & AIM 2022 Real-Time Single-Image Super-Resolution Challenge with PSNR of 30.03 dB and NPU runtime of 19.20 ms.
This enables viewers to watch multiple video contents using a single device. The basic concept of this paper is that the server is asked to combine multiple streams into one bit-stream based in a compressed domain. In other words, this paper presents a new compressed domain combiner that works in boundary macroblocks of input videos with re-calculating intra prediction mode, intra prediction MVD, a re-allocation of the coefficient table, and border extension methods. The rest of the macroblocks of the input video data are achieved simply by copying them. Simulation experiments have demonstrated the possibility and effectiveness of the proposed algorithm by showing that it is able to generate more than 103 frames per second, stitching four 480p-sized images into each frame.
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