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Previous research indicated that uniform deployment was not the most efficient way for saving energy in wireless sensor networks. The location nearer to a sink should have more sensors deployed. This paper describes a moving algorithm for achieving the non-uniform deployment (MAND). With MAND, mobile sensors can move to appropriate locations to prolong the system lifetime. With two refinements on MAND (called EMAND), not only the coverage of the network is maintained, but the lifetime can be improved enormously. Simulation results show that EMAND performs well in the environment with more sensing events.

A first dual-standard video encoder and decoder LSI providing VP8 (i.e. video format of WebM project for use of web's video) or H.264/AVC video recording and playback simultaneously is implemented with 28nm CMOS and occupies 1.94mm 2 of core area. Several area-efficient techniques are realized, leading to 43.6% of area reduction. A new rate control is designed to facilitate the adaptation of video data and frame rates for network services. Two fast algorithms and new bool encoder/decoder are proposed to enhance power efficiency. This chip consumes 28.15mW and 10.02mW of VP8 encoder and decoder average power for 1080p@30fps at 0.9V, respectively.

Prediction using the ground truth sounds like an oxymoron in machine learning. However, such an unrealistic setting was used in hundreds, if not thousands of papers in the area of finding graph representations. To evaluate the multi-label problem of node classification by using the obtained representations, many works assume that the number of labels of each test instance is known in the prediction stage. In practice such ground truth information is rarely available, but we point out that such an inappropriate setting is now ubiquitous in this research area. We detailedly investigate why the situation occurs. Our analysis indicates that with unrealistic information, the performance is likely over-estimated. To see why suitable predictions were not used, we identify difficulties in applying some multi-label techniques. For the use in future studies, we propose simple and effective settings without using practically unknown information. Finally, we take this chance to compare major graph-representation learning methods on multi-label node classification.