No abstract

Wireless energy transfer based on magnetic resonant coupling is a promising technology to replenish energy to sensor nodes in a wireless sensor network (WSN). However, charging sensor node one at a time poses a serious scalability problem. Recent advances in magnetic resonant coupling shows that multiple nodes can be charged at the same time. In this paper, we exploit this multi-node wireless energy transfer technology to address energy issue in a WSN. We consider a wireless charging vehicle (WCV) periodically traveling inside a WSN and charging sensor nodes wirelessly. We propose a cellular structure that partitions the two-dimensional plane into adjacent hexagonal cells. The WCV visits these cells and charge sensor nodes from the center of a cell. We pursue a formal optimization framework by jointly optimizing traveling path, flow routing and charging time. By employing discretization and a novel Reformulation-Linearization Technique (RLT), we develop a provably nearoptimal solution for any desired level of accuracy.

Wireless sensor networks that operate on batteries have limited network lifetime. There have been extensive recent research efforts on how to design protocols and algorithms to prolong network lifetime. However, due to energy constraint, even under the most efficient protocols and algorithms, the network lifetime may still be unable to meet the mission's requirements. In this paper, we consider the energy provisioning (EP) problem for a two-tiered wireless sensor network. In addition to provisioning additional energy on the existing nodes, we also consider deploying relay nodes (RNs) into the network to mitigate network geometric deficiencies and prolong network lifetime. We formulate the joint problem of EP and RN placement (EP-RNP) into a mixed-integer nonlinear programming (MINLP) problem. Since an MINLP problem is NP-hard in general, and even state-of-theart software and techniques are unable to offer satisfactory solutions, we develop a heuristic algorithm, called Smart Pairing and INtelligent Disc Search (SPINDS), to address this problem. We show a number of novel algorithmic design techniques in the design of SPINDS that effectively transform a complex MINLP problem into a linear programming (LP) problem without losing critical points in its search space. Through numerical results, we show that SPINDS offers a very attractive solution and some important insights to the EP-RNP problem.

Abstract. Hashing is one of the most popular and powerful approximate nearest neighbor search techniques for large-scale image retrieval. Most traditional hashing methods first represent images as off-the-shelf visual features and then produce hashing codes in a separate stage. However, off-the-shelf visual features may not be optimally compatible with the hash code learning procedure, which may result in sub-optimal hash codes. Recently, deep hashing methods have been proposed to simultaneously learn image features and hash codes using deep neural networks and have shown superior performance over traditional hashing methods. Most deep hashing methods are given supervised information in the form of pairwise labels or triplet labels. The current state-of-the-art deep hashing method DPSH [1], which is based on pairwise labels, performs image feature learning and hash code learning simultaneously by maximizing the likelihood of pairwise similarities. Inspired by DPSH [1], we propose a triplet label based deep hashing method which aims to maximize the likelihood of the given triplet labels. Experimental results show that our method outperforms all the baselines on CIFAR-10 and NUS-WIDE datasets, including the state-of-the-art method DPSH [1] and all the previous triplet label based deep hashing methods.

No abstract