The advances in developing the Internet of Underwater Things (IoUT) can lead to numerous applications, namely, environmental monitoring, underwater navigation, and surveillance. Magnetic Induction (MI) is a promising ...
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The advances in developing the Internet of Underwater Things (IoUT) can lead to numerous applications, namely, environmental monitoring, underwater navigation, and surveillance. Magnetic Induction (MI) is a promising communication solution for IoUT networks. In this article, we focus on a deep-ocean monitoring network, where the underwater relays communicate using MI induction. Placement of the relays in such a system is an exciting and challenging task. Therefore, we propose an optimal relay placement solution for relays in MI-based IoUT networks to improve network throughput. First, we formulate the relay placement problem as a non-convex optimization problem. Then, we propose a global optimization technique based on reverseconvexprogramming (RCP) to solve the non-convex function. Finally, we compare the results of the proposed scheme with the uniform deployment to show its effectiveness.
We exploit the biconvex nature of the Euclidean non-negative matrix factorization (NMF) optimization problem to derive optimization schemes based on sequential quadratic and second order cone programming. We show that...
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We exploit the biconvex nature of the Euclidean non-negative matrix factorization (NMF) optimization problem to derive optimization schemes based on sequential quadratic and second order cone programming. We show that for ordinary NMF, our approach performs as well as existing state-of-the-art algorithms, while for sparsity-constrained NMF, as recently proposed by P.O. Hoyer in JMLR 5 (2004), it outperforms previous methods. In addition, we show how to extend NMF learning within the same optimization framework in order to make use of class membership information in supervised learning problems.
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