Caitao Zhan

TL;DR: This paper proposes an efficient algorithm for the MTL problem based on the hypothesis-based Bayesian approach called MAP and develops optimized techniques based on MAP with significantly improved computational and training costs, including a novel interpolation method, ILDW, which helps minimize the training cost.

TL;DR: This paper addresses the problem of localizing an (illegal) transmitter using a distributed set of sensors using greedy approximation algorithms for the optimization problem of selecting a given number of sensors in order to maximize an appropriately defined objective function of localization accuracy.

TL;DR: In this paper, a novel deep-learning approach is proposed to address the multiple transmitters localization (MTL) problem, where the first step maps an input image representing sensor readings to an image representing distribution of transmitter locations and the second object detection step derives precise locations of transmitters from the image of transmitter distributions.

TL;DR: In this paper , a novel deep-learning approach is proposed to address the multiple transmitter localization problem, where the first step maps an input image representing sensor readings to an image representing the distribution of transmitter locations, and the second object detection step derives precise locations of transmitters from the image of transmitter distributions.

TL;DR: This paper designs greedy approximation algorithms for the optimization problem of selecting a given number of sensors in order to maximize an appropriately defined objective function of localization accuracy and develops techniques to significantly reduce the time complexity of the designed algorithms by incorporating certain observations and reasonable assumptions.