1 code implementation • 7 Apr 2022 • Shunan Sheng, Qikun Xiang, Ido Nevat, Ariel Neufeld
Firstly, we develop algorithms to perform approximate Likelihood Ratio Tests on the time-series observations, compressing them to a single bit for both point sensors and integral sensors.
no code implementations • 2 Sep 2020 • Biswadeep Chakraborty, Dinil Mon Divakaran, Ido Nevat, Gareth W. Peters, Mohan Gurusamy
In this work, we take a more realistic approach, and argue that feature extraction has a cost, and the costs are different for different features.
no code implementations • 16 Aug 2019 • Qikun Xiang, Ido Nevat, Gareth W. Peters
Spatial regression of random fields based on potentially biased sensing information is proposed in this paper.
no code implementations • 12 Nov 2017 • Pengfei Zhang, Ido Nevat, Gareth W. Peters, Wolfgang Fruehwirt, Yongchao Huang, Ivonne Anders, Michael Osborne
Next, building on the S-BLUE, we address the second problem, and develop an efficient algorithm for query based sensor set selection with performance guarantee.