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This letter investigates how to place the received-signal-strength (RSS) sensors to improve the static target localization accuracy in the three-dimensional (3-D) space. By using the A-optimality criterion, i.e., minimizing the trace of the inverse Fisher information matrix (FIM), a new optimal RSS sensor placement strategy is developed when sensors can be placed freely in the 3-D space.
In this letter, we consider the problem of direction-of-arrival (DOA) estimation with one-bit quantized array measurements. With analysis, it is shown that, under mild conditions the one-bit covariance matrix can be approximated by the sum of a scaled unquantized covariance matrix and a scaled identity matrix.
Lecture Date: June 17, 2019
Chapter: Italy
Chapter Chair: Mauro Barni
Topic: Signal Enhancement in Consumer Products
Lecture Date: June 18, 2019
Chapter: Atlanta
Chapter Chair: Alessio Medda
Topic: Distributed Algorithms for Principal Component Analysis
December 10-12, 2019
Location: Ajman, United Arab Emirates
June 8-11, 2020
Location: Changed to--Virtual Conference
Lecture Date: August 5, 2019
Chapter: Tokyo
Chapter Chair: Kazuya Takeda
Topic: Intelligent ear-level devices for hearing
enhancement and health and wellness monitoring
Lecture Date: August 12, 2019
Chapter: Beijing
Chapter Chair: Qiuqi Ruan
Topic: Tackling the ultimate cocktail party problem
using joint auditory attention decoding and beamforming
This paper presents a flexible array response control algorithm via oblique projection, abbreviated as FARCOP, and its application to array pattern synthesis. The proposed FARCOP algorithm stems from the adaptive array theory, and it can flexibly, precisely and simultaneously adjust the array response levels at multiple angles based on an arbitrarily given weight vector.
Intelligent mobile platforms such as smart vehicles and drones have recently become the focus of attention for onboard deployment of machine learning mechanisms to enable low latency decisions with low risk of privacy breach.
In this paper, we propose a novel sparse signal recovery algorithm called the trainable iterative soft thresholding algorithm (TISTA). The proposed algorithm consists of two estimation units: a linear estimation unit and a minimum mean squared error (MMSE) estimator based shrinkage unit.
In classification theory, it is generally assumed that the data are independent and identically distributed. However, in many practical applications, we face a set of observations that are collected sequentially with a dependence structure among samples.
PhD Position : Image Analysis of dynamic MRI data to study musculoskeletal disorders
Lab