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The Latest News, Articles, and Events in Signal Processing
The IEEE Signal Processing Society Chapter of the Year Award will be presented for the ninth time in 2020. The award will be presented annually to a Chapter that has provided their membership with the highest quality of programs, activities and services. The award will be presented annually in conjunction with the International Conference on Acoustics, Speech and Signal Processing (ICASSP) to the Chapter’s representative.
Please visit the Conferences and Events page on the IEEE Signal Processing Society website for Upcoming Lectures by Distinguished Lecturers.
The Signal Processing Society (SPS) has 12 Technical Committees and 3 Special Interest Groups (SIGs) that support a broad selection of signal processing-related activities defined by the scope of the Society.
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
The list of tables of contents (TOCs) are now available for May 2019. The TOCs are formatted to the style of its publication and offers not only links to the full issue on the front cover, but also links to the individual articles within each TOC to take you to IEEEXplore®.
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
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