The OU Analytics tool provides IEEE volunteer access to member data. It replaces SAMIEEE which was sunset in May. The OU Analytics User Group was formed to facilitate discussions among users, identify the top issues affecting user productivity, and provide a communication path between users and IEEE staff working to improve OU Analytics.
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.
Attn: Section Chairs, Section Student Activities Chairs, and Section Student Representatives
Below are the actions that have been approved by the MGA Board in relation to the Section - Student Branch Connection.
A Chapter is required to report and file not less than two (2) technical meetings per year. Changes to its roster of officers are to be submitted in a timely manner using electronic reporting tools provided by MGA.
The IEEE Signal Processing Society Malaysia Chapter has been selected as the recipient of the 2018 Chapter of the Year Award!
Please be advised that IEEE gauges the vitality of an OU by tracking how many meetings are reported during the year through vTools. If an OU reports "0" meetings for three consecutive years the OU/Chapter is placed on a dissolution list that is reviewed at the November Board Meeting.
A new online portal is available for ordering Membership Development (MD) materials. Volunteers now have the ability to customize the quantities and items included to support in recruiting new members, retaining those whose membership has lapsed, and recovering former members.
Please visit the Conferences and Events page on the IEEE Signal Processing Society website for Upcoming Lectures by Distinguished Lecturers.
The School of Informatics at the University of Edinburgh are currently recruiting faculty at the Lecturer/Senior Lecturer/Reader level (similar to Assistant/Associate Professor) in several areas, including speech technology.
In this paper, four iterative algorithms for learning analysis operators are presented. They are built upon the same optimization principle underlying both Analysis K-SVD and Analysis SimCO. The forward and sequential analysis operator learning (AOL) algorithms are based on projected gradient descent with optimally chosen step size. The implicit AOL algorithm is inspired by the implicit Euler scheme for solving ordinary differential equations and does not require to choose a step size.
@ieeeSPS
11.3K subscribers‧180 videos
