CEPD News 
July, 2013 - Vol 6, Issue 2
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Near Field Communications

What is NFC?

NFC stands for Near Field Communication. NFC is a subset of RFID. It transmits at a frequency of 13.56 MHz and has a maximum range of four inches. The short range is intended to increase security. NFC transmissions are encrypted to add a layer of security.

NFC can communicate with active devices or passive targets. Active devices can be two cell phones exchanging data. NFC is being used for mobile payments where your device can store your credit card information. Passive devices can be tags or cards that contain an id number. These tags can contain personal data, shopping information or just be used as a form of id.

CEPD has experience with NFC and other wireless technologies. If you need assistance with development of your wireless application CEPD can help. -GB

Society News

The next IEEE Signal Processing Society meeting will be July 22 at 6:00pm - 8:00pm.
The meeting will be held at DeVry University, Room 121, 1870 W. 122nd Ave. in Westminster.
The cost is free. Refreshments and snacks will be provided for the meet and greet, beginning at 6:00.
ICA and IVA: Theory, Connections, and Applications to Medical Imaging
by Tülay Adali
Data-driven methods are based on a simple generative model and hence can minimize the assumptions on the nature of data. They have emerged as promising alternatives to the traditional model-based approaches in many applications where the underlying dynamics are hard to characterize. Independent component analysis (ICA), in particular, has been a popular data-driven approach and an active area of research. Starting from a simple linear mixing model and imposing the constraint of statistical independence on the underlying components, ICA can recover the linearly mixed components subject to only a scaling and permutation ambiguity. It has been successfully applied to numerous data analysis problems in areas as diverse as biomedicine, communications, finance, geophysics, and remote sensing.

This talk reviews the fundamentals and properties of ICA, and provides a unified view of two main approaches for achieving ICA, those that make use of non-Gaussianity and sample dependence. Then, the generalization of ICA for analysis of multiple datasets, independent vector analysis (IVA), is introduced and the connections between ICA and IVA are highlighted, especially in the way both approaches make use of signal diversity. Examples are presented to demonstrate the application of ICA and IVA to analysis of functional magnetic resonance imaging data as well as fusion of data from multiple imaging modalities.

Tülay Adali (F) received the Ph.D. degree in electrical engineering from North Carolina State University, Raleigh, in 1992 and joined the faculty at the University of Maryland Baltimore County (UMBC), Baltimore, the same year where she currently is a Professor in the Department of Computer Science and Electrical Engineering. She has held visiting positions at École Supérieure de Physique et de Chimie Industrielles, Paris, France; Technical University of Denmark, Lyngby, Denmark; Katholieke Universiteit, Leuven, Belgium; University of Campinas, Brazil; and University of Newcastle, Australia.  Prof. Adali is a Fellow of the IEEE and the AIMBE, and the recipient of a 2010 IEEE Signal Processing Society Best Paper Award and an NSF CAREER Award. Prof. Adali's research interests are in the areas of statistical signal processing, machine learning for signal processing, and biomedical data analysis. 
More information is available on the IEEE SPS website. 
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