INFONET 국외출장 보고서

1. 보고자 인적사항

성 명	성진택
소 속 / 과정 및 학번	정보통신공학부 / 박사과정 20102043
여행국가 및 방문기관	독일 다름슈타트 공과대학 (SPAWC 2013, STATOS)
여 행 기 간	2013.06.14. ~ 2013.06.20.
여 행 목 적	SPAWC 논문 발표 및 STATOS 워크숍 참석

2. 업 무 수 행 내 용

회의명	기간	장소
STATOS 2013	2013.06.15.~16.	다름슈타트 공과대학
SPAWC 2013	2013.06.16.~19.	다름슈타트 공과대학

1) Paper presentation in SPAWC 2013: "Exact Outge Probability of Two Nodes for Cooperative Networks using GF(4)"

2) Schedule of STATOS tutorial:

Saturday, June 15th, 2013

7:30-8:30 am:	Registration and Welcome
8:30-10:00 am:	K. Ma, “Semidefinite Relaxation: Classical Developments and Forefront Advances”
10:00-11:00 am:	B. Ottersten, “Multi-antenna Signal Processing in Satellite Communications”
11:00-11:30 am	Coffee break
11:30-12:30 pm:	N. Sidiropoulos, “Analyzing (Big) Data Boxes: From Uniqueness of Tensor Decomposition to Multi-Way Compressed Sensing”
12:30-2:00pm	Lunch
2:00-3:00pm:	A.-J. van der Veen, “Signal Processing Tools for Radio Astronomy”
3:00-4:00pm:	S. Barbarossa, “Distributed sensing algorithms for cognitive networks”
4:00-4:30pm	Coffee break
4:30-5:30pm:	G. B. Giannakis, “Compendious State Inference for Wireless Cognitive Networks”

Sunday, June 16th, 2013

8:30-10:00am	W. Yin, “Distributed sparse optimization”
10:00-11:15am:	M. Amin, “Compressive sensing for urban radar”
11:15-11:30am	Coffee break
11:15-12:30pm:	P. P. Vaidyanathan, “Sparse sensing with coprime sampling lattices”
12:30-2:00pm	Coffee break
2:00-3:00pm:	Y. Eldar, “Sub-Nyquist Sampling: Bounds, Correlations and Hardware”
3:00-4:00pm:	G. Leus, “Compressive Power Spectrum Sensing”

3. 업무수행으로 습득한 연구관련 내용(자료가 있으실 경우 첨부하여주시기 바랍니다.)

I got some information from the tutorial of STATOS, "Sparse sensing with coprime sampling lattices" by P.P. Vaidynathan (Prof. of Caltech). He presented that a pair of uniform samplers provides us the reduction of sampling rates. It turns out that under some conditions of stationarity it is possible to recover second order statistical information, which is sufficient for many applications. For example, from samples of x(t) taken at the sparse rates fs=M and fs=N (where M and N are arbitrarily large but coprime), it is possible to estimate the autocorrelations at the dense sampling rate fs: The enabling principle in these applications comes from the theory of sparse coprime sampling. This talk gives an overview of sparse coprime sampling theory.

4. 업무수행에서 얻은 내용이 본인의 앞으로 연구방향에 끼친 영향

In the tutorial session, “Random Matrix Advances in Signal Processing” presented by Mérouane Debbah and Romain Couillet, they introduce basic notions of random matrix theory with applications to signal processing in large dimensional systems, such as detection and statistical inference in large population sizes. In the first part of this tutorial, they introduce theoretical tools of random matrix theory, and in the second part on applications of these methods such as eigen-based source detection, improved DoA estimation, statistical inference in large sensor networks, etc. In the third part, they provide an introduction to recent work on robust estimation and random matrix theory. The main aim of this tutorial is to present the field of random matrix theory and with the methodological tools to tackle signal processing problems. This talk provides us with the signal process technique in large dimensional systems.

5. 업무수행으로 보고, 느꼈던 부분

Throughout this conference, I felt the importance of signal processing in the area of communications. If we utilize a proper signal processing technique on the solving problems, many research areas including radar systems, wireless networks, and cloud computing would provide meaningful result. Another thing I felt is the shift of amount of data processing into the large size of systems. So, many researchers have focused on the signal processing of the large size to store, regenerate, extract, and process big data.

2013. 06. 24.

보고자  성 진 택