BLU 596 Seminars

Topic: Hyperspectral Data Compression

Abstract 
Hyperspectral data is composed of a set of correlated band images. In order to efficiently compress the hyperspectral imagery, this inherent correlation may be exploited by means of spectral decorrelators. In the first part of the talk, wavelet transform based spectral decorrelators will be discussed. On the other hand, sparse models provide data representations in the fewest possible number of nonzero elements. This enables sparse models to be utilized for data compression purposes. In the second half of the talk, a framework for sparsity-based hyperspectral image compression methods using online learning will be presented.

Bio 
Behçet Uğur Töreyin received the B.S. degree from the Middle East Technical University, Ankara, Turkey in 2001 and the M.S. and Ph.D. degrees from Bilkent University, Ankara, in 2003 and 2009, respectively, all in electrical and electronics engineering. He is now a faculty member with the Informatics Institute at Istanbul Technical University. His research interests broadly lie in signal processing and pattern recognition with applications to image/video analysis and communication systems. His research is focused on developing novel algorithms to analyze and compress signals from multitude of sensors such as visible/infra-red/hyperspectral cameras, microphones, passive infra-red sensors, vibration sensors and spectrum sensors for wireless communications. 
http://faculty.itu.edu.tr/toreyin/

Topic: A Hybrid CFD/Lumped-Capacitance Model For Simulating Data Center Transients

Speaker: Hamza Salih Erden

Date: 07 March 2017, 13:40 Room 408

Abstract 
Transient thermal events in air-cooled data centers may lead to undesirable operating conditions such as the formation of hot spots and associated degradation of equipment reliability. This seminar introduces a fast-executing hybrid computational fluid dynamics (CFD)/Lumped-Capacitance model for predicting server inlet temperatures resulting from common transient events such as power losses and cooling interruption. The model uses initial steady-state CFD or experimental data in combination with several lumped-capacitance models of the various thermal masses in the data center. The model predictions have been compared with experimental data obtained in a three-rack data-center test cell and found to agree well with the experimental measurements. Examples of the application of the model to more realistic data center configurations are also given.

Bio:
Hamza Salih Erden received his B.S. degree in Mechanical Engineering at Istanbul Technical University in 2007 and M.S. and Ph.D. degrees from Syracuse University, New York, in 2009 and 2013 in Mechanical and Aerospace Engineering. He joined ITU Informatics Institute in 2015 as an Assistant professor after his two-year post-doctoral study at Syracuse University. Dr. Erden focuses on the modeling, analysis and design of thermal systems for energy-efficient buildings, particularly for mission critical facilities like data centers and their supporting infrastructure.