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Source URL: www.nanonex.com

Torsten Kröger Stanford University & Google Torsten Kroeger is a roboticist at Google X and a visiting researcher at Stanford University. He received his Master‘s degree in Electrical Engineering from TU Braunschweig,

Source URL: www.interact25.org

MT4 (Contributed Oral) 2:15 PM – 2:30 PM On-chip Localized Surface Plasmon Resonance (LSPR) Sensing Maysamreza Chamanzar and Ali Adibi School of Electrical and Computer Engineering, Georgia Institute of Technology, Atl

Source URL: users.ece.cmu.edu

Understanding and Optimizing Asynchronous Low-Precision Stochastic Gradient Descent Christopher De Sa Matthew Feldman Christopher Ré Kunle Olukotun Departments of Electrical Engineering and Computer Science Stanford Uni

Source URL: www.cs.cornell.edu

INSTITUTE OF ELECTRICAL ENGINEERING Slovak A c a d e m y of Sciences Bratislava Správa o činnosti Elektrotechnického ústavu SAV

Source URL: www.elu.sav.sk

Thinner Clouds with Preallocation∗ Ittay Eyal1 , Flavio Junqueira2 , and Idit Keidar1 1 Department of Electrical Engineering, Technion, Haifa, Israel Research, Cambridge, UK

Source URL: webee.technion.ac.il

• Computing
• System software
• Computer architecture
• Cloud computing
• As a service
• Infrastructure as a service
• Hyper-V
• VM
• Virtual machine
• OpenVMS
• Elasticity
• Temporal isolation among virtual machines

Nonlinear Analysis: Modelling and Control, 2010, Vol. 15, No. 4, 451–458 Modeling nonlinear systems using multiple piecewise linear equations G.K. Lowe, M.A. Zohdy Department of Electrical and Computer Engineering

Source URL: www.lana.lt

Scalable Anonymous Communication with Provable Security Prateek Mittal, Nikita Borisov∗ Dept. of Electrical and Computer Engineering, U. of Illinois at Urbana-Champaign, Carmela Troncoso, Alfredo Rial†

Source URL: carmelatroncoso.com

Economics of Network Pricing with Multiple ISPs Srinivas Shakkottai R. Srikant Dept. of Electrical and Computer Engineering, and

Source URL: www.ece.tamu.edu

An Improved FOCUSS-Based Learning Algorithm for Solving Sparse Linear Inverse Problems Joseph F. Murray and Kenneth Kreutz-Delgado Electrical and Computer Engineering University of California, San Diego La Jolla, CA 9209

Source URL: dsp.ucsd.edu