题 目：Reliability-aware Fog Resource Provisioning for Deadline-driven IoT Services
时 间：2019年1月18日（周五）上午10:00
地 点：清水河校区科研楼B302
主讲人：新泽西理工学院 Nirwan Ansari 教授，IEEE Fellow
主要内容：
Magnetic Resonance Imaging (MRI) is well known to be slow in data acquisition, which limits imaging of moving organs and causes patient discomfort. Because the MRI acquisition time is directly related to the amount of data to be acquired, to address this issue of acquisition speed, mathematical models and algorithms have been developed to allow the MR images to be reconstructed from data acquired far below the Nyquist rate.Sparsity and low-rank models have been successfully used for fast imaging. Here we will introduce a new manifold framework which extends the existing models to more general ones. Specifically, the MR image series/patches are assumed to be on a smooth manifold, and the underlying manifold geometry is learned through training data. Moreover, low-dimensional embeddings which preserve the learned manifold geometry and effect parsimonious data representations are computed. Capitalizing on the learned manifold geometry, computational algorithms are introduced to reconstruct dynamic MR images from highly undersampled k-space data. Some examples on phantom and in-vivo data sets will be presented.
主讲人简介：
Nirwan Ansari is Distinguished Professor of Electrical and Computer Engineering at the New Jersey Institute of Technology (NJIT). He has also been a visiting (chair) professor at several universities.
Professor Ansari authored Green Mobile Networks: A Networking Perspective (IEEE-Wiley, 2017) with T. Han, and co-authored two other books. He has also (co-)authored more than 550 technical publications, over 250 published in widely cited journals/magazines. He has guest-edited a number of special issues covering various emerging topics in communications and networking. He has served on the editorial/advisory board of over ten journals including as Senior Technical Editor of IEEE Communications Magazine. His current research focuses on green communications and networking, cloud computing, drone-assisted networking, and various aspects of broadband networks.
Professor Ansari was elected to serve in the IEEE Communications Society (ComSoc) Board of Governors as a member-at-large, has chaired some ComSoc technical and steering committees, has been serving in many committees such as the IEEE Fellow Committee, and has been actively organizing numerous IEEE International Conferences/Symposia/Workshops. He has frequently been delivering keynote addresses, distinguished lectures, tutorials, and invited talks. Some of his recognitions include IEEE Fellow, several Excellence in Teaching Awards, a few best paper awards, the NCE Excellence in Research Award, the ComSoc TC-CSR Distinguished Technical Achievement Award, the ComSoc AHSN TC Technical Recognition Award, the IEEE TCGCC Distinguished Technical Achievement Recognition Award, the NJ Inventors Hall of Fame Inventor of the Year Award, the Thomas Alva Edison Patent Award, Purdue University Outstanding Electrical and Computer Engineering Award, and designation as a COMSOC Distinguished Lecturer. He has also been granted 38 U.S. patents.
He received a Ph.D. from Purdue University---West Lafayette, IN, an MSEE from the University of Michigan---Ann Arbor, MI, and a BSEE (summa cum laude with a perfect GPA) from NJIT---Newark, NJ.
