Hayder Radha
Professor and Associate Chair for Research Michigan State University
- East Lansing MI
Hayder Radha is an expert in autonomous vehicle research, coding and communications, image and video compression and image processing.
Media
Biography
Hayder Radha is available to discuss the pedestrian death caused by an autonomous vehicle. He also can discuss how MSU is working with a GM-sponsored competition to create fully autonomous vehicles, ones that can better avoid future fatalities.
Industry Expertise
Areas of Expertise
Accomplishments
Microsoft Research Content and Curriculum Award
Microsoft Research Content and Curriculum Award
Philips Research Group Accomplishment Award
Philips Research Group Accomplishment Award, 2000
Education
Columbia University
Ph.D.
1993
Columbia University
Ph.M.
1991
Purdue University
M.S.
1986
News
MSU shows off autonomous vehicle technology at the 2018 auto show
WZZM online
2018-01-16
Michigan State University's autonomous vehicle technology brings "superhuman" perception to a vehicle, said Dr. Hayder Radha, an engineering professor and director of the MSU Connected and Autonomous Networked Vehicles for Active Safety program, or CANVAS.
MSU SHOWCASES AUTONOMOUS VEHICLE TECHNOLOGY AT 2018 NAIAS
MSU Today online
2018-01-10
“Part of our work focuses on integrating the vehicle with its environment,” said Hayder Radha, professor of electrical and computer engineering and director of MSU’s Connected and Autonomous Networked Vehicles for Active Safety, or CANVAS. “MSU is a recognized leader in computer vision, radars and antenna design, and the areas that are at the core of self-driving vehicles like high-assurance computing and related technologies”...
University presidents: Prepare for global economy
The Detroit News online
2017-03-23
Michigan State professor Hayder Radha came from Baghdad, Iraq. He helped pioneer the rollout of digital TV in the U.S., developed key aspects of internet video streaming and holds 30 U.S. patents. Today, he directs MSU’s autonomous vehicle research program and partners with Michigan auto manufacturers to develop technologies for advanced mobility...
Could a self-driving car drive better in snow than a human? MSU researcher says yes
West Michigan online
2016-09-27
(NEWSCHANNEL 3) - Driving is a stressful part of life in Michigan this time of year, but researchers at Michigan State University say soon something else may be able to do it for you.
MSU researchers are working to develop ways for autonomous vehicles to drive in snow, just as well as a human driver.
Dr. Hayder Radha, a professor of electrical and computer engineering at MSU, said "Absolutely... in fact we do not believe it is something far-fetched" when asked if there would be a chance of a self-driving car that could do a better job handling snow than a human driver in the future...
Journal Articles
Network completion by leveraging similarity of nodes
Download PDF Social Network Analysis and Mining2016
The analysis of social networks has attracted much attention in recent years. Link prediction is an important aspect of social network analysis and an area of key research within that is the network completion problem, where it is assumed that only a small sample of a network (e.g., a complete or partially observed subgraph of a social graph) is observed and we would like to infer the unobserved part of the network.
RPCA-KFE: Key Frame Extraction for Video using Robust Principal Component Analysis
Image Processing, IEEE Transactions2015
Key frame extraction algorithms consider the problem of selecting a subset of the most informative frames from a video to summarize its content. Several applications, such as video summarization, search, indexing, and prints from video, can benefit from extracted key frames of the video under consideration.
Common and Innovative Visuals: A sparsity modeling framework for video
Image Processing, IEEE Transactions2014
Efficient video representation models are critical for
many video analysis and processing tasks. In this paper, we present a framework based on the concept of finding the sparsest solution to model video frames. To model the spatio-temporal information, frames from one scene are decomposed into two components: (i) a common frame, which describes the visual information common to all the frames in the scene/segment, and (ii) a set of innovative frames, which depicts the dynamic behaviour of the scene.
Heterogeneity Image Patch Index and Its Application to Consumer Video Summarization
Image Processing, IEEE TransactionsAutomatic video summarization is indispensable for fast browsing and efficient management of large video libraries. In this paper, we introduce an image feature that we refer to as heterogeneity image patch (HIP) index. The proposed HIP index provides a new entropy-based measure of the heterogeneity of patches within any picture.
