IEEE Awards Booklet - 2017 - 22

2017 ieee technical field awards

IEEE Leon K. Kirchmayer Graduate Teaching Award
Sponsored by the Leon K. Kirchmayer Memorial Fund

IEEE Koji Kobayashi Computers and
Communications Award
Sponsored by NEC Corporation

C.-C. Jay Kuo

Kannan Ramchandran

For inspirational guidance of graduate
students and curriculum development in
the area of multimedia signal processing

For pioneering contributions to the
theory and practice of distributed source
and storage coding

Ask students and colleagues to describe C.-C. Jay Kuo and you
will hear words such as "passionate teacher," "outstanding scholar," "great professional leader," and "unparalleled innovator." Since
1989 at the University of Southern California, Kuo has taught
over 3,000 students, guided over 130 students to Ph.D. degrees,
and supervised 25 postdoctoral research fellows. His Introduction
to Digital Image Processing is one of the most popular courses
among electrical engineering graduate students, and he continually revises the curriculum to address current trends. His Multimedia Data Compression graduate course is based on his own
lecture notes. Keys to Kuo's popularity in the classroom are his
enthusiastic teaching style and his broad and deep knowledge of
the technology and product trends of the multimedia industry.
An IEEE Fellow, Kuo is a Dean's Professor in Electrical Engineering at the University of Southern California, Los Angeles,
CA, USA.

The methods developed and made practical by Kannan Ramchandran for distributed source coding and distributed storage
coding are benefiting image and video communications and large
data storage systems. Ramchandran connected distributed source
coding theory to channel coding approaches that could be applied to real applications, such as video. To overcome the unreliability of nodes in large distributed systems where data is stored
over multiple nodes for redundancy, Ramchandran created regenerating codes. With these codes, he demonstrated how considerably less data is needed to be transferred over the network when
a failed node is repaired, while maintaining minimal storage overhead.Variants of these codes have saved companies like Microsoft
hundreds of millions of dollars in data center costs and will be part
of future releases of the Apache Hadoop open-source framework.
An IEEE Fellow, Ramchandran is a professor of electrical engineering and computer science with the University of California,
Berkeley, Berkeley, CA, USA.

IEEE William E. Newell Power Electronics Award

IEEE Daniel E. Noble Award for Emerging Technologies

Sponsored by the IEEE Power Electronics Society

Sponsored by the Motorola Solutions Foundation

Seung-Ki Sul

Miguel A.L. Nicolelis

For contributions to the sensorless control
of rotating field electrical machines

For seminal contributions to
brain-machine interfaces

An international leader in power electronics technologies, SeungKi Sul's innovations concerning sensorless control techniques are
improving the reliability and reducing the cost of motor drive
systems critical to a wide range of applications. Sul developed a sinusoidal pulsating injection method for sensorless control to realize high-performance alternating-current motor control without
using a position or speed sensor even at low speeds. Sul's methods
have been commercialized and applied to motion and traction
control in elevators to enable smooth starting, oil pump drives in
automobiles that reduce cost and improve vehicle reliability, sensorless traction motors in electric and hybrid vehicles, and even
washing machines. His recently developed sensorless drive featuring pulsating square wave voltage has important implications for
traction control in humanoid robots.
An IEEE Fellow, Sul is a professor with Department of Electrical and Computer Engineering at Seoul National University,
Seoul, Korea.

In creating the field of neuroengineering, Miguel A.L. Nicolelis' pioneering work on brain-machine interfaces has completely changed people's perception of what brains can do and how
such research can be rapidly applied to help humans. Nicolelis
demonstrated that humans can use raw brain activity to directly
communicate with mechanical, electronic, and virtual devices in
real time and in a closed control loop. He played a key role in
the development of a robotic exoskeleton that can help paralyzed
individuals to walk. He focused on methods to read a paraplegic
person's brain waves and decode and use them to move hydraulic
drivers on the suit. His work has great implications for patients
with epilepsy, Parkinson's disease, and spinal cord injury.
An IEEE Member, Nicolelis is the Duke School of Medicine
Distinguished Professor of Neuroscience at Duke University,
Durham, NC, USA.

22 | 2017 IEEE awards bOOkLET

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