- Date: May 22, 2019
Awarded to: Siriramya Bhamidipati, Kyeong Jin Kim, Hongbo Sun, Philip Orlik
MERL Contacts: Kyeong Jin (K.J.) Kim; Hongbo Sun
Research Areas: Artificial Intelligence, Communications, Machine Learning, Signal Processing, Information Security
Brief - MERL researchers, Kyeong Jin Kim, Hongbo Sun, Philip Orlik, along with lead author and former MERL intern Siriramya Bhamidipati were awarded the Smart Grid Symposium Best Paper Award at this year's International Conference on Communications (ICC) held in Shanghai, China. There paper titled "GPS Spoofing Detection and Mitigation in PMUs Using Distributed Multiple Directional Antennas," described a technique to rapidly detect and mitigate GPS timing attacks/errors via hardware (antennas) and signal processing (Kalman Filtering)
-
- Date: January 2, 2019
Awarded to: Siheng Chen
MERL Contact: Siheng Chen
Research Area: Signal Processing
Brief - MERL researcher, Siheng Chen, has won an IEEE Young Author Best Paper award for his paper entitled "Discrete Signal Processing on Graphs: Sampling Theory". This paper, published in the December 2015 issue of IEEE Transactions on Signal Processing, proposes a sampling theory for signals that are supported on either directed or undirected graphs. The theory follows the same paradigm as classical sampling theory and shows that perfect recovery is possible for graph signals bandlimited under the graph Fourier transform. The award honors the authors of an especially meritorious paper dealing with a subject related to IEEE's technical scope and appearing in one if its journals within a three year window of eligibility.
-
- Date: April 23, 2019
Awarded to: Teng-yok Lee
MERL Contact: Teng-Yok Lee
Research Areas: Artificial Intelligence, Computer Vision, Data Analytics, Machine Learning
Brief - MERL researcher Teng-yok Lee has won the Best Visualization Note Award at the PacificVis 2019 conference held in Bangkok Thailand, from April 23-26, 2019. The paper entitled "Space-Time Slicing: Visualizing Object Detector Performance in Driving Video Sequences" presents a visualization method called Space-Time Slicing to assist a human developer in the development of object detectors for driving applications without requiring labeled data. Space-Time Slicing reveals patterns in the detection data that can suggest the presence of false positives and false negatives.
-
- Date: January 16, 2019
Awarded to: Daniel Dinis
MERL Contact: Rui Ma
Research Areas: Communications, Signal Processing
Brief - Former MERL intern Daniel Dinis from University of Aveiro (UA), Portugal has received the 2018 IBM Scientific Award with Honorable Mention referring to the contributions on "Real-time Tunable Delta-sigma modulators for All-Digital RF Transmitters" in his Ph.D. study.
The award-winning work includes research conducted under the supervision of Arnaldo Oliveira and José Neto Vieira, professors from the Department of Electronics and Information Technology (DETI) of the UA, as well as contributions made during Daniel's 7 month internship in 2017 at MERL.
The ceremony for the presentation of the 28th IBM Scientific Prize took on January 16th, at the Noble Hall of the Superior Technical Institute. It was chaired by Marcelo Rebelo de Sousa, President of the Portuguese Republic.
-
- Date: November 16, 2018
Awarded to: Ziming Zhang, Alan Sullivan, Hideaki Maehara, Kenji Taira, Kazuo Sugimoto
MERL Contacts: Alan Sullivan; Ziming Zhang
Research Areas: Artificial Intelligence, Computer Vision, Machine Learning
Brief - Researchers and developers from MERL, Mitsubishi Electric and Mitsubishi Electric Engineering (MEE) have been recognized with an R&D100 award for the development of a deep learning-based water detector. Automatic detection of water levels in rivers and streams is critical for early warning of flash flooding. Existing systems require a height gauge be placed in the river or stream, something that is costly and sometimes impossible. The new deep learning-based water detector uses only images from a video camera along with 3D measurements of the river valley to determine water levels and warn of potential flooding. The system is robust to lighting and weather conditions working well during the night as well as during fog or rain. Deep learning is a relatively new technique that uses neural networks and AI that are trained from real data to perform human-level recognition tasks. This work is powered by Mitsubishi Electric's Maisart AI technology.
-
- Date: April 17, 2018
Awarded to: Zhong-Qiu Wang
MERL Contact: Jonathan Le Roux
Research Areas: Speech & Audio, Artificial Intelligence
Brief - Former MERL intern Zhong-Qiu Wang (Ph.D. Candidate at Ohio State University) has received a Best Student Paper Award at the 2018 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP 2018) for the paper "Multi-Channel Deep Clustering: Discriminative Spectral and Spatial Embeddings for Speaker-Independent Speech Separation" by Zhong-Qiu Wang, Jonathan Le Roux, and John Hershey. The paper presents work performed during Zhong-Qiu's internship at MERL in the summer 2017, extending MERL's pioneering Deep Clustering framework for speech separation to a multi-channel setup. The award was received on behalf on Zhong-Qiu by MERL researcher and co-author Jonathan Le Roux during the conference, held in Calgary April 15-20.
-
- Date: November 30, 2017
Awarded to: Yan Zhu, Makoto Imamura, Daniel Nikovski, Eamonn Keogh
MERL Contact: Daniel Nikovski
Research Areas: Data Analytics
Brief - Yan Zhu, a former MERL intern from the University of California at Riverside has won the Best Student Paper Award at the International Conference on Data Mining in 2017, for her work on time series chains, a novel primitive for time series analysis. The work was done in collaboration with Makoto Imamura, formerly at Information Technology Center/AI Department, and currently a professor at Tokai University in Tokyo, Japan, Daniel Nikovski from MERL, and Yan's advisor, Prof. Eamonn Keogh from UC Riverside, whose lab has had a long and fruitful collaboration with MERL and Mitsubishi Electric.
-
- Date: August 4, 2017
Awarded to: David Zhuzhunashvili and Andrew Knyazev
Research Area: Machine Learning
Brief - David Zhuzhunashvili, an undergraduate student at UC Boulder, Colorado, and Andrew Knyazev, Distinguished Research Scientist at MERL, received the 2017 Graph Challenge Student Innovation Award. Their poster "Preconditioned Spectral Clustering for Stochastic Block Partition Streaming Graph Challenge" was accepted to the 2017 IEEE High Performance Extreme Computing Conference (HPEC '17), taking place 12-14 September 2017 (http://www.ieee-hpec.org/), and the paper was accepted to the IEEE Xplore HPEC proceedings.
HPEC is the premier conference in the world on the convergence of High Performance and Embedded Computing. DARPA/Amazon/IEEE Graph Challenge is a special HPEC event. Graph Challenge encourages community approaches to developing new solutions for analyzing graphs derived from social media, sensor feeds, and scientific data to enable relationships between events to be discovered as they unfold in the field. The 2017 Streaming Graph Challenge is Stochastic Block Partition. This challenge seeks to identify optimal blocks (or clusters) in a larger graph with known ground-truth clusters, while performance is evaluated compared to baseline Python and C codes, provided by the Graph Challenge organizers.
The proposed approach is spectral clustering that performs block partition of graphs using eigenvectors of a matrix representing the graph. Locally Optimal Block Preconditioned Conjugate Gradient (LOBPCG) method iteratively approximates a few leading eigenvectors of the symmetric graph Laplacian for multi-way graph partitioning. Preliminary tests for all static cases for the Graph Challenge demonstrate 100% correctness of partition using any of the IEEE HPEC Graph Challenge metrics, while at the same time also being approximately 500-1000 times faster compared to the provided baseline code, e.g., 2M static graph is 100% correctly partitioned in ~2,100 sec. Warm-starts of LOBPCG further cut the execution time 2-3x for the streaming graphs.
-
- Date: October 15, 2016
Awarded to: Anthony Vetro
MERL Contact: Anthony Vetro Brief - Anthony Vetro was recognized by APSIPA (Asia-Pacific Signal and Information Processing Association) as a 2016 Industrial Distinguished Leader. This distinction is reserved for selected APSIPA members with extraordinary accomplishments in any of the fields related to APSIPA scope. A list of past recipients can be found online: http://www.apsipa.org/industrial.htm.
-
- Date: March 24, 2016
Awarded to: Toshiaki Koike-Akino, Keisuke Kojima, David S. Millar, Kieran Parsons, Tsuyoshi Yoshida, Takashi Sugihara
MERL Contacts: Toshiaki Koike-Akino; Keisuke Kojima; David Millar; Kieran Parsons
Research Areas: Communications, Electronic and Photonic Devices, Signal Processing
Brief - Five papers from the Optical Comms team were presented at the 2016 Optical Fiber Conference (OFC) held in Anaheim, USA in March 2016. The papers relate to enhanced modulation formats, constellation shaping, chromatic dispersion estimation, low complexity adaptive equalization and coding for coherent optical links. The top-scored paper studied optimal selection of coding and modulation sets to jointly maximize nonlinear tolerance and spectral efficiency.
-
- Date: March 31, 2016
Awarded to: Andrew Knyazev
Research Areas: Control, Optimization, Dynamical Systems, Machine Learning, Data Analytics, Communications, Signal Processing
Brief - Andrew Knyazev selected as a Fellow of the Society for Industrial and Applied Mathematics (SIAM) for contributions to computational mathematics and development of numerical methods for eigenvalue problems.
Fellowship honors SIAM members who have made outstanding contributions to the fields served by the SIAM. Andrew Knyazev was among a distinguished group of members nominated by peers and selected for the 2016 Class of Fellows.
-
- Date: January 6, 2016
Awarded to: Andrew Knyazev Brief - Andrew Knyazev is awarded the title of Professor Emeritus at the University of Colorado Denver effective 1/31/2016. The award letter from the Chancellor of the University of Colorado Denver provides examples of the record of excellence over 20 years of contributions to the university such as 2008 CU Denver Excellence in Research Award, 2000 Teaching Excellence Award for the college, supervision of Ph.D. students, and two decades of uninterrupted external research funding from the US National Science Foundation and Department of Energy.
-
- Date: December 15, 2015
Awarded to: John R. Hershey, Takaaki Hori, Jonathan Le Roux and Shinji Watanabe
MERL Contacts: Takaaki Hori; Jonathan Le Roux
Research Areas: Speech & Audio, Artificial Intelligence
Brief - The results of the third 'CHiME' Speech Separation and Recognition Challenge were publicly announced on December 15 at the IEEE Automatic Speech Recognition and Understanding Workshop (ASRU 2015) held in Scottsdale, Arizona, USA. MERL's Speech and Audio Team, in collaboration with SRI, ranked 2nd out of 26 teams from Europe, Asia and the US. The task this year was to recognize speech recorded using a tablet in real environments such as cafes, buses, or busy streets. Due to the high levels of noise and the distance from the speaker's mouth to the microphones, this is very challenging task, where the baseline system only achieved 33.4% word error rate. The MERL/SRI system featured state-of-the-art techniques including multi-channel front-end, noise-robust feature extraction, and deep learning for speech enhancement, acoustic modeling, and language modeling, leading to a dramatic 73% reduction in word error rate, down to 9.1%. The core of the system has since been released as a new official challenge baseline for the community to use.
-
- Date: December 1, 2015
Awarded to: Mark A. Davenport, Petros T. Boufounos, Michael B. Wakin and Richard G. Baraniuk
MERL Contact: Petros Boufounos
Research Areas: Computational Sensing
Brief - Petros Boufounos is a recipient of the 2015 IEEE Signal Processing Society Best Paper Award for the paper that he co-authored with Mark A. Davenport, Michael B. Wakin and Richard G. Baraniuk on "Signal Processing with Compressive Measurements" which was published in the April 2010 issue of IEEE Journal of Selected Topics in Signal Processing. The Best Paper Award honors the author(s) of a paper of exceptional merit dealing with a subject related to the Society's technical scope, and appearing in one of the Society's solely owned transactions or the Journal of Selected Topics in Signal Processing. Eligibility is based on a five-year window: for example, for the 2015 Award, the paper must have appeared in one of the Society's Transactions between January 1, 2010 and December 31, 2014.
-
- Date: September 30, 2015
Awarded to: Mitsubishi Electric Corp.
Research Area: Computer Vision
Brief - Mitsubishi Electric Corp. (MELCO) advertisements based on 3D reconstruction received a Gold medal and a Bronze medal in the Fujisankei Newspaper. "Will I fit?", "He'll fit just fine.", and "Oops, did you think in 3D?".
-
- Date: April 14, 2015
Awarded to: Anthony Vetro
Awarded by: InterNational Committee for Information Technology Standards (INCITS)
MERL Contact: Anthony Vetro Brief - The INCITS Gene Milligan Award for Effective Committee Management recognizes individuals who, as officers, have provided outstanding leadership to the subgroup in its national and/or international work, have demonstrated proficiency in achieving consensus in the national and/or international arenas and have followed the approved procedures in an exemplary fashion. Anthony Vetro was the recipient of this award in 2015. He has more than 15 years of participation in standards development, including service as the INCITS L3.1 Vice-Chair and Chair and an active technical participant in the MPEG committee.
-
- Date: May 1, 2014
Awarded to: Dehong Liu and Petros T. Boufounos
Awarded for: "Synthetic Aperture Imaging Using a Randomly Steered Spotlight"
Awarded by: IEEE Geoscience and Remote Sensing Society (GRSS)
MERL Contacts: Dehong Liu; Petros Boufounos
Research Area: Computational Sensing
Brief - Dehong Liu and Petros T. Boufounos are the recipients of the the IEEE Geoscience and Remote Sensing Society 2014 Symposium Prize Paper Award for their paper "Synthetic Aperture Imaging Using a Randomly Steered Spotlight," presented at IGARSS 2013 (TR2013-070).
-
- Date: March 11, 2014
Awarded to: Yuuki Tachioka
Awarded for: "Effectiveness of discriminative approaches for speech recognition under noisy environments on the 2nd CHiME Challenge"
Awarded by: Acoustical Society of Japan (ASJ)
MERL Contact: Jonathan Le Roux
Research Area: Speech & Audio
Brief - MELCO researcher Yuuki Tachioka received the Awaya Prize Young Researcher Award from the Acoustical Society of Japan (ASJ) for "effectiveness of discriminative approaches for speech recognition under noisy environments on the 2nd CHiME Challenge", which was based on joint work with MERL Speech & Audio team researchers Shinji Watanabe, Jonathan Le Roux and John R. Hershey.
-
- Date: September 26, 2013
Awarded to: Jonathan Le Roux
Awarded for: "A new non-negative dynamical system for speech and audio modeling"
Awarded by: Acoustical Society of Japan (ASJ)
MERL Contact: Jonathan Le Roux
Research Area: Speech & Audio
-
- Date: June 1, 2013
Awarded to: Yuuki Tachioka, Shinji Watanabe, Jonathan Le Roux and John R. Hershey
Awarded for: "Discriminative Methods for Noise Robust Speech Recognition: A CHiME Challenge Benchmark"
Awarded by: International Workshop on Machine Listening in Multisource Environments (CHiME)
MERL Contact: Jonathan Le Roux
Research Area: Speech & Audio
Brief - The results of the 2nd 'CHiME' Speech Separation and Recognition Challenge are out! The team formed by MELCO researcher Yuuki Tachioka and MERL Speech & Audio team researchers Shinji Watanabe, Jonathan Le Roux and John Hershey obtained the best results in the continuous speech recognition task (Track 2). This very challenging task consisted in recognizing speech corrupted by highly non-stationary noises recorded in a real living room. Our proposal, which also included a simple yet extremely efficient denoising front-end, focused on investigating and developing state-of-the-art automatic speech recognition back-end techniques: feature transformation methods, as well as discriminative training methods for acoustic and language modeling. Our system significantly outperformed other participants. Our code has since been released as an improved baseline for the community to use.
-