Signal Processing
Acquisition and processing of information.
Our research in the area of signal processing encompasses a wide range of work in the areas of communications, sensing, estimation, localization, and speech and visual information processing. We explore novel approaches for signal acquisition and coding, methods to filter and recover signals in the presence of noise and other degrading factors, and techniques that infer meaning from the processed signals.
Quick Links
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Researchers
Toshiaki
Koike-Akino
Keisuke
Kojima
Philip
Orlik
Kieran
Parsons
David
Millar
Pu
(Perry)
WangKyeong Jin
(K.J.)
KimYe
Wang
Karl
Berntorp
Bingnan
Wang
Petros
Boufounos
Rui
Ma
Hassan
Mansour
Yebin
Wang
Stefano
Di Cairano
Dehong
Liu
Koon Hoo
Teo
Jianlin
Guo
Mouhacine
Benosman
Hongbo
Sun
Jinyun
Zhang
Devesh
Jha
Chungwei
Lin
Yanting
Ma
Tim
Marks
Anthony
Vetro
Avishai
Weiss
William
Yerazunis
Takaaki
Hori
Jonathan
Le Roux
Suhas
Lohit
Marcel
Menner
Rien
Quirynen
Huifang
Sun
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Awards
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AWARD Outstanding Presentation Award at the 28th Conference of Information Processing Society of Japan/Consumer Device & Systems Date: October 20, 2020
Awarded to: Yukimasa Nagai, Takenori Sumi, Jianlin Guo, Philip Orlik, Hiroshi Mineno
MERL Contacts: Jianlin Guo; Philip Orlik
Research Areas: Communications, Optimization, Signal ProcessingBrief- MELCO and MERL researchers have won "Outstanding Presentation Award" at 28th Conference of Information Processing Society of Japan (IPSJ)/Consumer Device & Systems held on September 29-30, 2020. The paper titled "IEEE 802.19.3 Standardization for Coexistence of IEEE 802.11ah and IEEE 802.15.4g Systems in Sub-1 GHz Frequency Bands" reports IEEE 802.19.3 standard development on coexistence between IEEE 802.11ah and IEEE 802.15.4g systems in the Sub-1 GHz frequency bands. MERL and MELCO have been leading this standard development and made major technical contributions, which propose methods to mitigate interference in smart meter systems. The authors are Yukimasa Nagai, Takenori Sumi, Jianlin Guo, Philip Orlik and Hiroshi Mineno.
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AWARD Best Paper AWARD at International Workshop on Informatics (IWIN) 2020 Date: September 11, 2020
Awarded to: Yukimasa Nagai, Jianlin Guo, Takenori Sumi, Philip Orlik, Hiroshi Mineno
MERL Contact: Jianlin Guo
Research Areas: Communications, Signal ProcessingBrief- MELCO and MERL researchers have won one of two Best Paper Awards at International Workshop on Informatics (IWIN) 2020. The paper titled 'Hybrid CSMA/CA for Sub-1 GHz Frequency Band Coexistence of IEEE 802.11ah and IEEE 802.15.4g', reports research on the severity of interference between IEEE 802.11ah and IEEE 802.15.4g based networks and also proposes methods to mitigate this interference in smart meter systems. This research reported in this paper has also informed several of MELCO/MERL's contributions to the IEEE P802.19.3 task group which is developing standards to allow for improved coexistence in outdoor metering systems. Authors are Yukimasa Nagai, Jianlin Guo, Takenori Sumi, Philip Orlik and Hiroshi Mineno.
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AWARD Best Student Paper Award at the IEEE Conference on Control Technology and Applications Date: August 26, 2020
Awarded to: Marcus Greiff, Anders Robertsson, Karl Berntorp
MERL Contact: Karl Berntorp
Research Areas: Control, Signal ProcessingBrief- Marcus Greiff, a former MERL intern from the Department of Automatic Control, Lund University, Sweden, won one of three 2020 CCTA Outstanding Student Paper Awards and the Best Student Paper Award at the 2020 IEEE Conference on Control Technology and Applications. The research leading up to the awarded paper titled 'MSE-Optimal Measurement Dimension Reduction in Gaussian Filtering', concerned how to select a reduced set of measurements in estimation applications while minimally degrading performance, was done in collaboration with Karl Berntorp at MERL.
See All Awards for Signal Processing -
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News & Events
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NEWS MERL published four papers in 2020 IEEE Global Communications Conference Date: December 7, 2020 - December 11, 2020
Where: Taipei, Taiwan
MERL Contacts: Kyeong Jin (K.J.) Kim; Toshiaki Koike-Akino; Philip Orlik; Pu (Perry) Wang; Ye Wang
Research Areas: Communications, Computational Sensing, Machine Learning, Signal ProcessingBrief- MERL researchers have published four papers in 2020 IEEE Global Communications Conference (GlobeComm). This conference is one of the two IEEE Communications Societies flagship conferences dedicated to Communications for Human and Machine Intelligence. Topics of the published papers include, transmit diversity schemes, coding for molecular networks, and location and human activity sensing via WiFi signals.
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EVENT MERL Virtual Open House 2020 Date & Time: Wednesday, December 9, 2020; 1:00-5:00PM EST
MERL Contacts: Elizabeth Phillips; Jeroen van Baar; Anthony Vetro
Location: Virtual
Research Areas: Applied Physics, Artificial Intelligence, Communications, Computational Sensing, Computer Vision, Control, Data Analytics, Dynamical Systems, Electric Systems, Electronic and Photonic Devices, Machine Learning, Multi-Physical Modeling, Optimization, Robotics, Signal Processing, Speech & AudioBrief- MERL will host a virtual open house on December 9, 2020. Live sessions will be held from 1-5pm EST, including an overview of recent activities by our research groups and a talk by Prof. Pierre Moulin of University of Illinois at Urbana-Champaign on adversarial machine learning. Registered attendees will also be able to browse our virtual booths at their convenience and connect with our research staff on engagement opportunities including internship, post-doc and research scientist openings, as well as visiting faculty positions.
Registration: https://mailchi.mp/merl/merl-virtual-open-house-2020
Schedule: https://www.merl.com/events/voh20
Current internship and employment openings:
https://www.merl.com/internship/openings
https://www.merl.com/employment/employment
Information about working at MERL:
https://www.merl.com/employment
- MERL will host a virtual open house on December 9, 2020. Live sessions will be held from 1-5pm EST, including an overview of recent activities by our research groups and a talk by Prof. Pierre Moulin of University of Illinois at Urbana-Champaign on adversarial machine learning. Registered attendees will also be able to browse our virtual booths at their convenience and connect with our research staff on engagement opportunities including internship, post-doc and research scientist openings, as well as visiting faculty positions.
See All News & Events for Signal Processing -
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Research Highlights
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Internships
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SP1542: Research in Computational Sensing
The Computational Sensing team at MERL is seeking motivated and qualified individuals to assist in the development of computational methods for a variety of sensing applications. Ideal candidates should be Ph.D. students and have solid background and publication record in any of the following, or related areas: imaging inverse problems, learning for inverse problems, large-scale optimization, blind inverse scattering, radar/lidar/sonar imaging, sensing of dynamical systems, or wave-based inversion. Experience with experimentally measured data is desirable. Publication of the results produced during our internships is expected. The duration of the internships is anticipated to be 3-6 months. Start date is flexible. This internship is preferred to be onsite at MERL, but may be done remotely where you live if the COVID pandemic makes it necessary.
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SP1467: Machine learning for GNSS applications
MERL is seeking a highly motivated, qualified intern to join a thirteen weeks internship program. The ideal candidate will be expected to carry out research on Machine Learning for various GNSS applications. The candidate is expected to develop innovative machine learning technologies to increase accuracy and secrecy. Candidates should have strong knowledge about GNSS signal processing, handling RINEX data, neural network and learning techniques, such as feature extraction, deep machine learning, reinforcement learning, and distributed learning. Proficient programming skills with Python, MATLAB, and C++, and strong mathematical analysis will be additional assets to this position. Candidates in their junior or senior years of a Ph.D. program are encouraged to apply. This internship is preferred to be onsite at MERL, but may be done remotely where you live if the COVID pandemic makes it necessary.
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SP1510: Learning for inverse problems and dynamical systems
The Computational Sensing team at MERL is seeking motivated and qualified individuals to develop algorithms that solve inverse problems in computational sensing that incorporate deep learning architectures for a variety of sensing applications. The project goal is to improve the performance and develop an analysis of algorithms used for inverse problems by incorporating new tools from machine learning and artificial intelligence. Ideal candidates should be Ph.D. students and have solid background and publication record in any of the following, or related areas: imaging inverse problems, large-scale optimization, plug-and-play priors, learning-based modeling for imaging, learning theory for computational imaging, and Koopman theory/dynamic mode decomposition. Publication of the results produced during our internships is expected. The duration of the internships is anticipated to be 3-6 months. Start date is flexible. This internship is preferred to be onsite at MERL, but may be done remotely where you live if the COVID pandemic makes it necessary.
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Recent Publications
- "A dCDD-Based Transmit Diversity Scheme for Downlink Pseudo-NOMA Systems", IEEE Transactions on Wireless Communications, December 2020.BibTeX TR2020-176 PDF
- @article{Kim2020dec2,
- author = {Kim, Kyeong Jin and Liu, Hongwu and Lei, Hongjiang and Ding, Zhiguo and Orlik, Philip V. and Poor, H. Vincent},
- title = {A dCDD-Based Transmit Diversity Scheme for Downlink Pseudo-NOMA Systems},
- journal = {IEEE Transactions on Wireless Communications},
- year = 2020,
- month = dec,
- url = {https://www.merl.com/publications/TR2020-176}
- }
, - "Distributed Coding of Quantized Random Projections", IEEE Transactions on Signal Processing, DOI: 10.1109/TSP.2020.3029499, Vol. 68, pp. 5924-5939, December 2020.BibTeX TR2020-157 PDF
- @article{Goukhshtein2020dec,
- author = {Goukhshtein, Maxim and Boufounos, Petros T. and Koike-Akino, Toshiaki and Draper, Stark C.},
- title = {Distributed Coding of Quantized Random Projections},
- journal = {IEEE Transactions on Signal Processing},
- year = 2020,
- volume = 68,
- pages = {5924--5939},
- month = dec,
- doi = {10.1109/TSP.2020.3029499},
- issn = {1941-0476},
- url = {https://www.merl.com/publications/TR2020-157}
- }
, - "Polar Coding with Chemical Reaction Networks for Molecular Communications", IEEE Global Communications Conference (GLOBECOM), December 2020.BibTeX TR2020-160 PDF Video
- @inproceedings{Matsumine2020dec,
- author = {Matsumine, Toshiki and Koike-Akino, Toshiaki and Wang, Ye},
- title = {Polar Coding with Chemical Reaction Networks for Molecular Communications},
- booktitle = {IEEE Global Communications Conference (GLOBECOM)},
- year = 2020,
- month = dec,
- url = {https://www.merl.com/publications/TR2020-160}
- }
, - "Exploiting linear substructure in linear regression Kalman filters", IEEE Annual Conference on Decision and Control (CDC), December 2020.BibTeX TR2020-171 PDF
- @inproceedings{Greiff2020dec,
- author = {Greiff, Marcus and Robertsson, Anders and Berntorp, Karl},
- title = {Exploiting linear substructure in linear regression Kalman filters},
- booktitle = {IEEE Annual Conference on Decision and Control (CDC)},
- year = 2020,
- month = dec,
- url = {https://www.merl.com/publications/TR2020-171}
- }
, - "Backhaul Reliability Analysis on Cluster-Based Transmit Diversity Schemes in Private Networks", IEEE Global Communications Conference (GLOBECOM), December 2020.BibTeX TR2020-170 PDF
- @inproceedings{Kim2020dec,
- author = {Kim, Kyeong Jin and Liu, Hongwu and Yeoh, Phee Lep and Orlik, Philip V. and Poor, H. Vincent},
- title = {Backhaul Reliability Analysis on Cluster-Based Transmit Diversity Schemes in Private Networks},
- booktitle = {IEEE Global Communications Conference (GLOBECOM)},
- year = 2020,
- month = dec,
- url = {https://www.merl.com/publications/TR2020-170}
- }
, - "Fingerprinting-Based Indoor Localization with Commercial MMWave WiFi: NLOS Propagation", IEEE Global Communications Conference (GLOBECOM), December 2020.BibTeX TR2020-159 PDF
- @inproceedings{Wang2020dec,
- author = {Wang, Pu and Koike-Akino, Toshiaki and Orlik, Philip V.},
- title = {Fingerprinting-Based Indoor Localization with Commercial MMWave WiFi: NLOS Propagation},
- booktitle = {IEEE Global Communications Conference (GLOBECOM)},
- year = 2020,
- month = dec,
- url = {https://www.merl.com/publications/TR2020-159}
- }
, - "Human Pose and Seat Occupancy Classification with Commercial MMWave WiFi", IEEE Global Communications Conference (GLOBECOM), December 2020.BibTeX TR2020-158 PDF
- @inproceedings{Yu2020dec,
- author = {Yu, Jianyuan and Wang, Pu and Koike-Akino, Toshiaki and Wang, Ye and Orlik, Philip V.},
- title = {Human Pose and Seat Occupancy Classification with Commercial MMWave WiFi},
- booktitle = {IEEE Global Communications Conference (GLOBECOM)},
- year = 2020,
- month = dec,
- url = {https://www.merl.com/publications/TR2020-158}
- }
, - "Graph Cross Networks with Vertex Infomax Pooling", Advances in Neural Information Processing Systems (NeurIPS), December 2020.BibTeX TR2020-167 PDF
- @inproceedings{Li2020dec,
- author = {Li, Maosen and Chen, Siheng and Zhang, Ya},
- title = {Graph Cross Networks with Vertex Infomax Pooling},
- booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
- year = 2020,
- month = dec,
- url = {https://www.merl.com/publications/TR2020-167}
- }
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- "A dCDD-Based Transmit Diversity Scheme for Downlink Pseudo-NOMA Systems", IEEE Transactions on Wireless Communications, December 2020.
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Videos
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Multiview Sensing with Unknown Permutations: An Optimal Transport Approach
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[ECOC 2020] End-to-End Deep Learning Optimization for Phase-Noise Robust Optical Communications
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Imaging for inverse scattering in Reflection Tomography
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All Digital Transmitter
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Machine Learning Power Amplifier
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Polar Coding with Chemical Reaction Networks for Molecular Communications
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EMI reduction in PWM inverters using adaptive frequency modulated carriers
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[ACP 2020] Inverse Design of Nanophotonic Devices using Deep Neural Networks
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Through-the-Wall Imaging
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Software Downloads