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
Philip V.
Orlik
Kieran
Parsons
Pu
(Perry)
WangYe
Wang
Karl
Berntorp
Petros T.
Boufounos
Hassan
Mansour
Stefano
Di Cairano
Bingnan
Wang
Jianlin
Guo
Dehong
Liu
Yebin
Wang
Wataru
Tsujita
Koon Hoo
Teo
Mouhacine
Benosman
Yanting
Ma
Matthew
Brand
Joshua
Rapp
Devesh K.
Jha
Chungwei
Lin
Hongbo
Sun
Jinyun
Zhang
Ankush
Chakrabarty
Anthony
Vetro
Abraham
Goldsmith
Jonathan
Le Roux
Suhas
Lohit
Tim K.
Marks
Avishai
Weiss
William S.
Yerazunis
Jose
Amaya
Anoop
Cherian
Vedang M.
Deshpande
Chiori
Hori
Sameer
Khurana
Pedro
Miraldo
Huifang
Sun
Abraham P.
Vinod
Ryoma
Yataka
Jing
Liu
Shinya
Tsuruta
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Awards
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AWARD Best paper award at PHMAP 2023 Date: September 14, 2023
Awarded to: Dehong Liu, Anantaram Varatharajan, and Abraham Goldsmith
MERL Contacts: Abraham Goldsmith; Dehong Liu
Research Areas: Electric Systems, Signal ProcessingBrief- MERL researchers Dehong Liu, Anantaram Varatharajan, and Abraham Goldsmith were awarded one of three best paper awards at Asia Pacific Conference of the Prognostics and Health Management Society 2023 (PHMAP23) held in Tokyo from September 11th to 14th, 2023, for their co-authored paper titled 'Extracting Broken-Rotor-Bar Fault Signature of Varying-Speed Induction Motors.'
PHMAP is a biennial international conference specialized in prognostics and health management. PHMAP23 attracted more than 300 attendees from worldwide and published more than 160 regular papers from academia and industry including aerospace, production, civil engineering, electronics, and so on.
- MERL researchers Dehong Liu, Anantaram Varatharajan, and Abraham Goldsmith were awarded one of three best paper awards at Asia Pacific Conference of the Prognostics and Health Management Society 2023 (PHMAP23) held in Tokyo from September 11th to 14th, 2023, for their co-authored paper titled 'Extracting Broken-Rotor-Bar Fault Signature of Varying-Speed Induction Motors.'
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AWARD MERL’s Paper on Wi-Fi Sensing Earns Top 3% Paper Recognition at ICASSP 2023, Selected as a Best Student Paper Award Finalist Date: June 9, 2023
Awarded to: Cristian J. Vaca-Rubio, Pu Wang, Toshiaki Koike-Akino, Ye Wang, Petros Boufounos and Petar Popovski
MERL Contacts: Petros T. Boufounos; Toshiaki Koike-Akino; Pu (Perry) Wang; Ye Wang
Research Areas: Artificial Intelligence, Communications, Computational Sensing, Dynamical Systems, Machine Learning, Signal ProcessingBrief- A MERL Paper on Wi-Fi sensing was recognized as a Top 3% Paper among all 2709 accepted papers at the 2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP 2023). Co-authored by Cristian Vaca-Rubio and Petar Popovski from Aalborg University, Denmark, and MERL researchers Pu Wang, Toshiaki Koike-Akino, Ye Wang, and Petros Boufounos, the paper "MmWave Wi-Fi Trajectory Estimation with Continous-Time Neural Dynamic Learning" was also a Best Student Paper Award finalist.
Performed during Cristian’s stay at MERL first as a visiting Marie Skłodowska-Curie Fellow and then as a full-time intern in 2022, this work capitalizes on standards-compliant Wi-Fi signals to perform indoor localization and sensing. The paper uses a neural dynamic learning framework to address technical issues such as low sampling rate and irregular sampling intervals.
ICASSP, a flagship conference of the IEEE Signal Processing Society (SPS), was hosted on the Greek island of Rhodes from June 04 to June 10, 2023. ICASSP 2023 marked the largest ICASSP in history, boasting over 4000 participants and 6128 submitted papers, out of which 2709 were accepted.
- A MERL Paper on Wi-Fi sensing was recognized as a Top 3% Paper among all 2709 accepted papers at the 2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP 2023). Co-authored by Cristian Vaca-Rubio and Petar Popovski from Aalborg University, Denmark, and MERL researchers Pu Wang, Toshiaki Koike-Akino, Ye Wang, and Petros Boufounos, the paper "MmWave Wi-Fi Trajectory Estimation with Continous-Time Neural Dynamic Learning" was also a Best Student Paper Award finalist.
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AWARD Best Paper Award of 2022 IPSJ Transactions on Consumer Devices & Systems Date: March 27, 2023
Awarded to: Yukimasa Nagai, Takenori Sumi, Jianlin Guo, Philip Orlik, Hiroshi Mineno
MERL Contacts: Jianlin Guo; Philip V. Orlik; Kieran Parsons
Research Areas: Communications, Signal ProcessingBrief- MELCO/MERL research paper “IEEE 802.19.3 Standardization for Coexistence of IEEE 802.11ah and IEEE 802.15.4g Systems in Sub-1GHz Frequency Bands” has won the Best Paper Award of the 2022 IPSJ Transactions on Consumer Devices and Systems. The Information Processing Society of Japan (IPSJ) award was established in 1970 and is conferred on the authors of particularly excellent papers, which are published in the IPSJ journals and transactions. Our paper was published by the IPSJ Transaction on Consumer Device and System Vol. 29 in 2021 and authors are Yukimasa Nagai, Takenori Sumi, Jianlin Guo, Philip Orlik and Hiroshi Mineno.
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News & Events
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EVENT MERL Contributes to ICASSP 2024 Date: Sunday, April 14, 2024 - Friday, April 19, 2024
Location: Seoul, South Korea
MERL Contacts: Petros T. Boufounos; François Germain; Chiori Hori; Sameer Khurana; Toshiaki Koike-Akino; Jonathan Le Roux; Hassan Mansour; Zexu Pan; Kieran Parsons; Joshua Rapp; Anthony Vetro; Pu (Perry) Wang; Gordon Wichern; Ryoma Yataka
Research Areas: Artificial Intelligence, Computational Sensing, Machine Learning, Robotics, Signal Processing, Speech & AudioBrief- MERL has made numerous contributions to both the organization and technical program of ICASSP 2024, which is being held in Seoul, Korea from April 14-19, 2024.
Sponsorship and Awards
MERL is proud to be a Bronze Patron of the conference and will participate in the student job fair on Thursday, April 18. Please join this session to learn more about employment opportunities at MERL, including openings for research scientists, post-docs, and interns.
MERL is pleased to be the sponsor of two IEEE Awards that will be presented at the conference. We congratulate Prof. Stéphane G. Mallat, the recipient of the 2024 IEEE Fourier Award for Signal Processing, and Prof. Keiichi Tokuda, the recipient of the 2024 IEEE James L. Flanagan Speech and Audio Processing Award.
Jonathan Le Roux, MERL Speech and Audio Senior Team Leader, will also be recognized during the Awards Ceremony for his recent elevation to IEEE Fellow.
Technical Program
MERL will present 13 papers in the main conference on a wide range of topics including automated audio captioning, speech separation, audio generative models, speech and sound synthesis, spatial audio reproduction, multimodal indoor monitoring, radar imaging, depth estimation, physics-informed machine learning, and integrated sensing and communications (ISAC). Three workshop papers have also been accepted for presentation on audio-visual speaker diarization, music source separation, and music generative models.
Perry Wang is the co-organizer of the Workshop on Signal Processing and Machine Learning Advances in Automotive Radars (SPLAR), held on Sunday, April 14. It features keynote talks from leaders in both academia and industry, peer-reviewed workshop papers, and lightning talks from ICASSP regular tracks on signal processing and machine learning for automotive radar and, more generally, radar perception.
Gordon Wichern will present an invited keynote talk on analyzing and interpreting audio deep learning models at the Workshop on Explainable Machine Learning for Speech and Audio (XAI-SA), held on Monday, April 15. He will also appear in a panel discussion on interpretable audio AI at the workshop.
Perry Wang also co-organizes a two-part special session on Next-Generation Wi-Fi Sensing (SS-L9 and SS-L13) which will be held on Thursday afternoon, April 18. The special session includes papers on PHY-layer oriented signal processing and data-driven deep learning advances, and supports upcoming 802.11bf WLAN Sensing Standardization activities.
Petros Boufounos is participating as a mentor in ICASSP’s Micro-Mentoring Experience Program (MiME).
About ICASSP
ICASSP is the flagship conference of the IEEE Signal Processing Society, and the world's largest and most comprehensive technical conference focused on the research advances and latest technological development in signal and information processing. The event attracts more than 3000 participants.
- MERL has made numerous contributions to both the organization and technical program of ICASSP 2024, which is being held in Seoul, Korea from April 14-19, 2024.
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TALK [MERL Seminar Series 2023] Dr. Kristina Monakhova presents talk titled Robust and Physics-informed machine learning for low light imaging Date & Time: Tuesday, November 28, 2023; 12:00 PM
Speaker: Kristina Monakhova, MIT and Cornell
MERL Host: Joshua Rapp
Research Areas: Computational Sensing, Computer Vision, Machine Learning, Signal ProcessingAbstract- Imaging in low light settings is extremely challenging due to low photon counts, both in photography and in microscopy. In photography, imaging under low light, high gain settings often results in highly structured, non-Gaussian sensor noise that’s hard to characterize or denoise. In this talk, we address this by developing a GAN-tuned physics-based noise model to more accurately represent camera noise at the lowest light, and highest gain settings. Using this noise model, we train a video denoiser using synthetic data and demonstrate photorealistic videography at starlight (submillilux levels of illumination) for the first time.
For multiphoton microscopy, which is a form a scanning microscopy, there’s a trade-off between field of view, phototoxicity, acquisition time, and image quality, often resulting in noisy measurements. While deep learning-based methods have shown compelling denoising performance, can we trust these methods enough for critical scientific and medical applications? In the second part of this talk, I’ll introduce a learned, distribution-free uncertainty quantification technique that can both denoise and predict pixel-wise uncertainty to gauge how much we can trust our denoiser’s performance. Furthermore, we propose to leverage this learned, pixel-wise uncertainty to drive an adaptive acquisition technique that rescans only the most uncertain regions of a sample. With our sample and algorithm-informed adaptive acquisition, we demonstrate a 120X improvement in total scanning time and total light dose for multiphoton microscopy, while successfully recovering fine structures within the sample.
- Imaging in low light settings is extremely challenging due to low photon counts, both in photography and in microscopy. In photography, imaging under low light, high gain settings often results in highly structured, non-Gaussian sensor noise that’s hard to characterize or denoise. In this talk, we address this by developing a GAN-tuned physics-based noise model to more accurately represent camera noise at the lowest light, and highest gain settings. Using this noise model, we train a video denoiser using synthetic data and demonstrate photorealistic videography at starlight (submillilux levels of illumination) for the first time.
See All News & Events for Signal Processing -
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Research Highlights
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Internships
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ST1763: Technologies for Multimodal Tracking and Imaging
MERL is seeking a motivated intern to assist in developing hardware and algorithms for multimodal imaging applications. The project involves integration of radar, camera, and depth sensors in a variety of sensing scenarios. The ideal candidate should have experience with FMCW radar and/or depth sensing, and be fluent in Python and scripting methods. Familiarity with optical tracking of humans and experience with hardware prototyping is desired. Good knowledge of computational imaging and/or radar imaging methods is a plus.
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ST2083: Deep Learning for Radar Perception
The Computation Sensing team at MERL is seeking a highly motivated intern to conduct fundamental research in radar perception. Expertise in deep learning-based object detection, multiple object tracking, data association, and representation learning (detection points, heatmaps, and raw radar waveforms) is required. Previous hands-on experience on open indoor/outdoor radar datasets is a plus. Familiarity with the concept of FMCW, MIMO, and range-Doppler-angle spectrum is an asset. The intern will collaborate with a small group of MERL researchers to develop novel algorithms, design experiments with MERL in-house testbed, and prepare results for patents and publication. The expected duration of the internship is 3 months with a flexible start date.
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ST2090: Radiation Source Localization
The Computational Sensing Team at MERL is seeking an intern to work on estimation algorithms for radioactive source localization. The candidate should have experience with statistical modeling and estimation theory. A detailed knowledge of interactions of particles with matter, imaging inverse problems, and/or computed tomography is preferred. Hands-on experience with high-energy physics simulators (e.g., Geant4) is beneficial but not required. Strong programming skills in Python are essential. Publication of the results produced during our internships is expected. The duration is anticipated to be 3-6 months.
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Recent Publications
- "Implicit Neural Representation for Low-Overhead Graph-Based Holographic-Type Communications", IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP), April 2024.BibTeX TR2024-022 PDF
- @inproceedings{Fujihashi2024apr,
- author = {Fujihashi, Takuya and Kato, Sorachi and Koike-Akino, Toshiaki},
- title = {Implicit Neural Representation for Low-Overhead Graph-Based Holographic-Type Communications},
- booktitle = {IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP)},
- year = 2024,
- month = apr,
- url = {https://www.merl.com/publications/TR2024-022}
- }
, - "Tracking Beyond the Unambiguous Range with Modulo Single-Photon Lidar", IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP), DOI: 10.1109/ICASSP48485.2024.10446835, March 2024, pp. 6-10.BibTeX TR2024-021 PDF
- @inproceedings{Fernandez-Menduina2024mar,
- author = {Fernandez-Menduina, Samuel and Rapp, Joshua and Mansour, Hassan and Greiff, Marcus and Parsons, Kieran},
- title = {Tracking Beyond the Unambiguous Range with Modulo Single-Photon Lidar},
- booktitle = {IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP)},
- year = 2024,
- pages = {6--10},
- month = mar,
- doi = {10.1109/ICASSP48485.2024.10446835},
- url = {https://www.merl.com/publications/TR2024-021}
- }
, - "Single-pixel imaging of dynamic flows using Neural ODE regularization", IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP), March 2024.BibTeX TR2024-024 PDF
- @inproceedings{Sholokhov2024mar,
- author = {Sholokhov, Aleksei and Rapp, Joshua and Nabi, Saleh and Brunton, Steven and Kutz, Nathan and Mansour, Hassan},
- title = {Single-pixel imaging of dynamic flows using Neural ODE regularization},
- booktitle = {IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP)},
- year = 2024,
- month = mar,
- url = {https://www.merl.com/publications/TR2024-024}
- }
, - "Object Trajectory Estimation with Multi-Band Wi-Fi Neural Dynamic Fusion", IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP), March 2024.BibTeX TR2024-019 PDF
- @inproceedings{Kato2024mar,
- author = {Kato, Sorachi and Wang, Pu and Koike-Akino, Toshiaki and Fujihashi, Takuya and Mansour, Hassan and Boufounos, Petros T.},
- title = {Object Trajectory Estimation with Multi-Band Wi-Fi Neural Dynamic Fusion},
- booktitle = {IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP)},
- year = 2024,
- month = mar,
- url = {https://www.merl.com/publications/TR2024-019}
- }
, - "Monostatic DMG Passive Sensing with Hypothesis Testing", IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP), March 2024.BibTeX TR2024-020 PDF
- @inproceedings{Wang2024mar,
- author = {Wang, Pu and Boufounos, Petros T.},
- title = {Monostatic DMG Passive Sensing with Hypothesis Testing},
- booktitle = {IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP)},
- year = 2024,
- month = mar,
- url = {https://www.merl.com/publications/TR2024-020}
- }
, - "Radar Perception with Scalable Connective Temporal Relations for Autonomous Driving", IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP), March 2024.BibTeX TR2024-023 PDF
- @inproceedings{Yataka2024mar,
- author = {Yataka, Ryoma and Wang, Pu and Boufounos, Petros T. and Takahashi, Ryuhei},
- title = {Radar Perception with Scalable Connective Temporal Relations for Autonomous Driving},
- booktitle = {IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP)},
- year = 2024,
- month = mar,
- url = {https://www.merl.com/publications/TR2024-023}
- }
, - "Graph-Based EEG Signal Compression for Human-Machine Interaction", IEEE Access, DOI: 10.1109/ACCESS.2023.3347592, Vol. 12, No. IEEE, pp. 1163-1171, March 2024.BibTeX TR2024-015 PDF
- @article{Fujihashi2024mar,
- author = {Fujihashi, Takuya and Koike-Akino, Toshiaki},
- title = {Graph-Based EEG Signal Compression for Human-Machine Interaction},
- journal = {IEEE Access},
- year = 2024,
- volume = 12,
- number = {IEEE},
- pages = {1163--1171},
- month = mar,
- doi = {10.1109/ACCESS.2023.3347592},
- issn = {2169-3536},
- url = {https://www.merl.com/publications/TR2024-015}
- }
, - "Wi-Fi based Indoor Monitoring Enhanced by Multimodal Fusion", IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP), March 2024.BibTeX TR2024-012 PDF
- @inproceedings{Hori2024mar,
- author = {Hori, Chiori and Wang, Pu and Rahman, Mahbub and Vaca-Rubio, Cristian and Khurana, Sameer and Cherian, Anoop and Le Roux, Jonathan},
- title = {Wi-Fi based Indoor Monitoring Enhanced by Multimodal Fusion},
- booktitle = {IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP)},
- year = 2024,
- month = mar,
- url = {https://www.merl.com/publications/TR2024-012}
- }
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- "Implicit Neural Representation for Low-Overhead Graph-Based Holographic-Type Communications", IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP), April 2024.
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Software & Data Downloads