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
Devesh K.
Jha
Chungwei
Lin
Hongbo
Sun
Jinyun
Zhang
Joshua
Rapp
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
Jing
Liu
Shinya
Tsuruta
Ryoma
Yataka
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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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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.
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NEWS MERL Researchers give a Tutorial Talk on Quantum Machine Learning for Sensing and Communications at IEEE VCC Date: November 28, 2023 - November 30, 2023
Where: Virtual
MERL Contacts: Toshiaki Koike-Akino; Pu (Perry) Wang
Research Areas: Artificial Intelligence, Communications, Computational Sensing, Machine Learning, Signal ProcessingBrief- On November 28, 2023, MERL researchers Toshiaki Koike-Akino and Pu (Perry) Wang will give a 3-hour tutorial presentation at the first IEEE Virtual Conference on Communications (VCC). The talk, titled "Post-Deep Learning Era: Emerging Quantum Machine Learning for Sensing and Communications," addresses recent trends, challenges, and advances in sensing and communications. P. Wang presents use cases, industry trends, signal processing, and deep learning for Wi-Fi integrated sensing and communications (ISAC), while T. Koike-Akino discusses the future of deep learning, giving a comprehensive overview of artificial intelligence (AI) technologies, natural computing, emerging quantum AI, and their diverse applications. The tutorial is conducted virtually.
IEEE VCC is a new fully virtual conference launched from the IEEE Communications Society, gathering researchers from academia and industry who are unable to travel but wish to present their recent scientific results and engage in conducive interactive discussions with fellow researchers working in their fields. It is designed to resolve potential hardship such as pandemic restrictions, visa issues, travel problems, or financial difficulties.
- On November 28, 2023, MERL researchers Toshiaki Koike-Akino and Pu (Perry) Wang will give a 3-hour tutorial presentation at the first IEEE Virtual Conference on Communications (VCC). The talk, titled "Post-Deep Learning Era: Emerging Quantum Machine Learning for Sensing and Communications," addresses recent trends, challenges, and advances in sensing and communications. P. Wang presents use cases, industry trends, signal processing, and deep learning for Wi-Fi integrated sensing and communications (ISAC), while T. Koike-Akino discusses the future of deep learning, giving a comprehensive overview of artificial intelligence (AI) technologies, natural computing, emerging quantum AI, and their diverse applications. The tutorial is conducted virtually.
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Research Highlights
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Internships
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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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ST2082: Integrated Sensing and Communication (ISAC)
The Computational Sensing team at MERL is seeking a highly motivated intern to conduct fundamental research in integrated sensing and communication (ISAC) with a focus on signal processing, model-based learning, and optimization. Expertise in joint waveform/sequence optimization, integrated ISAC precoder/combiner design, model-based learning for ISAC, and downlink/uplink/active sensing under timing and frequency offsets is highly desired. Familiarity with IEEE 802.11 (ac/ax/ad/ay) standards is a plus but not required. The intern will collaborate with a small group of MERL researchers to develop novel algorithms, design experiments using MERL in-house testbed, and prepare results for publication. The expected duration of the internship is 3 months with a flexible start date.
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EA2096: Sensing data fusion
MERL is looking for a self-motivated intern to work on sensing data fusion with applicatino to condition monitoring, fault detection, and predictive maintenance. The ideal candidate would be a Ph.D. candidate in electrical engineering or computer science with solid research background in signal processing and machine learning. Background in electric machine, system control and automation is preferred. Proficiency in MATLAB is necessary. The intern is expected to collaborate with MERL researchers to perform simulations, analyze experimental data, and prepare manuscripts for scientific publications. The total duration is anticipated to be 3-6 months and the start date is flexible. This internship requires work that can only be done at MERL.
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Recent Publications
- "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}
- }
, - "Graph-Based EEG Signal Compression for Human-Machine Interaction", IEEE Access, 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,
- month = mar,
- 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}
- }
, - "Implicit Neural Representation-based Hybrid Digital-Analog Image Delivery", IEEE International Conference on Computing, Networking and Communications (ICNC), February 2024.BibTeX TR2024-007 PDF
- @inproceedings{Kuwabara2024feb,
- author = {Kuwabara,Akihiro and Osako Yutaro and Kato, Sorachi and Fujihashi, Takuya and Koike-Akino, Toshiaki and Watanabe, Takashi},
- title = {Implicit Neural Representation-based Hybrid Digital-Analog Image Delivery},
- booktitle = {IEEE International Conference on Computing, Networking and Communications (ICNC)},
- year = 2024,
- month = feb,
- url = {https://www.merl.com/publications/TR2024-007}
- }
, - "A model of spatial resolution uncertainty for Compton camera imaging", International Conference on Advancements in Nuclear Instrumentation Measurement Methods and their Applications (ANIMMA), DOI: 10.1051/epjconf/202328810002, January 2024, pp. 10002.BibTeX TR2024-005 PDF
- @inproceedings{Ma2024jan,
- author = {Ma, Yanting and Rapp, Joshua and Boufounos, Petros T. and Mansour, Hassan},
- title = {A model of spatial resolution uncertainty for Compton camera imaging},
- booktitle = {Advancements in Nuclear Instrumentation Measurement Methods and their Applications (ANIMMA)},
- year = 2024,
- pages = 10002,
- month = jan,
- publisher = {EPJ Web of Conferences, 288},
- doi = {10.1051/epjconf/202328810002},
- url = {https://www.merl.com/publications/TR2024-005}
- }
, - "The Role of Detection Times in Reflectivity Estimation with Single-Photon Lidar", IEEE Journal of Selected Topics in Quantum Electronics, DOI: 10.1109/JSTQE.2023.3333834, Vol. 30, No. 1, pp. 8800114:1-14, January 2024.BibTeX TR2024-003 PDF
- @article{Kitichotkul2024jan,
- author = {Kitichotkul, Ruangrawee and Rapp, Joshua and Goyal, Vivek K},
- title = {The Role of Detection Times in Reflectivity Estimation with Single-Photon Lidar},
- journal = {IEEE Journal of Selected Topics in Quantum Electronics},
- year = 2024,
- volume = 30,
- number = 1,
- pages = {8800114:1--14},
- month = jan,
- doi = {10.1109/JSTQE.2023.3333834},
- url = {https://www.merl.com/publications/TR2024-003}
- }
, - "RIS-Assisted Joint Preamble Detection and Localization", IEEE International Workshop on Computational Advances in Multi-Sensor Adaptive Processing (CAMSAP), DOI: 10.1109/CAMSAP58249.2023.10403493, December 2023.BibTeX TR2023-142 PDF
- @inproceedings{Nuti2023dec,
- author = {Nuti, Pooja and Kim, Kyeong Jin and Wang, Pu and Koike-Akino, Toshiaki and Parsons, Kieran},
- title = {RIS-Assisted Joint Preamble Detection and Localization},
- booktitle = {IEEE International Workshop on Computational Advances in Multi-Sensor Adaptive Processing (CAMSAP)},
- year = 2023,
- month = dec,
- doi = {10.1109/CAMSAP58249.2023.10403493},
- isbn = {979-8-3503-4453-0},
- url = {https://www.merl.com/publications/TR2023-142}
- }
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- "Object Trajectory Estimation with Multi-Band Wi-Fi Neural Dynamic Fusion", IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP), March 2024.
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