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)
Wang
Ye
Wang
Petros T.
Boufounos
Hassan
Mansour
Stefano
Di Cairano
Dehong
Liu
Jianlin
Guo
Bingnan
Wang
Yebin
Wang
Wataru
Tsujita
Joshua
Rapp
Yanting
Ma
Matthew
Brand
Devesh K.
Jha
Chungwei
Lin
Hongbo
Sun
Jinyun
Zhang
Ankush
Chakrabarty
Anthony
Vetro
Avishai
Weiss
Abraham
Goldsmith
Jonathan
Le Roux
Suhas
Lohit
Tim K.
Marks
William S.
Yerazunis
Wael H.
Ali
Anoop
Cherian
Radu
Corcodel
Vedang M.
Deshpande
Chiori
Hori
Pedro
Miraldo
James
Queeney
Huifang
Sun
Abraham P.
Vinod
Jing
Liu
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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.
See All Awards for Signal Processing -
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News & Events
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TALK [MERL Seminar Series 2025] Qing Qu presents talk titled The Emergence of Generalizability and Semantic Low-Dim Subspaces in Diffusion Models Date & Time: Wednesday, March 5, 2025; 12:00 PM
Speaker: Qing Qu, University of Michigan
MERL Host: Pu (Perry) Wang
Research Areas: Artificial Intelligence, Computational Sensing, Machine Learning, Signal ProcessingAbstract
Recent empirical studies have shown that diffusion models possess a unique reproducibility property, transiting from memorization to generalization as the number of training samples increases. This demonstrates that diffusion models can effectively learn image distributions and generate new samples. Remarkably, these models achieve this even with a small number of training samples, despite the challenge of large image dimensions, effectively circumventing the curse of dimensionality. In this work, we provide theoretical insights into this phenomenon by leveraging two key empirical observations: (i) the low intrinsic dimensionality of image datasets and (ii) the low-rank property of the denoising autoencoder in trained diffusion models. With these setups, we rigorously demonstrate that optimizing the training loss of diffusion models is equivalent to solving the canonical subspace clustering problem across the training samples. This insight has practical implications for training and controlling diffusion models. Specifically, it enables us to precisely characterize the minimal number of samples necessary for accurately learning the low-rank data support, shedding light on the phase transition from memorization to generalization. Additionally, we empirically establish a correspondence between the subspaces and the semantic representations of image data, which enables one-step, transferrable, efficient image editing. Moreover, our results have profound practical implications for training efficiency and model safety, and they also open up numerous intriguing theoretical questions for future research.
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TALK [MERL Seminar Series 2025] David Lindell presents talk titled Imaging Dynamic Scenes from Seconds to Picoseconds Date & Time: Wednesday, January 29, 2025; 1:00 PM
Speaker: David Lindell, University of Toronto
MERL Host: Joshua Rapp
Research Areas: Computational Sensing, Computer Vision, Signal ProcessingAbstract
The observed timescales of the universe span from the exasecond scale (~1e18 seconds) down to the zeptosecond scale (~1e-21 seconds). While specialized imaging systems can capture narrow slices of this temporal spectrum in the ultra-fast regime (e.g., nanoseconds to picoseconds; 1e-9 to 1e-12 s), they cannot simultaneously capture both slow (> 1 second) and ultra-fast events (< 1 nanosecond). Further, ultra-fast imaging systems are conventionally limited to single-viewpoint capture, hindering 3D visualization at ultra-fast timescales. In this talk, I discuss (1) new computational algorithms that turn a single-photon detector into an "ultra-wideband" imaging system that captures events from seconds to picoseconds; and (2) a method for neural rendering using multi-viewpoint, ultra-fast videos captured using single-photon detectors. The latter approach enables rendering videos of propagating light from novel viewpoints, observation of viewpoint-dependent changes in light transport predicted by Einstein, recovery of material properties, and accurate 3D reconstruction from multiply scattered light. Finally, I discuss future directions in ultra-wideband imaging.
See All News & Events for Signal Processing -
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Research Highlights
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Internships
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EA0070: Internship - Multi-modal sensor fusion
MERL is looking for a self-motivated intern to work on multi-modal sensor fusion for health condition monitoring and predictive maintenance of motor drive systems. 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. Experience in motor drive system is a plus. The intern is expected to collaborate with MERL researchers to collect data, explore multi-modal data relationship, and prepare manuscripts for publications. The total duration is anticipated to be 3 months and the start date is flexible.
Required Specific Experience
- Experience with multi-modal sensor fusion.
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CI0082: Internship - Quantum AI
MERL is excited to announce an internship opportunity in the field of Quantum Machine Learning (QML) and Quantum AI (QAI). We are seeking a highly motivated and talented individual to join our research team. This is an exciting opportunity to make a real impact in the field of quantum computing and AI, with the aim of publishing at leading research venues.
Responsibilities:
- Conduct cutting-edge research in quantum machine learning.
- Collaborate with a team of experts in quantum computing, deep learning, and signal processing.
- Develop and implement algorithms using PyTorch and PennyLane.
- Publish research results at leading research venues.
Qualifications:
- Currently pursuing a PhD or a post-graduate researcher in a relevant field.
- Strong background and solid publication records in quantum computing, deep learning, and signal processing.
- Proficient programming skills in PyTorch and PennyLane are highly desirable.
What We Offer:
- An opportunity to work on groundbreaking research in a leading research lab.
- Collaboration with a team of experienced researchers.
- A stimulating and supportive work environment.
If you are passionate about quantum machine learning and meet the above qualifications, we encourage you to apply. Please submit your resume and a brief cover letter detailing your research experience and interests. Join us at MERL and contribute to the future of quantum machine learning!
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ST0116: Internship - 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, pose estimation, segmentation, multiple object tracking (MOT), and representation learning on radar data is required. Previous hands-on experience with open indoor and outdoor radar datasets is a plus. Familiarity with basic radar concepts and MERL's recent work in radar perception is an asset. The intern will work closely with MERL researchers to develop novel algorithms, design experiments with MERL in-house testbed, and prepare results for patents and publication. The internship is expected to last 3 months with a preferred start date after June 2025.
Required Specific Experience
- Solid understanding of state-of-the-art perception frameworks including transformer-based (e.g., DETR) and diffusion-based (e.g., DiffusionDet) methods.
- Hands-on experience with open large-scale radar datasets such as MMVR, HIBER, RADIATE, and K-Radar.
- Proficiency in Python and experience with job scheduling on GPU clusters using tools like Slurm.
- Proven publication records in top-tier venues such as CVPR, ICCV, ECCV, NeurIPS.
- Knowledge of basic radar concepts such as FMCW, MIMO, (micro-) Doppler signature, radar point clouds, heatmaps, and raw ADC waveforms.
- Familiarity with MERL's recent radar perception research such as TempoRadar, SIRA, MMVR, and RETR.
See All Internships for Signal Processing -
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Openings
See All Openings at MERL -
Recent Publications
- , "Doppler Single-Photon Lidar", IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP), March 2025.BibTeX TR2025-028 PDF
- @inproceedings{Kitichotkul2025mar,
- author = {Kitichotkul, Ruangrawee and Rapp, Joshua and Ma, Yanting and Mansour, Hassan},
- title = {{Doppler Single-Photon Lidar}},
- booktitle = {IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP)},
- year = 2025,
- month = mar,
- url = {https://www.merl.com/publications/TR2025-028}
- }
- , "Indoor Airflow Imaging Using Physics-Informed Schlieren Tomography", IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP), March 2025.BibTeX TR2025-035 PDF
- @inproceedings{Teh2025mar,
- author = {Teh, Arjun and Ali, Wael H. and Rapp, Joshua and Mansour, Hassan},
- title = {{Indoor Airflow Imaging Using Physics-Informed Schlieren Tomography}},
- booktitle = {IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP)},
- year = 2025,
- month = mar,
- url = {https://www.merl.com/publications/TR2025-035}
- }
- , "Enabling DMG Wi-Fi Sensing in Data Transmission Intervals by Exploiting Beam Training Codebook", IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP), March 2025.BibTeX TR2025-026 PDF
- @inproceedings{Attiah2025mar,
- author = {Attiah, Kareem and Wang, Pu and Mansour, Hassan and Koike-Akino, Toshiaki and Boufounos, Petros T.},
- title = {{Enabling DMG Wi-Fi Sensing in Data Transmission Intervals by Exploiting Beam Training Codebook}},
- booktitle = {IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP)},
- year = 2025,
- month = mar,
- url = {https://www.merl.com/publications/TR2025-026}
- }
- , "Multi-View Radar Detection Transformer with Differentiable Positional Encoding", IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP), March 2025.BibTeX TR2025-027 PDF
- @inproceedings{Yataka2025mar,
- author = {Yataka, Ryoma and Wang, Pu and Boufounos, Petros T. and Takahashi, Ryuhei},
- title = {{Multi-View Radar Detection Transformer with Differentiable Positional Encoding}},
- booktitle = {IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP)},
- year = 2025,
- month = mar,
- url = {https://www.merl.com/publications/TR2025-027}
- }
- , "Bayesian Measurement Masks for GNSS Positioning", IEEE Conference on Decision and Control (CDC), December 2024.BibTeX TR2024-172 PDF
- @inproceedings{Greiff2024dec,
- author = {Greiff, Marcus and {Di Cairano}, Stefano and Berntorp, Karl},
- title = {{Bayesian Measurement Masks for GNSS Positioning}},
- booktitle = {IEEE Conference on Decision and Control (CDC)},
- year = 2024,
- month = dec,
- url = {https://www.merl.com/publications/TR2024-172}
- }
- , "A Model-Based Approach for Improving Reinforcement Learning Efficiency Leveraging Expert Observations", IEEE Conference on Decision and Control (CDC), DOI: 10.1109/CDC56724.2024.10885921, December 2024, pp. 1147-1152.BibTeX TR2024-178 PDF
- @inproceedings{Ozcan2024dec,
- author = {Ozcan, Erhan Can and Giammarino, Vittorio and Queeney, James and Paschalidis, Ioannis Ch.},
- title = {{A Model-Based Approach for Improving Reinforcement Learning Efficiency Leveraging Expert Observations}},
- booktitle = {IEEE Conference on Decision and Control (CDC)},
- year = 2024,
- pages = {1147--1152},
- month = dec,
- doi = {10.1109/CDC56724.2024.10885921},
- url = {https://www.merl.com/publications/TR2024-178}
- }
- , "Asynchronous Variational-Bayes Kalman Filtering", IEEE Conference on Decision and Control (CDC), December 2024.BibTeX TR2024-177 PDF
- @inproceedings{Greiff2024dec2,
- author = {Greiff, Marcus and Berntorp, Karl},
- title = {{Asynchronous Variational-Bayes Kalman Filtering}},
- booktitle = {IEEE Conference on Decision and Control (CDC)},
- year = 2024,
- month = dec,
- url = {https://www.merl.com/publications/TR2024-177}
- }
- , "ulti-layered Surface Estimation for Low-cost Optical Coherence Tomography", IEEE Transactions on Computational Imaging, DOI: 10.1109/TCI.2024.3497602, Vol. 10, pp. 1706-1721, December 2024.BibTeX TR2024-164 PDF
- @article{Rapp2024dec,
- author = {Rapp, Joshua and Mansour, Hassan and Boufounos, Petros T. and Koike-Akino, Toshiaki and Parsons, Kieran},
- title = {{ulti-layered Surface Estimation for Low-cost Optical Coherence Tomography}},
- journal = {IEEE Transactions on Computational Imaging},
- year = 2024,
- volume = 10,
- pages = {1706--1721},
- month = dec,
- doi = {10.1109/TCI.2024.3497602},
- url = {https://www.merl.com/publications/TR2024-164}
- }
- , "Doppler Single-Photon Lidar", IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP), March 2025.
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Videos
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Software & Data Downloads
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Stabilizing Subject Transfer in EEG Classification with Divergence Estimation -
Learned Born Operator for Reflection Tomographic Imaging -
Radar dEtection TRansformer -
Millimeter-wave Multi-View Radar Dataset -
Nonparametric Score Estimators -
Convergent Inverse Scattering using Optimization and Regularization -
One-Bit CRB
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