Computational Sensing
Utilizing computation to improve sensing capabilities.
Our research in the area of computational sensing focuses on signal acquisition and design, signal modeling and reconstruction algorithms, including inverse problems, as well as array signal processing techniques.
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Researchers
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Awards
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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 Joshua Rapp wins Best Dissertation Award from the IEEE Signal Processing Society Date: December 20, 2021
Awarded to: Joshua Rapp
MERL Contact: Joshua Rapp
Research Areas: Computational Sensing, Signal ProcessingBrief- Joshua Rapp has won the 2021 Best PhD Dissertation Award from the IEEE Signal Processing Society.
The award recognizes a PhD thesis completed on a signal processing subject within the past three years for its relevant work in signal processing while stimulating further research in the field.
Dr. Rapp completed his PhD at Boston University in 2020 with a thesis entitled "Probabilistic Modeling for Single-Photon Lidar." The dissertation tackles challenges of the acquisition and processing of 3D depth maps reconstructed from time-of-flight data captured one photon at a time.
The award will be presented at the 2022 IEEE International Conference on Image Processing (ICIP) in France.
- Joshua Rapp has won the 2021 Best PhD Dissertation Award from the IEEE Signal Processing Society.
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AWARD Petros Boufounos Elevated to IEEE Fellow Date: January 1, 2022
Awarded to: Petros T. Boufounos
MERL Contact: Petros T. Boufounos
Research Areas: Computational Sensing, Signal ProcessingBrief- MERL’s Petros Boufounos has been elevated to IEEE Fellow, effective January 2022, for “contributions to compressed sensing.”
IEEE Fellow is the highest grade of membership of the IEEE. It honors members with an outstanding record of technical achievements, contributing importantly to the advancement or application of engineering, science and technology, and bringing significant value to society. Each year, following a rigorous evaluation procedure, the IEEE Fellow Committee recommends a select group of recipients for elevation to IEEE Fellow. Less than 0.1% of voting members are selected annually for this member grade elevation.
- MERL’s Petros Boufounos has been elevated to IEEE Fellow, effective January 2022, for “contributions to compressed sensing.”
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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 ProcessingAbstractRecent 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 ProcessingAbstractThe 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.
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Research Highlights
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Internships
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CV0064: Internship - Robust Estimation for Computer Vision
MERL is looking for a self-motivated graduate student to work on robust estimation in Computer Vision. Based on the candidate’s interests, the intern can work on a variety of topics such as (but not limited to) camera pose estimation, 3D registration, camera calibration, pose-graph optimization, and transformation averaging. The ideal candidate would be a PhD student with a strong background in 3D computer vision, RANSAC, and graduated non-convexity algorithms, and good programming skills in C/C++ and/or Python. The candidate must have published at least one paper in a top-tier computer vision, machine learning, or robotics venue, such as CVPR, ECCV, ICCV, NeurIPS, ICRA, or IROS. The intern will collaborate with MERL researchers to derive and implement new algorithms for V-SLAM, conduct experiments, and report findings. A submission to a top-tier conference is expected. The duration of the internship and start date are flexible.
Required Specific Experience
- Experience with 3D computer vision, RANSAC, or graduated non-convexity algorithms for computer vision.
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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.
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ST0096: Internship - 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.
Required Specific Experience
- Experience with Python and Python Deep Learning Frameworks.
- Experience with FMCW radar and/or Depth Sensors.
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Openings
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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}
- }
, - "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}
- }
, - "RETR: Multi-View Radar Detection Transformer for Indoor Perception", Advances in Neural Information Processing Systems (NeurIPS), A. Globerson and L. Mackey and D. Belgrave and A. Fan and U. Paquet and J. Tomczak and C. Zhang, Eds., November 2024, pp. 19839-19869.BibTeX TR2024-159 PDF Software
- @inproceedings{Yataka2024nov3,
- author = {Yataka, Ryoma and Cardace, Adriano and Wang, Pu and Boufounos, Petros T. and Takahashi, Ryuhei},
- title = {{RETR: Multi-View Radar Detection Transformer for Indoor Perception}},
- booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
- year = 2024,
- editor = {A. Globerson and L. Mackey and D. Belgrave and A. Fan and U. Paquet and J. Tomczak and C. Zhang},
- pages = {19839--19869},
- month = nov,
- publisher = {Curran Associates, Inc.},
- url = {https://www.merl.com/publications/TR2024-159}
- }
, - "Single-pixel imaging of spatio-temporal flows using differentiable latent dynamics", IEEE Transactions on Computational Imaging, DOI: 10.1109/TCI.2024.3434541, Vol. 10, pp. 1124-1138, October 2024.BibTeX TR2024-151 PDF
- @article{Sholokhov2024oct,
- author = {Sholokhov, Aleksei and Nabi, Saleh and Rapp, Joshua and Brunton, Steven and Kutz, Nathan and Boufounos, Petros T. and Mansour, Hassan},
- title = {{Single-pixel imaging of spatio-temporal flows using differentiable latent dynamics}},
- journal = {IEEE Transactions on Computational Imaging},
- year = 2024,
- volume = 10,
- pages = {1124--1138},
- month = oct,
- doi = {10.1109/TCI.2024.3434541},
- url = {https://www.merl.com/publications/TR2024-151}
- }
, - "Spatial-Domain Mutual Interference Mitigation for MIMO-FMCW Automotive Radar", IEEE Transactions on Vehicular Technology, DOI: 10.1109/TVT.2024.3467917, September 2024.BibTeX TR2024-148 PDF
- @article{Jin2024sep,
- author = {Jin, Sian and Wang, Pu and Boufounos, Petros T. and Orlik, Philip V. and Takahashi, Ryuhei and Roy, Sumit},
- title = {{Spatial-Domain Mutual Interference Mitigation for MIMO-FMCW Automotive Radar}},
- journal = {IEEE Transactions on Vehicular Technology},
- year = 2024,
- month = sep,
- doi = {10.1109/TVT.2024.3467917},
- issn = {1939-9359},
- url = {https://www.merl.com/publications/TR2024-148}
- }
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- "Doppler Single-Photon Lidar", IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP), March 2025.
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