MERL - Mitsubishi Electric Research Laboratories

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

  • Awards

    •  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 Processing
      Brief
      • 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.
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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 Processing
      Brief
      • 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.
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    •  AWARD    Best Paper - Honorable Mention Award at WACV 2021
      Date: January 6, 2021
      Awarded to: Rushil Anirudh, Suhas Lohit, Pavan Turaga
      MERL Contact: Suhas Lohit
      Research Areas: Computational Sensing, Computer Vision, Machine Learning
      Brief
      • A team of researchers from Mitsubishi Electric Research Laboratories (MERL), Lawrence Livermore National Laboratory (LLNL) and Arizona State University (ASU) received the Best Paper Honorable Mention Award at WACV 2021 for their paper "Generative Patch Priors for Practical Compressive Image Recovery".

        The paper proposes a novel model of natural images as a composition of small patches which are obtained from a deep generative network. This is unlike prior approaches where the networks attempt to model image-level distributions and are unable to generalize outside training distributions. The key idea in this paper is that learning patch-level statistics is far easier. As the authors demonstrate, this model can then be used to efficiently solve challenging inverse problems in imaging such as compressive image recovery and inpainting even from very few measurements for diverse natural scenes.
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  • News & Events

    •  EVENT    MERL Contributes to ICASSP 2023
      Date: Sunday, June 4, 2023 - Saturday, June 10, 2023
      Location: Rhodes Island, Greece
      MERL Contacts: Petros T. Boufounos; Francois Germain; Toshiaki Koike-Akino; Jonathan Le Roux; Dehong Liu; Suhas Lohit; Yanting Ma; Hassan Mansour; Joshua Rapp; Anthony Vetro; Pu (Perry) Wang; Gordon Wichern
      Research Areas: Artificial Intelligence, Computational Sensing, Machine Learning, Signal Processing, Speech & Audio
      Brief
      • MERL has made numerous contributions to both the organization and technical program of ICASSP 2023, which is being held in Rhodes Island, Greece from June 4-10, 2023.

        Organization

        Petros Boufounos is serving as General Co-Chair of the conference this year, where he has been involved in all aspects of conference planning and execution.

        Perry Wang is the organizer of a special session on Radar-Assisted Perception (RAP), which will be held on Wednesday, June 7. The session will feature talks on signal processing and deep learning for radar perception, pose estimation, and mutual interference mitigation with speakers from both academia (Carnegie Mellon University, Virginia Tech, University of Illinois Urbana-Champaign) and industry (Mitsubishi Electric, Bosch, Waveye).

        Anthony Vetro is the co-organizer of the Workshop on Signal Processing for Autonomous Systems (SPAS), which will be held on Monday, June 5, and feature invited talks from leaders in both academia and industry on timely topics related to autonomous systems.

        Sponsorship

        MERL is proud to be a Silver Patron of the conference and will participate in the student job fair on Thursday, June 8. 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. Rabab Ward, the recipient of the 2023 IEEE Fourier Award for Signal Processing, and Prof. Alexander Waibel, the recipient of the 2023 IEEE James L. Flanagan Speech and Audio Processing Award.

        Technical Program

        MERL is presenting 13 papers in the main conference on a wide range of topics including source separation and speech enhancement, radar imaging, depth estimation, motor fault detection, time series recovery, and point clouds. One workshop paper has also been accepted for presentation on self-supervised music source separation.

        Perry Wang has been invited to give a keynote talk on Wi-Fi sensing and related standards activities at the Workshop on Integrated Sensing and Communications (ISAC), which will be held on Sunday, June 4.

        Additionally, Anthony Vetro will present a Perspective Talk on Physics-Grounded Machine Learning, which is scheduled for Thursday, June 8.

        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 2000 participants each year.
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    •  TALK    [MERL Seminar Series 2023] Prof. Mark Ku presents talk titled A beginner’s guide to quantum sensing illustrated with nitrogen vacancy centers in diamond
      Date & Time: Wednesday, May 17, 2023; 1:00 PM
      Speaker: Mark Ku, The University of Delaware
      MERL Host: Chungwei Lin
      Research Areas: Applied Physics, Computational Sensing
      Abstract
      • Quantum technology holds potential for revolutionizing how information is processed, transmitted, and acquired. While quantum computation and quantum communication have been among the well-known examples of quantum technology, it is increasingly recognized that quantum sensing is the application with the most potential for immediate wide-spread practical utilization. In this talk, I will provide an overview of the field of quantum sensing with nitrogen vacancy (NV) centers in diamond as a specific example. I will introduce the physical system of NV and describe some basic quantum sensing protocols. Then, I will present some state-of-the-art and examples where quantum sensors such as NV can accomplish what traditional sensors cannot. Lastly, I will discuss potential future directions in the area of NV quantum sensing.
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  • Research Highlights

  • Internships

    • ST1762: Computational Sensing Technologies

      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, deep learning for inverse problems, large-scale optimization, blind inverse scattering, radar/lidar/THz imaging, joint communications and sensing, multimodal sensor fusion, object or human tracking, 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.

    • 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.

    • ST2025: Background Oriented Schlieren Tomography

      The Computational Sensing team at MERL is seeking motivated and qualified individuals to develop algorithms that can perform background oriented Schlieren (BOS) tomography. The project goal is to utilize both analytical and learning-based architectures to enable the reconstruction of 3D air flows in an indoor setting from BOS measurements. 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, learning-based modeling for imaging, Schlieren tomography, physics informed neural networks. 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.


    See All Internships for Computational Sensing

  • Recent Publications

    •  Kim, K.J., Vinod, A.P., Guo, J., Deshpande, V.M., Parsons, K., "Spectrum Sharing-inspired Safe Motion Planning", IEEE International Conference on Communications Workshops (ICC), May 2023.
      BibTeX TR2023-049 PDF
      • @inproceedings{Kim2023may2,
      • author = {Kim, Kyeong Jin and Vinod, Abraham P. and Guo, Jianlin and Deshpande, Vedang M. and Parsons, Kieran},
      • title = {Spectrum Sharing-inspired Safe Motion Planning},
      • booktitle = {IEEE International Conference on Communications Workshops (ICC)},
      • year = 2023,
      • month = may,
      • url = {https://www.merl.com/publications/TR2023-049}
      • }
    •  Berk, A., Ma, Y., Boufounos, P.T., Wang, P., Mansour, H., "Deep Proximal Gradient Method for Learned Convex Regularizers", IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP), May 2023.
      BibTeX TR2023-032 PDF
      • @inproceedings{Berk2023may,
      • author = {Berk, Aaron and Ma, Yanting and Boufounos, Petros T. and Wang, Pu and Mansour, Hassan},
      • title = {Deep Proximal Gradient Method for Learned Convex Regularizers},
      • booktitle = {IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP)},
      • year = 2023,
      • month = may,
      • url = {https://www.merl.com/publications/TR2023-032}
      • }
    •  Jin, S., Wang, P., Boufounos, P.T., Takahashi, R., Roy, S., "Spatial-Domain Object Detection Under MIMO-FMCW Automotive Radar Interference", IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP), May 2023.
      BibTeX TR2023-027 PDF
      • @inproceedings{Jin2023may,
      • author = {Jin, Sian and Wang, Pu and Boufounos, Petros T. and Takahashi, Ryuhei and Roy, Sumit},
      • title = {Spatial-Domain Object Detection Under MIMO-FMCW Automotive Radar Interference},
      • booktitle = {IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP)},
      • year = 2023,
      • month = may,
      • url = {https://www.merl.com/publications/TR2023-027}
      • }
    •  Ulvog, A., Rapp, J., Koike-Akino, T., Mansour, H., Boufounos, P.T., Parsons, K., "Phase Unwrapping in Correlated Noise for FMCW LIDAR Depth Estimation", IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP), May 2023.
      BibTeX TR2023-028 PDF
      • @inproceedings{Ulvog2023may,
      • author = {Ulvog, Alfred and Rapp, Joshua and Koike-Akino, Toshiaki and Mansour, Hassan and Boufounos, Petros T. and Parsons, Kieran},
      • title = {Phase Unwrapping in Correlated Noise for FMCW LIDAR Depth Estimation},
      • booktitle = {IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP)},
      • year = 2023,
      • month = may,
      • url = {https://www.merl.com/publications/TR2023-028}
      • }
    •  Vaca-Rubio, C., Wang, P., Koike-Akino, T., Wang, Y., Boufounos, P.T., Popovski, P., "mmWave Wi-Fi Trajectory Estimation with Continuous-Time Neural Dynamic Learning", IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP), May 2023.
      BibTeX TR2023-033 PDF
      • @inproceedings{Vaca-Rubio2023may,
      • author = {Vaca-Rubio, Cristian and Wang, Pu and Koike-Akino, Toshiaki and Wang, Ye and Boufounos, Petros T. and Popovski, Petar},
      • title = {mmWave Wi-Fi Trajectory Estimation with Continuous-Time Neural Dynamic Learning},
      • booktitle = {IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP)},
      • year = 2023,
      • month = may,
      • url = {https://www.merl.com/publications/TR2023-033}
      • }
    •  Zhao, Q., Ma, Y., Boufounos, P.T., Nabi, S., Mansour, H., "Deep Born Operator Learning for Reflection Tomographic Imaging", IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP), May 2023.
      BibTeX TR2023-029 PDF
      • @inproceedings{Zhao2023may,
      • author = {Zhao, Qingqing and Ma, Yanting and Boufounos, Petros T. and Nabi, Saleh and Mansour, Hassan},
      • title = {Deep Born Operator Learning for Reflection Tomographic Imaging},
      • booktitle = {IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP)},
      • year = 2023,
      • month = may,
      • url = {https://www.merl.com/publications/TR2023-029}
      • }
    •  Shimoya, R., Morimoto, T., van Baar, J., Boufounos, P.T., Ma, Y., Mansour, H., "Learning Occlusion-Aware Dense Correspondences for Multi-Modal Images", IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS), DOI: 10.1109/​AVSS56176.2022.9959354, November 2022, pp. 1-8.
      BibTeX TR2022-149 PDF
      • @inproceedings{Shimoya2022nov,
      • author = {Shimoya, Ryosuke and Morimoto, Tahashi and van Baar, Jeroen and Boufounos, Petros T. and Ma, Yanting and Mansour, Hassan},
      • title = {Learning Occlusion-Aware Dense Correspondences for Multi-Modal Images},
      • booktitle = {IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS)},
      • year = 2022,
      • pages = {1--8},
      • month = nov,
      • doi = {10.1109/AVSS56176.2022.9959354},
      • isbn = {978-1-6654-6382-9},
      • url = {https://www.merl.com/publications/TR2022-149}
      • }
    •  Mansour, H., Lohit, S., Boufounos, P.T., "Distributed Radar Autofocus Imaging Using Deep Priors", IEEE International Conference on Image Processing (ICIP), DOI: 10.1109/​ICIP46576.2022.9897332, October 2022, pp. 2511-2515.
      BibTeX TR2022-129 PDF Video
      • @inproceedings{Mansour2022oct,
      • author = {Mansour, Hassan and Lohit, Suhas and Boufounos, Petros T.},
      • title = {Distributed Radar Autofocus Imaging Using Deep Priors},
      • booktitle = {IEEE International Conference on Image Processing (ICIP)},
      • year = 2022,
      • pages = {2511--2515},
      • month = oct,
      • doi = {10.1109/ICIP46576.2022.9897332},
      • url = {https://www.merl.com/publications/TR2022-129}
      • }
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