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.

  • 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.
    •  
    •  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.
    •  
    •  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


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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.

    • ST1916: Lidar Architectures & Algorithms

      MERL is seeking a motivated intern to develop algorithms for novel lidar systems. The ideal candidate would be a senior PhD student with a strong foundation in at least some of the following areas: statistical signal processing, stochastic processes, estimation, sampling theory. Hands-on experience with lidar systems, lasers, photodetectors, or other optics hardware is a bonus. Strong programming skills in MATLAB or Python are essential. Publication of the results produced during our internships is expected. Duration is anticipated to be 3 months.

    • 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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  • Recent Publications

    •  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), November 2022.
      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,
      • month = nov,
      • 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), October 2022.
      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,
      • month = oct,
      • url = {https://www.merl.com/publications/TR2022-129}
      • }
    •  Zheng, X., Liu, D., Inoue, H., Kanemaru, M., "Eccentricity Severity Estimation of Induction Machines using a Sparsity-Driven Regression Model", The Fourteenth Annual Energy Conversion Congress and Exposition, October 2022.
      BibTeX TR2022-126 PDF
      • @inproceedings{Zheng2022oct,
      • author = {Zheng, Xiangtian and Liu, Dehong and Inoue, Hiroshi and Kanemaru, Makoto},
      • title = {Eccentricity Severity Estimation of Induction Machines using a Sparsity-Driven Regression Model},
      • booktitle = {The Fourteenth Annual Energy Conversion Congress and Exposition},
      • year = 2022,
      • month = oct,
      • url = {https://www.merl.com/publications/TR2022-126}
      • }
    •  Wang, S., Guo, J., Wang, P., Parsons, K., Orlik, P.V., Nagai, Y., Sumi, T., Pathak, P., ,, "X-Disco: Cross-technology Neighbor Discovery", IEEE International Conference on Sensing, Communication, and Networking, September 2022.
      BibTeX TR2022-119 PDF
      • @inproceedings{Wang2022sep2,
      • author = {Wang, Shuai and Guo, Jianlin and Wang, Pu and Parsons, Kieran and Orlik, Philip V. and Nagai, Yukimasa and Sumi, Takenori and Pathak, Parth and},
      • title = {X-Disco: Cross-technology Neighbor Discovery},
      • booktitle = {IEEE International Conference on Sensing, Communication, and Networking},
      • year = 2022,
      • month = sep,
      • url = {https://www.merl.com/publications/TR2022-119}
      • }
    •  Rapp, J., Mansour, H., Boufounos, P.T., Orlik, P.V., Koike-Akino, T., Parsons, K., "Maximum Likelihood Surface Profilometry via Optical Coherence Tomography", IEEE International Conference on Image Processing (ICIP), DOI: 10.1109/​ICIP46576.2022.9897247, September 2022, pp. 1561-1565.
      BibTeX TR2022-117 PDF
      • @inproceedings{Rapp2022sep,
      • author = {Rapp, Joshua and Mansour, Hassan and Boufounos, Petros T. and Orlik, Philip V. and Koike-Akino, Toshiaki and Parsons, Kieran},
      • title = {Maximum Likelihood Surface Profilometry via Optical Coherence Tomography},
      • booktitle = {IEEE International Conference on Image Processing (ICIP)},
      • year = 2022,
      • pages = {1561--1565},
      • month = sep,
      • doi = {10.1109/ICIP46576.2022.9897247},
      • issn = {978-1-6654-9620-9},
      • isbn = {2381-8549},
      • url = {https://www.merl.com/publications/TR2022-117}
      • }
    •  Mansour, H., Boufounos, P.T., "Temporal Super-resolution for Wire Position Estimation in Electric Discharge Machines," Tech. Rep. TR2022-124, Mitsubishi Electric Research Laboratories, September 2022.
      BibTeX TR2022-124 PDF
      • @techreport{Mansour2022sep,
      • author = {Mansour, Hassan and Boufounos, Petros T.},
      • title = {Temporal Super-resolution for Wire Position Estimation in Electric Discharge Machines},
      • institution = {MERL website},
      • year = 2022,
      • month = sep,
      • url = {https://www.merl.com/publications/TR2022-124}
      • }
    •  Xia, H., Wang, P., Koike-Akino, T., Wang, Y., Orlik, P.V., Ding, Z., "Adversarial Bi-Regressor Network for Domain Adaptive Regression", International Joint Conference on Artificial Intelligence (IJCAI), Lud De Raedt, Eds., DOI: 10.24963/​ijcai.2022/​501, July 2022, pp. 3608-3614.
      BibTeX TR2022-103 PDF
      • @inproceedings{Xia2022jul,
      • author = {Xia, Haifeng and Wang, Pu and Koike-Akino, Toshiaki and Wang, Ye and Orlik, Philip V. and Ding, Zhengming},
      • title = {Adversarial Bi-Regressor Network for Domain Adaptive Regression},
      • booktitle = {International Joint Conference on Artificial Intelligence (IJCAI)},
      • year = 2022,
      • editor = {Lud De Raedt},
      • pages = {3608--3614},
      • month = jul,
      • publisher = {IJCAI},
      • doi = {10.24963/ijcai.2022/501},
      • url = {https://www.merl.com/publications/TR2022-103}
      • }
    •  Jin, S., Pu, W., Boufounos, P.T., Orlik, P.V., Takahashi, R., Roy, S., "Spatial-Domain Mutual Interference Mitigation for Slow-Time MIMO-FMCW Automotive Radar", IEEE Sensor Array and Multichannel Signal Processing Workshop (SAM), DOI: 10.1109/​SAM53842.2022.9827852, June 2022.
      BibTeX TR2022-067 PDF
      • @inproceedings{Jin2022jun,
      • author = {Jin, Sian and Pu, Wang and Boufounos, Petros T. and Orlik, Philip V. and Takahashi, Ryuhei and Roy, Sumit},
      • title = {Spatial-Domain Mutual Interference Mitigation for Slow-Time MIMO-FMCW Automotive Radar},
      • booktitle = {IEEE Sensor Array and Multichannel Signal Processing Workshop (SAM)},
      • year = 2022,
      • month = jun,
      • doi = {10.1109/SAM53842.2022.9827852},
      • issn = {2151-870X},
      • isbn = {978-1-6654-0633-8},
      • url = {https://www.merl.com/publications/TR2022-067}
      • }
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  • Videos

  • Software Downloads