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

    •  TALK    [MERL Seminar Series 2023] Prof. Shaowu Pan presents talk titled Neural Implicit Flow
      Date & Time: Wednesday, March 1, 2023; 1:00 PM
      Speaker: Shaowu Pan, Rensselaer Polytechnic Institute
      MERL Host: Saviz Mowlavi
      Research Areas: Computational Sensing, Data Analytics, Machine Learning
      Abstract
      • High-dimensional spatio-temporal dynamics can often be encoded in a low-dimensional subspace. Engineering applications for modeling, characterization, design, and control of such large-scale systems often rely on dimensionality reduction to make solutions computationally tractable in real-time. Common existing paradigms for dimensionality reduction include linear methods, such as the singular value decomposition (SVD), and nonlinear methods, such as variants of convolutional autoencoders (CAE). However, these encoding techniques lack the ability to efficiently represent the complexity associated with spatio-temporal data, which often requires variable geometry, non-uniform grid resolution, adaptive meshing, and/or parametric dependencies. To resolve these practical engineering challenges, we propose a general framework called Neural Implicit Flow (NIF) that enables a mesh-agnostic, low-rank representation of large-scale, parametric, spatial-temporal data. NIF consists of two modified multilayer perceptrons (MLPs): (i) ShapeNet, which isolates and represents the spatial complexity, and (ii) ParameterNet, which accounts for any other input complexity, including parametric dependencies, time, and sensor measurements. We demonstrate the utility of NIF for parametric surrogate modeling, enabling the interpretable representation and compression of complex spatio-temporal dynamics, efficient many-spatial-query tasks, and improved generalization performance for sparse reconstruction.
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    •  NEWS    MERL Researchers gave a Tutorial Talk on Quantum Machine Learning for Sensing and Communications at IEEE GLOBECOM
      Date: December 8, 2022
      MERL Contacts: Toshiaki Koike-Akino; Pu (Perry) Wang
      Research Areas: Artificial Intelligence, Communications, Computational Sensing, Machine Learning, Signal Processing
      Brief
      • On December 8, 2022, MERL researchers Toshiaki Koike-Akino and Pu (Perry) Wang gave a 3.5-hour tutorial presentation at the IEEE Global Communications Conference (GLOBECOM). The talk, titled "Post-Deep Learning Era: Emerging Quantum Machine Learning for Sensing and Communications," addressed recent trends, challenges, and advances in sensing and communications. P. Wang presented on use cases, industry trends, signal processing, and deep learning for Wi-Fi integrated sensing and communications (ISAC), while T. Koike-Akino discussed 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 was conducted remotely. MERL's quantum AI technology was partly reported in the recent press release (https://us.mitsubishielectric.com/en/news/releases/global/2022/1202-a/index.html).

        The IEEE GLOBECOM is a highly anticipated event for researchers and industry professionals in the field of communications. Organized by the IEEE Communications Society, the flagship conference is known for its focus on driving innovation in all aspects of the field. Each year, over 3,000 scientific researchers submit proposals for program sessions at the annual conference. The theme of this year's conference was "Accelerating the Digital Transformation through Smart Communications," and featured a comprehensive technical program with 13 symposia, various tutorials and workshops.
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  • Research Highlights

  • Internships

    • 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.

    • ST1750: THz (Terahertz) Sensing

      The Signal Processing (SP) group at MERL is seeking a highly motivated intern to conduct fundamental research in THz (Terahertz) sensing. Expertise in statistical inference, unsupervised anomaly detection, and deep learning (spatial-temporal representation learning) is required. Previous hands-on experience in THz data analysis is a plus. Familiarity with python and deep learning libraries is a must. The intern will collaborate with a small group of MERL researchers to develop novel algorithms, design experiments with collaborators, and prepare results for patents and publication. The expected duration of the internship is 3 months with a flexible start date.

    • 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.


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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), 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}
      • }
    •  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, DOI: 10.1109/​ECCE50734.2022.9947498, 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,
      • publisher = {IEEE},
      • doi = {10.1109/ECCE50734.2022.9947498},
      • issn = {2329-3748},
      • isbn = {978-1-7281-9387-8},
      • 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 Video
      • @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 = {2381-8549},
      • isbn = {978-1-6654-9620-9},
      • 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 = {Mitsubishi Electric Research Laboratories},
      • 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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