MERL - Mitsubishi Electric Research Laboratories

Share:

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.

  • Quick Links

  • Researchers

  • Awards

    •  AWARD   2015 IEEE Signal Processing Society Best Paper Award
      Date: December 1, 2015
      Awarded to: Mark A. Davenport, Petros T. Boufounos, Michael B. Wakin and Richard G. Baraniuk
      MERL Contact: Petros Boufounos
      Research Area: Computational Sensing
      Brief
      • Petros Boufounos is a recipient of the 2015 IEEE Signal Processing Society Best Paper Award for the paper that he co-authored with Mark A. Davenport, Michael B. Wakin and Richard G. Baraniuk on "Signal Processing with Compressive Measurements" which was published in the April 2010 issue of IEEE Journal of Selected Topics in Signal Processing. The Best Paper Award honors the author(s) of a paper of exceptional merit dealing with a subject related to the Society's technical scope, and appearing in one of the Society's solely owned transactions or the Journal of Selected Topics in Signal Processing. Eligibility is based on a five-year window: for example, for the 2015 Award, the paper must have appeared in one of the Society's Transactions between January 1, 2010 and December 31, 2014.
    •  
    •  AWARD   GRSS 2014 Symposium Prize Paper Award
      Date: May 1, 2014
      Awarded to: Dehong Liu and Petros T. Boufounos
      Awarded for: "Synthetic Aperture Imaging Using a Randomly Steered Spotlight"
      Awarded by: IEEE Geoscience and Remote Sensing Society (GRSS)
      MERL Contacts: Dehong Liu; Petros Boufounos
      Research Area: Computational Sensing
      Brief
      • Dehong Liu and Petros T. Boufounos are the recipients of the the IEEE Geoscience and Remote Sensing Society 2014 Symposium Prize Paper Award for their paper "Synthetic Aperture Imaging Using a Randomly Steered Spotlight," presented at IGARSS 2013 (TR2013-070).
    •  
    •  AWARD   MMSP 2012 Top 10% Paper Award
      Date: September 1, 2012
      Awarded to: Mu Li, Shantanu Rane and Petros Boufounos
      Awarded for: "Quantized Embeddings of Scale-Invariant Image Features for Mobile Augmented Reality"
      Awarded by: IEEE International Workshop on Multimedia Signal Processing (MMSP)
      MERL Contact: Petros Boufounos
      Research Areas: Digital Video, Computational Sensing
    •  

    See All Awards for MERL

  • News & Events

    •  NEWS   IEEE-NH ComSig lecture by MERL's Petros Boufounos
      Date: April 4, 2019
      Where: Nashua Public Library, Nashua, NH
      MERL Contact: Petros Boufounos
      Research Areas: Computational Sensing, Signal Processing
      Brief
      • MERL's Petros Boufounos gave a lecture for the IEEE-NH ComSig chapter at the Nashua Public Library as part of the IEEE Signal Processing Society Distinguished Lecturer series.

        Title: "An Inverse Problem Framework for Array Processing Systems."

        Abstract: Array-based sensing systems, such as ultrasonic, radar and optical (LIDAR) are becoming increasingly important in a variety of applications, including robotics, autonomous driving, medical imaging, and virtual reality, among others. This has led to continuous improvements in sensing hardware, but also to increasing demand for theory and methods to inform the system design and improve the processing. In this talk we will discuss how recent advances in formulating and solving inverse problems, such as compressed sensing, blind deconvolution, and sparse signal modeling can be applied to significantly reduce the cost and improve the capabilities of array-based and multichannel sensing systems. We show that these systems share a common mathematical framework, which allows us to describe both the acquisition hardware and the scene being acquired. Under this framework we can exploit prior knowledge on the scene, the system, and a variety of errors that might occur, allowing for significant improvements in the reconstruction accuracy. Furthermore, we can consider the design of the system itself in the context of the inverse problem, leading to designs that are more efficient, more accurate, or less expensive, depending on the application. In the talk we will explore applications of this model to LIDAR and depth sensing, radar and distributed radar, and ultrasonic sensing. In the context of these applications, we will describe how different models can lead to improved specifications in ultrasonic systems, robustness to position and timing errors in distributed array systems, and cost reduction and new capabilities in LIDAR systems.
    •  
    •  NEWS   MERL presenting 16 papers at ICASSP 2019
      Date: May 12, 2019 - May 17, 2019
      Where: Brighton, UK
      MERL Contacts: Petros Boufounos; Anoop Cherian; Chiori Hori; Takaaki Hori; Toshiaki Koike-Akino; Jonathan Le Roux; Dehong Liu; Hassan Mansour; Tim Marks; Niko Moritz; Philip Orlik; Anthony Vetro; Pu (Perry) Wang; Gordon Wichern
      Research Areas: Computational Sensing, Computer Vision, Machine Learning, Signal Processing, Speech & Audio
      Brief
      • MERL researchers will be presenting 16 papers at the IEEE International Conference on Acoustics, Speech & Signal Processing (ICASSP), which is being held in Brighton, UK from May 12-17, 2019. Topics to be presented include recent advances in speech recognition, audio processing, scene understanding, computational sensing, and parameter estimation. MERL is also a sponsor of the conference and will be participating in the student career luncheon; please join us at the lunch to learn about our internship program and career opportunities.

        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.
    •  

    See All News & Events for Computational Sensing

  • Internships

    • SP1430: WiFi Sensing

      The Signal Processing (SP) group at MERL is seeking a highly motivated intern to conduct fundamental research in wireless sensing using communication signals such as 5G, WiFi, and Bluetooth. Previous experience on occupancy sensing, people counting, localization, device-free pose/gesture recognition with machine learning approaches is highly preferred. Familiarity with IEEE 802.11 (ac/ad/ay)standards is a plus. The intern will collaborate with a small group of MERL researchers to develop novel algorithms, collect real-world channel measurements, and prepare results for publication. Senior Ph.D. students with research focuses on wireless communications, machine learning, signal processing, optimization, applied mathematics, or related areas are encouraged to apply. The expected duration of the internship is 3 months with a flexible start date.

    • SP1398: Electrical machine modeling

      MERL is looking for a self-motivated intern to work on electrical machine modelling and signal processing. The ideal candidate would be a Ph.D. candidate in electrical engineering with solid research background in electrical machines, signal processing, and electrical circuit analysis. Experience in transient analysis of electrical machines is desirable. Proficiency in MATLAB and simulink is necessary. The intern is expected to collaborate with MERL researchers to build models, develop algorithms, and prepare manuscripts for scientific publications. The total duration is 3 months.

    • SP1371: Object Tracking and Perception for Autonomous Driving

      The Signal Processing (SP) group at MERL is seeking a highly motivated intern to conduct fundamental research in automotive radar-based object tracking and perception for autonomous driving. Previous experience on multiple (point and extended) object tracking, data association, and data-driven object detection/tracking is highly preferred. Knowledge about automotive radar schemes (MIMO array and waveform modulation (FMCW, PMCW, and OFDM)) and hands-on experience on open automotive datasets are a plus. Knowledge on vehicle dynamics is an asset. The intern will collaborate with a small group of MERL researchers to develop novel algorithms, conduct field measurements, data analysis (Python & MATLAB), and prepare results for patents and publication. Senior Ph.D. students with research focuses on signal processing, machine learning, optimization, applied mathematics, or related areas are encouraged to apply. The expected duration of the internship is 3 months with a flexible start date.


    See All Internships for Computational Sensing

  • Recent Publications

    •  Wang, P., Li, H., "Target Detection with Imperfect Waveform Separation in Distributed MIMO Radar", IEEE Transactions on Signal Processing, January 2020.
      BibTeX Download PDFAbout TR2020-007
      • @article{Wang2020jan,
      • author = {Wang, Pu and Li, Hongbin},
      • title = {Target Detection with Imperfect Waveform Separation in Distributed MIMO Radar},
      • journal = {IEEE Transactions on Signal Processing},
      • year = 2020,
      • month = jan,
      • url = {https://www.merl.com/publications/TR2020-007}
      • }
    •  Pajovic, M., Wang, P., Koike-Akino, T., Sun, H., Orlik, P.V., "Fingerprinting-Based Indoor Localization with Commercial MMWave WiFi – Part I: RSS and Beam Indices", IEEE Global Communications Conference (GLOBECOM), December 2019.
      BibTeX Download PDFAbout TR2019-141
      • @inproceedings{Pajovic2019dec,
      • author = {Pajovic, Milutin and Wang, Pu and Koike-Akino, Toshiaki and Sun, Haijian and Orlik, Philip V.},
      • title = {Fingerprinting-Based Indoor Localization with Commercial MMWave WiFi – Part I: RSS and Beam Indices},
      • booktitle = {IEEE Global Communications Conference (GLOBECOM)},
      • year = 2019,
      • month = dec,
      • url = {https://www.merl.com/publications/TR2019-141}
      • }
    •  Kao, J.-Y., Ortega, A., Tian, D., Mansour, H., Vetro, A., "Graph Based Skeleton Modeling for Human Activity Analysis", IEEE International Conference on Image Processing (ICIP), September 2019.
      BibTeX Download PDFAbout TR2019-037
      • @inproceedings{Kao2019sep,
      • author = {Kao, Jiun-Yu and Ortega, Antonio and Tian, Dong and Mansour, Hassan and Vetro, Anthony},
      • title = {Graph Based Skeleton Modeling for Human Activity Analysis},
      • booktitle = {IEEE International Conference on Image Processing (ICIP)},
      • year = 2019,
      • month = sep,
      • url = {https://www.merl.com/publications/TR2019-037}
      • }
    •  Bose, A., Kadu, A., Mansour, H., Wang, P., Boufounos, P.T., Orlik, P.V., Soltanalian, M., "THz Multi-Layer Imaging via Nonlinear Inverse Scattering", International Conference on Infrared, Millimeter, and Terahertz Waves (IRMMW-THz), September 2019.
      BibTeX Download PDFAbout TR2019-091
      • @inproceedings{Bose2019sep,
      • author = {Bose, Arindam and Kadu, Ajinkya and Mansour, Hassan and Wang, Pu and Boufounos, Petros T. and Orlik, Philip V. and Soltanalian, Mojtaba},
      • title = {THz Multi-Layer Imaging via Nonlinear Inverse Scattering},
      • booktitle = {International Conference on Infrared, Millimeter, and Terahertz Waves (IRMMW-THz)},
      • year = 2019,
      • month = sep,
      • url = {https://www.merl.com/publications/TR2019-091}
      • }
    •  Wang, P., Koike-Akino, T., Bose, A., Ma, R., Orlik, P.V., Tsujita, W., Sadamoto, K., Tsutada, H., Soltanalian, M., "Learning-Based Shadow Mitigation for Terahertz Multi-Layer Imaging", International Conference on Infrared, Millimeter, and Terahertz Waves (IRMMW-THz), September 2019.
      BibTeX Download PDFAbout TR2019-090
      • @inproceedings{Wang2019sep,
      • author = {Wang, Pu and Koike-Akino, Toshiaki and Bose, Arindam and Ma, Rui and Orlik, Philip V. and Tsujita, Wataru and Sadamoto, Kota and Tsutada, Hiroyuki and Soltanalian, Mojtaba},
      • title = {Learning-Based Shadow Mitigation for Terahertz Multi-Layer Imaging},
      • booktitle = {International Conference on Infrared, Millimeter, and Terahertz Waves (IRMMW-THz)},
      • year = 2019,
      • month = sep,
      • url = {https://www.merl.com/publications/TR2019-090}
      • }
    •  Yamashita, G., Tsujita, W., Tsutada, H., Ma, R., Wang, P., Orlik, P.V., Suzuki, S., Dobroiu, A., Asada, M., "Terahertz Polarimetric Sensing for Linear Encoder Based on a Resonant-Tunneling-Diode and CFRP polarizing plates", International Conference on Infrared, Millimeter, and Terahertz Waves (IRMMW-THz), September 2019.
      BibTeX Download PDFAbout TR2019-089
      • @inproceedings{Yamashita2019sep,
      • author = {Yamashita, Genki and Tsujita, Wataru and Tsutada, Hiroyuki and Ma, Rui and Wang, Pu and Orlik, Philip V. and Suzuki, Safumi and Dobroiu, Adrian and Asada, M.},
      • title = {Terahertz Polarimetric Sensing for Linear Encoder Based on a Resonant-Tunneling-Diode and CFRP polarizing plates},
      • booktitle = {International Conference on Infrared, Millimeter, and Terahertz Waves (IRMMW-THz)},
      • year = 2019,
      • month = sep,
      • url = {https://www.merl.com/publications/TR2019-089}
      • }
    •  Greiff, M., Robertsson, A., Berntorp, K., "Performance Bounds in Positioning with the VIVE Lighthouse System", International Conference on Information Fusion (FUSION), July 2019.
      BibTeX Download PDFAbout TR2019-068
      • @inproceedings{Greiff2019jul,
      • author = {Greiff, Marcus and Robertsson, Anders and Berntorp, Karl},
      • title = {Performance Bounds in Positioning with the VIVE Lighthouse System},
      • booktitle = {International Conference on Information Fusion (FUSION)},
      • year = 2019,
      • month = jul,
      • url = {https://www.merl.com/publications/TR2019-068}
      • }
    •  Koga, S., Benosman, M., Borggaard, J., "Learning-Based Robust Observer Design for Coupled Thermal and Fluid Systems", American Control Conference (ACC), July 2019.
      BibTeX Download PDFAbout TR2019-067
      • @inproceedings{Koga2019jul,
      • author = {Koga, Shumon and Benosman, Mouhacine and Borggaard, Jeff},
      • title = {Learning-Based Robust Observer Design for Coupled Thermal and Fluid Systems},
      • booktitle = {American Control Conference (ACC)},
      • year = 2019,
      • month = jul,
      • url = {https://www.merl.com/publications/TR2019-067}
      • }
    See All Publications for Computational Sensing

  • Videos