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   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   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 T. 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.
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    •  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 T. 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).
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  • News & Events

    •  EVENT   Prof. Melanie Zeilinger of ETH to give keynote at MERL's Virtual Open House
      Date & Time: Thursday, December 9, 2021; 1:00pm - 5:30pm EST
      Speaker: Prof. Melanie Zeilinger, ETH
      Location: Virtual Event
      Research Areas: Applied Physics, Artificial Intelligence, Communications, Computational Sensing, Computer Vision, Control, Data Analytics, Dynamical Systems, Electric Systems, Electronic and Photonic Devices, Machine Learning, Multi-Physical Modeling, Optimization, Robotics, Signal Processing, Speech & Audio, Digital Video, Human-Computer Interaction, Information Security
      Brief
      • MERL is excited to announce the second keynote speaker for our Virtual Open House 2021:
        Prof. Melanie Zeilinger from ETH .

        Our virtual open house will take place on December 9, 2021, 1:00pm - 5:30pm (EST).

        Join us to learn more about who we are, what we do, and discuss our internship and employment opportunities. Prof. Zeilinger's talk is scheduled for 3:15pm - 3:45pm (EST).

        Registration: https://mailchi.mp/merl/merlvoh2021

        Keynote Title: Control Meets Learning - On Performance, Safety and User Interaction

        Abstract: With increasing sensing and communication capabilities, physical systems today are becoming one of the largest generators of data, making learning a central component of autonomous control systems. While this paradigm shift offers tremendous opportunities to address new levels of system complexity, variability and user interaction, it also raises fundamental questions of learning in a closed-loop dynamical control system. In this talk, I will present some of our recent results showing how even safety-critical systems can leverage the potential of data. I will first briefly present concepts for using learning for automatic controller design and for a new safety framework that can equip any learning-based controller with safety guarantees. The second part will then discuss how expert and user information can be utilized to optimize system performance, where I will particularly highlight an approach developed together with MERL for personalizing the motion planning in autonomous driving to the individual driving style of a passenger.
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    •  EVENT   Prof. Ashok Veeraraghavan of Rice University to give keynote at MERL's Virtual Open House
      Date & Time: Thursday, December 9, 2021; 1:00pm - 5:30pm EST
      Speaker: Prof. Ashok Veeraraghavan, Rice University
      Location: Virtual Event
      Research Areas: Applied Physics, Artificial Intelligence, Communications, Computational Sensing, Computer Vision, Control, Data Analytics, Dynamical Systems, Electric Systems, Electronic and Photonic Devices, Machine Learning, Multi-Physical Modeling, Optimization, Robotics, Signal Processing, Speech & Audio, Digital Video, Human-Computer Interaction, Information Security
      Brief
      • MERL is excited to announce the first keynote speaker for our Virtual Open House 2021:
        Prof. Ashok Veeraraghavan from Rice University.

        Our virtual open house will take place on December 9, 2021, 1:00pm - 5:30pm (EST).

        Join us to learn more about who we are, what we do, and discuss our internship and employment opportunities. Prof. Veeraraghavan's talk is scheduled for 1:15pm - 1:45pm (EST).

        Registration: https://mailchi.mp/merl/merlvoh2021

        Keynote Title: Computational Imaging: Beyond the limits imposed by lenses.

        Abstract: The lens has long been a central element of cameras, since its early use in the mid-nineteenth century by Niepce, Talbot, and Daguerre. The role of the lens, from the Daguerrotype to modern digital cameras, is to refract light to achieve a one-to-one mapping between a point in the scene and a point on the sensor. This effect enables the sensor to compute a particular two-dimensional (2D) integral of the incident 4D light-field. We propose a radical departure from this practice and the many limitations it imposes. In the talk we focus on two inter-related research projects that attempt to go beyond lens-based imaging.

        First, we discuss our lab’s recent efforts to build flat, extremely thin imaging devices by replacing the lens in a conventional camera with an amplitude mask and computational reconstruction algorithms. These lensless cameras, called FlatCams can be less than a millimeter in thickness and enable applications where size, weight, thickness or cost are the driving factors. Second, we discuss high-resolution, long-distance imaging using Fourier Ptychography, where the need for a large aperture aberration corrected lens is replaced by a camera array and associated phase retrieval algorithms resulting again in order of magnitude reductions in size, weight and cost. Finally, I will spend a few minutes discussing how the wholistic computational imaging approach can be used to create ultra-high-resolution wavefront sensors.
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  • Research Highlights

  • Internships

    • SP1762: 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.

    • MD1648: THz Electronic Sensing

      MERL is looking for a senior Ph.D. student to join our team to conduct application-motivated research and experiments. The candidate must have hands-on practical lab experiment experience on millimeter-wave, sub-THz, or THz for sensing, radar, and other applications. Skills of using RF/Microwave Lab equipment are necessary. Knowledge of solid-state device physics, high frequency, and high speed integrated circuit (IC) chip design, and signal processing is desired. The internship is expected to be 3-6 months, starting date is flexible after September. This internship requires work that can only be done at MERL.

    • SP1763: 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. This internship is preferred to be onsite at MERL, but may be done remotely where you live if the COVID pandemic makes it necessary.


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  • Recent Publications

    •  Wang, P., Koike-Akino, T., Ma, R., Orlik, P.V., Yamashita, G., Tsujita, W., Nakajima, M., "Learning-Based THz Multi-Layer Imaging for High-Capacity Positioning", International Conference on Infrared, Millimeter, and Terahertz Waves (IRMMW-THz), DOI: 10.1109/​IRMMW-THz50926.2021.9566940, November 2021.
      BibTeX TR2021-098 PDF
      • @inproceedings{Wang2021nov,
      • author = {Wang, Perry and Koike-Akino, Toshiaki and Ma, Rui and Orlik, Philip V. and Yamashita, Genki and Tsujita, Wataru and Nakajima, M.},
      • title = {Learning-Based THz Multi-Layer Imaging for High-Capacity Positioning},
      • booktitle = {International Conference on Infrared, Millimeter, and Terahertz Waves (IRMMW-THz)},
      • year = 2021,
      • month = nov,
      • publisher = {IEEE},
      • doi = {10.1109/IRMMW-THz50926.2021.9566940},
      • issn = {2162-2035},
      • isbn = {978-1-7281-9424-0},
      • url = {https://www.merl.com/publications/TR2021-098}
      • }
    •  Yao, G., WANG, P., Berntorp, K., Mansour, H., Boufounos, P.T., Orlik, P.V., "Extended Object Tracking with Spatial Model Adaptation Using Automotive Radar", International Conference on Information Fusion (FUSION), November 2021.
      BibTeX TR2021-138 PDF
      • @inproceedings{Yao2021nov,
      • author = {Yao, Gang and WANG, PU and Berntorp, Karl and Mansour, Hassan and Boufounos, Petros T. and Orlik, Philip V.},
      • title = {Extended Object Tracking with Spatial Model Adaptation Using Automotive Radar},
      • booktitle = {International Conference on Information Fusion (FUSION)},
      • year = 2021,
      • month = nov,
      • url = {https://www.merl.com/publications/TR2021-138}
      • }
    •  Shi, L., Liu, D., Thornton, J.E., "Robust Camera Pose Estimation for Image Stitching", IEEE International Conference on Image Processing (ICIP), DOI: 10.1109/​ICIP42928.2021.9506680, September 2021.
      BibTeX TR2021-113 PDF
      • @inproceedings{Shi2021sep,
      • author = {Shi, Laixi and Liu, Dehong and Thornton, Jay E.},
      • title = {Robust Camera Pose Estimation for Image Stitching},
      • booktitle = {IEEE International Conference on Image Processing (ICIP)},
      • year = 2021,
      • month = sep,
      • publisher = {IEEE},
      • doi = {10.1109/ICIP42928.2021.9506680},
      • isbn = {978-1-6654-4115-5},
      • url = {https://www.merl.com/publications/TR2021-113}
      • }
    •  Jain, S., Corcodel, R., van Baar, J., "Visual 3D Perception for Interactive Robotic Tactile Data Acquisition", IEEE International Conference on Automation Science and Engineering (CASE 2021), August 2021.
      BibTeX TR2021-092 PDF
      • @inproceedings{Jain2021aug,
      • author = {Jain, Siddarth and Corcodel, Radu and van Baar, Jeroen},
      • title = {Visual 3D Perception for Interactive Robotic Tactile Data Acquisition},
      • booktitle = {2021 IEEE International Conference on Automation Science and Engineering (CASE)},
      • year = 2021,
      • month = aug,
      • url = {https://www.merl.com/publications/TR2021-092}
      • }
    •  Ma, Y., Boufounos, P.T., Mansour, H., Aeron, S., "Multiview Sensing with Unknown Permutations: An Optimal Transport Approach", IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP), DOI: 10.1109/​ICASSP39728.2021.9415075, June 2021, pp. 1440-1444.
      BibTeX TR2021-047 PDF
      • @inproceedings{Ma2021jun,
      • author = {Ma, Yanting and Boufounos, Petros T. and Mansour, Hassan and Aeron, Shuchin},
      • title = {Multiview Sensing with Unknown Permutations: An Optimal Transport Approach},
      • booktitle = {IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP)},
      • year = 2021,
      • pages = {1440--1444},
      • month = jun,
      • doi = {10.1109/ICASSP39728.2021.9415075},
      • issn = {1520-6149},
      • isbn = {978-1-7281-7606-2},
      • url = {https://www.merl.com/publications/TR2021-047}
      • }
    •  Hyder, R., Mansour, H., Ma, Y., Boufounos, P.T., Wang, P., "A Consensus Equilibrium Solution for Deep Image Prior Powered by Red", IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP), DOI: 10.1109/​ICASSP39728.2021.9414290, June 2021, pp. 1380-1384.
      BibTeX TR2021-046 PDF
      • @inproceedings{Hyder2021jun,
      • author = {Hyder, Rakib and Mansour, Hassan and Ma, Yanting and Boufounos, Petros T. and Wang, Perry},
      • title = {A Consensus Equilibrium Solution for Deep Image Prior Powered by Red},
      • booktitle = {IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP)},
      • year = 2021,
      • pages = {1380--1384},
      • month = jun,
      • doi = {10.1109/ICASSP39728.2021.9414290},
      • issn = {2379-190X},
      • isbn = {978-1-7281-7605-5},
      • url = {https://www.merl.com/publications/TR2021-046}
      • }
    •  Yao, G., Wang, P., Berntorp, K., Mansour, H., Boufounos, P.T., Orlik, P.V., "Extended Object Tracking with Automotive Radar Using B-Spline Chained Ellipses Model", IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP), DOI: 10.1109/​ICASSP39728.2021.9415080, June 2021, pp. 8408-8412.
      BibTeX TR2021-048 PDF Video
      • @inproceedings{Yao2021jun,
      • author = {Yao, Gang and Wang, Perry and Berntorp, Karl and Mansour, Hassan and Boufounos, Petros T. and Orlik, Philip V.},
      • title = {Extended Object Tracking with Automotive Radar Using B-Spline Chained Ellipses Model},
      • booktitle = {IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP)},
      • year = 2021,
      • pages = {8408--8412},
      • month = jun,
      • publisher = {IEEE},
      • doi = {10.1109/ICASSP39728.2021.9415080},
      • issn = {2379-190X},
      • isbn = {978-1-7281-7605-5},
      • url = {https://www.merl.com/publications/TR2021-048}
      • }
    •  Kadu, A., Mansour, H., Boufounos, P.T., "High-Contrast Reflection Tomography with Total-Variation Constraints", IEEE Transactions on Computational Imaging, DOI: 10.1109/​TCI.2020.3038171, Vol. 6, pp. 1523-1536, March 2021.
      BibTeX TR2021-013 PDF
      • @article{Kadu2021mar,
      • author = {Kadu, Ajinkya and Mansour, Hassan and Boufounos, Petros T.},
      • title = {High-Contrast Reflection Tomography with Total-Variation Constraints},
      • journal = {IEEE Transactions on Computational Imaging},
      • year = 2021,
      • volume = 6,
      • pages = {1523--1536},
      • month = mar,
      • doi = {10.1109/TCI.2020.3038171},
      • url = {https://www.merl.com/publications/TR2021-013}
      • }
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  • Videos