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.
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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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  • News & Events

    •  NEWS    MERL Scientists Presenting 5 Papers at IEEE International Conference on Communications (ICC) 2022
      Date: May 16, 2022 - May 20, 2022
      Where: Seoul, Korea
      MERL Contacts: Jianlin Guo; Kyeong Jin (K.J.) Kim; Toshiaki Koike-Akino; Philip V. Orlik; Kieran Parsons; Pu (Perry) Wang; Ye Wang
      Research Areas: Artificial Intelligence, Communications, Computational Sensing, Computer Vision, Machine Learning, Signal Processing
      Brief
      • MERL Connectivity & Information Processing Team scientists remotely presented 5 papers at the IEEE International Conference on Communications (ICC) 2022, held in Seoul Korea on May 16-20, 2022. Topics presented include recent advancements in communications technologies, deep learning methods, and quantum machine learning (QML). Presentation videos are also found on our YouTube channel. In addition, K. J. Kim organized "Industrial Private 5G-and-beyond Wireless Networks Workshop" at the conference.

        IEEE ICC is one of two IEEE Communications Society’s flagship conferences (ICC and Globecom). Each year, close to 2,000 attendees from over 70 countries attend IEEE ICC to take advantage of a program which consists of exciting keynote session, robust technical paper sessions, innovative tutorials and workshops, and engaging industry sessions. This 5-day event is known for bringing together audiences from both industry and academia to learn about the latest research and innovations in communications and networking technology, share ideas and best practices, and collaborate on future projects.
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    •  TALK    [MERL Seminar Series 2022] Beyond the First Portrait of a Black Hole
      Date & Time: Tuesday, February 15, 2022; 1:00 PM EST
      Speaker: Katie Bouman, California Institute of Technology
      MERL Host: Joshua Rapp
      Research Area: Computational Sensing
      Abstract
      • As imaging requirements become more demanding, we must rely on increasingly sparse and/or noisy measurements that fail to paint a complete picture. Computational imaging pipelines, which replace optics with computation, have enabled image formation in situations that are impossible for conventional optical imaging. For instance, the first black hole image, published in 2019, was only made possible through the development of computational imaging pipelines that worked alongside an Earth-sized distributed telescope. However, remaining scientific questions motivate us to improve this computational telescope to see black hole phenomena still invisible to us and to meaningfully interpret the collected data. This talk will discuss how we are leveraging and building upon recent advances in machine learning in order to achieve more efficient uncertainty quantification of reconstructed images as well as to develop techniques that allow us to extract the evolving structure of our own Milky Way's black hole over the course of a night, perhaps even in three dimensions.
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  • Research Highlights

  • Internships

    • ST1791: Single Pixel Imaging

      The Computational Sensing team at MERL is seeking motivated and qualified individuals to design sensing mechanisms and develop algorithms that perform high quality image and video reconstruction from a single pixel detector. The project goal is to improve the performance and develop robust methods that can reduce the number of snapshots required for image formation. Ideal candidates should be Ph.D. students and have solid background and publication record in any of the following, or related areas: compressed sensing, imaging inverse problems, large-scale optimization, plug-and-play priors, learning-based modeling for imaging, learning theory for computational imaging. 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.

    • 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.

    • 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.


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  • Recent Publications

    •  Koike-Akino, T., Pu, W., Wang, Y., "Quantum Transfer Learning for Wi-Fi Sensing", IEEE International Conference on Communications (ICC), May 2022.
      BibTeX TR2022-044 PDF Video Presentation
      • @inproceedings{Koike-Akino2022may2,
      • author = {Koike-Akino, Toshiaki and Pu, Wang and Wang, Ye},
      • title = {Quantum Transfer Learning for Wi-Fi Sensing},
      • booktitle = {IEEE International Conference on Communications (ICC)},
      • year = 2022,
      • month = may,
      • url = {https://www.merl.com/publications/TR2022-044}
      • }
    •  Peng, K.-C., "Iterative Self Knowledge Distillation --- From Pothole Classification To Fine-Grained And COVID Recognition", IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP), Gan, W.-S. and Ma, K. K., Eds., DOI: 10.1109/​ICASSP43922.2022.9746470, April 2022, pp. 3139-3143.
      BibTeX TR2022-020 PDF Video Presentation
      • @inproceedings{Peng2022apr,
      • author = {Peng, Kuan-Chuan},
      • title = {Iterative Self Knowledge Distillation --- From Pothole Classification To Fine-Grained And COVID Recognition},
      • booktitle = {IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP)},
      • year = 2022,
      • editor = {Gan, W.-S. and Ma, K. K.},
      • pages = {3139--3143},
      • month = apr,
      • publisher = {IEEE},
      • doi = {10.1109/ICASSP43922.2022.9746470},
      • issn = {1520-6149},
      • isbn = {978-1-6654-0541-6},
      • url = {https://www.merl.com/publications/TR2022-020}
      • }
    •  Jin, S., Pu, W., Boufounos, P.T., Orlik, P.V., Roy, S., "Automotive Radar Interference Mitigation with Fast-Time-Frequency Mode Retrieval", IEEE Radar Conference (RadarCon), March 2022.
      BibTeX TR2022-029 PDF
      • @inproceedings{Jin2022mar,
      • author = {Jin, Sian and Pu, Wang and Boufounos, Petros T. and Orlik, Philip V. and Roy, Sumit},
      • title = {Automotive Radar Interference Mitigation with Fast-Time-Frequency Mode Retrieval},
      • booktitle = {IEEE Radar Conference (RadarCon)},
      • year = 2022,
      • month = mar,
      • url = {https://www.merl.com/publications/TR2022-029}
      • }
    •  Yu, L., Liu, D., Mansour, H., Boufounos, P.T., "Fast and High-Quality Blind Multi-Spectral Image Pansharpening", IEEE Transactions on Geoscience and Remote Sensing, DOI: 10.1109/​TGRS.2021.3091329, Vol. 60, pp. 1-17, January 2022.
      BibTeX TR2022-004 PDF
      • @article{Yu2022jan,
      • author = {Yu, Lantao and Liu, Dehong and Mansour, Hassan and Boufounos, Petros T.},
      • title = {Fast and High-Quality Blind Multi-Spectral Image Pansharpening},
      • journal = {IEEE Transactions on Geoscience and Remote Sensing},
      • year = 2022,
      • volume = 60,
      • pages = {1--17},
      • month = jan,
      • doi = {10.1109/TGRS.2021.3091329},
      • issn = {1558-0644},
      • url = {https://www.merl.com/publications/TR2022-004}
      • }
    •  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, pp. 1-8.
      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,
      • pages = {1--8},
      • month = nov,
      • isbn = {IEEE Xplore},
      • 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}
      • }
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  • Videos

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