Signal Processing

Acquisition and processing of information.

Our research in the area of signal processing encompasses a wide range of work in the areas of communications, sensing, estimation, localization, and speech and visual information processing. We explore novel approaches for signal acquisition and coding, methods to filter and recover signals in the presence of noise and other degrading factors, and techniques that infer meaning from the processed signals.

  • 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   Toshiaki Koike-Akino elected Fellow of Optica
      Date: November 18, 2021
      Awarded to: Toshiaki Koike-Akino
      MERL Contact: Toshiaki Koike-Akino
      Research Areas: Communications, Electronic and Photonic Devices, Signal Processing
      Brief
      • Toshiaki Koike-Akino's research activities in communications, error control coding and optical technologies at MERL have earned him election as a Fellow Member of Optica (formerly OSA), the foremost professional association in optics and photonics worldwide. Fellow membership in Optica is limited to no more than ten percent of the membership and is reserved for members who have served with distinction in the advancement of optics and photonics. Koike-Akino is one of 106 members from 24 countries in Optica’s 2022 Fellows Class, elected during the Board of Directors of Optica meeting held on 2nd of November, 2021.

        “Congratulations to the 2022 Optica Fellows,” said 2021 President Connie Chang-Hasnain, University of California, Berkeley, USA. “These members exemplify what it means to be a leader in optics and photonics. Your election, by your peers, confirms the important contributions made within our field. Thank you for your dedication to Optica, and for advancing the science of light.”

        Koike-Akino's elevation to Fellow is specifically “for outstanding and innovative contributions to R&D in enabling technologies for optical communications, including nonlinear equalizers, high-dimensional modulations, and FEC (Forward Error Correction),” said Meredith Smith, Director, Optica Awards and Honors Office. "Again, congratulations on joining this esteemed group of Optica members."

        About Optica

        Optica (formerly OSA) is dedicated to promoting the generation, application, archiving and dissemination of knowledge in optics and photonics worldwide. Founded in 1916, it is the leading organization for scientists, engineers, business professionals, students and others interested in the science of light. Optica’s renowned publications, meetings, online resources and in-person activities fuel discoveries, shape real-life applications and accelerate scientific, technical and educational achievement.
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    •  AWARD   Mitsubishi Electric US Receives a 2022 CES Innovation Award for Touchless Elevator Control Jointly Developed with MERL
      Date: November 17, 2021
      Awarded to: Elevators and Escalators Division of Mitsubishi Electric US, Inc.
      MERL Contacts: Daniel N. Nikovski; William S. Yerazunis
      Research Areas: Data Analytics, Machine Learning, Signal Processing
      Brief
      • The Elevators and Escalators Division of Mitsubishi Electric US, Inc. has been recognized as a 2022 CES® Innovation Awards honoree for its new PureRide™ Touchless Control for elevators, jointly developed with MERL. Sponsored by the Consumer Technology Association (CTA), the CES Innovation Awards is the largest and most influential technology event in the world. PureRide™ Touchless Control provides a simple, no-touch product that enables users to call an elevator and designate a destination floor by placing a hand or finger over a sensor. MERL initiated the development of PureRide™ in the first weeks of the COVID-19 pandemic by proposing the use of infra-red sensors for operating elevator call buttons, and participated actively in its rapid implementation and commercialization, resulting in a first customer installation in October of 2020.
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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

    • SP1730: Advanced Signal Processing for RF-controlled metasurface

      MERL is seeking a highly motivated, qualified intern to carry out research on Advanced Signal Processing for RF-controlled meta-surfaces. The candidate is expected to develop innovative signal processing for RF-controlled meta-surfaces aiding various applications. Candidates should have strong knowledge of machine learning, channel estimation, beamforming, interference mitigation, optimization, and electromagnetic field analysis. Proficient programming skills with Python and MATLAB and strong mathematical analysis will be additional assets to this position. Candidates in their junior or senior years of a Ph.D. program are encouraged to apply. The expected duration of the internship is 3-6 months, with a flexible start date. This internship is preferred to be onsite at MERL, but may be done remotely where you live if the COVID pandemic makes it necessary.

    • 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.

    • CV1770: Vital signs estimation using computer vision, machine learning, and signal processing

      MERL is seeking a highly motivated intern to conduct original research in the area of monitoring vital signs such as heart rate, heart rate variability, breathing rate, and blood pressure, from video of a person. the successful candidate will use the latest methods in deep learning, computer vision, and signal processing to derive and implement new models, collect data, conduct experiments, and prepare results for publication, all in collaboration with MERL researchers. The candidate should be a PhD student in computer vision with a strong publication record and experience in computer vision, signal processing, machine learning, and health monitoring. Strong programming skills (Python, Pytorch/Tensorflow, Matlab, C/C++, etc.) are expected. This internship is preferred to be onsite at MERL, but may be done remotely where you live if the COVID pandemic makes it necessary.


    See All Internships for Signal Processing
  • 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}
      • }
    •  Demir, A., Koike-Akino, T., Wang, Y., Erdogmus, D., Haruna, M., "EEG-GNN: Graph Neural Networks for Classification of Electroencephalogram (EEG) Signals", International IEEE EMBS Conference on Neural Engineering, October 2021.
      BibTeX TR2021-136 PDF Video Presentation
      • @inproceedings{Demir2021oct,
      • author = {Demir, Andac and Koike-Akino, Toshiaki and Wang, Ye and Erdogmus, Deniz and Haruna, Masaki},
      • title = {EEG-GNN: Graph Neural Networks for Classification of Electroencephalogram (EEG) Signals},
      • booktitle = {International IEEE EMBS Conference on Neural Engineering},
      • year = 2021,
      • month = oct,
      • url = {https://www.merl.com/publications/TR2021-136}
      • }
    •  Zhang, Z., Liu, D., "A Graph-based Method to Extract Broken Rotor Bar Fault Signature in Varying Speed Operation", ICEMS 2021, October 2021.
      BibTeX TR2021-135 PDF
      • @inproceedings{Zhang2021oct,
      • author = {Zhang, Zhe and Liu, Dehong},
      • title = {A Graph-based Method to Extract Broken Rotor Bar Fault Signature in Varying Speed Operation},
      • booktitle = {ICEMS 2021},
      • year = 2021,
      • month = oct,
      • url = {https://www.merl.com/publications/TR2021-135}
      • }
    •  Rakin, A.S., Wang, Y., Aeron, S., Koike-Akino, T., Moulin, P., Parsons, K., "Towards Universal Adversarial Examples and Defenses", IEEE Information Theory Workshop, DOI: 10.1109/​ITW48936.2021.9611439, October 2021.
      BibTeX TR2021-125 PDF Video
      • @inproceedings{Rakin2021oct,
      • author = {Rakin, Adnan S and Wang, Ye and Aeron, Shuchin and Koike-Akino, Toshiaki and Moulin, Pierre and Parsons, Kieran},
      • title = {Towards Universal Adversarial Examples and Defenses},
      • booktitle = {IEEE Information Theory Workshop},
      • year = 2021,
      • month = oct,
      • publisher = {IEEE},
      • doi = {10.1109/ITW48936.2021.9611439},
      • isbn = {978-1-6654-0312-2},
      • url = {https://www.merl.com/publications/TR2021-125}
      • }
    •  Jurdi, R., Guo, J., Kim, K.J., Orlik, P.V., Nagai, Y., "Queueing Delay Analysis of Mixed Traffic in Time Sensitive Networks", International Conference on Intelligent Manufacturing and Automation Engineering (ICIMA), October 2021.
      BibTeX TR2021-122 PDF
      • @inproceedings{Jurdi2021oct,
      • author = {Jurdi, Rebal and Guo, Jianlin and Kim, Kyeong Jin and Orlik, Philip V. and Nagai, Yukimasa},
      • title = {Queueing Delay Analysis of Mixed Traffic in Time Sensitive Networks},
      • booktitle = {International Conference on Intelligent Manufacturing and Automation Engineering (ICIMA)},
      • year = 2021,
      • month = oct,
      • url = {https://www.merl.com/publications/TR2021-122}
      • }
    •  Matsumine, T., Koike-Akino, T., Ochiai, H., "A Low-Complexity Probabilistic Amplitude Shaping with Short Linear Block Codes", IEEE Transactions on Communications, September 2021.
      BibTeX TR2021-117 PDF
      • @article{Matsumine2021sep,
      • author = {Matsumine, Toshiki and Koike-Akino, Toshiaki and Ochiai, Hideki},
      • title = {A Low-Complexity Probabilistic Amplitude Shaping with Short Linear Block Codes},
      • journal = {IEEE Transactions on Communications},
      • year = 2021,
      • month = sep,
      • url = {https://www.merl.com/publications/TR2021-117}
      • }
    •  Kannapiran, S., van Baar, J., Berman, S., "A Visual Inertial Odometry Framework for 3D Points, Lines and Planes", IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), September 2021.
      BibTeX TR2021-131 PDF
      • @inproceedings{Kannapiran2021sep,
      • author = {Kannapiran, Shenbagaraj and van Baar, Jeroen and Berman, Spring},
      • title = {A Visual Inertial Odometry Framework for 3D Points, Lines and Planes},
      • booktitle = {IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)},
      • year = 2021,
      • month = sep,
      • url = {https://www.merl.com/publications/TR2021-131}
      • }
    See All Publications for Signal Processing
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