MERL - Mitsubishi Electric Research Laboratories

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.

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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   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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  • News & Events

    •  EVENT   Toshiaki Koike-Akino Gives Seminar Talk at IEEE Boston Photonics
      Date & Time: Thursday, December 9, 2021; 7pm EST
      Speaker: Toshiaki Koike-Akino
      MERL Contact: Toshiaki Koike-Akino
      Location: virtual
      Research Areas: Communications, Machine Learning, Signal Processing
      Brief
      • Toshiaki Koike-Akino (Signal Processing group, Network Intelligence Team) is giving an invited talk titled, `Evolution of Machine Learning for Photonic Research' for the Boston Photonic Chapter of the IEEE Photonic Society on December 9. The talk covers recent MERL research on machine learning for nonlinearity compensation and nanophotonic device design.
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    •  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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  • Research Highlights

  • Internships

    • SP1753: Algorithms for Coherent Imaging Systems

      MERL is seeking an intern to work on estimation algorithms for coherent optical imaging. The ideal candidate would be a senior PhD student working in coherent imaging. The candidate should have experience in statistical modeling and estimation. A detailed knowledge of optical interferometry and imaging with a focus on either optical coherence tomography, optical coherence microscopy or FMCW LIDAR is also preferred. Strong programming skills in MATLAB or Python are essential. Publication of the results produced during our internships is expected. Duration is anticipated to be 3 to 6 months.

    • MD1761: Blind signal decomposition

      MERL is seeking a self-motivated intern to work on blind signal decomposition. The ideal candidate would be a senior PhD student with solid background in signal processing, sparse representation, and optimization. Prior experience in array signal processing, compressive sensing, and spectrum analysis is preferred. Skills in Python and/or Matlab are required. The intern is expected to collaborate with MERL researchers to build models, develop algorithms, and prepare manuscripts for scientific publications. The expected duration of the internship is 3 months and the start date is flexible. This internship requires work that can only be done at MERL.

    • SP1748: Learning-based Wireless Sensing

      The Signal Processing (SP) group at MERL is seeking a highly motivated intern to conduct fundamental research in learning-based wireless sensing using communication signals (e.g., Wi-Fi) and other RF signals (such as millimeter-wave sensing waveforms). Expertise in deep learning in one of the following areas: localization, occupancy sensing, device-free pose/gesture recognition, skeleton tracking, and multi-modal fusion, is highly preferred. Familiarity with IEEE 802.11 (g/n/ac/ad/ay)standards 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 using MERL in-house testbed, and prepare results for publication. The expected duration of the internship is 3 months with a flexible start date. This internship requires work that can only be done at MERL.


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  • Recent Publications

    •  Fujihashi, T., Koike-Akino, T., Watanabe, T., "Overhead Reduction for Graph-Based Point Cloud Delivery Using Non-Uniform Quantization", IEEE International Conference on Consumer Electronics (ICCE), January 2022.
      BibTeX TR2022-005 PDF
      • @inproceedings{Fujihashi2022jan,
      • author = {Fujihashi, Takuya and Koike-Akino, Toshiaki and Watanabe, Takashi},
      • title = {Overhead Reduction for Graph-Based Point Cloud Delivery Using Non-Uniform Quantization},
      • booktitle = {IEEE International Conference on Consumer Electronics (ICCE)},
      • year = 2022,
      • month = jan,
      • url = {https://www.merl.com/publications/TR2022-005}
      • }
    •  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, 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,
      • month = jan,
      • url = {https://www.merl.com/publications/TR2022-004}
      • }
    •  Kojima, K., Tang, Y., Wang, Y., Koike-Akino, T., "Machine Learning for design and optimization of photonic devices" in Machine Learning for Future Fiber Optic Communication Systems (Elsevier Book), November 2021.
      BibTeX TR2021-142 PDF
      • @incollection{Kojima2021nov,
      • author = {Kojima, Keisuke and Tang, Yingheng and Wang, Ye and Koike-Akino, Toshiaki},
      • title = {Machine Learning for design and optimization of photonic devices},
      • booktitle = {Machine Learning for Future Fiber Optic Communication Systems (Elsevier Book)},
      • year = 2021,
      • month = nov,
      • url = {https://www.merl.com/publications/TR2021-142}
      • }
    •  Furuichi, T., Ma, R., Koike-Akino, T., Komatsuszaki, Y., "Full-Range Three-Stage 16GSa/s Riemann Pump RF-Power DAC in GaN HEMT", Asia-Pacific Microwave Conference (APMC) 2021, November 2021.
      BibTeX TR2021-141 PDF
      • @inproceedings{Furuichi2021nov,
      • author = {Furuichi, Tomoyuki and Ma, Rui and Koike-Akino, Toshiaki and Komatsuszaki, Yuji},
      • title = {Full-Range Three-Stage 16GSa/s Riemann Pump RF-Power DAC in GaN HEMT},
      • booktitle = {Asia-Pacific Microwave Conference (APMC) 2021},
      • year = 2021,
      • month = nov,
      • url = {https://www.merl.com/publications/TR2021-141}
      • }
    •  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}
      • }
    •  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, DOI: 10.1109/​EMBC46164.2021.9630194, 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,
      • publisher = {IEEE},
      • doi = {10.1109/EMBC46164.2021.9630194},
      • issn = {2694-0604},
      • isbn = {978-1-7281-1179-7},
      • 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, DOI: 10.23919/​ICEMS52562.2021.9634664, 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,
      • publisher = {IEEE},
      • doi = {10.23919/ICEMS52562.2021.9634664},
      • issn = {2642-5513},
      • isbn = {978-8-9865-1021-8},
      • url = {https://www.merl.com/publications/TR2021-135}
      • }
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