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

    •  NEWS    MERL Researchers gave a Tutorial Talk on Quantum Machine Learning for Sensing and Communications at IEEE GLOBECOM
      Date: December 8, 2022
      MERL Contacts: Toshiaki Koike-Akino; Pu (Perry) Wang
      Research Areas: Artificial Intelligence, Communications, Computational Sensing, Machine Learning, Signal Processing
      Brief
      • On December 8, 2022, MERL researchers Toshiaki Koike-Akino and Pu (Perry) Wang gave a 3.5-hour tutorial presentation at the IEEE Global Communications Conference (GLOBECOM). The talk, titled "Post-Deep Learning Era: Emerging Quantum Machine Learning for Sensing and Communications," addressed recent trends, challenges, and advances in sensing and communications. P. Wang presented on use cases, industry trends, signal processing, and deep learning for Wi-Fi integrated sensing and communications (ISAC), while T. Koike-Akino discussed the future of deep learning, giving a comprehensive overview of artificial intelligence (AI) technologies, natural computing, emerging quantum AI, and their diverse applications. The tutorial was conducted remotely. MERL's quantum AI technology was partly reported in the recent press release (https://us.mitsubishielectric.com/en/news/releases/global/2022/1202-a/index.html).

        The IEEE GLOBECOM is a highly anticipated event for researchers and industry professionals in the field of communications. Organized by the IEEE Communications Society, the flagship conference is known for its focus on driving innovation in all aspects of the field. Each year, over 3,000 scientific researchers submit proposals for program sessions at the annual conference. The theme of this year's conference was "Accelerating the Digital Transformation through Smart Communications," and featured a comprehensive technical program with 13 symposia, various tutorials and workshops.
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    •  NEWS    MERL's Quantum Machine Learning Technology Featured in Mitsubishi Electric Corporation Press Release
      Date: December 2, 2022
      MERL Contacts: Toshiaki Koike-Akino; Kieran Parsons; Pu (Perry) Wang; Ye Wang
      Research Areas: Artificial Intelligence, Computational Sensing, Machine Learning, Signal Processing, Human-Computer Interaction
      Brief
      • Mitsubishi Electric Corporation announced its development of a quantum artificial intelligence (AI) technology that automatically optimizes inference models to downsize the scale of computation with quantum neural networks. The new quantum AI technology can be integrated with classical machine learning frameworks for diverse solutions.

        Mitsubishi Electric has confirmed that the technology can be incorporated in the world's first applications for terahertz (THz) imaging, Wi-Fi indoor monitoring, compressed sensing, and brain-computer interfaces. The technology is based on recent research by MERL's Connectivity & Information Processing team and Computational Sensing team.

        Mitsubishi Electric's new quantum machine learning (QML) technology realizes compact inference models by fully exploiting the enormous capacity of quantum computers to express exponentially larger-state space with the number of quantum bits (qubits). In a hybrid combination of both quantum and classical AI, the technology can compensate for limitations of classical AI to achieve superior performance while significantly downsizing the scale of AI models, even when using limited data.
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  • Research Highlights

  • Internships

    • CI1951: Multipath IoT Networking

      MERL is seeking a highly motivated and qualified intern to conduct research on emerging IoT networking. The candidate is expected to develop innovative networking technologies to achieve efficient network traffic delivery. The candidate should have knowledge of networking protocols such as multi-path TCP/UDP and RPL and network simulation tools such as ns3. Knowledge of the data protection such as erasure coding is a plus. Candidates in their junior or senior years of a Ph.D. program are encouraged to apply. Start date for this internship is flexible and the duration is 3 months.

    • ST1763: 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.

    • ST1762: 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.


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  • Recent Publications

    •  Soushi Ueno, , Fujihashi, T., Koike-Akino, T., Watanabe, T., "Point Cloud Soft Multicast for Untethered XR Users", IEEE Transactions on Multimedia, December 2022.
      BibTeX TR2022-164 PDF
      • @article{SoushiUeno;Fujihashi2022dec,
      • author = {Soushi Ueno and Fujihashi, Takuya and Koike-Akino, Toshiaki and Watanabe, Takashi},
      • title = {Point Cloud Soft Multicast for Untethered XR Users},
      • journal = {IEEE Transactions on Multimedia},
      • year = 2022,
      • month = dec,
      • url = {https://www.merl.com/publications/TR2022-164}
      • }
    •  Greiff, M., Berntorp, K., "Distributed Kalman Filtering: When to Share Measurements", IEEE Conference on Decision and Control (CDC), DOI: 10.1109/​CDC51059.2022.9993404, December 2022, pp. 5399-5404.
      BibTeX TR2022-158 PDF
      • @inproceedings{Greiff2022dec,
      • author = {Greiff, Marcus and Berntorp, Karl},
      • title = {Distributed Kalman Filtering: When to Share Measurements},
      • booktitle = {IEEE Conference on Decision and Control (CDC)},
      • year = 2022,
      • pages = {5399--5404},
      • month = dec,
      • publisher = {IEEE},
      • doi = {10.1109/CDC51059.2022.9993404},
      • issn = {2576-2370},
      • isbn = {978-1-6654-6761-2},
      • url = {https://www.merl.com/publications/TR2022-158}
      • }
    •  Singla, V., Aeron, S., Koike-Akino, T., Parsons, K., Brand, M.E., Wang, Y., "Learning with noisy labels using low-dimensional model trajectory", NeurIPS 2022 Workshop on Distribution Shifts (DistShift), December 2022.
      BibTeX TR2022-156 PDF
      • @inproceedings{Singla2022dec,
      • author = {Singla, Vasu and Aeron, Shuchin and Koike-Akino, Toshiaki and Parsons, Kieran and Brand, Matthew E. and Wang, Ye},
      • title = {Learning with noisy labels using low-dimensional model trajectory},
      • booktitle = {NeurIPS 2022 Workshop on Distribution Shifts (DistShift)},
      • year = 2022,
      • month = dec,
      • url = {https://www.merl.com/publications/TR2022-156}
      • }
    •  Liu, D., Inoue, H., Kanemaru, M., "Robust Motor Current Signature Analysis (MCSA)-based Fault Detection under Varying Operating Conditions", 2022 International Conference on Electrical Machines and Systems, DOI: 10.1109/​ICEMS56177.2022.9983454, November 2022.
      BibTeX TR2022-150 PDF
      • @inproceedings{Liu2022nov2,
      • author = {Liu, Dehong and Inoue, Hiroshi and Kanemaru, Makoto},
      • title = {Robust Motor Current Signature Analysis (MCSA)-based Fault Detection under Varying Operating Conditions},
      • booktitle = {2022 25th International Conference on Electrical Machines and Systems},
      • year = 2022,
      • month = nov,
      • publisher = {IEEE},
      • doi = {10.1109/ICEMS56177.2022.9983454},
      • issn = {2642-5513},
      • isbn = {978-1-6654-9302-4},
      • url = {https://www.merl.com/publications/TR2022-150}
      • }
    •  Shimoya, R., Morimoto, T., van Baar, J., Boufounos, P.T., Ma, Y., Mansour, H., "Learning Occlusion-Aware Dense Correspondences for Multi-Modal Images", IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS), November 2022.
      BibTeX TR2022-149 PDF
      • @inproceedings{Shimoya2022nov,
      • author = {Shimoya, Ryosuke and Morimoto, Tahashi and van Baar, Jeroen and Boufounos, Petros T. and Ma, Yanting and Mansour, Hassan},
      • title = {Learning Occlusion-Aware Dense Correspondences for Multi-Modal Images},
      • booktitle = {IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS)},
      • year = 2022,
      • month = nov,
      • url = {https://www.merl.com/publications/TR2022-149}
      • }
    •  Skvortcov, P., Koike-Akino, T., Millar, D.S., Kojima, K., Parsons, K., "Dual Coding Concatenation for Burst-Error Correction in Probabilistic Amplitude Shaping", IEEE Journal of Lightwave Technology, DOI: 10.1109/​JLT.2022.3178675, Vol. 40, No. 16, pp. 5502-5513, November 2022.
      BibTeX TR2022-145 PDF
      • @article{Skvortcov2022nov,
      • author = {Skvortcov, Pavel and Koike-Akino, Toshiaki and Millar, David S. and Kojima, Keisuke and Parsons, Kieran},
      • title = {Dual Coding Concatenation for Burst-Error Correction in Probabilistic Amplitude Shaping},
      • journal = {IEEE Journal of Lightwave Technology},
      • year = 2022,
      • volume = 40,
      • number = 16,
      • pages = {5502--5513},
      • month = nov,
      • doi = {10.1109/JLT.2022.3178675},
      • issn = {1558-2213},
      • url = {https://www.merl.com/publications/TR2022-145}
      • }
    •  Yu, X., Smedemark-Margulies, N., Aeron, S., Koike-Akino, T., Moulin, P., Brand, M.E., Parsons, K., Wang, Y., "Improving Adversarial Robustness by Learning Shared Information", Pattern Recognition, DOI: 10.1016/​j.patcog.2022.109054, Vol. 134, pp. 109054, November 2022.
      BibTeX TR2022-141 PDF
      • @article{Yu2022nov,
      • author = {Yu, Xi and Smedemark-Margulies, Niklas and Aeron, Shuchin and Koike-Akino, Toshiaki and Moulin, Pierre and Brand, Matthew E. and Parsons, Kieran and Wang, Ye},
      • title = {Improving Adversarial Robustness by Learning Shared Information},
      • journal = {Pattern Recognition},
      • year = 2022,
      • volume = 134,
      • pages = 109054,
      • month = nov,
      • doi = {10.1016/j.patcog.2022.109054},
      • issn = {0031-3203},
      • url = {https://www.merl.com/publications/TR2022-141}
      • }
    •  Xia, H., Wang, P., Ding, Z., "Incomplete Multi-view Domain Adaptation via Channel Enhancement and Knowledge Transfer", European Conference on Computer Vision (ECCV), DOI: 10.1007/​978-3-031-19830-4_12, October 2022.
      BibTeX TR2022-134 PDF
      • @inproceedings{Xia2022oct,
      • author = {Xia, Haifeng and Wang, Pu and Ding, Zhengming},
      • title = {Incomplete Multi-view Domain Adaptation via Channel Enhancement and Knowledge Transfer},
      • booktitle = {European Conference on Computer Vision (ECCV)},
      • year = 2022,
      • month = oct,
      • doi = {10.1007/978-3-031-19830-4_12},
      • isbn = {978-3-031-19830-4},
      • url = {https://www.merl.com/publications/TR2022-134}
      • }
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