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   Outstanding Presentation Award at the 28th Conference of Information Processing Society of Japan/Consumer Device & Systems
      Date: October 20, 2020
      Awarded to: Yukimasa Nagai, Takenori Sumi, Jianlin Guo, Philip Orlik, Hiroshi Mineno
      MERL Contacts: Jianlin Guo; Philip Orlik
      Research Areas: Communications, Optimization, Signal Processing
      Brief
      • MELCO and MERL researchers have won "Outstanding Presentation Award" at 28th Conference of Information Processing Society of Japan (IPSJ)/Consumer Device & Systems held on September 29-30, 2020. The paper titled "IEEE 802.19.3 Standardization for Coexistence of IEEE 802.11ah and IEEE 802.15.4g Systems in Sub-1 GHz Frequency Bands" reports IEEE 802.19.3 standard development on coexistence between IEEE 802.11ah and IEEE 802.15.4g systems in the Sub-1 GHz frequency bands. MERL and MELCO have been leading this standard development and made major technical contributions, which propose methods to mitigate interference in smart meter systems. The authors are Yukimasa Nagai, Takenori Sumi, Jianlin Guo, Philip Orlik and Hiroshi Mineno.
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    •  AWARD   Best Paper AWARD at International Workshop on Informatics (IWIN) 2020
      Date: September 11, 2020
      Awarded to: Yukimasa Nagai, Jianlin Guo, Takenori Sumi, Philip Orlik, Hiroshi Mineno
      MERL Contact: Jianlin Guo
      Research Areas: Communications, Signal Processing
      Brief
      • MELCO and MERL researchers have won one of two Best Paper Awards at International Workshop on Informatics (IWIN) 2020. The paper titled 'Hybrid CSMA/CA for Sub-1 GHz Frequency Band Coexistence of IEEE 802.11ah and IEEE 802.15.4g', reports research on the severity of interference between IEEE 802.11ah and IEEE 802.15.4g based networks and also proposes methods to mitigate this interference in smart meter systems. This research reported in this paper has also informed several of MELCO/MERL's contributions to the IEEE P802.19.3 task group which is developing standards to allow for improved coexistence in outdoor metering systems. Authors are Yukimasa Nagai, Jianlin Guo, Takenori Sumi, Philip Orlik and Hiroshi Mineno.
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    •  AWARD   Best Student Paper Award at the IEEE Conference on Control Technology and Applications
      Date: August 26, 2020
      Awarded to: Marcus Greiff, Anders Robertsson, Karl Berntorp
      MERL Contact: Karl Berntorp
      Research Areas: Control, Signal Processing
      Brief
      • Marcus Greiff, a former MERL intern from the Department of Automatic Control, Lund University, Sweden, won one of three 2020 CCTA Outstanding Student Paper Awards and the Best Student Paper Award at the 2020 IEEE Conference on Control Technology and Applications. The research leading up to the awarded paper titled 'MSE-Optimal Measurement Dimension Reduction in Gaussian Filtering', concerned how to select a reduced set of measurements in estimation applications while minimally degrading performance, was done in collaboration with Karl Berntorp at MERL.
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  • News & Events


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  • Research Highlights

  • Internships

    • SP1460: Advanced Vehicular Technologies

      MERL is seeking a highly motivated, qualified intern to collaborate with the Signal Processing group and the Control for Autonomy team in developing technologies for Connected Automated Vehicles. The ideal candidate is expected to be involved in research on collaborative learning between infrastructure and vehicles. The candidate is expected to develop learning-based technologies to achieve vehicle coordination, estimation and GNSS-based localization using data and computation sharing between vehicle and infrastructure. The candidates should have knowledge of machine learning, connected vehicles and V2X communications. Knowledge of one or more traffic and/or multi-vehicle simulators (SUMO, Vissim, etc.) and GNSS is a plus. 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 start date in September/October 2020.

    • SP1468: Quantum Machine Learning

      MERL is seeking an intern to work on research for quantum machine learning (QML). The ideal candidate is an experienced PhD student or post-graduate researcher having an excellent background in quantum computing, deep learning, and signal processing. Proficient programming skills with PyTorch, Qiskit, and PennyLane will be additional assets to this position. Also note that we wish to fill this position as soon as possible and expect that the candidate will be available during this fall/winter. This internship is preferred to be onsite at MERL, but may be done remotely where you live if the COVID pandemic makes it necessary.

    • SP1542: Research in Computational Sensing

      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, learning for inverse problems, large-scale optimization, blind inverse scattering, radar/lidar/sonar imaging, 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. 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, B., Hotta, A., "Contactless Eddy Current Sensing for Carbon Fiber Reinforced Polymer Defect Detection", Biennial IEEE Conference on Electromagnetic Field Computation (CEFC), November 2020.
      BibTeX TR2020-148 PDF
      • @inproceedings{Wang2020nov2,
      • author = {Wang, Bingnan and Hotta, Akira},
      • title = {Contactless Eddy Current Sensing for Carbon Fiber Reinforced Polymer Defect Detection},
      • booktitle = {Biennial IEEE Conference on Electromagnetic Field Computation (CEFC)},
      • year = 2020,
      • month = nov,
      • url = {https://www.merl.com/publications/TR2020-148}
      • }
    •  Wang, P., Koike-Akino, T., Orlik, P.V., Sertel, K., Yamashita, G., Tsujita, W., Tsutada, H., "Terahertz QR Positioning: Experimental Results", International Conference on Infrared, Millimeter, and Terahertz Waves (IRMMW-THz), November 2020.
      BibTeX TR2020-146 PDF
      • @inproceedings{Wang2020nov,
      • author = {Wang, Pu and Koike-Akino, Toshiaki and Orlik, Philip V. and Sertel, Kubilay and Yamashita, Genki and Tsujita, Wataru and Tsutada, Hiroyuki},
      • title = {Terahertz QR Positioning: Experimental Results},
      • booktitle = {International Conference on Infrared, Millimeter, and Terahertz Waves (IRMMW-THz)},
      • year = 2020,
      • month = nov,
      • url = {https://www.merl.com/publications/TR2020-146}
      • }
    •  Yamashita, G., Tsujita, W., Tsutada, H., Ma, R., Wang, P., Orlik, P.V., "Evaluation of Position Error of Terahertz Polarimetric Encoder By Ray-Tracing Method", International Conference on Infrared, Millimeter, and Terahertz Waves (IRMMW-THz), November 2020.
      BibTeX TR2020-145 PDF
      • @inproceedings{Yamashita2020nov,
      • author = {Yamashita, Genki and Tsujita, Wataru and Tsutada, Hiroyuki and Ma, Rui and Wang, Pu and Orlik, Philip V.},
      • title = {Evaluation of Position Error of Terahertz Polarimetric Encoder By Ray-Tracing Method},
      • booktitle = {International Conference on Infrared, Millimeter, and Terahertz Waves (IRMMW-THz)},
      • year = 2020,
      • month = nov,
      • url = {https://www.merl.com/publications/TR2020-145}
      • }
    •  Tang, Y., Kojima, K., Koike-Akino, T., Wang, Y., Wu, P., TaherSima, M., Jha, D., Parsons, K., Qi, M., "Generative Deep Learning Model for Inverse Design of Integrated Nanophotonic Devices", Lasers and Photonics Reviews, DOI: 10.1002/lpor.202000287, Vol. 2020, pp. 2000287, October 2020.
      BibTeX TR2020-135 PDF
      • @article{Tang2020oct,
      • author = {Tang, Yingheng and Kojima, Keisuke and Koike-Akino, Toshiaki and Wang, Ye and Wu, Pengxiang and TaherSima, Mohammad and Jha, Devesh and Parsons, Kieran and Qi, Minghao},
      • title = {Generative Deep Learning Model for Inverse Design of Integrated Nanophotonic Devices},
      • journal = {Lasers and Photonics Reviews},
      • year = 2020,
      • volume = 2020,
      • pages = 2000287,
      • month = oct,
      • doi = {10.1002/lpor.202000287},
      • url = {https://www.merl.com/publications/TR2020-135}
      • }
    •  Nagai, Y., Sumi, T., Guo, J., Orlik, P.V., Mineno, H., "Hybrid CSMA/CA for Sub-1 GHz Frequency Band Coexistence of IEEE 802.11ah and IEEE 802.15.4g", Consumer Device System 28th Research Presentation, September 2020.
      BibTeX TR2020-133 PDF
      • @inproceedings{Nagai2020sep,
      • author = {Nagai, Yukimasa and Sumi, Takenori and Guo, Jianlin and Orlik, Philip V. and Mineno, Hiroshi},
      • title = {Hybrid CSMA/CA for Sub-1 GHz Frequency Band Coexistence of IEEE 802.11ah and IEEE 802.15.4g},
      • booktitle = {Consumer Device System 28th Research Presentation},
      • year = 2020,
      • month = sep,
      • url = {https://www.merl.com/publications/TR2020-133}
      • }
    •  Xia, Y., Wang, P., Berntorp, K., Boufounos, P.T., Orlik, P.V., Svensson, L., Granstrom, K., "Extended Object Tracking with Automotive Radar Using Learned Structural Measurement Model", IEEE Radar Conference (RadarCon), September 2020.
      BibTeX TR2020-131 PDF
      • @inproceedings{Xia2020sep,
      • author = {Xia, Yuxuan and Wang, Pu and Berntorp, Karl and Boufounos, Petros T. and Orlik, Philip V. and Svensson, Lennart and Granstrom, Karl},
      • title = {Extended Object Tracking with Automotive Radar Using Learned Structural Measurement Model},
      • booktitle = {IEEE Radar Conference (RadarCon)},
      • year = 2020,
      • month = sep,
      • url = {https://www.merl.com/publications/TR2020-131}
      • }
    •  Kojima, K., Tang, Y., Koike-Akino, T., Wang, Y., Jha, D., Parsons, K., TaherSima, M., Sang, F., Klamkin, J., Qi, M., "Inverse Design of Nanophotonic Devices using Deep Neural Networks", Asia Communications and Photonics Conference (ACP), September 2020.
      BibTeX TR2020-130 PDF Video
      • @inproceedings{Kojima2020sep,
      • author = {Kojima, Keisuke and Tang, Yingheng and Koike-Akino, Toshiaki and Wang, Ye and Jha, Devesh and Parsons, Kieran and TaherSima, Mohammad and Sang, Fengqiao and Klamkin, Jonathan and Qi, Minghao},
      • title = {Inverse Design of Nanophotonic Devices using Deep Neural Networks},
      • booktitle = {Asia Communications and Photonics Conference (ACP)},
      • year = 2020,
      • month = sep,
      • url = {https://www.merl.com/publications/TR2020-130}
      • }
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