MERL - Mitsubishi Electric Research Laboratories

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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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  • Awards

    •  AWARD   Excellent Presentation Award
      Date: January 25, 2021
      Awarded to: Takenori Sumi, Yukimasa Nagai, Jianlin Guo, Philip Orlik, Tatsuya Yokoyama, Hiroshi Mineno
      MERL Contacts: Jianlin Guo; Philip Orlik
      Research Areas: Communications, Machine Learning, Signal Processing
      Brief
      • MELCO and MERL researchers have won "Excellent Presentation Award" at the IPSJ/CDS30 (Information Processing Society of Japan/Consumer Devices and Systems 30th conferences) held on January 25, 2021. The paper titled "Sub-1 GHz Coexistence Using Reinforcement Learning Based IEEE 802.11ah RAW Scheduling" addresses coexistence between IEEE 802.11ah and IEEE 802.15.4g systems in the Sub-1 GHz frequency bands. This paper proposes a novel method to allocate IEEE 802.11 RAW time slots using a Q-Learning technique. MERL and MELCO have been leading IEEE 802.19.3 coexistence standard development and this paper is a good candidate for future standard enhancement. The authors are Takenori Sumi, Yukimasa Nagai, Jianlin Guo, Philip Orlik, Tatsuya Yokoyama and Hiroshi Mineno.
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    •  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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  • News & Events

    •  TALK   Prof. Pere Gilabert gave an invited talk at MERL on Machine Learning for Digital Predistortion Linearization of High Efficient Power Amplifier
      Date & Time: Tuesday, February 16, 2021; 11:00-12:00
      Speaker: Prof. Pere Gilabert, Universitat Politecnica de Catalunya, Barcelona, Spain
      MERL Host: Rui Ma
      Research Areas: Communications, Electronic and Photonic Devices, Machine Learning, Signal Processing
      Brief
      • Digital predistortion (DPD) linearization is the most common and spread solution to cope with power amplifiers (PA) inherent linearity versus efficiency trade-off. The use of new radio 5G spectrally efficient signals with high peak-to-average power ratios (PAPR) occupying wider bandwidths only aggravates such compromise. When considering wide bandwidth signals, carrier aggregation or multi-band configurations in high efficient transmitter architectures, such as Doherty PAs, load-modulated balanced amplifiers, envelope tracking PAs or outphasing transmitters, the number of parameters required in the DPD model to compensate for both nonlinearities and memory effects can be unacceptably high. This has a negative impact in the DPD model extraction/adaptation, because it increases the computational complexity and drives to over-fitting and uncertainty.
        This talk will discuss the use of machine learning techniques for DPD linearization. The use of artificial neural networks (ANNs) for adaptive DPD linearization and approaches to reduce the coefficients adaptation time will be discussed. In addition, an overview on several feature-extraction techniques used to reduce the number of parameters of the DPD linearization system as well as to ensure proper, well-conditioned estimation for related variables will be presented.
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    •  NEWS   MERL published four papers in 2020 IEEE Global Communications Conference
      Date: December 7, 2020 - December 11, 2020
      Where: Taipei, Taiwan
      MERL Contacts: Kyeong Jin (K.J.) Kim; Toshiaki Koike-Akino; Philip Orlik; Pu (Perry) Wang; Ye Wang
      Research Areas: Communications, Computational Sensing, Machine Learning, Signal Processing
      Brief
      • MERL researchers have published four papers in 2020 IEEE Global Communications Conference (GlobeComm). This conference is one of the two IEEE Communications Societies flagship conferences dedicated to Communications for Human and Machine Intelligence. Topics of the published papers include, transmit diversity schemes, coding for molecular networks, and location and human activity sensing via WiFi signals.
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  • Research Highlights

  • Internships

    • CA1519: Estimation for High-Precision Positioning

      MERL is seeking a highly motivated candidate for development of next-generation high-precision positioning methods for autonomous systems applications, e.g., autonomous driving. The candidate will work with the Control for Autonomy team and the Signal Processing group in developing satellite-based positioning methods using information from multiple sources. Previous experience with at least some of the Bayesian inference, distributed estimation, satellite navigation systems, is highly desirable. Solid knowledge in MATLAB is required, working experience in C/C++ is desired, and previous experience with satellite navigation packages such as RTKLib is a merit. PhD candidates meeting the above requirements are encouraged to apply. The expected duration of the internship is 3-6 months with 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.

    • 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.

    • SP1307: Vehicular traffic environment sensing

      MERL is seeking a highly motivated, qualified intern to join a three month internship program. The ideal candidate will be expected to carry out research on environmental sensing in high frequency bands. The candidate is expected to develop innovative sensing technologies. Candidates should have strong knowledge about neural network and learning techniques, such as machine learning, deep learning, shallow learning, and distributed learning. In addition, understanding of spectrum sensing and wireless communications technologies is necessary. Proficient programming skills with Python, MATLAB, and C++, 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.


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  • Recent Publications

    •  Berntorp, K., "Online Bayesian Inference and Learning of Gaussian-Process State-SpaceModels", Automatica, DOI: https:/​/​doi.org/​10.1016/​j.automatica.2021.109613, Vol. 129, March 2021.
      BibTeX TR2021-026 PDF
      • @article{Berntorp2021mar,
      • author = {Berntorp, Karl},
      • title = {Online Bayesian Inference and Learning of Gaussian-Process State-SpaceModels},
      • journal = {Automatica},
      • year = 2021,
      • volume = 129,
      • month = mar,
      • doi = {https://doi.org/10.1016/j.automatica.2021.109613},
      • issn = {0005-1098},
      • url = {https://www.merl.com/publications/TR2021-026}
      • }
    •  Sanz-Gorrachategui, I., Pastor-Flores, P., Pajovic, M., Wang, Y., Orlik, P.V., Bernal-Ruiz, C., Bono-Nuez, A., Artal-Sevil, J.S., "Remaining Useful Life Estimation for LFP Cells in Second Life Applications", IEEE Transactions on Instrumentation and Measurement, DOI: 10.1109/​TIM.2021.3055791, March 2021.
      BibTeX TR2021-023 PDF
      • @article{Sanz-Gorrachategui2021mar,
      • author = {Sanz-Gorrachategui, Ivan and Pastor-Flores, Pablo and Pajovic, Milutin and Wang, Ye and Orlik, Philip V. and Bernal-Ruiz, Carlos and Bono-Nuez, Antonio and Artal-Sevil, Jesús Sergio},
      • title = {Remaining Useful Life Estimation for LFP Cells in Second Life Applications},
      • journal = {IEEE Transactions on Instrumentation and Measurement},
      • year = 2021,
      • month = mar,
      • doi = {10.1109/TIM.2021.3055791},
      • url = {https://www.merl.com/publications/TR2021-023}
      • }
    •  Kadu, A., Mansour, H., Boufounos, P.T., "High-Contrast Reflection Tomography with Total-Variation Constraints", IEEE Transactions on Computational Imaging, March 2021.
      BibTeX TR2021-013 PDF
      • @article{Kadu2021mar,
      • author = {Kadu, Ajinkya and Mansour, Hassan and Boufounos, Petros T.},
      • title = {High-Contrast Reflection Tomography with Total-Variation Constraints},
      • journal = {IEEE Transactions on Computational Imaging},
      • year = 2021,
      • month = mar,
      • url = {https://www.merl.com/publications/TR2021-013}
      • }
    •  Demir, A., Koike-Akino, T., Wang, Y., Erdogmus, D., "AutoBayes: Automated Bayesian Graph Exploration for Nuisance-Robust Inference", IEEE Access, DOI: 10.1109/​ACCESS.2021.3064530, Vol. 9, pp. 39955-39972, March 2021.
      BibTeX TR2021-016 PDF
      • @article{Demir2021mar,
      • author = {Demir, Andac and Koike-Akino, Toshiaki and Wang, Ye and Erdogmus, Deniz},
      • title = {AutoBayes: Automated Bayesian Graph Exploration for Nuisance-Robust Inference},
      • journal = {IEEE Access},
      • year = 2021,
      • volume = 9,
      • pages = {39955--39972},
      • month = mar,
      • doi = {10.1109/ACCESS.2021.3064530},
      • issn = {2169-3536},
      • url = {https://www.merl.com/publications/TR2021-016}
      • }
    •  Kojima, K., Tang, Y., Koike-Akino, T., Wang, Y., Jha, D., TaherSima, M., Parsons, K., "Application of Deep Learning for Nanophotonic Device Design", SPIE Photonics West, Bahram Jalali and Ken-ichi Kitayama, Eds., DOI: 10.1117/​12.2579104, March 2021.
      BibTeX TR2020-182 PDF Video
      • @inproceedings{Kojima2021mar,
      • author = {Kojima, Keisuke and Tang, Yingheng and Koike-Akino, Toshiaki and Wang, Ye and Jha, Devesh and TaherSima, Mohammad and Parsons, Kieran},
      • title = {Application of Deep Learning for Nanophotonic Device Design},
      • booktitle = {SPIE Photonics West},
      • year = 2021,
      • editor = {Bahram Jalali and Ken-ichi Kitayama},
      • month = mar,
      • publisher = {SPIE},
      • doi = {10.1117/12.2579104},
      • url = {https://www.merl.com/publications/TR2020-182}
      • }
    •  Skvortcov, P., Phillips, I., Forysiak, W., Koike-Akino, T., Kojima, K., Parsons, K., Millar, D.S., "Huffman-Coded Sphere Shaping for Extended-Reach Single-Span Links", IEEE Journal of Selected Topics in Quantum Electronics, DOI: 10.1109/​JSTQE.2021.3055476, Vol. 27, No. 3, February 2021.
      BibTeX TR2021-007 PDF
      • @article{Skvortcov2021feb,
      • author = {Skvortcov, Pavel and Phillips, Ian and Forysiak, Wladek and Koike-Akino, Toshiaki and Kojima, Keisuke and Parsons, Kieran and Millar, David S.},
      • title = {Huffman-Coded Sphere Shaping for Extended-Reach Single-Span Links},
      • journal = {IEEE Journal of Selected Topics in Quantum Electronics},
      • year = 2021,
      • volume = 27,
      • number = 3,
      • month = feb,
      • doi = {10.1109/JSTQE.2021.3055476},
      • issn = {1558-4542},
      • url = {https://www.merl.com/publications/TR2021-007}
      • }
    •  Xia, Y., Wang, P., Berntorp, K., Svensson, L., Granstrom, K., Mansour, H., Boufounos, P.T., Orlik, P.V., "Learning-based Extended Object Tracking Using Hierarchical Truncation Measurement Model with Automotive Radar", IEEE Journal of Selected Topics in Signal Processing, February 2021.
      BibTeX TR2021-006 PDF
      • @article{Xia2021feb,
      • author = {Xia, Yuxuan and Wang, Pu and Berntorp, Karl and Svensson, Lennart and Granstrom, Karl and Mansour, Hassan and Boufounos, Petros T. and Orlik, Philip V.},
      • title = {Learning-based Extended Object Tracking Using Hierarchical Truncation Measurement Model with Automotive Radar},
      • journal = {IEEE Journal of Selected Topics in Signal Processing},
      • year = 2021,
      • month = feb,
      • url = {https://www.merl.com/publications/TR2021-006}
      • }
    •  Li, S., Mansour, H., Wakin, M., "Recovery Analysis of Damped Spectrally Sparse Signals and Its Relation to MUSIC", Information and Inference: A Journal of the IMA, January 2021.
      BibTeX TR2021-008 PDF
      • @article{Li2021jan,
      • author = {Li, Shuang and Mansour, Hassan and Wakin, Michael},
      • title = {Recovery Analysis of Damped Spectrally Sparse Signals and Its Relation to MUSIC},
      • journal = {Information and Inference: A Journal of the IMA},
      • year = 2021,
      • month = jan,
      • url = {https://www.merl.com/publications/TR2021-008}
      • }
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