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    Best paper award at PHMAP 2023
      Date: September 14, 2023
      Awarded to: Dehong Liu, Anantaram Varatharajan, and Abraham Goldsmith
      MERL Contacts: Abraham Goldsmith; Dehong Liu
      Research Areas: Electric Systems, Signal Processing
      Brief
      • MERL researchers Dehong Liu, Anantaram Varatharajan, and Abraham Goldsmith were awarded one of three best paper awards at Asia Pacific Conference of the Prognostics and Health Management Society 2023 (PHMAP23) held in Tokyo from September 11th to 14th, 2023, for their co-authored paper titled 'Extracting Broken-Rotor-Bar Fault Signature of Varying-Speed Induction Motors.'

        PHMAP is a biennial international conference specialized in prognostics and health management. PHMAP23 attracted more than 300 attendees from worldwide and published more than 160 regular papers from academia and industry including aerospace, production, civil engineering, electronics, and so on.
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    •  AWARD    MERL’s Paper on Wi-Fi Sensing Earns Top 3% Paper Recognition at ICASSP 2023, Selected as a Best Student Paper Award Finalist
      Date: June 9, 2023
      Awarded to: Cristian J. Vaca-Rubio, Pu Wang, Toshiaki Koike-Akino, Ye Wang, Petros Boufounos and Petar Popovski
      MERL Contacts: Petros T. Boufounos; Toshiaki Koike-Akino; Pu (Perry) Wang; Ye Wang
      Research Areas: Artificial Intelligence, Communications, Computational Sensing, Dynamical Systems, Machine Learning, Signal Processing
      Brief
      • A MERL Paper on Wi-Fi sensing was recognized as a Top 3% Paper among all 2709 accepted papers at the 2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP 2023). Co-authored by Cristian Vaca-Rubio and Petar Popovski from Aalborg University, Denmark, and MERL researchers Pu Wang, Toshiaki Koike-Akino, Ye Wang, and Petros Boufounos, the paper "MmWave Wi-Fi Trajectory Estimation with Continous-Time Neural Dynamic Learning" was also a Best Student Paper Award finalist.

        Performed during Cristian’s stay at MERL first as a visiting Marie Skłodowska-Curie Fellow and then as a full-time intern in 2022, this work capitalizes on standards-compliant Wi-Fi signals to perform indoor localization and sensing. The paper uses a neural dynamic learning framework to address technical issues such as low sampling rate and irregular sampling intervals.

        ICASSP, a flagship conference of the IEEE Signal Processing Society (SPS), was hosted on the Greek island of Rhodes from June 04 to June 10, 2023. ICASSP 2023 marked the largest ICASSP in history, boasting over 4000 participants and 6128 submitted papers, out of which 2709 were accepted.
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    •  AWARD    Best Paper Award of 2022 IPSJ Transactions on Consumer Devices & Systems
      Date: March 27, 2023
      Awarded to: Yukimasa Nagai, Takenori Sumi, Jianlin Guo, Philip Orlik, Hiroshi Mineno
      MERL Contacts: Jianlin Guo; Philip V. Orlik; Kieran Parsons
      Research Areas: Communications, Signal Processing
      Brief
      • MELCO/MERL research paper “IEEE 802.19.3 Standardization for Coexistence of IEEE 802.11ah and IEEE 802.15.4g Systems in Sub-1GHz Frequency Bands” has won the Best Paper Award of the 2022 IPSJ Transactions on Consumer Devices and Systems. The Information Processing Society of Japan (IPSJ) award was established in 1970 and is conferred on the authors of particularly excellent papers, which are published in the IPSJ journals and transactions. Our paper was published by the IPSJ Transaction on Consumer Device and System Vol. 29 in 2021 and authors are Yukimasa Nagai, Takenori Sumi, Jianlin Guo, Philip Orlik and Hiroshi Mineno.
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  • News & Events

    •  NEWS    Toshiaki Koike-Akino to give a tutorial talk at ISIT 2025 Quantum Hackathon
      Date: June 22, 2025
      Where: IEEE International Symposium on Information Theory (ISIT)
      MERL Contact: Toshiaki Koike-Akino
      Research Areas: Artificial Intelligence, Communications, Data Analytics, Machine Learning, Optimization, Signal Processing, Human-Computer Interaction, Information Security
      Brief
      • Toshiaki Koike-Akino is invited to present a tutorial talk at IEEE ISIT 2025 Quantum Hackathon, to be held at Ann Arbor, Michigan, USA. The talk, entitled "Emerging Quantum AI Technology", will discuss the recent trends, challenges, and applications of quantum artificial intelligence (QAI) technologies.

        The ISIT 2025 Quantum Hackathon invites participants to explore the intersection of quantum computing and information theory. Participants will work with quantum simulators, available quantum hardware, and state-of-the-art development kits to create innovative solutions that connect quantum advancements with challenges in communication and signal processing.

        The IEEE International Symposium on Information Theory (ISIT) is the flagship conference of the IEEE Information Theory Society. The symposium centers around the presentation in all of the areas of information theory, including source and channel coding, communication theory and systems, cryptography and security, detection and estimation, networks, pattern recognition and learning, statistics, stochastic processes and complexity, and signal processing.
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    •  NEWS    MERL Papers and Workshops at CVPR 2025
      Date: June 11, 2025 - June 15, 2025
      Where: Nashville, TN, USA
      MERL Contacts: Matthew Brand; Moitreya Chatterjee; Anoop Cherian; François Germain; Michael J. Jones; Toshiaki Koike-Akino; Jing Liu; Suhas Lohit; Tim K. Marks; Pedro Miraldo; Kuan-Chuan Peng; Naoko Sawada; Pu (Perry) Wang; Ye Wang
      Research Areas: Artificial Intelligence, Computer Vision, Machine Learning, Signal Processing, Speech & Audio
      Brief
      • MERL researchers are presenting 2 conference papers, co-organizing two workshops, and presenting 7 workshop papers at the IEEE Conference on Computer Vision and Pattern Recognition (CVPR) 2025 conference, which will be held in Nashville, TN, USA from June 11-15, 2025. CVPR is one of the most prestigious and competitive international conferences in the area of computer vision. Details of MERL contributions are provided below:


        Main Conference Papers:

        1. "UWAV: Uncertainty-weighted Weakly-supervised Audio-Visual Video Parsing" by Y.H. Lai, J. Ebbers, Y. F. Wang, F. Germain, M. J. Jones, M. Chatterjee

        This work deals with the task of weakly‑supervised Audio-Visual Video Parsing (AVVP) and proposes a novel, uncertainty-aware algorithm called UWAV towards that end. UWAV works by producing more reliable segment‑level pseudo‑labels while explicitly weighting each label by its prediction uncertainty. This uncertainty‑aware training, combined with a feature‑mixup regularization scheme, promotes inter‑segment consistency in the pseudo-labels. As a result, UWAV achieves state‑of‑the‑art performance on two AVVP datasets across multiple metrics, demonstrating both effectiveness and strong generalizability.

        Paper: https://www.merl.com/publications/TR2025-072

        2. "TailedCore: Few-Shot Sampling for Unsupervised Long-Tail Noisy Anomaly Detection" by Y. G. Jung, J. Park, J. Yoon, K.-C. Peng, W. Kim, A. B. J. Teoh, and O. Camps.

        This work tackles unsupervised anomaly detection in complex scenarios where normal data is noisy and has an unknown, imbalanced class distribution. Existing models face a trade-off between robustness to noise and performance on rare (tail) classes. To address this, the authors propose TailSampler, which estimates class sizes from embedding similarities to isolate tail samples. Using TailSampler, they develop TailedCore, a memory-based model that effectively captures tail class features while remaining noise-robust, outperforming state-of-the-art methods in extensive evaluations.

        paper: https://www.merl.com/publications/TR2025-077


        MERL Co-Organized Workshops:

        1. Multimodal Algorithmic Reasoning (MAR) Workshop, organized by A. Cherian, K.-C. Peng, S. Lohit, H. Zhou, K. Smith, L. Xue, T. K. Marks, and J. Tenenbaum.

        Workshop link: https://marworkshop.github.io/cvpr25/

        2. The 6th Workshop on Fair, Data-Efficient, and Trusted Computer Vision, organized by N. Ratha, S. Karanam, Z. Wu, M. Vatsa, R. Singh, K.-C. Peng, M. Merler, and K. Varshney.

        Workshop link: https://fadetrcv.github.io/2025/


        Workshop Papers:

        1. "FreBIS: Frequency-Based Stratification for Neural Implicit Surface Representations" by N. Sawada, P. Miraldo, S. Lohit, T.K. Marks, and M. Chatterjee (Oral)

        With their ability to model object surfaces in a scene as a continuous function, neural implicit surface reconstruction methods have made remarkable strides recently, especially over classical 3D surface reconstruction methods, such as those that use voxels or point clouds. Towards this end, we propose FreBIS - a neural implicit‑surface framework that avoids overloading a single encoder with every surface detail. It divides a scene into several frequency bands and assigns a dedicated encoder (or group of encoders) to each band, then enforces complementary feature learning through a redundancy‑aware weighting module. Swapping this frequency‑stratified stack into an off‑the‑shelf reconstruction pipeline markedly boosts 3D surface accuracy and view‑consistent rendering on the challenging BlendedMVS dataset.

        paper: https://www.merl.com/publications/TR2025-074

        2. "Multimodal 3D Object Detection on Unseen Domains" by D. Hegde, S. Lohit, K.-C. Peng, M. J. Jones, and V. M. Patel.

        LiDAR-based object detection models often suffer performance drops when deployed in unseen environments due to biases in data properties like point density and object size. Unlike domain adaptation methods that rely on access to target data, this work tackles the more realistic setting of domain generalization without test-time samples. We propose CLIX3D, a multimodal framework that uses both LiDAR and image data along with supervised contrastive learning to align same-class features across domains and improve robustness. CLIX3D achieves state-of-the-art performance across various domain shifts in 3D object detection.

        paper: https://www.merl.com/publications/TR2025-078

        3. "Improving Open-World Object Localization by Discovering Background" by A. Singh, M. J. Jones, K.-C. Peng, M. Chatterjee, A. Cherian, and E. Learned-Miller.

        This work tackles open-world object localization, aiming to detect both seen and unseen object classes using limited labeled training data. While prior methods focus on object characterization, this approach introduces background information to improve objectness learning. The proposed framework identifies low-information, non-discriminative image regions as background and trains the model to avoid generating object proposals there. Experiments on standard benchmarks show that this method significantly outperforms previous state-of-the-art approaches.

        paper: https://www.merl.com/publications/TR2025-058

        4. "PF3Det: A Prompted Foundation Feature Assisted Visual LiDAR 3D Detector" by K. Li, T. Zhang, K.-C. Peng, and G. Wang.

        This work addresses challenges in 3D object detection for autonomous driving by improving the fusion of LiDAR and camera data, which is often hindered by domain gaps and limited labeled data. Leveraging advances in foundation models and prompt engineering, the authors propose PF3Det, a multi-modal detector that uses foundation model encoders and soft prompts to enhance feature fusion. PF3Det achieves strong performance even with limited training data. It sets new state-of-the-art results on the nuScenes dataset, improving NDS by 1.19% and mAP by 2.42%.

        paper: https://www.merl.com/publications/TR2025-076

        5. "Noise Consistency Regularization for Improved Subject-Driven Image Synthesis" by Y. Ni., S. Wen, P. Konius, A. Cherian

        Fine-tuning Stable Diffusion enables subject-driven image synthesis by adapting the model to generate images containing specific subjects. However, existing fine-tuning methods suffer from two key issues: underfitting, where the model fails to reliably capture subject identity, and overfitting, where it memorizes the subject image and reduces background diversity. To address these challenges, two auxiliary consistency losses are porposed for diffusion fine-tuning. First, a prior consistency regularization loss ensures that the predicted diffusion noise for prior (non- subject) images remains consistent with that of the pretrained model, improving fidelity. Second, a subject consistency regularization loss enhances the fine-tuned model’s robustness to multiplicative noise modulated latent code, helping to preserve subject identity while improving diversity. Our experimental results demonstrate the effectiveness of our approach in terms of image diversity, outperforming DreamBooth in terms of CLIP scores, background variation, and overall visual quality.

        paper: https://www.merl.com/publications/TR2025-073

        6. "LatentLLM: Attention-Aware Joint Tensor Compression" by T. Koike-Akino, X. Chen, J. Liu, Y. Wang, P. Wang, M. Brand

        We propose a new framework to convert a large foundation model such as large language models (LLMs)/large multi- modal models (LMMs) into a reduced-dimension latent structure. Our method uses a global attention-aware joint tensor decomposition to significantly improve the model efficiency. We show the benefit on several benchmark including multi-modal reasoning tasks.

        paper: https://www.merl.com/publications/TR2025-075

        7. "TuneComp: Joint Fine-Tuning and Compression for Large Foundation Models" by T. Koike-Akino, X. Chen, J. Liu, Y. Wang, P. Wang, M. Brand

        To reduce model size during post-training, compression methods, including knowledge distillation, low-rank approximation, and pruning, are often applied after fine- tuning the model. However, sequential fine-tuning and compression sacrifices performance, while creating a larger than necessary model as an intermediate step. In this work, we aim to reduce this gap, by directly constructing a smaller model while guided by the downstream task. We propose to jointly fine-tune and compress the model by gradually distilling it to a pruned low-rank structure. Experiments demonstrate that joint fine-tuning and compression significantly outperforms other sequential compression methods.

        paper: https://www.merl.com/publications/TR2025-079
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  • Research Highlights

  • Internships

    • EA0151: Internship - Physics-informed machine learning

      MERL is looking for a self-motivated intern to work on physics-informed machine learning with application to electric machine condition monitoring and predictive maintenance. The ideal candidate would be a Ph.D. student in electrical engineering or computer science with solid research background in electric machines, signal processing, and machine learning. Proficiency in Python and Matlab is required. The intern is expected to collaborate with MERL researchers to build machine learning model for multi-modal data analysis, prepare technical reports, and draft manuscripts for scientific publications. The total duration is anticipated to be 3-6 months. The start date is flexible. This internship requires work that can only be done at MERL.

    • CI0082: Internship - Quantum AI

      MERL is excited to announce an internship opportunity in the field of Quantum Machine Learning (QML) and Quantum AI (QAI). We are seeking a highly motivated and talented individual to join our research team. This is an exciting opportunity to make a real impact in the field of quantum computing and AI, with the aim of publishing at leading research venues.

      Responsibilities:

      • Conduct cutting-edge research in quantum machine learning.
      • Collaborate with a team of experts in quantum computing, deep learning, and signal processing.
      • Develop and implement algorithms using PyTorch and PennyLane.
      • Publish research results at leading research venues.

      Qualifications:

      • Currently pursuing a PhD or a post-graduate researcher in a relevant field.
      • Strong background and solid publication records in quantum computing, deep learning, and signal processing.
      • Proficient programming skills in PyTorch and PennyLane are highly desirable.

      What We Offer:

      • An opportunity to work on groundbreaking research in a leading research lab.
      • Collaboration with a team of experienced researchers.
      • A stimulating and supportive work environment.

      If you are passionate about quantum machine learning and meet the above qualifications, we encourage you to apply. Please submit your resume and a brief cover letter detailing your research experience and interests. Join us at MERL and contribute to the future of quantum machine learning!

    • EA0069: Internship - PWM inverter switching loss reduction

      MERL is looking for a self-motivated intern to work on PWM inverter simulation and design. The ideal candidate would be a Ph.D. candidate in electrical engineering with solid research background in power electronics, control, and optimization. Experience in switching loss reduction modulation is desired. The intern is expected to collaborate with MERL researchers to carry out simulations, optimize design, analyze results, and prepare manuscripts for scientific publications. The total duration is 3 months.

      Required Specific Experience

      • Experience with simulation tools for PWM inverter design.


    See All Internships for Signal Processing

  • Openings


    See All Openings at MERL

  • Recent Publications

    •  Guo, P., Liu, D., Wang, B., Wang, Y., Inoue, H., Kanemaru, M., "Deep Generalized Canonical Correlation Analysis for Motor Fault Diagnosis", 2025 IEEE Industry Applications Society Annual Meeting (IAS), June 2025.
      BibTeX TR2025-085 PDF
      • @inproceedings{Guo2025jun2,
      • author = {Guo, Peikun and Liu, Dehong and Wang, Bingnan and Wang, Yebin and Inoue, Hiroshi and Kanemaru, Makoto},
      • title = {{Deep Generalized Canonical Correlation Analysis for Motor Fault Diagnosis}},
      • booktitle = {2025 IEEE Industry Applications Society Annual Meeting (IAS)},
      • year = 2025,
      • month = jun,
      • url = {https://www.merl.com/publications/TR2025-085}
      • }
    •  Abdallah, A., Zhou, S., Wang, P., "IEEE 802.11bf Multistatic Sensing with Unsynchronized Receivers", IEEE Statistical Signal Processing Workshop (SSP), June 2025.
      BibTeX TR2025-080 PDF
      • @inproceedings{Abdallah2025jun,
      • author = {Abdallah, Ayah and Zhou, Shengli and Wang, Pu},
      • title = {{IEEE 802.11bf Multistatic Sensing with Unsynchronized Receivers}},
      • booktitle = {IEEE Statistical Signal Processing Workshop (SSP)},
      • year = 2025,
      • month = jun,
      • url = {https://www.merl.com/publications/TR2025-080}
      • }
    •  Teh, A., Ali, W.H., Rapp, J., Mansour, H., "Indoor Airflow Imaging Using Physics-Informed Background-Oriented Schlieren Tomography", International Symposium on Computational Sensing (ISCS), June 2025.
      BibTeX TR2025-071 PDF
      • @inproceedings{Teh2025jun,
      • author = {Teh, Arjun and Ali, Wael H. and Rapp, Joshua and Mansour, Hassan},
      • title = {{Indoor Airflow Imaging Using Physics-Informed Background-Oriented Schlieren Tomography}},
      • booktitle = {International Symposium on Computational Sensing (ISCS)},
      • year = 2025,
      • month = jun,
      • url = {https://www.merl.com/publications/TR2025-071}
      • }
    •  Ding, Q., Oppenheim, A.V., Boufounos, P.T., Gustavsson, S., Grover, J.A., Baran, T.A., Oliver, W.D., "Pulse Design of Baseband Flux Control for Adiabatic Controlled-Phase Gates in Superconducting Circuits", Physical Review Applied, June 2025.
      BibTeX TR2025-070 PDF
      • @article{Ding2025jun,
      • author = {Ding, Qi and Oppenheim, Alan V. and Boufounos, Petros T. and Gustavsson, Simon and Grover, Jeffrey A. and Baran, Thomas A. and Oliver, William D.},
      • title = {{Pulse Design of Baseband Flux Control for Adiabatic Controlled-Phase Gates in Superconducting Circuits}},
      • journal = {Physical Review Applied},
      • year = 2025,
      • month = jun,
      • url = {https://www.merl.com/publications/TR2025-070}
      • }
    •  Fujihashi, T., Koike-Akino, T., Watanabe, T., "Rateless Deep Joint Source Channel Coding for 3D Point Cloud", IEEE Access, DOI: 10.1109/​ACCESS.2025.3546514, Vol. 13, pp. 39585-39599, June 2025.
      BibTeX TR2025-069 PDF
      • @article{Fujihashi2025jun,
      • author = {Fujihashi, Takuya and Koike-Akino, Toshiaki and Watanabe, Takashi},
      • title = {{Rateless Deep Joint Source Channel Coding for 3D Point Cloud}},
      • journal = {IEEE Access},
      • year = 2025,
      • volume = 13,
      • pages = {39585--39599},
      • month = jun,
      • doi = {10.1109/ACCESS.2025.3546514},
      • issn = {2169-3536},
      • url = {https://www.merl.com/publications/TR2025-069}
      • }
    •  Zhou, Z., Di Cairano, S., Wang, Y., Berntorp, K., "Simultaneous Collision Detection and Force Estimation for Dynamic Quadrupedal Locomotion", IEEE International Conference on Robotics and Automation (ICRA), May 2025.
      BibTeX TR2025-063 PDF
      • @inproceedings{Zhou2025may,
      • author = {Zhou, Ziyi and {Di Cairano}, Stefano and Wang, Yebin and Berntorp, Karl},
      • title = {{Simultaneous Collision Detection and Force Estimation for Dynamic Quadrupedal Locomotion}},
      • booktitle = {IEEE International Conference on Robotics and Automation (ICRA)},
      • year = 2025,
      • month = may,
      • url = {https://www.merl.com/publications/TR2025-063}
      • }
    •  Pan, C."., Brand, M., "Inverse Design of Multilayer Broadband “RGBP” Freeform Metalens for Dual-Functional Color-sorting and Polarization Imaging", Conference on Lasers and Electro-Optics (CLEO), May 2025.
      BibTeX TR2025-055 PDF
      • @inproceedings{Pan2025may,
      • author = {Pan, Cindy "Hsin" and Brand, Matthew},
      • title = {{Inverse Design of Multilayer Broadband “RGBP” Freeform Metalens for Dual-Functional Color-sorting and Polarization Imaging}},
      • booktitle = {Conference on Lasers and Electro-Optics (CLEO)},
      • year = 2025,
      • month = may,
      • url = {https://www.merl.com/publications/TR2025-055}
      • }
    •  Kitichotkul, R., Rapp, J., Ma, Y., Mansour, H., "Simultaneous Range and Velocity Measurement with Doppler Single-Photon Lidar", Optica, DOI: 10.1364/​OPTICA.555984, Vol. 12, pp. 604-613, April 2025.
      BibTeX TR2025-050 PDF
      • @article{Kitichotkul2025apr,
      • author = {Kitichotkul, Ruangrawee and Rapp, Joshua and Ma, Yanting and Mansour, Hassan},
      • title = {{Simultaneous Range and Velocity Measurement with Doppler Single-Photon Lidar}},
      • journal = {Optica},
      • year = 2025,
      • volume = 12,
      • pages = {604--613},
      • month = apr,
      • doi = {10.1364/OPTICA.555984},
      • url = {https://www.merl.com/publications/TR2025-050}
      • }
    See All Publications for Signal Processing

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