Pu (Perry) Wang

Pu (Perry) Wang
Perry's Quick Links

  • Biography

    Pu (Perry) Wang was an intern at the Mitsubishi Electric Research Laboratories (MERL), Cambridge, MA, in the summer of 2010. Before returning to MERL in 2016, he was a Research Scientist at Schlumberger-Doll Research, Cambridge, MA, contributing to developments of logging-while-drilling Acoustics/NMR products. His research focuses on signal processing, Bayesian inference, deep learning, and their industrial applications. He received the IEEE Jack Neubauer Memorial Award from IEEE Vehicular Technology Society in 2013 and was recognized as a Distinguished Speaker by the Society of Petrophysicists and Well Log Analysts (SPWLA) for his contributions to borehole NMR imaging in 2017. Current or past roles include serving as a Senior Area Editor (SAE) for IEEE Signal Processing Letters, a Guest Editor for IEEE Signal Processing Magazine and IEEE Sensors Journal, a Member of the IEEE SPS Signal Processing Theory and Methods (SPTM) Technical Committee, a Member of the IEEE ComSoc integrated sensing and communication emerging technology initiative (ISAC-ETI), and a voting member of the IEEE 802.11 Standards Association.

  • Recent News & Events

    •  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; 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
    •  
    •  EVENT    MERL Contributes to ICASSP 2025
      Date: Sunday, April 6, 2025 - Friday, April 11, 2025
      Location: Hyderabad, India
      MERL Contacts: Wael H. Ali; Petros T. Boufounos; Radu Corcodel; François Germain; Chiori Hori; Siddarth Jain; Devesh K. Jha; Toshiaki Koike-Akino; Jonathan Le Roux; Yanting Ma; Hassan Mansour; Yoshiki Masuyama; Joshua Rapp; Diego Romeres; Anthony Vetro; Pu (Perry) Wang; Gordon Wichern
      Research Areas: Artificial Intelligence, Communications, Computational Sensing, Electronic and Photonic Devices, Machine Learning, Robotics, Signal Processing, Speech & Audio
      Brief
      • MERL has made numerous contributions to both the organization and technical program of ICASSP 2025, which is being held in Hyderabad, India from April 6-11, 2025.

        Sponsorship

        MERL is proud to be a Silver Patron of the conference and will participate in the student job fair on Thursday, April 10. Please join this session to learn more about employment opportunities at MERL, including openings for research scientists, post-docs, and interns.

        MERL is pleased to be the sponsor of two IEEE Awards that will be presented at the conference. We congratulate Prof. Björn Erik Ottersten, the recipient of the 2025 IEEE Fourier Award for Signal Processing, and Prof. Shrikanth Narayanan, the recipient of the 2025 IEEE James L. Flanagan Speech and Audio Processing Award. Both awards will be presented in-person at ICASSP by Anthony Vetro, MERL President & CEO.

        Technical Program

        MERL is presenting 15 papers in the main conference on a wide range of topics including source separation, sound event detection, sound anomaly detection, speaker diarization, music generation, robot action generation from video, indoor airflow imaging, WiFi sensing, Doppler single-photon Lidar, optical coherence tomography, and radar imaging. Another paper on spatial audio will be presented at the Generative Data Augmentation for Real-World Signal Processing Applications (GenDA) Satellite Workshop.

        MERL Researchers Petros Boufounos and Hassan Mansour will present a Tutorial on “Computational Methods in Radar Imaging” in the afternoon of Monday, April 7.

        Petros Boufounos will also be giving an industry talk on Thursday April 10 at 12pm, on “A Physics-Informed Approach to Sensing".

        About ICASSP

        ICASSP is the flagship conference of the IEEE Signal Processing Society, and the world's largest and most comprehensive technical conference focused on the research advances and latest technological development in signal and information processing. The event has been attracting more than 4000 participants each year.
    •  

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  • Awards

    •  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.
    •  
    See All Awards for MERL

  • Research Highlights

  • MERL Publications

    •  Santra, A., Wang, P., Shaker, G., Mysore, B.S., Dolmans, G., Chen, Y., Shariati, N., Pandharipande, A., "Machine Learning-Powered Radio Frequency Sensing: A Review", IEEE Sensors Journal, June 2025.
      BibTeX TR2025-099 PDF
      • @article{Santra2025jun,
      • author = {Santra, Avik and Wang, Pu and Shaker, George and Mysore, Bhavani Shankar and Dolmans, Guido and Chen, Yan and Shariati, Negin and Pandharipande, Ashish},
      • title = {{Machine Learning-Powered Radio Frequency Sensing: A Review}},
      • journal = {IEEE Sensors Journal},
      • year = 2025,
      • month = jun,
      • url = {https://www.merl.com/publications/TR2025-099}
      • }
    •  Chen, X., Liu, J., Wang, Y., Brand, M., Wang, P., Koike-Akino, T., "TuneComp: Joint Fine-Tuning and Compression for Large Foundation Models", IEEE Conference on Computer Vision and Pattern Recognition (CVPR) workshop on Efficient and On-Device Generation, June 2025.
      BibTeX TR2025-079 PDF
      • @inproceedings{Chen2025jun,
      • author = {Chen, Xiangyu and Liu, Jing and Wang, Ye and Brand, Matthew and Wang, Pu and Koike-Akino, Toshiaki},
      • title = {{TuneComp: Joint Fine-Tuning and Compression for Large Foundation Models}},
      • booktitle = {IEEE Conference on Computer Vision and Pattern Recognition (CVPR) workshop on Efficient and On-Device Generation},
      • year = 2025,
      • month = jun,
      • url = {https://www.merl.com/publications/TR2025-079}
      • }
    •  Koike-Akino, T., Chen, X., Liu, J., Wang, Y., Wang, P., Brand, M., "LatentLLM: Attention-Aware Joint Tensor Compression", IEEE Conference on Computer Vision and Pattern Recognition (CVPR) Workshop, June 2025.
      BibTeX TR2025-075 PDF
      • @inproceedings{Koike-Akino2025jun,
      • author = {Koike-Akino, Toshiaki and Chen, Xiangyu and Liu, Jing and Wang, Ye and Wang, Pu and Brand, Matthew},
      • title = {{LatentLLM: Attention-Aware Joint Tensor Compression}},
      • booktitle = {IEEE Conference on Computer Vision and Pattern Recognition (CVPR) Workshop},
      • year = 2025,
      • month = jun,
      • url = {https://www.merl.com/publications/TR2025-075}
      • }
    •  Guo, J., Parsons, K., Nagai, Y., Sumi, T., Sakaguchi, N., Wang, P., Orlik, P.V., "Modeling Multipath TCP Over Heterogeneous WiFi and 5G Networks", IEEE International Conference on Communications (ICC), June 2025.
      BibTeX TR2025-084 PDF
      • @inproceedings{Guo2025jun,
      • author = {Guo, Jianlin and Parsons, Kieran and Nagai, Yukimasa and Sumi, Takenori and Sakaguchi, Naotaka and Wang, Pu and Orlik, Philip V.},
      • title = {{Modeling Multipath TCP Over Heterogeneous WiFi and 5G Networks}},
      • booktitle = {IEEE International Conference on Communications (ICC)},
      • year = 2025,
      • month = jun,
      • url = {https://www.merl.com/publications/TR2025-084}
      • }
    •  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}
      • }
    See All MERL Publications for Perry

  • Other Publications

    •  P. Wang, S. Bose and S.K. Sinha, "Dipole Shear Anisotropy Using Logging-While-Drilling Sonic Tools", SPWLA 57th Annual Logging Symposium, June 2016.
      BibTeX
      • @Inproceedings{WangBose2016,
      • author = {Wang, P. and Bose, S. and Sinha, S.K.},
      • title = {Dipole Shear Anisotropy Using Logging-While-Drilling Sonic Tools},
      • booktitle = {SPWLA 57th Annual Logging Symposium},
      • year = 2016,
      • month = jun
      • }
    •  P. Wang, V. Jain and L. Venkataramanan, "Sparse Bayesian T1-T2 Inversion from Borehole NMR Measurements", SPWLA 57th Annual Logging Symposium, June 2016.
      BibTeX
      • @Inproceedings{WangJain2016,
      • author = {Wang, P. and Jain, V. and Venkataramanan, L.},
      • title = {Sparse Bayesian T1-T2 Inversion from Borehole NMR Measurements},
      • booktitle = {SPWLA 57th Annual Logging Symposium},
      • year = 2016,
      • month = jun
      • }
    •  P. Wang, H. Li, Besson O. and Fang J., "Knowledge-aided hyperparameter-free Bayesian detection in stochastic homogeneous environments", IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), March 2016.
      BibTeX
      • @Inproceedings{WangLi2016,
      • author = {Wang, P. and Li, H. and O., Besson and J., Fang},
      • title = {Knowledge-aided hyperparameter-free {Bayesian} detection in stochastic homogeneous environments},
      • booktitle = {IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)},
      • year = 2016,
      • month = mar
      • }
    •  J. Fang, Y. Shen, H. Li and P. Wang, "Pattern-Coupled Sparse Bayesian Learning for Recovery of Block-Sparse Signals", IEEE Transactions on Signal Processing, Vol. 63, No. 2, pp. 360-372, Jan 2015.
      BibTeX
      • @Article{6967808,
      • author = {Fang, J. and Shen, Y. and Li, H. and Wang, P.},
      • title = {Pattern-Coupled Sparse Bayesian Learning for Recovery of Block-Sparse Signals},
      • journal = {IEEE Transactions on Signal Processing},
      • year = 2015,
      • volume = 63,
      • number = 2,
      • pages = {360--372},
      • month = {Jan}
      • }
    •  P. Wang, H. Li and B. Himed, Bayesian Radar Detection in Interference, SciTech Publishing, 2015.
      BibTeX
      • @Book{WangLiHimed16,
      • author = {Wang, P. and Li, H. and Himed, B.},
      • title = {Bayesian Radar Detection in Interference},
      • booktitle = {Modern Radar Detection Theory},
      • year = 2015,
      • editor = {De Maio, A. and Greco, M.S.},
      • chapter = 5,
      • pages = {133--162},
      • address = {Edison, NJ},
      • publisher = {SciTech Publishing}
      • }
    •  P. Wang and S. Bose, "Cramer-Rao Bounds for Broadband Dispersion Extraction of Borehole Acoustic Modes", IEEE Signal Processing Letters, Vol. 21, No. 9, pp. 1083-1087, Sept 2014.
      BibTeX
      • @Article{6813598,
      • author = {Wang, P. and Bose, S.},
      • title = {Cramer-Rao Bounds for Broadband Dispersion Extraction of Borehole Acoustic Modes},
      • journal = {IEEE Signal Processing Letters},
      • year = 2014,
      • volume = 21,
      • number = 9,
      • pages = {1083--1087},
      • month = {Sept}
      • }
    •  P. Wang, Z. Wang, H. Li and B. Himed, "Knowledge-Aided Parametric Adaptive Matched Filter With Automatic Combining for Covariance Estimation", IEEE Transactions on Signal Processing, Vol. 62, No. 18, pp. 4713-4722, Sept 2014.
      BibTeX
      • @Article{6853408,
      • author = {Wang, P. and Wang, Z. and Li, H. and Himed, B.},
      • title = {Knowledge-Aided Parametric Adaptive Matched Filter With Automatic Combining for Covariance Estimation},
      • journal = {IEEE Transactions on Signal Processing},
      • year = 2014,
      • volume = 62,
      • number = 18,
      • pages = {4713--4722},
      • month = {Sept}
      • }
    •  P. Wang, H. Li and B. Himed, "A Parametric Moving Target Detector for Distributed MIMO Radar in Non-Homogeneous Environment", IEEE Transactions on Signal Processing, Vol. 61, No. 9, pp. 2282-2294, May 2013.
      BibTeX
      • @Article{6450110,
      • author = {Wang, P. and Li, H. and Himed, B.},
      • title = {A Parametric Moving Target Detector for Distributed MIMO Radar in Non-Homogeneous Environment},
      • journal = {IEEE Transactions on Signal Processing},
      • year = 2013,
      • volume = 61,
      • number = 9,
      • pages = {2282--2294},
      • month = may
      • }
    •  I. Djurovic, M. Simeunovic, S. Djukanovic and P. Wang, "A Hybrid CPF-HAF Estimation of Polynomial-Phase Signals: Detailed Statistical Analysis", IEEE Transactions on Signal Processing, Vol. 60, No. 10, pp. 5010-5023, Oct 2012.
      BibTeX
      • @Article{6222371,
      • author = {Djurovic, I. and Simeunovic, M. and Djukanovic, S. and Wang, P.},
      • title = {A Hybrid CPF-HAF Estimation of Polynomial-Phase Signals: Detailed Statistical Analysis},
      • journal = {IEEE Transactions on Signal Processing},
      • year = 2012,
      • volume = 60,
      • number = 10,
      • pages = {5010--5023},
      • month = {Oct}
      • }
    •  P. Wang, J. Fang, H. Li and B. Himed, "Detection With Target-Induced Subspace Interference", IEEE Signal Processing Letters, Vol. 19, No. 7, pp. 403-406, July 2012.
      BibTeX
      • @Article{6193410,
      • author = {Wang, P. and Fang, J. and Li, H. and Himed, B.},
      • title = {Detection With Target-Induced Subspace Interference},
      • journal = {IEEE Signal Processing Letters},
      • year = 2012,
      • volume = 19,
      • number = 7,
      • pages = {403--406},
      • month = jul
      • }
    •  P. Wang, H. Li and B. Himed, "Knowledge-Aided Parametric Tests for Multichannel Adaptive Signal Detection", IEEE Transactions on Signal Processing, Vol. 59, No. 12, pp. 5970-5982, Dec 2011.
      BibTeX
      • @Article{6020816,
      • author = {Wang, P. and Li, H. and Himed, B.},
      • title = {Knowledge-Aided Parametric Tests for Multichannel Adaptive Signal Detection},
      • journal = {IEEE Transactions on Signal Processing},
      • year = 2011,
      • volume = 59,
      • number = 12,
      • pages = {5970--5982},
      • month = {Dec}
      • }
    •  P. Wang, H. Li and B. Himed, "Moving Target Detection Using Distributed MIMO Radar in Clutter With Nonhomogeneous Power", IEEE Transactions on Signal Processing, Vol. 59, No. 10, pp. 4809-4820, Oct 2011.
      BibTeX
      • @Article{5934613,
      • author = {Wang, P. and Li, H. and Himed, B.},
      • title = {Moving Target Detection Using Distributed MIMO Radar in Clutter With Nonhomogeneous Power},
      • journal = {IEEE Transactions on Signal Processing},
      • year = 2011,
      • volume = 59,
      • number = 10,
      • pages = {4809--4820},
      • month = {Oct}
      • }
    •  P. Wang, H. Li and B. Himed, "Parametric Rao Tests for Multichannel Adaptive Detection in Partially Homogeneous Environment", IEEE Transactions on Aerospace and Electronic Systems, Vol. 47, No. 3, pp. 1850-1862, July 2011.
      BibTeX
      • @Article{5937269,
      • author = {Wang, P. and Li, H. and Himed, B.},
      • title = {Parametric Rao Tests for Multichannel Adaptive Detection in Partially Homogeneous Environment},
      • journal = {IEEE Transactions on Aerospace and Electronic Systems},
      • year = 2011,
      • volume = 47,
      • number = 3,
      • pages = {1850--1862},
      • month = jul
      • }
    •  P. Wang, H. Li, I. Djurovic and B. Himed, "Integrated Cubic Phase Function for Linear FM Signal Analysis", IEEE Transactions on Aerospace and Electronic Systems, Vol. 46, No. 3, pp. 963-977, July 2010.
      BibTeX
      • @Article{5545167,
      • author = {Wang, P. and Li, H. and Djurovic, I. and Himed, B.},
      • title = {Integrated Cubic Phase Function for Linear FM Signal Analysis},
      • journal = {IEEE Transactions on Aerospace and Electronic Systems},
      • year = 2010,
      • volume = 46,
      • number = 3,
      • pages = {963--977},
      • month = jul
      • }
    •  P. Wang, J. Fang, N. Han and H. Li, "Multiantenna-Assisted Spectrum Sensing for Cognitive Radio", IEEE Transactions on Vehicular Technology, Vol. 59, No. 4, pp. 1791-1800, May 2010.
      BibTeX
      • @Article{5345867,
      • author = {Wang, P. and Fang, J. and Han, N. and Li, H.},
      • title = {Multiantenna-Assisted Spectrum Sensing for Cognitive Radio},
      • journal = {IEEE Transactions on Vehicular Technology},
      • year = 2010,
      • volume = 59,
      • number = 4,
      • pages = {1791--1800},
      • month = may
      • }
    •  P. Wang, H. Li, I. Djurovic and B. Himed, "Performance of Instantaneous Frequency Rate Estimation Using High-Order Phase Function", IEEE Transactions on Signal Processing, Vol. 58, No. 4, pp. 2415-2421, April 2010.
      BibTeX
      • @Article{5290077,
      • author = {Wang, P. and Li, H. and Djurovic, I. and Himed, B.},
      • title = {Performance of Instantaneous Frequency Rate Estimation Using High-Order Phase Function},
      • journal = {IEEE Transactions on Signal Processing},
      • year = 2010,
      • volume = 58,
      • number = 4,
      • pages = {2415--2421},
      • month = apr
      • }
    •  P. Wang, H. Li and B. Himed, "A Bayesian Parametric Test for Multichannel Adaptive Signal Detection in Nonhomogeneous Environments", IEEE Signal Processing Letters, Vol. 17, No. 4, pp. 351-354, April 2010.
      BibTeX
      • @Article{5371946,
      • author = {Wang, P. and Li, H. and Himed, B.},
      • title = {A Bayesian Parametric Test for Multichannel Adaptive Signal Detection in Nonhomogeneous Environments},
      • journal = {IEEE Signal Processing Letters},
      • year = 2010,
      • volume = 17,
      • number = 4,
      • pages = {351--354},
      • month = apr
      • }
    •  P. Wang, H. Li and B. Himed, "A New Parametric GLRT for Multichannel Adaptive Signal Detection", IEEE Transactions on Signal Processing, Vol. 58, No. 1, pp. 317-325, Jan 2010.
      BibTeX
      • @Article{5210195,
      • author = {Wang, P. and Li, H. and Himed, B.},
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      • }

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  • MERL Issued Patents

    • Title: "System and Method for Detecting an Object in a Scene"
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      Issue Date: Jan 28, 2025
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      Inventors: Wang, Pu; Jin, Sian
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      Issue Date: Jan 28, 2025
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      Inventors: Millar, David; Atalar, Okan; Kojima, Keisuke; Koike-Akino, Toshiaki; Wang, Pu; Parsons, Kieran
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      Issue Date: Nov 26, 2024
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      Issue Date: Oct 1, 2024
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      Inventors: Guo, Jianlin; Wang, Pu; Parsons, Kieran; Orlik, Philip V.; Nagai, Yukimasa; Sumi, Takenori
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      Issue Date: Aug 13, 2024
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      Inventors: Wang, Pu; Yu, Jianyuan; Koike-Akino, Toshiaki; Orlik, Philip V.
      Patent No.: 12,063,620
      Issue Date: Aug 13, 2024
    • Title: "System and Method for Active Carrier Sense Based CSMA/CA for IEEE 802.15.4 System to Avoid Packet Discard Caused by Interference"
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      Patent No.: 11,985,705
      Issue Date: May 14, 2024
    • Title: "System and Method for Tracking an Expanded State of a Moving Object Using a Compound Measurement Model"
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      Patent No.: 11,914,023
      Issue Date: Feb 27, 2024
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      Issue Date: Feb 13, 2024
    • Title: "System and Method for Tracking Expanded State of Moving Object with Model Geometry Learning"
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      Patent No.: 11,879,964
      Issue Date: Jan 23, 2024
    • Title: "System and Method for Tracking Expanded State of an Object"
      Inventors: Wang, Pu; Xia, Yuxuan; Berntorp, Karl; Koike-Akino, Toshiaki; Mansour, Hassan; Boufounos, Petros T.; Orlik, Philip V.
      Patent No.: 11,619,494
      Issue Date: Apr 4, 2023
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      Inventors: Wang, Pu; Boufounos, Petros T.; Mansour, Hassan; Orlik, Philip V.
      Patent No.: 11,567,183
      Issue Date: Jan 31, 2023
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      Inventors: Millar, David; Wang, Pu; Parsons, Kieran; Orlik, Philip V.
      Patent No.: 11,346,932
      Issue Date: May 31, 2022
    • Title: "Optical Coherence Tomography (OCT) System for Producing Profilometry Measurements of a Specimen"
      Inventors: Millar, David; Yurdakul, Celalettin; Wang, Pu; Parsons, Kieran
      Patent No.: 11,262,184
      Issue Date: Mar 1, 2022
    • Title: "Localization using Millimeter Wave Beam Attributes for Keyless Entry Applications"
      Inventors: Koike-Akino, Toshiaki; Wang, Pu; Pajovic, Milutin; Orlik, Philip V.
      Patent No.: 11,249,181
      Issue Date: Feb 15, 2022
    • Title: "Localization using Millimeter Wave Beam Attributes"
      Inventors: Pajovic, Milutin; Koike-Akino, Toshiaki; Wang, Pu; Orlik, Philip V.
      Patent No.: 11,122,397
      Issue Date: Sep 14, 2021
    • Title: "Symbol Detection of Massive MIMO Systems with Unknown Symbol-Dependent Transmit-Side Impairments"
      Inventors: Wang, Pu; Koike-Akino, Toshiaki; Pajovic, Milutin; Orlik, Philip V.
      Patent No.: 11,121,816
      Issue Date: Sep 14, 2021
    • Title: "Learning-Based See-Through Sensing Suitable for Factory Automation"
      Inventors: Wang, Pu; Koike-Akino, Toshiaki; Orlik, Philip V.; Bose, Arindam
      Patent No.: 11,061,388
      Issue Date: Jul 13, 2021
    • Title: "Reference-Free Nonlinearity Correction for FMCW-Based Sensing Systems"
      Inventors: Wang, Pu; Millar, David; Lin, Chungwei; Parsons, Kieran; Orlik, Philip V.
      Patent No.: 10,969,465
      Issue Date: Apr 6, 2021
    • Title: "Systems and Methods for Speed Estimation of Contactless Encoder Systems"
      Inventors: Wang, Pu; Orlik, Philip V.; Sadamoto, Kota; Tsujita, Wataru
      Patent No.: 10,866,124
      Issue Date: Dec 15, 2020
    • Title: "Methods and Systems for Terahertz-Based Positioning"
      Inventors: Wang, Pu; Fu, Haoyu; Orlik, Philip V.; Koike-Akino, Toshiaki; Ma, Rui; Wang, Bingnan
      Patent No.: 10,795,151
      Issue Date: Oct 6, 2020
    • Title: "System and Method for Angular-Domain Channel Estimation of Massive MIMO System with Low-Resolution ADC with Time-Varying Thresholds"
      Inventors: Wang, Pu; Pajovic, Milutin; Orlik, Philip V.; Boufounos, Petros T.
      Patent No.: 10,541,839
      Issue Date: Jan 21, 2020
    • Title: "System and Method for Parameter Estimation of Hybrid Sinusoidal FM-Polynomial Phase Signal"
      Inventors: Wang, Pu; Orlik, Philip V.; Sadamoto, Kota; Tsujita, Wataru
      Patent No.: 10,407,274
      Issue Date: Sep 10, 2019
    • Title: "System and Method for Channel Estimation in mmWave Communications Exploiting Joint AoD-AoA Angular Spread"
      Inventors: Wang, Pu; Pajovic, Milutin; Orlik, Philip V.
      Patent No.: 10,382,230
      Issue Date: Aug 13, 2019
    • Title: "Digital Beamforming Transmitter Array System with Hardware Sharing and Reduction"
      Inventors: Wang, Bingnan; Peng, Zhengyu; Kim, Kyeong-Jin; Wang, Pu; Ma, Rui; Teo, Koon Hoo
      Patent No.: 10,270,510
      Issue Date: Apr 23, 2019
    • Title: "See-Through Sensing for Image Reconstruction of Structure of Target Object"
      Inventors: Wang, Pu; Koike-Akino, Toshiaki; Fu, Haoyu; Orlik, Philip V.
      Patent No.: 10,217,252
      Issue Date: Feb 26, 2019
    • Title: "Method for Detecting Targets Using Space-Time Adaptive Processing"
      Inventors: Pun, Man On; Wang, Pu; Sahinoglu, Zafer
      Patent No.: 8,907,841
      Issue Date: Dec 9, 2014
    • Title: "Persymmetric Parametric Adaptive Matched Filters for Detecting Targets Using Space-Time Adaptive Processing of Radar Signals"
      Inventors: Sahinoglu, Zafer; Wang, Pu; Pun, Man On
      Patent No.: 8,284,098
      Issue Date: Oct 9, 2012
    • Title: "Method for Detecting Targets Using Space-Time Adaptive Processing and Shared Knowledge of the Environment"
      Inventors: Pun, Man On; Sahinoglu, Zafer; Wang, Pu
      Patent No.: 8,138,963
      Issue Date: Mar 20, 2012
    See All Patents for MERL