Pu (Perry) Wang

  • 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 2024
      Date: June 17, 2024 - June 21, 2024
      Where: Seattle, WA
      MERL Contacts: Petros T. Boufounos; Moitreya Chatterjee; Anoop Cherian; Michael J. Jones; Toshiaki Koike-Akino; Jonathan Le Roux; Suhas Lohit; Tim K. Marks; Pedro Miraldo; Jing Liu; Kuan-Chuan Peng; Pu (Perry) Wang; Ye Wang; Matthew Brand
      Research Areas: Artificial Intelligence, Computational Sensing, Computer Vision, Machine Learning, Speech & Audio
      Brief
      • MERL researchers are presenting 5 conference papers, 3 workshop papers, and are co-organizing two workshops at the CVPR 2024 conference, which will be held in Seattle, June 17-21. CVPR is one of the most prestigious and competitive international conferences in computer vision. Details of MERL contributions are provided below.

        CVPR Conference Papers:

        1. "TI2V-Zero: Zero-Shot Image Conditioning for Text-to-Video Diffusion Models" by H. Ni, B. Egger, S. Lohit, A. Cherian, Y. Wang, T. Koike-Akino, S. X. Huang, and T. K. Marks

        This work enables a pretrained text-to-video (T2V) diffusion model to be additionally conditioned on an input image (first video frame), yielding a text+image to video (TI2V) model. Other than using the pretrained T2V model, our method requires no ("zero") training or fine-tuning. The paper uses a "repeat-and-slide" method and diffusion resampling to synthesize videos from a given starting image and text describing the video content.

        Paper: https://www.merl.com/publications/TR2024-059
        Project page: https://merl.com/research/highlights/TI2V-Zero

        2. "Long-Tailed Anomaly Detection with Learnable Class Names" by C.-H. Ho, K.-C. Peng, and N. Vasconcelos

        This work aims to identify defects across various classes without relying on hard-coded class names. We introduce the concept of long-tailed anomaly detection, addressing challenges like class imbalance and dataset variability. Our proposed method combines reconstruction and semantic modules, learning pseudo-class names and utilizing a variational autoencoder for feature synthesis to improve performance in long-tailed datasets, outperforming existing methods in experiments.

        Paper: https://www.merl.com/publications/TR2024-040

        3. "Gear-NeRF: Free-Viewpoint Rendering and Tracking with Motion-aware Spatio-Temporal Sampling" by X. Liu, Y-W. Tai, C-T. Tang, P. Miraldo, S. Lohit, and M. Chatterjee

        This work presents a new strategy for rendering dynamic scenes from novel viewpoints. Our approach is based on stratifying the scene into regions based on the extent of motion of the region, which is automatically determined. Regions with higher motion are permitted a denser spatio-temporal sampling strategy for more faithful rendering of the scene. Additionally, to the best of our knowledge, ours is the first work to enable tracking of objects in the scene from novel views - based on the preferences of a user, provided by a click.

        Paper: https://www.merl.com/publications/TR2024-042

        4. "SIRA: Scalable Inter-frame Relation and Association for Radar Perception" by R. Yataka, P. Wang, P. T. Boufounos, and R. Takahashi

        Overcoming the limitations on radar feature extraction such as low spatial resolution, multipath reflection, and motion blurs, this paper proposes SIRA (Scalable Inter-frame Relation and Association) for scalable radar perception with two designs: 1) extended temporal relation, generalizing the existing temporal relation layer from two frames to multiple inter-frames with temporally regrouped window attention for scalability; and 2) motion consistency track with a pseudo-tracklet generated from observational data for better object association.

        Paper: https://www.merl.com/publications/TR2024-041

        5. "RILA: Reflective and Imaginative Language Agent for Zero-Shot Semantic Audio-Visual Navigation" by Z. Yang, J. Liu, P. Chen, A. Cherian, T. K. Marks, J. L. Roux, and C. Gan

        We leverage Large Language Models (LLM) for zero-shot semantic audio visual navigation. Specifically, by employing multi-modal models to process sensory data, we instruct an LLM-based planner to actively explore the environment by adaptively evaluating and dismissing inaccurate perceptual descriptions.

        Paper: https://www.merl.com/publications/TR2024-043

        CVPR Workshop Papers:

        1. "CoLa-SDF: Controllable Latent StyleSDF for Disentangled 3D Face Generation" by R. Dey, B. Egger, V. Boddeti, Y. Wang, and T. K. Marks

        This paper proposes a new method for generating 3D faces and rendering them to images by combining the controllability of nonlinear 3DMMs with the high fidelity of implicit 3D GANs. Inspired by StyleSDF, our model uses a similar architecture but enforces the latent space to match the interpretable and physical parameters of the nonlinear 3D morphable model MOST-GAN.

        Paper: https://www.merl.com/publications/TR2024-045

        2. “Tracklet-based Explainable Video Anomaly Localization” by A. Singh, M. J. Jones, and E. Learned-Miller

        This paper describes a new method for localizing anomalous activity in video of a scene given sample videos of normal activity from the same scene. The method is based on detecting and tracking objects in the scene and estimating high-level attributes of the objects such as their location, size, short-term trajectory and object class. These high-level attributes can then be used to detect unusual activity as well as to provide a human-understandable explanation for what is unusual about the activity.

        Paper: https://www.merl.com/publications/TR2024-057

        MERL co-organized workshops:

        1. "Multimodal Algorithmic Reasoning Workshop" by A. Cherian, K-C. Peng, S. Lohit, M. Chatterjee, H. Zhou, K. Smith, T. K. Marks, J. Mathissen, and J. Tenenbaum

        Workshop link: https://marworkshop.github.io/cvpr24/index.html

        2. "The 5th Workshop on Fair, Data-Efficient, and Trusted Computer Vision" by K-C. Peng, et al.

        Workshop link: https://fadetrcv.github.io/2024/

        3. "SuperLoRA: Parameter-Efficient Unified Adaptation for Large Vision Models" by X. Chen, J. Liu, Y. Wang, P. Wang, M. Brand, G. Wang, and T. Koike-Akino

        This paper proposes a generalized framework called SuperLoRA that unifies and extends different variants of low-rank adaptation (LoRA). Introducing new options with grouping, folding, shuffling, projection, and tensor decomposition, SuperLoRA offers high flexibility and demonstrates superior performance up to 10-fold gain in parameter efficiency for transfer learning tasks.

        Paper: https://www.merl.com/publications/TR2024-062
    •  
    •  EVENT    MERL Contributes to ICASSP 2024
      Date: Sunday, April 14, 2024 - Friday, April 19, 2024
      Location: Seoul, South Korea
      MERL Contacts: Petros T. Boufounos; François Germain; Chiori Hori; Sameer Khurana; Toshiaki Koike-Akino; Jonathan Le Roux; Hassan Mansour; Kieran Parsons; Joshua Rapp; Anthony Vetro; Pu (Perry) Wang; Gordon Wichern; Ryoma Yataka
      Research Areas: Artificial Intelligence, Computational Sensing, Machine Learning, Robotics, Signal Processing, Speech & Audio
      Brief
      • MERL has made numerous contributions to both the organization and technical program of ICASSP 2024, which is being held in Seoul, Korea from April 14-19, 2024.

        Sponsorship and Awards

        MERL is proud to be a Bronze Patron of the conference and will participate in the student job fair on Thursday, April 18. 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. Stéphane G. Mallat, the recipient of the 2024 IEEE Fourier Award for Signal Processing, and Prof. Keiichi Tokuda, the recipient of the 2024 IEEE James L. Flanagan Speech and Audio Processing Award.

        Jonathan Le Roux, MERL Speech and Audio Senior Team Leader, will also be recognized during the Awards Ceremony for his recent elevation to IEEE Fellow.

        Technical Program

        MERL will present 13 papers in the main conference on a wide range of topics including automated audio captioning, speech separation, audio generative models, speech and sound synthesis, spatial audio reproduction, multimodal indoor monitoring, radar imaging, depth estimation, physics-informed machine learning, and integrated sensing and communications (ISAC). Three workshop papers have also been accepted for presentation on audio-visual speaker diarization, music source separation, and music generative models.

        Perry Wang is the co-organizer of the Workshop on Signal Processing and Machine Learning Advances in Automotive Radars (SPLAR), held on Sunday, April 14. It features keynote talks from leaders in both academia and industry, peer-reviewed workshop papers, and lightning talks from ICASSP regular tracks on signal processing and machine learning for automotive radar and, more generally, radar perception.

        Gordon Wichern will present an invited keynote talk on analyzing and interpreting audio deep learning models at the Workshop on Explainable Machine Learning for Speech and Audio (XAI-SA), held on Monday, April 15. He will also appear in a panel discussion on interpretable audio AI at the workshop.

        Perry Wang also co-organizes a two-part special session on Next-Generation Wi-Fi Sensing (SS-L9 and SS-L13) which will be held on Thursday afternoon, April 18. The special session includes papers on PHY-layer oriented signal processing and data-driven deep learning advances, and supports upcoming 802.11bf WLAN Sensing Standardization activities.

        Petros Boufounos is participating as a mentor in ICASSP’s Micro-Mentoring Experience Program (MiME).

        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 attracts more than 3000 participants.
    •  

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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.
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    See All Awards for MERL
  • Research Highlights

  • Internships with Perry

    • ST2083: Deep Learning for Radar Perception

      The Computation Sensing team at MERL is seeking a highly motivated intern to conduct fundamental research in radar perception. Expertise in deep learning-based object detection, multiple object tracking, data association, and representation learning (detection points, heatmaps, and raw radar waveforms) is required. Previous hands-on experience on open indoor/outdoor radar datasets is a plus. Familiarity with the concept of FMCW, MIMO, and range-Doppler-angle spectrum is an asset. The intern will collaborate with a small group of MERL researchers to develop novel algorithms, design experiments with MERL in-house testbed, and prepare results for patents and publication. The expected duration of the internship is 3 months with a flexible start date.

    See All Internships at MERL
  • MERL Publications

    •  Chen, X., Liu, J., Wang, Y., Wang, P., Brand, M., Wang, G., Koike-Akino, T., "SuperLoRA: Parameter-Efficient Unified Adaptation for Large Vision Models", IEEE Conference on Computer Vision and Pattern Recognition (CVPR), June 2024.
      BibTeX TR2024-062 PDF
      • @inproceedings{Chen2024jun,
      • author = {Chen, Xiangyu and Liu, Jing and Wang, Ye and Wang, Pu and Brand, Matthew and Wang, Guanghui and Koike-Akino, Toshiaki}},
      • title = {SuperLoRA: Parameter-Efficient Unified Adaptation for Large Vision Models},
      • booktitle = {IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},
      • year = 2024,
      • month = jun,
      • url = {https://www.merl.com/publications/TR2024-062}
      • }
    •  Yataka, R., Wang, P., Boufounos, P.T., Takahashi, R., "SIRA: Scalable Inter-frame Relation and Association for Radar Perception", IEEE Conference on Computer Vision and Pattern Recognition (CVPR), June 2024.
      BibTeX TR2024-041 PDF
      • @inproceedings{Yataka2024jun,
      • author = {Yataka, Ryoma and Wang, Pu and Boufounos, Petros T. and Takahashi, Ryuhei},
      • title = {SIRA: Scalable Inter-frame Relation and Association for Radar Perception},
      • booktitle = {IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},
      • year = 2024,
      • month = jun,
      • url = {https://www.merl.com/publications/TR2024-041}
      • }
    •  Guo, J., Parsons, K., Nagai, Y., Sumi, T., Sakaguchi, N., Tsuchida, H., Wang, P., Orlik, P.V., "Multipath TCP Over Multi-Hop Heterogeneous Wireless IoT Networks", IEEE International Conference on Communications (ICC), June 2024.
      BibTeX TR2024-071 PDF
      • @inproceedings{Guo2024jun,
      • author = {Guo, Jianlin and Parsons, Kieran and Nagai, Yukimasa and Sumi, Takenori and Sakaguchi, Naotaka and Tsuchida, Hikaru and Wang, Pu and Orlik, Philip V.}},
      • title = {Multipath TCP Over Multi-Hop Heterogeneous Wireless IoT Networks},
      • booktitle = {IEEE International Conference on Communications (ICC)},
      • year = 2024,
      • month = jun,
      • url = {https://www.merl.com/publications/TR2024-071}
      • }
    •  Vaca-Rubio, C., Wang, P., Koike-Akino, T., Wang, Y., Boufounos, P.T., Popovski, P., "Object Trajectory Estimation with Continuous-Time Neural Dynamic Learning of Millimeter-Wave Wi-Fi", IEEE Journal of Selected Topics in Signal Processing, April 2024.
      BibTeX TR2024-044 PDF
      • @article{Vaca-Rubio2024apr,
      • author = {Vaca-Rubio, Cristian and Wang, Pu and Koike-Akino, Toshiaki and Wang, Ye and Boufounos, Petros T. and Popovski, Petar},
      • title = {Object Trajectory Estimation with Continuous-Time Neural Dynamic Learning of Millimeter-Wave Wi-Fi},
      • journal = {IEEE Journal of Selected Topics in Signal Processing},
      • year = 2024,
      • month = apr,
      • url = {https://www.merl.com/publications/TR2024-044}
      • }
    •  Nagai, Y., Guo, J., Sumi, T., Parsons, K., Orlik, P.V., Rolfe, B.A., Wang, P., "Improve IEEE 802.15.4 Network Reliability by Suspendable CSMA/CA", IEEE Wireless Communications and Networking Conference (WCNC), April 2024.
      BibTeX TR2024-039 PDF
      • @inproceedings{Nagai2024apr,
      • author = {Nagai, Yukimasa and Guo, Jianlin and Sumi, Takenori and Parsons, Kieran and Orlik, Philip V. and Rolfe, Benjamin A. and Wang, Pu},
      • title = {Improve IEEE 802.15.4 Network Reliability by Suspendable CSMA/CA},
      • booktitle = {IEEE Wireless Communications and Networking Conference (WCNC)},
      • year = 2024,
      • month = apr,
      • url = {https://www.merl.com/publications/TR2024-039}
      • }
    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.},
      • title = {A New Parametric GLRT for Multichannel Adaptive Signal Detection},
      • journal = {IEEE Transactions on Signal Processing},
      • year = 2010,
      • volume = 58,
      • number = 1,
      • pages = {317--325},
      • month = {Jan}
      • }
    •  P. Wang, H. Li, I. Djurovic and B. Himed, "Instantaneous Frequency Rate Estimation for High-Order Polynomial-Phase Signals", IEEE Signal Processing Letters, Vol. 16, No. 9, pp. 782-785, Sept 2009.
      BibTeX
      • @Article{5071278,
      • author = {Wang, P. and Li, H. and Djurovic, I. and Himed, B.},
      • title = {Instantaneous Frequency Rate Estimation for High-Order Polynomial-Phase Signals},
      • journal = {IEEE Signal Processing Letters},
      • year = 2009,
      • volume = 16,
      • number = 9,
      • pages = {782--785},
      • month = {Sept}
      • }
    •  P. Wang, I. Djurovic and J. Yang, "Generalized High-Order Phase Function for Parameter Estimation of Polynomial Phase Signal", IEEE Transactions on Signal Processing, Vol. 56, No. 7, pp. 3023-3028, July 2008.
      BibTeX
      • @Article{4545291,
      • author = {Wang, P. and Djurovic, I. and Yang, J.},
      • title = {Generalized High-Order Phase Function for Parameter Estimation of Polynomial Phase Signal},
      • journal = {IEEE Transactions on Signal Processing},
      • year = 2008,
      • volume = 56,
      • number = 7,
      • pages = {3023--3028},
      • month = jul
      • }
    •  Q. Zhao, H. Li and P. Wang, "Performance of Cooperative Relay With Binary Modulation in Nakagami- Fading Channels", IEEE Transactions on Vehicular Technology, Vol. 57, No. 5, pp. 3310-3315, Sept 2008.
      BibTeX
      • @Article{4425810,
      • author = {Zhao, Q. and Li, H. and Wang, P.},
      • title = {Performance of Cooperative Relay With Binary Modulation in Nakagami- Fading Channels},
      • journal = {IEEE Transactions on Vehicular Technology},
      • year = 2008,
      • volume = 57,
      • number = 5,
      • pages = {3310--3315},
      • month = {Sept}
      • }
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  • MERL Issued Patents

    • Title: "System and Method for Active Carrier Sense Based CSMA/CA for IEEE 802.15.4 System to Avoid Packet Discard Caused by Interference"
      Inventors: Guo, Jianlin; Orlik, Philip V.; Nagai, Yukimasa; Sumi, Takenori; Wang, Pu; Parsons, Kieran
      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"
      Inventors: Wang, Pu; Mansour, Hassan; Berntorp, Karl; Boufounos, Petros T.; Orlik, Philip V.
      Patent No.: 11,914,023
      Issue Date: Feb 27, 2024
    • Title: "Multi-Band Wi-Fi Fusion for WLAN Sensing"
      Inventors: Wang, Pu; Yu, Jianyuan; Koike-Akino, Toshiaki; Wang, Ye; Orlik, Philip V.
      Patent No.: 11,902,811
      Issue Date: Feb 13, 2024
    • Title: "System and Method for Tracking Expanded State of Moving Object with Model Geometry Learning"
      Inventors: Wang, Pu; Berntorp, Karl; Xia, Yuxuan; Mansour, Hassan; Boufounos, Petros T.; Orlik, Philip V.
      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
    • Title: "Radar Detection of Moving Object with Waveform Separation Residual"
      Inventors: Wang, Pu; Boufounos, Petros T.; Mansour, Hassan; Orlik, Philip V.
      Patent No.: 11,567,183
      Issue Date: Jan 31, 2023
    • Title: "Frequency Modulated Image Reconstruction"
      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