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 Researchers to Present 2 Conference and 11 Workshop Papers at NeurIPS 2024
      Date: December 10, 2024 - December 15, 2024
      Where: Advances in Neural Processing Systems (NeurIPS)
      MERL Contacts: Petros T. Boufounos; Matthew Brand; Ankush Chakrabarty; Anoop Cherian; François Germain; Toshiaki Koike-Akino; Christopher R. Laughman; Jonathan Le Roux; Jing Liu; Suhas Lohit; Tim K. Marks; Yoshiki Masuyama; Kieran Parsons; Kuan-Chuan Peng; Diego Romeres; Pu (Perry) Wang; Ye Wang; Gordon Wichern
      Research Areas: Artificial Intelligence, Communications, Computational Sensing, Computer Vision, Control, Data Analytics, Dynamical Systems, Machine Learning, Multi-Physical Modeling, Optimization, Robotics, Signal Processing, Speech & Audio, Human-Computer Interaction, Information Security
      Brief
      • MERL researchers will attend and present the following papers at the 2024 Advances in Neural Processing Systems (NeurIPS) Conference and Workshops.

        1. "RETR: Multi-View Radar Detection Transformer for Indoor Perception" by Ryoma Yataka (Mitsubishi Electric), Adriano Cardace (Bologna University), Perry Wang (Mitsubishi Electric Research Laboratories), Petros Boufounos (Mitsubishi Electric Research Laboratories), Ryuhei Takahashi (Mitsubishi Electric). Main Conference. https://neurips.cc/virtual/2024/poster/95530

        2. "Evaluating Large Vision-and-Language Models on Children's Mathematical Olympiads" by Anoop Cherian (Mitsubishi Electric Research Laboratories), Kuan-Chuan Peng (Mitsubishi Electric Research Laboratories), Suhas Lohit (Mitsubishi Electric Research Laboratories), Joanna Matthiesen (Math Kangaroo USA), Kevin Smith (Massachusetts Institute of Technology), Josh Tenenbaum (Massachusetts Institute of Technology). Main Conference, Datasets and Benchmarks track. https://neurips.cc/virtual/2024/poster/97639

        3. "Probabilistic Forecasting for Building Energy Systems: Are Time-Series Foundation Models The Answer?" by Young-Jin Park (Massachusetts Institute of Technology), Jing Liu (Mitsubishi Electric Research Laboratories), François G Germain (Mitsubishi Electric Research Laboratories), Ye Wang (Mitsubishi Electric Research Laboratories), Toshiaki Koike-Akino (Mitsubishi Electric Research Laboratories), Gordon Wichern (Mitsubishi Electric Research Laboratories), Navid Azizan (Massachusetts Institute of Technology), Christopher R. Laughman (Mitsubishi Electric Research Laboratories), Ankush Chakrabarty (Mitsubishi Electric Research Laboratories). Time Series in the Age of Large Models Workshop.

        4. "Forget to Flourish: Leveraging Model-Unlearning on Pretrained Language Models for Privacy Leakage" by Md Rafi Ur Rashid (Penn State University), Jing Liu (Mitsubishi Electric Research Laboratories), Toshiaki Koike-Akino (Mitsubishi Electric Research Laboratories), Shagufta Mehnaz (Penn State University), Ye Wang (Mitsubishi Electric Research Laboratories). Workshop on Red Teaming GenAI: What Can We Learn from Adversaries?

        5. "Spatially-Aware Losses for Enhanced Neural Acoustic Fields" by Christopher Ick (New York University), Gordon Wichern (Mitsubishi Electric Research Laboratories), Yoshiki Masuyama (Mitsubishi Electric Research Laboratories), François G Germain (Mitsubishi Electric Research Laboratories), Jonathan Le Roux (Mitsubishi Electric Research Laboratories). Audio Imagination Workshop.

        6. "FV-NeRV: Neural Compression for Free Viewpoint Videos" by Sorachi Kato (Osaka University), Takuya Fujihashi (Osaka University), Toshiaki Koike-Akino (Mitsubishi Electric Research Laboratories), Takashi Watanabe (Osaka University). Machine Learning and Compression Workshop.

        7. "GPT Sonography: Hand Gesture Decoding from Forearm Ultrasound Images via VLM" by Keshav Bimbraw (Worcester Polytechnic Institute), Ye Wang (Mitsubishi Electric Research Laboratories), Jing Liu (Mitsubishi Electric Research Laboratories), Toshiaki Koike-Akino (Mitsubishi Electric Research Laboratories). AIM-FM: Advancements In Medical Foundation Models: Explainability, Robustness, Security, and Beyond Workshop.

        8. "Smoothed Embeddings for Robust Language Models" by Hase Ryo (Mitsubishi Electric), Md Rafi Ur Rashid (Penn State University), Ashley Lewis (Ohio State University), Jing Liu (Mitsubishi Electric Research Laboratories), Toshiaki Koike-Akino (Mitsubishi Electric Research Laboratories), Kieran Parsons (Mitsubishi Electric Research Laboratories), Ye Wang (Mitsubishi Electric Research Laboratories). Safe Generative AI Workshop.

        9. "Slaying the HyDRA: Parameter-Efficient Hyper Networks with Low-Displacement Rank Adaptation" by Xiangyu Chen (University of Kansas), Ye Wang (Mitsubishi Electric Research Laboratories), Matthew Brand (Mitsubishi Electric Research Laboratories), Pu Wang (Mitsubishi Electric Research Laboratories), Jing Liu (Mitsubishi Electric Research Laboratories), Toshiaki Koike-Akino (Mitsubishi Electric Research Laboratories). Workshop on Adaptive Foundation Models.

        10. "Preference-based Multi-Objective Bayesian Optimization with Gradients" by Joshua Hang Sai Ip (University of California Berkeley), Ankush Chakrabarty (Mitsubishi Electric Research Laboratories), Ali Mesbah (University of California Berkeley), Diego Romeres (Mitsubishi Electric Research Laboratories). Workshop on Bayesian Decision-Making and Uncertainty. Lightning talk spotlight.

        11. "TR-BEACON: Shedding Light on Efficient Behavior Discovery in High-Dimensions with Trust-Region-based Bayesian Novelty Search" by Wei-Ting Tang (Ohio State University), Ankush Chakrabarty (Mitsubishi Electric Research Laboratories), Joel A. Paulson (Ohio State University). Workshop on Bayesian Decision-Making and Uncertainty.

        12. "MEL-PETs Joint-Context Attack for the NeurIPS 2024 LLM Privacy Challenge Red Team Track" by Ye Wang (Mitsubishi Electric Research Laboratories), Tsunato Nakai (Mitsubishi Electric), Jing Liu (Mitsubishi Electric Research Laboratories), Toshiaki Koike-Akino (Mitsubishi Electric Research Laboratories), Kento Oonishi (Mitsubishi Electric), Takuya Higashi (Mitsubishi Electric). LLM Privacy Challenge. Special Award for Practical Attack.

        13. "MEL-PETs Defense for the NeurIPS 2024 LLM Privacy Challenge Blue Team Track" by Jing Liu (Mitsubishi Electric Research Laboratories), Ye Wang (Mitsubishi Electric Research Laboratories), Toshiaki Koike-Akino (Mitsubishi Electric Research Laboratories), Tsunato Nakai (Mitsubishi Electric), Kento Oonishi (Mitsubishi Electric), Takuya Higashi (Mitsubishi Electric). LLM Privacy Challenge. Won 3rd Place Award.

        MERL members also contributed to the organization of the Multimodal Algorithmic Reasoning (MAR) Workshop (https://marworkshop.github.io/neurips24/). Organizers: Anoop Cherian (Mitsubishi Electric Research Laboratories), Kuan-Chuan Peng (Mitsubishi Electric Research Laboratories), Suhas Lohit (Mitsubishi Electric Research Laboratories), Honglu Zhou (Salesforce Research), Kevin Smith (Massachusetts Institute of Technology), Tim K. Marks (Mitsubishi Electric Research Laboratories), Juan Carlos Niebles (Salesforce AI Research), Petar Veličković (Google DeepMind).
    •  
    •  NEWS    Jianlin Guo delivered a keynote in IEEE ICC 2024 Workshop
      Date: June 13, 2024
      Where: IEEE International Conference on Communications (ICC)
      MERL Contacts: Jianlin Guo; Philip V. Orlik; Kieran Parsons; Pu (Perry) Wang
      Research Areas: Communications, Machine Learning, Signal Processing
      Brief
      • Jianlin Guo delivered a keynote titled "Private IoT Networks" in the IEEE International Conference on Communications (ICC) 2024 Workshop "Industrial Private 5G-and-Beyond Wireless Networks", held in Denver, Colorado from June 9-13. The ICC is one of two IEEE Communications Society’s flagship conferences.

        Abstract: With the advent of private 5G-and-Beyond communication technologies, private IoT networks have been emerging. In private IoT networks, network owners have full control on the network resource management. However, to fully realize private IoT networks, the upper layer technologies need to be developed as well. This keynote presents machine learning based anomaly detection in manufacturing systems, innovative multipath TCP technologies over heterogeneous wireless IoT networks, novel channel resource scheduling in private 5G networks and efficient wireless coexistence of the heterogeneous wireless systems.
    •  

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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

  • Internships with Perry

    • ST0116: Internship - 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, pose estimation, segmentation, multiple object tracking (MOT), and representation learning on radar data is required. Previous hands-on experience with open indoor and outdoor radar datasets is a plus. Familiarity with basic radar concepts and MERL's recent work in radar perception is an asset. The intern will work closely with MERL researchers to develop novel algorithms, design experiments with MERL in-house testbed, and prepare results for patents and publication. The internship is expected to last 3 months with a preferred start date after June 2025.

      Required Specific Experience

      • Solid understanding of state-of-the-art perception frameworks including transformer-based (e.g., DETR) and diffusion-based (e.g., DiffusionDet) methods.
      • Hands-on experience with open large-scale radar datasets such as MMVR, HIBER, RADIATE, and K-Radar.
      • Proficiency in Python and experience with job scheduling on GPU clusters using tools like Slurm.
      • Proven publication records in top-tier venues such as CVPR, ICCV, ECCV, NeurIPS.
      • Knowledge of basic radar concepts such as FMCW, MIMO, (micro-) Doppler signature, radar point clouds, heatmaps, and raw ADC waveforms.
      • Familiarity with MERL's recent radar perception research such as TempoRadar, SIRA, MMVR, and RETR.

    See All Internships at MERL

  • MERL Publications

    •  Chen, X., Wang, Y., Brand, M., Wang, P., Liu, J., Koike-Akino, T., "Slaying the HyDRA: Parameter-Efficient Hyper Networks with Low-Displacement Rank Adaptation", Advances in Neural Information Processing Systems (NeurIPS), December 2024.
      BibTeX TR2024-157 PDF Presentation
      • @inproceedings{Chen2024dec,
      • author = {{Chen, Xiangyu and Wang, Ye and Brand, Matthew and Wang, Pu and Liu, Jing and Koike-Akino, Toshiaki}},
      • title = {Slaying the HyDRA: Parameter-Efficient Hyper Networks with Low-Displacement Rank Adaptation},
      • booktitle = {Workshop on Adaptive Foundation Models: Evolving AI for Personalized and Efficient Learning at Neural Information Processing Systems (NeurIPS)},
      • year = 2024,
      • month = dec,
      • url = {https://www.merl.com/publications/TR2024-157}
      • }
    •  Chen, X., Liu, J., Wang, Y., Wang, P., Brand, M., Wang, G., Koike-Akino, T., "SuperLoRA: Parameter-Efficient Unified Adaptation of Large Foundation Models", British Machine Vision Conference (BMVC), November 2024.
      BibTeX TR2024-156 PDF Presentation
      • @inproceedings{Chen2024nov,
      • 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 of Large Foundation Models},
      • booktitle = {British Machine Vision Conference (BMVC)},
      • year = 2024,
      • month = nov,
      • url = {https://www.merl.com/publications/TR2024-156}
      • }
    •  Yataka, R., Cardace, A., Wang, P., Boufounos, P.T., Takahashi, R., "RETR: Multi-View Radar Detection Transformer for Indoor Perception", Advances in Neural Information Processing Systems (NeurIPS), November 2024.
      BibTeX TR2024-159 PDF Software
      • @inproceedings{Yataka2024nov3,
      • author = {Yataka, Ryoma and Cardace, Adriano and Wang, Pu and Boufounos, Petros T. and Takahashi, Ryuhei}},
      • title = {RETR: Multi-View Radar Detection Transformer for Indoor Perception},
      • booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
      • year = 2024,
      • month = nov,
      • url = {https://www.merl.com/publications/TR2024-159}
      • }
    •  Jin, S., Wang, P., Boufounos, P.T., Orlik, P.V., Takahashi, R., Roy, S., "Spatial-Domain Mutual Interference Mitigation for MIMO-FMCW Automotive Radar", IEEE Transactions on Vehicular Technology, DOI: 10.1109/​TVT.2024.3467917, September 2024.
      BibTeX TR2024-148 PDF
      • @article{Jin2024sep,
      • author = {Jin, Sian and Wang, Pu and Boufounos, Petros T. and Orlik, Philip V. and Takahashi, Ryuhei and Roy, Sumit}},
      • title = {Spatial-Domain Mutual Interference Mitigation for MIMO-FMCW Automotive Radar},
      • journal = {IEEE Transactions on Vehicular Technology},
      • year = 2024,
      • month = sep,
      • doi = {10.1109/TVT.2024.3467917},
      • issn = {1939-9359},
      • url = {https://www.merl.com/publications/TR2024-148}
      • }
    •  Rahman, M., Yataka, R., Kato, S., Wang, P., Li, P., Cardace, A., Boufounos, P.T., "MMVR: Millimeter-wave Multi-View Radar Dataset and Benchmark for Indoor Perception", European Conference on Computer Vision (ECCV), DOI: 10.1007/​978-3-031-72986-7_18, September 2024, pp. 306–322.
      BibTeX TR2024-117 PDF Data
      • @inproceedings{Rahman2024sep,
      • author = {Rahman, Mahbub and Yataka, Ryoma and Kato, Sorachi and Wang, Pu and Li, Peizhao and Cardace, Adriano and Boufounos, Petros T.}},
      • title = {MMVR: Millimeter-wave Multi-View Radar Dataset and Benchmark for Indoor Perception},
      • booktitle = {European Conference on Computer Vision (ECCV)},
      • year = 2024,
      • pages = {306–322},
      • month = sep,
      • publisher = {Springer},
      • doi = {10.1007/978-3-031-72986-7_18},
      • url = {https://www.merl.com/publications/TR2024-117}
      • }
    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,
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      • @Article{4425810,
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      • volume = 57,
      • number = 5,
      • pages = {3310--3315},
      • month = {Sept}
      • }

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