Research Interest: Statistical signal processing theory and applications

More specifically,

  • Biological signal processing, biomedical imaging and modeling
  • Digital fingerprinting, Information security and Digital rights management
  • Wireless communications and networking
  • Genomic signal processing and statistics
  • Radar signal processing (target detection and tracking)

Right now, my research works mainly focus on “Biomedical signal processing, imaging and modeling” and “Multimedia security”.


  • Biomedical signal processing, imaging and modeling: There is often an essential trade-off between quality of clinical data, and the risks associated with obtaining the data. Therefore, non-invasive neurological recordings, such as positron emission tomography (PET), functional magnetic resonance imaging (fMRI), electroencephalogram (EEG), and surface electromyograph (EMG) data, draw increasing research and clinical interest in brain disease study. However, despite these great efforts, the use of such non-invasive technologies in routine clinical practice is still far away from expectation. A key reason is the insufficient accuracy and capability in information extraction of the existing data analysis and modeling approaches. Thus, the central theme of my work has been to address this fundamental trade-off employing advanced statistical signal processing and network modeling techniques so that more relevant and accurate information can be distilled from non-invasive neurological recordings. The developed methods will be widely applicable to different brain and movement disorders. Overall my work will advance understanding of disease progression, and have potential for the translation into improved health. To follow this central theme,  we have focused on non-invasive modeling and analysis in extracting characteristic spatial-temporal signatures:
    • Computed simultaneous imaging of multiple biomarkers: The long term goal is to develop and validate novel computational methods for characterizing multiple biomarkers (e.g. tissue components with different kinetics, specific/nonspecific receptor bindings) in bio-medical imaging. We proposed an integrated scheme to estimate the kinetic parameters, reveal and quantify the spatial and temporal heterogeneity of underlying biomarkers. Both MRI and PET images will be studied.
    • EEG/EMG network analysis to monitor recovery: A fundamental obstacle in rehabilitation science is the difficulty in quantifying “recovery” after, e.g., stroke. Inter-subject variability of recovery patterns complicates the ability to derive robust measures. However, our work at UBC on exploring connectivity between EEG nodes and muscles provides a new, powerful avenue and inspires new research directions in sEMG. We believe that network analysis is suitable to monitor recovery.
    • Inferring the interactions between brain regions using fMRI, e.g. to disentangle disease effects from compensatory mechanisms:  Altered patterns of dynamic connectivity between brain regions appear to be characteristic of several diseases. For instance, many of the cognitive deficits seen in PD are the result of a functional disconnection between frontal regions and the basal ganglia. To infer connectivity between regions, we have explored different statistical and modeling approaches. The benefit of these approaches are their solid basis in statistics and information theory, and the fact that they allow the model of the electrophysiological signal to incorporate non-linear, dynamic and stochastic aspects of the underlying system in a robust way.
    • Potential non-invasive treatment in PD with virtual environments (VE): As the above research attempt to describe brain activity in a spatial-temporal domain, we are currently attempting to control brain activity with appropriate, novel stimuli using VE technology, as alternate non-invasive (i.e. non-pharmacological, non-surgical) treatments in those PD patients for whom pharmacological treatment is no longer effective or surgical therapy is inappropriate.


  • Genomic signal processing & statistics: Recent advances in genome study have stimulated synergetic research in many cross-disciplinary areas. Genomic data, especially microarray gene expression data, represents enormous challenges of signal processing and statistics in processing these vast data to reveal the complex biological functionality.
    • Model-based genomic/proteomic signal processing for cancer classification & prediction: Cancer is the fourth most common disease and the second leading cause of death in the United States. Cancer means a significant financial burden to the health care system, in addition to the tremendous toll on patients and their families.  Despite many advances derived from important innovations in technology during the last decades, in the field of cancer medicine, limited successes are still overshadowed by the tremendous morbidity & mortality incurred by this devastating disease. Therefore, accurate detection, classification and early prediction of cancer is a research topic with significant importance. My one recent work is on cancer classification, early detection and prediction by studying the different expression profiles between microarray gene expression samples from cancer and normal subjects. Together with my collaborators at Maryland, we proposed a novel model-driven classification approach, and developed an eigen pattern analysis technique helping predict the transition from healthy to cancer state (see our Bioinformatics paper). This invention has a strong potential to advance clinical capability of early cancer diagnosis.
    • Modeling of genetic regulatory networks by incorporating genomic data sources: Modeling and determining of genetic regulatory networks from genomic data poses one key scientific challenge with potentially high industrial pay-offs.
    • Identification of cell-cycle related genes:  (see our Bioinformatics paper).

I have co-authored the following book in the EURASIP Book Series on Signal Processing and Communications, entitled as "Genomic Signal Processing and Statistics" (ISBN: 977-5945-07-0, Edited by: Edward R. Dougherty, Ilya Shmulevich, Jie Chen, and Z. Jane Wang.)  This book aims to address current genomic challenges by exploiting potential synergies between genomics, signal processing, and statistics, with special emphasis on signal processing and statistical tools for structural and functional understanding of genomic data.

  • Digital multimedia security
    • Information management and security in media-sharing networks: Recent massive sharing and distribution of multimedia over networks creates a technological revolution to the entertainment and media industries and introduces the new concept of web-based social networking communities. However, it also poses new challenges to the efficient, scalable, reliable exchange of multimedia over networks. The objective is to establish a multimedia management and security framework to provide effective management, secure and reliable sharing of digital media in large-scale social networks via investigating both fundamental technologies and system design methodologies.
    • Collusion-resistant multimedia fingerprinting for digital forensic applications: The global nature of the Internet has brought media closer to both authorized users and adversaries. It is now easy for a group of users with differently marked versions of the same content to work together and collectively mount attacks against the fingerprints. These attacks, known as collusion attacks, provide a cost-effective method for removing an identifying fingerprint. Thus, collusion poses a strong threat to protecting the value of multimedia and enforcing usage policies. The goal of the proposed research is to establish a holistic framework for multimedia forensics that is capable of withstanding collusion attacks. We plan to investigate the effectiveness of collusion attacks and to develop collusion-resistant countermeasures for digital fingerprinting capable of supporting multimedia forensics.

Together with K. J. Ray Liu, Wade Trappe, Min Wu, and Hong Zhao, I co-authored a book entitled as Multimedia Fingerprinting Forensics for Traitor Tracing. This book provides comprehensive coverage of emerging multimedia fingerprinting technology, which tracks culprits involved in the illegal manipulation and unauthorized usage of multimedia content. Also, please refer to our multimedia forensics related publications for details.

  • Wireless communications and bio-sensor networks
    • A Unified Framework for Resource Allocation over Wireless Networks:
    • Channel Estimation with Multiple Antennas (An important problem in Cross-Layer Optimization for Space-Time MANET): Since the topology of an MANET is typically arbitrary and keeps changing, the capacity of MANET can be limited. Accurate channel estimation techniques and efficient decoding algorithms are critical to the performance of such systems. With the dynamic MANET channel modeling, we address joint channel estimation and decoding for MIMO-OFDM systems.
    • RFID-based sensor networks for detecting and tracking mobile targets: The overall goal of this project is to contribute to the development of low-power integrated circuit design, data fusion and tracking algorithms, localization techniques, cooperation protocol design, as well as privacy and security mechanisms for advanced radio frequency identification (RFID) systems with sensing capabilities. 
    • Secure and reliable wireless body area sensor networks:  Wireless body area sensor networks (WBASNs) consist of multiple sensor nodes capable of sampling, processing, and communicating one or more vital signs (e.g., heart rate, brain activity, blood pressure, oxygen saturation) and/or environmental parameters (location, temperature, humidity, light) over extended periods. The overall goal of this project is to contribute to the development of the channel models, protocol designs, security methods, cross-layer designs and sensor data processing techniques that will make WBASNs more secure, reliable, and effective and thereby make their widespread deployment practical and commercially viable.
  • Statistical signal processing (detection, segmentation and classification)
    • Signal (transient) detection and parameter estimation: Efforts were taken to the transient detection, including a novel adaptive Page procedure was carried out to efficiently detect a transient with unknown strength and location but with temporal contiguity, and we proposed a number of improved power-law statistics for a  remarkably robust detection of transient signals;  we have worked out a wavelet-based structure for the detection of long-duration narrowband processes and generalizations were given to CFAR operation in both prewhitened and unwhitened cases, and to the detection of multi-band signals. It would be useful for effective and efficient improvement of signal detection systems, such as underwater passive surveillance systems.
    • Segmentation and classification of time-series for the purpose of recognition: A two-stage approach to segment the Gaussian data with unknown piecewise constant variances was presented. Based on simulations, we found the performance of this novel segmenter is compared to the optimal maximum likelihood segmenter using dynamic programming, but the computational burden of our implementation is startlingly small. Furthermore, a new approach using Class-Specific features was presented for the joint segmentation and Classification of a time Series. The applications to autoregressive AR processes and to multiple structures illustrate the good performance and the remarkable computation efficiency of this approach.
    • Monitoring industrial processes, for fault detection and condition-based maintenance: I have worked on one NSF founded project, Surface Grinding Monitoring by Processing of Acoustic Emission (AE) Signals. Overall, the main purpose of this research is to investigate in-process grinding process monitoring using AE (including employing innovative signal processing tools) to analyze the relationships between AE signatures and contact initiation, work piece surface burn, and cracks generation during ceramic grinding.
  • Radar signal processing
    • Monopulse radar angle estimation for unresolved targets: Most present-day radar systems use monopulse techniques to extract angular measurements of sub-beam accuracy. We presented several DOA estimators of two unresolved Rayleigh targets, including a combined approach based on fusing the results from different approaches.
  • Power engineering: My undergraduate research focused on the applications of high voltage technology. A patent was granted for the efficient ozone generator, which is very useful to improve the food safety.
    • X. Yang, Z. Wang, X. Gao and R. Dai, "New Method for Treatment of Pesticide Remains of Vegetables and Fruits", Journal of Tsinghua University (Sci. Tech.), Vol. 37, No. 9, Sep., 1997.
    • [Patents:]X. Yang, Z. Wang, X. Sheng and Q. Qi,  The Efficient Ozone Generator (ZL 96 2 13551.8), June 1996, Beijing, China.

Publications: or Download my Curriculum Vitae (in pdf format)


  • Dr. Peter Willett (UConn)
  • Dr. K. J. Ray Liu (UMCP)
  • Dr. Min Wu (UMCP)
  • Dr. Wade Trappe (WINLAB/Rutgers)
  • Dr. Joseph Wang (VirTech)
  • Dr. Jie Chen (UAlberta)
  • Dr. Vicky H. Zhao (UAlberta)
  • Dr. Zsolt Szabo (JHU)
  • Dr. Martin McKeown (UBC)
  • Dr. Janice Eng (UBC)
  • Dr. Mark Carpenter (UBC)


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Last Update: 07/01/08
(c) Copyright 2004 Z. Jane Wang. All rights reserved.