Research Interest: Statistical signal processing theory and
signal processing, biomedical imaging and modeling
fingerprinting, Information security and Digital rights management
communications and networking
signal processing and statistics
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.
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.
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
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
- 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
- Secure and reliable wireless body area sensor
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
- Statistical signal processing (detection, segmentation and
- 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
- 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
- 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,
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)
Martin McKeown (UBC)
Janice Eng (UBC)
Mark Carpenter (UBC)
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Last Update: 07/01/08
(c) Copyright 2004 Z. Jane Wang. All rights reserved.