Projects
Learning for Biometric Signal Recognition
Biometrics is an important component in security-related applications such as access control, forensic investigation, and identity fraud protection. We focused on the very important problem of feature extraction through subspace learning in biometric systems.
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Privacy Protected Surveillance Using Secure Visual Object Coding
The Secure Shape and Texture Set Partitioning in Hierarchical Trees (SecST-SPIHT) secure visual object coder allows individual, arbitrarily shaped objects to be efficiently coded (compressed) and encrypted. We use this in surveillance applications to protect the privacy of individuals appearing in the surveillance footage.
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Wireless Multi Input Multi Output (MIMO) Channel Simulation Package
The project aimed to provide a MATLAB software package for simulating wireless MIMO
communication channels under practical assumptions. The final software provides an
accurate simulation test bed which incorporates the effect of various pulse-shapes,
fading models, noise structures, channel estimators, synchronization strategies, etc.
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Network for Effective Collaboration Technologies through Advanced Research
The Network for Effective Collaboration Technologies through Advanced Research (NECTAR) is a NSERC network of Canada's leading university researchers in human-computer interaction (HCI) and computer-supported cooperative work (CSCW).
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Multilinear Subspace Learning (MSL)
This project aims to provide an overview of resources concerned with theories and applications of multilinear subspace learning (MSL). The origin of MSL traces back to multi-way analysis in the 1960s and they have been studied extensively in face and gait recognition.
With more connections revealed and analogies drawn between multilinear algorithms and their linear counterparts, MSL has become an exciting area to explore for applications involving large-scale multidimensional (tensorial) data as well as a challenging problem for machine learning researchers to tackle.
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