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Computer Sciences

Electrical & Computer Engineering Faculty Publications

2010

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Full-Text Articles in Engineering

Security In Ad Hoc Networks And Pervasive Computing, Isaac Z. Wu, X.-Y. Li, M. Song, C.-M. Liu Jan 2010

Security In Ad Hoc Networks And Pervasive Computing, Isaac Z. Wu, X.-Y. Li, M. Song, C.-M. Liu

Electrical & Computer Engineering Faculty Publications

Pervasive computing is an exciting and blooming research field, in which innovative techniques and applications are continuously emerging and aim to provide ambient and personalized services to users with high quality. Ad hoc networks are wireless, self-organizing systems formed by cooperating nodes within communication range of each other that form temporary networks. Their topology is dynamic, decentralized, ever changing and the nodes may move around arbitrarily. The last few years have witnessed a wealth of research ideas on ad hoc networking that are moving rapidly into implemented standards. Technology under development for ad hoc networks and pervasive computing is making …


Prediction Of Brain Tumor Progression Using A Machine Learning Technique, Yuzhong Shen, Debrup Banerjee, Jiang Li, Adam Chandler, Yufei Shen, Frederic D. Mckenzie, Jihong Wang, Nico Karssemeijer (Ed.), Ronald M. Summers (Ed.) Jan 2010

Prediction Of Brain Tumor Progression Using A Machine Learning Technique, Yuzhong Shen, Debrup Banerjee, Jiang Li, Adam Chandler, Yufei Shen, Frederic D. Mckenzie, Jihong Wang, Nico Karssemeijer (Ed.), Ronald M. Summers (Ed.)

Electrical & Computer Engineering Faculty Publications

A machine learning technique is presented for assessing brain tumor progression by exploring six patients' complete MRI records scanned during their visits in the past two years. There are ten MRI series, including diffusion tensor image (DTI), for each visit. After registering all series to the corresponding DTI scan at the first visit, annotated normal and tumor regions were overlaid. Intensity value of each pixel inside the annotated regions were then extracted across all of the ten MRI series to compose a 10 dimensional vector. Each feature vector falls into one of three categories:normal, tumor, and normal but progressed to …