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Full-Text Articles in Engineering
Tpm: Cloud-Based Tele Ptsd Monitor Using Multi-Dimensional Information, Roger Xu, Gang Mei, Guangfan Zhang, Pan Gao, Aaron Pepe, Jiang Li, James D. Westwood (Ed.), Susan W. Westwood (Ed.), Li Felländer-Tsai (Ed.), Randy S. Haluck (Ed.), Richard A. Robb (Ed.), Steven Senger (Ed.), Kirby G. Vosburgh (Ed.)
Tpm: Cloud-Based Tele Ptsd Monitor Using Multi-Dimensional Information, Roger Xu, Gang Mei, Guangfan Zhang, Pan Gao, Aaron Pepe, Jiang Li, James D. Westwood (Ed.), Susan W. Westwood (Ed.), Li Felländer-Tsai (Ed.), Randy S. Haluck (Ed.), Richard A. Robb (Ed.), Steven Senger (Ed.), Kirby G. Vosburgh (Ed.)
Electrical & Computer Engineering Faculty Publications
An automated system that can remotely and non-intrusively screen individuals at high risk for Post-Traumatic Stress Disorder (PTSD) and monitor their progress during treatment would be desired by many Veterans Affairs (VAs) as well as other PTSD treatment and research organizations. In this paper, we present an automated, cloud-based Tele-PTSD Monitor (TPM) system based on the fusion of multiple sources of information. The TPM system can be hosted in a cloud environment and accessed through landline or cell phones, or on the Internet through a web portal or mobile application (app).
Hyperspectral Image Classification Using A Spectral-Spatial Sparse Coding Model, Ender Oguslu, Guoqing Zhou, Jiang Li, Lorenzo Bruzzone (Ed.)
Hyperspectral Image Classification Using A Spectral-Spatial Sparse Coding Model, Ender Oguslu, Guoqing Zhou, Jiang Li, Lorenzo Bruzzone (Ed.)
Electrical & Computer Engineering Faculty Publications
We present a sparse coding based spectral-spatial classification model for hyperspectral image (HSI) datasets. The proposed method consists of an efficient sparse coding method in which the l1/lq regularized multi-class logistic regression technique was utilized to achieve a compact representation of hyperspectral image pixels for land cover classification. We applied the proposed algorithm to a HSI dataset collected at the Kennedy Space Center and compared our algorithm to a recently proposed method, Gaussian process maximum likelihood (GP-ML) classifier. Experimental results show that the proposed method can achieve significantly better performances than the GP-ML classifier when training data …