We propose a hybrid system of combining mobile sensor nodes and fixed sensor nodes for robust and effective public management, analysis, and prediction. In the proposed system, user privacy protection (on device, edge, and cloud) is implemented via the design of multiple levels of data sensitivity protection: User activities and location data are anonymized, consented, and aggregated (via edge computing at public infrastructures) before uploading for analysis; Specially designed event tokens are distributed for information/notification flowing and matching, and sharing user sensitive data is avoided; Event tokens also link hierarchical user groups combined with blockchain based technologies. Our system facilitates matching between user private data and public data without compromising privacy.

In this paper, we propose an interactive segmentation method to apply user information during the segmentation of a specific anatomic structure. This method is formulated to use belief propagation to minimize a global cost function according to local level sets. The propagation starts with one user labeled point, and iteratively extends the user information from the labeled pixel to its neighborhood by calculating the beliefs of the pixels in the same level as the labeled pixel. Since the segmentation relies on both local user information and global image features, it is less interrupted by noise, and works well even the target is not obvious to its neighbor. The promising segmentation results also show that our method is robust to the objects with high shape variation and inhomogeneous intensity value appearance.

Hyperspectral imaging is becoming increasingly important in a variety of applications. These images contain a large number of contiguous bands to provide information at a fine spectral resolution and, therefore, cannot be displayed directly using an RGB color display. There has been some recent work on the problem of fusing hyperspectral images to three-band images for color display purposes. In this paper, we evaluate the performance of our recently proposed approach based on independent component analysis, correlation coefficient and mutual information (ICA- CCMI) to fuse the information from a large number of bands to three images suitable for color display. Depending on whether the reference images are available or not, several image quality metrics such as entropy and edge correlation have been proposed and employed to evaluate the fusion performance via three widely used hyperspectral image datasets.

The problem of dimensionality reduction for color representation of hyperspectral images has received recent attention. In this paper, several independent component analysis (ICA) based approaches are proposed to reduce the dimensionality of hyperspectral images for visualization. We also develop a simple but effective method, based on correlation coefficient and mutual information (CCMI), to select the suitable independent components for RGB color representation. Experimental results are presented to illustrate the performance of our approaches.