Survivality Modeling for Quantitative Security Assessment in Ubiquitous Computing Systems

Abstract. Ubiquitous computing is about networked processors, which is constructed not only with one computer but with networks of computers. Security solutions usually lack a clear definition of survivality. Thus, this paper deals with a method of quantitatively assessing the system security based on the survivality. Since a logical step towards modeling survivality is to have a set of requirements first, attack-type modeling is constructed firstly. As the case study, we analyze the TCP-SYN attack and Code-Red worm attack according to both the attack-type model and survivality model. 1

Real-Time Target Detection Architecture Based on Reduced Complexity Hyperspectral Processing

This paper presents a real-time target detection architecture for hyperspectral image processing. The architecture is based on a reduced complexity algorithm for high-throughput applications.We propose an efficient pipelined processing element architecture and a scalable multiple-processing element architecture by exploiting data partitioning. We present a processing unit modeling based on the data reduction algorithm in hyperspectral image processing and propose computing structure, that is, to optimize memory usage and eliminates memory bottleneck. We investigate the interconnection topology for the multipleprocessing element architecture to improve the speed. The proposed architecture is designed and implemented in FPGA to illustrate the relationship between hardware complexity and execution throughput of hyperspectral image processing for target detection

Spectral Content Characterization for Efficient Image Detection Algorithm Design

This paper presents spectral characterization for efficient image detection using hyperspectral processing techniques. We investigate the relationship between the number of used bands and the performance of the detection process in order to find the optimal number of band reductions. The band reduction significantly reduces computation and implementation complexity of the algorithms. Specifically, we define and characterize the contribution coefficient for each band. Based on the coefficients, we heuristically select the required minimum bands for the detection process. We have shown that the small number of bands is efficient for effective detection. The proposed algorithm is suitable for low-complexity and real-time applications

Spectral Content Characterization for Efficient Image Detection Algorithm Design

This paper presents spectral characterization for efficient image detection using hyperspectral processing techniques. We investigate the relationship between the number of used bands and the performance of the detection process in order to find the optimal number of band reductions. The band reduction significantly reduces computation and implementation complexity of the algorithms. Specifically, we define and characterize the contribution coefficient for each band. Based on the coefficients, we heuristically select the required minimum bands for the detection process. We have shown that the small number of bands is efficient for effective detection. The proposed algorithm is suitable for low-complexity and real-time applications.</p

Learning Spatio-Temporal Topology of a Multi-Camera Network by Tracking Multiple People

Abstract—This paper presents a novel approach for representing the spatio-temporal topology of the camera network with overlapping and non-overlapping fields of view (FOVs). The topology is determined by tracking moving objects and establishing object correspondence across multiple cameras. To track people successfully in multiple camera views, we used the Merge-Split (MS) approach for object occlusion in a single camera and the grid-based approach for extracting the accurate object feature. In addition, we considered the appearance of people and the transition time between entry and exit zones for tracking objects across blind regions of multiple cameras with non-overlapping FOVs. The main contribution of this paper is to estimate transition times between various entry and exit zones, and to graphically represent the camera topology as an undirected weighted graph using the transition probabilities. Keywords—Surveillance, multiple camera, people tracking, topology. I

Iterative Object Localization Algorithm Using Visual Images with a Reference Coordinate

We present a simplified algorithm for localizing an object using multiple visual images that are obtained from widely used digital imaging devices. We use a parallel projection model which supports both zooming and panning of the imaging devices. Our proposed algorithm is based on a virtual viewable plane for creating a relationship between an object position and a reference coordinate. The reference point is obtained from a rough estimate which may be obtained from the preestimation process. The algorithm minimizes localization error through the iterative process with relatively low-computational complexity. In addition, nonlinearity distortion of the digital image devices is compensated during the iterative process. Finally, the performances of several scenarios are evaluated and analyzed in both indoor and outdoor environments