In microprocessor-based systems, such as the cloud computing infrastructure, high reliability is essential. As multiprocessor systems become more widespread and increasingly complex, system-level diagnosis will increasingly be adopted to determine their robustness. In this paper, we consider a pessimistic diagnostic strategy for hypermesh multiprocessor systems under the PMC model. The pessimistic strategy is a diagnostic process whereby all faulty processors are correctly identified and at most one fault-free processor may be misjudged to be a faulty processor. We first determine the pessimistic diagnosability of a hypermesh to be${2}{{n}}({{k}} - {1}) - {{k}}$. We then propose an efficient pessimistic diagnostic algorithm to identify at most${ 2}{{n}}({{k}} - { 1}) - {{k}}$faults in${{O}}({{N}})$time, where${\mbi{k}}$is the radix,${\mbi{n}}$is the number of dimensions, and${{N}} = {{k^n}}$is the total number of processors. This result is superior to the best precise diagnostic algorithm, which runs in${{O}}({{N}}{\log _{{k}}}{{N}})$time. Furthermore, the Cartesian product network, a subgraph of the hypermesh and the proposed algorithm can be employed to determine faults in the product network.

Most image fusion techniques are designed for multispectral (MS) images with natural color responses. If there is an unnatural spectral response, the fused image will be unsuitable for visualization. To enhance the vegetation content in such cases, applying the normalized difference vegetation index (NDVI) to tune the color of MS images is a feasible approach. However, the NDVI is usually used to generate lower resolution vegetation maps, and particularly, the threshold needs to be chosen manually for various scenes. In order to avert this drawback, using VI with a fixed threshold, a method is developed for IKONOS and QuickBird images in this letter, which integrates a fast technique of intensity-hue-saturation fusion. For MS images with unnatural color responses on vegetation areas, experimental results demonstrate that the proposed method can effectively enhance and extract vegetation information from the IKONOS and QuickBird images.

To increase the capacity of the hidden secret information and to provide a stego-image imperceptible for human vision, a novel steganographic approach based on tri-way pixel-value differencing (TPVD) is presented in this paper. This approach uses three different directional edges effectively to design the tri-way differencing scheme and to remove the capacity limitation of the original PVD method using only one direction. In addition, we propose an optimal selection approach for the reference point and adaptive rules to reduce distortion brought from setting larger embedding capacity. The theoretical estimation and experimental results demonstrate that our scheme can provide a superior embedding capacity. Besides, the embedded confidential information can be extracted from stego-images without the assistance of original images.

The fast intensity-hue-saturation (IHS) methods can quickly merge massive volumes of data from new satellite imagery. To minimize the color distortion from IHS fusion, Choi used a tradeoff parameter in his new fast IHS approach to control the spatial and spectral resolution of the fused image. However, the method is not general and requires manual adjustment of the tradeoff parameter in order to keep spatial details while preserving spectral characteristics. To tackle this problem, a modified approach that is a tunable IHS-Brovey method is presented in this letter. Experimental results demonstrate that the proposed technique can provide significant improvements over the original method, that is, to achieve a best tradeoff for spatial details and color distortion

Biometrics is inherently a reliable technique to identify human's authentication by his or her own physiological or behavioral characteristics. Empirical mode decomposition (EMD), a multiresolution decomposition technique, is adaptive and appears to be suitable for nonlinear, non-stationary data analysis. EMD analyzes the signal locally and separates the component holding locally the highest frequency from the rest into a separate component. In this paper, we adopt the EMD approach to extract residual components from the iris image as the features for recognition. Three different similarity measures have been evaluated. Experimental results show that three metrics have achieved similar performance. Therefore, the proposed method has demonstrated to be promising for iris recognition and EMD is suitable for feature extraction.

Diagnosability has played an important role in the reliability of an interconnection network. The classical problem of fault diagnosis is discussed widely and the diagnosability of many well-known networks have been explored. We introduce a new measure of diagnosability, called conditional diagnosability, by restricting that any faulty set cannot contain all the neighbors of any vertex in the graph. Based on this requirement, the conditional diagnosability of the n-dimensional hypercube is shown to be 4(n - 2) +1, which is about four times as large as the classical diagnosability. Besides, we propose some useful conditions for verifying if a system is t-diagnosable and introduce a new concept, called a strongly t-diagnosable system, under the PMC model. Applying these concepts and conditions, we investigate some t-diagnosable networks which are also strongly t-diagnosable.

Among various image fusion methods, intensity-hue-saturation (IHS) technique is capable of quickly merging the massive volumes of data. For IKONOS imagery, IHS can yield satisfactory "spatial" enhancement but may introduce "spectral" distortion, appearing as a change in colors between compositions of resampled and fused multispectral bands. To solve this problem, a fast IHS fusion technique with spectral adjustment is presented. The experimental results demonstrate that the proposed approach can provide better performance than the original IHS method, both in processing speed and image quality.

Diagnosability is an important factor in measuring the reliability of an interconnection network, while the (node) connectivity is used to measure the fault tolerance of an interconnection network. We observe that there is a close relationship between the connectivity and the diagnosability. According to our results, a t-regular and t-connected network with at least 2t + 3 nodes is t-diagnosable. Furthermore, the diagnosability of the product networks is also investigated in this work. The product networks, including hypercube, mesh, and tori, comprise very important classes of interconnection networks. Herein, different combinations of t-diagnosable and t-connected are employed to study the diagnosability of the product networks.

An n-dimensional crossed cube, CQ/sub n/, is a variation of hypercubes. In this paper, we give a new shortest path routing algorithm based on a new distance measure defined herein. In comparison with Efe's algorithm, which generates one shortest path in O(n/sup 2/) time, our algorithm can generate more shortest paths in O(n) time. Based on a given shortest path routing algorithm, we consider a new performance measure of interconnection networks called edge congestion. Using our shortest path routing algorithm and assuming that message exchange between all pairs of vertices is equally probable, we show that the edge congestion of crossed cubes is the same as that of hypercubes. Using the result of edge congestion, we can show that the bisection width of crossed cubes is 2/sup n-1/. We also prove that wide diameter and fault diameter are [n/2]+2. Furthermore, we study embedding of cycles in cross cubes and construct more types than previous work of cycles of length at least four.