A new fuzzy C-means method for magnetic resonance image brain segmentation

In this paper, we introduce a new fuzzy c-means (FCM) method in order to improve the magnetic resonance images’ (MRIs) segmentation. The proposed method combines the FCM and possiblistic c-means (PCM) functions using a weighted Gaussian function. The weighted Gaussian function is given to indicate the spatial influence of the neighbouring pixels on the central pixel. The parameters of weighting coefficients are automatically determined in the implementation using the Gaussian function for every pixel in the image. The proposed method is realised by modifying the objective function of the PCM algorithm to produce memberships and possibilities simultaneously, along with the usual point prototypes or cluster centres for each cluster. The membership values can be interpreted as degrees of possibility of the points belonging to the classes, that is, the compatibilities of the points with the class prototypes to overcome the coincident clusters problem of PCM. The efficiency of the proposed algorithm is demonstrated by extensive segmentation experiments using MRIs and comparison with other state-of-the-art algorithms. In the proposed method, the effect of noise is controlled by incorporating the possibility (typicality) function in addition to the membership function. Consideration of these constraints can greatly control the noise in the image as shown in our experiments.     

Review:Data center network architecture in cloud computing:review,taxonomy, and open research issues

The data center network(DCN), which is an important component of data centers, consists of a large number of hosted servers and switches connected with high speed communication links. A DCN enables the deployment of resources centralization and on-demand access of the information and services of data centers to users. In recent years, the scale of the DCN has constantly increased with the widespread use of cloud-based services and the unprecedented amount of data delivery in/between data centers, whereas the traditional DCN architecture lacks aggregate bandwidth, scalability, and cost effectiveness for coping with the increasing demands of tenants in accessing the services of cloud data centers. Therefore, the design of a novel DCN architecture with the features of scalability, low cost, robustness, and energy conservation is required. This paper reviews the recent research findings and technologies of DCN architectures to identify the issues in the existing DCN architectures for cloud computing. We develop a taxonomy for the classification of the current DCN architectures, and also qualitatively analyze the traditional and contemporary DCN architectures. Moreover, the DCN architectures are compared on the basis of the significant characteristics, such as bandwidth, fault tolerance, scalability, overhead, and deployment cost. Finally, we put forward open research issues in the deployment of scalable, low-cost, robust, and energy-efficient DCN architecture, for data centers in computational clouds.     

IDDQ Testing of Submicron CMOS—by Cooling?

The usability of I_DDQ testing is limited by the subthreshold currents of the low-V_T, submicron MOS transistors in the low bias voltage circuits. The paper addresses the cooling of the chip in order to overcome this problem. Experimental results concerning the effect of cooling on the threshold voltage and subthreshold current are presented in the range of –75...25 Centigrade. The subthreshold currents decrease by a factor of about 100–1000 by cooling-down the chip to –75 Centigrade.     

Elaboration and Characterization of Amorphous Silicon Carbide Thin Films (a-SiC) by Sputerring Magnetron Technique for Photoelectrochemical CO2 Conversion

Silicon - Semiconductors as photoelectric catalysis is recognized as a promising strategy for simultaneous face energy crisis and environmental pollution. In this study, amorphous silicon carbide...     

An agent-based approach for dynamic adjustment of scheduled jobs in computational grids

Grid computing is a newly developed technology for complex systems with large-scale resource sharing, wide-area communication, and multi-institutional collaboration. Grid scheduling is an important infrastructure in the grid computing environment. Most of the existing grids scheduling methods focus on maximizing processor utilization without taking grid load into consideration. This may lead to significant inefficiencies in performance such as large job queues and processing delays. In this paper, we propose a multiagent-based scheduling system for computational grids with a new approach. Agent technology is suitable for a computational grid because of the dynamic, heterogeneous, and autonomous nature of the grid. The main idea of the proposed system is a combination of a static scheduling using a fixed scheduling algorithm and a dynamic adjustment through the autonomous behavior of agents. The superiority of the proposed system, in reducing the load of the grid and minimizing the response time for executing user applications, is demonstrated by simulation experiments.