Blind image separation based on exponentiated transmuted Weibull distribution

In recent years the processing of blind image separation has been investigated. As a result, a number of feature extraction algorithms for direct application of such image structures have been developed. For example, separation of mixed fingerprints found in any crime scene, in which a mixture of two or more fingerprints may be obtained, for identification, we have to separate them. In this paper, we have proposed a new technique for separating a multiple mixed images based on exponentiated transmuted Weibull distribution. To adaptively estimate the parameters of such score functions, an efficient method based on maximum likelihood and genetic algorithm will be used. We also calculate the accuracy of this proposed distribution and compare the algorithmic performance using the efficient approach with other previous generalized distributions. We find from the numerical results that the proposed distribution has flexibility and an efficient resultComment: 14 pages, 12 figures, 4 tables. International Journal of Computer Science and Information Security (IJCSIS),Vol. 14, No. 3, March 2016 (pp. 423-433

An Investigation of the Mechanical and Physical Characteristics of Cement Paste Incorporating Different Air Entraining Agents using X-ray Micro-Computed Tomography

Improving the thermal insulation properties of cement-based materials is the key to reducing energy loss and consumption in buildings. Lightweight cement-based composites can be used efficiently for this purpose, as a structural material with load bearing ability or as a non-structural one for thermal insulation. In this research, lightweight cement pastes containing fly ash and cement were prepared and tested. In these mixes, three different techniques for producing air voids inside the cement paste were used through the incorporation of aluminum powder (AL), air entraining agent (AA), and hollow microspheres (AS). Several experiments were carried out in order to examine the structural and physical characteristics of the cement composites, including dry density, compressive strength, porosity and absorption. A Hot Disk device was used to evaluate the thermal conductivity of different cement composites. In addition, X-ray micro-computed tomography (micro-CT) was adopted to investigate the microstructure of the air-entrained cement pastes and the spatial distribution of the voids inside pastes without destroying the specimens. The experimental results obtained showed that AS specimens with admixture of hollow microspheres can improve the compressive strength of cement composites compared to other air entraining admixtures at the same density level. It was also confirmed that the incorporation of aluminum powder creates large voids, which have a negative effect on specimens’ strength and absorption.EC/H2020/841592/EU/Ultra-Lightweight Concrete for 3D printing technologies/Ultra-LightCon-3

New Non-deterministic Approaches for Register Allocation

In this paper two algorithms for register allocation are presented. The first algorithm is a simulated annealing algorithm. The core of the algorithm is the Metropolis procedure. The algorithm presented in the paper has a linear time asymptotic complexity. The second algorithm is a genetic algorithm. The algorithm has a linear time complexity

Predicting Intermediate Storage Performance for Workflow Applications

Configuring a storage system to better serve an application is a challenging task complicated by a multidimensional, discrete configuration space and the high cost of space exploration (e.g., by running the application with different storage configurations). To enable selecting the best configuration in a reasonable time, we design an end-to-end performance prediction mechanism that estimates the turn-around time of an application using storage system under a given configuration. This approach focuses on a generic object-based storage system design, supports exploring the impact of optimizations targeting workflow applications (e.g., various data placement schemes) in addition to other, more traditional, configuration knobs (e.g., stripe size or replication level), and models the system operation at data-chunk and control message level. This paper presents our experience to date with designing and using this prediction mechanism. We evaluate this mechanism using micro- as well as synthetic benchmarks mimicking real workflow applications, and a real application.. A preliminary evaluation shows that we are on a good track to meet our objectives: it can scale to model a workflow application run on an entire cluster while offering an over 200x speedup factor (normalized by resource) compared to running the actual application, and can achieve, in the limited number of scenarios we study, a prediction accuracy that enables identifying the best storage system configuration