A generalized scheme for mapping parallel algorithms

A generalized mapping strategy that uses a combination of graph theory, mathematical programming, and heuristics is proposed. The authors use the knowledge from the given algorithm and the architecture to guide the mapping. The approach begins with a graphical representation of the parallel algorithm (problem graph) and the parallel computer (host graph). Using these representations, the authors generate a new graphical representation (extended host graph) on which the problem graph is mapped. An accurate characterization of the communication overhead is used in the objective functions to evaluate the optimality of the mapping. An efficient mapping scheme is developed which uses two levels of optimization procedures. The objective functions include minimizing the communication overhead and minimizing the total execution time which includes both computation and communication times. The mapping scheme is tested by simulation and further confirmed by mapping a real world application onto actual distributed environments     

Comprehensive study of RF analysis of G/GO-based NEMS shunt switch

Microsystem Technologies - High isolation and low insertion loss are the key design parameters for the NEMS switch at high frequency. The comprehensive study of radio frequency (RF) performance...     

Automated bank cheque verification using image processing and deep learning methods

Multimedia Tools and Applications - Automated bank cheque verification using image processing is an attempt to complement the present cheque truncation system, as well as to provide an alternate...     

Target based image fusion using multi-scale feature selection in three regions

Image captured by low dynamic range (LDR) camera fails to capture entire exposure level of scene, and instead only covers certain range of exposures. In order to cover entire exposure level in single image, bracketed exposure LDR images are combined. The range of exposures in different images results in information loss in certain regions. These regions need to be addressed and based on this motive a novel methodology of layer based fusion is proposed to generate high dynamic range image. High and low-frequency layers are formed by dividing each image based on pixel intensity variations. The regions are identified based on information loss section created in differently exposed images. High-frequency layers are combined using region based fusion with Dense SIFT which is used as activity level testing measure. Low-frequency layers are combined using weighted sum. Finally combined high and low-frequency layers are merged together on pixel to pixel basis to synthesize fused image. Objective analysis is performed to compare the quality of proposed method with state-of-the-art. The measures indicate superiority of the proposed method.

     

Design of backtracking search heuristics for parameter estimation of power signals

Neural Computing and Applications - This study presents a novel implementation of evolutionary heuristics through backtracking search optimization algorithm (BSA) for accurate, efficient and robust...     

A SAS macro for the analysis of cell survival curves

This paper describes a SAS macro for the statistical analyses of cell survival data obtained after radiation treatment using the methods of R.E. Tarone et al. (Mutation Research 111 (1983) 79-96). These analyses are usually required on a routine basis by all biomedical research laboratories involved in cell survival assays generating dose-response curves aimed at characterizing radiosensitive mutant cell strains or individuals whose body cells exhibit enhanced sensitivity to radiation and other genotoxic agents. Statistical methods of linear regression are applied to data from repeated experiments with a cell line/strain and weighted estimates of a common slope and its variance are obtained. The methods are currently implemented in two APL programs. These programs are not easily accessible to most biomedical statisticians and researchers because APL is not a common software tool for statistical analysis. Implementation of these methods in SAS, a widely used commercial software for statistical analysis, is expected to help resolve this issue. We illustrate the application of the macro using an example data set obtained in our laboratory, and hope that other investigators may find it useful in analyzing their data.