A balanced bin sort for hypercube multicomputers

We propose a balanced bin sort for hypercube multicomputers. This sorting algorithm has an empirically measured expected run time that is greater than that of hyperquicksort but less than that of bitonic sort. Also, its space requirements are less than that of hyperquicksort but more than that of bitonic sort. So, it is useful in situations in which there is some excess memory but not enough to run hyperquicksort.This research was supported, in part, by the National Science Foundation under grants DCR 84-20935 and MIP 86-17374.     

Normal state of short coherence length superconductors: Qualitative differences between spin and charge correlations

The temperature scaleT* at which the amplitude of the order parameter is formed is in general much higher than theT _c at which phase coherence is established in short coherence length superconductors. Using quantum Monte Carlo simulations for the attractive Hubbard model we show that for a degenerate 2D system the spin and charge responses show qualitatively different behavior in the normal state in the temperature rangeT _c <T<T*. We find that _s is stronglyT-dependent whiledn/d is independent ofT. A pseudo-gap develops in the one-particle density of states and leads to anomalous behavior of the NMR relaxation rate 1/T ₁ T _s (T) characteristic of spin gap behavior in the high-T _c materials.     

Hyperspectral image classification using multiobjective optimization

Hyperspectral images constitute a substantial amount of data in the form of spectral bands. This information is used for land cover analysis, specifically in classifying a hyperspectral pixel, which is a popular domain in remote sensing. This paper proposed an efficient framework to classify spectral-spatial hyperspectral images by employing multiobjective optimization. Spectral-spatial features of hyperspectral images are passed for optimization. As hyperspectral images have a high dimensional feature set, many classifiers cannot perform well. Multiobjective optimization reduces the feature set without affecting the discrimination ability of the classifier. The proposed work is validated on a standard hyperspectral image set, Pavia University and Kennedy Space Centre.

     

Medical data fusion algorithm based on Internet of things

In order to explore the data fusion algorithm in medical Internet of things, the monitoring of medical data in the Internet of things is discussed and studied focusing on data fusion and related routing technology. According to the particularity of the data in the medical Internet of things, a data fusion cluster-tree construction algorithm based on event-driven (DFCTA) is proposed. The fusion delay problem in the network is analyzed, and the minimum fusion delay method is proposed by calculation of the fusion waiting time of the nodes. Finally, the intelligent health management data fusion system in the medical Internet of things is designed. Aiming at the characteristics of multilevel integration of multisource heterogeneous data fusion for intelligent health management, the data fusion architecture of fusion tree composed of fusion nodes is proposed. The experiment shows that the DFCTA algorithm has good fusion performance. Based on the above findings, it is concluded that the algorithm is a fast and reliable method, which has important practical significance.     

Computing Hough transforms on hypercube multicomputers

Efficient algorithms to compute the Hough transform on MIMD and SIMD hypercube multicomputer are developed. Our algorithms can compute p angles of the Hough transform of an N × N image, p N, in 0(p + log N) time on both MIMD and SIMD hypercubes. These algorithms require 0(N ²) processors. We also consider the computation of the Hough transform on MIMD hypercubes with a fixed number of processors. Experimental results on an NCUBE/7 hypercube are presented.This research was supported by the National Science Foundation under grants DCR84-20935 and 86-17374. All correspondence should be mailed to Sanjay Ranka.     

Processing of Amorphous Calcium Aluminate Powders at < 900°C

Aluminum chelate and calcium nitrate precursors were used to synthesize amorphous calcium aluminate powders by solgel processing at < 900°C. The method of preparation and results of characterization of the gels by thermogravimetric analysis, X-ray diffraction, scanning Auger microscopy, and single-point BET analysis are presented. An optimum heat-treatment schedule consisting of heating the gel to 900°C at 5°C/min and holding for 16 h was developed to produce highly reactive, X-ray amorphous calcium aluminate powders.     

The subspace Gaussian mixture model—A structured model for speech recognition

We describe a new approach to speech recognition, in which all Hidden Markov Model (HMM) states share the same Gaussian Mixture Model (GMM) structure with the same number of Gaussians in each state. The model is defined by vectors associated with each state with a dimension of, say, 50, together with a global mapping from this vector space to the space of parameters of the GMM. This model appears to give better results than a conventional model, and the extra structure offers many new opportunities for modeling innovations while maintaining compatibility with most standard techniques.     

A novel approach for analyzing fuzzy system reliability using different types of intuitionistic fuzzy failure rates of components

This paper addresses the fuzzy system reliability analysis using different types of intuitionistic fuzzy numbers. Till now, in the literature, to analyze the fuzzy system reliability, it is assumed that the failure rates of all components of a system follow the same type of fuzzy set or intuitionistic fuzzy set. However, in practical problems, such type of situation rarely occurs. Therefore, in the present paper, a new algorithm has been introduced to construct the membership function and non-membership function of fuzzy reliability of a system having components following different types of intuitionistic fuzzy failure rates. Functions of intuitionistic fuzzy numbers are calculated to construct the membership function and non-membership function of fuzzy reliability via non-linear programming techniques. Using the proposed algorithm, membership functions and non-membership functions of fuzzy reliability of a series system and a parallel systems are constructed. Our study generalizes the various works of the literature. Numerical examples are given to illustrate the proposed algorithm.