Development of Low Density,Heat Resistant and Broadband Microwave Absorbing Materials (MAMs) for Stealth Applications

The development of low density and broadband microwave absorbers are the need of the hour to cater for the needs of all military platforms for stealth technology. The low density and broadband properties can be inculcated in microwave absorbers using dielectric lossy materials (e.g. carbon fibres, carbon nanotubes, carbon black, fullerene, graphite, graphene and silicon carbide fibre). Therefore, we designed low density and heat resistant microwave absorbing materials (MAMs) using a novel approach of ceramic fibre board manufacturing technology. The microwave absorbing composites were prepared with varying percentage of milled carbon fibres, discontinuous aluminosilicate fibres and silicone resin as the matrix. The physico-mechanical properties of microwave absorbing composites were determined. Reflection loss of microwave absorbing composites was measured in the frequency range 2–18 GHz by unique single horn interferometry technique. The electromagnetic properties were measured in X-band using free space measurement system. Based on these properties the effect of thickness on the microwave absorbing properties in X-band was simulated The effect of weight % variation of milled carbon fibres on the microwave absorbing properties of composites have been studied in the frequency range 2–18 GHz.     

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.

     

Autonomic cloud resource provisioning and scheduling using meta-heuristic algorithm

We investigate that resource provisioning and scheduling is a prominent problem due to heterogeneity as well as dispersion of cloud resources. Cloud service providers are building more and more datacenters due to demand of high computational power which is a serious threat to environment in terms of energy requirement. To overcome these issues, we need an efficient meta-heuristic technique that allocates applications among the virtual machines fairly and optimizes the quality of services (QoS) parameters to meet the end user objectives. Binary particle swarm optimization (BPSO) is used to solve real-world discrete optimization problems but simple BPSO does not provide optimal solution due to improper behavior of transfer function. To overcome this problem, we have modified transfer function of binary PSO that provides exploration and exploitation capability in better way and optimize various QoS parameters such as makespan time, energy consumption, and execution cost. The computational results demonstrate that modified transfer function-based BPSO algorithm is more efficient and outperform in comparison with other baseline algorithm over various synthetic datasets.

     

A parallel computational approach for similarity search using Bloom filters

Finding similar items in a large and unstructured dataset is a challenging task in many applications of data science, such as searching, indexing, and retrieval. With the increasing data volume and demand for real time responses, similarity search has gained much consideration. In this paper, a parallel computational approach for similarity search using Bloom filters (PCASSB) has been proposed, which uses Bloom filter for the representation of features of document and comparison with user's query. Query features are stored in integer query array (^IQ_A), an array of integer. The PCASSB, an approximate similarity search technique, has been implemented on graphics processing unit with compute unified device architecture as the programming platform. To compute the similarity score between query and reference dataset, Dice coefficient has been used as a baseline method. The accuracy of the results generated by PCASSB is compared with the baseline method and other state‐of‐the‐art methods. The experimental results show that the proposed technique is quite effective in processing large number of text documents as it takes less computational time.     

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.     

An integrated framework for product line design for modular products: product attribute and functionality-driven perspective

The purpose of this research is to facilitate original equipment manufacturers operating in a single market segment to frame their product line design strategy that pertains to offering right product attributes with right attribute level in the right product profile within a market segment. Through this research, we attempt to establish a link between functional level design of product attributes with commercial objectives of the enterprise. Initially, by deriving the functional importance of product attribute levels of individual product attributes within a product profile, demand and functional importance data are generated. Utilising the function-based cost estimating framework and multi-linear regression methodology, we determine the cost and product development time coefficients for respective product attributes. Finally, a mixed integer quadratic programming-based mathematical formulation is developed that includes maximisation of product premium and minimisation of various costs as major objectives under the assumption that manufacturer seeks to offer optimal number of product profiles within the market segment. Employing the commercial solver LINGO, the integrated framework is solved. The entire framework is illustrated using the operator cabin of heavy construction machinery.     

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 numerical study of singularly perturbed generalized Burgers-Huxley equation using three-step Taylor-Galerkin method

In the present work, a numerical study has been carried out for the singularly perturbed generalized Burgers-Huxley equation using a three-step Taylor-Galerkin finite element method. A Burgers-Huxley equation represents the traveling wave phenomena. In singular perturbed problems, a very small positive parameter, ?, called the singular perturbation parameter is multiplied with the highest order derivative term. As this parameter tends towards zero, the problem exhibits boundary layers. The traditional methods fail to capture the boundary layers when ? becomes very small. In this paper a three-step Taylor-Galerkin finite element method is used to capture the boundary layers. The method is third-order accurate and has inbuilt upwinding. Stability analysis has been carried out and the numerical results show that the method is efficient in capturing the boundary layers.