Estimation of power density in IMPATT using different materials

ABSTRACT

The RF output power dissipated per unit area is calculated using Runge-Kutta method for the high-moderate-moderate-high (n⁺-n-p-p⁺) doping profile of double drift region (DDR)-based impact avalanche transit time (IMPATT) diode by taking different substrate at Ka band. Those substrates are silicon, gallium arsenide, germanium, wurtzite gallium nitride, indium phosphide and 4H-silicon carbide. A comparative study regarding power dissipation ability by the IMPATT using different material is being presented thereby modelling the DDR IMPATT diode in a one-dimensional structure. The IMPATT based on 4H-SiC element has highest power density in the order of 10¹⁰ Wm^?2 and the Si-based counterpart has lowest power density of order 10⁶ Wm^?2 throughout the Ka band. So, 4H-SiC-based IMPATT should be preferable over others for the power density preference based application. This result will be helpful to estimate the power density of the IMPATT for any doping profile and to select the proper element for the optimum design of the IMPATT as far as power density is concerned in the Ka band. Also, we have focused on variation of power density with different junction temperatures and modelled the heat sink with analysis of thermal resistances.     

Unconventional Field Dependence of Magnetoresistance of (TMTSF)2ClO4 Studied by 46-T Pulsed Magnetic Field System

A new TTP donor, Me-DH-TTP (2-methyl-5-(1,3-dithiolan-2-yliden)-1,3,4,6-tetrathiapentalene), was designed to realize a system with large on-site Coulomb repulsion as compared with the previously known bis-fused type TTP donors. Probably as a consequence, (Me-DH-TTP)₂AsF₆ exhibits semiconducting behavior from room temperature to liquid helium temperature. By increasing pressure, metallic behavior appears below 300 K, and with distinct metal-insulator (M-I) transition up to 2.2 GPa. This M-I transition suddenly disappears beyond 2.5 GPa, and metallic state is stabilized down to 2.6 K. We discuss the possibility of quantum critical point around 2.4 GPa.     

Electrical conductivity of a novel cast 6351 Al–Al4SiC4 in situ composite

In this research work, electrical conductivity of a novel cast 6351 Al–Al₄SiC₄ composite has been studied for varying Al₄SiC₄ particle content (2–7 vol%). Analytical models were developed on the basis of free electron theory and local resistance approach in order to predict electrical conductivity of metal matrix composites. The electrical conductivity of cast 6351 Al–Al₄SiC₄ composite was found to decrease with increasing Al₄SiC₄ content. Overprediction of results and more % deviation of the predicted electrical conductivity from experimental value were observed for local-resistance-approach-based model. However, the model developed on the basis of electron theory considering scattering of electrons at particle–matrix interface accurately predicted the experimental electrical conductivity data.     

Phase Transformation of Metastable ZnSnO3 Upon Thermal Decomposition by In‐Situ Temperature‐Dependent Raman Spectroscopy

Temperature‐dependent in‐situ Raman spectroscopy is used to investigate the phase transformation of zinc metastannate (ZnSnO₃) to zinc orthostannate (Zn₂SnO₄) induced upon annealing in the ambient. ZnSnO₃ microcubes (MCs) were synthesized at room temperature using a simple aqueous synthesis process, followed by characterization using electron microscopy, X‐ray diffraction (XRD), Fourier transform infrared (FTIR) spectroscopy, and thermogravimetric analysis (TGA). Annealing of the ZnSnO₃ MCs was carried out up to 1000°C, while recording the Raman spectra in‐situ at regular intervals. Phase transformation from metastannate to orthostannate was found to begin around 500°C with an activation energy of ~0.965 eV followed by the recrystallization into the inverse spinel orthostannate phase at ~750°C. Results from this study provide detailed understanding of the phase transformation behavior of perovskite ZnSnO₃ to inverse spinel Zn₂SnO₄ upon thermal annealing.     

Investigation of Eccentric Open Eye Formation in a Slab Caster Tundish

Metallurgical and Materials Transactions B - Inert gas shrouding in tundish can result in the formation of a tundish open eye (TOE) due to the presence of reversed flows on the upper surface of the...     

Hierarchical Fusion of Multiple Classifiers for Hyperspectral Data Analysis

Many classification problems involve high dimensional inputs and a large number of classes. Multiclassifier fusion approaches to such difficult problems typically centre around smart feature extraction, input resampling methods, or input space partitioning to exploit modular learning. In this paper, we investigate how partitioning of the output space (i.e. the set of class labels) can be exploited in a multiclassifier fusion framework to simplify such problems and to yield better solutions. Specifically, we introduce a hierarchical technique to recursively decompose a C-class problem into C_1 two-(meta) class problems. A generalised modular learning framework is used to partition a set of classes into two disjoint groups called meta-classes. The coupled problems of finding a good partition and of searching for a linear feature extractor that best discriminates the resulting two meta-classes are solved simultaneously at each stage of the recursive algorithm. This results in a binary tree whose leaf nodes represent the original C classes. The proposed hierarchical multiclassifier framework is particularly effective for difficult classification problems involving a moderately large number of classes. The proposed method is illustrated on a problem related to classification of landcover using hyperspectral data: a 12-class AVIRIS subset with 180 bands. For this problem, the classification accuracies obtained were superior to most other techniques developed for hyperspectral classification. Moreover, the class hierarchies that were automatically discovered conformed very well with human domain experts’ opinions, which demonstrates the potential of using such a modular learning approach for discovering domain knowledge automatically from data. Received: 21 November 2000, Received in revised form: 02 November 2001, Accepted: 13 December 2001     

CONSENSUS-BASED ENSEMBLES OF SOFT CLUSTERINGS

The problem of obtaining a single “consensus” clustering solution from a multitude or ensemble of clusterings of a set of objects, has attracted much interest recently because of its numerous practical applications. While a wide variety of approaches including graph partitioning, maximum likelihood, genetic algorithms, and voting-merging have been proposed so far to solve this problem, virtually all of them work on hard partitionings, i.e., where an object is a member of exactly one cluster in any individual solution. However, many clustering algorithms such as fuzzy c-means naturally output soft partitionings of data, and forcibly hardening these partitions before applying a consensus method potentially involves loss of valuable information. In this article we propose several consensus algorithms that can be applied directly to soft clusterings. Experimental results over a variety of real-life datasets are also provided to show that using soft clusterings as input does offer significant advantages, especially when dealing with vertically partitioned data.     

Monopoly money: The effect of payment coupling and form on spending behavior.

This article examines consumer spending as a function of payment mode both when the modes differ in terms of payment coupling (association between purchase decision and actual parting of money) and physical form as well as when the modes differ only in terms of form. Study 1 demonstrates that consumers are willing to spend more when a credit card logo is present versus absent. Study 2 shows that the credit card effect can be attenuated when people estimate their expenses using a decomposition strategy (vs. a holistic one). Noting that credit card and cash payments differ in terms of payment coupling and form, Studies 3 and 4 examine consumer spending when the payment mode differs only in physical form. Study 3 demonstrates that consumers spend more when they are spending scrip (a form of stored value certificate) versus cash of the same face value. Study 4 shows that the difference in spending across payment modes (cash and gift certificates) is attenuated by altering the salience of parting with money through contextual manipulations of the differences between cash and gift certificates. (PsycINFO Database Record (c) 2010 APA, all rights reserved)