A physics‐based model of DC and microwave characteristics of GaN/AlGaN HEMTs

A physics‐based model of AlGaN/GaN High Electron Mobility Transistor (HEMT) is developed for the analysis of DC and microwave characteristics. Large‐ and small‐signal parameters are calculated for a given device dimensions and operating conditions. Spontaneous and piezoelectric polarizations at the heterointerface and finite effective width of the 2DEG gas have been incorporated in the analysis. The model predicts a maximum drain current of 523 mA/mm and transconductance of 138 mS/mm for a 1 μm × 75 μm device, which are in agreement with the experimental data. © 2007 Wiley Periodicals, Inc. Int J RF and Microwave CAE, 2007.     

Starting open source collaborative innovation: the antecedents of network formation in community source

Specific needs in the area of enterprise applications have led to a new type of collaborative open source innovation development across institution borders: community source. We use the Kuali community source network, a jointly managed, border‐spanning organization that supplies the institutions that created it, to describe how community source works. This study builds a theoretical basis for understanding the individual and institutional factors affecting community source network formation and the decision by organizations to join a community source network. We identify eight antecedents of decisions about forming or joining community source initiatives: motives, learning, trust, norms and monitoring, institutional similarity, external funding, hostile external environment and information technology. © 2016 John Wiley & Sons Ltd     

Source cell-phone recognition from recorded speech using non-speech segments

In a recent study, we have introduced the problem of identifying cell-phones using recorded speech and shown that speech signals convey information about the source device, making it possible to identify the source with some accuracy. In this paper, we consider recognizing source cell-phone microphones using non-speech segments of recorded speech. Taking an information-theoretic approach, we use Gaussian Mixture Model (GMM) trained with maximum mutual information (MMI) to represent device-specific features. Experimental results using Mel-frequency and linear frequency cepstral coefficients (MFCC and LFCC) show that features extracted from the non-speech segments of speech contain higher mutual information and yield higher recognition rates than those from speech portions or the whole utterance. Identification rate improves from 96.42% to 98.39% and equal error rate (EER) reduces from 1.20% to 0.47% when non-speech parts are used to extract features. Recognition results are provided with classical GMM trained both with maximum likelihood (ML) and maximum mutual information (MMI) criteria, as well as support vector machines (SVMs). Identification under additive noise case is also considered and it is shown that identification rates reduces dramatically in case of additive noise.     

Mapping streaming applications on multiprocessors with time-division-multiplexed network-on-chip

This paper addresses mapping of streaming applications (such as MPEG) on multiprocessor platforms with time-division-multiplexed network-on-chip. In particular, we solve processor selection, path selection and router configuration problems. Given the complexity of these problems, state of the art approaches in this area largely rely on greedy heuristics, which do not guarantee optimality. Our approach is based on a constraint programming formulation that merges a number of steps, usually tackled in sequence in classic approaches. Thus, our method has the potential of finding optimal solutions with respect to resource usage under throughput constraints. The experimental evaluation presented in here shows that our approach is capable of exploring a range of solutions while giving the designer the opportunity to emphasize the importance of various design metrics.     

A tutorial on modelling and inference in undirected graphical models for hyperspectral image analysis

ABSTRACT

Undirected graphical models have been successfully used to jointly model the spatial and the spectral dependencies in earth observing hyperspectral images. They produce less noisy, smooth, and spatially coherent land-cover maps and give top accuracies on many datasets. Moreover, they can easily be combined with other state-of-the-art approaches, such as deep learning. This has made them an essential tool for remote-sensing researchers and practitioners. However, graphical models have not been easily accessible to the larger remote-sensing community as they are not discussed in standard remote-sensing textbooks and not included in the popular remote-sensing software and toolboxes. In this tutorial, we provide a theoretical introduction to Markov random fields and conditional random fields-based spatial–spectral classification for land-cover mapping along with a detailed step-by-step practical guide on applying these methods using freely available software. Furthermore, the discussed methods are benchmarked on four public hyperspectral datasets for a fair comparison among themselves and easy comparison with the vast number of methods in literature which use the same datasets. The source code necessary to reproduce all the results in the paper is published on-line to make it easier for the readers to apply these techniques to different remote-sensing problems.     

A survey of identity and handoff management approaches for the future Internet

Since its inception almost 40 years ago, the Internet has evolved and changed immensely. New technology solutions are desired to keep up with this unprecedented growth. Besides the traditional computing devices, different types of mobile devices need to be supported by the future Internet architecture. In this work, a survey of identity and handoff management solutions proposed in future Internet architectures is presented. Mobility protocols developed by the Internet Engineering Task Force initiatives are discussed to give the background on the user mobility support challenges with the current architecture. The next generation network architectures supported by global initiatives are presented and analyzed in terms of their support for seamless user and device mobility. Furthermore, this survey is extended to include the architectures proposed for wireless mesh networks, which are envisioned to be a part of the next generation networks with their self organizing and self configuring network characteristics.     

Provide Virtual Distributed Environments for Grid computing on demand

Grid users always expect to meet some challenges to employ Grid resources, such as customized computing environment and QoS support. In this paper, we propose a new methodology for Grid computing – to use virtual machines as computing resources and provide Virtual Distributed Environments (VDE) for Grid users. It is declared that employing virtual environment for Grid computing can bring various advantages, for instance, computing environment customization, QoS guarantee and easy management. A light weight Grid middleware, Grid Virtualization Engine, is developed accordingly to provide functions of building virtual environment for Grids. We also present a typical use case, on-demand build a virtual e-Science infrastructure to justify the methodology.     

Learning Automata from Ordered Examples

Connectionist learning models have had considerable empirical success, but it is hard to characterize exactly what they learn. The learning of finite-state languages (FSL) from example strings is a domain which has been extensively studied and might provide an opportunity to help understand connectionist learning. A major problem is that traditional FSL learning assumes the storage of all examples and thus violates connectionist principles. This paper presents a provably correct algorithm for inferring any minimum-state deterministic finite-state automata (FSA) from a complete ordered sample using limited total storage and without storing example strings. The algorithm is an iterative strategy that uses at each stage a current encoding of the data considered so far, and one single sample string. One of the crucial advantages of our algorithm is that the total amount of space used in the course of learning for encoding any finite prefix of the sample is polynomial in the size of the inferred minimum state deterministic FSA. The algorithm is also relatively efficient in time and has been implemented. More importantly, there is a connectionist version of the algorithm that preserves these properties. The connectionist version requires much more structure than the usual models and has been implemented using the Rochester Connectionist Simulator. We also show that no machine with finite working storage can iteratively identify the FSL from arbitrary presentations.     

A Note on Square Rooting of Time Functions of Turing Machines

Abstract. No abstract.     

Indentation studies on two-phase bicrystals of alpha-beta brass with various phase boundary geometries

Indentation studies were carried out on a two-phase model system of alpha-beta brass so as to understand the role of phase boundary on the propagation of deformation. Unlike in the case of uniaxial loading, the phase boundary acts as a very effective barrier for slip propagation, irrespective of its geometry.