Pair-copula based mixture models and their application in clustering

Finite mixtures are often used to perform model based clustering of multivariate data sets. In real life applications, such data may exhibit complex nonlinear form of dependence among the variables. Also, the individual variables (margins) may follow different families of distributions. Most of the existing mixture models are unable to accommodate these two aspects of the data. This paper presents a finite mixture model that involves a pair-copula based construction of a multivariate distribution. Such a model de-couples the margins and the dependence structures. Hence, the margins can be modeled using different families. Again, many possible dependence structures can also be studied using different copulas. The resulting mixture model (called DVMM) is then capable of capturing a broad family of distributions including non-Gaussian models. Here we study DVMM in the context of clustering of multivariate data. We design an expectation maximization procedure for estimating the mixture parameters. We perform extensive experiments on the basis of a number of well-known data sets. A detailed evaluation of the clustering quality obtained by DVMM in comparison to other mixture models is presented. The experimental results show that the performance of DVMM is quite satisfactory.     

SWGMM: a semi-wrapped Gaussian mixture model for clustering of circular–linear data

Finite mixture models are widely used to perform model-based clustering of multivariate data sets. Most of the existing mixture models work with linear data; whereas, real-life applications may involve multivariate data having both circular and linear characteristics. No existing mixture models can accommodate such correlated circular–linear data. In this paper, we consider designing a mixture model for multivariate data having one circular variable. In order to construct a circular–linear joint distribution with proper inclusion of correlation terms, we use the semi-wrapped Gaussian distribution. Further, we construct a mixture model (termed SWGMM) of such joint distributions. This mixture model is capable of approximating the distribution of multi-modal circular–linear data. An unsupervised learning of the mixture parameters is proposed based on expectation maximization method. Clustering is performed using maximum a posteriori criterion. To evaluate the performance of SWGMM, we choose the task of color image segmentation in LCH space. We present comprehensive results and compare SWGMM with existing methods. Our study reveals that the proposed mixture model outperforms the other methods in most cases.     

On routing in large WDM networks

An important problem in WDM network design is to construct a logical topology and determine an optimal routing over that topology. Mixed Integer Linear Program (MILP) formulations to generate optimal solutions for this problem have been presented. Such formulations are computationally intractable, even for moderate sized networks. A standard approach is to decouple the problem of logical topology design and the problem of routing the traffic on this logical topology. Heuristics for finding the logical topology exist and a straight-forward linear program (LP), based on the node-arc formulation can be used to solve the routing problem over a given logical topology. We have found that such LP formulations become computationally infeasible for large networks. In this paper, we present a new formulation, based on the arc-chain representation, for optimally routing the specified traffic over a given logical topology to minimize the congestion of the network. We have used the revised simplex method incorporating an implicit column generation technique, and exploited the special Generalized Upper Bounding structure as well as the possibility of eta-factorization for efficiently updating the dual variables and finding the solution. Experimental results on a number of networks demonstrate the suitability of this approach.     

Gate‐Controlled Energy Barrier at a Graphene/Molecular Semiconductor Junction

The formation of an energy‐barrier at a metal/molecular semiconductor junction is a universal phenomenon which limits the performance of many molecular semiconductor‐based electronic devices, from field‐effect transistors to light‐emitting diodes. In general, a specific metal/molecular semiconductor combination of materials leads to a fixed energy‐barrier. However, in this work, a graphene/C₆₀ vertical field‐effect transistor is presented in which control of the interfacial energy‐barrier is demonstrated, such that the junction switches from a highly rectifying diode at negative gate voltages to a highly conductive nonrectifying behavior at positive gate voltages and at room temperature. From the experimental data, an energy‐barrier modulation of up to 660 meV, a transconductance of up to five orders of magnitude, and a gate‐modulated photocurrent are extracted. The ability to tune the graphene/molecular semiconductor energy‐barrier provides a promising route toward novel, high performance molecular devices.     

Study of Optimal Adaptive Rule in Testing Composite Hypothesis

Abstract

In this article on sequential adaptive testing, we have studied the optimal allocation between two populations for testing a composite hypothesis involving the parameters, with the goal of decreasing allocation of one of the treatments to the order of the logarithm of the sample size while decreasing the probability of incorrect selection to zero. We have proved the result for large sample sizes both mathematically and by simulation studies.     

Investigation of Pt and Pd Modified WO<Subscript>3</Subscript> and ZnO Based Thin Film Sensors for Ethanol Sensing

Ethanol sensors based on different WO₃ and ZnO structures are studied in the present work. The XRD and SEM processes are used to characterize the sensing layer’s surface morphology which reveals the presence of nanoparticle in sensing layer. Further reducing the nanoparticle diameter by the addition of palladium (Pd) and platinum (Pt) for both the sensors (ZnO and WO₃) gives good results on sensitivity, operating temperature, response time and recovery time. Nanoparticle diameter for undoped WO₃, Pd-modified WO₃ and Pt-modified WO₃ based sensors is 11.8, 6 and 5.4 nm, whereas nanoparticle diameter for undoped, Pd-modified and Pt-modified ZnO based sensors is 20, 14 and 11 nm, respectively. Analysis of dynamic response of the sensors when exposed to different concentrations of ethanol vapour (from 500 to 10,000 ppm) and various temperatures indicate the improvement in sensitivity up to 77.2% for WO₃ and 74.6% for ZnO based sensors.     

Analysis of Temperature Effect in Quadruple Gate Nano-scale FinFET

Silicon - Quadruple gate FinFET is a promising candidate among other multi-gate MOS devices due to it’s better scalability and higher short channel effect suppression capability in advanced...     

A new method to test system diagnosability

A new necessary and sufficient condition for t-diagnosability of a system is proposed. It is shown that the new scheme is computationally more efficient than the existing one for checking the diagnosability of a system.