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...     

Identifying crop water stress using deep learning models

Neural Computing and Applications - The identification of water stress is a major challenge for timely and effective irrigation to ensure global food security and sustainable agriculture. Several...     

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.     

Predictive models for multibiometric systems

Recognizing a subject given a set of biometrics is a fundamental pattern recognition problem. This paper builds novel statistical models for multibiometric systems using geometric and multinomial distributions. These models are generic as they are only based on the similarity scores produced by a recognition system. They predict the bounds on the range of indices within which a test subject is likely to be present in a sorted set of similarity scores. These bounds are then used in the multibiometric recognition system to predict a smaller subset of subjects from the database as probable candidates for a given test subject. Experimental results show that the proposed models enhance the recognition rate beyond the underlying matching algorithms for multiple face views, fingerprints, palm prints, irises and their combinations.     

Initial Deposition of Colloidal Particles on a Rough Nanofiltration Membrane

The initial rate of colloid deposition onto semi‐permeable membranes is largely controlled by the coupled influence of permeation drag and particle‐membrane colloidal interactions. Recent studies show that the particle‐membrane interactions are subject to immense local variations due to the inherent morphological heterogeneity (roughness) of reverse osmosis (RO) and nanofiltration (NF) membranes. This experimental investigation reports the effect of membrane roughness on the initial deposition of polystyrene latex particles on a rough NF membrane during cross flow membrane filtration under different operating pressures and solution chemistries. Atomic force microscopy was used to characterize the roughness of the membrane and observe the structure of particle deposits. At the initial stages of fouling, the AFM images show that more particles preferentially accumulate near the “peaks” than in the “valleys” of the rough NF membrane surface.     

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.     

An algorithm for recognizing palm polygons

A polygonP is said to be apalm polygon if there exists a pointxP such that the Euclidean shortest path fromx to any pointyP makes only left turns or only right turns. The set of all such pointsx is called thepalm kernel. In this paper we propose an O(E) time algorithm for recognizing a palm polygonP, whereE is the size of the visibility graph ofP. The algorithm recognizes the given polygonP as a palm polygon by computing the palm kernel ofP. If the palm kernel is not empty,P is a palm polygon.The extended abstract of this paper was reported at the Second Canadian Conference in Computational Geometry, pp. 246–251, 1990     

Analyzing multi-hop routing feasibility for sensor data harvesting using mobile sinks

This paper presents a modeling framework for characterizing the feasibility and impacts of multi-hop packet routing in sensor networks with mobile sinks. Data collection in sensor networks using mobile sinks has recently been investigated to improve energy performance at the cost of collection delay. Although the data collection can be accomplished with varying degrees of multi-hop routing, for a given data generation rate, as the extent of multi-hop routing increases, the round traversal time of the sink decreases. At the same time, the interference experienced by the mobile sink-to-sensor links and the consequent upload time go up. This paper characterizes these competing effects and develops a methodology for determining the extent of multi-hop routing that is feasible for given network and application parameters such as sensor data generation rate, wireless link capacity between sensors and mobile sink, the speed of the mobile sink and node density.     

Algorithms for computing diffuse reflection paths in polygons

Let s be a point source of light inside a polygon P of n vertices. A polygonal path from s to some point t inside P is called a diffuse reflection path if the turning points of the path lie on edges of?P. A?diffuse reflection path is said to be optimal if it has the minimum number of reflections on the path. The problem of computing a diffuse reflection path from s to t inside P has not been considered explicitly in the past. We present three different algorithms for this problem which produce suboptimal paths. For constructing such a path, the first algorithm uses a greedy method, the second algorithm uses a transformation of a minimum link path, and the third algorithm uses the edge–edge visibility graph of?P. The first two algorithms are for polygons without holes, and they run in O(n+klogn) time, where k denotes the number of reflections in the constructed path. The third algorithm is for polygons with or without holes, and it runs in O(n ²) time. The number of reflections in the path produced by this third algorithm can be at most three times that of an optimal diffuse reflection path. Though the combinatorial approach used in the third algorithm gives a better bound on the number of reflections on the path, the first and the second algorithms stand on the merit of their elegant geometric approaches based on local geometric information.