Epitaxial growth of GaN on (0001) Al<Subscript>2</Subscript>O<Subscript>3</Subscript> via solution-cast seed layer formation process using Ga(mDTC)<Subscript>3</Subscript>

This paper reports an alternative method for the growth of GaN epitaxial layer on (0001) Al₂O₃ substrate by hot-wall vapor phase epitaxy technique. Tris (N,N-dimethyldithiocarbamato)-gallium (III), Ga(mDTC)₃ was introduced as a precursor material for the seed layer formation in the growth of GaN. Optimal growth conditions with seed layers formed by the Ga(mDTC)₃ concentration of 0.047 mol/L were identified: Growth temperature was found to be 850 °C, and optimal distance between the reactant outlet and substrate was determined to be 12.5 cm. Characterization results showed that this growth method produce high-crystallinity GaN epitaxial layers at a relatively lower growth temperature compared to the existing growth techniques and simplify the growth process.     

Thermal properties and crystalline structure of liquid crystalline poly(ethylene terephthalate‐co‐2(3)‐chloro‐1,4‐phenylene terephthalate)

Thermal properties and crystalline structure of liquid crystalline (LC) poly(ethylene terephthalate‐co‐2(3)‐chloro‐1,4‐phenylene terephthalate) [copoly(ET/CPT)] were investigated using differential scanning calorimetry (DSC), thermogravimetry (TGA), limiting oxygen index (LOI) measurement, electron dispersive X‐ray analysis (EDX), X‐ray diffractometry, and infrared spectrometry (IR). The thermal transition temperatures of copoly(ET/CPT) were changed with the composition. Copoly(ET/CPT) showed two thermal decomposition steps and the residues at 700°C and LOI values of copoly(ET/CPT) were almost proportional to its chlorine content. The activation energy of thermal decomposition of LC units was very low compared to that of poly(ethylene terephthalate)(PET) units. Crystal structure of copoly(ET/CPT) (20/80) was of triclinic system with the lattice constants of a = 9.98 A?, b = 8.78 A?, c = 12.93 A?, α = 97.4°, β = 96.1°, and γ = 90.8°, which is very close to that of poly(chloro‐p‐phenylene terephthlate) (PCPT) with the lattice constants of a = 9.51 A?, b = 8.61 A?, c = 12.73 A?, α = 96.8°, β = 95.4°, and γ = 90.8°. When copoly(ET/CPT)(50/50) was annealed at 220°C in vacuum, crystallization induced sequential reordering (CISR) was not observed but the heat of fusion was slightly increased due to the increase of the trans isomer content in PET units. © 2002 Wiley Periodicals, Inc. J Appl Polym Sci 84: 1286–1294, 2002; DOI 10.1002/app.10451     

Rate-distortion optimized tree-structured compression algorithms for piecewise polynomial images.

This paper presents novel coding algorithms based on tree-structured segmentation, which achieve the correct asymptotic rate-distortion (R-D) behavior for a simple class of signals, known as piecewise polynomials, by using an R-D based prune and join scheme. For the one-dimensional case, our scheme is based on binary-tree segmentation of the signal. This scheme approximates the signal segments using polynomial models and utilizes an R-D optimal bit allocation strategy among the different signal segments. The scheme further encodes similar neighbors jointly to achieve the correct exponentially decaying R-D behavior (D(R) - c(o)2(-c1R)), thus improving over classic wavelet schemes. We also prove that the computational complexity of the scheme is of O(N log N). We then show the extension of this scheme to the two-dimensional case using a quadtree. This quadtree-coding scheme also achieves an exponentially decaying R-D behavior, for the polygonal image model composed of a white polygon-shaped object against a uniform black background, with low computational cost of O(N log N). Again, the key is an R-D optimized prune and join strategy. Finally, we conclude with numerical results, which show that the proposed quadtree-coding scheme outperforms JPEG2000 by about 1 dB for real images, like cameraman, at low rates of around 0.15 bpp.     

Lipid peroxides, glutathione, gamma-glutamylcysteine synthetase and gamma-glutamyltranspeptidase activities in several tissues of rats following water-immersion stress

The effects of water-immersion restraint (WIR) stress on lipid peroxide, glutathione (GSH), glutathione peroxidase (GSH-Px), gamma-glutamylcysteine synthetase (gamma-GCS) and gamma-glutamyltranspeptidase (gamma-GT) activities in several tissues of rats were investigated. Hepatic and intestinal lipid peroxide levels were increased significantly in the WIR stress group. In both tissues, GSH levels were significantly decreased and gamma-GCS activity was significantly increased. In addition, gamma-GT activities remained unchanged in both tissues following WIR stress. However, lipid peroxide and GSH levels did not change in the stomach and brain in the WIR stress group compared to the control group. These results suggest that lipid peroxidation, but not the depression of GSH synthesis and/or the increase of GSH breakdown may be a factor in hepatic and intestinal GSH reduction following WIR stress.     

A Video Server cost/performance Estimator tool

The Video Server Estimator (VSE) is an analytical tool which allows a user to perform a cost/performance analysis of video servers with hierarchical storage. The underlying model comprises multiple systems, main memory, expanded storage, disks and a tape library. The main objective of the tool is to optimally allocate the video files to different storage media based on the system parameters and the video file request probability distribution. The cost and the size of the video server that can accommodate a customer profile are determined. Furthermore, the impact of design parameters on the cost and performance are examined through a parametric analysis.     

Application of genetic algorithm-based intuitionistic fuzzy weighted c-ordered-means algorithm to cluster analysis

Knowledge and Information Systems - With the advance of information technology, many fields have begun using data clustering to reveal data structures and obtain useful information. Most of the...     

Deep neural network-based fusion model for emotion recognition using visual data

The Journal of Supercomputing - In this study, we present a fusion model for emotion recognition based on visual data. The proposed model uses video information as its input and generates emotion...     

Integrating local action elements for action analysis

In this paper, we propose a framework for human action analysis from video footage. A video action sequence in our perspective is a dynamic structure of sparse local spatial–temporal patches termed action elements, so the problems of action analysis in video are carried out here based on the set of local characteristics as well as global shape of a prescribed action. We first detect a set of action elements that are the most compact entities of an action, then we extend the idea of Implicit Shape Model to space time, in order to properly integrate the spatial and temporal properties of these action elements. In particular, we consider two different recipes to construct action elements: one is to use a Sparse Bayesian Feature Classifier to choose action elements from all detected Spatial Temporal Interest Points, and is termed discriminative action elements. The other one detects affine invariant local features from the holistic Motion History Images, and picks up action elements according to their compactness scores, and is called generative action elements. Action elements detected from either way are then used to construct a voting space based on their local feature representations as well as their global configuration constraints. Our approach is evaluated in the two main contexts of current human action analysis challenges, action retrieval and action classification. Comprehensive experimental results show that our proposed framework marginally outperforms all existing state-of-the-arts techniques on a range of different datasets.     

On enabling integrated process compliance with semantic constraints in process management systems

Key to broad use of process management systems (PrMS) in practice is their ability to foster and ease the implementation, execution, monitoring, and adaptation of business processes while still being able to ensure robust and error-free process enactment. To meet these demands a variety of mechanisms has been developed to prevent errors at the structural level (e.g., deadlocks). In many application domains, however, processes often have to comply with business level rules and policies (i.e., semantic constraints) as well. Hence, to ensure error-free executions at the semantic level, PrMS need certain control mechanisms for validating and ensuring the compliance with semantic constraints. In this paper, we discuss fundamental requirements for a comprehensive support of semantic constraints in PrMS. Moreover, we provide a survey on existing approaches and discuss to what extent they are able to meet the requirements and which challenges still have to be tackled. In order to tackle the particular challenge of providing integrated compliance support over the process lifecycle, we introduce the SeaFlows framework. The framework introduces a behavioural level view on processes which serves a conceptual process representation for constraint specification approaches. Further, it provides general compliance criteria for static compliance validation but also for dealing with process changes. Altogether, the SeaFlows framework can serve as formal basis for realizing integrated support of semantic constraints in PrMS.     

A new ECG beat clustering method based on kernelized fuzzy c-means and hybrid ant colony optimization for continuous domains

The kernelized fuzzy c-means algorithm uses kernel methods to improve the clustering performance of the well known fuzzy c-means algorithm by mapping a given dataset into a higher dimensional space non-linearly. Thus, the newly obtained dataset is more likely to be linearly seprable. However, to further improve the clustering performance, an optimization method is required to overcome the drawbacks of the traditional algorithms such as, sensitivity to initialization, trapping into local minima and lack of prior knowledge for optimum paramaters of the kernel functions. In this paper, to overcome these drawbacks, a new clustering method based on kernelized fuzzy c-means algorithm and a recently proposed ant based optimization algorithm, hybrid ant colony optimization for continuous domains, is proposed. The proposed method is applied to a dataset which is obtained from MIT–BIH arrhythmia database. The dataset consists of six types of ECG beats including, Normal Beat (N), Premature Ventricular Contraction (PVC), Fusion of Ventricular and Normal Beat (F), Artrial Premature Beat (A), Right Bundle Branch Block Beat (R) and Fusion of Paced and Normal Beat (f). Four time domain features are extracted for each beat type and training and test sets are formed. After several experiments it is observed that the proposed method outperforms the traditional fuzzy c-means and kernelized fuzzy c-means algorithms.