Reliability analysis of star type local area networks

The present work studies the reliability of a star type computer networking system, normally used for Local Area Networks (LAN). The regenerative point technique has been used to derive expressions for various reliability characteristics of interest. Numerical computations have also been done to present the results graphically.     

White electroluminescence from stacked organic light emitting diode

Electroluminescent zinc complex [(2-(2-hydroxyphenyl)benzoxazole)(2-methyl-8-hydoxyquinoline)] zinc [Zn(hpb)mq] has been synthesized. It has been used with a blue emitting zinc complex Zn(hpb)₂, to fabricate stacked organic light emitting diode (OLED). Thickness of layers of these materials has been optimized to achieve white light emission. The maximum luminescence of the device was 8390 Cd/m² at 14 V. Commission Internationale de l’Eclairage coordinates of the device, with 40 nm thickness of Zn(hpb)₂ and 15 nm thickness of Zn(hpb)mq, were (0.29, 0.38) at 7 V and were well within the white region. A model has been presented for simulating the electroluminescence (EL) spectrum of stacked OLED based on Gaussian disorder model using Monte-Carlo technique. From the model the energetic disorders, highest ocuppied molecular orbital and lowest unoccupied molecular orbital positions of different emissive layers and the excimer layer have been estimated.     

C–V index of hyperabrupt p-n junctions

C–V index n for hyperabrupt p⁺-n junctions with exponentially retrograded n-region has been computed numerically for different values of parameters characterizing the impurity profile and the results have been plotted graphically. Although n is found to vary with the bias across the junction for any given impurity distribution, the maximum value nmax of n is determined only by the ratio of the background concentration to the crossover concentration in the retrograded region. By making this ratio R₀ smaller and smaller, values of n substantially larger than unity can be obtained. Practical considerations, however, limit the maximum value of n to about 10. An empirical relation expressing nmax as a function of the ratio R₀ has been obtained. Calculated results are compared with the values of n measured on hyperabrupt junctions fabricated by a double diffusion process.     

Segmented organosiloxane copolymers. 1. Synthesis of siloxane—urea copolymers

Siloxane—urea linked segmented copolymers were synthesized from ,ω-bis(aminopropyl)poly dimethylsiloxane oligomers and various aromatic and cycloaliphatic diisocyanates such as MDI, TDI and H-MDI. Molecular weights of the siloxane oligomers were varied in the range of 1000–4000 g mole⁻¹ and high molecular weight linear copolymers were obtained by employing homogeneous solution polymerization techniques. The solvent used during the reactions has a very critical role on the average molecular weight of the final product. Bis(2-ethoxy ethyl) ether (EEE) and THF were most useful. Chain extenders such as aliphatic or cycloaliphatic diamines were also used in some reactions and in this case polar cosolvents such as NMP were also required. The yields were usually very high and the amorphous products obtained from the simple stoichiometric linking reaction were soluble in various common organic solvents. Formation of urea linkages during the polymerization were followed by FTi.r. spectroscopy. The copolymers were characterized by g.p.c. and intrinsic viscosity measurements, FTi.r., n.m.r. and thermal analysis. All these results together with mechanical characterization described in the accompanying second paper of this series indicate the formation of novel, segmented elastomeric siloxane—urea copolymers.     

Simulation of laminar and turbulent impeller stirred tanks using immersed boundary method and large eddy simulation technique in multi-block curvilinear geometries

Impeller stirred tanks are commonly used in the chemical processing industries (CPI) for a variety of mixing and blending technologies. Such processes require accurate modeling of the turbulent flow in the tank over a range of operating conditions (e.g. impeller speed), and in addition, require a computationally efficient solution strategy that can represent moving rigid geometric parts (impellers) in the tank. In the present study, a methodology is proposed that combines the advantages of the immersed boundary method (IBM) to represent moving rigid geometries with the efficiency of multi-block structured curvilinear meshes (to minimize wasted grid points) for the representation of overall complex domains. The IBM implementation on a multi-block curvilinear mesh is advocated for the simulations of impeller stirred tank reactors (STR) and has distinct advantages over other competing methods. In the present work, the curvilinear-IBM methodology is further combined with the curvilinear coordinate implementation of large eddy simulation (LES) technique to address the issue of modeling unsteady turbulent flows in the STR. To verify the implementation of IBM in a multi-block curvilinear geometry, a laminar STR with a stack of four pitched blade impellers on a single shaft is simulated and compared against experimental data. Verification of the combined IBM-LES implementation strategy in curvilinear coordinates is done through comparisons with the measurements of turbulent flow in a baffled STR with a single pitched blade impeller. For both laminar and turbulent STR, the predictions are in very good agreement with measurements. It is suggested here that this methodology can be reliably used as a predictive tool for the flow fields in STRs with complex geometries.     

An effective scheme for image texture classification based on binary local structure pattern

Effectiveness of local binary pattern (LBP) features is well proven in the field of texture image classification and retrieval. This paper presents a more effective completed modeling of the LBP. The traditional LBP has a shortcoming that sometimes it may represent different structural patterns with same LBP code. In addition, LBP also lacks global information and is sensitive to noise. In this paper, the binary patterns generated using threshold as a summation of center pixel value and average local differences are proposed. The proposed local structure patterns (LSP) can more accurately classify different textural structures as they utilize both local and global information. The LSP can be combined with a simple LBP and center pixel pattern to give a completed local structure pattern (CLSP) to achieve higher classification accuracy. In order to make CLSP insensitive to noise, a robust local structure pattern (RLSP) is also proposed. The proposed scheme is tested over three representative texture databases viz. Outex, Curet, and UIUC. The experimental results indicate that the proposed method can achieve higher classification accuracy while being more robust to noise.     

Tectonic evolution of Duns in Kumaun Sub‐Himalaya,India: a remote sensing and GIS‐based study

The tectonic evolution of the Sub‐Himalayan longitudinal intermontane valleys, called ‘Duns’, in the Kumaun region of India is not yet understood as the thick forest cover and extensive anthropogenic modifications of the landscape have made it difficult to identify the various tectonic and geomorphic features. In this paper we present a conceptual model of the tectonic evolution of Kota and Pawalgarh Duns of the region, based on interpretation of digitally processed IRS P6 LISS III imagery and analyses of Digital Terrain Models (DTMs) in a Geographic Information System (GIS), together with field investigations. The Duns were formed as the fault‐bend folded hills came up in front of the southern piedmont zone of the frontal Himalaya along the Himalayan Frontal Thrust (HFT) and the Pawalgarh Fault. The landscape of the Duns is further modified as a result of movement along subsequently developed transverse faults.     

A systematic review on clustering and routing techniques based upon LEACH protocol for wireless sensor networks

In recent years, Wireless Sensor Networks (WSNs) have emerged as a new powerful technology used in many applications such as military operations, surveillance system, Intelligent Transport Systems (ITS) etc. These networks consist of many Sensor Nodes (SNs), which are not only used for monitoring but also capturing the required data from the environment. Most of the research proposals on WSNs have been developed keeping in view of minimization of energy during the process of extracting the essential data from the environment where SNs are deployed. The primary reason for this is the fact that the SNs are operated on battery which discharges quickly after each operation. It has been found in literature that clustering is the most common technique used for energy aware routing in WSNs. The most popular protocol for clustering in WSNs is Low Energy Adaptive Clustering Hierarchy (LEACH) which is based on adaptive clustering technique. This paper provides the taxonomy of various clustering and routing techniques in WSNs based upon metrics such as power management, energy management, network lifetime, optimal cluster head selection, multihop data transmission etc. A comprehensive discussion is provided in the text highlighting the relative advantages and disadvantages of many of the prominent proposals in this category which helps the designers to select a particular proposal based upon its merits over the others.     

On clustering massive text and categorical data streams

In this paper, we will study the data stream clustering problem in the context of text and categorical data domains. While the clustering problem has been studied recently for numeric data streams, the problems of text and categorical data present different challenges because of the large and un-ordered nature of the corresponding attributes. Therefore, we will propose algorithms for text and categorical data stream clustering. We will propose a condensation based approach for stream clustering which summarizes the stream into a number of fine grained cluster droplets. These summarized droplets can be used in conjunction with a variety of user queries to construct the clusters for different input parameters. Thus, this provides an online analytical processing approach to stream clustering. We also study the problem of detecting noisy and outlier records in real time. We will test the approach for a number of real and synthetic data sets, and show the effectiveness of the method over the baseline OSKM algorithm for stream clustering.