VLSI Implementation of Forward Error Control Technique for ATM Networks

In asynchronous transfer mode (ATM) networks, fixed length cells of 53 bytes are transmitted. A cell may be discarded during transmission due to buffer overflow or a detection of errors. Cell discarding seriously degrades transmission quality. The quality degradation can be reduced by employing efficient forward error control (FEC) to recover discarded cells. In this paper, we present the design and implementation of decoding equipment for FEC in ATM networks based on a single parity check (SPC) product code using very‐large‐scale integration (VLSI) technology. FEC allows the destination to reconstruct missing data cells by using redundant parity cells that the source adds to each block of data cells. The functionality of the design has been tested using the Model Sim 5.7cXE Simulation Package. The design has been implemented for a 5 ° 5 matrix of data cells in a Virtex‐E XCV 3200E FG1156 device. The simulation and synthesis results show that the decoding function can be completed in 81 clock cycles with an optimum clock of 56.8 MHz. A test bench was written to study the performance of the decoder, and the results are presented.     

Optimal server allocations for streaming multimedia applications on the Internet

In this paper, we address the server selection problem for streaming applications on the Internet. The architecture we consider is similar to the content distribution networks consisting of geographically dispersed servers and user populations over an interconnected set of metropolitan areas. Server selection issues for Web-based applications in such an environment have been widely addressed; the selection is mostly based on proximity measured using packet delay. Such a greedy or heuristic approach to server selection will not address the capacity planning problem evident in multimedia applications. For such applications, admission control becomes an essential part of their design to maintain Quality of Service (QoS). Our objective in providing a solution to the server selection problem is threefold: first, to direct clients to the nearest server; second, to provide multiple sources to diffuse network load; third, to match server capacity to user demand so that optimal blocking performance can be expected. We accomplish all three objectives by using a special type of Linear Programming (LP) formulation called the Transportation Problem (TP). The objective function in the TP is to minimize the cost of serving a video request from user population x using server y as measured by network distance. The optimal allocation between servers and user populations from TP results in server clusters, the aggregated capacity of each cluster designed to meet the demands of its designated user population. Within a server cluster, we propose streaming protocols for request handling that will result in a balanced load. We implement threshold-based admission control in individual servers within a cluster to enforce the fair share of the server resource to its designated user population. The blocking performance is used as a trigger to find new optimal allocations when blocking rates become unacceptable due to change in user demands. We substantiate the analytical model with an extensive simulation for analyzing the performance of the proposed clustered architecture and the protocols. The simulation results show significant difference in overall blocking performance between optimal and suboptimal allocations in as much as 15% at moderate to high workloads. We also show that the proposed cluster protocols result in lower packet loss and latencies by forcing path diversity from multiple sources for request delivery.     

Processing of carbon-fiber reinforced (SiC + ZrC) mini-composites by soft-solution approach and their characterization

Carbon-fiber reinforced (SiC + ZrC) mini-composites have been prepared via soft-solution process using inorganic precursors. In this process, water-soluble compounds have been used to act as precursor materials to impregnate the fiber tow. Thermal analysis provided the temperature range for the pyrolysis to convert the precursors into the desired (SiC + ZrC) matrix. X-ray diffraction of the composites confirmed the phase formation and the crystallite size of these phases were in the range of 25–40 nm. Cross-sectional microstructures of the composites have shown the matrix formation around each individual fiber. The mechanical properties revealed that the tensile strength and fracture energy of the composites pyrolyzed at 1600 °C were significantly higher with typical composite failure behavior, as compared to those pyrolyzed at 1700 °C. The statistical size effects of the tensile strength were investigated on the basis of the Weibull statistics.     

Influence of geometry on gas holdups in a reversed flow jet loop reactor

The effects of ratio of draft tube to reactor diameter (D_d/D), liquid nozzle diameter (d_N), aeration tube diameter (d_G) and immersion height of the two-fluid nozzle into the draft tube (H_N) on overall and annulus gas holdups for the air-water system were evaluated experimentally in a reversed flow jet loop reactor over wide ranges of gas and liquid flow rates. Both the gas holdups increased with increasing gas and liquid flow rates and with decreasing d_N and H_N. The influence of d_G on gas holdups is found to vary with gas flow rates. Correlations are proposed to predict gas holdups.     

Inferential estimation of molecular weights of polybutadiene rubber by neural networks

Molecular weight distribution, which is characterized by its averages like number average (Mn) and weight average (Mw), is one of the important properties of polybutadiene rubber (PBR), and it is difficult to measure. The objective of this work is to develop models to predict Mn and Mw from readily available process variables. Neural networks that are capable of mapping highly complex and non‐linear dependencies have been adapted to develop models for the Mn and Mw of PBR. The molecular weight distribution and its averages of PBR samples collected over a wide range of operating conditions were measured by the conventional Gel Permeable Chromatograph (GPC) method. Neural networks were trained with relevant data to predict Mn and Mw from process variables. The trained networks were found to generalize well when tested with new data. © 2005 Wiley Periodicals, Inc. J Appl Polym Sci 96: 1611–1618, 2005     

Statistical analysis of accurate prediction of local atmospheric optical attenuation with a new model according to weather together with beam wandering compensation system: a season-wise experimental investigation

Abstract

Atmospheric parameters strongly affect the performance of Free Space Optical Communication (FSOC) system when the optical wave is propagating through the inhomogeneous turbulent medium. Developing a model to get an accurate prediction of optical attenuation according to meteorological parameters becomes significant to understand the behaviour of FSOC channel during different seasons. A dedicated free space optical link experimental set-up is developed for the range of 0.5 km at an altitude of 15.25 m. The diurnal profile of received power and corresponding meteorological parameters are continuously measured using the developed optoelectronic assembly and weather station, respectively, and stored in a data logging computer. Measured meteorological parameters (as input factors) and optical attenuation (as response factor) of size [177147 × 4] are used for linear regression analysis and to design the mathematical model that is more suitable to predict the atmospheric optical attenuation at our test field. A model that exhibits the R² value of 98.76% and average percentage deviation of 1.59% is considered for practical implementation. The prediction accuracy of the proposed model is investigated along with the comparative results obtained from some of the existing models in terms of Root Mean Square Error (RMSE) during different local seasons in one-year period. The average RMSE value of 0.043-dB/km is obtained in the longer range dynamic of meteorological parameters variations.     

Synthesis and Structure Determination of the Adducts of Dibenzo[a,l]pyrene Diol Epoxides and Deoxyadenosine or Deoxyguanosine

Abstract

(±)?Syn?dibenzo[a,l]pyrene diol epoxide (DB[a,l]PDE) and (±)?anti?DB[a,l]PDE were reacted with deoxyadenosine (dA) or deoxyguanosine (dG) in dimethylformamide at 100 °C for 30 min. The crude products were purified by reverse phase HPLC under gradient and isocratic conditions. The structure of each adduct was assigned by 1D and 2D NMR spectra and by fast atom bombardment mass spectrometry. Five adducts were isolated from the reaction of (±)?syn?DB[a,l]PDE and dA: syn?DB[a,l]PDE?N⁶dA?1, syn?DB[a,l]PDE?N⁶dA?2, syn?DB[a,l]PDE?N⁶dA?3, syn?DB[a,l]PDE?N⁶dA?4 and syn?DB[a,l]PDE?N7Ade. Four adducts were isolated from the reaction of (±)?anti?DB[a,l]PDE and dA: anti?DB[a,l]PDE?N⁶dA?1, anti?DB[a,l]PDE?N⁶dA?2, anti?DB[a,l]PDE?N⁶dA?3 and anti?DB[a,l]PDE?N⁶dA?4. Two adducts were isolated from the reaction of (±)?syn?DB[a,l]PDE and dG: (±)?11,12,13?trihydroxy?tetrahydroDB[a,l]P?14?N²dG and (±)?11,12,13?trihydroxy?tetrahydroDB[a,l]P?14?N7Gua. Two adducts were isolated from the reaction of (±)?anti?DB[a,l]PDE and dG: (±)?11,12,13?trihydroxy?tetrahydroDB[a,l]P?14?N²dG and (±)?11,12,13?trihydroxy?tetrahydroDB[a,l]P?14?N7Gua.     

Energy-efficient routing protocol and optimized passive clustering in WSN for SMART grid applications

Due to low cost, ease of implementation and flexibility of wireless sensor networks (WSNs), WSNs are considered to be an essential technology to support the smart grid (SG) application. The prime concern is to increase the lifetime in order to find the active sensor node and thereby to find once the sensor node (SN) dies in any region. For this reason, an energy-efficient Dynamic Source Routing (DSR) protocol needs to provide the right stability region with a prolonged network lifetime. This work is an effort to extend the network's existence by finding and correcting the considerable energy leveraging behaviors of WSN. We build a comprehensive model based on real measures of SG path loss for different conditions by using the characteristics of WSN nodes and channel characteristics. This method also establishes a hierarchical network structure of balanced clusters and an energy-harvesting SN. The cluster heads (CHs) are chosen by these SN using a low overhead passive clustering strategy. The cluster formation method is focused on the use of passive clustering of the particle swarm optimization (PSO). For the sake of eliminating delayed output in the WSN, energy competent dynamic source routing protocol (EC-DSR) is used. Chicken swarm optimization (CSO) in which optimum cluster path calculation shall be done where distance and residual energy should be regarded as limitation. Finally, the results are carried out with regard to the packet distribution ratio, throughput, overhead management, and average end-to-end delay to demonstrate the efficiency of the proposed system.