VLC Enabled Hybrid Wireless Network for B5G/6G Communications

Wireless Personal Communications - In the present scenario, there is a boom in the demand of the users to achieve increased capacity, high data, low latency, and high-performance rates. 5G New...     

Analytic models for the latency and steady-state throughput of TCP Tahoe, Reno, and SACK

Continuing the process of improvements made to TCP through the addition of new algorithms in Tahoe and Reno, TCP SACK aims to provide robustness to TCP in the presence of multiple losses from the same window. In this paper we present analytic models to estimate the latency and steady-state throughput of TCP Tahoe, Reno, and SACK and validate our models using both simulations and TCP traces collected from the Internet. In addition to being the first models for the latency of finite Tahoe and SACK flows, our model for the latency of TCP Reno gives a more accurate estimation of the transfer times than existing models. The improved accuracy is partly due to a more accurate modeling of the timeouts, evolution of cwnd during slow start and the delayed ACK timer. Our models also show that, under the losses introduced by the droptail queues which dominate most routers in the Internet, current implementations of SACK can fail to provide adequate protection against timeouts and a loss of roughly more than half the packets in a round will lead to timeouts. We also show that with independent losses SACK performs better than Tahoe and Reno and, as losses become correlated, Tahoe can outperform both Reno and SACK.     

A Convenient Synthesis of Some Coumarin Derivatives Using SnCl2 · 2H2O as Catalyst

Some substituted coumarins have been synthesized by von-Pechmann condensation using SnCl₂ · 2H₂O (10 mol %) as catalyst in ethanolic medium. The reactions are simple, easy in handling and environmentally benign.     

A KBES for integrated engineering

The expert process of engineering involves application of knowledge fundamental to the engineering process at one end, and of compiled knowledge in the form of heuristics gained through many years of experience at the other. Developments in Artificial Intelligence have made the process of computer aided engineering richer by enabling the simulation of this human process of engineering, which until now has been difficult to capture. Knowledge Based Expert Systems (KBES) in engineering developed so far have addressed only narrow domains of the entire process of engineering. This paper discusses a KBES shell (IES) that integrates various AI technologies, which makes it possible to implement new problem-solving strategies required for efficient handling of the entire engineering process. Issues in the integrated process of engineering are discussed first, in order to evolve a specification for the shell. The blackboard architecture is shown to be the ideal backbone for such a shell. The details of inference techniques, the knowledge based backtracking mechanism and the DBMS used in the IES are presented. The application of the IES is demonstrated using the domain of integrated engineering of steel industrial structures as an example. This application encompasses the engineering activities of conceptual design, detailed design, documentation and construction planning.     

On the improvement of detection characteristics of trans-stilbene crystals by improving crystal perfection

Organic molecular scintillating crystals are noted for their good timing and particle discrimination process. Trans-stilbene is one such candidate noted for its good particle detection characteristics for the past five decades. Progressive strengthening of detection characteristics of trans-stilbene has been attempted by improving crystal perfection. A series of timing resolution studies have been carried out for the Bridgman grown trans-stilbene crystals under different experimental conditions. The results were compared with the previously reported values. Pulse shape discrimination process has been carried out for ²⁴¹Am and ²⁵²Cf sources and good discrimination has been obtained for gamma-alpha and gamma-neutron sources from the grown organic phosphor crystal. Electronic Publication     

Space and time efficient parallel algorithms and software for EST clustering

Expressed sequence tags, abbreviated as ESTs, are DNA molecules experimentally derived from expressed portions of genes. Clustering of ESTs is essential for gene recognition and for understanding important genetic variations such as those resulting in diseases. We present the algorithmic foundations and implementation of PaCE, a parallel software system we developed for large-scale EST clustering. The novel features of our approach include 1) design of space-efficient algorithms to limit the space required to linear in the size of the input data set, 2) a combination of algorithmic techniques to reduce the total work without sacrificing the quality of EST clustering, and 3) use of parallel processing to reduce runtime and facilitate clustering of large data sets. Using a combination of these techniques, we report the clustering of 327,632 rat ESTs in 47 minutes, and 420,694 Triticum aestivum ESTs in 3 hours and 15 minutes, using a 60-processor IBM xSeries cluster. These problems are well beyond the capabilities of state-of-the-art sequential software. We also present thorough experimental evaluation of our software including quality assessment using benchmark Arabidopsis EST data.     

Performance and buffering requirements of Internet protocols overATM ABR and UBR services

ATM networks are quickly being adopted as backbones over various parts of the Internet. This article studies the dynamics and performance of the TCP/IP protocol over the ABR and UBR services of ATM networks. Specifically the buffering requirements in the ATM switches as well as the ATM edge devices. It is shown that with a good switch algorithm, ABR pushes congestion to the edges of the ATM network while UBR leaves it inside the ATM portion. As a result, the switch ABR buffer requirement for zero-packet-loss high-throughput TCP transmission is a sublinear function of the number of TCP connections     

Cationic surfactant-selective potentiometric sensors based on neutral ion-pair carrier complexes

Poly(vinyl chloride) (PVC) membrane electrodes to determine monomer concentrations of tetradecyltrimethylammonium ion (TTA⁺) and hexadecylpyridinium ion (HPy⁺) based on neutral ion-pair carrier complexes of tetradecyltrimethylammonium dodecyl sulfate (TTA⁺-DS⁻) and hexadecylpyridinium dodecyl sulfate (HPy⁺-DS⁻), respectively, are reported here. The electrodes exhibit a Nernstian slope of 59 mV per decade for TTA⁺ and a sub-Nernstian slope of 34.5 mV per decade for HPy⁺ ions. The TTA⁺ ion-selective electrode (ISE) and the HPy⁺-ISE can determine the monomer units down to concentrations as low as 4.0×10⁻⁴M and 1.66×10⁻⁵ M, respectively. The effect of various additives, such as the anionic polyelectrolyte sodium dextran sulfate and macrocyclic β-cyclodextrin, on the surface activity exhibited by the cationic surfactants TTA⁺-DS⁻ and HPy⁺-DS⁻, in the presence of background concentrations of NaCl was also examined with surfactant-selective sensors. The Gibbs free energy of micelle formation (ΔG _m ) of both surfactants in the presence of various additives was calculated and found to be less favorable with respect to an increase in the amount of additives due to less availability of cationic surfactant monomer units. The ion-pair complexes TTA⁺-DS⁻ and HPy⁺-DS⁻ were found to behave as selective carrier compounds in PVC membranes in determining the concentration of monomer units of both TTA⁺ and HPy⁺, respectively. The proposed sensors worked well at a fairly acidic pH of 1–6.5 with response time of 60 s. The sensors responded well to the surfactant ions even in the presence of additives at lower concentration. The lifetime of the sensors is 3 mon.     

Prediction of properties of rubber by using artificial neural networks

Different rubber formulations were designed using nitrile rubber and a mixed crosslinking system consisting of sulfur/accelerator and electron beam radiation. Based on the experimental results, an artificial neural network (ANN) was constructed to simulate the mechanical properties and volume fraction of rubber. The ANN could predict accurately the above properties for a series of nitrile rubber compounds. However, the number of training data played a key role in the ANN predictive quality. In addition, the more complex the nonlinear relation between input and output was, the larger was the number of training dataset required. The predicted results were further validated using another mathematical model. The constructed ANN was verified with a completely different styrene butadiene rubber system. The prediction was found to be extremely good. © 2006 Wiley Periodicals, Inc. J Appl Polym Sci 100: 2227–2237, 2006     

Yield optimization for complex reactor systems

For complex reactions, the choice of reactor type is important for attaining optimum product yields. Strategies for specifying reactor types have beenIn this paper, product yield in a Van de Vusse reaction scheme has been studied for a wide range of the rate constants, in a reactor system consisting