Capacity of Multiservice WCDMA Networks with Variable GoS

Traditional definitions of capacity of CDMA networks are either related to the number of calls they can handle (pole capacity) or to the arrival rate that guarantees that the rejection rate (or outage) is below a given fraction (Erlang capacity). We extend the latter definition to other quality of service (QoS). We consider best-effort (BE) traffic sharing the network resources with real-time (RT) applications. As is often the case in CDMA systems, we assume that the BE traffic access is done using a time-shared channel (such as the HDR or the HSDPA). BE applications can adapt their instantaneous transmission rate to the available one and thus need not be subject to admission control or outages. Their meaningful QoS is the average delay (i.e. the sojourn time). The delay aware capacity is then defined as the arrival rate of BE calls that the system can handle such that their expected delay is bounded by a given constant. We compute in this paper both the blocking probability of the RT traffic having an adaptive Grade of Service (GoS) as well as the expected delay of the BE traffic for an uplink multicell WCDMA system. This yields the Erlang capacity for former and the delay capacity for the latter. A shorter version of this work was presented at the IEEE WCNC 2003 in New Orleans, LA, USA. Nidhi Hegde received the B.Sc. degree with specialization in Biochemistry (1995) from the University of Alberta, Canada, and the M.Sc (1997) in Computer Science and the Ph.D (2000) degree in Telecommunications and Computer Networking, both at the University of Missouri-Kansas City, USA. She has been with France Telecom R&D since January 2005. Her research interests include performance evaluation of networks, with an emphasis on wireless networks. E. Altman received the B.Sc. degree in electrical engineering (1984), the B.A. degree in physics (1984) and the Ph.D. degree in electrical engineering (1990), all from the Technion-Israel Institute, Haifa. In (1990) he further received his B.Mus. degree in music composition in Tel-Aviv university. Since 1990, he has been with INRIA (National research institute in informatics and control) in Sophia-Antipolis, France. His current research interests include performance evaluation and control of telecommunication networks and in particular congestion control, wireless communications and networking games. He is in the editorial board of several scientific journals: Stochastic Models, JEDC, COMNET, SIAM SICON and WINET. He has been the (co)chairman of the program committee of several international conferences and workshops (on game theory, networking games and mobile networks). More informaion can be found at     

Constrained MIMO dynamic discrete-time modeling exploiting optimal experimental design

This article presents a new multiple input, multiple output (MIMO) constrained discrete-time modeling (DTM) approach for dynamic block-oriented processes that does not require the nonlinear steady state characteristics to be known prior to model development. This approach uses an efficient statistical experimental design to provide design points for sequential step tests. The DTM is developed from this data in two stages. In the first stage, the ultimate response (steady state) model is determined from just the ultimate response data of the sequential step tests. In the second stage, the dynamic parameters are estimated under the constraint of the fitted ultimate response model obtained in the first stage. The constrained formulation is given for MIMO Hammerstein and Wiener block-oriented systems. Comparison of the proposed constrained DTM method is made with unconstrained DTM and constrained continuous-time modeling (CTM). Prediction accuracy of the proposed method is significantly better than unconstrained DTM and comparable to constrained CTM for the process studied.     

Interpenetrating polymer networks: A review on synthesis and properties

A review containing 83 references on the chemistry aspects (mechanism and structure–property relationships) for IPN synthesis is presented.     

Antibacterial potential of silver nanoparticles biosynthesised using Canarium ovatum leaves extract

Silver nanoparticles (AgNPs) have been extensively used as antibacterial agents, owing to their ease of preparation. In the present study, leaves extract of Canarium ovatum have been employed for the biosynthesis of silver nanoparticles (CO‐AgNPs). CO‐AgNPs were synthesised under very mild, eco‐friendly manner where the plant extract acted both as reducing and capping agent. These AgNPs were synthesised by taking into account several parameters, that included, time of reaction, concentration of AgNO₃, amount of extract and temperature of reaction. The optimisation studies suggested efficient synthesis of CO‐AgNPs at 25°C when 1.5 mM AgNO₃ was reduced with 1:20 ratio of plant extract for 40 min. Size determination studies done on dynamic light scattering and scanning electron microscope suggested of spherical shape nanoparticles of size 119.7 ± 7 nm and 50–80 nm, respectively. Further, characterisations were done by Fourier transform infrared and energy‐dispersive X‐ray spectroscopy to evaluate the functional groups and the purity of CO‐AgNPs. The antibacterial efficacy of CO‐AgNPs was determined against the bacterial strain Pseudomonas aeruginosa. As evident from disc diffusion method studies, CO‐AgNPs remarkably inhibited the growth of the tested microorganism. This study suggested that C. ovatum extract efficiently synthesises CO‐AgNPs with significant antibacterial properties and can be good candidates for therapeutics.Inspec keywords: antibacterial activity, nanoparticles, silver, nanofabrication, particle size, light scattering, scanning electron microscopy, Fourier transform infrared spectra, X‐ray chemical analysis, microorganisms, biomedical materials, nanomedicineOther keywords: antibacterial potential, silver nanoparticles, biosynthesis, Canarium ovatum leave extract, plant extract, reducing agent, capping agent, antibacterial agents, reaction time, reaction temperature, dynamic light scattering, scanning electron microscopy, spherical shape nanoparticles, Fourier transform infrared spectroscopy, functional groups, bacterial strain Pseudomonas aeruginosa, disc diffusion method, microorganism, energy‐dispersive X‐ray spectroscopy, temperature 25 degC, time 40 min, Ag     

Optimizing MapReduce for energy efficiency

The efficient use of energy is essential to address concerns of cost and sustainability. Many data centers contain MapReduce clusters to process Big Data applications. A large number of machines and fault tolerance capabilities make MapReduce clusters energy inefficient. In this paper, we present a Configurator based on performance and energy models to improve the energy efficiency of MapReduce systems. Our solution is novel as it takes into account the dependence of the performance and energy consumption of a cluster on MapReduce parameters. While this dependence is known, we are the first to model it and design a Configurator to optimize these parameter settings for maximizing the energy efficiency of MapReduce systems. Our empirical evaluations show that the Configurator can result in up to 50% improvement in the energy efficiency of typical MapReduce applications in two architecturally different clusters.     

Hydrogen evolution by water splitting using novel composite zeolite-based photocatalyst

Novel zeolite-based material showing photocatalytic properties in the visible light have been synthesized by incorporating TiO₂, heteropolyacid (HPA) and transition metal, namely cobalt. This material shows high efficiency for water splitting under visible light irradiation. Hydrogen generation to the tune of 2171 μmol/h/g of TiO₂ has been achieved for the composite photocatalyst synthesized as compared to H₂ evolution rate to the tune of 131.6 μmol/h/g of TiO₂ for Degussa P25. This suggests that the TiO₂ which gets effectively dispersed and stabilized on the surface of zeolite works synergistically with cobalt and heteropolyacid to make the material active in visible light for evolution of hydrogen from water. TiO₂ is the photocatalyst, HPA functions as the dye sensitiser as well as redox system; zeolite functions as support matrix and as electron acceptor in synergy with cobalt. The probable mechanism for improved hydrogen evolution rate using such composite photocatalyst has been discussed.     

Joint watermarking and encryption for still visual data

Joint watermarking and encryption is an upcoming security solution that combines leading but complementary techniques to achieve an enhanced security level. Real time applications using joint watermarking and encryption framework has three requirements: data to be efficiently compressed, watermarking technique to sustain compression, and encryption technique to be developed in a way so as not to disturb the compression efficiency. Finding an optimal solution that combines the three techniques while fulfilling these requirements is a challenging problem. This paper thus, proposes a wavelet domain based joint watermarking and encryption framework that employs singular value decomposition based watermark embedding and sign bit encryption prior to compression. The varying significance of different subbands has been considered to encrypt the data without adversely effecting the compression ratio. Experimental analysis using various evaluation parameters and attack scenarios has revealed the ability of the proposed framework to prove content-ownership, even from the encrypted data. Comparative analysis with the existing techniques reflect its ability to provide better security with less computational resources. This makes it a preferable solution for data security at all stages of data archival, transmission or distribution.     

A comparison of multiple behavior models in a simulation of the aftermath of an improvised nuclear detonation

We describe a large-scale simulation of the aftermath of a hypothetical 10kT improvised nuclear detonation at ground level, near the White House in Washington DC. We take a synthetic information approach, where multiple data sets are combined to construct a synthesized representation of the population of the region with accurate demographics, as well as four infrastructures: transportation, healthcare, communication, and power. In this article, we focus on the model of agents and their behavior, which is represented using the options framework. Six different behavioral options are modeled: household reconstitution, evacuation, healthcare-seeking, worry, shelter-seeking, and aiding & assisting others. Agent decision-making takes into account their health status, information about family members, information about the event, and their local environment. We combine these behavioral options into five different behavior models of increasing complexity and do a number of simulations to compare the models.