An analytical model for capacity evaluation of VoIP on HCCA and TCP file transfers over EDCA in an IEEE 802.11e WLAN

In the context of the IEEE 802.11e standard for WLANs, we provide an analytical model for obtaining the maximum number of VoIP calls that can be supported on HCCA, such that the delay QoS constraint of the accepted calls is met, when TCP downloads are coexistent on EDCA. In this scenario, we derive the TCP download throughput by using an analytical model for the case where only TCP sessions are present in the WLAN. We show that the analytical model for combined voice and TCP transfers provides accurate results in comparison with simulations (using ns-2).

An analytical model for the capacity estimation of combined VoIP and TCP file transfers over EDCA in an IEEE 802.11e WLAN

In this paper we develop and numerically explore the modeling heuristic of using saturation attempt probabilities as state dependent attempt probabilities in an IEEE 802.11e infrastructure network carrying packet telephone calls and TCP controlled file downloads, using Enhanced Distributed Channel Access (EDCA). We build upon the fixed point analysis and performance insights in [1]. When there are a certain number of nodes of each class contending for the channel (i.e., have nonempty queues), then their attempt probabilities are taken to be those obtained from saturation analysis for that number of nodes. Then we model the system queue dynamics at the network nodes. With the proposed heuristic, the system evolution at channel slot boundaries becomes a Markov renewal process, and regenerative analysis yields the desired performance measures.The results obtained from this approach match well with ns2 simulations. We find that, with the default IEEE 802.11e EDCA parameters for AC 1 and AC 3, the voice call capacity decreases if even one file download is initiated by some station. Subsequently, reducing the voice calls increases the file download capacity almost linearly (by 1/3 Mbps per voice call for the 11 Mbps PHY).

Electronic phase separation in correlated oxides: The phenomenon, its present status and future prospects

Many transition metal oxide materials of high chemical purity are not necessarily monophasic. Thus, single crystals of chemically pure rare earth manganites and cobaltates of the general formula Ln1-xAxMO3 (Ln=rare earth metal, A=alkaline earth metal, M=Mn, Co) exhibit the phenomenon of electronic phase separation wherein phases of different electronic and magnetic properties coexist. Such phase separation, the length scale of which can vary anywhere between a few nanometers to microns, gives distinct signatures in X-ray and neutron diffraction patterns, electrical and magnetic properties, as well as in NMR and other spectroscopies. While the probe one employs to investigate electronic phase separation depends on the length scale, it is noteworthy that direct imaging of the inhomogeneities has been accomplished. Some understanding of this phenomenon has been possible on the basis of some of the theoretical models, but we are far from unraveling the varied aspects of this new phenomenon. Herein, we present the highlights of experimental techniques and theoretical approaches, and comment on the future outlook for this fascinating phenomenon

Electronic absorption spectra of selenocarbonyl and thiocarbonyl compounds

Electronic absorption spectra of a variety of thiocarbonyl and selenocarbonyl compounds have been compared to point out their similarities. Interesting correlations between the absorption maxima and electronegativities of substituents in both the seleno- and thio-carbonyl compounds have been reported.

Study of the electronic structures of high Tc cuprate superconductors by electron energy loss and secondary electron emission spectroscopies

Energy loss spectra of superconducting YBa2Cu3O6.9' Bi1.5Pb0.5Ca2.5Sr1.5Cu3O10+δ and Tl2CaBa2Cu3O8 obtained at primary electron energies in the 170–310 eV range show features reflecting the commonalities in their electronic structures. The relative intensity of the plasmon peak shows a marked drop across the transition temperature. Secondary electron emission spectra of the cuprates also reveal some features of the electronic structure.

Electronic-Transitions In Hydrogen-Bonded Dimers Of Carboxylic-Acids In The Vapor-Phase - An Electron-Energy Loss Spectroscopic Study

An electron energy loss spectroscopic study of the formic acid dimer has shown bands centred around 7.2, 8.5, 9.8, and 11.1 eV, of which the first and the third bands are assigned to n- rc* transitions and the other two to n-n* transitions; similar transitions are found in the acetic acid dimer.

A study of the electronic structures of n---v addition compounds of BH3 by a combined use of ups and eels

Orbital energies and electronic transition energies of BH3·H2S and BH3·CO obtained from ultraviolet (HeI) photoelectron spectroscopy and electron energy loss spectroscopy are discussed in the light of quantum mechanical calculations. BH3·H2O has been characterized, for the first time, by means of the HeI spectrum and the ionization energies assigned to the various orbitals based on calculations.

Electronic structure of high-Tc Ba0.6K0.4BiO3 by x-ray photoelectron spectroscopy

We have investigated the electronic structure of Ba1-xKxBiO3 (0

Learning dynamic prices in multiseller electronic retail markets with price sensitive customers, stochastic demands, and inventory replenishments

In this paper, we use reinforcement learning (RL) as a tool to study price dynamics in an electronic retail market consisting of two competing sellers, and price sensitive and lead time sensitive customers. Sellers, offering identical products, compete on price to satisfy stochastically arriving demands (customers), and follow standard inventory control and replenishment policies to manage their inventories. In such a generalized setting, RL techniques have not previously been applied. We consider two representative cases: 1) no information case, were none of the sellers has any information about customer queue levels, inventory levels, or prices at the competitors; and 2) partial information case, where every seller has information about the customer queue levels and inventory levels of the competitors. Sellers employ automated pricing agents, or pricebots, which use RL-based pricing algorithms to reset the prices at random intervals based on factors such as number of back orders, inventory levels, and replenishment lead times, with the objective of maximizing discounted cumulative profit. In the no information case, we show that a seller who uses Q-learning outperforms a seller who uses derivative following (DF). In the partial information case, we model the problem as a Markovian game and use actor-critic based RL to learn dynamic prices. We believe our approach to solving these problems is a new and promising way of setting dynamic prices in multiseller environments with stochastic demands, price sensitive customers, and inventory replenishments.