A connection-level call admission control using genetic algorithm for MultiClass multimedia services in wireless networks

Call admission control in a wireless cell in a personal communication system (PCS) can be modeled as an M/M/C/C queuing system with m classes of users. Semi-Markov Decision Process (SMDP) can be used to optimize channel utilization with upper bounds on handoff blocking probabilities as Quality of Service constraints. However, this method is too time-consuming and therefore it fails when state space and action space are large. In this paper, we apply a genetic algorithm approach to address the situation when the SMDP approach fails. We code call admission control decisions as binary strings, where a value of “1” in the position i (i=1,…m) of a decision string stands for the decision of accepting a call in class-i; a value of “0” in the position i of the decision string stands for the decision of rejecting a call in class-i. The coded binary strings are feed into the genetic algorithm, and the resulting binary strings are founded to be near optimal call admission control decisions. Simulation results from the genetic algorithm are compared with the optimal solutions obtained from linear programming for the SMDP approach. The results reveal that the genetic algorithm approximates the optimal approach very well with less complexity

Practical Certificateless Aggregate Signatures From Bilinear Maps

Aggregate signature is a digital signature with a striking property that anyone can aggregate n individual signatures on n different messages which are signed by n distinct signers, into a single compact signature to reduce computational and storage costs. In this work, two practical certificateless aggregate signature schemes are proposed from bilinear maps. The first scheme CAS-1 reduces the costs of communication and signer-side computation but trades off the storage, while CAS-2 minimizes the storage but sacrifices the communication costs. One can choose either of the schemes by consideration of the application requirement. Compare with ID-based schemes, our schemes do not entail public key certificates as well and achieve the trust level 3, which imply the frauds of the authority are detectable. Both of the schemes are proven secure in the random oracle model by assuming the intractability of the computational Diffie-Hellman problem over the groups with bilinear maps, where the forking lemma technique is avoided

Quakes in Solid Quark Stars

A starquake mechanism for pulsar glitches is developed in the solid quark star model. It is found that the general glitch natures (i.e., the glitch amplitudes and the time intervals) could be reproduced if solid quark matter, with high baryon density but low temperature, has properties of shear modulus \mu = 10^{30~34} erg/cm^3 and critical stress \sigma_c = 10^{18~24} erg/cm^3. The post-glitch behavior may represent a kind of damped oscillations.Comment: 11 pages, 4 figures (but Fig.3 is lost), a complete version can be obtained by http://vega.bac.pku.edu.cn/~rxxu/publications/index_P.htm, a new version to be published on Astroparticle Physic

Self-organizing nonlinear output (SONO): A neural network suitable for cloud patch-based rainfall estimation at small scales

Accurate measurement of rainfall distribution at various spatial and temporal scales is crucial for hydrological modeling and water resources management. In the literature of satellite rainfall estimation, many efforts have been made to calibrate a statistical relationship (including threshold, linear, or nonlinear) between cloud infrared (IR) brightness temperatures and surface rain rates (RR). In this study, an automated neural network for cloud patch-based rainfall estimation, entitled self-organizing nonlinear output (SONO) model, is developed to account for the high variability of cloud-rainfall processes at geostationary scales (i.e., 4 km and every 30 min). Instead of calibrating only one IR-RR function for all clouds the SONO classifies varied cloud patches into different clusters and then searches a nonlinear IR-RR mapping function for each cluster. This designed feature enables SONO to generate various rain rates at a given brightness temperature and variable rain/no-rain IR thresholds for different cloud types, which overcomes the one-to-one mapping limitation of a single statistical IR-RR function for the full spectrum of cloud-rainfall conditions. In addition, the computational and modeling strengths of neural network enable SONO to cope with the nonlinearity of cloud-rainfall relationships by fusing multisource data sets. Evaluated at various temporal and spatial scales, SONO shows improvements of estimation accuracy, both in rain intensity and in detection of rain/no-rain pixels. Further examination of the SONO adaptability demonstrates its potentiality as an operational satellite rainfall estimation system that uses the passive microwave rainfall observations from low-orbiting satellites to adjust the IR-based rainfall estimates at the resolution of geostationary satellites. Copyright 2005 by the American Geophysical Union

Specific heat and thermal conductivity of ferromagnetic magnons in Yttrium Iron Garnet

The specific heat and thermal conductivity of the insulating ferrimagnet Y$_3$Fe$_5$O$_{12}$ (Yttrium Iron Garnet, YIG) single crystal were measured down to 50 mK. The ferromagnetic magnon specific heat $C$$_m$ shows a characteristic $T^{1.5}$ dependence down to 0.77 K. Below 0.77 K, a downward deviation is observed, which is attributed to the magnetic dipole-dipole interaction with typical magnitude of 10$^{-4}$ eV. The ferromagnetic magnon thermal conductivity $\kappa_m$ does not show the characteristic $T^2$ dependence below 0.8 K. To fit the $\kappa_m$ data, both magnetic defect scattering effect and dipole-dipole interaction are taken into account. These results complete our understanding of the thermodynamic and thermal transport properties of the low-lying ferromagnetic magnons.Comment: 5 pages, 5 figure

New transformation of Wigner operator in phase space quantum mechanics for the two-mode entangled case

As a natural extension of Fan's paper (arXiv: 0903.1769vl [quant-ph]) by employing the formula of operators' Weyl ordering expansion and the bipartite entangled state representation we find new two-fold complex integration transformation about the Wigner operator (in its entangled form) in phase space quantum mechanics and its inverse transformation. In this way, some operator ordering problems can be solved and the contents of phase space quantum mechanics can be enriched.Comment: 8 pages, 0 figure

Nodeless superconductivity in Ca3Ir4Sn13: evidence from quasiparticle heat transport

We report resistivity $\rho$ and thermal conductivity $\kappa$ measurements on Ca$_3$Ir$_4$Sn$_{13}$ single crystals, in which superconductivity with $T_c \approx 7$ K was claimed to coexist with ferromagnetic spin-fluctuations. Among three crystals, only one crystal shows a small hump in resistivity near 20 K, which was previously attributed to the ferromagnetic spin-fluctuations. Other two crystals show the $\rho \sim T^2$ Fermi-liquid behavior at low temperature. For both single crystals with and without the resistivity anomaly, the residual linear term $\kappa_0/T$ is negligible in zero magnetic field. In low fields, $\kappa_0(H)/T$ shows a slow field dependence. These results demonstrate that the superconducting gap of Ca$_3$Ir$_4$Sn$_{13}$ is nodeless, thus rule out nodal gap caused by ferromagnetic spin-fluctuations.Comment: 5 pages, 4 figure

Visualizing urban microclimate and quantifying its impact on building energy use in San Francisco

Weather data at nearby airports are usually used in building energy simulation to estimate energy use in buildings or evaluate building design or retrofit options. However, due to urbanization and geography characteristics, local weather conditions can differ significantly from those at airports. This study presents the visualization of 10-year hourly weather data measured at 27 sites in San Francisco, aiming to provide insights into the urban microclimate and urban heat island effect in San Francisco and how they evolve during the recent decade. The 10-year weather data are used in building energy simulations to investigate its influence on energy use and electrical peak demand, which informs the city's policy making on building energy efficiency and resilience. The visualization feature is implemented in CityBES, an open web-based data and computing platform for urban building energy research