Coherent Graphene Devices: Movable Mirrors, Buffers and Memories

We theoretically report that, at a sharp electrostatic step potential in graphene, massless Dirac fermions can obtain Goos-H\"{a}nchen-like shifts under total internal reflection. Based on these results, we study the coherent propagation of the quasiparticles along a sharp graphene \emph{p-n-p} waveguide and derive novel dispersion relations for the guided modes. Consequently, coherent graphene devices (e.g. movable mirrors, buffers and memories) induced only by the electric field effect can be proposed.Comment: 12 pages, 5 figure

Coexistence of full which-path information and interference in Wheelers delayed choice experiment with photons

We present a computer simulation model that is a one-to-one copy of an experimental realization of Wheeler's delayed choice experiment that employs a single photon source and a Mach-Zehnder interferometer composed of a 50/50 input beam splitter and a variable output beam splitter with adjustable reflection coefficient $R$ (V. Jacques {\sl et al.}, Phys. Rev. Lett. 100, 220402 (2008)). For $0\le R\le 0.5$, experimentally measured values of the interference visibility $V$ and the path distinguishability $D$, a parameter quantifying the which-path information WPI, are found to fulfill the complementary relation $V^2+D^2\le 1$, thereby allowing to obtain partial WPI while keeping interference with limited visibility. The simulation model that is solely based on experimental facts, that satisfies Einstein's criterion of local causality and that does not rely on any concept of quantum theory or of probability theory, reproduces quantitatively the averages calculated from quantum theory. Our results prove that it is possible to give a particle-only description of the experiment, that one can have full WPI even if D=0, V=1 and therefore that the relation $V^2+D^2\le 1$ cannot be regarded as quantifying the notion of complementarity.Comment: Physica E, in press; see also http://www.compphys.ne

Distributed Clustering in Cognitive Radio Ad Hoc Networks Using Soft-Constraint Affinity Propagation

Absence of network infrastructure and heterogeneous spectrum availability in cognitive radio ad hoc networks (CRAHNs) necessitate the self-organization of cognitive radio users (CRs) for efficient spectrum coordination. The cluster-based structure is known to be effective in both guaranteeing system performance and reducing communication overhead in variable network environment. In this paper, we propose a distributed clustering algorithm based on soft-constraint affinity propagation message passing model (DCSCAP). Without dependence on predefined common control channel (CCC), DCSCAP relies on the distributed message passing among CRs through their available channels, making the algorithm applicable for large scale networks. Different from original soft-constraint affinity propagation algorithm, the maximal iterations of message passing is controlled to a relatively small number to accommodate to the dynamic environment of CRAHNs. Based on the accumulated evidence for clustering from the message passing process, clusters are formed with the objective of grouping the CRs with similar spectrum availability into smaller number of clusters while guaranteeing at least one CCC in each cluster. Extensive simulation results demonstrate the preference of DCSCAP compared with existing algorithms in both efficiency and robustness of the clusters

Diffusion induced decoherence of stored optical vortices

We study the coherence properties of optical vortices stored in atomic ensembles. In the presence of thermal diffusion, the topological nature of stored optical vortices is found not to guarantee slow decoherence. Instead the stored vortex state has decoherence surprisingly larger than the stored Gaussian mode. Generally, the less phase gradient, the more robust for stored coherence against diffusion. Furthermore, calculation of coherence factor shows that the center of stored vortex becomes completely incoherent once diffusion begins and, when reading laser is applied, the optical intensity at the center of the vortex becomes nonzero. Its implication for quantum information is discussed. Comparison of classical diffusion and quantum diffusion is also presented.Comment: 5 pages, 2 figure

Modelling human behaviours and reactions under dangerous environment

This paper describes the framework of a real-time simulation system to model human behavior and reactions in dangerous environments. The system utilizes the latest 3D computer animation techniques, combined with artificial intelligence, robotics and psychology, to model human behavior, reactions and decision making under expected/unexpected dangers in real-time in virtual environments. The development of the system includes: classification on the conscious/subconscious behaviors and reactions of different people; capturing different motion postures by the Eagle Digital System; establishing 3D character animation models; establishing 3D models for the scene; planning the scenario and the contents; and programming within Virtools (TM) Dev. Programming within Virtools (TM) Dev is subdivided into modeling dangerous events, modeling character's perceptions, modeling character's decision making, modeling character's movements, modeling character's interaction with environment and setting up the virtual cameras. The real-time simulation of human reactions in hazardous environments is invaluable in military defense, fire escape, rescue operation planning, traffic safety studies, and safety planning in chemical factories, the design of buildings, airplanes, ships and trains. Currently, human motion modeling can be realized through established technology, whereas to integrate perception and intelligence into virtual human's motion is still a huge undertaking. The challenges here are the synchronization of motion and intelligence, the accurate modeling of human's vision, smell, touch and hearing, the diversity and effects of emotion and personality in decision making. There are three types of software platforms which could be employed to realize the motion and intelligence within one system, and their advantages and disadvantages are discussed

Calibrating the {\alpha} parameter of convective efficiency using observed stellar properties

Context. Synthetic model atmosphere calculations are still the most commonly used tool when determining precise stellar parameters and stellar chemical compositions. Besides three-dimensional models that consistently solve for hydrodynamic processes, one-dimensional models that use an approximation for convective energy transport play the major role. Aims. We use modern Balmer-line formation theory as well as spectral energy distribution (SED) measurements for the Sun and Procyon to calibrate the model parameter {\alpha} that describes the efficiency of convection in our 1D models. Convection was calibrated over a significant range in parameter space, reaching from F-K along the main sequence and sampling the turnoff and giant branch over a wide range of metallicities. This calibration was compared to theoretical evaluations and allowed an accurate modeling of stellar atmospheres. Methods. We used Balmer-line fitting and SED fits to determine the convective efficiency parameter {\alpha}. Both methods are sensitive to the structure and temperature stratification of the deeper photosphere. Results. While SED fits do not allow a precise determination of the convective parameter for the Sun and Procyon, they both favor values significantly higher than 1.0. Balmer-line fitting, which we find to be more sensitive, suggests that the convective efficiency parameter {\alpha} is $\approx$ 2.0 for the main sequence and quickly decreases to $\approx$ 1.0 for evolved stars. These results are highly consistent with predictions from 3D models. While the values on the main sequence fit predictions very well, measurements suggest that the decrease of convective efficiency as stars evolve to the giant branch is more dramatic than predicted by models.Comment: 14 pages, 16 figures, accepted for publication in A&

Pavlov's dog associative learning demonstrated on synaptic-like organic transistors

In this letter, we present an original demonstration of an associative learning neural network inspired by the famous Pavlov's dogs experiment. A single nanoparticle organic memory field effect transistor (NOMFET) is used to implement each synapse. We show how the physical properties of this dynamic memristive device can be used to perform low power write operations for the learning and implement short-term association using temporal coding and spike timing dependent plasticity based learning. An electronic circuit was built to validate the proposed learning scheme with packaged devices, with good reproducibility despite the complex synaptic-like dynamic of the NOMFET in pulse regime