Multiple Timescale Energy Scheduling for Wireless Communication with Energy Harvesting Devices

The primary challenge in wireless communication with energy harvesting devices is to efficiently utilize the harvesting energy such that the data packet transmission could be supported. This challenge stems from not only QoS requirement imposed by the wireless communication application, but also the energy harvesting dynamics and the limited battery capacity. Traditional solar predictable energy harvesting models are perturbed by prediction errors, which could deteriorate the energy management algorithms based on this models. To cope with these issues, we first propose in this paper a non-homogenous Markov chain model based on experimental data, which can accurately describe the solar energy harvesting process in contrast to traditional predictable energy models. Due to different timescale between the energy harvesting process and the wireless data transmission process, we propose a general framework of multiple timescale Markov decision process (MMDP) model to formulate the joint energy scheduling and transmission control problem under different timescales. We then derive the optimal control policies via a joint dynamic programming and value iteration approach. Extensive simulations are carried out to study the performances of the proposed schemes

Localization of strongly correlated electrons as Jahn-Teller polarons in manganites

A realistic modeling of manganites should include the Coulomb repulsion between $e_g$ electrons, the Hund's rule coupling to $t_{2g}$ spins, and Jahn-Teller phonons. Solving such a model by dynamical mean field theory, we report large magnetoresistances and spectra in good agreement with experiments. The physics of the unusual, insulating-like paramagnetic phase is determined by correlated electrons which are-due to strong correlations-easily trapped as Jahn-Teller polarons.Comment: 4 pages, 3 figure

Nd-doped aluminum oxide integrated amplifiers at 880 nm, 1060 nm, and 1330 nm

Neodymium-doped Al2O3 layers were deposited on thermally oxidized Si substrates and channel waveguides were patterned using reactive-ion etching. Internal net gain on the Nd3+ transitions at 880, 1064, and 1330 nm was investigated,\ud yielding a maximum gain of 6.3 dB/cm at 1064 nm. Values for the energy-transfer upconversion parameter for different Nd3+\ud concentrations were deduced

Shear capacity of reinforced concrete beams using neural network

NoOptimum multi-layered feed-forward neural network (NN) models using a resilient back-propagation algorithm and early stopping technique are built to predict the shear capacity of reinforced concrete deep and slender beams. The input layer neurons represent geometrical and material properties of reinforced concrete beams and the output layer produces the beam shear capacity. Training, validation and testing of the developed neural network have been achieved using 50%, 25%, and 25%, respectively, of a comprehensive database compiled from 631 deep and 549 slender beam specimens. The predictions obtained from the developed neural network models are in much better agreement with test results than those determined from shear provisions of different codes, such as KBCS, ACI 318-05, and EC2. The mean and standard deviation of the ratio between predicted using the neural network models and measured shear capacities are 1.02 and 0.18, respectively, for deep beams, and 1.04 and 0.17, respectively, for slender beams. In addition, the influence of different parameters on the shear capacity of reinforced concrete beams predicted by the developed neural network shows consistent agreement with those experimentally observed

Effective spin model for the spin-liquid phase of the Hubbard model on the triangular lattice

We show that the spin liquid phase of the half-filled Hubbard model on the triangular lattice can be described by a pure spin model. This is based on a high-order strong coupling expansion (up to order 12) using perturbative continuous unitary transformations. The resulting spin model is consistent with a transition from three-sublattice long-range magnetic order to an insulating spin liquid phase, and with a jump of the double occupancy at the transition. Exact diagonalizations of both models show that the effective spin model is quantitatively accurate well into the spin liquid phase, and a comparison with the Gutzwiller projected Fermi sea suggests a gapless spectrum and a spinon Fermi surface.Comment: 4 pages, 4 figures, published versions with additional dat

Tunneling, dissipation, and superfluid transition in quantum Hall bilayers

We study bilayer quantum Hall systems at total Landau level filling factor $\nu=1$ in the presence of interlayer tunneling and coupling to a dissipative normal fluid. Describing the dynamics of the interlayer phase by an effective quantum dissipative XY model, we show that there exists a critical dissipation $\sigma_c$ set by the conductance of the normal fluid. For $\sigma > \sigma_c$, interlayer tunnel splitting drives the system to a $\nu=1$ quantum Hall state. For $\sigma <\sigma_c$, interlayer tunneling is irrelevant at low temperatures, the system exhibits a superfluid transition to a collective quantum Hall state supported by spontaneous interlayer phase coherence. The resulting phase structure and the behavior of the in-plane and tunneling currents are studied in connection to experiments.Comment: 4 RevTex pages, revised version, to appear in Phys. Rev. Let