20,461 research outputs found
Broadband reflectionless metasheets: Frequency-selective transmission and perfect absorption
Energy of propagating electromagnetic waves can be fully absorbed in a thin
lossy layer, but only in a narrow frequency band, as follows from the causality
principle. On the other hand, it appears that there are no fundamental
limitations on broadband matching of thin absorbing layers. However, known thin
absorbers produce significant reflections outside of the resonant absorption
band. In this paper we explore possibilities to realize a thin absorbing layer
which produces no reflected waves in a very wide frequency range, while the
transmission coefficient has a narrow peak of full absorption. Here we show,
both theoretically and experimentally, that a wide-band-matched thin resonant
absorber, invisible in reflection, can be realized if one and the same resonant
mode of the absorbing array unit cells is utilized to create both electric and
magnetic responses. We test this concept using chiral particles in each unit
cells, arranged in a periodic planar racemic array, utilizing chirality
coupling in each unit cell but compensating the field coupling at the
macroscopic level. We prove that the concept and the proposed realization
approach also can be used to create non-reflecting layers for full control of
transmitted fields. Our results can have a broad range of potential
applications over the entire electromagnetic spectrum including, for example,
perfect ultra-compact wave filters and selective multi-frequency sensors.Comment: 9 pages, 10 figure
An Approximate Inner Bound to the QoS Aware Throughput Region of a Tree Network under IEEE 802.15.4 CSMA/CA and Application to Wireless Sensor Network Design
We consider a tree network spanning a set of source nodes that generate
measurement packets, a set of additional relay nodes that only forward packets
from the sources, and a data sink. We assume that the paths from the sources to
the sink have bounded hop count. We assume that the nodes use the IEEE 802.15.4
CSMA/CA for medium access control, and that there are no hidden terminals. In
this setting, starting with a set of simple fixed point equations, we derive
sufficient conditions for the tree network to approximately satisfy certain
given QoS targets such as end-to-end delivery probability and delay under a
given rate of generation of measurement packets at the sources (arrival rates
vector). The structures of our sufficient conditions provide insight on the
dependence of the network performance on the arrival rate vector, and the
topological properties of the network. Furthermore, for the special case of
equal arrival rates, default backoff parameters, and for a range of values of
target QoS, we show that among all path-length-bounded trees (spanning a given
set of sources and BS) that meet the sufficient conditions, a shortest path
tree achieves the maximum throughput
Backpropagation training in adaptive quantum networks
We introduce a robust, error-tolerant adaptive training algorithm for
generalized learning paradigms in high-dimensional superposed quantum networks,
or \emph{adaptive quantum networks}. The formalized procedure applies standard
backpropagation training across a coherent ensemble of discrete topological
configurations of individual neural networks, each of which is formally merged
into appropriate linear superposition within a predefined, decoherence-free
subspace. Quantum parallelism facilitates simultaneous training and revision of
the system within this coherent state space, resulting in accelerated
convergence to a stable network attractor under consequent iteration of the
implemented backpropagation algorithm. Parallel evolution of linear superposed
networks incorporating backpropagation training provides quantitative,
numerical indications for optimization of both single-neuron activation
functions and optimal reconfiguration of whole-network quantum structure.Comment: Talk presented at "Quantum Structures - 2008", Gdansk, Polan
Characterization of the on-body path Loss at 2.45 GHz and energy efficient WBAN design for dairy cows
Wireless body area networks (WBANs) provide promising applications in the healthcare monitoring of dairy cows. The characterization of the path loss (PL) between on-body nodes constitutes an important step in the deployment of a WBAN. In this paper, the PL between nodes placed on the body of a dairy cow was determined at 2.45 GHz. Finite-difference time domain simulations with two half-wavelength dipoles placed 20 mm above a cow model were performed using a 3-D electromagnetic solver. Measurements were conducted on a live cow to validate the simulation results. Excellent agreement between measurements and simulations was achieved and the obtained PL values as a function of the transmitter-receiver separation were well fitted by a lognormal PL model with a PL exponent of 3.1 and a PL at reference distance ( 10 cm) of 44 dB. As an application, the packet error rate ( PER) and the energy efficiency of different WBAN topologies for dairy cows (i.e., single-hop, multihop, and cooperative networks) were investigated. The analysis results revealed that exploiting multihop and cooperative communication schemes decrease the PER and increase the optimal payload packet size. The analysis results revealed that exploiting multihop and cooperative communication schemes increase the optimal payload packet size and improve the energy efficiency by 30%
Implications of Selfish Neighbor Selection in Overlay Networks
In a typical overlay network for routing or content sharing, each node must select a fixed number of immediate overlay neighbors for routing traffic or content queries. A selfish node entering such a network would select neighbors so as to minimize the weighted sum of expected access costs to all its destinations. Previous work on selfish neighbor selection has built intuition with simple models where edges are undirected, access costs are modeled by hop-counts, and nodes have potentially unbounded degrees. However, in practice, important constraints not captured by these models lead to richer games with substantively and fundamentally different outcomes. Our work models neighbor selection as a game involving directed links, constraints on the number of allowed neighbors, and costs reflecting both network latency and node preference. We express a node's "best response" wiring strategy as a k-median problem on asymmetric distance, and use this formulation to obtain pure Nash equilibria. We experimentally examine the properties of such stable wirings on synthetic topologies, as well as on real topologies and maps constructed from PlanetLab and AS-level Internet measurements. Our results indicate that selfish nodes can reap substantial performance benefits when connecting to overlay networks composed of non-selfish nodes. On the other hand, in overlays that are dominated by selfish nodes, the resulting stable wirings are optimized to such great extent that even non-selfish newcomers can extract near-optimal performance through naive wiring strategies.Marie Curie Outgoing International Fellowship of the EU (MOIF-CT-2005-007230); National Science Foundation (CNS Cybertrust 0524477, CNS NeTS 0520166, CNS ITR 0205294, EIA RI 020206
A generative modeling approach for benchmarking and training shallow quantum circuits
Hybrid quantum-classical algorithms provide ways to use noisy
intermediate-scale quantum computers for practical applications. Expanding the
portfolio of such techniques, we propose a quantum circuit learning algorithm
that can be used to assist the characterization of quantum devices and to train
shallow circuits for generative tasks. The procedure leverages quantum hardware
capabilities to its fullest extent by using native gates and their qubit
connectivity. We demonstrate that our approach can learn an optimal preparation
of the Greenberger-Horne-Zeilinger states, also known as "cat states". We
further demonstrate that our approach can efficiently prepare approximate
representations of coherent thermal states, wave functions that encode
Boltzmann probabilities in their amplitudes. Finally, complementing proposals
to characterize the power or usefulness of near-term quantum devices, such as
IBM's quantum volume, we provide a new hardware-independent metric called the
qBAS score. It is based on the performance yield in a specific sampling task on
one of the canonical machine learning data sets known as Bars and Stripes. We
show how entanglement is a key ingredient in encoding the patterns of this data
set; an ideal benchmark for testing hardware starting at four qubits and up. We
provide experimental results and evaluation of this metric to probe the trade
off between several architectural circuit designs and circuit depths on an
ion-trap quantum computer.Comment: 16 pages, 9 figures. Minor revisions. As published in npj Quantum
Informatio
- …