311 research outputs found
Can Electromagnetic Information Theory Improve Wireless Systems? A Channel Estimation Example
Electromagnetic information theory (EIT) is an emerging interdisciplinary
subject that integrates classical Maxwell electromagnetics and Shannon
information theory. The goal of EIT is to uncover the information transmission
mechanisms from an electromagnetic (EM) perspective in wireless systems.
Existing works on EIT are mainly focused on the analysis of degrees-of-freedom
(DoF), system capacity, and characteristics of the electromagnetic channel.
However, these works do not clarify how EIT can improve wireless communication
systems. To answer this question, in this paper, we provide a novel
demonstration of the application of EIT. By integrating EM knowledge into the
classical MMSE channel estimator, we observe for the first time that EIT is
capable of improving the channel estimation performace. Specifically, the EM
knowledge is first encoded into a spatio-temporal correlation function (STCF),
which we term as the EM kernel. This EM kernel plays the role of side
information to the channel estimator. Since the EM kernel takes the form of
Gaussian processes (GP), we propose the EIT-based Gaussian process regression
(EIT-GPR) to derive the channel estimations. In addition, since the EM kernel
allows parameter tuning, we propose EM kernel learning to fit the EM kernel to
channel observations. Simulation results show that the application of EIT to
the channel estimator enables it to outperform traditional isotropic MMSE
algorithm, thus proving the practical values of EIT.Comment: Electromagnetic information theory (EIT) is an emerging
interdisciplinary subject, aiming at providing a unified analytical framework
for wireless systems as well as guiding practical system design. This paper
answers the question: "How can we improve wireless communication systems via
EIT"
Nonlinear behavior of the Chinese SSEC index with a unit root: Evidence from threshold unit root tests
We investigate the behavior of the Shanghai Stock Exchange Composite (SSEC)
index for the period from 1990:12 to 2007:06 using an unconstrained two-regime
threshold autoregressive (TAR) model with an unit root developed by Caner and
Hansen. The method allows us to simultaneously consider non-stationarity and
nonlinearity in financial time series. Our finding indicates that the Shanghai
stock market exhibits nonlinear behavior with two regimes and has unit roots in
both regimes. The important implications of the threshold effect in stock
markets are also discussed.Comment: 10 Elsart pages + 5 tables + 1 eps figur
Micro/Nano Gas Sensors: A New Strategy Towards In-Situ Wafer-Level Fabrication of High-Performance Gas Sensing Chips
Nano-structured gas sensing materials, in particular nanoparticles, nanotubes, and nanowires, enable high sensitivity at a ppb level for gas sensors. For practical applications, it is highly desirable to be able to manufacture such gas sensors in batch and at low cost. We present here a strategy of in-situ wafer-level fabrication of the high-performance micro/nano gas sensing chips by naturally integrating microhotplatform (MHP) with nanopore array (NPA). By introducing colloidal crystal template, a wafer-level ordered homogenous SnO_2 NPA is synthesized in-situ on a 4-inch MHP wafer, able to produce thousands of gas sensing units in one batch. The integration of micromachining process and nanofabrication process endues micro/nano gas sensing chips at low cost, high throughput, and with high sensitivity (down to ~20 ppb), fast response time (down to ~1 s), and low power consumption (down to ~30 mW). The proposed strategy of integrating MHP with NPA represents a versatile approach for in-situ wafer-level fabrication of high-performance micro/nano gas sensors for real industrial applications
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