An Performance Study for Sectorised Antenna based Doppler Diversity in High-Speed Railway Communications

The wireless channel of High-Speed Railway communication system is rapidly time-varying. The orthogonal frequency division multiplexing transmitting over this channel will be exposed to the intercarrier interference caused by large Doppler spread. The sectorised antenna can be employed for Doppler mitigation and obtaining Doppler diversity gain. In this paper the performance of this directional antenna is analyzed. The preferable partition scheme for the omnidirectional antenna and the optimal Doppler compensation frequency are addressed firstly. And the uncorrelated property of the signal received from the different sectorised antennas is demonstrated originally which can be utilized for Doppler diversity gain. Finally, it is proved by the simulation results that this architecture will allows us to achieve remarkable performance under high mobility conditions

Enhanced interlayer neutral excitons and trions in trilayer van der Waals heterostructures

Vertically stacked van der Waals heterostructures constitute a promising platform for providing tailored band alignment with enhanced excitonic systems. Here we report observations of neutral and charged interlayer excitons in trilayer WSe2-MoSe2-WSe2 van der Waals heterostructures and their dynamics. The addition of a WSe2 layer in the trilayer leads to significantly higher photoluminescence quantum yields and tunable spectral resonance compared to its bilayer heterostructures at cryogenic temperatures. The observed enhancement in the photoluminescence quantum yield is due to significantly larger electron-hole overlap and higher light absorbance in the trilayer heterostructure, supported via first-principle pseudopotential calculations based on spin-polarized density functional theory. We further uncover the temperature- and power-dependence, as well as time-resolved photoluminescence of the trilayer heterostructure interlayer neutral excitons and trions. Our study elucidates the prospects of manipulating light emission from interlayer excitons and designing atomic heterostructures from first-principles for optoelectronics.Comment: 25 pages, 5 figures(Maintext). 9 pages, 7 figures(Supplementary Information). - Accepted for publication in npg: 2D materials and applications and reformatted to its standard. - Updated co-authors and references. - Title and abstract are modified for clarity. - Errors have been corrected, npg: 2D materials and applications (2018

ERP is the Key Point of Supply Chain Management

Information technology is changing the mode of customer service , material purchasing , price making in an enterprise .To keep advantage , an enterprise has to take supply chain management . By sharing information and making plans together, an enterprise and its providers , its customers ,can improve their whole logistic efficiency through supply chain management. ERP is classic of information management in modern enterprises. ERP manages and controls effectively every node of supply chain inside an enterprise. It’s the information flat on which an enterprise manages and makes decision. It is the key point of supply chain management

Topological and control theoretic properties of Hamilton-Jacobi equations via Lax-Oleinik commutators

In the context of weak KAM theory, we discuss the commutators $\{T^-_t\circ T^+_t\}_{t\geqslant0}$ and $\{T^+_t\circ T^-_t\}_{t\geqslant0}$ of Lax-Oleinik operators. We characterize the relation $T^-_t\circ T^+_t=Id$ for both small time and arbitrary time $t$. We show this relation characterizes controllability for evolutionary Hamilton-Jacobi equation. Based on our previous work on the cut locus of viscosity solution, we refine our analysis of the cut time function $\tau$ in terms of commutators $T^+_t\circ T^-_t-T^+_t\circ T^-_t$ and clarify the structure of the super/sub-level set of the cut time function $\tau$

Optimal transport in the frame of abstract Lax-Oleinik operator revisited

This is our first paper on the extension of our recent work on the Lax-Oleinik commutators and its applications to the intrinsic approach of propagation of singularities of the viscosity solutions of Hamilton-Jacobi equations. We reformulate Kantorovich-Rubinstein duality theorem in the theory of optimal transport in terms of abstract Lax-Oleinik operators, and analyze the relevant optimal transport problem in the case the cost function $c(x,y)=h(t_1,t_2,x,y)$ is the fundamental solution of Hamilton-Jacobi equation. For further applications to the problem of cut locus and propagation of singularities in optimal transport, we introduce corresponding random Lax-Oleinik operators. We also study the problem of singularities for $c$-concave functions and its dynamical implication when $c$ is the fundamental solution with $t_2-t_1\ll1$ and $t_2-t_1<\infty$, and $c$ is the Peierls' barrier respectively

Topology of singular set of semiconcave function via Arnaud's theorem

We proved the (local) path-connectedness of certain subset of the singular set of semiconcave functions with linear modulus in general. In some sense this result is optimal. The proof is based on a theorem by Marie-Claude Arnaud (M.-C. Arnaud, \textit{Pseudographs and the Lax-Oleinik semi-group: a geometric and dynamical interpretation}. Nonlinearity, \textbf{24}(1): 71-78, 2011.). We also gave a new proof of the theorem in time-dependent case

Deep Neural Networks are Adaptive to Function Regularity and Data Distribution in Approximation and Estimation

Deep learning has exhibited remarkable results across diverse areas. To understand its success, substantial research has been directed towards its theoretical foundations. Nevertheless, the majority of these studies examine how well deep neural networks can model functions with uniform regularity. In this paper, we explore a different angle: how deep neural networks can adapt to different regularity in functions across different locations and scales and nonuniform data distributions. More precisely, we focus on a broad class of functions defined by nonlinear tree-based approximation. This class encompasses a range of function types, such as functions with uniform regularity and discontinuous functions. We develop nonparametric approximation and estimation theories for this function class using deep ReLU networks. Our results show that deep neural networks are adaptive to different regularity of functions and nonuniform data distributions at different locations and scales. We apply our results to several function classes, and derive the corresponding approximation and generalization errors. The validity of our results is demonstrated through numerical experiments