IR-UWB系统几种新型调制技术

IR-UWB(Impulse Radio-Ultra wide Bandwidth,冲激无线电-超宽带技术)是短距离无线通信中一个非常有前景的技术.该文介绍了几种有关IR-UWB信号的比较新型的调制技术,例如伪混沌编码跳时调制、脉冲位置幅度调制、自适应调制等,分析了其调制方法的基本原理,并给出了误码率分析.     

Steepest-descent method-based adaptive frequency domain equalizer in coherent optical orthogonal frequency division multiplexing with quadrature phase shift keying modulation

An adaptive frequency domain equalizer (A-FDE) in quadrature phase shift keying-coherent optical-orthogonal frequency division multiplexing (QPSK-CO-OFDM) system is proposed in this paper. A-FDE uses steepest descent method to search the optimal equalization parameters. And through applying the adaptive control factor (ACF) to monitor the state of A-FDE, the interference from phase noise (PN) and chromatic dispersion (CD) can be mitigated. The numerical simulation shows that the performance of A-FDE has a remarkable improvement on combating PN and CD comparing with the conventional decision-directed equalizer (DDE). And the A-FDE can achieve the forward-error-correction (FEC) threshold in noisier scenario. Furthermore, because the calculation of ACF only bases on valid data instead of pilot symbols (PSs), A-FDE has low PS overhead merit.     

Simultaneous analysis of multiple PCR amplicons enhances capillary SSCP discrimination of MHC alleles

Major histocompatibility complex (MHC) genotyping still remains one of the most challenging issues for evolutionary ecologists. To date, none of the proposed methods have proven to be perfect, and all provide both important pros and cons. Although denaturing capillary electrophoresis has become a popular alternative, allele identification commonly relies upon conformational polymorphisms of two single‐stranded DNA molecules at the most. Using the MHC class II (β chain, exon 2) of the black kite (Aves: Accipitridae) as our model system, we show that the simultaneous analysis of overlapping PCR amplicons from the same target region substantially enhances allele discrimination. To cover this aim, we designed a multiplex PCR capable to generate four differentially sized and labeled amplicons from the same allele. Informative peaks to assist allele calling then fourfold those generated by the analysis of single PCR amplicons. Our approach proved successful to differentiate all the alleles (N=13) isolated from eight unrelated birds at a single optimal run temperature and electrophoretic conditions. In particular, we emphasize that this approach may constitute a straightforward and cost‐effective alternative for the genotyping of single or duplicated MHC genes displaying low to moderate sets of divergent alleles.     

Adaptive finite element methods for the identification of distributed parameters in elliptic equation

In this paper, adaptive finite element method is developed for the estimation of distributed parameter in elliptic equation. Both upper and lower error bound are derived and used to improve the accuracy by appropriate mesh refinement. An efficient preconditioned project gradient algorithm is employed to solve the nonlinear least-squares problem arising in the context of parameter identification problem. The efficiency of our error estimators is demonstrated by some numerical experiments.      

An adaptive memory algorithm for the k-coloring problem

Let G=(V,E) be a graph with vertex set V and edge set E. The k-coloring problem is to assign a color (a number chosen in {1,…,k}) to each vertex of G so that no edge has both endpoints with the same color. The adaptive memory algorithm is a hybrid evolutionary heuristic that uses a central memory. At each iteration, the information contained in the central memory is used for producing an offspring solution which is then possibly improved using a local search algorithm. The so obtained solution is finally used to update the central memory. We describe in this paper an adaptive memory algorithm for the k-coloring problem. Computational experiments give evidence that this new algorithm is competitive with, and simpler and more flexible than, the best known graph coloring algorithms.     

An FPGA based scalable architecture of a stochastic state point process filter (SSPPF) to track the nonlinear dynamics underlying neural spiking

Recent studies have verified the efficiency of stochastic state point process filter (SSPPF) in coefficients tracking in the modeling of the mammalian nervous system. In this study, a hardware architecture of SSPPF is both designed and implemented on a field-programmable gate array (FPGA). It provides a time-efficient method to investigate the nonlinear neural dynamics through coefficients tracking of a generalized Laguerre–Volterra model describing the spike train transformations of different brain sub-regions. The proposed architecture is able to process matrices and vectors with arbitrary sizes. It is designed to be scalable in parallel degree and to provide different customizable levels of parallelism, by exploring the intrinsic parallelism of the FPGA. Multiple architectures with different degrees of parallelism are explored. This design maintains numerical precision and the proposed parallel architectures for coefficients estimation are also much more power efficient.     

A Moderate Deviation Principle for <Emphasis Type="Italic">m</Emphasis>-Dependent Random Variables with Unbounded <Emphasis Type="Italic">m</Emphasis>

In this paper, a theorem on the moderate deviation principle for random arrays under m-dependence with unbounded m is established. This partially extends the results of Chen (Stat. Probab. Lett. 35:123–134, 1997). As an application, the moderate deviation principle for the truncation estimator of the variance in the analysis of time series is obtained.      

Matching inductive search bias and problem structure in continuous Estimation-of-Distribution Algorithms

Research into the dynamics of Genetic Algorithms (GAs) has led to the field of Estimation-of-Distribution Algorithms (EDAs). For discrete search spaces, EDAs have been developed that have obtained very promising results on a wide variety of problems. In this paper we investigate the conditions under which the adaptation of this technique to continuous search spaces fails to perform optimization efficiently. We show that without careful interpretation and adaptation of lessons learned from discrete EDAs, continuous EDAs will fail to perform efficient optimization on even some of the simplest problems. We reconsider the most important lessons to be learned in the design of EDAs and subsequently show how we can use this knowledge to extend continuous EDAs that were obtained by straightforward adaptation from the discrete domain so as to obtain an improvement in performance. Experimental results are presented to illustrate this improvement and to additionally confirm experimentally that a proper adaptation of discrete EDAs to the continuous case indeed requires careful consideration.     

Building nonredundant adaptive wavelets by update lifting

In this paper, we propose a technique for building adaptive wavelets by means of an extension of the lifting scheme. Our scheme comprises an adaptive update lifting step and a fixed prediction lifting step. The adaptivity consists hereof that the system can choose between two different update filters, and that this choice is triggered by the local gradient of the original signal. If the gradient is large (in some seminorm sense) it chooses one filter, if it is small the other. We derive necessary and sufficient conditions for the invertibility of such an adaptive system for various scenarios. Furthermore, we present some examples to illustrate our theoretical results.