细胞信号传导理论的建立及其启示

细胞信号传导理论的提出 ,极大地深化了以往对细胞生物学的认识。该理论在形成和发展的过程中 ,移植嫁接了信息科学的新原理和新方法 ,促进了人类对复杂的生命系统的研究和了解 ,同时这种移植嫁接使生命现象及其规律的研究反过来为生物电子学及生物信息学的进一步发展奠定了良好的基础 ,加速了生物电子学和生物信息学时代的到来     

测定光纤折射率的简易方法

本文利用光导纤维中光速的测定原理,采用一种方波调制信号,应用具有异或逻辑功能的门电路进行相差测量的巧妙方法得到光在光导纤维中的传输速度,再利用,从而快速测得光纤的折射率[1]。     

新型流水线ADC的设计与分析

设计和分析了一种新型的流水线式模数转换器。电路设计主要包括一种开关采样差分折叠式共源共栅增益级、两个时钟控制动态比较器组成的两位模数转换器、两位数模转换器。由于采用了电容下极板采样、全差分和开关栅电压自举,有效地消除了开关管的电荷注入效应、时钟馈通效应引起的采样信号的误差,提高了模数转换器的线性度、信噪比、转换精度和速度。该转换器的设计是在0.6μm CMOS工艺下实现,转换器在采样频率为5MHz、信号频率为500kHz时功耗为70mW;SFDR为80 dB。     

Bayesian estimation of quantile distributions

Use of Bayesian modelling and analysis has become commonplace in many disciplines (finance, genetics and image analysis, for example). Many complex data sets are collected which do not readily admit standard distributions, and often comprise skew and kurtotic data. Such data is well-modelled by the very flexibly-shaped distributions of the quantile distribution family, whose members are defined by the inverse of their cumulative distribution functions and rarely have analytical likelihood functions defined. Without explicit likelihood functions, Bayesian methodologies such as Gibbs sampling cannot be applied to parameter estimation for this valuable class of distributions without resorting to numerical inversion. Approximate Bayesian computation provides an alternative approach requiring only a sampling scheme for the distribution of interest, enabling easier use of quantile distributions under the Bayesian framework. Parameter estimates for simulated and experimental data are presented.     

圆阵中相干信号的高分辨阵列测向技术

在分析均匀线阵中相干信号高分辨测向原理的基础上,提出了一种适合于圆阵的相干信号高分辨阵列测向技术,理论分析和计算机模拟都证明了该技术的正确性和可行性。     

WSNs中基于协作通信的分簇路由算法研究

针对无线传感器网络数据信息传输可靠性,提出一种无线传感器网络中基于协作通信的分簇路由算法。该算法主要对分簇、簇间路由和簇内路由三个阶段进行了设计,依据信噪比寻找满足网络数据传输可靠性的最佳路由,实现源节点和目的节点之间的协作通信。仿真结果表明,无线传感器网络中基于协作通信的分簇路由算法能够有效地优化网络路径,提高频谱利用率,增强数据信息传输的可靠性。     

非媒体信息、非媒体机构与大众传媒的有限功能——兼及一种去媒体中心化的舆论监督观

由于信息类型的多样性与社会分工的多元化,不论是在环境监测还是在舆论监督中,我们都不能对大众媒体寄予太多的期望.对媒体功能的分析不能忽视两个事实:一是媒体只是社会信息传播系统中的一个组成部分,只承担了一部分的信息传播功能;第二,媒体的能力是有限的,它无法呈现全部的信息,其洞察、监督及信息收集与分析能力都有很大的局限性.通过区分媒体信息与非媒体信息、媒体信息机构与非媒体信息机构,我们可以客观地审视其社会功能.正如大众传媒的有限效果一样,大众传媒的功能也是有限的,受其他社会装置的制约.若从舆论监督的角度看,没有其他“非舆论监督”机制或机构的配合,大众传媒的舆论监督必然事倍功半,即便在最为民主的体制下也是如此.     

Autocovariance Estimation in Regression with a Discontinuous Signal and m‐Dependent Errors: A Difference‐Based Approach

We discuss a class of difference‐based estimators for the autocovariance in nonparametric regression when the signal is discontinuous and the errors form a stationary m‐dependent process. These estimators circumvent the particularly challenging task of pre‐estimating such an unknown regression function. We provide finite‐sample expressions of their mean squared errors for piecewise constant signals and Gaussian errors. Based on this, we derive biased‐optimized estimates that do not depend on the unknown autocovariance structure. Notably, for positively correlated errors, that part of the variance of our estimators that depend on the signal is minimal as well. Further, we provide sufficient conditions for ‐consistency; this result is extended to piecewise Hölder regression with non‐Gaussian errors. We combine our biased‐optimized autocovariance estimates with a projection‐based approach and derive covariance matrix estimates, a method that is of independent interest. An R package, several simulations and an application to biophysical measurements complement this paper.     

Estimating Spatial Autocorrelation With Sampled Network Data

Spatial autocorrelation is a parameter of importance for network data analysis. To estimate spatial autocorrelation, maximum likelihood has been popularly used. However, its rigorous implementation requires the whole network to be observed. This is practically infeasible if network size is huge (e.g., Facebook, Twitter, Weibo, WeChat, etc.). In that case, one has to rely on sampled network data to infer about spatial autocorrelation. By doing so, network relationships (i.e., edges) involving unsampled nodes are overlooked. This leads to distorted network structure and underestimated spatial autocorrelation. To solve the problem, we propose here a novel solution. By temporarily assuming that the spatial autocorrelation is small, we are able to approximate the likelihood function by its first-order Taylor’s expansion. This leads to the method of approximate maximum likelihood estimator (AMLE), which further inspires the development of paired maximum likelihood estimator (PMLE). Compared with AMLE, PMLE is computationally superior and thus is particularly useful for large-scale network data analysis. Under appropriate regularity conditions (without assuming a small spatial autocorrelation), we show theoretically that PMLE is consistent and asymptotically normal. Numerical studies based on both simulated and real datasets are presented for illustration purpose.