无线传感器网络RSSI测距方法与精度分析

基于RSSI的测距技术是一项低成本的距离测量技术。分析了接收信号强度指示器(RSSI)多种 测距模型,结 合采用IEEE802.15.4协议的CC2430芯片,设计了测距实验,获取了多组数据,通过对实验数 据的分析,提出结合信标节点确定参数、高斯拟合确定测量值的RSSI测距处理方法。实验证 明,该方法能提高RSSI测距的抗干扰能力,20 m内节点间的测距精度能达到15 m 以下。     

Characterizing Gaussian Models of the Term Structure of Interest Rates

Models of the term structure of interest rates are considered for which, under the martingale measure, instantaneous forward rates are Gaussian. The possible forms of the covariance structure are characterized under appropriate formulations of the Markov property. It is demonstrated that imposing Markovian assumptions limits severely the covariances that may be obtained and that the strongest such formulation together with stationarity implies that the whole forward rate surface is necessarily a Gaussian random field described by just three parameters.     

Probabilistic recalibration of forecasts

We present a scheme by which a probabilistic forecasting system whose predictions have a poor probabilistic calibration may be recalibrated through the incorporation of past performance information in order to produce a new forecasting system that is demonstrably superior to the original, inasmuch as one may use it to win wagers consistently against someone who is using the original system. The scheme utilizes Gaussian process (GP) modeling to estimate a probability distribution over the probability integral transform (PIT) of a scalar predictand. The GP density estimate gives closed-form access to information entropy measures that are associated with the estimated distribution, which allows the prediction of winnings in wagers against the base forecasting system. A separate consequence of the procedure is that the recalibrated forecast has a uniform expected PIT distribution. One distinguishing feature of the procedure is that it is appropriate even if the PIT values are not i.i.d. The recalibration scheme is formulated in a framework that exploits the deep connections among information theory, forecasting, and betting. We demonstrate the effectiveness of the scheme in two case studies: a laboratory experiment with a nonlinear circuit and seasonal forecasts of the intensity of the El Niño-Southern Oscillation phenomenon.     

Simulation modeling and optimization for ambulance allocation considering spatiotemporal stochastic demand

To ensure a timely response to emergencies, governments are obliged to implement effective ambulance allocation plans. In practice, an emergency medical service (EMS) system works in an uncertain environment, with stochastic demand, response-times, and travel-times. This uncertainty significantly affects ambulance allocation planning. However, few studies in this field adequately consider the effect of spatiotemporal uncertainty in demand, because it is difficult to measure it quantitatively. As a result, few analytic models capture the dynamic nature of an EMS system and, thus, the allocation plans they generate are not efficient in practice. Therefore, this study proposes a simulation-based optimization method for ambulance allocation. A simulation model is constructed to mimic the operational processes of an EMS system, and to evaluate the performance of an ambulance allocation plan in an uncertain environment. Gaussian mixture model clustering is used to derive the uncertain spatial demand. Then, the simulation generates emergency demand based on the obtained spatial distribution. A Gaussian-process-based search algorithm is used together with the simulation model to identify optimal solutions. To validate the proposed method, a case study is conducted using data on emergency patients in the Shanghai Songjiang District. Compared with the current plan adopted in Songjiang, the experimental results demonstrate that the delay time and frequency of the EMS system can be reduced significantly by employing the proposed methods. Furthermore, nearly 41% of the allocation cost can be saved.     

CHANGE DETECTION OF ORDERS IN STOCK MARKETS USING A GAUSSIAN MIXTURE MODEL

We propose a method for detecting changes in the order balance in stock markets by applying a stochastic model to the feature vectors extracted from the order‐book data of stocks. First, the data are divided into training and test periods. Next, a Gaussian mixture model is estimated from the feature vectors extracted from the order‐book data in the training period. Finally, the goodness of fit of the feature vectors in the test period over this model is calculated. Using the proposed method, we found that the order balances of stocks for which insider trading was reported were unusual. Copyright © 2014 John Wiley & Sons, Ltd.     

获得三维曲面相对大小高斯曲率的方法与实现

本方法在不使用任何参照物的前提下,从对象图像中获得曲面的三维表面重建信息。通过物体自身的多角度旋转并拍摄多枚照片,并从多枚照片中获得的数据使用神经网络的学习数据。从而最终获得相对的高斯曲率大小值、曲率的符号以及曲面的类型。从而达到无任何参照物的前提下,获得对象物体的三维形状复原信息。     

A Skew‐normal copula‐driven GLMM

This paper presents a method for fitting a copula‐driven generalized linear mixed models. For added flexibility, the skew‐normal copula is adopted for fitting. The correlation matrix of the skew‐normal copula is used to capture the dependence structure within units, while the fixed and random effects coefficients are estimated through the mean of the copula. For estimation, a Monte Carlo expectation–maximization algorithm is developed. Simulations are shown alongside a real data example from the Framingham Heart Study.     

On an automatic and optimal importance sampling approach with applications in finance

Calculating high-dimensional integrals efficiently is essential and challenging in many scientific disciplines, such as pricing financial derivatives. This paper proposes an exponentially tilted importance sampling based on the criterion of minimizing the variance of the importance sampling estimators, and its contribution is threefold: (1) A theoretical foundation to guarantee the existence, uniqueness, and characterization of the optimal tilting parameter is built. (2) The optimal tilting parameter can be searched via an automatic Newton’s method. (3) Simplified yet competitive tilting formulas are further proposed to reduce heavy computational cost and numerical instability in high-dimensional cases. Numerical examples in pricing path-dependent derivatives and basket default swaps are provided.     

基于伽罗华域高斯列消元法的RS码盲识别

为解决高码率RS（Reed Solomon）码盲识别问题,提出了一种基于伽罗华域高斯列消元法的RS码盲识别方法。先利用矩阵秩的差值函数识别符号数及码长;再遍历此时符号数对应的本原多项式,对矩阵进行伽罗华域高斯列消元,并引入熵函数差值来识别本原多项式;最后求码字多项式的根,其中连续根即为生成多项式的根。该方法可以较好地识别RS码码长、生成多项式及本原多项式,并且避免了遍历符号数时多次进行伽罗华域傅里叶变换的繁琐过程。仿真结果表明,在误码率为3×10－3的情况下,对RS码的识别概率高于90%。