Estimating Attitude from Vector Observations Using a Genetic Algorithm-Embedded Quaternion Particle Filter 介紹了一種基于四元數粒子濾波的方法
標簽： Algorithm-Embedded Observations Estimating Quaternion
上傳時間： 2013-12-29
上傳用戶：集美慧
runs Kalman-Bucy filter over Observations matrix Z for 1-step prediction onto matrix X (X can = Z) with model order p V = initial covariance of observation sequence noise returns model parameter estimation sequence A, sequence of predicted outcomes y_pred and error matrix Ey (reshaped) for y and Ea for a along with inovation prob P = P(y_t | D_t-1) = evidence
標簽： matrix Observations Kalman-Bucy prediction
上傳時間： 2016-04-28
上傳用戶：huannan88
This talk centered on Hamming s Observations and research on the question"Why do so few scientists make significant contributions and so many are forgotten in the long run?"
標簽： Observations scientists centered research
上傳時間： 2013-12-19
上傳用戶：671145514
ObsReduce is an MS Windows program that reduces Observations of satellites relative to the background stars into their precise coordinates.
標簽： Observations satellites ObsReduce backgroun
上傳時間： 2014-01-18
上傳用戶：上善若水
The subroutines glkern.f and lokern.f use an efficient and fast algorithm for automatically adaptive nonparametric regression estimation with a kernel method. Roughly speaking, the method performs a local averaging of the Observations when estimating the regression function. Analogously, one can estimate derivatives of small order of the regression function.
標簽： automatically subroutines and algorithm
上傳時間： 2015-11-25
上傳用戶：luke5347
In this paper, we consider the problem of filtering in relational hidden Markov models. We present a compact representation for such models and an associated logical particle filtering algorithm. Each particle contains a logical formula that describes a set of states. The algorithm updates the formulae as new Observations are received. Since a single particle tracks many states, this filter can be more accurate than a traditional particle filter in high dimensional state spaces, as we demonstrate in experiments.
標簽： relational filtering consider problem
上傳時間： 2016-01-02
上傳用戶：海陸空653
The algorm of viterbi. You talk to your friend three days in a row and discover that on the first day he went for a walk, on the second day he went shopping, and on the third day he cleaned his apartment. You have two questions: What is the overall probability of this sequence of Observations? And what is the most likely sequence of rainy/sunny days that would explain these Observations? The first question is answered by the forward algorithm the second question is answered by the Viterbi algorithm. These two algorithms are structurally so similar (in fact, they are both instances of the same abstract algorithm) that they can be implemented in a single function:
標簽： discover viterbi algorm friend
上傳時間： 2016-02-16
上傳用戶：xc216
％ EM algorithm for k multidimensional Gaussian mixture estimation ％ ％ Inputs: ％ X(n,d) - input data, n=number of Observations, d=dimension of variable ％ k - maximum number of Gaussian components allowed ％ ltol - percentage of the log likelihood difference between 2 iterations ([] for none) ％ maxiter - maximum number of iteration allowed ([] for none) ％ pflag - 1 for plotting GM for 1D or 2D cases only, 0 otherwise ([] for none) ％ Init - structure of initial W, M, V: Init.W, Init.M, Init.V ([] for none) ％ ％ Ouputs: ％ W(1,k) - estimated weights of GM ％ M(d,k) - estimated mean vectors of GM ％ V(d,d,k) - estimated covariance matrices of GM ％ L - log likelihood of estimates ％
標簽： multidimensional estimation algorithm Gaussian
上傳時間： 2013-12-03
上傳用戶：我們的船長
he algorithm is equivalent to Infomax by Bell and Sejnowski 1995 [1] using a maximum likelihood formulation. No noise is assumed and the number of Observations must equal the number of sources. The BFGS method [2] is used for optimization. The number of independent components are calculated using Bayes Information Criterion [3] (BIC), with PCA for dimension reduction.
標簽： equivalent likelihood algorithm Sejnowski
上傳時間： 2016-09-17
上傳用戶：Altman
This paper addresses the subject of SQL Injection in a Microsoft SQL Server/IIS/Active Server Pages environment, but most of the techniques discussed have equivalents in other database environments. It should be viewed as a "follow up", or perhaps an appendix, to the previous paper, "Advanced SQL Injection". The paper covers in more detail some of the points described in its predecessor, providing examples to clarify areas where the previous paper was perhaps unclear. An effective method for privilege escalation is described that makes use of the openrowset function to scan a network. A novel method for extracting information in the absence of helpful error messages is described the use of time delays as a transmission channel. Finally, a number of miscellaneous Observations and useful hints are provided, collated from responses to the original paper, and various conversations around the subject of SQL injection in a SQL Server environment.
標簽： Server SQL Injection Microsoft
上傳時間： 2014-07-28
上傳用戶：xhz1993
