RANSAC (RANdom SAmple Consensus) is an iterative method to ESTIMATE parameters of a mathematical model from a set of observed data which contains outliers. Source code is in Document
Features a unique program to ESTIMATE the power spectral density. The spectrum containing all significant details is calculated from a time series model. Model type as well as model order are determined automatically from the data, using statistical criteria. Robust estimation algorithms and order selection criteria are used to obtain reliable results. Unlike in FFT analysis, where the experimenter has to set the amount of smoothing of the raw FFT, the right level of detail is assessed using the data only.
Many thermal metrics exist for integrated circuit (IC) packages ranging from θja to Ψjt.Often, these thermal metrics are misapplied by customers who try to use them to ESTIMATE junction temperatures in their systems.
為了提高直接轉矩控制（DTC）系統定子磁鏈估計精度，降低電流、電壓測量的隨機誤差，提出了一種基于擴展卡爾曼濾波（EKF）實現異步電機轉子位置和速度估計的方法。擴展卡爾曼濾波器是建立在基于旋轉坐標系下由定子電流、電壓、轉子轉速和其它電機參量所構成的電機模型上，將定子電流、定子磁鏈、轉速和轉子角位置作為狀態變量，定子電壓為輸入變量，定子電流為輸出變量，通過對磁鏈和轉速的閉環控制提高定子磁鏈的估計精度，實現了異步電機的無速度傳感器直接轉矩控制策略，仿真結果驗證了該方法的可行性,提高了直接轉矩的控制性能。
Abstract:
In order to improve the Direct Torque Control（DTC） system of stator flux estimation accuracy and reduce the current， voltage measurement of random error， a novel method to ESTIMATE the speed and rotor position of asynchronous motor based on extended Kalman filter was introduced. EKF was based on d-p axis motor and other motor parameters （state vector: stator current， stator flux linkage， rotor angular speed and position； input: stator voltage； output: staror current). EKF was designed for stator flux and rotor speed estimation in close-loop control. It can improve the ESTIMATEd accuracy of stator flux. It is possible to ESTIMATE the speed and rotor position and implement asynchronous motor drives without position and speed sensors. The simulation results show it is efficient and improves the control performance.
ICA is used to classify text in extension to the latent semantic indexing framework. ICA show to align the context grouping structure well in a human sense [1], thus can be used for unsupervised classification. The demonstration shows this on medical abstracts (MED dataset), that uses BIC to ESTIMATE the number of classes and produces keywords for each class. The icaML algorithm is used.
this demo is to show you how to implement a generic SIR (a.k.a. particle, bootstrap, Monte Carlo) filter to ESTIMATE the hidden states of a nonlinear, non-Gaussian state space model.
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.
