面向?qū)ο髷?shù)據(jù)庫開發(fā)時，需要將數(shù)據(jù)庫中的表寫成類使用，重復(fù)工作代碼量巨大，該工具支持SQLServer2000數(shù)據(jù)庫，可以將數(shù)據(jù)庫中的表，批量轉(zhuǎn)換在Delphi類．支持2種格式： 1. 類聲明格式；(需要開發(fā)者在屬性上按Shift+Ctrl+C) 2. 直接轉(zhuǎn)換并保存Unit文件；
標(biāo)簽： 對象 數(shù)據(jù)庫
上傳時間： 2013-12-08
上傳用戶：frank1234
This paper examines the asymptotic (large sample) performance of a family of non-data aided feedforward (NDA FF) nonlinear least-squares (NLS) type carrier frequency estimators for burst-mode phase shift keying (PSK) modulations transmitted through AWGN and flat Ricean-fading channels. The asymptotic performance of these estimators is established in closed-form expression and compared with the modified Cram`er-Rao bound (MCRB). A best linear unbiased estimator (BLUE), which exhibits the lowest asymptotic variance within the family of NDA FF NLS-type estimators, is also proposed.
標(biāo)簽： performance asymptotic examines non-data
上傳時間： 2015-12-30
上傳用戶：225588
We present a particle filter construction for a system that exhibits time-scale separation. The separation of time-scales allows two simplifications that we exploit: i) The use of the averaging principle for the dimensional reduction of the system needed to solve for each particle and ii) the factorization of the transition probability which allows the Rao-Blackwellization of the filtering step. Both simplifications can be implemented using the coarse projective integration framework. The resulting particle filter is faster and has smaller variance than the particle filter based on the original system. The convergence of the new particle filter to the analytical filter for the original system is proved and some numerical results are provided.
標(biāo)簽： construction separation time-scale particle
上傳時間： 2016-01-02
上傳用戶：fhzm5658
matlab 實現(xiàn)系統(tǒng)的參數(shù)計算，系統(tǒng)單位階躍響應(yīng)的相關(guān)參數(shù)計算。Matlab real system parameters, the system unit step response of the relevant parameters.
上傳時間： 2014-01-08
上傳用戶：曹云鵬
Example - 3-D Stem Plot of an FFTFor example, fast Fourier transforms are calculated at points around the unit circle on the complex plane. So, it is interesting to visualize the plot around the unit circle. Calculating the unit circle.
標(biāo)簽： calculated transforms Example Fourier
上傳時間： 2013-12-17
上傳用戶：wpwpwlxwlx
類 • Person：PTS系統(tǒng)中的一個抽象類。 • Customer： Person類繼承下來的客戶即病人類。 • Prescription：病人處方類，根據(jù)處方可以為病人取藥，供為處方取藥的次數(shù)。 • Medicine： 藥品類，提供處方中給病人的藥物，藥品的單位用枚舉類Unit實現(xiàn)，給出藥品得副作用。 • Unit： 單位枚舉類，為藥品提供各種不同單位。
標(biāo)簽： 8226 Person Prescription Customer
上傳時間： 2016-03-12
上傳用戶：ddddddos
The result is an IS-95CDMA forward link software simulation package ,which mimics real-time data communication from a basestation to a cellular unit. The package simulates an IS-95CDMA forward link cellular system consisting of 3 major components:Transmitter, Communication Channel and Receiver.
標(biāo)簽： simulation real-time software forward
上傳時間： 2016-03-14
上傳用戶：大融融rr
PlotSphereIntensity(azimuth, elevation) PlotSphereIntensity(azimuth, elevation, intensity) h = PlotSphereIntensity(...) Plots the intensity (as color) of a number of points on a unit sphere. Input: azimuth (phi), in degrees elevation (theta), in degrees intensity (optional, if not provided, a green sphere is produced) All inputs must be vectors or matrices of the same size. Data does not have to be evenly spaced. When there aren t enough points to draw a smooth sphere, additional points (with color) are interpolated. Output: h - a handle to the patch object The axes are also plotted: positive x axis is red positive y axis is green positive z axis is blue
標(biāo)簽： PlotSphereIntensity elevation azimuth intensity
上傳時間： 2014-01-15
上傳用戶：ruan2570406
Probabilistic Principal Components Analysis. [VAR, U, LAMBDA] = PPCA(X, PPCA_DIM) computes the principal ％ component subspace U of dimension PPCA_DIM using a centred covariance matrix X. The variable VAR contains the off-subspace variance (which is assumed to be spherical), while the vector LAMBDA contains the variances of each of the principal components. This is computed using the eigenvalue and eigenvector decomposition of X.
標(biāo)簽： Probabilistic Components Principal Analysis
上傳時間： 2016-04-28
上傳用戶：qb1993225
The MDP toolbox proposes functions related to the resolution of discrete-time Markov Decision Process : finite horizon, value iteration, policy iteration, linear programming algorithms with some variants. The functions (m-functions) were developped with MATLAB v6.0 (one of the functions requires the Mathworks Optimization Toolbox) by the decision team of the Biometry and Artificial Intelligence Unit of INRA Toulouse (France). The version 2.0 (February 2005) handles sparse matrices and contains an example
標(biāo)簽： discrete-time resolution functions Decision
上傳時間： 2014-01-01
上傳用戶：xuanjie
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