This sample program generates two sine waves called X and Y. It will then calculate the normalized magnitude and phase of the two waveforms using the following formulas: Mag = sqrt(X^2 + Y^2)/sqrt(GainX^2 + GainY^2) Phase = (long) (atan2PU(X,Y) * 360) The program will prompt the user to change the gain and frequency of the X and Y waveforms.
標簽： Y. normalized generates calculate
上傳時間： 2014-01-06
上傳用戶：123456wh
This a GA implementation using binary and real coded variables. Mixed variables can be used. Constraints can also be handled. All constraints must be greater-than-equal-to type (g >= 0) and normalized (see the sample problem in prob1 in objective()).
標簽： variables implementation Constra binary
上傳時間： 2015-03-16
上傳用戶：qiao8960
基于Volterra濾波器混沌時間序列多步預測 作者：陸振波，海軍工程大學 歡迎同行來信交流與合作，更多文章與程序下載請訪問我的個人主頁 電子郵件：luzhenbo@sina.com 個人主頁：luzhenbo.88uu.com.cn 參考文獻： 1、張家樹.混沌時間序列的Volterra自適應預測.物理學報.2000.03 2、Scott C.Douglas, Teresa H.-Y. Meng, normalized Data Nonlinearities for LMS Adaptation. IEEE Trans.Sign.Proc. Vol.42 1994 文件說明： 1、original_MultiStepPred_main.m 程序主文件,直接運行此文件即可 2、original_train.m 訓練函數 3、original_test.m 測試函數 4、LorenzData.dll 產生Lorenz離散序列 5、normalize_1.m 歸一化 6、PhaSpaRecon.m 相空間重構 7、PhaSpa2VoltCoef.dll 構造 Volterra 自適應 FIR 濾波器的輸入信號矢量 Un 8、TrainTestSample_2.m 將特征矩陣前 train_num 個為訓練樣本，其余為測試樣本 9、FIR_NLMS.dll NLMS自適應算法
上傳時間： 2013-12-16
上傳用戶：talenthn
一種 較新的聚類算法 Dominant-set 的代碼，包括聚類算法的代碼和測試代碼。該算法最大特點 就是基于圖理論的 ，相對于normalized Cut,計算復雜度低很多，況且能自動決定類的個數
標簽： Dominant-set 聚類算法 代碼
上傳時間： 2013-12-20
上傳用戶：417313137
iris localization using integro differential operator. The rar contains 5 files in order to computer the integro differential operator of the normalized contour of the iris and puil boundaries and then add circles to the respective boundaries.
標簽： differential localization contains computer
上傳時間： 2017-03-23
上傳用戶：希醬大魔王
The existence of numerous imaging modalities makes it possible to present different data present in different modalities together thus forming multimodal images. Component images forming multimodal images should be aligned, or registered so that all the data, coming from the different modalities, are displayed in proper locations. The term image registration is most commonly used to denote the process of alignment of images , that is of transforming them to the common coordinate system. This is done by optimizing a similarity measure between the two images. A widely used measure is Mutual Information (MI). This method requires estimating joint histogram of the two images. Experiments are presented that demonstrate the approach. The technique is intensity-based rather than feature-based. As a comparative assessment the performance based on normalized mutual information and cross correlation as metric have also been presented.
標簽： present modalities existence different
上傳時間： 2017-04-03
上傳用戶：qunquan
Computes all eigenvalues and eigenvectors of a real symmetric matrix a, ! which is of size n by n, stored in a physical np by np array. ! On output, elements of a above the diagonal are destroyed. ! d returns the eigenvalues of a in its first n elements. ! v is a matrix with the same logical and physical dimensions as a, ! whose columns contain, on output, the normalized eigenvectors of a. ! nrot returns the number of Jacobi rotations that were required. ! Please notice that the eigenvalues are not ordered on output. ! If the sorting is desired, the addintioal routine "eigsrt" ! can be invoked to reorder the output of jacobi.
上傳時間： 2016-06-04
上傳用戶：1512313
We are currently witnessing an increase in telecommunications norms and standards given the recent advances in this domain. The increasing number of normalized standards paves the way for an increase in the range of offers and services available for each consumer. Moreover, the majority of available radio frequencies have already been allocated.
標簽： Allocation Resource Radio
上傳時間： 2020-06-01
上傳用戶：shancjb
