As a consequence, more exact models of devices can
be retained for analysis rather than the approximate models commonly introduced
for the sake of computational simplicity. A computer icon appears in the margin
with each introduction of MATLAB analysis.
As a consequence, more exact models of devices can
be retained for analysis rather than the approximate models commonly introduced
for the sake of computational simplicity. A computer icon appears in the margin
with each introduction of MATLAB analysis.
linux下8139網(wǎng)卡驅(qū)動(dòng)分析,非常徹底,本人照此移植成功了嵌入式設(shè)備上的網(wǎng)口-8139 NIC drivers under analysis, very thorough, I do the successful transplantation of embedded devices on the LAN
AIOTrade is a free, open source stock technical analysis platform built on pure java. Its pluggable architecture is also ideal for custom features extending, such as indicators and charts. It Requires JRE 1.5.0+.
A Tutorial on Principal Component Analysis.Principal component analysis (PCA) is a mainstay of modern data analysis - a black box that
is widely used but poorly understood. The goal of this paper is to dispel the magic behind this
black box.
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.
