Then, we apply the matrix to express the

linear transformation layer of these block ciphers and give a detailed process.

They cover the basics of probability theory, the digital features of random variables, methods of describing random signals, complex stochastic processes, the

linear transformation of the stationary stochastic process, the stationary narrow band stochastic process, the nonlinear transformation method of stationary random processes, and the nonstationary stochastic process and methods of analyzing it in modern signal processing.

Linear transformation functions are applied on the overall partition of the fuzzy sets and will either linearly expand or contract all the fuzzy sets (see Figure 1).

The PCA finds an orthogonal

linear transformation that converts the data to a new coordinate system so that the largest change in any projection of data comes from the first coordinate, the second largest change is on the second coordinate, and so on.

For our purpose, the observation that ILI is required to register information in experience is vital, first because it means that the form of information in the sensory cortex, digital information, is not the form of information in experience, and second because just such a

linear transformation would be required to convert the digital information in a sensory cortex into the kind of information, by which a critical instability on the cortex could represent it in experience.

* The proposed HVS-based algorithm focuses on the three sub-models :maximum likelihood estimation method,blind deconvolution and dehazing using

linear transformation.

Where [[bar.[epsilon]].sub.p] indicates the plastic strain vector under plane stress condition [[epsilon].sup.p] = {[[epsilon].sup.p.sub.11], [[epsilon].sup.p.sub.22,[[epsilon].sup.p.sub.33],[[epsilon].sup.p.sub.12]]}, while [beta] is a positive semi-definite matrix which characterizes the

linear transformation of the strain vector, reads

proposes a zero-dimensional Grobner basis construction method by choosing suitable term order and variable order after performing an in-depth study on the

linear transformation and the system of multivariate polynomial equations of AES-256.

Source data representation can be obtained by

linear transformation of target data according to Jhuo et al.

Namely,

linear transformation matrix learned in the high dimensional feature space can more appropriately map samples into their class labels and has more powerful discriminating ability.

It can be known from Figure 1 that T is composed of the nonlinear layer S and the

linear transformation L.

So, the name of the transition matrix of the state is quite suitable for a matrix [PHI][t.sub.0]), as it reflects the state of the system at the moment [t.sub.0] with the help of a

linear transformation: