This ECMA Standard specifies the form and establishes the interpretation of programs written in the C# programming language. It specifies
The Representation of C# programs
The syntax and constraints of the C# language
The semantic rules for interpreting C# programs
The restrictions and limits imposed by a conforming implementation of C#.
In the field of biometrics, palmprint is a novel but promising technology. Limited work has been reported on palmprint identification and verification, despite the importance of palmprint features. There are many unique features in a palmprint image that can be used for personal identification. Principal lines, wrinkles, ridges, minutiae points, singular points, and texture are regarded as useful features for palmprint Representation.
這是個(gè)反向工程delphi的程序的全部源代碼.能分析幾乎所有的結(jié)構(gòu)
Revendepro is a program to reverse engineer Delphi program. Revendepro finds almost all structures (classes, types, procedures, etc) in the program, and generates the pascal Representation, procedures will be written in assembler. Due to some limitation in assembler the generated output can not be recompiled.
The main features of the considered identification problem are that there is no an a priori separation of the variables into inputs and outputs and the approximation criterion, called misfit, does not depend on the model Representation. The misfit is defined as the minimum of the l2-norm between the given time series and a time series that is consistent with the approximate model. The misfit is equal to zero if and only if the model is exact and the smaller the misfit is (by definition) the more accurate the model is. The considered model class consists of all linear time-invariant systems of bounded complexity and the complexity is specified by the number of inputs and the smallest number of lags in a difference equation Representation. We present a Matlab function for approximate identification based on misfit minimization. Although the problem formulation is Representation independent, we use input/state/output Representations of the system in order
The toolbox solves a variety of approximate modeling problems for linear static models. The model can be parameterized in kernel, image, or input/output form and the approximation criterion, called misfit, is a weighted norm between the given data and data that is consistent with the model. There are three main classes of functions in the toolbox: transformation functions, misfit computation functions, and approximation functions. The approximation functions derive an approximate model from data, the misfit computation functions are used for validation and comparison of models, and the transformation functions are used for deriving one model Representation from another.
KEYWORDS: Total least squares, generalized total least squares, software implementation.
迭代自適應(yīng)Simpson，Lobatto積分
In almost every standard book on numerics quadrature algorithms like the adaptive Simpson or the adaptive Lobatto algorithm are presented in a recursive way. The benefit of the recursive programming is the compact and clear Representation. However, recursive quadrature algorithms might be transformed into iterative quadrature algorithms without major modifications in the structure of the algorithm.
We present iterative adaptive quadrature algorithm (adaptiveSimpson and adaptiveLobatto), which preserves the compactness and the clarity of the recursive algorithms (e.g. quad, quadv, and quadl). Our iterative algorithm provides a parallel calculation of the integration function, which leads to tremendous gain in run-time, in general. Our results suggest a general iterative and not a recursive implementation of adaptive quadrature formulas, once the programming language permits parallel access to the integration function. For details the attached PDF file Conrad_08.pdf.
CCE is a multi-instance learning method solving multi-instance problems through adapting multi-instance Representation to single-instance algorithms, which is quite different from existing multi-instance learning algorithms which attempt to adapt single-instance algorithms to multi-instance Representation
H.264/AVC, the result of the collaboration between the ISO/IEC
Moving Picture Experts Group and the ITU-T Video Coding
Experts Group, is the latest standard for video coding. The goals
of this standardization effort were enhanced compression efficiency,
network friendly video Representation for interactive
(video telephony) and non-interactive applications (broadcast,
streaming, storage, video on demand). H.264/AVC provides
gains in compression efficiency of up to 50％ over a wide range
of bit rates and video resolutions compared to previous standards.
Compared to previous standards, the decoder complexity
is about four times that of MPEG-2 and two times that of
MPEG-4 Visual Simple Profile. This paper provides an overview
of the new tools, features and complexity of H.264/AVC.
