penMesh is a generic and efficient data structure for representing and manipulating polygonal meshes. OpenMesh is developed at the Computer Graphics Group, RWTH Aachen , as part of the OpenSGPlus project, is funded by the German Ministry for Research and Education ( BMBF), and will serve as geometry kernel upon which the so-called high level primitives (e.g. subdivision surfaces or progressive meshes) of OpenSGPlus are built. It was designed with the following goals in mind : Flexibility : provide a basis for many different algorithms without the need for adaptation. Efficiency : maximize time efficiency while keeping memory usage as low as possible. Ease of use : wrap complex internal structure in an easy-to-use interface.
標簽： manipulating representing and efficient
上傳時間： 2015-10-14
上傳用戶：米卡
Support servers like Tomcat, Resin, WebSphere, WebLogic etc. Multi-languages(English/Chinese...) Support RSS 2.0 RSS syndication Unlimited XML news source Customizable fetch time interval Customizable max entry count Customizable max content size
標簽： Multi-languages WebSphere WebLogic Support
上傳時間： 2015-10-17
上傳用戶：x4587
acm HDOJ 1051WoodenSticks Description: There is a pile of n wooden sticks. The length and weight of each stick are known in advance. The sticks are to be processed by a woodworking machine in one by one fashion. It needs some time, called setup time, for the machine to prepare processing a stick. The setup times are associated with cleaning operations and changing tools and shapes in the machine. The setup times of the woodworking machine are given as follows: (a) The setup time for the first wooden stick is 1 minute. (b) Right after processing a stick of length l and weight w , the machine will need no setup time for a stick of length l and weight w if l<=l and w<=w . Otherwise, it will need 1 minute for setup.
標簽： WoodenSticks Description length wooden
上傳時間： 2014-03-08
上傳用戶：netwolf
Learning Kernel Classifiers: Theory and Algorithms, Introduction This chapter introduces the general problem of machine learning and how it relates to statistical inference. 1.1 The Learning Problem and (Statistical) Inference It was only a few years after the introduction of the first computer that one of man’s greatest dreams seemed to be realizable—artificial intelligence. Bearing in mind that in the early days the most powerful computers had much less computational power than a cell phone today, it comes as no surprise that much theoretical research on the potential of machines’ capabilities to learn took place at this time. This becomes a computational problem as soon as the dataset gets larger than a few hundred examples.
標簽： Introduction Classifiers Algorithms introduces
上傳時間： 2015-10-20
上傳用戶：aeiouetla
In 1960, R.E. Kalman published his famous paper describing a recursive solution to the discrete-data linear filtering problem. Since that time, due in large part to advances in digital computing, the Kalman filter has been the subject of extensive research and application, particularly in the area of autonomous or assisted navigation.
標簽： R.E. discrete-dat describing published
上傳時間： 2015-10-22
上傳用戶：2404
In 1960, R.E. Kalman published his famous paper describing a recursive solution to the discretedata linear filtering problem [Kalman60]. Since that time, due in large part to advances in digital computing, the Kalman filter has been the subject of extensive research and application, particularly in the area of autonomous or assisted navigation. A very “friendly” introduction to the general idea of the Kalman filter can be found in Chapter 1 of [Maybeck79], while a more complete introductory discussion can be found in [Sorenson70], which also contains some interesting historical narrative.
標簽： R.E. discretedata describing published
上傳時間： 2015-10-22
上傳用戶：a673761058
Many applications in computer graphics require complex, highly detailed models. However, the level of detail actually necessary may vary considerably. To control processing time, it is often desirable to use approximations in place of excessively detailed models.
標簽： applications computer graphics detailed
上傳時間： 2014-11-22
上傳用戶：拔絲土豆
Verilog HDL: Magnitude For a vector (a,b), the magnitude representation is the following: A common approach to implementing these arithmetic functions is to use the Coordinate Rotation Digital Computer (CORDIC) algorithm. The CORDIC algorithm calculates the trigonometric functions of sine, cosine, magnitude, and phase using an iterative process. It is made up of a series of micro-rotations of the vector by a set of predetermined constants, which are powers of two. Using binary arithmetic, this algorithm essentially replaces multipliers with shift and add operations. In a Stratix™ device, it is possible to calculate some of these arithmetic functions directly, without having to implement the CORDIC algorithm.
標簽： representation Magnitude the magnitude
上傳時間： 2013-12-24
上傳用戶：金宜
Input The first line of the input contains a single integer T (1 <= T <= 20), the number of test cases. Then T cases follow. The first line of each case contains N, and the second line contains N integers giving the time for each people to cross the river. Each case is preceded by a blank line. There won t be more than 1000 people and nobody takes more than 100 seconds to cross. Output For each test case, print a line containing the total number of seconds required for all the N people to cross the river. Sample Input 1 4 1 2 5 10 Sample Output 17
標簽： the contains integer number
上傳時間： 2015-10-27
上傳用戶：plsee
Solutions are obtained for Poissson, diffusion, or wave PDEs homogeneous or nonhomogeneous equations and/or boundary conditions rectangular, cylindrical, or spherical coordinates time, Laplace, or frequency domains Dirichlet, Neumann, Robin, singular, periodic, or incoming/outgoing boundary conditions. Output is suitable for pasting into LaTeX documents.
標簽： nonhomogeneous homogeneous Solutions diffusion
上傳時間： 2015-10-30
上傳用戶：JasonC
