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.
Tracking a moving object through several frames, provided changes from frame to frame are on the order of +-(10 + "X Range") pixels in the X direction and +-(10 + "Y Range") in the Y direction is done automatically because of a relatively large Area of exploration during the search for an optimal (new) position for a particular control point and a very strong force exerted by large values of the image gradient.
This the implementation of structural SVM for training complex alignment models for protein sequence alignment, especially for homology modeling. The structural SVM algorithm can incorporate many relevant features like secondary structure, relative exposed surface Area, profiles and their various interaction into the alignment model. It was developed under Linux and compiles under gcc, built upon the svm^light software by Thorsten Joachims.
IT++ is a C++ library of mathematical, signal processing and communication system routines/functions. Its main use is in simulation of communication systems or for performing research in the Area of communications.
This title demonstrates how to develop computer programmes which solve specific engineering problems using the finite element method. It enables students, scientists and engineers to assemble their own computer programmes to produce numerical results to solve these problems. The first three editions of Programming the Finite Element Method established themselves as an authority in this Area. This fully revised 4th edition includes completely rewritten programmes with a unique description and list of parallel versions of programmes in Fortran 90. The Fortran programmes and subroutines described in the text will be made available on the Internet via anonymous ftp, further adding to the value of this title.
Sector is a system infrastructure software that provides functionality for distributed data storage, access, and analysis/processing. It automatically manages large volumetric data across servers or clusters, even those over distributed wide Area high speed networks. Sector provides simple tools and APIs to access and/or process the data. Data and server locations are transparent to users, as the whole Sector network is a single networked super computer to the users.
Intro/: Directory containing introductory examples.
HelloWorld.c A simple program that draws a box and writes "Hello World" in
HelloWorld.f it.
data The data file for the introductory progressive example.
Lines.c Reads the data from file "data" and plots just the curve with
Lines.f no labels, viewport or anything indicating quantity or units.
Viewport.c Restricts the graph to a viewport and frames the viewport,
Viewport.f leaving the remainder of the Area for labels, etc.
CharLbls.c Adds labels for the chart title, X-axis title, and Y-axis
CharLbls.f title.
Tics.c Adds tic marks to the viewport edges, but since clipping was
Tics.f not set correctly, tics extend outside the viewport.
Clip.c Sets clipping such that tic marks are clipped at the viewport
Clip.f boundaries.
TicLabels.c Adds numeric tic labels to the graph this is the final
TicLabels.f installment of the progressive example.
北京大學ACM題
Here is a geometric problem. You have an angle and some squares in the first quadrant of the plane rectangular coordinates. The vertex of the angle is fixed on the origin O of the coordinates, and both of its radial lines are specified by the input. The sizes of the squares are also specified by the input, and the squares can shift vertically and horizontally. Now your job is to use the squares and the radial lines of the angle to enclose the maximum Area, which excludes the Area of the squares (see Figure 1). You should note that the edges of the squares must be parallel to the axes.
New training algorithm for linear classification SVMs that can be much faster than SVMlight for large datasets. It also lets you direcly optimize multivariate performance measures like F1-Score, ROC-Area, and the Precision/Recall Break-Even Point.
