SOUNDSC(Y,...) is the same as SOUND(Y,...) except the data is
scaled so that the sound is played as loud as possible without
clipping. The mean of the data is removed.
2． Using Gaussian elimination method and Gaussian elimination method with row scaled method to solve the following tri-diagonal system
for n=10 and 100
Analog Inputs and Outputs in an S7 PLC are represented in the PLC as a 16-bit integer. Over
the nominal span of the analog input or output, the value of this integer will range between -
27648 and +27648. However, it is easier to use the analog values if they are scaled to the
same units and ranges as the process being controlled. This applications tip describes
methods for scaling analog values to and from engineering units.
The use of the Wind River VxWorks Real-Time Operating System (RTOS) on Virtex™-4embedded PowerPC™ processors continues to be a popular choice for high performanceFPGA designs. The introduction of the Wind River Workbench design environment has enableda new and easier way for designers to control the configuration of the VxWorks kernel. Thisguide shows the steps required to build and configure a ML403 Embedded DevelopmentPlatform to boot and run the VxWorks RTOS. A VxWorks bootloader is created, programmedinto Flash, and used to boot the design. The concepts presented here can be scaled to anyPowerPC enabled development platform.
The data plane of the reference design consists of a configurable multi-channel XBERT modulethat generates and checks high-speed serial data transmitted and received by the MGTs. Eachchannel in the XBERT module consists of two MGTs (MGTA and MGTB), which physicallyoccupy one MGT tile in the Virtex-4 FPGA. Each MGT has its own pattern checker, but bothMGTs in a channel share the same pattern generator. Each channel can load a differentpattern. The MGT serial rate depends on the reference clock frequency and the internal PMAdivider settings. The reference design can be scaled anywhere from one channel (two MGTs)to twelve channels (twenty-four MGTs).
Displaying large amounts of technical data in a chart can be a frustrating task. You can find tons of charting controls with fancy effects and useless features, but when it comes to displaying many curves at once, independently scaled on different axes, most of them fail.
When working with mathematical simulations or engineering problems, it is not unusual to handle curves that contains thousands of points. Usually, displaying all the points is not useful, a number of them will be rendered on the same pixel since the screen precision is finite. Hence, you use a lot of resource for nothing!
This article presents a fast 2D-line approximation algorithm based on the Douglas-Peucker algorithm (see [1]), well-known in the cartography community. It computes a hull, scaled by a tolerance factor, around the curve by choosing a minimum of key points. This algorithm has several advantages:
這是一個基于Douglas-Peucker算法的二維估值算法。
//
// Histogram Sample
// This sample shows how to use the Sample Grabber filter for video image processing.
// Conceptual background:
// A histogram is just a frequency count of every pixel value in the image.
// There are various well-known mathematical operations that you can perform on an image
// using histograms, to enhance the image, etc.
// Histogram stretch (aka automatic gain control):
// Stretches the image histogram to fill the entire range of values. This is a "point operation,"
// meaning each pixel is scaled to a new value, without examining the neighboring pixels. The
// histogram stretch does not actually require you to calculate the full histogram. The scaling factor
// is calculated from the minimum and maximum values in the image.
