A Java virtual machine instruction consists of an opcode specifying the operation to be performed, followed by zero or more operands embodying values to be operated upon. This chapter gives details about the format of each Java virtual machine instruction and the operation it performs.
A Java virtual machine instruction consists of an opcode specifying the operation to be performed, followed by zero or more operands embodying values to be operated upon. This chapter gives details about the format of each Java virtual machine instruction and the operation it performs.
This example program shows how to configure and use the A/D Converter of the following microcontroller:
STMicroelectronics ST10F166
After configuring the A/D, the program reads the A/D result and outputs the converted value using the serial port.
To run this program...
Build the project (Project Menu, Build Target)
Start the debugger (Debug Menu, Start/Stop Debug Session)
View the Serial Window (View Menu, Serial Window #1)
View the A/D converter peripheral (Peripheral Menu, A/D Converter)
Run the program (Debug Menu, Go)
A debug script (debug.ini) creates buttons that set different analog values in A/D channels. As the program runs, you will see the A/D input and output change.
Other buttons create signals that generate sine wave or sawtooth patterns as analog inputs. µ Vision3 users may enable the built-in Logic Analyzer to view, measure and compare these input signals graphically.
SelectionDemo also has code (not included in the preceding snippet) that changes the table s selection orientation. By changing a couple of boolean values, you can make the table allow either column selections or individual cell selections, instead of row selections.
This m-file displays the time waveform for the Gaussian pulse function and the first and second derivatives of the Gaussian pulse function for a 0.5 nanosecond pulse width. Other values of pulse widths may be used by changing fs,t,t1. The program uses the actual first and second derivative equations for the Gaussian pulse waveforms. The first derivative is considered to be the monocycle or monopulse as discussed in most papers. The second derivative is the waveform generated from a dipole antenna used in a UWB system. Other information is contained in the file.
The tar file contains the following files:
ptfsf.c: heart of the perfect TFSF code
ptfsf.h: header file for same
ptfsf-demo.c: FDTD code which demonstrates use of perfect TFSF code. Essentially this program used to generate results shown in the paper
ptfsf-file-maker.c: code to generate an incident-field file using the "perfect" incident fields
ptfsf-demo-file.c: FDTD code which uses the perfect incident fields stored in a file
fdtdgen.h: defines macros used in much of my code
Makefile: simple make-file to compile programs
Also include are some simple script files to run the programs with reasonable values.
The code assumes a two-dimensional computational domain with TMz polarization (i.e., non-zero field Ez, Hx, and Hy). The program is currently written so that the incident field always strikes the lower-left corner of the total-field region first. (If you want a different corner, that should be a fairly simple tweak to the code, but for now you ll have to make that tweak yourself.)
Routine mar1psd: To compute the power spectum by AR-model parameters.
Input parameters:
ip : AR model order (integer)
ep : White noise variance of model input (real)
ts : Sample interval in seconds (real)
a : Complex array of AR parameters a(0) to a(ip)
Output parameters:
psdr : Real array of power spectral density values
psdi : Real work array
in chapter 12
The Hopfield model is a distributed model of an associative memory. Neurons are pixels and can take the values of -1 (off) or +1 (on). The network has stored a certain number of pixel patterns. During a retrieval phase, the network is started with some initial configuration and the network dynamics evolves towards the stored pattern which is closest to the initial configuration.
This is SvmFu, a package for training and testing support vector
machines (SVMs). It s written in C++. It uses templates. The
advantage of templates is that the types of kernel values and data
points can be varied to suit the problem.
The Valgrind distribution has multiple tools. The most popular is the memory checking tool (called Memcheck) which can detect many common memory errors such as:
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touching memory you shouldn t (eg. overrunning heap block boundaries)
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using values before they have been initialized
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incorrect freeing of memory, such as double-freeing heap blocks
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memory leaks.
