This project is created using the Keil ARM CA Compiler.
The Logic Analyzer built into the simulator may be used to monitor and display any variable or peripheral I/O register. It is already configured to show the PWM output signal on PORT3.0 and PORT3.1
This ARM Example may be debugged using only the uVision Simulator and your PC--no additional hardware or evaluation boards are required. The Simulator provides cycle-accurate simulation of all on-chip peripherals of the ADuC7000 device series.
You may create various input signals like digital pulses, sine waves, sawtooth waves, and square waves using signal functions which you write in C. Signal functions run in the background in the simulator within timing constraints you configure. In this example, several signal functions are defined in the included Startup_SIM.INI file.
Setting and Changing Column Widths
By default, all columns in a table start out with equal width, and the columns automatically fill the entire width of the table. When the table becomes wider or narrower (which might happen when the user resizes the window containing the table), all the column widths change appropriately.
The code is fairly straightforward, except perhaps for the call to convertColumnIndexToModel. That call is necessary because if the user moves the columns around, the view s index for the column doesn t match the model s index for the column. For example, the user might drag the Vegetarian column (which the model considers to be at index 4) so it s displayed as the first column — at view index 0. Since prepareRenderer gives us the view index, we need to translate the view index to a model index so we can be sure we re dealing with the intended column
The project KEIL_IODemo shows how to use memory allocation routines (malloc) and char I/O (printf, scanf) via a serial interface with the Keil ARM toolchain.
The I/O functions are adapted for the Analog Devices ADuC7000 series using the SERIAL.C module.
The example also shows the efficiency of the Keil CA ARM Compiler run-time library which is tuned for single chip systems.
UART I/O and Memory Allocation Example for GNU
The project GNU_IODemo shows how to use memory allocation routines (malloc) and char I/O (printf, scanf) via a serial interface with the GNU toolchain.
The I/O functions are adapted for the Analog Devices ADuC7000 series using the SERIAL.C module.
The example also shows the efficiency of the Keil CA ARM Compiler run-time library which is tuned for single chip systems.
For the incomplete methods, we kept the representation of the queens by a table and the method of calculation to determine if two queens are in conflict, which is much faster for this kind of problems than the representation by a matrix.
heuristics: descent.
Tests: 100 queens in less than 1 second and 67 iterations. 500 queens in 1 second and 257 iterations. 1000 queens in 11 seconds and 492 iterations.
heuristics: Simulated annealing.
Tests: 100 queens in less than 1 second and 47 iterations. 500 queens in 5 seconds and 243 iterations. 1000 queens in 13 seconds and 497 iterations.
heuristics: based on Simulated Annealing.
Tests: 100 queens in less than 1 second and 60 iterations. 500 queens in 1 second and 224 iterations. 1000 queens in 5 seconds and 459 iterations. 10 000 queens in 20 minutes 30 seconds and 4885 iterations.
This book is about using Python to get jobs done on Windows.This intended to be a practical book focused on tasks. It doesn t aim to teach Python programming, although we do provide a brief tutorial. Instead, it aims to cover:How Python works on Windows The key integration technologies supported by Python on Windows, such as the Win32 extensions, which let you call the Windows API, and the support for COM Examples in many topic areas showing what Python can do and how to put it to work.
GUI Ant-Miner is a tool for extracting classification rules from data. It is an updated version of a data mining algorithm called Ant-Miner (Ant Colony-based Data Miner), which was proposed in 2002 by Parpinelli, Lopes and Freitas.
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.)
The module LSQ is for unconstrained linear least-squares fitting. It is
based upon Applied Statistics algorithm AS 274 (see comments at the start
of the module). A planar-rotation algorithm is used to update the QR-
factorization. This makes it suitable for updating regressions as more
data become available. The module contains a test for singularities which
is simpler and quicker than calculating the singular-value decomposition.
An important feature of the algorithm is that it does not square the condition
number. The matrix X X is not formed. Hence it is suitable for ill-
conditioned problems, such as fitting polynomials.
By taking advantage of the MODULE facility, it has been possible to remove
many of the arguments to routines. Apart from the new function VARPRD,
and a back-substitution routine BKSUB2 which it calls, the routines behave
as in AS 274.
