This white paper discusses how market trends, the need for increased productivity, and new legislation have
accelerated the use of safety systems in industrial machinery. This TÜV-qualified FPGA design methodology is
changing the paradigms of safety designs and will greatly reduce development effort, system complexity, and time to
market. This allows FPGA users to design their own customized safety controllers and provides a significant
competitive advantage over traditional microcontroller or ASIC-based designs.
Introduction
The basic motivation of deploying functional safety systems is to ensure safe operation as well as safe behavior in
cases of failure. Examples of functional safety systems include train brakes, proximity sensors for hazardous areas
around machines such as fast-moving robots, and distributed control systems in process automation equipment such
as those used in petrochemical plants.
The International Electrotechnical Commission’s standard, IEC 61508: “Functional safety of
electrical/electronic/programmable electronic safety-related systems,” is understood as the standard for designing
safety systems for electrical, electronic, and programmable electronic (E/E/PE) equipment. This standard was
developed in the mid-1980s and has been revised several times to cover the technical advances in various industries.
In addition, derivative standards have been developed for specific markets and applications that prescribe the
particular requirements on functional safety systems in these industry applications. Example applications include
process automation (IEC 61511), machine automation (IEC 62061), transportation (railway EN 50128), medical (IEC
62304), automotive (ISO 26262), power generation, distribution, and transportation.
圖Figure 1. Local Safety System
This algorithm was developed by Professor Ronald L. Rivest of MIT and can be found presented in several languages. What I provide to you here is a C++ derivative of the original C implementation of Professor Rivets. The library code itself is platform-independant and has been tested in Redhat Linux. I ve included the sample code and makefile that I used for the Linux test. The demo, however, was written with Visual C++ 6 on a Windows 2000 platform.
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 code, images and designs for this book are released under a Creative Commons Attribution-NonCommercial-ShareAlike 2.5 License.
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You are free:
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Under the following conditions:
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*Any of these conditions can be waived if you get permission from the copyright holder.
CONTACT ME
Please address any questions to info@andybudd.com.
This source file may be used and distributed without restriction provided that this copyright statement is not removed from the file and that any derivative work contains the original copyright notice and the associated disclaimer.
This m file simulates a differential phase shift keyed (DPSK) ultra wide bandwidth(UWB) system using a fifth derivative waveform equation of a Gaussian pulse.
WB_BPSK_Analysis.rar:BPSK modulation and link analysis of UWB monocycle and doublet waveforms.Revised 1/2/05-JC.This m file plots the time and frequency waveforms for BPSK 1st and 2nd derivative equations used in UWB system analysis.
Basic function to locate and measure the positive peaks in a noisy
data sets. Detects peaks by looking for downward zero-crossings
in the smoothed third derivative that exceed SlopeThreshold
and peak amplitudes that exceed AmpThreshold. Determines,
position, height, and approximate width of each peak by least-squares
curve-fitting the log of top part of the peak with a parabola.
This contribution provides functions for finding an optimum parameter set using the evolutionary algorithm of Differential Evolution. Simply speaking: If you have some complicated function of which you are unable to compute a derivative, and you want to find the parameter set minimizing the output of the function, using this package is one possible way to go.
