This document specifies a subset of the C programming language which is intended to be suitable
for embedded automotive systems up to and including safety integrity level 3 (as defined in the
MISRA Guidelines). It contains a list of rules concerning the use of the C programming language
together with justifications and examples.
This project asks you to write a program that allows you to specify an initial
configuration. The program follows the rules of Life (listed shortly) to show the
continuing behavior of the configuration.
This program implements a PIC-based fuzzy inference engine for the Fudge fuzzy development system from Motorola.
It works by taking the output from Fudge for the 68HC11 processor, and converting it to a MPASM compatible assembler file using the convert
batch file.
This file can then be incorporated with fuzzy.asm to create a fuzzy inference engine.
Tool chain
----------
FUDGE -> Fuzzy rules -> MC68HC11.ASM -> CONVERT.BAT -> rules.ASM
-> MPASM FUZZY.ASM -> INTEL HEX
Fuzzy input registers
---------------------
current_ins 1..8 x 8-bit raw inputs
Fuzzy inference function
------------------------
FuzzyEngine
Fuzzy output registers
----------------------
cog_outs 1..8 x 8-bit raw outputs
The book consists of three sections. The first, foundations, provides a tutorial overview of the principles underlying data mining algorithms and their application. The presentation emphasizes intuition rather than rigor. The second section, data mining algorithms, shows how algorithms are constructed to solve specific problems in a principled manner. The algorithms covered include trees and rules for classification and regression, association rules, belief networks, classical statistical models, nonlinear models such as neural networks, and local memory-based models. The third section shows how all of the preceding analysis fits together when applied to real-world data mining problems. Topics include the role of metadata, how to handle missing data, and data preprocessing.
Guided vehicles (GVs) are commonly used for the internal transportation of loads in warehouses, production plants and terminals. These guided vehicles can be routed with a variety of vehicle dispatching rules in an attempt to meet performance criteria such as minimizing the average load waiting times. In this research, we use simulation models of three companies to evaluate the performance of several real-time vehicle dispatching rules, in part described in the literature. It appears that there
is a clear difference in average load waiting time between the different dispatching rules in the different environments. Simple rules, based on load and vehicle proximity (distance-based) perform best for all cases. The penalty for this is a relatively high maximum load waiting time. A distance-based rule with time truncation, giving more priority to loads that have to wait longer than a time threshold, appears to yield the best possible overall performance. A rule that particularly considers load-waiting time performs poor overall. We also show that using little pre-arrival information of loads leads to a significant improvement in the performance of the dispatching rules without changing their performance ranking.
We’re living through exciting times. The landscape of what computers can do is
changing by the week. Tasks that only a few years ago were thought to require
higher cognition are getting solved by machines at near-superhuman levels of per-
formance. Tasks such as describing a photographic image with a sentence in idiom-
atic English, playing complex strategy game, and diagnosing a tumor from a
radiological scan are all approachable now by a computer. Even more impressively,
computers acquire the ability to solve such tasks through examples, rather than
human-encoded of handcrafted rules.
Machinelearninghasgreatpotentialforimprovingproducts,processesandresearch.Butcomputers
usually do not explain their predictions which is a barrier to the adoption of machine learning.
This book is about making machine learning models and their decisions interpretable.
After exploring the concepts of interpretability, you will learn about simple, interpretable models
such as decision trees, decision rules and linear regression. Later chapters focus on general model-
agnosticmethodsforinterpretingblackboxmodelslikefeatureimportanceandaccumulatedlocal
effects and explaining individual predictions with Shapley values and LIME.
Wherever possible the overall technique used for this series will be "definition by example" withgeneric formulae included for use in other applications. To make stability analysis easy we will usemore than one tool from our toolbox with data sheet information, tricks, rules-of-thumb, SPICESimulation, and real-world testing all accelerating our design of stable operational amplifier (op amp)circuits. These tools are specifically targeted at voltage feedback op amps with unity-gain bandwidths<20 MHz, although many of the techniques are applicable to any voltage feedback op amp. 20 MHz ischosen because as we increase to higher bandwidth circuits there are other major factors in closing theloop: such as parasitic capacitances on PCBs, parasitic inductances in capacitors, parasitic inductancesand capacitances in resistors, etc. Most of the rules-of-thumb and techniques were developed not justfrom theory but from the actual building of real-world circuits with op amps <20 MHz.
This document provides general hardware and layoutconsiderations and guidelines for hardware engineersimplementing a DDR3 memory subsystem.The rules and recommendations in this document serve as aninitial baseline for board designers to begin their specificimplementations, such as fly-by memory topology.
