Yet another Java implementation for the addictive Minesweeper game. This game comes with a number of options unavailable in Windows s version, such as allowing more than one mines in a square.
New users and old of optimization in MATLAB will find useful tips and tricks in this document, as well as examples one can use as templates for their own problems.
Use this tool by editing the file optimtips.m, then execute blocks of code in cell mode from the editor, or best, publish the file to HTML. Copy and paste also works of course.
Some readers may find this tool valuable if only for the function pleas - a partitioned least squares solver based on lsqnonlin.
This is a work in progress, as I fully expect to add new topics as I think of them or as suggestions are made. Suggestions for topics I ve missed are welcome, as are corrections of my probable numerous errors. The topics currently covered are listed below
Finds a (near) optimal solution to the Traveling Salesman Problem (TSP) by setting up a Genetic Algorithm (GA) to search for the shortest path (least distance needed to travel to each city exactly once)
This paper examines the asymptotic (large sample) performance
of a family of non-data aided feedforward (NDA FF) nonlinear
Least-squares (NLS) type carrier frequency estimators for burst-mode
phase shift keying (PSK) modulations transmitted through AWGN and
flat Ricean-fading channels. The asymptotic performance of these estimators
is established in closed-form expression and compared with the
modified Cram`er-Rao bound (MCRB). A best linear unbiased estimator
(BLUE), which exhibits the lowest asymptotic variance within the family
of NDA FF NLS-type estimators, is also proposed.
Ink Blotting
One method for escaping from a maze is via ‘ink-blotting’. In this method your starting square
is marked with the number ‘1’. All free, valid squares north, south, east and west around the
number ‘1‘ are marked with a number ‘2’. In the next step, all free, valid squares around the two
are marked with a ‘3’ and the process is repeated iteratively until :
The exit is found (a free square other than the starting position is reached on the very edge
of the maze), or,
No more free squares are available, and hence no exit is possible.
Traveling Salesman Problem (TSP) has been an interesting problem for a long
time in classical optimization techniques which are based on linear and nonlinear
programming. TSP can be described as follows: Given a number of cities to visit
and their distances from all other cities know, an optimal travel route has to be
found so that each city is visited one and only once with the least possible distance
traveled. This is a simple problem with handful of cities but becomes complicated
as the number increases.
PCA and PLS aims:to get some
insight into the bilinear factor models Principal Component Analysis
(PCA) and Partial Least Squares (PLS) regression, focusing on the
mathematics and numerical aspects rather than how s and why s of
data analysis practice. For the latter part it is assumed (but not
absolutely necessary) that the reader is already familiar with these
methods. It also assumes you have had some preliminary experience
with linear/matrix algebra.
μC/OS-II Goals
Probably the most important goal of μC/OS-II was to make it backward compatible with μC/OS (at least from an
application’s standpoint). A μC/OS port might need to be modified to work with μC/OS-II but at least, the application
code should require only minor changes (if any). Also, because μC/OS-II is based on the same core as μC/OS, it is just
as reliable. I added conditional compilation to allow you to further reduce the amount of RAM (i.e. data space) needed
by μC/OS-II. This is especially useful when you have resource limited products. I also added the feature described in
the previous section and cleaned up the code.
Where the book is concerned, I wanted to clarify some of the concepts described in the first edition and provide
additional explanations about how μC/OS-II works. I had numerous requests about doing a chapter on how to port
μC/OS and thus, such a chapter has been included in this book for μC/OS-II.
This example demo code is provided as is and has no warranty,
implied or otherwise. You are free to use/modify any of the provided
code at your own risk in your applications with the expressed limitation
of liability (see below) so long as your product using the code contains
at least one uPSD products (device).
