This book uses the Python language to teach pro
-
gramming concepts and problem
-solving skills, without assuming any previous program- ming experience. With easy-to-understand examples, pseudocode, flowcharts, and other
tools, the student learns how to design the logic of programs and then implement those
programs using Python. This book is ideal for an introductory programming course or a
programming logic and design course using Python as the language.
As with all the boolts in the
Starting Out With
series, the hallmark of this text is its clear,
friendly, and easy
-to-understand writing. In addition, it is rich in example programs that
are concise and practical. The programs in this book include short examples that highlight
specific programming topics, as well as more involved examples that focus on problem
solving. Each chapter provides one or more case studies that provide step
-by-step analysis
of a specific problem and shows the student how to solve it.
exercie 8
ssd1
Object-Oriented Programming and Design Introduction This course introduces students to problem solving by means of object-oriented design and implementation. Emphasis is on problem analysis and solution design, documentation and implementation. Students use commercial software libraries, and create software projects
exm 3
Object-Oriented Programming and Design Introduction This course introduces students to problem solving by means of object-oriented design and implementation. Emphasis is on problem analysis and solution design, documentation and implementation. Students use commercial software libraries, and create software projects
Learning Kernel Classifiers: Theory and Algorithms, Introduction This chapter introduces the general problem of machine learning and how it relates to statistical inference. 1.1 The Learning Problem and (Statistical) Inference It was only a few years after the introduction of the first computer that one of man’s greatest dreams seemed to be realizable—artificial intelligence. Bearing in mind that in the early days the most powerful computers had much less computational power than a cell phone today, it comes as no surprise that much theoretical research on the potential of machines’ capabilities to learn took place at this time. This becomes a computational problem as soon as the dataset gets larger than a few hundred examples.
Just what is a regular expression, anyway?
Take the tutorial to get the long answer. The short answer is that a regular expression
is a compact way of describing complex patterns in texts. You can use them to search
for patterns and, once found, to modify the patterns in complex ways. You can also use
them to launch programmatic actions that depend on patterns.
A tongue-in-cheek comment by programmers is worth thinking about: "Sometimes you
have a programming problem and it seems like the best solution is to use regular
expressions now you have two problems." Regular expressions are amazingly
powerful and deeply expressive. That is the very reason writing them is just as
error-prone as writing any other complex programming code. It is always better to
solve a genuinely simple problem in a simple way when you go beyond simple, think
about regular expressions.
Tutorial: Using regular expressions
A major goal of this book is to show to make devices that are inherently reliable by design. While a lot of attention has been given to “quality improvement,” the majority of the emphasis has been placed on the processes that occur after the design of a product is complete. Design deficiencies are a significant problem, and can be exceedingly difficult to identify in the field. These types of quality problems can be addressed in the design phase with relatively little effort, and with far less expense than will be incurred later in the process. Unfortunately, there are many hardware designers and organizations that, for various reasons, do not understand the significance and expense of an unreliable design. The design methodology presented in this text is intended to address this problem.
We consider the problem of target localization by a
network of passive sensors. When an unknown target emits an
acoustic or a radio signal, its position can be localized with multiple
sensors using the time difference of arrival (TDOA) information.
In this paper, we consider the maximum likelihood formulation
of this target localization problem and provide efficient convex
relaxations for this nonconvex optimization problem.We also propose
a formulation for robust target localization in the presence of
sensor location errors. Two Cramer-Rao bounds are derived corresponding
to situations with and without sensor node location errors.
Simulation results confirm the efficiency and superior performance
of the convex relaxation approach as compared to the
existing least squares based approach when large sensor node location
errors are present.
The wide deployment of wireless networks and mobile technologies, along with the
significant increase in the number of mobile device users, have created a very strong
demand on various wireless-based, mobile-based software application systems and
enabling technologies. This not only provides many new business opportunities and
challenges to wireless and networking service providers, mobile technology ven-
dors, and software industry and solution integrators, butalso changes and enhances
people’s lives in many areas, including communications, information sharing and
exchange, commerce, home environment, education, and entertainment. Business
organizations and government agencies face new pressure fortechnology updatesto
upgrade their networking infrastructures with wireless connectivity to enhance
enterprise-oriented systems and solutions.
Traveling Salesperson Problem
Our branch-and-strategy splits a branch and bound solution into two groups:
one group including a particular arc and the other excluding this arc.
1.Each splitting incurs a lower bound and we shall traverse the searching tree with the "lower" lower bound.
2.If a constant subtracted from any row
or any column of the cost matrix, an
optimal solution does not change.
