The need for accurate monitoring and analysis of sequential data arises in many scientic, industrial
and nancial problems. Although the Kalman lter is effective in the linear-Gaussian
case, new methods of dealing with sequential data are required with non-standard models.
Recently, there has been renewed interest in simulation-based techniques. The basic idea behind
these techniques is that the current state of knowledge is encapsulated in a representative
sample from the appropriate posterior distribution. As time goes on, the sample evolves and
adapts recursively in accordance with newly acquired data. We give a critical review of recent
developments, by reference to oil well monitoring, ion channel monitoring and tracking
problems, and propose some alternative algorithms that avoid the weaknesses of the current
methods.
小波神經網絡好文章！A method for fault detection is proposed using a trained neural network as the nominal
model of the system to be monitored. Partial physical knowledge, if available, can be combined
with the nominal model to perform fault isolation.
This the second tutorial of the Writing Device Drivers series. There seems to be a lot of interest in the topic, so this article will pick up where the first left off. The main focus of these articles will be to build up little by little the knowledge needed to write device drivers. In this article, we will be building on the same example source code used in part one. In this article, we will expand on that code to include Read functionality, Handle Input/Ouput Controls also known as IOCTLs, and learn a bit more about IRPs.
Adaptive Filter. This script shows the BER performance of several types of equalizers in a static channel with a null in the passband. The script constructs and implements a linear equalizer object and a decision feedback equalizer (DFE) object. It also initializes and invokes a maximum likelihood sequence estimation (MLSE) equalizer. The MLSE equalizer is first invoked with perfect channel knowledge, then with a straightforward but imperfect channel estimation technique.
The market for miniature computer programming is exploding. C++ Footprint and Performance Optimization supplies programmers the knowledge they need to write code for the increasing number of hand-held devices, wearable computers, and intelligent appliances.
This book gives readers valuable knowledge and programming techniques that are not currently part of traditional programming training.
In the world of C++ programming, all other things being equal, programs that are smaller and faster are better.
C++ Footprint and Performance Optimization contains case studies and sample code to give readers concrete examples and proven solutions to problems that don t have cut and paste solutions.
Data mining (DM) is the extraction of hidden predictive information from large databases
(DBs). With the automatic discovery of knowledge implicit within DBs, DM uses
sophisticated statistical analysis and modeling techniques to uncover patterns and relationships
hidden in organizational DBs. Over the last 40 years, the tools and techniques to
process structured information have continued to evolve from DBs to data warehousing
(DW) to DM. DW applications have become business-critical. DM can extract even more
value out of these huge repositories of information.
The second volume in the Write Great Code series supplies the critical information that today s computer science students don t often get from college and university courses: How to carefully choose their high-level language statements to produce efficient code. Write Great Code, Volume 2: Thinking Low-Level, Writing High-Level, teaches software engineers how compilers translate high-level language statements and data structures into machine code. Armed with this knowledge, a software engineer can make an informed choice concerning the use of those high-level structures to help the compiler produce far better machine code--all without having to give up the productivity and portability benefits of using a high-level language
The core of Java(TM) technology, the Java virtual machine is an abstract computing machine that enables the Java(TM) platform to host applications on any computer or operating system without rewriting or recompiling. Anyone interested in designing a language or writing a compiler for the Java virtual machine must have an in-depth understanding of its binary class format and instruction set. If you are programming with the Java programming language, knowledge of the Java virtual machine will give you valuable insight into the Java platform s security capabilities and cross-platform portability. It will increase your understanding of the Java programming language, enabling you to improve the security and performance of your programs.
Welcome to Beginning Algorithms, a step-by-step introduction to computing algorithms for the real world.
Developers use algorithms and data structures every day of their working lives. Having a good understanding
of these algorithms and knowledge of when to apply them is essential to producing software
that not only works correctly, but also performs efficiently.
This book aims to explain those algorithms and data structures most commonly encountered in day-today
software development, while remaining at all times practical, concise, and to the point, with little or
no verbiage to distract from the core concepts and examples.
Matlab is an ideal tool for simulating digital communications systems, thanks to
its easy scripting language and excellent data visualization capabilities. One of the
most frequent simulation tasks in the field of digital communications is bit-error-
rate testing of modems. The bit-error-rate performance of a receiver is a figure of
merit that allows different designs to be compared in a fair manner. Performing
bit-error-rate testing withMatlab is very simple, but does require some prerequisite
knowledge
