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.
In 1960, R.E. Kalman published his famous paper describing a recursive solution
to the discrete-data linear filtering Problem. Since that time, due in large part to advances
in digital computing, the Kalman filter has been the subject of extensive research
and application, particularly in the area of autonomous or assisted
navigation.
In 1960, R.E. Kalman published his famous paper describing a recursive solution to the discretedata
linear filtering Problem [Kalman60]. Since that time, due in large part to advances in digital
computing, the
Kalman filter
has been the subject of extensive research and application,
particularly in the area of autonomous or assisted navigation. A very “friendly” introduction to the
general idea of the Kalman filter can be found in Chapter 1 of [Maybeck79], while a more complete
introductory discussion can be found in [Sorenson70], which also contains some interesting
historical narrative.
Mobile phones are constantly decreasing in size, thereby complicating the acoustical
functionality. Signal processing methods can be used to partially mitigate
this Problem. In this paper we suggest a method which uses multiple spectral
subtraction functions and two microphones, introducing only a short signal delay.
The ability to write efficient, high-speed arithmetic routines ultimately depends
upon your knowledge of the elements of arithmetic as they exist on a computer. That
conclusion and this book are the result of a long and frustrating search for
information on writing arithmetic routines for real-time embedded systems.
With instruction cycle times coming down and clock rates going up, it would
seem that speed is not a Problem in writing fast routines. In addition, math
coprocessors are becoming more popular and less expensive than ever before and are
readily available. These factors make arithmetic easier and faster to use and
implement. However, for many of you the systems that you are working on do not
include the latest chips or the faster processors. Some of the most widely used
microcontrollers used today are not Digital Signal Processors (DSP), but simple
eight-bit controllers such as the Intel 8051 or 8048 microprocessors.
From the point of view of quality management, it is an important issue to reduce the transmission time in
the network. The quickest path Problem is to 6ndthe path in the network to senda given amount of data from
the source to the sink such that the transmission time is minimized.
SharpZipLib之前叫做NZipLib，完全由 C# 開發的壓縮庫，支持Zip, GZip, Tar and BZip2 ，為2007年8月最新0852release版的源文件和文檔說明！
Changes for v0.85.2 release
Minor tweaks for CF, ZipEntryFactory and ZipFile.
Fix for zip testing and Zip64 local header patching.
FastZip revamped to handle file attributes on extract + other fixes
Null ref in path filter fixed.
Extra data handling fixes
Revamped build and conditional compilation handling
Many bug fixes for Zip64.
Minor improvements to C# samples.
ZIP-1341 Non ascii zip password handling fix.
ZIP-355 Fix for zip compression Problem at low levels
SharpZipLib之前叫做NZipLib，完全由 C# 開發的壓縮庫，支持Zip, GZip, Tar and BZip2 ，為2007年8月最新0852release版的代碼實例！
Changes for v0.85.2 release
Minor tweaks for CF, ZipEntryFactory and ZipFile.
Fix for zip testing and Zip64 local header patching.
FastZip revamped to handle file attributes on extract + other fixes
Null ref in path filter fixed.
Extra data handling fixes
Revamped build and conditional compilation handling
Many bug fixes for Zip64.
Minor improvements to C# samples.
ZIP-1341 Non ascii zip password handling fix.
ZIP-355 Fix for zip compression Problem at low levels
Carrier-phase synchronization can be approached in a
general manner by estimating the multiplicative distortion (MD) to which
a baseband received signal in an RF or coherent optical transmission
system is subjected. This paper presents a unified modeling and
estimation of the MD in finite-alphabet digital communication systems. A
simple form of MD is the camer phase exp GO) which has to be estimated
and compensated for in a coherent receiver. A more general case with
fading must, however, allow for amplitude as well as phase variations of
the MD.
We assume a state-variable model for the MD and generally obtain a
nonlinear estimation Problem with additional randomly-varying system
parameters such as received signal power, frequency offset, and Doppler
spread. An extended Kalman filter is then applied as a near-optimal
solution to the adaptive MD and channel parameter estimation Problem.
Examples are given to show the use and some advantages of this scheme.
