本文簡單討論并總結了VHDL、Verilog，System verilog 這三中語言的各自特點和區別As the number of enhancements to variousHardware Description Languages (HDLs) hasincreased over the PAST year, so too has the complexityof determining which language is best fora particular design. Many designers and organizationsare contemplating whether they shouldswitch from one HDL to another.
This program demonstrates using watchdog timers to invoke deadline
handlers. CoordinatorTask sends data to the organizer.
OrganizerTask receives data from the coordinatorTask, and resets
the coordinatorTask when no data is sent by the coordinatorTask in
the PAST five seconds (deadline time). This demonstration program
is automatically stopped after twenty seconds.
This document outlines what is necessary to install and run the LEACH protocol on version
2.27 of ns2. At the time of this writing, this is the newest version of ns2. The LEACH implementation
was written as a stand-alone application. Thus, in the PAST a version compiled for LEACH may or may
not work for other protocols. In addition, the original version of LEACH was compiled for version
2.5b which is an outdated version of ns2.
Introduction
Some times it is required that we build a shared library (DLL) from an m-file. M-files are functions that are written in Matlab editor and can be used from Matlab command prompt. In m-files, we employ Matlab built-in functions or toolbox functions to compute something. In my PAST articles, I showed you some ways to use Matlab engine (vis. API, C++ class or Matlab engine API) for employing Matlab built-in functions, but what about functions that we develop? How can we use them in VC? Is there any interface? This article shows you an idea to employ your own Matlab functions.
將數字時間轉換為英語口語表達形式，控制臺形式。其實核心代碼為一個類，可以自己修改輸出形式。
比如輸入
8 15
10 45
5 30
2 20
2 40
0 0
就能轉換為：
It s twenty PAST eight
It s a quarter PAST eight
It s a quarter to eleven
It s half PAST five
It s twenty PAST two
It s twenty to three
注意： 輸入 0 0 后結束并顯示結果
方式為每行兩個數，中間用空格空開
第一個數0到12表示小時，第二個數0到59表示分鐘
Abstract-The effect of the companding process on QAM signals
has been under investigation for the PAST several years. The
compander, included in the PCM telephone network to improve
voice performance, has an unusual affect on digital QAM data
signals which are transmitted over the same channel. The quantization
noise, generated by the companding process which is multiplicative
(and asymmetric), degrades the detectability performance
of the outermost points of the QAM constellation more
than that of the inner points.
The combined effect of the companding noise and the inherent
white gaussian noise of the system, leads us to a re-examination of
signal constellation design.
In this paper we investigate the detectability performance of a
number of candidates for signal constellations including, a typical
rectangular QAM constellation, the same constellation with the
addition of a smear-desmear operation, and two new improved
QAM constellation designs with two-dimensional warpi
The Kalman filter is a set of mathematical equations that provides an efficient computational
[recursive] means to estimate the state of a process, in a way that minimizes
the mean of the squared error. The filter is very powerful in several aspects:
it supports estimations of PAST, present, and even future states, and it can do so even
when the precise nature of the modeled system is unknown.
As I write this foreword, I am collaborating with four leading user interface
(UI) component vendors on a presentation for the 2004 JavaOneSM conference.
In our presentation, the vendors will show how they leverage JavaServerTM
Faces technology in their products. While developing the presentation, I am
learning some things about the work we’ve been doing on JavaServer Faces for
the PAST three years. The vendors have their own set of concerns unique to
adapting their product for JavaServer Faces, but they all voice one opinion
loud and clear: they are very relieved to finally have a standard for web-based
user interfaces.
The software and hardware development fields evolved along separate paths through the end of the 20th century. We seem to have come full circle, however. The previously rigid hardware on which our programs run is softening in many ways. Embedded systems are largely responsible for this softening. These hidden computing systems drive the electronic products around us, including consumer products like digital cameras and personal digital assistants, office automation equipment like copy machines and printers, medical devices like heart monitors and ventilators, and automotive electronics like cruise controls and antilock brakes.
Embedded systems force designers to work under incredibly tight time-tomarket, power consumption, size, performance, flexibility, and cost constraints.
Many technologies introduced over the PAST two decades have sought to help satisfy these constraints. To understand these technologies, it is important to first distinguish the underlying embedded systems elements.
