Beginning with an overview of SQL Server 2000, this book discusses online transaction processing (OLTP) and online analytical processing (OLAP), features a tour of different SQL Server releases, and offers a guide to installation. The author describes and demonstrates the changes since SQL Server 7.0, thoroughly exploring SQL Server 2000 s capacity as a Web-enabled database server. Readers are then immersed in advanced database administration topics such as performance optimization and debugging techniques.
SAM9261 BasicMMU Example code with ADS1.2 (163 kB) The goal of this project is to show how to use a PC100 SDRAM and the MMU to perform a rating with a 100MHz Bus Clock. The rating is based on Dhrystone 2.1. It shows the rate when I+D Caches are disabled or enabled, with or without MMU and I Cache is disable or enabled, with or without MMU.
This leon3 design is tailored to the Altera NiosII Startix2
Development board, with 16-bit DDR SDRAM and 2 Mbyte of SSRAM.
As of this time, the DDR interface only works up to 120 MHz.
At 130, DDR data can be read but not written.
NOTE: the test bench cannot be simulated with DDR enabled
because the Altera pads do not have the correct delay models.
* How to program the flash prom with a FPGA programming file
1. Create a hex file of the programming file with Quartus.
2. Convert it to srecord and adjust the load address:
objcopy --adjust-vma=0x800000 output_file.hexout -O srec fpga.srec
3. Program the flash memory using grmon:
flash erase 0x800000 0xb00000
flash load fpga.srec
siptapi
A TAPI driver for SIP. With this TAPI driver you have a click2dial feature with any TAPI enabled application (e.g. MS Outlook) and any SIP account (e.g. freeworlddialup or iptel.org).
JTop monitors the CPU usage of all threads in a remote application
which has remote management enabled. JTop demonstrates the use of
the java.lang.management API to obtain the CPU consumption for
each thread.
ITU-T G.729語音壓縮算法。
description:
Fixed-point description of commendation G.729 with ANNEX B Coding of Speech at 8 kbit/s using Conjugate-Structure Algebraic-Code-Excited Linear-Prediction (CS-ACELP) with Voice Activity Decision(VAD), Discontinuous Transmission(DTX), and Comfort Noise Generation(CNG).
Hidden_Markov_model_for_automatic_speech_recognition
This code implements in C++ a basic left-right hidden Markov model
and corresponding Baum-Welch (ML) training algorithm. It is meant as
an example of the HMM algorithms described by L.Rabiner (1) and
others. Serious students are directed to the sources listed below for
a theoretical description of the algorithm. KF Lee (2) offers an
especially good tutorial of how to build a speech recognition system
using hidden Markov models.
