Matsig is an object-oriented signal class library for MATLAB 6.5 and later. It implements a signal class, simplifying operations and manipulations common in audio signal processing and speech processing
The DSPLIB is a collection of 39 high-level optimized DSP functions for the TMS320C64x device. This source code library includes C-callable functions (ANSI-C language compatible) for general signal processing math and vector functions.
The DSPLIB is a collection of 39 high-level optimized DSP functions for the TMS320C64x device. This source code library includes C-callable functions (ANSI-C language compatible) for general signal processing math and vector functions.
With the advent of multimedia, digital signal processing (DSP) of sound has emerged from the shadow of bandwidth-limited speech processing. Today, the main appli- cations of audio DSP are high quality audio coding and the digital generation and manipulation of music signals. They share common research topics including percep- tual measurement techniques and analysis/synthesis methods. Smaller but nonetheless very important topics are hearing aids using signal processing technology and hardware architectures for digital signal processing of audio. In all these areas the last decade has seen a significant amount of application oriented research.
This document accompanies a sample co-installer that can be used in conjunction with an INF file to install additional device INF files on the target system during a device installation. The instructions herein apply to the Microsoft Windows 2000 and Windows XP and Windows Server 2003 operating systems.
The sample co-installer described in this article interprets CopyINF directives in a [DDInstall] section in an INF file. The sample demonstrates using a co-installer to perform processing after a device has been installed, parsing the INF section that is being used for the installation, and the use of the SetupCopyOEMInf, SetupGetInfInformation, SetupQueryInfOriginalFileInformation and SetupDiGetActualSectionToInstall APIs.
the code environment s C++. it is in image processing field. this code Read TIFF Image then it excute a Median Filter Classical on gray images. after that it Write TIFFU nsigned Char.
In this paper, we describe the development of a rapidly reconfigurable system in which the users’ tacit knowledge and requirements are
elicited via a process of Interactive Evolution, finding the image processing parameters to achieve the required goals without any need for
specialised knowledge of the machine vision system. We show that the resulting segmentation can be quickly and easily evolved from
scratch, and achieves detection rates comparable to those of a hand-tuned system on a hot-rolled steel defect recognition problem.
This paper shows the development of a 1024-point
radix-4 FFT VHDL core for applications in hardware signal processing, targeting low-cost FPGA technologies. The developed core is targeted into a Xilinx廬 Spartan鈩?3 XC3S200 FPGA with the inclusion of a VGA display interface and an external 16-bit data acquisition system for performance evaluation purposes. Several tests were performed in order to verify FFT core functionality, besides the time performance analysis highlights the core advantages over commercially available DSPs and Pentium-based PCs. The core is compared with similar third party IP cores targeting resourceful FPGA technologies. The novelty of this work is to provide a lowcost, resource efficient core for spectrum analysis
applications.
The GIFT (the GNU Image-Finding Tool) is a Content Based Image Retrieval System (CBIRS: http://en.wikipedia.org/wiki/CBIR). It enables you to do Query By Example (QBE: http://en.wikipedia.org/wiki/QBE) on images, giving you the opportunity to improve query results by relevance feedback. For processing your queries the program relies entirely on the content of the images, freeing you from the need to annotate all images before querying the collection.
The philosophy of the book is to present various pattern recognition tasks in
a unified way, including image analysis, speech processing, and communication applications. Despite their differences, these areas do share common features and their study can only benefit from a unified approach.
