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V4_SI
- 针对FPGA设计的信号完整性分析，以及设计指导,网页视频和音频，识货的下-FPGA design for signal integrity analysis, and design guidelines, web video and audio,识货next
A61EDAn
- 分析了各种视频采集方案的研究现状。对如何采用CCD 摄像头采集高分辨率、高质量的图像以及基于FPGA 的嵌入式视频图像采集系统的实现方法进行了研究。采用了以摄像头+ 解码芯片模式为采集方案， 针对视频频解码芯片ADV7181B，实现了I2C 总线配置、ITU656 解码、VGA 显示模块的设计。设计的视频采集控制器已经在Altera 公司的CycloneII系列FPGA(EP2C35)上实现。结果显 -Status of a variety of video capture programs
DDigital_videS
- 配合DSP做的例子，前段视频采集和转换后， 通过切换SRAM中的数据到DPSS后端处理和FPGA采集操作，具有 一般通用性，更重要的是测试代码丰富，加深理解 -Example, with the DSP do to the DPSS back-end processing and FPGA front-end video capture and conversion, by switching the SRAM data acquisition operations, with genera
Bssppartan3a
- 一种基于xilinx公司的FPGA开发板spartan3的一个用键盘控制制vga输出的vhdl源代码程序源码，能实现高清晰的视频输出. -Based xilinx company FPGA development board spartan3 of a keyboard control system vga output vhdl source code program source code, can achieve high-definition video output.
FFPGA_vidiop
- 基于fpga的实时视频采集与与显示高速化 小型化-fpga -Based fpga real-time video capture and display high-speed small-fpga
VHDL_Complacated_divider_multiplier_technic
- Described as a way difficult to express in the language VHDL complicated point calculation method can be easily expressed in a FPGA-based 2 n wins to reference video signal YCbCr to RGB conversion will be created. This material has a lot of technic
DE2_TV
- 基于fpga的视频处理系统，在Altera的DE2-70开发板上实现。-Fpga-based video processing system, the Altera DE2-70 development board to achieve
project_3
- 视频处理,视频处理在FPGA 上的实现。已验证。-video process
51--AVR--MSP430--PIC--STM8--STM32
- 各种单片机(51、AVR、MSP430、PIC、STM8、STM32)、ARM、DSP、TFT彩屏、C语言，WiFi小车，FPGA、LINUX、机器人,CAD、VB、Protel99、Protues、Unix、UCOSII等各种视频教程超800G-Various microcontroller (51, AVR, MSP430, PIC, STM8, STM32), ARM, DSP, TFT color screen, C language, WiFi car, FPGA, LINUX, ro
嵌入式视频处理基本原理-中文版
- 嵌入式视频处理基础，对各种视频格式及视频数据流信号解码与编码进行了详细的介绍，做视频处理不可多得的工具书(Embedded video processing basis, a variety of video formats and video data stream decoding and coding are introduced in detail, very helpful for video processing)
pid-fpga-vhdl-master
- 6. Show how accurate your predicted model is, also explain in what situation and why it does (not) perform that well (in report and video). 7. If you re-train the network for your own custom images, you can choose different training options. Explain