IMX230 is a diagonal 5.867 (Type1/2.4) 21Mega-pixel CMOS active pixel type stacked image sensor with a square pixel array. It adopts Exmor-RSTM technology to achieve high speed image capturing by column parallel ADC circuits and high sensitivity and low noise image

CV-H1X Terminal-Software 用户手册

2021.09.29 c2960x-universalk9-tar.152-7.E5.tar

华中科技大学软件学院软件体系结构Software Architecture华中科技大学软件学院软件体系结构Software Architecture

蒂娜 基于编程语言的实时软渲染器。 签出以获得API演示入门。 结帐有关应用示例。 注意：渲染器以前已被命名为Taichi三，见底部。 安装 最终用户可以从PyPI安装Tina： python3 -m pip install taichi-tina 特征 以下是Tina当前拥有的重要功能： 粒子docs/particles.py 三角形网格docs/triangle.py 连接网格tests/connect.py 平滑阴影docs/smooth.py 网格- examples/meshgrid_wave.py 网格线框和抗锯齿docs/wireframe.py 从粒子构造表面examples/mciso_mpm3d.py 前景色和背景色- examples/pars_mpm3d.py 照明和材料docs/lighting.py 加载GLTF场景docs/gltf.py 转换模型docs/transform.py 屏幕绽放效果tests/blooming.py 时间抗锯齿，TAA- docs/options.py 快速近似抗锯齿，FXAA- tests

matlab代码仿真功放软件定义无线电中的数字预失真 MP型号 IIT Roorkee的Meenakshi Rawat教授的部分实习生工作 注意：系统实现的 Verilog 封装顶层模块文件和 matlab 代码是保密的 整个系统级架构在 Xilinx Vivado 中实现 使用的硬件 针对 Zynq:registered:-7000 XC7Z045-2FFG900C SoC 的 ZC706 评估板设计 AD9371 射频收发器 硬件设置 在片上系统 (SoC) 平台上设计并实现了基于软件定义无线电 (SDR) 的收发器系统，该系统由高速 Arm 嵌入式处理器和可重新配置的 FPGA 组成。 由于 PA 在饱和区的非线性行为，PA 在其输出端以相邻通道中频谱再生的形式产生失真。 这种频谱再生表示为相邻信道泄漏功率比 (ACPR)。 为了以高效率保持良好的线性度，需要增加 PA 的线性工作区域。 因此，在任何 SDR 收发器系统中，PA 的线性化是提高功率和频谱效率的一个重要方面。 具有 20 MHz 带宽和 11 dB 峰均功率比 (PAPR) 的长期演进 (LTE) 信号用于仿真和测量目的。 LTE 信号

The ITK Software Guide 给予ITK5.0.0进行更新的，且高清，有书签，希望给入门者一点帮助。

ITK Software Guide Book1 & Book2 pdf (for v4.13.0)

GNU广播USRP AoA GNU Radio软件包实现了MUSIC和根MUSIC到达角算法，并带有提供USRP设备相位同步所必需的模块。 这是的更新和改进版本。 已实现的块： 示例性GR GUI： 安装步骤： cd git clone https://github.com/MarcinWachowiak/GNU-Radio-USRP-Beamforming cd GNU-Radio-USRP-Beamforming/gr-aoa mkdir build cd build cmake .. make -j$(nproc) sudo make install sudo ldconfig 该存储库还包含MUSIC算法的仿真和以Python实现的参数分析。 示范性仿真结果：

Making Software - What Really Works, and Why We Believe It. Does the MMR vaccine cause autism? Does watching violence on TV make children more violent? Are some programming languages better than others? People argue about these questions every day. Every serious attempt to answer the first two questions relies on the scientific method: careful collection of evidence, and impartial evaluation of its implications. Until recently, though, only a few people have tried to apply these techniques to the third. When it comes to computing, it often seems that a couple glasses of beer and an anecdote about a startup in Warsaw are all the “evidence” most programmers expect. That is changing, thanks in part to the work of the contributors to this book. Drawing on fields as diverse as data mining, cognitive psychology, and sociology, they and their colleagues are creating an evidence-based approach to software engineering. By gathering evidence drawn from a myriad of primary sources and analyzing the results, they are shedding new light onto some vexing questions of software development. What do most programmers get wrong in their first job? Does test-driven development lead to better code? What about pair programming, or code reviews? Is it possible to predict the likely number of bugs in a piece of code before it’s released? If so, how?