Digital Image Processing_3ed_Gonzalez 数字图像处理 冈萨雷斯第三版 英文版

音视频采集以及美颜滤镜

音视频编辑播放

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This lecture series gives comprehensive overview of the broad field of advanced radar systems, signal and data processing. The series starts with a lecture by U. Nickel in which the basic and fundamental of signal processing for phased array radar and their problems with grating lobes, ambiguities, and angle estimation for instance. The lecture “Advanced target tracking techniques” by W. Koch gives a short introduction to the principle of target tracking and several approaches are discussed for sequential track extraction and for phased-array radars. In the third lecture P. Berens gives an introduction to the synthetic aperture radar (SAR). T. Johnsen provides an overview of bi- and multistatic radar and their associated problems like synchronization, timing, and signal processing. The second lecture of U. Nickel focuses on the problem of adaptive array signal processing and provides the fundamental understanding for the next two lectures. The focus of these lectures, presented by W. Bürger, is on space-time adaptive processing. In his second lecture P. Berens continues with the topic of the synthetic aperture radar and expands the presented techniques to wideband SAR and multichannel SAR/MTI systems. W. Koch’s second paper focuses on sensor data and information fusion, which is essential to extract key-information for the final judgement using several sensors. In summery, this Lecture Series presents a unique overview of the state of the art of advanced radar and the associated signal and data processing research. It offers a variety of material for all those being involved in this scientific area, e.g. students, university teachers, researchers, industrial system designers, and military users.

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Simon Haykin著 附录彩色，全本清晰版 适合进一步学习雷达知识者 -------------------------------- 小闻子专业推荐 质量保证

GAP8边缘计算模块的规格书，适用于边缘计算，嵌入式AI开发人员。本文介绍了GAP8的详细属性和一些应用场景，全英文，后续可能会翻译

一本提高GPU编程的好书，采用CUDA技术，容易理解！

波网 实现，用于音频源分离。 对于（改进的）Pytorch版本，请单击。 对于Tensorflow 2 / Keras中的第三方实施（不是我本人），请单击。 听的例子 听人声的分离结果和多仪器分离结果 什么是Wave-U-Net？ Wave-U-Net是一种适用于音频源分离任务的卷积神经网络，直接在原始音频波形上工作，中。 Wave-U-Net是U-Net架构对一维时域的一种改编，可以执行端到端音频源分离。 通过一系列涉及一维卷积的下采样和上采样块以及下采样/上采样过程，可以在抽象和时间分辨率的多个尺度/级别上计算特征，并进行组合以进行预测。 有关网络体系结构的摘要，请参见下图。 参加SiSec分离竞赛 Wave-U-Net还以提交和的参加了，并取得了良好的性能，特别是考虑到我们使用的数据集与许多其他提交相比所使用的有限数据集，尽管端到端数据处理更为耗时（我们还必须从数据中学习频