离散小波变换dwt matlab代码论文代码“使用完全卷积去噪自动编码器消除细胞外神经记录中的噪声” 抽象的 细胞外录音受到大量噪声源的严重污染，使降噪过程成为一项极具挑战性的任务，必须对其进行有效的尖峰分拣才能解决。 为此，我们提出了一种利用此问题的端到端深度学习方法，该方法利用了完全卷积去噪自动编码器，该编码器学会了从嘈杂的多通道输入中产生干净的神经元活动信号。 在模拟数据上的实验结果表明，我们提出的方法可以显着改善受噪声破坏的神经信号的质量，优于广泛使用的小波去噪技术。 要求 Python（已通过v3.8测试）：用于数据生成和网络开发 Matlab（经过R2020b测试）：用于开发小波去噪方法以比较网络的性能 为了安装必要的Python库，请运行以下命令： pip install -r requirements.txt 注意：要运行数据集生成脚本，您还应该安装MEArec Python库。 可以找到说明。 数据集 用于训练和评估的细胞外录音有两种格式，即.mat和.tfrecord 。 . |-- data/ | |-- mat/ | |-- TFRecord/ . 数据组织如下

The Scientist and Engineer's Guide to Digital Signal Processing

开放式图像信号处理器（openISP） 介绍 图像信号处理器（ISP）是执行数字图像处理的应用处理器，专门用于将RAW图像（从Imaging Sensors获取）转换为RGB / YUV图像（以进行进一步处理或显示）。 目标 该项目旨在提供ISP的概述，并从硬件角度激发整个ISP管道和一些调整功能。 提议的ISP管道包括以下模块，坏点校正（DPC），黑电平补偿（BLC），镜头阴影校正（LSC），抗混叠噪声滤波器（ANF），自动白平衡增益控制（AWB），彩色滤光片阵列插值（CFA），伽玛校正（GC），色彩校正矩阵（CCM），色彩空间转换（CSC），用于亮度和色度的噪声过滤器（NF），边缘增强（EE），伪彩色抑制（FCS），色相/饱和度控制（HSC）和亮度/对比度控制（BCC）。 ISP管道体系结构参考[1]，直接从本书中获取。 将来将实现一些高级功能，例如宽/高动态范围（W / HDR）和

处理信号时的标准操作是对语音信号进行低通/带通/高通滤波，以消除无关的低频和高频分量，例如在录音过程中经常产生的 DC 偏移、嗡嗡声和噪声。 本练习描述了一个 MATLAB 练习，用于设计合适的 FIR 滤波器（基于用户规范），然后使用设计的滤波器对语音信号进行滤波。 文件“1.8 Filter Signal.pdf”提供了本练习的用户指南。

Digital Signal Processing with Matlab Examples, Vol. 2：Decomposition, Recovery, Data-Based Actions，2017.(944s).pdf

This book can be used in the following ways: 1. For a DSP course with a laboratory component, using parts of Chapters 1–9. If needed, the book can be supplemented with some additional theoretical materials, since its emphasis is on the practical aspects of DSP. It is possible to cover Chapter 7 on adaptive filtering following Chapter 4 on finite impulse response (FIR) filtering (since there is only one example in Chapter 7 that uses materials from Chapter 5). It is my conviction that adaptive filtering should be incorporated into an undergraduate course in DSP. 2. For a laboratory course using many of the examples and experiments from Chapters 1–7 and Chapter 9. The beginning of the semester can be devoted to short programming examples and experiments and the remainder of the semester for a final project. The wide range of sample projects (for both undergraduate and graduate students) discussed in Chapter 10 can be very valuable. 3. For a senior undergraduate or first-year graduate design project course using selected materials from Chapters 1–10. 4. For the practicing engineer as a tutorial and reference, and for workshops and seminars, using selected materials throughout the book.

Digital Signal Processing- Laboratory Experiments Using C

Digital Signal Processing and Noise Reduction 4th Edition

Understanding Digital Signal Processing, Third Edition, is quite simply the best resource for engineers and other technical professionals who want to master and apply today’s latest DSP techniques. Richard G. Lyons has updated and expanded his best-selling second edition to reflect the newest technologies, building on the exceptionally readable coverage that made it the favorite of DSP professionals worldwide. He has also added hands-on problems to every chapter, giving students even more of the practical experience they need to succeed. Comprehensive in scope and clear in approach, this book achieves the perfect balance between theory and practice, keeps math at a tolerable level, and makes DSP exceptionally accessible to beginners without ever oversimplifying it. Readers can thoroughly grasp the basics and quickly move on to more sophisticated techniques. This edition adds extensive new coverage of FIR and IIR filter analysis techniques, digital differentiators, integrators, and matched filters. Lyons has significantly updated and expanded his discussions of multirate processing techniques, which are crucial to modern wireless and satellite communications. He also presents nearly twice as many DSP Tricks as in the second edition—including techniques even seasoned DSP professionals may have overlooked. Coverage includes New homework problems that deepen your understanding and help you apply what you’ve learned Practical, day-to-day DSP implementations and problem-solving throughout Useful new guidance on generalized digital networks, including discrete differentiators, integrators, and matched filters Clear descriptions of statistical measures of signals, variance reduction by averaging, and real-world signal-to-noise ratio (SNR) computation A significantly expanded chapter on sample rate conversion (multirate systems) and associated filtering techniques New guidance on implementing fast convolution, IIR filter scaling, and more Enhanced coverage of analyzing digital filter behavior and performance for diverse communications and biomedical applications Discrete sequences/systems, periodic sampling, DFT, FFT, finite/infinite impulse response filters, quadrature (I/Q) processing, discrete Hilbert transforms, binary number formats, and much more

Computer-Based Exercises for Signal Processing Using MATLAB 5 配套源码