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Galileo_singlefrequency
Galileo单频定位程序，能基于rinex文件实现高精度定位，供编写时参考(Galileo single-frequency positioning program can achieve high-precision positioning based on RINEX file for reference when writing.)
- 2020-07-04 14:20:01下载
- 积分：1
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PyTorch教程
说明： PyTorch是一个开源的Python机器学习库，基于Torch，用于自然语言处理等应用程序。(Pytorch is an open source Python machine learning library, based on torch, used for natural language processing and other applications.)
- 2021-01-24 20:06:17下载
- 积分：1
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teardrop_smb
针对早期windows系统SMB协议的攻击
前提时先安装安装python
在命令提示符窗口中首先进入py命令所在的文件夹
直接键入python xx.py后回车确认(Attacks on SMB Protocol of Early Windows System
Install Python first
First enter the folder where the PY command resides in the command prompt window
Type Python xx.py directly and return to confirm)
- 2020-06-23 15:40:02下载
- 积分：1
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pysot-toolkit-master
说明： 目标跟踪算法评测集。可以评测vot otb got10k等数据集。(Evaluation set of target tracking algorithm. It can evaluate data sets such as VOT OTB got10k.)
- 2020-11-19 21:25:56下载
- 积分：1
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Internet-socket
用于TCP,UCP协议进行通讯(For TCP, UCP protocol for communication)
- 2017-06-08 20:43:04下载
- 积分：1
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grid
网格搜索法寻找最优的惩罚因子，SVM,经测试可以用(SVM find C gamma)
- 2012-01-03 17:48:46下载
- 积分：1
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djangobook
django book for developing networking applications
- 2014-11-26 23:11:25下载
- 积分：1
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SCUC
- 2022-02-04 07:59:31下载
- 积分：1
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自动提取应力结果保存
说明： 采用Python写的abaqus自动提取应力结果的命令(A command written by Python to automatically extract stress results from ABAQUS)
- 2021-03-03 11:59:33下载
- 积分：1
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joint_sparse_algorithms-master
说明： 我们描述了所提出的方法对超声（US）信号的压缩多路复用的直接应用。该技术利用压缩多路复用器架构进行信号压缩，并依靠频域中US信号的联合稀疏性进行信号重建。由于换能器元件具有压电特性，因此可以获得有关US信号频率支持的准确先验知识，并且可以在联合稀疏算法中使用。
我们在数值实验中验证了所提出的方法，并显示了它们在秩次缺陷情况下相对于最新方法的优越性。我们还证明，与没有已知支持的重建相比，该技术可显着提高体内颈动脉图像的图像质量。(We describe a direct application of the proposed methods for compressive multiplexing of ultrasound (US) signals. The technique exploits the compressive multiplexer architecture for signal compression and relies on joint-sparsity of US signals in the frequency domain for signal reconstruction. Due to piezo-electric properties of transducer elements, accurate prior knowledge of the frequency support of US signals is available and can be used in joint-sparse algorithms.
We validate the proposed methods on numerical experiments and show their superiority against state-of-the-art approaches in rank-defective cases. We also demonstrate that the techniques lead to a significant increase of the image quality on in vivo carotid images compared to reconstruction without known support.)
- 2020-03-16 16:45:38下载
- 积分：1
