- .NET-Framework-3.5 本书全面讲解了.net Framework3.5开发中各方面的技术要点
- applausl Privacy Preserving and Collusion Resistance in a Location Proof Updating System Abstract: APPLAUS generate location proofs and sends the updates to a location proof server Pseudonyms are used to protect source location privacy from the attackers. An authorized verifier can query and retrieve location proofs from the server. Our location proof system guarantees user location privacy from every party.
- Privacy-Preserving-Delegated-Access-Control-in-Pu The main objective of this project is establishing Two Layer Encryption (TLE) and Virtual Private Database (VPD) for a invulnerable data storage system that allow multiple functions when the storage system is distributed and has no central authority.
- 暗原色先验 基于何凯明博士
- glfdiff 用于对函数进行分数阶求解
- COT 阶次分析进行等角度采样以及频率曲线的拟合(Equal Angle Sampling and Curve Fitting for Order Analysis)
文件名称:利用拉普拉斯先验的压缩感知稀疏重构的贝叶斯方法
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利用拉普拉斯先验的压缩感知稀疏重构的贝叶斯方法(The bayesian method of sparse reconstruction using Laplace's prior compressed sensing)
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下载文件列表
| 文件名 | 大小 | 更新时间 |
|---|---|---|
| 利用拉普拉斯先验的压缩感知稀疏重构的贝叶斯方法\Bayesian Compressive Sensing Using Laplace Priors.pdf | 1946405 | 2019-11-19 |
| 利用拉普拉斯先验的压缩感知稀疏重构的贝叶斯方法\Bayesian Regularization and Pruning Using a Laplace Prior.pdf | 1225617 | 2019-10-11 |
| 利用拉普拉斯先验的压缩感知稀疏重构的贝叶斯方法\FAST BAYESIAN COMPRESSIVE SENSING USING LAPLACE PRIORS.pdf | 123067 | 2019-11-21 |
| 利用拉普拉斯先验的压缩感知稀疏重构的贝叶斯方法\FastLaplace.m | 8339 | 2019-03-26 |
| 利用拉普拉斯先验的压缩感知稀疏重构的贝叶斯方法\example.m | 2016 | 2019-03-26 |
| 利用拉普拉斯先验的压缩感知稀疏重构的贝叶斯方法\readme.m | 1844 | 2019-03-26 |
| 利用拉普拉斯先验的压缩感知稀疏重构的贝叶斯方法 | 0 | 2019-11-21 |
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