Question

请大侠翻译一下基于互信息的分布式贝叶斯压缩感知 方面一篇专业的英语短文，翻译成中文，不要百度翻译 谷歌

需要精确的翻译哦 谢谢哦 请 在线等哦 翻译的好的话 我会附加分数哦
Next, one cognitive radio user ( SNR= 10) is selected randomly to verify the effect of cooperation based on mutual information. The improvement on reconstruction performance is given in Fig1 3. Here, / no cooper ation0 means each cognitive radio user perform compressive sampling as well as the reconstruction independently, / with mutual information0 denotes the performance of proposed algorithm, that is, to get information from 4 certain cognitive radio users correlated by mutual information, and / no mutual information0 means that the under lying cognitive radio user randomly selects 4 cognitive radio user s for cooperation. From Fig1 3, we known that the cooperation scheme based on mutual information greatly eliminates the fluctuation of reconstruction err or existing in the Bayesian scheme. Meanwhile, the mutual information scheme can effectively improve the precision of recovery when compared with the randomly selected neighbor scheme. Also, the simulation examines whether more neighbors can bring in better performance. Obviously, in the scenario where the channel quality worsens ( SNR= 7) , a marginal performance improvement can be gained from this kind of cooperation ( Fig1 4) .

Answer 1

下一步，一个认知无线电用户（信噪比=10）是随机挑选验证效果的合作基础上，互信息。改进的性能是在骨盆重建3。在这里，/没有古柏ation0每个认知用户执行压缩采样以及重建与独立，相互information0表示算法的性能，这是，获得的信息从4某些认知无线电用户相关的互信息，和/互不information0指在说谎认知无线电用户随机选择4认知无线电用户合作。从图1的3，我们知道，合作方案基于互信息大大消除波动犯错或重建现有的贝叶斯方法。同时，互信息的方案能有效地提高精度的复苏相比，随机选择的邻居方案。此外，模拟检查是否有更多的邻居可以带来更好的性能。显然，在的情况下，信道质量恶化（信噪比=7），边缘性能的改进，可以从这种合作（图一：4）。

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百度翻译我还用问你吗

追答

压缩感知理论应用于分布式认知网络中时,由于每个认知用户所处的信道环境差别很大,因此频谱感知的精度相差很大.

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你以为你装了一B我就能把分给你吗

Answer 2

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