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Utilizing Csiszar Divergences to Analyze Deployments of Binary Sensors with Modulators

机译：利用CSISZAR分解分析模型传感器的部署

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Divergences or their counterpart (dis)similarity measures of two probability distributions play an important role in information theory. Especially, Csiszar divergences have many forms. Among so many forms of Csiszar divergences, we plan to find which divergence is the best suit of the analysis of binary sensor deployments. A binary sensor outputs a binary digit 1 or 0 when detecting an object or not, respectively. Recently, modulators made of opaque materials are utilized to modulate the sensing view of binary sensors to enhance their spatial awareness. In this paper, we construct two probability models of binary sensors modulated by modulators, i.e., an ideal model and an actual deployment model. Moreover, we utilize 13 forms of Csiszar divergences to analyze the distribution of those probability models. Based on the divergence calculation results, we classify the 13 divergences into five classifications. Furthermore, we propose a smoothing method to deal with the events which are absent in the experiments, i.e., occurring with zero probability. Our experiment results show that the smoothing method eliminates the zero probabilities and has little influence on the nonzero probabilities. Finally, we select the best divergence among the 13 divergences to analyze binary sensors modulated with modulators.
机译：两个概率分布的分歧或其对应（DIS）相似度测量在信息理论中起重要作用。特别是，CSISZAR分歧有很多形式。在这么多形式的CSISZAR分歧中，我们计划发现哪些分歧是二元传感器部署分析的最佳诉讼。二进制传感器分别在检测到对象时输出二进制数字1或0。最近，利用由不透明材料制成的调制器来调制二元传感器的感测视图，以增强它们的空间意识。在本文中，我们构造了由调制器调制的二元传感器的两个概率模型，即理想模型和实际部署模型。此外，我们利用13种形式的CSISZAR分歧来分析这些概率模型的分布。根据分歧计算结果，我们将13分的分歧分为五分类。此外，我们提出了一种平滑方法，以处理实验中不存在的事件，即，以零概率发生。我们的实验结果表明，平滑方法消除了零概率，对非零概率影响不大。最后，我们选择了13个分歧中的最佳分歧，以分析用调制器调制的二元传感器。

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