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Application of Quantum-Clustering on Thermograms of WiFi Circuits in Different Operation Modes

机译:量子聚类在不同操作模式下WiFi电路热量点的应用

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摘要

The purpose of this work is to evaluate the efficacy of applying a model-based quantum clustering (QC) algorithm on thermograms of functional modes in WiFi circuits. As unsupervised clustering algorithm, it can work on clusters of any shape and does not require any prior information. QC proves its efficacy for many applications, it has been tested, in this work, and compared with other algorithms which suffer randomness according to initialization. The tests are conducted on thermograms of an electronic chip in different operation modes. The benefits of QC are confirmed through performance analysis of clustering algorithms. Robustness analysis is also conducted against white-Gaussian noise clustering and so on classification of actual WiFi circuit operation modes based on thermograms.
机译:这项工作的目的是评估应用模型的量子聚类(QC)算法在WiFi电路中功能模式的热图中的功效。 作为无监督的聚类算法,它可以在任何形状的簇上工作,并且不需要任何先前的信息。 QC证明了其对许多应用的功效,在这项工作中已经过测试,并与根据初始化进行随机性的其他算法进行了测试。 测试在不同操作模式下对电子芯片的热量点进行进行。 通过对聚类算法的性能分析确认了QC的好处。 鲁棒性分析也针对Whib-Gaussian噪声聚类等基于热图的实际WiFi电路操作模式的分类。

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