摘要翻译：
本研究旨在通过电机加速老化时的年龄检测，说明互信息即自动互信息功能在电机状态监测中的适用性。利用人工感应电机实验中采集的振动数据，验证了原自动互信息函数算法及其在Verilog中的硬件实现。基于现场可编程逻辑阵列开发板，建立了一个面向工业和教育的概念模型，并以自动互信息函数为例进行了演示，同时提出了其它应用。对振动数据的吸引子重建并不是简单的。
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英文标题：
《Hardware implementation of auto-mutual information function for
  condition monitoring》
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作者：
Harun Siljak, Abdulhamit Subasi and Belle R. Upadhyaya
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最新提交年份：
2018
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分类信息：

一级分类：Computer Science        计算机科学
二级分类：Other Computer Science        其他计算机科学
分类描述：This is the classification to use for documents that do not fit anywhere else.
这是用于不适合其他任何地方的文档的分类。
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一级分类：Electrical Engineering and Systems Science        电气工程与系统科学
二级分类：Signal Processing        信号处理
分类描述：Theory, algorithms, performance analysis and applications of signal and data analysis, including physical modeling, processing, detection and parameter estimation, learning, mining, retrieval, and information extraction. The term "signal" includes speech, audio, sonar, radar, geophysical, physiological, (bio-) medical, image, video, and multimodal natural and man-made signals, including communication signals and data. Topics of interest include: statistical signal processing, spectral estimation and system identification; filter design, adaptive filtering / stochastic learning; (compressive) sampling, sensing, and transform-domain methods including fast algorithms; signal processing for machine learning and machine learning for signal processing applications; in-network and graph signal processing; convex and nonconvex optimization methods for signal processing applications; radar, sonar, and sensor array beamforming and direction finding; communications signal processing; low power, multi-core and system-on-chip signal processing; sensing, communication, analysis and optimization for cyber-physical systems such as power grids and the Internet of Things.
信号和数据分析的理论、算法、性能分析和应用，包括物理建模、处理、检测和参数估计、学习、挖掘、检索和信息提取。“信号”一词包括语音、音频、声纳、雷达、地球物理、生理、（生物）医学、图像、视频和多模态自然和人为信号，包括通信信号和数据。感兴趣的主题包括：统计信号处理、谱估计和系统辨识；滤波器设计；自适应滤波/随机学习；（压缩）采样、传感和变换域方法，包括快速算法；用于机器学习的信号处理和用于信号处理应用的机器学习；网络与图形信号处理；信号处理中的凸和非凸优化方法；雷达、声纳和传感器阵列波束形成和测向；通信信号处理；低功耗、多核、片上系统信号处理；信息物理系统的传感、通信、分析和优化，如电网和物联网。
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英文摘要：
  This study is aimed at showing applicability of mutual information, namely auto-mutual information function for condition monitoring in electrical motors, through age detection in accelerated motor aging. Vibration data collected in artificial induction motor experiment is used for verification of both the original auto-mutual information function algorithm and its hardware implementation in Verilog, produced from an initial version made with Matlab HDL (Hardware Description Language) Coder. A conceptual model for industry and education based on a field programmable logic array development board is developed and demonstrated on the auto-mutual information function example, while suggesting other applications as well. It has also been shown that attractor reconstruction for the vibration data cannot be straightforward.
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PDF链接：
https://arxiv.org/pdf/1801.08444