摘要翻译：
分析将是即将到来的智慧城市和物联网（IoT）的一部分。本工作的重点是近似分布信号分析。设想分布式物联网设备将记录信号，这些信号可能是物联网云感兴趣的。这些信号从物联网设备到物联网云的通信将需要（低通）近似。线性信号近似在文献中是众所周知的。将概述，在许多物联网分析问题中，希望近似信号（或其分析）总是过度预测精确信号（或其分析）。这种分布非线性逼近问题以前没有研究过。提出了一种基于IoT设备信号近似的IoT云中分布式过预测信号分析算法。对于这类可微信号，量化了IoT设备使用的信号近似带宽和IoT云信号分析中的近似误差之间的基本折衷。给出了仿真结果。
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英文标题：
《On Distributed Nonlinear Signal Analytics : Bandwidth and Approximation
  Error Tradeoffs》
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作者：
Vijay Anavangot and Animesh Kumar
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最新提交年份：
2018
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分类信息：

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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一级分类：Computer Science        计算机科学
二级分类：Information Theory        信息论
分类描述：Covers theoretical and experimental aspects of information theory and coding. Includes material in ACM Subject Class E.4 and intersects with H.1.1.
涵盖信息论和编码的理论和实验方面。包括ACM学科类E.4中的材料，并与H.1.1有交集。
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一级分类：Mathematics        数学
二级分类：Information Theory        信息论
分类描述：math.IT is an alias for cs.IT. Covers theoretical and experimental aspects of information theory and coding.
它是cs.it的别名。涵盖信息论和编码的理论和实验方面。
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英文摘要：
  Analytics will be a part of the upcoming smart city and Internet of Things (IoT). The focus of this work is approximate distributed signal analytics. It is envisaged that distributed IoT devices will record signals, which may be of interest to the IoT cloud. Communication of these signals from IoT devices to the IoT cloud will require (lowpass) approximations. Linear signal approximations are well known in the literature. It will be outlined that in many IoT analytics problems, it is desirable that the approximated signals (or their analytics) should always over-predict the exact signals (or their analytics). This distributed nonlinear approximation problem has not been studied before. An algorithm to perform distributed over-predictive signal analytics in the IoT cloud, based on signal approximations by IoT devices, is proposed. The fundamental tradeoff between the signal approximation bandwidth used by IoT devices and the approximation error in signal analytics at the IoT cloud is quantified for the class of differentiable signals. Simulation results are also presented.
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PDF链接：
https://arxiv.org/pdf/1805.08521