摘要翻译：
几十年来，波的时间反转已经成功地应用于通信、传感和成像等领域。在水声通信中的应用是我们特别感兴趣的，因为它将可逆过程（允许可逆的软件或硬件实现）和可逆介质（允许环境的可逆模型）结合在一起。这份正在进行的工作报告从可逆计算的角度讨论了声学时间反转的建模、分析和实现问题。我们展示了用可逆元胞自动机对时间反转通信过程中的可逆性进行建模和量化的潜力。在此基础上，提出了一种基于可逆电路的时间反转硬件实现方案。
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英文标题：
《Reversibility in space, time, and computation: the case of underwater
  acoustic communications》
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作者：
Harun Siljak
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最新提交年份：
2018
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分类信息：

一级分类：Physics        物理学
二级分类：Cellular Automata and Lattice Gases        元胞自动机与格子气体
分类描述：Computational methods, time series analysis, signal processing, wavelets, lattice gases
计算方法，时间序列分析，信号处理，小波，格子气体
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一级分类：Computer Science        计算机科学
二级分类：Emerging Technologies        新兴技术
分类描述：Covers approaches to information processing (computing, communication, sensing) and bio-chemical analysis based on alternatives to silicon CMOS-based technologies, such as nanoscale electronic, photonic, spin-based, superconducting, mechanical, bio-chemical and quantum technologies (this list is not exclusive). Topics of interest include (1) building blocks for emerging technologies, their scalability and adoption in larger systems, including integration with traditional technologies, (2) modeling, design and optimization of novel devices and systems, (3) models of computation, algorithm design and programming for emerging technologies.
涵盖基于硅CMOS技术替代品的信息处理（计算、通信、传感）和生物化学分析方法，如纳米级电子、光子、自旋、超导、机械、生物化学和量子技术（此列表不是唯一的）。感兴趣的主题包括：（1）新兴技术的构建块、其可伸缩性和在大型系统中的采用，包括与传统技术的集成；（2）新型设备和系统的建模、设计和优化；（3）新兴技术的计算模型、算法设计和编程。
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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：
  Time reversal of waves has been successfully used in communications, sensing and imaging for decades. The application in underwater acoustic communications is of our special interest, as it puts together a reversible process (allowing a reversible software or hardware realisation) and a reversible medium (allowing a reversible model of the environment). This work in progress report addresses the issues of modelling, analysis and implementation of acoustic time reversal from the reversible computation perspective. We show the potential of using reversible cellular automata for modelling and quantification of reversibility in the time reversal communication process. Then we present an implementation of time reversal hardware based on reversible circuits.
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PDF链接：
https://arxiv.org/pdf/1806.04223