Can software tools enable improved configuration, operation and exploitation of distributed and intelligent sensors for marine industrial applications

矿石资源的成功开采需要在比提取设备更小的尺度上进行准确的资源表征。传统的多普勒谱仪受到波束分离尺度均匀性假设的限制。为了抵消这一限制，设计了一种新型仪器，该仪器具有多个单波束声学多普勒剖面仪汇聚在单个焦点上。通过一系列的致动器，该系统能够在空间上重新定位上述光束的焦点。在我对会聚波束声学多普勒分析器（C-ADP）的研究中，我为系统实现了一个基本的智能水平，这样它就可以“选择”下一步要进行哪些测试，并调整传感器位置以考虑框架弯曲。虽然这些选择系统很简单，但它们代表了探索水下系统对环境刺激反应的起点，我相信这一概念将是下一代海洋传感的关键。子系统的时间同步也很重要，因为它允许多个传感器在数据收集期间避免相互干扰。在未来的分布式传感器网络中实现类似的精确时间同步将更加重要，避免干扰，同步数据时间戳并允许新的应用。博士学位为我的人生旅程提供了下一步，也为我提供了利用和进一步提高我在软件开发方面的技能来解决现实问题的绝佳机会。将信号处理、机器学习和/或其他人工智能工具和方法应用于复杂数据集的前景非常有趣，可能会使这些解决方案更加强大，并且可能“用户友好”（如果“套件”可以更自主、更可靠地完成任务，最终用户不必是专家）。我将寻求在我已经完成的软件自动化和传感器与智能软件集成方面的工作的基础上，改进海洋环境中的数据收集。专门开发软件工具，以改进分布式和智能传感器的配置，操作和开发，用于海洋工业应用。作为我经验最丰富的传感器，这项研究将从声学多普勒电流分析器（ADCPs）开始，并随着项目的进展引入其他传感器。adcp的另一个优势是，它们通常收集多种类型的环境数据（速度、压力、温度），为基于环境刺激的决策提供了多种选择。研究将作为能源系统研究所MSM小组的一部分进行，理想情况下包括与传感器制造商合作。工业合作伙伴的加入将使博士研究开发的解决方案能够在现实问题上得到实施和测试，并进一步获得合作伙伴的硬件和专业知识。在高层次上，这项研究和工作是关于低功耗智能计算提供良好的数据，最终目标是平台无关。

The successful exploitation of ORE resources requires accurate resource characterisation on scales smaller than that of the extraction device. Conventional acoustic doppler profilers are limited by the assumption of homogeneity on the scale of beam separation. To counteract this limitation a novel instrument was designed with multiple single beam acoustic doppler profilers converging on a single focal point. Through a series of actuators, the system is capable of spatially relocating the aforementioned focal point of the beams. In my work on the convergent-beam acoustic doppler profiler (C-ADP) I implemented a basic level of intelligence to the system such that it could "choose" which tests to run next and adjust sensor positions to account for frame bending. Whilst these choice systems were simple they represent a jumping off point for exploring the response of an underwater system to environmental stimuli, a concept I believe will be key to the next generation of marine sensing. Time synchronisation of sub-systems was also important as it allows the multiple sensors to avoid interfering with each other during data collection. Implementing a similarly precise time synchronisation in future distributed sensor networks would be even more important, avoiding interference, syncing data timestamps and allowing new applications.A PhD offers me the next step of my journey and the perfect opportunity to use and further my skills in software development to solve real-world problems. The prospect of applying signal processing, machine learning and/or other AI tools and methods to complex data sets is very interesting and likely will make these solutions more powerful and potentially "user friendly" (if the "kit" can do more both autonomously and reliably, the end-user doesn't have to be an expert). I would seek to build upon the work I have already done in software automation and integration of sensors with intelligent software to improve data collection in marine environments. Specifically developing software tools to enable improved configuration, operation and exploitation of distributed and intelligent sensors for marine industrial applications.As the sensors I have the most experience with, this research would start out with acoustic doppler current profilers (ADCPs) and bring in other sensors as the project progressed. An additional advantage that ADCPs present is that they typically collect several types of environmental data (velocity, pressure, temperature), offering many options for decision making based on environmental stimuli. Research would be undertaken as part of the MSM group in the Institute of Energy Systems, ideally including work with a sensor manufacturer. The inclusion of an industrial partner would allow solutions developed under PhD research to be implemented and tested on real-world problems, and further give access to the hardware and expertise of the partner. At a high-level this research and work is about low-power smart computing delivering good data and would aim to eventually be platform agnostic.