Towards Auto-Building of Embedded FPGA-based Soft Sensors for Wastewater Flow Estimation
<|reference_start|>Towards Auto-Building of Embedded FPGA-based Soft Sensors for Wastewater Flow Estimation: Executing flow estimation using Deep Learning (DL)-based soft sensors on resource-limited IoT devices has demonstrated promise in terms of reliability and energy efficiency. However, its application in the field of wastewater flow estimation remains underexplored due to: (1) a lack of available datasets, (2) inconvenient toolchains for on-device AI model development and deployment, and (3) hardware platforms designed for general DL purposes rather than being optimized for energy-efficient soft sensor applications. This study addresses these gaps by proposing an automated, end-to-end solution for wastewater flow estimation using a prototype IoT device.<|reference_end|>
@article{ling2024towards,
    title={Towards Auto-Building of Embedded FPGA-based Soft Sensors for Wastewater
  Flow Estimation},
    author={Tianheng Ling, Chao Qian, Gregor Schiele},
    journal={arXiv preprint arXiv:2407.05102},
    year={2024},
    archivePrefix={arXiv},
    eprint={2407.05102},
    primaryClass={eess.SP cs.AI}
}