Browsing by Author "Furuta, Daniel"

Now showing 1 - 4 of 4
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    Data and analysis code for "Capturing High Resolution Plant Movement in the Field"
    (2022-02-07) Heuschele, Deborah; Furuta, Daniel; Smith, Kevin; Marchetto, Peter; Jo.Heuschele@usda.gov; Heuschele, Deborah; USDA Agricultural Research Service
    This is the dataset and analysis code for the paper "Capturing High Resolution Plant Movement in the Field", in which we demonstrate a system to capture high resolution plant motion with small grains in natural conditions.
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    Dataset for "Characterization of inexpensive MOx sensor performance for trace methane detection"
    (2022-03-23) Furuta, Daniel; Sayahi, Tofigh; Li, Jinsheng; Wilson, Bruce; Presto, Albert; Li, Jiayu; lijiayu@umn.edu; Li, Jiayu; University of Minnesota BBE Air and Aerosol Sensing Group
    Laboratory calibration data for three replicates each of five types of inexpensive methane sensors in support of a study characterizing sensor suitability for atmospheric monitoring, with particular attention to sensitivity to humidity and temperature. Sensor performance from ambient levels to 10ppm was characterized with decaying methane pulses at five different temperatures. Methane, water vapor levels, and temperature were monitored with reference instruments.
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    Final report and simulation program for "Monitoring Methods for Prioritization and Assessment of Stormwater Practices"
    (2022) Furuta, Daniel; Wilson, Bruce; Chapman, John; University of Minnesota Department of Bioproducts and Biosystems Engineering
    This project developed a framework for simulating stormwater sampling and for evaluating the performance of monitoring methods for runoff pollution. This submission contains the final report and a self-contained program practitioners can use to compare sampling methods, along with source code for the simulation program.
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    Low-Cost Improvements in Pollution Monitoring Methods
    (2024) Furuta, Daniel
    Pollution research and mitigation efforts rely on the accuracy of data collected by monitoring programs. I present work on improving monitoring protocols for two important pollutants. As a step towards improving the spatiotemporal resolution and lowering the cost of ground-level methane sensing, I design and thoroughly characterize an inexpensive sensor node for near-atmospheric methane monitoring. In an effort to improve stormwater monitoring methods, I develop a new model for runoff pollution and use it to simulate stormwater sampling, resulting in two suggested protocol modifications with the potential to substantially improve accuracy without increased cost. Throughout, I focus in particular on developing devices and methods that will improve monitoring accuracy or increase monitoring resolution at the same or lower cost as current approaches.