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depletion, toxic algae, and pollutants. This natural sensitivity makes them powerful bio-sensors for environmental monitoring, capable of providing early warnings of ecosystem stress. However, harnessing this
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. It will use signals from different sources—such as radio signals and internal sensors— to maintain robust and accurate PNT, even when satellite signals are weak or missing. A built-in intelligent
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sensors systems and UAVs at different scales. In particular, we will combine borehole and surface GPR as well as small-scale EMI measurements with root and shoot observations in controlled experiments
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. It will use signals from different sources—such as radio signals and internal sensors— to maintain robust and accurate PNT, even when satellite signals are weak or missing. A built-in intelligent
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selectivity is the first important barrier to overcome in order to perform quantitative analyses for each pollutant and avoid ionic interference between the different sensors used in the project. Sensor
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using electromagnetic induction (EMI), and ground penetrating radar (GPR) will be combined with soil sensors systems and UAVs at different scales. In particular, we will combine borehole and surface GPR
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series data. Large data sets come with significant computational challenges. Tremendous algorithmic progress has been made in machine learning and related areas, but application to dynamic systems is
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different approaches, the most prevalent is polygraph testing which infers deception through the measurement and analysis of physiological responses (e.g., blood pressure, electrodermal activity). However
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across different imaging devices, including future sensors with unknown spectral sensitivities. Training The student will be based at the Colour & Imaging Lab at the School of Computing Sciences which has
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across Scotland’s west coast. It will evaluate the practicality of different image capture techniques and the potential of different sensor types (e.g., RGB, multispectral) to generate beach litter images