Application of an Ultrasonic Sensor to Monitor Soil Erosion and Deposition

While erosion and deposition are naturally occurring processes, these processes can be accelerated by human influences. The acceleration of erosion causes damage to human assets and costs billions of dollars to mitigate. Monitoring erosion at high resolutions can provide researchers and managers the data necessary to help manage erosion. Current erosion monitoring methods tend to be invasive to the area, record low frequency measurements, have a narrow spatial range of measurement, or are very expensive. There is a need for an affordable monitoring system capable of monitoring erosion and deposition non-invasively at a high resolution. The objectives of this research were to (1) design and construct a non-invasive sediment monitoring system (SMS) using an ultrasonic sensor capable of monitoring erosion and deposition continuously, (2) test the system in the lab and field, (3) and determine the applications and limitations of the system. The ultrasonic sensor measures the time of reflectance of sound waves to calculate the distance to the area non-invasively. The SMS was tested in the lab to determine the extent to which the soil type, slope, surface topography, change in distance and vegetation impact the SMS’s ultrasonic sensor’s measurement. It was found that the soil type, slope and surface topography had little effect on the measurement, but the change in distance of the measurement and the introduction of vegetation impacted the measurement. The error in measurement increased as the sensing distance increased, and vegetation interferes with the measurement. In the field during high flows, as erosion and deposition occur, the changes in distance were determined in near real-time, allowing for the calculation of erosion and deposition quantities. The system was deployed to monitor deposition on sandy streambanks in the Nebraska Sandhills and erosion on a streambank and field plot in Lincoln, Nebraska. The system was proven successful in measuring sediment change during high flow events but yielded some error; ±1.06 mm in controlled lab settings and ±10.79 mm when subjected to environmental factors such as temperature, relative humidity and wind. Advisors: Aaron Mittelstet and Nancy Shan

In-Place Activated BatchNorm for Memory-Optimized Training of DNNs

In this work we present In-Place Activated Batch Normalization (InPlace-ABN) - a novel approach to drastically reduce the training memory footprint of modern deep neural networks in a computationally efficient way. Our solution substitutes the conventionally used succession of BatchNorm + Activation layers with a single plugin layer, hence avoiding invasive framework surgery while providing straightforward applicability for existing deep learning frameworks. We obtain memory savings of up to 50% by dropping intermediate results and by recovering required information during the backward pass through the inversion of stored forward results, with only minor increase (0.8-2%) in computation time. Also, we demonstrate how frequently used checkpointing approaches can be made computationally as efficient as InPlace-ABN. In our experiments on image classification, we demonstrate on-par results on ImageNet-1k with state-of-the-art approaches. On the memory-demanding task of semantic segmentation, we report results for COCO-Stuff, Cityscapes and Mapillary Vistas, obtaining new state-of-the-art results on the latter without additional training data but in a single-scale and -model scenario. Code can be found at https://github.com/mapillary/inplace_abn