Faraday Cup Array Integrated with a Readout IC and Method for Manufacture Thereof

A detector array and method for making the detector array. The array includes a substrate including a plurality of trenches formed therein, and includes a plurality of collectors electrically isolated from each other, formed on the walls of the trenches, and configured to collect charge particles incident on respective ones of the collectors and to output from said collectors signals indicative of charged particle collection. The array includes a plurality of readout circuits disposed on a side of the substrate opposite openings to the collectors. The readout circuits are configured to read charge collection signals from respective ones of the plurality of collectors

Superfilling technology: transferring knowledge to industry from the National Institute of Standards and Technology

Economic analysis, Technology policy, Technology transfer, Semiconductors, Superfilling, O33, O31,

Remote Crop Mapping at Scale: Using Satellite Imagery and UAV-Acquired Data as Ground Truth

Timely and accurate agricultural information is needed to inform resource allocation and sustainable practices to improve food security in the developing world. Obtaining this information through traditional surveys is time consuming and labor intensive, making it difficult to collect data at the frequency and resolution needed to accurately estimate the planted areas of key crops and their distribution during the growing season. Remote sensing technologies can be leveraged to provide consistent, cost-effective, and spatially disaggregated data at high temporal frequency. In this study, we used imagery acquired from unmanned aerial vehicles to create a high-fidelity ground-truth dataset that included examples of large mono-cropped fields, small intercropped fields, and natural vegetation. The imagery was acquired in three rounds of flights at six sites in different agro-ecological zones to capture growing conditions. This dataset was used to train and test a random forest model that was implemented in Google Earth Engine for classifying cropped land using freely available Sentinel-1 and -2 data. This model achieved an overall accuracy of 83%, and a 91% accuracy for maize specifically. The model results were compared with Rwanda’s Seasonal Agricultural Survey, which highlighted biases in the dataset including a lack of examples of mixed land cover

Deep Neural Networks and Transfer Learning for Food Crop Identification in UAV Images

Accurate projections of seasonal agricultural output are essential for improving food security. However, the collection of agricultural information through seasonal agricultural surveys is often not timely enough to inform public and private stakeholders about crop status during the growing season. Acquiring timely and accurate crop estimates can be particularly challenging in countries with predominately smallholder farms because of the large number of small plots, intense intercropping, and high diversity of crop types. In this study, we used RGB images collected from unmanned aerial vehicles (UAVs) flown in Rwanda to develop a deep learning algorithm for identifying crop types, specifically bananas, maize, and legumes, which are key strategic food crops in Rwandan agriculture. The model leverages advances in deep convolutional neural networks and transfer learning, employing the VGG16 architecture and the publicly accessible ImageNet dataset for pretraining. The developed model performs with an overall test set F1 of 0.86, with individual classes ranging from 0.49 (legumes) to 0.96 (bananas). Our findings suggest that although certain staple crops such as bananas and maize can be classified at this scale with high accuracy, crops involved in intercropping (legumes) can be difficult to identify consistently. We discuss the potential use cases for the developed model and recommend directions for future research in this area