We demonstrate a sustainable, lithography-free process for generating non fading plasmonic colors with a prototype device that produces a wide range of vivid colors in red, green, and blue (RGB) ([0-1], [0-1], [0-1]) ...
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Nitrogen (N) is one of the essential nutrients required for healthy crop growth. Field phenotyping for nitrogen stress symptoms is laborious and time-consuming, that way, it is a major bottleneck in nutrition-inclusiv...
Nitrogen (N) is one of the essential nutrients required for healthy crop growth. Field phenotyping for nitrogen stress symptoms is laborious and time-consuming, that way, it is a major bottleneck in nutrition-inclusive agricultural research. Recent advancements in sensors and image processing facilitate color-based quantification of crop greenness from high-resolution RGB images. In this paper, we present unmanned aerial vehicle (UAV)-based digital field phenotyping for the estimation of crop nitrogen content. For this, we conducted a field experiment during the post-rainy season of 2021 at International Crops Research institute for Semi-Arid Tropics (ICRISAT), Hyderabad, India with long-stature cereal model crop, sorghum (Sorghum bicolor L.) cultivated under three different regimes varying in moisture and soil nitrogen content. A high-resolution RGB sensor (XenmuseX5S) mounted on DJI Matric 210 quadcopter was used for capturing the spatiotemporal imagery. Five different RGB spectrum vegetation indices indicating crop greenness were correlated with ground truth values of crop N content using simple linear regression and stepwise backward regression. With a prediction potential of R 2 =0.65 and MAE=0.27 for an independent dataset, we present a stepwise backward linear regression model as a promising approach for real-time estimation of the N status of sorghum crop.
Triboelectric nanogenerators (TENGs) have gained remarkable attention in energy harvesting and smart sensing, allowing for converting mechanical energy into electrical energy. Despite great potential and progress made...
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Driver state sensing technologies, such as vehicular systems, start to be widely considered by automotive manufacturers. To reduce the cost and minimize the intrusiveness towards driving, the majority of these systems...
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Driver state sensing technologies, such as vehicular systems, start to be widely considered by automotive manufacturers. To reduce the cost and minimize the intrusiveness towards driving, the majority of these systems rely on the in-cabin camera(s) and other optical sensors. With their great capabilities in detecting and intervening of driver distraction and inattention,these technologies may become key components in future vehicle safety and control systems. However, to the best of our knowledge,currently, there is no common standard available to objectively compare the performance of these technologies. Thus, it is imperative to develop one standardized process for evaluation *** this paper, we propose one systematic and standardized evaluation process after successfully addressing three difficulties:1) defining and selecting the important influential individual and environmental factors, 2) countering the effects of individual differences and randomness in driver behaviors, and 3) building a reliable in-vehicle driver head motion tracking tool to collect ground-truth motion data. We have collected data on a large scale on a commercial driver state-sensing platform. For each subject, 30 to 40 minutes of head motion data was collected and included variables, such as lighting conditions, head/face features,and camera locations. The collected data was analyzed based on a proposed performance measure. The results show that the developed process can efficiently evaluate an individual camerabased driver state sensing product, which builds a common base for comparing the performance of different systems.
Carrier transport in materials is often diffusive due to momentum-relaxing scattering with phonons and defects. Suppression of momentum-relaxing scattering can lead to the ballistic and hydrodynamic transport regimes,...
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Humans rely increasingly on sensors to address grand challenges and to improve quality of life in the era of digitalization and big data. For ubiquitous sensing, flexible sensors are developed to overcome the limitati...
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Humans rely increasingly on sensors to address grand challenges and to improve quality of life in the era of digitalization and big data. For ubiquitous sensing, flexible sensors are developed to overcome the limitations of conventional rigid counterparts. Despite rapid advancement in bench-side research over the last decade, the market adoption of flexible sensors remains limited. To ease and to expedite their deployment, here, we identify bottlenecks hindering the maturation of flexible sensors and propose promising solutions. We first analyze continued...challenges in achieving satisfactory sensing performance for real-world applications and then summarize issues in compatible sensor-biology interfaces, followed by brief discussions on powering and connecting sensor networks. Issues en route to commercialization and for sustainable growth of the sector are also analyzed, highlighting environmental concerns and emphasizing nontechnical issues such as business, regulatory, and ethical considerations. Additionally, we look at future intelligent flexible sensors. In proposing a comprehensive roadmap, we hope to steer research efforts towards common goals and to guide coordinated development strategies from disparate communities. Through such collaborative
computer-aided design (CAD) programs are essential to engineering as they allow for better designs through low-cost iterations. While CAD programs are typically taught to undergraduate students as a job skill, such so...
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Deep Neural Networks (DNNs) which are trained end-to-end have been successfully applied to solve complex problems that we have not been able to solve in past decades. Autonomous driving is one of the most complex prob...
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This study was primarily triggered by the requirements of crop breeding programs to rapidly, accurately and cost-effectively select crop genotypes with enhanced biomass Nitrogen (N) content (an essential indicator of ...
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This study was primarily triggered by the requirements of crop breeding programs to rapidly, accurately and cost-effectively select crop genotypes with enhanced biomass Nitrogen (N) content (an essential indicator of stover quality as feed) and, at the same time, possibly speed up the agriculture transition to the more sustainable and precise application of fertilizers (crop health status). This proof of concept study explores whether and how precisely it is possible to predict the N content in sorghum crop biomass using a minimum unmanned aerial vehicle (UAV)-carried imaging technology (Red Green Blue (RGB) camera) and a small dataset. To this end, we raised a post-rainy (rabi) 2021 and 2022 season sorghum crop in the field with standard and sub-optimal management practices (N and water stress) at the International Crop Research institute for Semi-arid Tropics (ICRISAT), India. RGB images were collected during different crop growth stages (rabi 2021, 2022) using a low-cost RGB camera DJI Zenmuse X5S mounted UAV DJI Matrice 210 *** collected three ground-truth points with corresponding RGB images to build the N-prediction model (rabi 2021) and then six ground-truth points with corresponding RGB to evaluate the model with a second independent test set (rabi 2022). We have assessed statistical and AI-based models for N prediction, namely multiple linear regression (MLR), multi-layer perceptron (MLP) and various convolutional neural network (CNN) architectures, as well as their combinations. The first two take as input five handcrafted features computed using RGB spectrum vegetation indices (VIs) while the CNN automatically extracts discriminant features from the raw images. These schemes have already been proposed in state-of-the-art and the novelty of our study lies in the assessment of several fusion strategies in order to leverage both the deep features learned automatically by the CNN and the human-expertise-based handcrafted features, harnessing, thereb
We implement extreme skin depth engineering (e-skid) in two dimensions to suppress or enhance evanescent coupling on-demand. This is demonstrated experimentally with a large gap, bendless directional coupler exhibitin...
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