In limited-resource edge computing circumstances such as on mobile devices, IoT devices, and electric vehicles, the energy-efficient optimized convolutional neural network (cnn) accelerator implemented on mobile Field...
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In limited-resource edge computing circumstances such as on mobile devices, IoT devices, and electric vehicles, the energy-efficient optimized convolutional neural network (cnn) accelerator implemented on mobile Field Programmable Gate Array (FPGA) is becoming more attractive due to its high accuracy and scalability. In recent days, mobile FPGAs such as the Xilinx PYNQ-Z1/Z2 and Ultra96, definitely have the advantage of scalability and flexibility for the implementation of deep learning algorithm-basedobjectdetection applications. It is also suitable for battery-powered systems, especially for drones and electric vehicles, to achieve energy efficiency in terms of power consumption and size aspect. However, it has the low and limited performance to achieve real-time processing. In this article, optimizing the accelerator design flow in the register-transfer level (RTL) will be introduced to achieve fast programming speed by applying low-power techniques on FPGA accelerator implementation. In general, most accelerator optimization techniques are conducted on the system level on the FPGA. In this article, we propose the reconfigurable accelerator design for a cnn-based object detection system on the register-transfer level on mobile FPGA. Furthermore, we present RTL optimization design techniques that will be applied such as various types of clock gating techniques to eliminate residual signals and to deactivate the unnecessarily active block. based on the analysis of the cnn-based object detection architecture, we analyze and classify the common computing operation components from the Convolutional Neuron Network, such as multipliers and adders. We implement a multiplier/adder unit to a universal computing unit and modularize it to be suitable for a hierarchical structure of RTL code. The proposed system design was tested with Resnet-20 which has 23 layers and it was trained with the dataset, CIFAR-10 which provides a test set of 10,000 images in several formats, an
Points of gaze (PoGs) and motor behaviors impact sport climbing performance. A large dataset of global PoGs and climbing holds (CHs) is needed. Recent eye-tracking devices capture only local views, leading to time-con...
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ISBN:
(纸本)9798331541859;9798331541842
Points of gaze (PoGs) and motor behaviors impact sport climbing performance. A large dataset of global PoGs and climbing holds (CHs) is needed. Recent eye-tracking devices capture only local views, leading to time-consuming global localization. This study aims to automate global PoG and CH computation. A wireless eye-tracking device records PoGs and CHs during climbs. Artificial landmarks aid in mapping to global space. A cnn-based framework detects and classifies landmarks. Local PoGs and CHs are transformed globally using a homography transform. Cross-validation assessed the method's success rates and accuracies. The optimal framework computed global PoGs and CHs for 2,460 climbing cases. CH success rates were 80.90% +/- 13.98%, with mean Euclidean distance errors of 0.0239 +/- 0.0216 m. PoG success rates were 80.79% +/- 10.74%. Processing time per frame averaged 115.14 +/- 6.80 ms. The datasets will analyze gaze behaviors' effects on climbing outcomes and inform a decision-support system for sport climbing.
The COVID-19 outbreak forced governments worldwide to impose lockdowns and quarantines to prevent virus transmission. As a consequence, there are disruptions in human and economic activities all over the globe. The re...
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The COVID-19 outbreak forced governments worldwide to impose lockdowns and quarantines to prevent virus transmission. As a consequence, there are disruptions in human and economic activities all over the globe. The recovery process is also expected to be rough. Economic activities impact social behaviors, which leave signatures in satellite images that can be automatically detected and classified. Satellite imagery can support the decision-making of analysts and policymakers by providing a different kind of visibility into the unfolding economic changes. In this article, we use a deep learning approach that combines strategic location sampling and an ensemble of lightweight convolutional neural networks (cnns) to recognize specific elements in satellite images that could be used to compute economic indicators based on it, automatically. This cnn ensemble framework ranked third place in the US Department of Defense xView challenge, the most advanced benchmark for objectdetection in satellite images. We show the potential of our framework for temporal analysis using the US IARPA Function Map of the World (fMoW) dataset. We also show results on real examples of different sites before and after the COVID-19 outbreak to illustrate different measurable indicators. Our code and annotated high-resolution aerial scenes before and after the outbreak are available on GitHub.(1) 1. https://***/maups/covid19-satellite-analysis.
This work introduces a novel solution to measure economic activity through remote sensing for a wide range of spatial areas. We hypothesize that disturbances in human behavior caused by major life-changing events leav...
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This work introduces a novel solution to measure economic activity through remote sensing for a wide range of spatial areas. We hypothesize that disturbances in human behavior caused by major life-changing events leave signatures in satellite imagery that allows devising relevant image-based indicators to estimate their impact and support decision-makers. We present a case study for the COVID-19 coronavirus outbreak, which imposed severe mobility restrictions and caused worldwide disruptions, using flying airplane detection around the 30 busiest airports in Europe to quantify and analyze the lockdown's effects and postlockdown recovery. Our solution won the rapid action coronavirus earth observation (RACE) upscaling challenge, sponsored by the European Space Agency and the European Commission, and now is integrated into the RACE dashboard. This platform combines satellite data and artificial intelligence to promote a progressive and safe reopening of essential activities. Code, trained model, and data are available at https://***/maups/covid19-custom-script-contest.
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