Traditional puppet manipulation systems often require human operators to be physically present in tight and cramped locations. This leads to challenges in the positioning and effective operation of puppets, particular...
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Terrestrial laser scanning (TLS) is the standard technique used to create accurate point clouds for digital forest inventories. However, the measurement process is demanding, requiring up to two days per hectare for d...
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ISBN:
(纸本)9798350377712;9798350377705
Terrestrial laser scanning (TLS) is the standard technique used to create accurate point clouds for digital forest inventories. However, the measurement process is demanding, requiring up to two days per hectare for data collection, significant data storage, as well as resource-heavy post-processing of 3D data. In this work, we present a real-time mapping and analysis system that enables online generation of forest inventories using mobile laser scanners that can be mounted e.g. on mobile robots. Given incrementally created and locally accurate submaps-data payloads-our approach extracts tree candidates using a custom, Voronoi-inspired clustering algorithm. Tree candidates are reconstructed using an algorithm based on the Hough transform, which enables robust modeling of the tree stem. Further, we explicitly incorporate the incremental nature of the data collection by consistently updating the database using a pose graph LiDAR SLAM system. This enables us to refine our estimates of the tree traits if an area is revisited later during a mission. We demonstrate competitive accuracy to TLS or manual measurements using laser scanners that we mounted on backpacks or mobile robots operating in conifer, broad-leaf and mixed forests. Our results achieve RMSE of 1.93 cm, a bias of 0.65 cm and a standard deviation of 1.81 cm (averaged across these sequences)-with no post-processing required after the mission is complete.
The cloud-native data analytics platforms for autonomous systems are thoroughly examined in this research, together with their theoretical underpinnings, methodological frameworks, empirical findings, and practical co...
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Aiming at a low-power embeddedreal-time sitting posture detection system, a real-time sitting posture detection system based on deep learning is designed. The system obtains the pressure of the sitting posture of the...
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The proceedings contain 60 papers. The topics discussed include: schedulability analysis for processors with aging-aware autonomic frequency scaling;an optimal real-time voltage and frequency scaling for uniform multi...
The proceedings contain 60 papers. The topics discussed include: schedulability analysis for processors with aging-aware autonomic frequency scaling;an optimal real-time voltage and frequency scaling for uniform multiprocessors;real-time scheduling of energy harvesting embeddedsystems with timed automata;thermal-constrained energy-aware partitioning for heterogeneous multi-core multiprocessor real-timesystems;MinMax: a sampling interval control algorithm for process control systems;the split-phase synchronisation technique: reducing the pessimism in the WCET analysis of parallelised hard real-time programs;MCFlow: a real-time multi-core aware middleware for dependent task graphs;partitioned scheduling of implicit-deadline sporadic task systems under multiple resource constraints;a parallel algorithm for EDF-schedulability analysis of multi-modal real-timesystems;and optimizing data allocation for loops on embeddedsystems with scratch-pad memory.
We present the preliminary design and results of QVis, a visual analytics tool for exploring quantum device performance data. QV is helps uncover temporal and multivariate variations in noise properties of quantum dev...
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In recent years, drones have found widespread applications in communications, disaster relief, and other fields. However, due to battery capacity constraints, the limited flight endurance of drones hinders their full ...
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Machine learning has attracted a lot of interest in the last few years as a solution to a variety of difficult challenges in many disciplines. An emerging area is that of embedded devices, where machine learning is de...
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Machine learning has attracted a lot of interest in the last few years as a solution to a variety of difficult challenges in many disciplines. An emerging area is that of embedded devices, where machine learning is deployed to efficiently carry out tasks like data analysis, prediction, and decision-making in real-timeapplications. Challenges such as the necessity for fast and effective algorithms and the restricted resources available in embeddedsystems to cover the computational and storage demands need to be confronted to successfully integrate machine learning models into embeddedsystems. This work aims to provide an overview of the use of machine learning in embeddedsystems, including past and current solutions, and to present the challenges that need to be addressed. Future directions for the use of machine learning in embeddedsystems are also discussed.
The utilization of online diagnosis as a prevalent data service in contemporary healthcare systems involves the real-time collection of physical data through Internet of Things (IoT) devices, such as body sensors and ...
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Computation intensive IoT applications have grown exponentially over the past few years, which has led to the rise in popularity of the edge, fog, and cloud computing paradigms among mobile devices with embedded smart...
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