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Residual Knowledge Retention For Edge Devices
2021 IEEE 30th International Symposium on Industrial Electronics (ISIE), 2021This paper proposes an approach for continual learning, Knowledge Retention (KR), that learns new information without accessing data from previous tasks. A KR unit is based on the embedding layer that identifies the important kernel in the convolution layer, which preserves key parameters and allows the weights to be reused across tasks.
Cheng-Fu Liou, Paul Kuo, Jiun-In Guo
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Parallelized edge-based droplet generation (EDGE) devices
Lab on a Chip, 2009We here report on three parallelized designs of the new edge-based droplet generation mechanism, which, unlike existing mechanisms, produces many equally sized droplets simultaneously at a single droplet formation unit. Operation of the scaled-out systems is straight forward; only the oil inlet pressure has to be controlled to let all the units produce
van Dijke, K.C. +3 more
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Stream Processing on Clustered Edge Devices
IEEE Transactions on Cloud Computing, 2022The Internet of Things continuously generates avalanches of raw sensor data to be transferred to the Cloud for processing and storage. Due to network latency and limited bandwidth, this vertical offloading model, however, fails to meet requirements of time-critical data-intensive applications which must act upon generated data with minimum time delays.
Rustem Dautov, Salvatore Distefano
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Trusted Video Streaming on Edge Devices
2021 IEEE International Conference on Pervasive Computing and Communications Workshops and other Affiliated Events (PerCom Workshops), 2021The ubiquitous operation of mobile and embedded devices has given an impetus to the development of sensing systems. Most applications on edge devices rely heavily on sensor inputs. Surveillance devices and autonomous vehicles often require high-frequency video sensor data for security provisions and decision making.
Narendra Prabhu +2 more
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ECG Beat Classification on Edge Device
2020 IEEE International Conference on Consumer Electronics (ICCE), 2020Feature transformation is a class of unsupervised learning methods that has been extensively applied and studied in computer vision. Little work has been done to adapt it to the end-to-end training of visual features on large-scale datasets on edge device.
Dennis Hou, Raymond Hou, Janpu Hou
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Contact and edge effects in graphene devices
Nature Nanotechnology, 2008Electrical transport studies on graphene have been focused mainly on the linear dispersion region around the Fermi level and, in particular, on the effects associated with the quasiparticles in graphene behaving as relativistic particles known as Dirac fermions. However, some theoretical work has suggested that several features of electron transport in
Lee, Eduardo J. H. +4 more
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Learning-from-signals on edge devices
IEEE Instrumentation & Measurement Magazine, 2012Machine learning tools are being developed that support increasingly complex learning-fromsignals on "edge" devices to meet the challenges of decentralized decision making. Edge devices in this context include any electronically enabled device that can sense, process and make decisions based on locally integrated information. Component systems that use
Michael Roy Moore, Mark A. Buckner
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Fast Sobel Edge Detection for IoT Edge Devices
SN Computer Science, 2022Rajeev Joshi +2 more
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Design of a Smart Leading Edge Device
2012To make use of low-drag future generation wings with high aspect ratio and low sweep for natural laminar flow, new high lift devices have to be developed [ACARE (Addendum to the Strategic Research Agenda, 2008), Horstmann (TELFONA, Contribution to Laminar Wing Development for Future Transport Aircraft, 2006)].
Kintscher, Markus, Wiedemann, Martin
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Evaluation of bandwidth enforcement at edge devices
Proceedings 10th IEEE International Conference on Networks (ICON 2002). Towards Network Superiority (Cat. No.02EX588), 2003Bandwidth enforcement at edge devices can allocate bandwidth resources according to organizational policy rules. Enterprises often employ such policy-based devices at their organizational edges to manage the narrow but expensive Internet access links.
H. Y. Wei, Y. D. Lin
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