Edge computing

Summary

Edge computing is a distributed computing paradigm that brings computation and data storage closer to the sources of data. This is expected to improve response times and save bandwidth. Edge computing is an architecture rather than a specific technology, and a topology- and location-sensitive form of distributed computing. The origins of edge computing lie in content distributed networks that were created in the late 1990s to serve web and video content from edge servers that were deployed close to users. In the early 2000s, these networks evolved to host applications and application components on edge servers, resulting in the first commercial edge computing services that hosted applications such as dealer locators, shopping carts, real-time data aggregators, and ad insertion engines. Internet of things (IoT) is an example of edge computing. A common misconception is that edge and IoT are synonymous. Definition One definition of edge computing is the use

Official source

https://en.wikipedia.org/wiki/Edge_computing

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Related publications (38)

New Measurement Method in Drug Sensing by Direct Total-Charge Detection in Voltammetry

Simone Aiassa, Sandro Carrara, Danilo Demarchi, José David González Martínez

Electrochemical biosensors are promoting point-of-care and wearable instrumentation due to their high versatility in measuring human metabolites. There is a considerable number of biological compounds that can be detected and measured through voltammetry based techniques. Voltmmetry some times requires peak identification and quantification that are non-trivial to be efficiently implemented by automatic instrumentation. To overcome the complexity of automatic peak estimation, we propose here an instrumentation circuit for edge-computing in pharmacology relying on an entirely novel measurement method via TotalCharge Detection in Cyclic voltammetry (TCDC). Namely, our TCDC method innovatively applies the coulometry measurement to the well-established voltammetry procedure. The proposed instrumentation accumulates the total charge exchanged in the faradaic process, exploiting a Nagaraj integrator as charge suppressor to fit the application-specific constraints. The work shows accurate simulations of the TCDC circuit on a set of experimental measures, acquired on paracetamol as benchmark drug. The proposed measurement technique and the developed circuit are compared to the peak detection method usually adopted in literature. The results demonstrate that the proposed system is a perfect trade-off between the doubled limit-of-detection and a tenfold reduction in measurement errors. At the same time, we eliminate any need for data oversampling and processing, promoting the TCDC as an efficient new measurement method for point-of-care and wearable monitoring of biological compounds.

IEEE2020

Prospection sur les images numériques proche infrarouge

Gilles Gachet

Introduction Les caméras multispectrales - Caractéristiques (tableau comparatif) Analyse du bruit (entropie) Analyse de la résolution effective (Full Width of edge response at Half Height) Comparaison des résolutions spatiales Formats de compression Rehaussement Interprétation visuelle Applications - Foresterie

2006

X-Stream: Edge-centric Graph Processing using Streaming Partitions

Ivo Mihailovic, Amitabha Roy, Willy Zwaenepoel

X-Stream is a system for processing both in-memory and out-of-core graphs on a single shared-memory machine. While retaining the scatter-gather programming model with state stored in the vertices, X-Stream is novel in (i) using an edge-centric rather than a vertex-centric implementation of this model, and (ii) streaming completely unordered edge lists rather than performing random access. This design is motivated by the fact that sequential bandwidth for all storage media (main memory, SSD, and magnetic disk) is substantially larger than random access bandwidth. We demonstrate that a large number of graph algorithms can be expressed using the edge-centric scatter-gather model. The resulting implementations scale well in terms of number of cores, in terms of number of I/O devices, and across different storage media. X-Stream competes favorably with existing systems for graph processing. Besides sequential access, we identify as one of the main contributors to better performance the fact that X-Stream does not need to sort edge lists during pre-processing.

ACM2013

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