Edge Computing
Edge Computing - Related Videos
10:25
Specimen Preparation, Imaging, and Analysis Protocols for Knife-edge Scanning Microscopy
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Cited by 16 •
2011
The full process from brain specimen preparation to serial sectioning imaging using the Knife-Edge Scanning Microscope, to data visualization and analysis is described. This technique is currently used to acquire mouse brain data, but it is applicable to other organs, other species.
10:23
A Computational Modeling Approach to Investigate the Influence of Hyperthermia on the Tumor Microenvironment
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Cited by 3 •
2023
The article describes a protocol to simulate the transient temperature profiles and the coupled spatiotemporal variation of the interstitial fluid pressure following the heating delivered by a dipolar radiofrequency hyperthermia system. The protocol can be used to assess the response of biophysical parameters characterizing the tumor microenvironment to interventional hyperthermia techniques.
04:06
Identification of Pre-weanling 7-Day-Old Mice with Pinna Edge Biopsies
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2025
The protocol presents a novel stepwise approach to producing a pinna-edge biopsy (PEB) in post-natal day (PND) 7 mice for the purpose of identification and simultaneous genotyping.
05:50
Measuring Cell-Edge Protrusion Dynamics during Spreading using Live-Cell Microscopy
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Cited by 2 •
2021
This protocol aims to measure the dynamic parameters (protrusions, retractions, ruffles) of protrusions at the edge of spreading cells.
01:10
Computed Tomography
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2023
Tomography refers to imaging by sections. Computed tomography (CT) is a non-invasive imaging technique that uses computers to analyze several cross-sectional X-rays to reveal minute details about structures in the body. The technique was invented in the 1970s and is based on the principle that as X-rays pass through the body, they are absorbed or reflected at different levels. In the technique, a patient lies on a motorized platform while a computerized axial tomography (CAT) scanner rotates...
FAQs
Edge Computing improves responsiveness by shortening the path between data generation and computation. Local servers, gateways, or embedded processors can process incoming information near the device, so an engineered system can react without waiting for all data to reach a distant cloud. The same placement reduces network traffic because less information must travel through the wider connection.
A coordinated edge and cloud design assigns different responsibilities across the architecture. Edge resources support immediate local processing and decision-making, while centralized cloud systems provide large-scale storage and analysis. This division allows an engineering system to preserve fast responses at the network edge without giving up broader computational or analytical capabilities.
Local processing can limit how much generated data must leave the device or site, supporting privacy within the system architecture. It can also improve resilience by allowing responses to continue when connectivity is limited. These properties are especially relevant for engineered systems that must operate near equipment, infrastructure, or devices despite unreliable communication with centralized services.
An engineering workflow begins by locating where data is generated and identifying which responses must occur quickly or remain available during limited connectivity. Designers can then place suitable servers, gateways, or embedded processors near those sources, assign local processing tasks, and coordinate the resulting architecture with cloud storage or analysis. This links physical placement to system requirements.
The approach supports real-time control of industrial equipment, autonomous systems, smart infrastructure, and Internet of Things devices. In these settings, nearby processing helps systems respond close to the point where information is produced. Its value is greatest when fast local action, reduced dependence on continuous connectivity, or coordination between physical devices and broader computing resources is important.
Engineers can assess whether the architecture enables faster local responses, lowers latency, and conserves bandwidth compared with relying entirely on centralized processing. They can also examine how well the system maintains operation under limited connectivity, protects locally generated information, and balances immediate edge decisions with large-scale cloud storage and analysis.