Analog Computers
Analog Computers - Related Videos
02:48
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2026
Source: Dembowski, J. A., Deluca, N. A. Purification of Viral DNA for the Identification of Associated Viral and Cellular Proteins. J. Vis. Exp. (2017)This video demonstrates the infection of stationary-phase mammalian cells with HSV-1 (Herpes Simplex Virus-1), followed by replication of the viral genome using host and viral replication machinery. A synthetic nucleoside analog is incorporated into newly synthesized viral DNA to label replicating genomes, which are subsequently isolated for...
10:13
Fentanyl Analog Screening using LC-TIMS-TOF MS/MS
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Cited by 1 •
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A method for the screening of fentanyl analogs based on their retention time, mobility, and mass spectrometry fragmentation pattern.
06:58
Cell Cycle Analysis in the C. elegans Germline with the Thymidine Analog EdU
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Cited by 31 •
2018
An imaging-based method is described that can be used to identify S-phase and analyze cell cycle dynamics in the C. elegans hermaphrodite germline using the thymidine analog EdU. This method requires no transgenes and is compatible with immunofluorescent staining.
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...
08:37
Fluorescence-based Monitoring of PAD4 Activity via a Pro-fluorescence Substrate Analog
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Cited by 3 •
2014
PAD4 is an enzyme responsible for the conversion of peptidyl-arginine to peptidyl-citrulline. Dysregulation of PAD4 has been implicated in a number of human diseases. A facile and high-throughput compatible fluorescence based PAD4 assay is described.
FAQs
Feedback networks connect signal paths so that a system’s output influences subsequent computation. When configured with amplifiers, integrators, summers, and multipliers, these paths represent relationships among changing quantities and reproduce the behavior described by differential equations. This arrangement allows engineers to examine dynamic behavior directly as the computation proceeds.
Each component performs a distinct mathematical operation on the represented signals. Amplifiers adjust signal magnitude, integrators represent accumulation over time, summers combine signal contributions, and multipliers form products between quantities. Engineers combine these functions in a configured network to construct a model of a physical system or mathematical relationship.
Continuously varying signals can track changing system quantities without first converting them into discrete numerical codes. As a result, the computer can reproduce a modeled process in real time, making changes in system behavior available as the computation unfolds. This direct representation is especially relevant when studying motion, circuits, or other dynamic processes.
Noise, component tolerances, and signal drift can all alter the physical quantities carrying the computation. These effects may cause the represented relationships to depart from the intended model, limiting accuracy even when the signal network is configured correctly. Engineers therefore interpret results with awareness that physical signal behavior affects computational precision.
An engineer first represents the relationships of the target system with signal paths, then routes those paths through suitable amplifiers, integrators, summers, and multipliers. Feedback networks are configured to reproduce the required differential equations or physical behavior. The resulting system can then operate in real time, providing a direct representation of the modeled process.
Analog computers are suited to engineering tasks involving dynamic-system simulation, control-system design, and rapid analysis of processes. They are particularly useful when the behavior of a mechanical system, electrical circuit, or similar process must be reproduced as it changes. Their real-time operation supports direct observation of the modeled dynamics during analysis.
The approach can reproduce relationships associated with mechanical motion, electrical circuits, and other physical processes described through changing quantities. By configuring signal-processing components and feedback paths, engineers build models that reflect a system’s dynamic behavior. This makes the method relevant for examining how physical systems evolve and for developing control-system designs.
The direct physical representation provides speed and intuitive visualization because changing signals correspond to changing quantities in the modeled system. However, the same physical implementation introduces sensitivity to noise, component tolerances, and signal drift. Engineers gain rapid, visually accessible analysis, but must accept accuracy limitations associated with the components and signals.