Sensor Digitization
Sensor Digitization - Related Videos
09:14
Glovebox and Impurity Sensors
0 Views •
2023
Source: Tamara M. Powers, Department of Chemistry, Texas A&M University The glovebox provides a straightforward means to handle air- and moisture-sensitive solids and liquids. The glovebox is what it sounds like: a box with gloves attached to one or more sides, which allows the user to perform manipulations within the glovebox under an inert atmosphere. For manipulations under inert atmospheres, chemists can choose between Schlenk or high-vacuum techniques and a glovebox. Schlenk and...
08:06
Use of a Low-flow Digital Anesthesia System for Mice and Rats
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Cited by 4 •
2016
Here, we present a protocol to more safely and efficiently administer anesthetic gas to mice using a digital, low flow anesthesia system utilizing a syringe-driven direct injection vaporizer.
09:30
Bacterial Detection & Identification Using Electrochemical Sensors
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Cited by 6 •
2013
We describe an electrochemical sensor assay method for rapid bacterial detection and identification. The assay involves a sensor array functionalized with DNA oligonucleotide capture probes for ribosomal RNA (rRNA) species-specific sequences. Sandwich hybridization of target rRNA with the capture probe and a horseradish peroxidase-linked DNA oligonucleotide detector probe produces a measurable amperometric current.
10:23
Glutamine Flux Imaging Using Genetically Encoded Sensors
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Cited by 7 •
2014
This article will demonstrate how to monitor glutamine dynamics in live cells using FRET. Genetically encoded sensors allow real-time monitoring of biological molecules at a subcellular resolution. Experimental design, technical details of the experimental settings, and considerations for post-experimental analyses will be discussed for genetically encoded glutamine sensors.
14:02
Measuring Engagement of Spectators of Social Digital Games
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2021
We propose a methodology that enables measuring engagement of spectators in a social digital game combining physiological and self-reported data. As this digital game involves a group of freely moving people, the experience is filmed using a synchronizing technique that links physiological data with events in the game.
FAQs
Sampling rate determines how frequently an analog neural signal is measured. A higher rate provides more measurements within the same time interval, which can help preserve and distinguish rapid electrical events. If sampling is too infrequent, brief changes may be poorly represented in the digital record, limiting later filtering, feature extraction, and interpretation.
Resolution controls how finely each sampled voltage or signal measurement can be assigned a numerical value, while input range determines the span of values that can be represented. Together, they influence whether meaningful differences in neural activity remain distinguishable without exceeding the measurable range. Appropriate settings support more informative computational analysis.
Electrical noise can alter the measured signal before or during conversion, causing the digital record to differ from the underlying neural activity. Its effect depends on the signal being recorded and on the converter settings, including sampling rate, resolution, and input range. Noise-aware acquisition is therefore important when comparing activity across recordings or experiments.
A typical workflow begins by sampling the sensor output at defined time intervals, assigning each measurement a finite numerical value, and encoding those values for computational use. The resulting record can then be stored and processed with filtering, feature extraction, visualization, or quantitative comparison. These stages convert recorded activity into data suitable for systematic analysis.
Digitized sensor data support experiments that measure electrical activity from neurons or broader brain regions. In electrophysiology and EEG, the records provide computationally accessible representations for examining neural signals. Neural-interface systems also rely on such data so recorded activity can be processed, characterized, and used within an electronic analysis workflow.
The digital record allows researchers to apply filtering, extract measurable features, visualize signal patterns, and compare results quantitatively across experiments. These operations can make electrical activity easier to inspect and evaluate consistently. The usefulness of the outcomes still depends on how accurately sampling, quantization, input range, and noise conditions captured the original signal.