Multi-sensor Fusion
Multi-sensor Fusion - Related Videos
02:45
Nuclear Fusion
0 Views •
2020
The process of converting very light nuclei into heavier nuclei is also accompanied by the conversion of mass into large amounts of energy, a process called fusion. The principal source of energy in the sun is a net fusion reaction in which four hydrogen nuclei fuse and ultimately produce one helium nucleus and two positrons. A helium nucleus has a mass that is 0.7% less than that of four hydrogen nuclei; this lost mass is converted into energy during the fusion. This reaction produces about...
09:19
Analysis of SNARE-mediated Membrane Fusion Using an Enzymatic Cell Fusion Assay
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2012
We have developed a cell fusion assay that quantifies SNARE-mediated membrane fusion events by activated expression of β-galactosidase.
09:14
Glovebox and Impurity Sensors
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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...
06:10
Live Imaging of Mouse Secondary Palate Fusion
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Cited by 10 •
2017
Here, we present a protocol for live imaging of mouse secondary palate fusion using confocal microscopy. This protocol can be used in combination with a variety of fluorescent reporter mouse lines, and with pathway inhibitors for mechanistic insight. This protocol can be adapted for live imaging in other developmental systems.
08:20
In Situ Soil Moisture Sensors in Undisturbed Soils
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Cited by 7 •
2022
The determination of soil water content is a critical mission requirement for many state and federal agencies. This protocol synthesizes multi-agency efforts to measure soil water content using buried in situ sensors.
FAQs
Calibration establishes how each sensor’s measurements relate to the system or environment, while synchronization ensures that observations correspond to the same moment. Without these steps, a camera, lidar, radar, inertial sensor, or temperature probe may report correctly on its own but produce inconsistent combined data. Proper alignment allows later estimation methods to compare and integrate observations meaningfully.
Probabilistic estimation represents uncertainty in sensor observations, allowing the system to account for noise rather than treating every measurement as equally reliable. Kalman filtering provides a way to combine observations over time while updating the estimated system state. This weighting can produce a more stable and accurate representation than relying on one imperfect measurement source.
Different sensing devices can compensate for weaknesses in one another. A camera, lidar, radar, inertial sensor, or temperature probe may provide useful information under conditions where another source becomes noisy, blocked, or otherwise limited. Combining their observations improves reliability because system decisions can draw on complementary evidence instead of depending entirely on a single sensor.
A typical engineering workflow begins by selecting sensing devices suited to the system and environment. Engineers then calibrate the sensors, synchronize their measurements, and align observations into a common representation. Probabilistic estimation or Kalman filtering can subsequently weight and combine the data. The integrated output supports detection, tracking, decision-making, or monitoring tasks.
Engineering applications include robot navigation, autonomous vehicles, industrial monitoring, and structural health assessment. In navigation and vehicles, fused observations support detection and tracking. Industrial and structural systems can combine measurements from multiple sources to characterize operating conditions or detect changes. These applications benefit when individual sensors provide incomplete or environmentally constrained observations.
The integrated result can provide a more complete, accurate, and reliable representation of a system or environment than isolated measurements. Depending on the application, this supports improved detection, tracking, decision-making, and overall system robustness. The value is especially apparent when noise, occlusion, or environmental conditions reduce the usefulness of individual sensing devices.