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Smoke detector mounted on a ceiling. A smoke detector is a device that senses smoke, typically as an indicator of fire.Smoke detectors/Alarms are usually housed in plastic enclosures, typically shaped like a disk about 150 millimetres (6 in) in diameter and 25 millimetres (1 in) thick, but shape and size vary.
A defect detector is a device used on railroads to detect axle and signal problems in passing trains. The detectors are normally integrated into the tracks and often include sensors to detect several different kinds of problems that could occur.
The rest of the engagement would be carried out optically. During the day, fighters would be directed with sufficient precision for them to come into visual contact with their targets, while during the night they would use their onboard aircraft interception (AI) radar to find the target after initial direction from the ground-based radars.
American inventor Leonarde Keeler testing his improved polygraph on Kohler, a former witness for the prosecution at the 1935 trial of Richard Hauptmann. A polygraph, often incorrectly referred to as a lie detector test, [1] [2] [3] is a junk science [4] [5] [6] device or procedure that measures and records several physiological indicators such as blood pressure, pulse, respiration, and skin ...
Many attempts have been made in the statistical and computer science communities to define an anomaly. The most prevalent ones include the following, and can be categorised into three groups: those that are ambiguous, those that are specific to a method with pre-defined thresholds usually chosen empirically, and those that are formally defined:
Frequency domain, polyphonic detection is possible, usually utilizing the periodogram to convert the signal to an estimate of the frequency spectrum.This requires more processing power as the desired accuracy increases, although the well-known efficiency of the FFT, a key part of the periodogram algorithm, makes it suitably efficient for many purposes.
An RBM can be viewed as a single layer architecture for unsupervised feature learning. In particular, the visible variables correspond to input data, and the hidden variables correspond to feature detectors. The weights can be trained by maximizing the probability of visible variables using Hinton's contrastive divergence (CD) algorithm. [24]
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