Zero crossing (or burst-firing) control is an approach for electrical control circuits that starts operation with the AC load voltage at close to 0 volts in the AC cycle.[1] This is in relation to solid state relays, such as triacs and silicon controlled rectifiers.[1] The purpose of the circuit is to start the triac conducting very near the time point when the load voltage is crossing zero volts (at the beginning or the middle of each AC cycle represented by a sine wave), so that the output voltage begins as a complete sine-wave half-cycle. In other words, if the controlling input signal is applied at any point during the AC output wave other than very close to the zero voltage point of that wave, the output of the switching device will "wait" to switch on until the output AC wave reaches its next zero point. This is useful when sudden turn-on in the middle of a sine-wave half cycle could cause undesirable effects like high frequency spikes for which the circuit or the environment is not expected to handle gracefully.
The point where the AC line voltage is 0 V is the zero cross point. When a triac is connected in its simplest form, it can clip the beginning of the voltage curve, due to the minimum gate voltage of the triac. A zero cross circuit works to correct this problem, so that the triac functions as well as possible. This is typically done with thyristors in two of the three phases.
Many opto-triacs come with zero cross circuits built in. They are often used to control larger, power triacs. In this setup triac turn-on delays will compound, so quick turn on times are important.
The corresponding phase angle circuits are more sophisticated and more expensive than zero cross circuits.
https://en.wikipedia.org/wiki/Zero_crossing_control
The zero-crossing rate (ZCR) is the rate at which a signal changes from positive to zero to negative or from negative to zero to positive.[1] Its value has been widely used in both speech recognition and music information retrieval, being a key feature to classify percussive sounds.[2]
ZCR is defined formally as
where is a signal of length and is an indicator function.
In some cases only the "positive-going" or "negative-going" crossings are counted, rather than all the crossings, since between a pair of adjacent positive zero-crossings there must be a single negative zero-crossing.
For monophonic tonal signals, the zero-crossing rate can be used as a primitive pitch detection algorithm. Zero crossing rates are also used for Voice activity detection (VAD), which determines whether human speech is present in an audio segment or not.
See also[edit]
In electronics[edit]
In alternating current, the zero-crossing is the instantaneous point at which there is no voltage present. In a sine wave or other simple waveform, this normally occurs twice during each cycle. It is a device for detecting the point where the voltage crosses zero in either direction.
The zero-crossing is important for systems that send digital data over AC circuits, such as modems, X10 home automation control systems, and Digital Command Control type systems for Lionel and other AC model trains.
Counting zero-crossings is also a method used in speech processing to estimate the fundamental frequency of speech.
In a system where an amplifier with digitally controlled gain is applied to an input signal, artifacts in the non-zero output signal occur when the gain of the amplifier is abruptly switched between its discrete gain settings. At audio frequencies, such as in modern consumer electronics like digital audio players, these effects are clearly audible, resulting in a 'zipping' sound when rapidly ramping the gain or a soft 'click' when a single gain change is made. Artifacts are disconcerting and clearly not desirable. If changes are made only at zero-crossings of the input signal, then no matter how the amplifier gain setting changes, the output also remains at zero, thereby minimizing the change. (The instantaneous change in gain will still produce distortion, but it will not produce a click.)
If electrical power is to be switched, no electrical interference is generated if switched at an instant when there is no current—a zero crossing. Early light dimmers and similar devices generated interference; later versions were designed to switch at the zero crossing.
In image processing[edit]
In the field of Digital Image Processing, great emphasis is placed on operators that seek out edges within an image. They are called 'Edge Detection' or 'Gradient filters'. A gradient filter is a filter that seeks out areas of rapid change in pixel value. These points usually mark an edge or a boundary. A Laplace filter is a filter that fits in this family, though it sets about the task in a different way. It seeks out points in the signal stream where the digital signal of an image passes through a pre-set '0' value, and marks this out as a potential edge point. Because the signal has crossed through the point of zero, it is called a zero-crossing. An example can be found here, including the source in Java.
In the field of Industrial radiography, it is used as a simple method for the segmentation of potential defects.[1]
References[edit]
- ^ Mery, Domingo (2015). Computer Vision for X-Ray Testing. Switzerland: Springer International Publishing. p. 271. ISBN 978-3319207469.
See also[edit]
In computer vision and image processing, a feature is a piece of information about the content of an image; typically about whether a certain region of the image has certain properties. Features may be specific structures in the image such as points, edges or objects. Features may also be the result of a general neighborhood operation or feature detection applied to the image. Other examples of features are related to motion in image sequences, or to shapes defined in terms of curves or boundaries between different image regions.
More broadly a feature is any piece of information which is relevant for solving the computational task related to a certain application. This is the same sense as feature in machine learning and pattern recognitiongenerally, though image processing has a very sophisticated collection of features. The feature concept is very general and the choice of features in a particular computer vision system may be highly dependent on the specific problem at hand.
https://en.wikipedia.org/wiki/Feature_(computer_vision)
In special relativity, a four-vector (or 4-vector)[1] is an object with four components, which transform in a specific way under Lorentz transformation. Specifically, a four-vector is an element of a four-dimensional vector space considered as a representation space of the standard representation of the Lorentz group, the (12,12) representation. It differs from a Euclidean vector in how its magnitude is determined. The transformations that preserve this magnitude are the Lorentz transformations, which include spatial rotations and boosts (a change by a constant velocity to another inertial reference frame).[2]: ch1
Four-vectors describe, for instance, position xμ in spacetime modeled as Minkowski space, a particle's four-momentum pμ, the amplitude of the electromagnetic four-potential Aμ(x) at a point x in spacetime, and the elements of the subspace spanned by the gamma matrices inside the Dirac algebra.
The Lorentz group may be represented by 4×4 matrices Λ. The action of a Lorentz transformation on a general contravariant four-vector X (like the examples above), regarded as a column vector with Cartesian coordinates with respect to an inertial frame in the entries, is given by
(matrix multiplication) where the components of the primed object refer to the new frame. Related to the examples above that are given as contravariant vectors, there are also the corresponding covariant vectors xμ, pμ and Aμ(x). These transform according to the rule
where T denotes the matrix transpose. This rule is different from the above rule. It corresponds to the dual representation of the standard representation. However, for the Lorentz group the dual of any representation is equivalent to the original representation. Thus the objects with covariant indices are four-vectors as well.
For an example of a well-behaved four-component object in special relativity that is not a four-vector, see bispinor. It is similarly defined, the difference being that the transformation rule under Lorentz transformations is given by a representation other than the standard representation. In this case, the rule reads X′ = Π(Λ)X, where Π(Λ) is a 4×4 matrix other than Λ. Similar remarks apply to objects with fewer or more components that are well-behaved under Lorentz transformations. These include scalars, spinors, tensors and spinor-tensors.
The article considers four-vectors in the context of special relativity. Although the concept of four-vectors also extends to general relativity, some of the results stated in this article require modification in general relativity.
https://en.wikipedia.org/wiki/Four-vector
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