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The gauss is the unit of magnetic flux density B in the system of Gaussian units and is equal to Mx/cm 2 or g/Bi/s 2, while the oersted is the unit of H-field. One tesla (T) corresponds to 10 4 gauss, and one ampere (A) per metre corresponds to 4π × 10 −3 oersted.
Magnetic induction B (also known as magnetic flux density) has the SI unit tesla [T or Wb/m 2]. [1] One tesla is equal to 10 4 gauss. Magnetic field drops off as the inverse cube of the distance ( 1 / distance 3 ) from a dipole source. Energy required to produce laboratory magnetic fields increases with the square of magnetic field. [2]
The tesla (symbol: T) is the unit of magnetic flux density (also called magnetic B-field strength) in the International System of Units (SI). One tesla is equal to one weber per square metre .
Successive over-relaxation (SOR) — a technique to accelerate the Gauss–Seidel method Symmetric successive over-relaxation (SSOR) — variant of SOR for symmetric matrices; Backfitting algorithm — iterative procedure used to fit a generalized additive model, often equivalent to Gauss–Seidel; Modified Richardson iteration
One difference between the Gaussian and SI systems is in the factor 4π in various formulas that relate the quantities that they define. With SI electromagnetic units, called rationalized, [3] [4] Maxwell's equations have no explicit factors of 4π in the formulae, whereas the inverse-square force laws – Coulomb's law and the Biot–Savart law – do have a factor of 4π attached to the r 2.
In the CGS system, the unit of the H-field is the oersted and the unit of the B-field is the gauss. In the SI system, the unit ampere per meter (A/m), which is equivalent to newton per weber, is used for the H-field and the unit of tesla is used for the B-field. [3]
In physics (specifically electromagnetism), Gauss's law, also known as Gauss's flux theorem (or sometimes Gauss's theorem), is one of Maxwell's equations. It is an application of the divergence theorem , and it relates the distribution of electric charge to the resulting electric field .
Markov random fields find application in a variety of fields, ranging from computer graphics to computer vision, [13] machine learning or computational biology, [14] [15] and information retrieval. [16] MRFs are used in image processing to generate textures as they can be used to generate flexible and stochastic image models.