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Orthogonal view is basically seeing something in 2 dimensions that is actually 3 dimensions. The projection lines in these views are orthogonal to the projection plane which causes it to be 2 dimensions.
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What is an orthogonal view?
Orthogonal view is basically seeing something in 2 dimensions that is actually 3 dimensions. The projection lines in these views are orthogonal to the projection plane which causes it to be 2 dimensions.
What is the definition of orthogonal signal space?
Orthogonal signal space is defined as the set of orthogonal functions, which are complete. In orthogonal vector space any vector can be represented by orthogonal vectors provided they are complete.Thus, in similar manner any signal can be represented by a set of orthogonal functions which are complete.
Can the difference of 2 vectors be orthogonal?
The answer will depend on orthogonal to WHAT!
What is orthogonal planning in ancient Greece?
it is planning of orthogonal planning
When was Orthogonal - novel - created?
Orthogonal - novel - was created in 2011.
What is the orthogonal planning in ancient Greece?
it is planning of orthogonal planning
Self orthogonal trajectories?
a family of curves whose family of orthogonal trajectories is the same as the given family, is called self orthogonal trajectories.
How do you use Orthogonal in a sentence?
Orthogonal is a term referring to something containing right angles. An example sentence would be: That big rectangle is orthogonal.
What has the author Richard Askey written?
Richard Askey has written: 'Three notes on orthogonal polynomials' -- subject(s): Orthogonal polynomials 'Recurrence relations, continued fractions, and orthogonal polynomials' -- subject(s): Continued fractions, Distribution (Probability theory), Orthogonal polynomials 'Orthogonal polynomials and special functions' -- subject(s): Orthogonal polynomials, Special Functions
What is self orthogonal?
Self orthogonal trajectories are a family of curves whose family of orthogonal trajectories is the same as the given family. This is a term that is not very widely used.
What does the mean of product of two orthogonal matrix is orthogonal in terms of rotation?
The mean of the product of two orthogonal matrices, which represent rotations, is itself an orthogonal matrix. This is because the product of two orthogonal matrices is orthogonal, preserving the property that the rows (or columns) remain orthonormal. When averaging these rotations, the resulting matrix maintains orthogonality, indicating that the averaged transformation still represents a valid rotation in the same vector space. Thus, the mean of the rotations captures a new rotation that is also orthogonal.
What is an orthogonal matrix?
A matrix A is orthogonal if itstranspose is equal to it inverse. So AT is the transpose of A and A-1 is the inverse. We have AT=A-1 So we have : AAT= I, the identity matrix Since it is MUCH easier to find a transpose than an inverse, these matrices are easy to compute with. Furthermore, rotation matrices are orthogonal. The inverse of an orthogonal matrix is also orthogonal which can be easily proved directly from the definition.
