0
What else can I help you with?
How do you fit the word orthogonal into a sentence without math?
jjj
Why use orthogonal trajectories?
we dont ever
Why use orthogonal signal space?
One reason is that anything which happens in one of the orthogonal directions has no effect on what happens in another orthogonal direction. Thus, for example, the horizontal component of a force will not have any effect in the vertical direction.
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 the orthogonal planning in ancient Greece?
it is planning of orthogonal planning
When was Orthogonal - novel - created?
Orthogonal - novel - was created in 2011.
What is 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.
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.
Prove that the product of two orthogonal matrices is orthogonal and so is the inverse of an orthogonal matrix What does this mean in terms of rotations?
To prove that the product of two orthogonal matrices ( A ) and ( B ) is orthogonal, we can show that ( (AB)^T(AB) = B^TA^TA = B^T I B = I ), which confirms that ( AB ) is orthogonal. Similarly, the inverse of an orthogonal matrix ( A ) is ( A^{-1} = A^T ), and thus ( (A^{-1})^T A^{-1} = AA^T = I ), proving that ( A^{-1} ) is also orthogonal. In terms of rotations, this means that the combination of two rotations (represented by orthogonal matrices) results in another rotation, and that rotating back (inverting) maintains orthogonality, preserving the geometric properties of rotations in space.
