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An orthogonal array is simple if it does not contain any repeated rows. (Subarrays of t columns may have repeated rows, as in the OA(18, 7, 3, 2) example pictured in this section.) An orthogonal array is linear if X is a finite field F q of order q (q a prime power) and the rows of the array form a subspace of the vector space (F q) k. [2]
It is associated with simplicity; the more orthogonal the design, the fewer exceptions. This makes it easier to learn, read and write programs in a programming language [citation needed]. The meaning of an orthogonal feature is independent of context; the key parameters are symmetry and consistency (for example, a pointer is an orthogonal concept).
To construct a (k + 2, n)-net from k MOLS(n), represent the MOLS as an orthogonal array, OA(k + 2, n) (see above). The ordered pairs of entries in each row of the orthogonal array in the columns labeled r and c, will be considered to be the coordinates of the n 2 points of the net. Each other column (that is, Latin square) will be used to ...
If each entry of an n × n Latin square is written as a triple (r,c,s), where r is the row, c is the column, and s is the symbol, we obtain a set of n 2 triples called the orthogonal array representation of the square. For example, the orthogonal array representation of the Latin square
An orthogonal instruction set does not impose a limitation that requires a certain instruction to use a specific register [1] so there is little overlapping of instruction functionality. [ 2 ] Orthogonality was considered a major goal for processor designers in the 1970s, and the VAX-11 is often used as the benchmark for this concept.
In linear algebra, orthogonalization is the process of finding a set of orthogonal vectors that span a particular subspace.Formally, starting with a linearly independent set of vectors {v 1, ... , v k} in an inner product space (most commonly the Euclidean space R n), orthogonalization results in a set of orthogonal vectors {u 1, ... , u k} that generate the same subspace as the vectors v 1 ...
Visual understanding of multiplication by the transpose of a matrix. If A is an orthogonal matrix and B is its transpose, the ij-th element of the product AA T will vanish if i≠j, because the i-th row of A is orthogonal to the j-th row of A. An orthogonal matrix is the real specialization of a unitary matrix, and thus always a normal matrix.
Download QR code; Print/export ... EFS—Encrypting File System; EGA—Enhanced Graphics Array; ... OFDM—Orthogonal Frequency-Division Multiplexing;