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Furthermore, ∥●∥ and |●| denote the norm and absolute operator, respectively, and E is the expectation operator.
where ({sigma _{0}^{2}}triangleq text {E} left {left |{s_{0}}(t) right |^{2} right }) denotes the desired signal power, E is an expectation operator, and |·| is an absolute operator.
where H t, u, v, j) and H(t − 1, u, v, j) represent the j th bins of the histograms of the (u, v th region in the t th and (t – 1 th frames, respectively, | · | denotes the absolute operator, and N b is the number of bins (here N b = 256).
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We denote by | A | the absolute value operator of A, that is, | A | = ( A ∗ A ) 1 2, where A ∗ is the adjoint operator of A. For positive real number a, b, the arithmetic-geometric mean inequality says that a b ≤ a + b 2. It is equivalent to ( a b ) m ≤ ( a + b 2 ) 2 m, m = 1, 2, …. (1.1).
It follows that the amount of NC traffic is MNC= min MA,MB) and the amount of UC traffic is MUC=|MA−MB|, where |·| is the absolute value operator.
where Γ stands for the complex-valued gamma function, β is a real positive number that controls the trade-off between TF resolution and cross-terms suppression [41], and. is the absolute function operator.
| to denote the absolute value operator.
This idea has been carried further by Bach [ 27], resulting in the Bolasso (bootstrap-enhanced least absolute shrinkage operator) algorithm for variable selection in linear regression.
There are two uses of the @ operator, absolute scaling and relative scaling: to scale the signal at α dB RMS relative to full scale (absolute scaling) to scale the signal at α dB RMS above the signal (relative scaling).
For example, the operator absolute value (Abs) can be applied to a float, a vector, or a matrix; this polymorphism provides needed simplicity in the function expression.
These data were first used to 'train' a least absolute shrinkage selection operator (LASSO) machine-learning algorithm in distinguishing patients from HCs.
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