The need for real-time computation of the Euclidean norm of a vector arises frequently in many signal processing applications such as vector median filtering, vector quantization and multiple-input multiple-output wireless communication systems. In this correspondence, we examine the properties of a linear combination of the 1-norm and the infinity norm as an approximation to the Euclidean norm of real-valued vectors. The approximation requires only two multiplications regardless of the vector length and does not require sorting of the absolute values of the vector entries. Numerical results show that the considered approximation incurs negligible performance degradations in typical applications.X1112sciescopu
For P 2 Z[x], let kPk denote the Euclidean norm of the coefficient vector of P. For an algebraic num...
Most of the objective (cost) functions in op-timization techniques utilize norms especially when dea...
This paper considers the problem of determining the row and/or column scaling of a matrix A that min...
The vector median filter has good filtering capabilities; nevertheless, its huge computational compl...
For reducing impulsive noise without degrading image contours, median filtering is a powerful tool. ...
A major drawback with vector median filters is their high computational complexity. A fast algorithm...
We consider the computation of the Euclidean (a.k.a. L2) norm of an ndimensional vector in floating-...
Approximation of digital signals by means of continuous-time functions is often required in many tas...
Maximum (or L_infinity) norm minimization subject to an underdetermined system of linear equations f...
Data measured in the real-world is often composed of both a true signal, such as an image or experim...
The operation ‘multiplication of a vector by a matrix ’ can be represented by a computational scheme...
AbstractThe operation of multiplication of a vector by a matrix can be represented by a computationa...
Both supremum norms and 2-norms have found a huge number of applications as fitting and approximatio...
Abstract—Maximum (or `∞) norm minimization subject to an underdetermined system of linear equations ...
Different norms are considered to replace the Euclidean norm in an algorithm given by Fan and Tits (...
For P 2 Z[x], let kPk denote the Euclidean norm of the coefficient vector of P. For an algebraic num...
Most of the objective (cost) functions in op-timization techniques utilize norms especially when dea...
This paper considers the problem of determining the row and/or column scaling of a matrix A that min...
The vector median filter has good filtering capabilities; nevertheless, its huge computational compl...
For reducing impulsive noise without degrading image contours, median filtering is a powerful tool. ...
A major drawback with vector median filters is their high computational complexity. A fast algorithm...
We consider the computation of the Euclidean (a.k.a. L2) norm of an ndimensional vector in floating-...
Approximation of digital signals by means of continuous-time functions is often required in many tas...
Maximum (or L_infinity) norm minimization subject to an underdetermined system of linear equations f...
Data measured in the real-world is often composed of both a true signal, such as an image or experim...
The operation ‘multiplication of a vector by a matrix ’ can be represented by a computational scheme...
AbstractThe operation of multiplication of a vector by a matrix can be represented by a computationa...
Both supremum norms and 2-norms have found a huge number of applications as fitting and approximatio...
Abstract—Maximum (or `∞) norm minimization subject to an underdetermined system of linear equations ...
Different norms are considered to replace the Euclidean norm in an algorithm given by Fan and Tits (...
For P 2 Z[x], let kPk denote the Euclidean norm of the coefficient vector of P. For an algebraic num...
Most of the objective (cost) functions in op-timization techniques utilize norms especially when dea...
This paper considers the problem of determining the row and/or column scaling of a matrix A that min...