OPTIMAL COMPRESSOR FUNCTION APPROXIMATION UTILIZING Q-FUNCTION APPROXIMATIONS

In this paper, we have proposed two solutions for approximating the optimal compressor function for the Gaussian source. Both solutions are based on approximating Q-function with exponential functions. These solutions differ in that the second one is given in parametric form and can be considered as a more general solution compared to the first one, which is a special case of the second solution for a specific value of the mentioned parameter. The approximated functions proposed in the paper facilitate designing scalar companding quantizers for the Gaussian source since with the application of these functions main difficulties occurred in designing the observed quantizers for the Gaussian source can be overcome

TWO-DIMENSIONAL GMM-BASED CLUSTERING IN THE PRESENCE OF QUANTIZATION NOISE

In this paper, unlike to the commonly considered clustering, wherein data attributes are accurately presented, it is researched how successful clustering can be performed when data attributes are represented with smaller accuracy, i.e. by using the small number of bits. In particular, the effect of data attributes quantization on the two-dimensional two-component Gaussian mixture model (GMM)-based clustering by using expectation–maximization (EM) algorithm is analyzed. An independent quantization of data attributes by using uniform quantizers with the support limits adjusted to the minimal and maximal attribute values is assumed. The analysis makes it possible to determine the number of bits for data presentation that provides the accurate clustering. These findings can be useful in clustering wherein before being grouped the data have to be represented with a finite small number of bits due to their transmission through the bandwidth-limited channel.

NOVEL EXPONENTIAL TYPE APPROXIMATIONS OF THE Q-FUNCTION

In this paper, we propose several solutions for approximating the Q-function using one exponential function or the sum of two exponential functions. As the novel Q-function approximations have simple analytical forms and are therefore very suitable for further derivation of expressions in closed forms, a large number of applications are feasible. The application of the novel exponential type approximations of the Q-function is especially important for overcoming issues arising in designing scalar companding quantizers for the Gaussian source, which are caused by the non-existence of a closed form expression for the Q-function. Since our approximations of the Q-function have simple analytical forms and are more accurate than the approximations of the Q-function previously used for the observed problem in the scalar companding quantization of the Gaussian source, their application, especially for this problem is of great importance