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

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.

QUANTITY DISCOUNTS IN SUPPLIER SELECTION PROBLEM BY USE OF FUZZY MULTI-CRITERIA PROGRAMMING

Supplier selection in supply chain is a multi-criteria problem that involves a number of quantitative and qualitative factors. This paper deals with a concrete problem of flour purchase by a company that manufactures bakery products and the purchasing price of flour depends on the quantity ordered. The criteria for supplier selection and quantities supplied by individual suppliers are: purchase costs, product quality and reliability of suppliers. The problem is solved using a model that combines revised weighting method and fuzzy multi-criteria linear programming (FMCLP). The paper highlights the efficiency of the proposed methodology in conditions when purchasing prices depend on order quantities

ONE-BIT QUANTIZER PARAMETRIZATION FOR ARBITRARY LAPLACIAN SOURCES

In this paper we suggest an exact formula for the total distortion of one-bit quantizer and for the arbitrary Laplacian probability density function (pdf). Suggested formula additionally extends normalized case of zero mean and unit variance, which is the most applied quantization case not only in traditional quantization rather in contemporary solutions that involve quantization. Additionally symmetrical quantizer’s representation levels are calculated from minimal distortion criteria. Note that one-bit quantization is the most sensitive quantization from the standpoint of accuracy degradation and quantization error, thus increasing importance of the suggested parameterization of one-bit quantizer

QUANTITY DISCOUNTS IN SUPPLIER SELECTION PROBLEM BY USE OF FUZZY MULTI-CRITERIA PROGRAMMING

Supplier selection in supply chain is a multi-criteria problem that involves a number of quantitative and qualitative factors. This paper deals with a concrete problem of flour purchase by a company that manufactures bakery products and the purchasing price of flour depends on the quantity ordered. The criteria for supplier selection and quantities supplied by individual suppliers are: purchase costs, product quality and reliability of suppliers. The problem is solved using a model that combines revised weighting method and fuzzy multi-criteria linear programming (FMCLP). The paper highlights the efficiency of the proposed methodology in conditions when purchasing prices depend on order quantities

OFDM LOW COMPLEXITY CHANNEL ESTIMATION USING TIME-FREQUENCY ADJUSTABLE WINDOW FUNCTIONS

In this paper, we introduce a low complexity algorithm for estimation of the channel transfer function in the OFDM communication system that is using a scattered pilot symbol grid. Although, the use of the scattered pilot grid enables implementation of the flexible, and adaptive radio interface, it suffers from a high estimation error at the edges of the symbol sequence. Due to the sampling in time, and frequency, the signal is circularly expanded in both domains, and this has to be taken into account when the signal is processed. The proposed algorithm is shaping the pilot symbol estimates in time, and frequency domain, such that the aliasing in both domains are reduced or eliminated. We achieve a significant reduction of the estimation error, with a linear increase in computational complexity

Analysis of the efficiency of the linearization techniques for solving multi-objective linear fractional programming problems by goal programming

This paper presents and analyzes the applicability of three linearization techniques used for solving multi-objective linear fractional programming problems using the goal programming method. The three linearization techniques are: (1) Taylor’s polynomial linearization approximation, (2) the method of variable change, and (3) a modification of the method of variable change proposed in [20]. All three linearization techniques are presented and analyzed in two variants: (a) using the optimal value of the objective functions as the decision makers’ aspirations, and (b) the decision makers’ aspirations are given by the decision makers. As the criteria for the analysis we use the efficiency of the obtained solutions and the difficulties the analyst comes upon in preparing the linearization models. To analyze the applicability of the linearization techniques incorporated in the linear goal programming method we use an example of a financial structure optimization problem

Vendor Selection and Supply Quotas Determination by Using the Analytic Hierarchy Process and a New Multi-objective Programming Method

In this article, we propose a new methodology for solving the vendor selection and the supply quotas determination problem. The proposed methodology combines the Analytic Hierarchy Process (AHP) for determining the coefficients of the objective functions and a new multiple objective programming method based on the cooperative game theory for vendor selection and supply quotas determination. The proposed methodology is tested on the problem of flour purchase by a company that manufactures bakery products. For vendor selection and supply quotas determination we use three complex criteria: (1) purchasing costs, (2) product quality, and (3) vendor reliability

A fuzzy goal programming approach to solving decentralized bi-level multi-objective linear fractional programming problems

This paper presents a new approach for solving decentralized bi-level multi-objective linear fractional programming problems. The main goal was to find a simple algorithm with high confidence of decision-makers in the results. First, all the linear fractional programming models on the given set of constraints were solved separately. Next, all the linear fractional objective functions were linearized, membership functions of objective functions and decision variables controlled by decision-makers at the highest level calculated, and a fuzzy multi-objective linear programming model formed and solved as linear goal programming problem by using simplex algorithm. The efficiency of the proposed algorithm was investigated using an economic example, and the obtained results compared with those obtained using an existing method