COMBINED TECHNIQUES FOR FORECASTING THE VOLUME OF PACKAGES IN INTERNAL POSTAL TRAFFIC OF SERBIA

The main goal of time series analysis is to explain the main features of the data in a chronological order and in the general case to predict future processes, products, service requirements, etc., using appropriate statistical models. In this paper, time series prediction was performed using a seasonal autoregressive integrated moving average model (SARIMA) in the XLSTAT add-in for Excel environment, as well as two artificial neural network (ANN) models - long short-term memory (LSTM) network and relatively new machine learning technique - extreme learning machines (ELM).  The proposed approaches were used for forecasting the volume of packages in the internal postal traffic of Serbia for the period 2014-2020. A comparison of the obtained modeling results with the original data was made and it was shown that the best modelling results were achieved by using ELM

FAST DOA ESTIMATION OF THE SIGNAL RECEIVED BY TEXTILE WEARABLE ANTENNA ARRAY BASED ON ANN MODEL

MLP_DoA module, being an integral part of the smart TWAA DoA subsystem, intended for fast DoA estimation is proposed. Multilayer perceptron network is used to create the MLP_DoA module that provides a radio gateway location in azimuthal plane at its output when a spatial correlation matrix, found by receiving the radio gateway signal using two-element textile wearable antenna array, is on its input. MLP_DoA network training with monitoring the generalization capabilities on the validation set of samples is applied. The accuracy of the proposed modeling approach is compared to the classical approach in MLP_DoA module training previously developed by the authors. Comparison of the presented ANN model with the root MUSIC algorithm in terms of accuracy and program execution time is also done

COMPARATIVE ANALYSIS OF DIFFERENT CAD METHODS FOR EXTRACTION OF THE HEMT NOISE WAVE MODEL PARAMETERS

The noise wave model has appeared as a very appropriate model for the purpose of transistor noise modeling at microwave frequencies. The transistor noise wave model parameters are usually extracted from the measured transistor noise parameters by using time-consuming optimization procedures in microwave circuit simulators. Therefore, three different Computer-Aided Design methods that enable more efficient automatic determination of these parameters in the case of high electron-mobility transistors were developed. All of these extraction methods are based on different noise de-embedding procedures, which are described in detail within this paper. In order to validate the presented extraction methods, they were applied for the noise modeling of a specific GaAs high electron-mobility transistor. Finally, the obtained results were used for the comparative analysis of the presented extraction approaches in terms of accuracy, complexity and effectiveness

HYBRID NEURAL LUMPED ELEMENT APPROACH IN INVERSE MODELING OF RF MEMS SWITCHES

RF MEMS switches have been efficiently exploited in various applications in communication systems. As the dimensions of the switch bridge influence the switch behaviour, during the design of a switch it is necessary to perform inverse modeling, i.e. to determine the bridge dimensions to ensure the desired switch characteristics, such as the resonant frequency. In this paper a novel inverse modeling approach based on combination of artificial neural networks and a lumped element circuit model has been considered. This approach allows determination of the bridge fingered part length for the given resonant frequency and the bridge solid part length, generating at the same time values of the elements of the switch lumped element model. Validity of the model is demonstrated by appropriate numerical examples

Intelligent Agents and Game-Based Learning Modules in a Learning Management System

Many researchers have taken a great deal of effort to promote high quality game-based learning applications, such as educational games, animations, simulations, animated or interactive simulation mechanisms in learning management system (LMS), and so on. The Bloom's taxonomy strategy was successfully implemented as effective gaming model in two different game-based learning applications (puzzle and platformer games) that motivate and actively engage college students in order to make learning process more enjoyable. During using the game-based modules in LMS Moodle, special attention was paid both to the integration of game-play aspects and the relationship between learning styles and game genres. In this paper we shall describe the proposed approach and introduce an adaptation and personalization of player as student model based on game genres. We have analyzed learning styles and teaching strategies that match the game features which resulted in embedding the analysis personalization and teaching strategies into the game. This article presents the effectiveness of agent-based approach in teaching strategies of Moodle gaming education resources

An analytical approach to the HEMT noise wave model parameter determination

This paper presents an analytical approach to determination of the noise wave model parameters for a high electron-mobility transistor working under different temperature and frequency conditions. The presented approach is composed of two steps and provides more efficient determination of these parameters than in the case of optimization procedures commonly applied for that purpose in circuit simulators. The first step is extraction of the noise parameters of transistor intrinsic circuit from the measured noise parameters of whole transistor using an analytical noise de-embedding procedure. The second step is calculation of the noise wave model parameters from the de-embedded intrinsic noise parameters using existing formulas. The accuracy of the presented approach is validated in a wide frequency and temperature range by comparison of the transistor noise parameters simulated for the determined noise wave model parameters with the measured noise parameters. [Project of the Serbian Ministry of Education, Science and Technological Development, Grant no. TR-32052