Bratislav Milovanovic

Alternative signal detection for neural network-based smart antenna

Neural network-based smart antennas are used for the solution of multiple-source tracking problems in the area of wireless communications. The architecture of the neural network is constructed in two stages, one stage for signal detection and the other for angle of arrival (AOA) estimation. The best candidates for this type of problem are radial basis function neural networks (RBFNN), applied in both stages. Progress is made by applying probabilistic neural networks (PNN) in the first stage. This rapidly reduces the time for network training. Simulation results are performed to investigate the performance of the algorithm.

Loaded cylindrical metallic cavities modeling using neural networks

The feasibility of using neural networks for resonant frequencies determination in loaded cylindrical metallic cavities is presented. The load in the form of a homogeneous dielectric slab with losses, which is located on the bottom of the cavity, is considered. The use of the classical multilayer perceptron (MLP) network is illustrated through the example of TM/sub 113/ modes determination in a microwave cavity with circular cross-section. An original approach for decreasing the neural network training set is used.

TLM modeling of the circular cylindrical cavity loaded by lossy dielectric sample of various geometric shapes.

The modeling of the cylindrical metallic cavity with circular cross-section loaded by lassy dielectric sample of various geometric shapes is done by using the Transmission-Line Matrix (TLIVP method. For modeling purposes, a hybrid symmetrical condensed node (HSCN) in cylindrical coordinates, developed and implemented in the appropriate software, is applied. The proposed TLM models for several characteristic geometric shapes of the lassy dielectric sample are described in detail, the modeling process is discussed and the influence of the load form on the resonant frequencies of the cavity is investigated In addition, the applied TL 11 approach is experimentally verifiedfor two cases of cavity load form.

A New Type of Turnstile Antenna

The paper presents a new type of turnstile antenna realized with two crossed dipoles parallelly connected. Feeding of the dipoles in phase quadrature to obtain omnidirectional radiation pattern is achieved by optimizing their impedances to be complex-conjugated. Dipoles are realized with aluminum strips. Balun - transition from the antenna to coaxial cable - is accomplished with concentrated parameters - two capacitors and two inductors. Presented concept of the turnstile antenna can be used in VHF and UHF frequency ranges. Measured results show very good agreement with those obtained by simulation. The antenna is also characterized by simple manufacturing and low cost.

Mode Tuning of Microwave Reasonator Loaded with Lossy Multilayer Dilectric

Multi-layer filled rectangular cavity resonances are investigated in this paper. A general computer program for the calculation of resonant frequencies has been developed. Characteristic equations are obtained on the basics of the transverse resonance method with impedance and admittance resonance conditions. The two resonance conditions give different results for lossy dielectric, which seems not to have been noticed before. Experimental results in the frequency range 0.5 to 1 GHz compares well with combined results for the two resonance conditions.

Modelling of the cylindrical metallic cavity loaded by lossy dielectric slab using neural networks

In this paper, the loaded cylindrical metallic cavity loaded by lossy dielectric slab is modelled using multilayer perceptron networks. A proper neural model is defined, which includes the dielectric slab losses and is used for modelling of the cavity with circular cross-section when lossy dielectric slab is placed at the bottom of the cavity. An original approach for significantly decreasing neural network training set is suggested. The results obtained show that the suggested approach provides microwave cavity modelling with high accuracy.

Efficient DOA estimation of impinging stochastic EM signal using neural networks

In this paper a method for the accurate and fast determination of direction of arrival (DOA) of impinging electromagnetic signal radiated from stochastic sources in the far-field is proposed. The method is based on neural models using MLP (Multi-Layer Perceptron) artificial neural network. To illustrate the applicability of the proposed method, two MLP models for one-dimensional (1D) DOA estimation (in azimuth plane) are presented: MLP model for the estimation of angle position of one stochastic source and MLP model for the estimation of two stochastic sources position at fixed angle distance. Presented models perform very fast 1D DOA estimation and therefore they are very suitable for the real time applications. The architecture of developed models, their training results and simulation results are described. in details.

Modeling of patch antennas using neural networks

In this paper patch antennas are modeled using neural model based on multi-layer perceptrons (MLP) network. Neural model is trained by data, which are obtained by electromagnetic simulation of antennas using HFSS 9.0 software. This model has four input parameters: patch antenna length L, patch antenna width W, deep of patch antenna slot l and width of patch antenna slot s and it enables quick and correct calculation of resonant frequency f/sub r/ and minimum value of S/sub 11/ parameter (S/sub 11min/).

The hybrid-neural empirical model for the electromagnetic field level prediction in urban environments

The application of multilayer perceptron networks to calculating the electromagnetic wave path loss in an urban environment for propagation through an area with low or high buildings is presented. A hybrid neural-empirical model, created in two phases, is proposed. The first phase implies the realization of an approximate (coarse) propagation model based on measured values. This model determines the propagation loss from the beginning of the area, based on the distance from the area beginning, the average building density, the partial loss of a single building, the distance from the transmitter and the exponential loss index of the area. In the second phase, a neural network and the approximate model are integrated in the hybrid (fine) model of the propagation area. The input parameters for the neural network are the distance from the area beginning and the average height of buildings in that area, while the output parameter is the partial loss of a single building. This value is used in the approximate model, in order to obtain the propagation area model with higher accuracy.

Implementation of temperature dependence in small-signal models of microwave transistors including noise

In this paper, the artificial neural network approach is proposed for prediction purposes of temperature behavior of microwave transistors. Neural networks are used for modeling of temperature dependencies of elements of transistor small-signal models including noise. These dependencies are extracted from transistor signal and noise data referred to a set of temperatures, The developed models are valid in the whole operational range of temperatures.