Baran Tander

Amplitude and Frequency Modulation behaviours of Cellular Neural Networks

As is well known, Amplitude Modulation (AM) and Frequency Modulation (FM) are the most popular modulation techniques in communications [1]. Classically, they are designed as individual systems at transmitters; however in this study, it is shown that, both can be realized under a single architecture, specifically on a Cellular Neural Network with an opposite sign template (CNN-OST). First, the CNN-OST is introduced and then, AM and FM behaviours of this structure are explained. Simulations are carried out where satisfactory results are found and finaly, the advantages and drawbacks of the proposed system are discussed.

Channel equalization with cellular neural networks

In this paper, a dynamic neural network structure called Cellular Neural Network (CNN) is employed for the equalization in digital communication. It is shown that, this nonlinear system is capable of suppressing the effect of intersymbol interference (ISI) and the noise at the channel. The architecture is a small-scaled, simple CNN containing 9 neurons, thus having only 19 weight coefficients. Proposed system is compared with linear transversal filters as well as with a Multilayer Perceptron (MLP) based equalizer.

Amplitude and Frequency Modulations with Cellular Neural Networks

Amplitude and frequency modulations are still the most popular modulation techniques in data transmission at telecommunication systems such as radio and television broadcasting, gsm etc. However, the architectures of these individual systems are totally different. In this paper, it is shown that a cellular neural network with an opposite—sign template, can behave either as an amplitude or a frequency modulator. Firstly, a brief information about these networks is given and then, the amplitude and frequency surfaces of the generated quasi-sine oscillations are sketched with respect to various values of their cloning templates. Secondly it is proved that any of these types of modulations can be performed by only varying the template components without ever changing their structure. Finally a circuit is designed, simulations are presented and performance of the proposed system is evaluated. The main contribution of this work is to show that both amplitude and frequency modulations can be realized under the same architecture with a simple technique, specifically by treating the input signals as template components.

DETECTION AND CLASSIFICATION OF VIEWER AGE RANGE SMART SIGNS AT TV BROADCAST

In this paper, the identification and classification of “Viewer Age Range Smart Signs”, designed by the Radio and Television Supreme Council of Turkey, to give age range information for the TV viewers, are realized. Therefore, the automatic detection at the broadcast will be possible, enabling the manufacturing of TV receivers which are sensible to these signs. The most important step at this process is the pattern recognition. Since the symbols that must be identified are circular, various circle detection techniques can be employed. In our study, first,two different circle segmentation methods for still images are analyzed, their advantages and drawbacks are discussed. A popular neural network structure called Multilayer Perceptron is employed for the classification. Afterwards, the same procedures are carried out for streaming video.All of the steps depicted above are realized on a standard PC.

Real time detection and classification of age range smart signs at TV broadcast

In this paper, real time detection and classification of the “Smart Signs”, which appear at the beginning and after the commercial breaks of the programs at national TV channels, that indicates the appropriate viewer ages for the broadcast, is realized by using a fast image processing technique. For this purpose, circular Hough Transform (CHT) and a circle detection algorithm depending on the determination of center of gravity of closed objects is carried out for the segmentation of these signs and afterwards a Multilayer Perceptron (MLP) is utilized for the classification process at the video signals.