Rasul Enayatifar

New method for edge detection and de noising via fuzzy cellular automata

In this article, a new method is presented for eliminating noise and for detecting image edges through the use of fuzzy cellular automata. The algorithm based on the proposed method is used for edge detection in Gray level images and for noise elimination in images containing salt and pepper noise. In this method, eight specific contiguity states are considered for each pixel and sixteen numbers are derived from these states. These numbers are used as input for the fuzzy member ship function. The fuzzy rule base is constructed in such a way as to correctly recognize the state of each pixel. The ability to detect edges in different directions and to determine suitable edges in noisy images are among the advantages of the proposed method. In comparison to common methods of edge detection, like the Sobel and the Robert methods of edge detection and the mean-filter and the median-filter methods of noise elimination, our proposed method shows a higher efficiency. n n xa0 n n Key words:xa0Fuzzy cellular automata, image processing, noise elimination, edge detecting.

A hybrid model based on differential fuzzy logic relationships and imperialist competitive algorithm for stock market forecasting

Graphical abstractDisplay Omitted HighlightsA differential fuzzy time series model is defined for forecast inside trend data.The differential fuzzy logical relationships and groups are established.The actual value at former state is added to the average of defuzzified values.The proposed method is combined with the ICA to enhance forecast accuracy.Frothy case studies are employed for validating the proposed method. In this study, a new kind of fuzzy set in fuzzy time series field is introduced. It works as a trend estimator to be appropriate for fuzzy time series forecasting by reconnoitering trend of data appropriately. First, the historical data are fuzzified into differential fuzzy sets, and then differential fuzzy relationships are calculated. Second, differential fuzzy logic groups are established by grouping differential fuzzy relationships. Finally, in the defuzzification step, the forecasts are calculated. However, for increasing the accuracy of the models, an evolutionary algorithm, namely imperialist competitive algorithm is injected, to train the model. A massive stock data from four main stock databases have been selected for model validation. The final project, has shown that outperformed its counterparts in term of accuracy.

Using the Chaotic Map in Image Steganography

Recent years have seen more interest from researchers in image encryption using chaotic signals. A new method was proposed for image Steganography in this article in which two chaotic signals for specifying the location of the different parts of the message in the picture. An 80-bit key was used to reach the preliminary measures of the two chaotic signals.One of the advantages of this method is its security which is caused by the chaotic signals and the higher range of the PSNR(42.06)

Farsi digit recognition via features extraction

In this paper, a new method is proposed to extract the features of a one-numberxa0Persian image in which for the final verification of the extracted features, a three-layer neural network (mesh) of Perceptron has been utilized. The method is capable of extracting some ideal features from a one-number image that are stable against rotation, movement, size change and noise. The method is examined on a database of 60000 discredited numbers, from which 40000 numbers were used in the training stage and 20000 ones were used for the experiment. The recognition percentage of 92.7% shows the great efficiency of the proposed method. n n xa0 n n Key words:xa0Features extraction, recognition of Persian numbers, perceptron neural network, standard deviance, average angle.

Energy-Efficient Routing Security and Control in Wireless Sensor Networks

Although several energy-conserving security architectures have been proposed in wireless sensor networks so far, these architectures still need improvements to resist against routing attacks. In this paper, we propose a key management scheme and a routing algorithm called Ripple-Beaconing, with no redundancy between them. The two improve routing security and control and help in node localization and malicious node detection with no considerable increase in energy consumption considering the existing schemes.

The Recognition of Handwritten Numbers by Extracting New Features Using Water Filling Method

In this paper, a new method is proposed to extract the features of a one-number Persian image in which for the final verification of the extracted features, a three-layer neural network (mesh) of Perceptron has been utilized. The method, which is called Water Filling method, is capable of extracting some ideal features from a one-number image that are stable against rotation, movement, size change and noise. The method is examined on a database of 60000 discretized numbers, from which 40000 numbers were used in the training stage and 20000 ones were used for the experiment. The recognition percentage of 92.7% shows the great efficiency of the proposed method.

Image Encryption Using Chaotic Signal and Max-Heap Tree

In this paper, a new method is proposed for image encryption using chaotic signals and Max-Heap tree. In this method, Max-Heap tree is utilized for further complexity of the encryption algorithm, higher security and changing the amount of gray scale of each pixel of the original image. Studying the obtained results of the performed experiments, high resistance of the proposed method against brute-force and statistical invasions is obviously illustrated. Also, the obtained entropy of the method which is about 7.9931 is very close to the ideal amount of 8.