مجله علوم و فناوری های پدافند نوین
پیاپی 2 (زمستان 1389)

Secret sharing schemes are useful in the management of cryptographic keys and in multiparty secure protocols. A secret sharing scheme permits a secret to be shared among participants in such a way that only authorized subsets of participants can recover the secret, but any unauthorized has absolutely no information on the secret. The set of all authorized subset defines the access structure to the secret. There are several approaches to the construction of secret sharing schemes. One of them is based on coding theory. It is to note that every linear code can be used to construct secret sharing schemes. On of the important problems of the secret sharing schemes is detection and correction of cheat. In this paper we present two hypotheses for detection and correction of cheat.

In this paper, neuron cell's action potential and its effective parameters over beating are analyzed. Cell's action potential mechanism can be simulated by fabrication of an equivalent circuit for directed ionic currents using a neuron modeling oscillator. Equivalent circuit consists of capacitor, resistor and transistor. Changing membrane properties such as capacitance and sodium-potassium leakage resistance causes a significant change in action potential. Then, the neuron cell moduling circuit is implemented in microcontroller. The equations should be digitized in order to be sent to microcontroller. Therefore, a discrete algorithm is presented to solve the equations and FHN equations are solved and simulated in discrete form. Then, microcontroller is programmed by the generated hex file of solved equations. In fact, each parameter of the model can be controlled in the fabricated circuit so that it becomes a proper simulator for cellular behavior such as disease and cells malfunction.

Modular addition modulo 2t is one of the most applicable operators in symmetric cryptography. Therefore, investigating the properties of this operator has a significant role in design and analysis of symmetric ciphers. Algebraic properties of this operator have been studied for two operands in [1]. In this contribution, to obtain more accurate results in this area, we generalize some of the algebraic properties of this operator for operands. More precisely, we consider the algebraic degree of the component Boolean functions of modular addition as a vectorial Boolean function and determine the number of terms and variables in these Boolean functions. After some theoretical analysis in special cases, we propose an efficient algorithm for finding the degree of these Boolean functions general case. Using this algorithm, the algebraic degree of the component Boolean functions for modular addition modulo 232, with three up to eight operands is calculated.

In this paper, a new method to improve sites access filtering is presented. This method is based on web visitor behavior analysis. In order to analyze the behavior of users, A procedure is proposed following which, based on the information in the log files of proxy servers, user’s behavior history was extracted and stocked in a data bank by a data-mining engine. Authorized or unauthorized access to the desired page, when a user requests a new page, is reported after investigation of the user’s behavior history. The above architecture was tested, using data from one of the globally credible sites, and the results were analyzed.

Due to the widespread use of dc motors, their position or speed control becomes very important. The aim in this paper is providing a new method for intelligent position control of separately excited dc motor using neural networks. For this purpose the NARMA-L2 neural network controller is used. In the position control using neural network, based position for neural network intended as input position (ideal), and after adapting of output position of motor to base position, Armature voltage become zero and motor stops working. In this way, neural network learns the used model of dc motors to control the position, then the input optimized for matching of model output position to ideal position. Advantage of the proposed neural network method is, correction coefficients during the working engine and Replies robustness to changes inertia moment (J) and friction (B). Unlike the PID controllers that will lose their optimization Performance with changing one of the model parameters, such as J & B, and need to reset the parameters of the controller, the proposed method needs not to redesign, and these parameters repair automatically. Proposed method is simulated using information from a separately excited dc motor in SIMULINK environment of MATLAB software. Results show that by using of proposed method, fluctuations in response and need times to reach the ideal situation decreases.

Usage of spatial information in the DOA estimation of signals, overcome some weaknesses of time-processing techniques. In this paper, following the Direct Data Domain Least Square (D3LS) technique, amplitude information of signals arrived at each element in an antenna array are arranged in a square matrix in which, except for the first row, other entries are combinations of reflective signals, noise and interference. By forming an equation including this matrix, weighting factors of antenna array, and antenna gain, coefficients are calculated in such a way that all signals which their DOA is not important, i.e. reflected signals, noise and etc., reach their least quantity. Then, by making a symmetric square matrix and modification of the main equation according to Linear Conjugate Gradient procedure, weighting factors of antenna are obtained. Finally, it is shown by simulation that this procedure will accelerate the convergence process remarkably compared with D3LS technique.

The recent advances in hyperspectral remote sensing technology have provided a very good source of information for reconnaissance purposes. This is because of high spatial and spectral resolutions of airborne hyperspectral imaging sensors, and also the use of spectral signature of phenomena and objects for identification of military targets. In this paper, the ability of hyperspectral imagery for detection of targets is investigated by providing two important analyses including anomaly detection and target recognition. Besides, in a case study, this ability is evaluated practically by performing the algorithms on a real hyperspectral image from the HyMap sensor. Afterwards, two criteria are presented for evaluation and control of hyperspectral camouflage and deception techniques. Finally, assuming consideration of the indirect measures of passive defence, the essential considerations of CC&D (Camouflage, Concealment, and Deception) measures are presented against the threats of hyperspectral imagery.

In this paper, exploitation of parallel matched filters bank based on binary phase code is proposed, to overcome shift Doppler in ground based radars which use pulse compression technique. The mismatch loss due to target Doppler shift in radar receiver reduced by odd or even number of these parallel filters. By increasing the number of filters over target velocity axis the central Doppler frequencies of the neighbor filters approach together and consequently the overlap increases and results in the straddling loss decrease in output. Using MATLAB software, the attenuation in the output of the receiver matched filter due to airborne target velocity was analyzed and a solution to calculate minimum number of filters is proposed for minimum loss of SNR. Finally, two methods for implementation of parallel matched filters on FPGA in the airborne targets radar receiver are presented and compared

In this paper an intelligent algorithm for clustering, between pulse modulation detection and interleaved pulses train identification is offered. In the proposed algorithm, for interleave pulses received clustering from SOFM neural network (due to its high accuracy compared with other neural networks such CLNN neural network and Fuzzy ART neural network) and for between pulse modulation detection from matrix multiplication method and for radar type identification from RBF neural network is used. The results of the proposed algorithm simulation shows with the presence of 5% noise and 5% missing pulses, clustering section accuracy is equal 91.8%, between pulse modulation detection section accuracy is equal 98% and radar type identification section accuracy is equal 99.2%. thus the resultant accuracy of the proposed algorithm is 89.244%.