Journal:Informatica
Volume 26, Issue 1 (2015), pp. 51–65
Abstract
Abstract
A nonlinear substitution operation of bytes is the main strength factor of the Advanced Encryption Standard (AES) and other modern cipher systems. In this paper we have presented a new simple algorithm to generate key-dependent S-boxes and inverse S-boxes for block cipher systems. The quality of this algorithm was tested by using NIST tests, and changing only one bit of the secret key to generate new key-dependent S-boxes. The fact that the S-boxes are key-dependent and unknown is the main strength of the algorithm, since the linear and differential cryptanalysis require known S-boxes. In the second section of the paper, we analyze S-boxes. In the third section we describe the key-dependent S-boxes and inverse S-boxes generation algorithm. Afterwards, we experimentally investigate the quality of the generated key-dependent S-boxes. Comparison results suggest that the key-dependent S-boxes have good performance and can be applied to AES.
Journal:Informatica
Volume 25, Issue 2 (2014), pp. 209–220
Abstract
The paper presents a novel algorithm for restoration of the missing samples in additive Gaussian noise based on the forward–backward autoregressive (AR) parameter estimation approach and the extrapolation technique. The proposed algorithm is implemented in two consecutive steps. In the first step, the forward–backward approach is used to estimate the parameters of the given neighbouring segments, while in the second step the extrapolation technique for the segments is applied to restore the samples of the missing segment. The experimental results demonstrate that the restoration error of the samples of the missing segment using the proposed algorithm is reduced as compared with the Burg algorithm.
Journal:Informatica
Volume 24, Issue 1 (2013), pp. 35–58
Abstract
The aim of the given paper is development of an approach based on reordering of observations to be processed for the extraction of an unmeasurable internal intermediate signal, that acts between linear dynamical and static nonlinear blocks of the Wiener system with hard-nonlinearity of the known structure. The technique based on the ordinary least squares (LS) and on data partition is used for the internal signal extraction. The results of numerical simulation and identification of a discrete-time Wiener system with five types of hard-nonlinearities, such as saturation, dead-zone, preload, backlash, and, discontinuous nonlinearity are given by computer.
Journal:Informatica
Volume 23, Issue 1 (2012), pp. 65–76
Abstract
In this paper a forward–backward basis function approach for instantaneous frequency estimation of the frequency-modulated signal in noisy environment is presented. At first, a forward–backward prediction approach is applied for least squares estimation of time-varying autoregressive parameters. A time-varying parameters are expressed as a summation of constants multiplied by basis functions. Then, the time-varying frequencies are extracted from the time-varying autoregressive parameters by calculating the angles of the estimation error filter polynomial roots. The experimental results are presented, which shows the superiority of the proposed method against the covariance (forward prediction) approach.
Journal:Informatica
Volume 22, Issue 2 (2011), pp. 177–188
Abstract
The paper presents a novel method for improving the estimates of closely-spaced frequencies of a short length signal in additive Gaussian noise based on the Burg algorithm with extrapolation. The proposed method is implemented in two consecutive steps. In the first step, the Burg algorithm is used to estimate the parameters of the predictive filter, while in the second step the extrapolation technique of the signal is used to improve the frequency estimates. The experimental results demonstrate that the frequency estimates of the short length signal, using the Burg algorithm with extrapolation, are more accurate than the frequency estimates using the Burg algorithm without extrapolation.
Journal:Informatica
Volume 20, Issue 1 (2009), pp. 23–34
Abstract
Advanced Encryption Standard (AES) block cipher system is widely used in cryptographic applications. A nonlinear substitution operation is the main factor of the AES cipher system strength. The purpose of the proposed approach is to generate the random S-boxes changing for every change of the secret key. The fact that the S-boxes are randomly key-dependent and unknown is the main strength of the new approach, since both linear and differential cryptanalysis require known S-boxes. In the paper, we briefly analyze the AES algorithm, substitution S-boxes, linear and differential cryptanalysis, and describe a randomly key-dependent S-box and inverse S-box generation algorithm. After that, we introduce the independency measure of the S-box elements, and experimentally investigate the quality of the generated S-boxes.
Journal:Informatica
Volume 17, Issue 2 (2006), pp. 199–206
Abstract
This paper presents an iterative autoregressive system parameter estimation algorithm in the presence of white observation noise. The algorithm is based on the parameter estimation bias correction approach. We use high order Yule–Walker equations, sequentially estimate the noise variance, and exploit these estimated variances for the bias correction. The improved performance of the proposed algorithm in the presence of white noise is demonstrated via Monte Carlo experiments.
Journal:Informatica
Volume 14, Issue 2 (2003), pp. 213–222
Abstract
This paper discusses the linear periodically time‐varying (LPTV) system parameter estimation using a block approach. An block algorithm is proposed for optimal estimation of the parameters of LPTV system from the input sequence and the output sequence corrupted by additive Gaussianly distributed noise. In the proposed method, the least squares error criterion has been used.The algorithm provides a useful computational tool based on an appropriate theoretical foundation for parameter estimation of linear time‐invariant (LTI) systems from input and output data. Simulation results are presented that demonstrate the performance of the approach.
Journal:Informatica
Volume 13, Issue 1 (2002), pp. 23–36
Abstract
It is shown that nonlinear Volterra, polynomial autoregressive, and bilinear filters have the same layered implementation procedure. Using the layered structure, the order of nonlinearity can be increased by adding more layers to the structure. The structure is modular and consists of the simple moving average (MA) or autoregressive (AR) filters which can be added to the structure to achieve a desired degree of complexity. In addition, the modular layered structures admit very large scale integration (VLSI) implementation of the polynomial nonlinear filters.
Journal:Informatica
Volume 11, Issue 1 (2000), pp. 97–110
Abstract
This paper contains measures to describe the matrix impulse response sensitivity of state space multivariable systems with respect to parameter perturbations. The parameter sensitivity is defined as an integral measure of the matrix impulse response with respect to the coefficients. A state space approach is used to find a realization of impulse response that minimizes a sensitivity measure.