Abstract: This work addresses the problem of designing an
algorithm capable of generating chaotic trajectories for mobile robots.
Particularly, the chaotic behavior is induced in the linear and angular
velocities of a Khepera III differential mobile robot by infusing them
with the states of the H´enon chaotic map. A possible application,
using the properties of chaotic systems, is patrolling a work area.
In this work, numerical and experimental results are reported and
analyzed. In addition, two quantitative numerical tests are applied in
order to measure how chaotic the generated trajectories really are.
Abstract: Fingerprints are suitable as long-term markers of human identity since they provide detailed and unique individual features which are difficult to alter and durable over life time. In this paper, we propose an algorithm to encrypt and decrypt fingerprint images by using a specially designed Elliptic Curve Cryptography (ECC) procedure based on block ciphers. In addition, to increase the confusing effect of fingerprint encryption, we also utilize a chaotic-behaved method called Arnold Cat Map (ACM) for a 2D scrambling of pixel locations in our method. Experimental results are carried out with various types of efficiency and security analyses. As a result, we demonstrate that the proposed fingerprint encryption/decryption algorithm is advantageous in several different aspects including efficiency, security and flexibility. In particular, using this algorithm, we achieve a margin of about 0.1% in the test of Number of Pixel Changing Rate (NPCR) values comparing to the-state-of-the-art performances.
Abstract: Generating random numbers are mainly used to create
secret keys or random sequences. It can be carried out by various
techniques. In this paper we present a very simple and efficient
pseudo random number generator (PRNG) based on chaotic maps
and S-Box tables. This technique adopted two main operations one to
generate chaotic values using two logistic maps and the second to
transform them into binary words using random S-Box tables.
The simulation analysis indicates that our PRNG possessing
excellent statistical and cryptographic properties.
Abstract: In this paper a new robust digital image watermarking
algorithm based on the Complex Wavelet Transform is proposed. This
technique embeds different parts of a watermark into different blocks
of an image under the complex wavelet domain. To increase security
of the method, two chaotic maps are employed, one map is used to
determine the blocks of the host image for watermark embedding,
and another map is used to encrypt the watermark image. Simulation
results are presented to demonstrate the effectiveness of the proposed
algorithm.
Abstract: Polynomial maps offer analytical properties used to obtain better performances in the scope of chaos synchronization under noisy channels. This paper presents a new method to simplify equations of the Exact Polynomial Kalman Filter (ExPKF) given in [1]. This faster algorithm is compared to other estimators showing that performances of all considered observers vanish rapidly with the channel noise making application of chaos synchronization intractable. Simulation of ExPKF shows that saturation drawn on the emitter to keep it stable impacts badly performances for low channel noise. Then we propose a particle filter that outperforms all other Kalman structured observers in the case of noisy channels.
Abstract: Symbolic dynamics studies dynamical systems on the basis of the symbol sequences obtained for a suitable partition of the state space. This approach exploits the property that system dynamics reduce to a shift operation in symbol space. This shift operator is a chaotic mapping. In this article we show that in the symbol space exist other chaotic mappings.
Abstract: Many systems in the natural world exhibit chaos or non-linear behavior, the complexity of which is so great that they appear to be random. Identification of chaos in experimental data is essential for characterizing the system and for analyzing the predictability of the data under analysis. The Lyapunov exponents provide a quantitative measure of the sensitivity to initial conditions and are the most useful dynamical diagnostic for chaotic systems. However, it is difficult to accurately estimate the Lyapunov exponents of chaotic signals which are corrupted by a random noise. In this work, a method for estimation of Lyapunov exponents from noisy time series using unscented transformation is proposed. The proposed methodology was validated using time series obtained from known chaotic maps. In this paper, the objective of the work, the proposed methodology and validation results are discussed in detail.