Computational Intelligence and Complexity Measures for Chaotic Information Processing

This dissertation investigates the application of computational intelligence methods in the analysis of nonlinear chaotic systems in the framework of many known and newly designed complex systems. Parallel comparisons are made between these methods. This provides insight into the difficult challenges facing nonlinear systems characterization and aids in developing a generalized algorithm in computing algorithmic complexity measures, Lyapunov exponents, information dimension and topological entropy. These metrics are implemented to characterize the dynamic patterns of discrete and continuous systems. These metrics make it possible to distinguish order from disorder in these systems. Steps required for computing Lyapunov exponents with a reorthonormalization method and a group theory approach are formalized. Procedures for implementing computational algorithms are designed and numerical results for each system are presented. The advance-time sampling technique is designed to overcome the scarcity of phase space samples and the buffer overflow problem in algorithmic complexity measure estimation in slow dynamics feedback-controlled systems. It is proved analytically and tested numerically that for a quasiperiodic system like a Fibonacci map, complexity grows logarithmically with the evolutionary length of the data block. It is concluded that a normalized algorithmic complexity measure can be used as a system classifier. This quantity turns out to be one for random sequences and a non-zero value less than one for chaotic sequences. For periodic and quasi-periodic responses, as data strings grow their normalized complexity approaches zero, while a faster deceasing rate is observed for periodic responses. Algorithmic complexity analysis is performed on a class of certain rate convolutional encoders. The degree of diffusion in random-like patterns is measured. Simulation evidence indicates that algorithmic complexity associated with a particular class of 1/n-rate code increases with the increase of the encoder constraint length. This occurs in parallel with the increase of error correcting capacity of the decoder. Comparing groups of rate-1/n convolutional encoders, it is observed that as the encoder rate decreases from 1/2 to 1/7, the encoded data sequence manifests smaller algorithmic complexity with a larger free distance value

Signal processing techniques for mobile multimedia systems

Recent trends in wireless communication systems show a significant demand for the delivery of multimedia services and applications over mobile networks - mobile multimedia - like video telephony, multimedia messaging, mobile gaming, interactive and streaming video, etc. However, despite the ongoing development of key communication technologies that support these applications, the communication resources and bandwidth available to wireless/mobile radio systems are often severely limited. It is well known, that these bottlenecks are inherently due to the processing capabilities of mobile transmission systems, and the time-varying nature of wireless channel conditions and propagation environments. Therefore, new ways of processing and transmitting multimedia data over mobile radio channels have become essential which is the principal focus of this thesis. In this work, the performance and suitability of various signal processing techniques and transmission strategies in the application of multimedia data over wireless/mobile radio links are investigated. The proposed transmission systems for multimedia communication employ different data encoding schemes which include source coding in the wavelet domain, transmit diversity coding (space-time coding), and adaptive antenna beamforming (eigenbeamforming). By integrating these techniques into a robust communication system, the quality (SNR, etc) of multimedia signals received on mobile devices is maximised while mitigating the fast fading and multi-path effects of mobile channels. To support the transmission of high data-rate multimedia applications, a well known multi-carrier transmission technology known as Orthogonal Frequency Division Multiplexing (OFDM) has been implemented. As shown in this study, this results in significant performance gains when combined with other signal-processing techniques such as spa ce-time block coding (STBC). To optimise signal transmission, a novel unequal adaptive modulation scheme for the communication of multimedia data over MIMO-OFDM systems has been proposed. In this system, discrete wavelet transform/subband coding is used to compress data into their respective low-frequency and high-frequency components. Unlike traditional methods, however, data representing the low-frequency data are processed and modulated separately as they are more sensitive to the distortion effects of mobile radio channels. To make use of a desirable subchannel state, such that the quality (SNR) of the multimedia data recovered at the receiver is optimized, we employ a lookup matrix-adaptive bit and power allocation (LM-ABPA) algorithm. Apart from improving the spectral efficiency of OFDM, the modified LM-ABPA scheme, sorts and allocates subcarriers with the highest SNR to low-frequency data and the remaining to the least important data. To maintain a target system SNR, the LM-ABPA loading scheme assigns appropriate signal constella tion sizes and transmit power levels (modulation type) across all subcarriers and is adapted to the varying channel conditions such that the average system error-rate (SER/BER) is minimised. When configured for a constant data-rate load, simulation results show significant performance gains over non-adaptive systems. In addition to the above studies, the simulation framework developed in this work is applied to investigate the performance of other signal processing techniques for multimedia communication such as blind channel equalization, and to examine the effectiveness of a secure communication system based on a logistic chaotic generator (LCG) for chaos shift-keying (CSK)

QAM-DWT-SVD Based Watermarking Scheme for Medical Images

This paper presents a new semi-blind image watermarking system for medical applications. The new scheme utilizes Singular Value Decomposition (SVD) and Discrete Wavelet Transform (DWT) to embed a textual data into original medical images. In particular, text characters are encoded by a Quadrature Amplitude Modulation (QAM-16). In order to increase the security of the system and protect then the watermark from several attacks, the embedded data is submitted to Arnold Transform before inserting it into the host medical image. To evaluate the performances of the scheme, several medical images have been used in the experiments. Simulation results show that the proposed watermarking system ensures good imperceptibility and high robustness against several attacks

Combined Industry, Space and Earth Science Data Compression Workshop

The sixth annual Space and Earth Science Data Compression Workshop and the third annual Data Compression Industry Workshop were held as a single combined workshop. The workshop was held April 4, 1996 in Snowbird, Utah in conjunction with the 1996 IEEE Data Compression Conference, which was held at the same location March 31 - April 3, 1996. The Space and Earth Science Data Compression sessions seek to explore opportunities for data compression to enhance the collection, analysis, and retrieval of space and earth science data. Of particular interest is data compression research that is integrated into, or has the potential to be integrated into, a particular space or earth science data information system. Preference is given to data compression research that takes into account the scien- tist's data requirements, and the constraints imposed by the data collection, transmission, distribution and archival systems

ON TURBO CODES AND OTHER CONCATENATED SCHEMES IN COMMUNICATION SYSTEMS

The advent of turbo codes in 1993 represented a significant step towards realising the ultimate capacity limit of a communication channel, breaking the link that was binding very good performance with exponential decoder complexity. Turbo codes are parallel concatenated convolutional codes, decoded with a suboptimal iterative algorithm. The complexity of the iterative algorithm increases only linearly with block length, bringing previously unprecedented performance within practical limits.. This work is a further investigation of turbo codes and other concatenated schemes such as the multiple parallel concatenation and the serial concatenation. The analysis of these schemes has two important aspects, their performance under optimal decoding and the convergence of their iterative, suboptimal decoding algorithm. The connection between iterative decoding performance and the optimal decoding performance is analysed with the help of computer simulation by studying the iterative decoding error events. Methods for good performance interleaver design and code design are presented and analysed in the same way. The optimal decoding performance is further investigated by using a novel method to determine the weight spectra of turbo codes by using the turbo code tree representation, and the results are compared with the results of the iterative decoder. The method can also be used for the analysis of multiple parallel concatenated codes, but is impractical for the serial concatenated codes. Non-optimal, non-iterative decoding algorithms are presented and compared with the iterative algorithm. The convergence of the iterative algorithm is investigated by using the Cauchy criterion. Some insight into the performance of the concatenated schemes under iterative decoding is found by separating error events into convergent and non-convergent components. The sensitivity of convergence to the Eb/Ng operating point has been explored.SateUite Research Centre Department of Communication and Electronic Engineerin

Investigation of MIMO Communications

This project focuses on the background of multiple input multiple output (MIMO) communication and its advantages over the other possible implementations. A background of wireless communication in regards to modulation types, information theory, antenna background and a hardware implementation using a VSA and VSG is provided. The theory is compared and verified with the hardware implementation with regards to the effect of the increase in the number of antennas and the parameter vector error magnitude

Journal of Telecommunications and Information Technology, 2006, nr 1

kwartalni

Optical Communication

Optical communication is very much useful in telecommunication systems, data processing and networking. It consists of a transmitter that encodes a message into an optical signal, a channel that carries the signal to its desired destination, and a receiver that reproduces the message from the received optical signal. It presents up to date results on communication systems, along with the explanations of their relevance, from leading researchers in this field. The chapters cover general concepts of optical communication, components, systems, networks, signal processing and MIMO systems. In recent years, optical components and other enhanced signal processing functions are also considered in depth for optical communications systems. The researcher has also concentrated on optical devices, networking, signal processing, and MIMO systems and other enhanced functions for optical communication. This book is targeted at research, development and design engineers from the teams in manufacturing industry, academia and telecommunication industries