Applications of nonlinear dynamics to information processing

The reported results are direct applications of nonlinear dynamics to information processing or are relevant for the applications. In the second chapter we describe a simple method for estimating the embedding dimension that can be used as a first step in constructing nonlinear models. The method for the reduction of measurement noise in chaotic systems that is presented in the third chapter is attractive in the cases where high accuracy is necessary. Next we propose how to overcome some problems encountered in constructing models of complex nonlinear systems. Finally, the behaviour of one-dimensional cellular automata useful for the detection of velocities of patterns is shown and explained in the last chapter. The method of estimating the embedding dimension is based on the idea that when the observed dynamical system is deterministic and smooth and the embedding dimension is correctly chosen, the relationship between the successive reconstructed state vectors should be described as a continuous mapping. To check if the given embedding dimension is a good one we search for pairs of state vectors whose distance is smaller than some number. For each pair we compute the distance between the successors of the elements of pairs and represent this distance graphically. When the embedding dimension is equal or larger than the minimum correct dimension, all distances are small in comparison to distances for incorrect dimensions. The method for noise reduction is developed assuming that the map of the system is known and the noise is bounded. The closer the initial condition is to the true state of the system, the longer the computed trajectory follows the observed trajectory. To reduce the uncertainty in knowing the given state we recursively search for the state for which the computed trajectory follows the observed trajectory as long as possible. The method is demonstrated on several twodimensional invertible and noninvertible chaotic maps. When the map is known exactly an arbitrary level of noise reduction can be achieved. With the increase of the complexity of a nonlinear system it is harder to construct its model. We propose to discover first how to construct a model of a similar but simple system. Discovered heuristics can be useful in modeling more complex systems. We demonstrate the approach by constructing a deterministic feed-forward neural network that can extract velocities of onedimensional patterns. Analysing simpler models we discovered how to estimate the necessary numbers of neurons; what are the useful ranges of the parameters of the network and what are the potential functional dependencies between the parameters. The class of one-dimensional cellular automata whose state is a function of both the previous state and a time-dependant input is described. As inputs we considered the sequences of binary strings that represent black-and-white objects moving in front of a white background. As outputs we considered the trajectory of the automaton. For some rules the automaton will evolve to the zero state for all velocities of the object except for the velocities in specific narrow range. The phenomenon is persistent even when a strong noise is present in input patterns but unreliable units of the automaton or having a more complex input break it down

Design and Implementation of Secure Chaotic Communication Systems

Chaotic systems have properties such as ergodicity, sensitivity to initial conditions/parameter mismatches, mixing property, deterministic dynamics, structure complexity, to mention a few, that map nicely with cryptographic requirements such as confusion, diffusion, deterministic pseudorandomness, algorithm complexity. Furthermore, the possibility of chaotic synchronization, where the master system (transmitter) is driving the slave system (receiver) by its output signal, made it probable for the possible utilization of chaotic systems to implement security in the communication systems. Many methods like chaotic masking, chaotic modulation, inclusion, chaotic shift keying (CSK) had been proposed however, many attack methods later showed them to be insecure. Different modifications of these methods also exist in the literature to improve the security, but almost all suffer from the same drawback. Therefore, the implementation of chaotic systems in security still remains a challenge. In this work, different possibilities on how it might be possible to improve the security of the existing methods are explored. The main problem with the existing methods is that the message imprint could be found in the dynamics of the transmitted signal, therefore by some signal processing or pattern classification techniques, etc, allow the exposition of the hidden message. Therefore, the challenge is to remove any pattern or change in dynamics that the message might bring in the transmitted signal

The Translocal Event and the Polyrhythmic Diagram

This thesis identifies and analyses the key creative protocols in translocal performance practice, and ends with suggestions for new forms of transversal live and mediated performance practice, informed by theory. It argues that ontologies of emergence in dynamic systems nourish contemporary practice in the digital arts. Feedback in self-organised, recursive systems and organisms elicit change, and change transforms. The arguments trace concepts from chaos and complexity theory to virtual multiplicity, relationality, intuition and individuation (in the work of Bergson, Deleuze, Guattari, Simondon, Massumi, and other process theorists). It then examines the intersection of methodologies in philosophy, science and art and the radical contingencies implicit in the technicity of real-time, collaborative composition. Simultaneous forces or tendencies such as perception/memory, content/ expression and instinct/intellect produce composites (experience, meaning, and intuition- respectively) that affect the sensation of interplay. The translocal event is itself a diagram - an interstice between the forces of the local and the global, between the tendencies of the individual and the collective. The translocal is a point of reference for exploring the distribution of affect, parameters of control and emergent aesthetics. Translocal interplay, enabled by digital technologies and network protocols, is ontogenetic and autopoietic; diagrammatic and synaesthetic; intuitive and transductive. KeyWorx is a software application developed for realtime, distributed, multimodal media processing. As a technological tool created by artists, KeyWorx supports this intuitive type of creative experience: a real-time, translocal “jamming” that transduces the lived experience of a “biogram,” a synaesthetic hinge-dimension. The emerging aesthetics are processual – intuitive, diagrammatic and transversal

Design and implementation of secure chaotic communication systems

Chaotic systems have properties such as ergodicity, sensitivity to initial conditions/parameter mismatches, mixing property, deterministic dynamics, structure complexity, to mention a few, that map nicely with cryptographic requirements such as confusion, diffusion, deterministic pseudorandomness, algorithm complexity. Furthermore, the possibility of chaotic synchronization, where the master system (transmitter) is driving the slave system (receiver) by its output signal, made it probable for the possible utilization of chaotic systems to implement security in the communication systems. Many methods like chaotic masking, chaotic modulation, inclusion, chaotic shift keying (CSK) had been proposed however, many attack methods later showed them to be insecure. Different modifications of these methods also exist in the literature to improve the security, but almost all suffer from the same drawback. Therefore, the implementation of chaotic systems in security still remains a challenge. In this work, different possibilities on how it might be possible to improve the security of the existing methods are explored. The main problem with the existing methods is that the message imprint could be found in the dynamics of the transmitted signal, therefore by some signal processing or pattern classification techniques, etc, allow the exposition of the hidden message. Therefore, the challenge is to remove any pattern or change in dynamics that the message might bring in the transmitted signal.EThOS - Electronic Theses Online ServiceGBUnited Kingdo

Using MapReduce Streaming for Distributed Life Simulation on the Cloud

Distributed software simulations are indispensable in the study of large-scale life models but often require the use of technically complex lower-level distributed computing frameworks, such as MPI. We propose to overcome the complexity challenge by applying the emerging MapReduce (MR) model to distributed life simulations and by running such simulations on the cloud. Technically, we design optimized MR streaming algorithms for discrete and continuous versions of Conway’s life according to a general MR streaming pattern. We chose life because it is simple enough as a testbed for MR’s applicability to a-life simulations and general enough to make our results applicable to various lattice-based a-life models. We implement and empirically evaluate our algorithms’ performance on Amazon’s Elastic MR cloud. Our experiments demonstrate that a single MR optimization technique called strip partitioning can reduce the execution time of continuous life simulations by 64%. To the best of our knowledge, we are the first to propose and evaluate MR streaming algorithms for lattice-based simulations. Our algorithms can serve as prototypes in the development of novel MR simulation algorithms for large-scale lattice-based a-life models.https://digitalcommons.chapman.edu/scs_books/1014/thumbnail.jp

Algorithmic Compositional Methods and their Role in Genesis: A Multi-Functional Real-Time Computer Music System

Algorithmic procedures have been applied in computer music systems to generate compositional products using conventional musical formalism, extensions of such musical formalism and extra-musical disciplines such as mathematical models. This research investigates the applicability of such algorithmic methodologies for real-time musical composition, culminating in Genesis, a multi-functional real-time computer music system written for Mac OS X in the SuperCollider object-oriented programming language, and contained in the accompanying DVD. Through an extensive graphical user interface, Genesis offers musicians the opportunity to explore the application of the sonic features of real-time sound-objects to designated generative processes via different models of interaction such as unsupervised musical composition by Genesis and networked control of external Genesis instances. As a result of the applied interactive, generative and analytical methods, Genesis forms a unique compositional process, with a compositional product that reflects the character of its interactions between the sonic features of real-time sound-objects and its selected algorithmic procedures. Within this thesis, the technologies involved in algorithmic methodologies used for compositional processes, and the concepts that define their constructs are described, with consequent detailing of their selection and application in Genesis, with audio examples of algorithmic compositional methods demonstrated on the accompanying DVD. To demonstrate the real-time compositional abilities of Genesis, free explorations with instrumentalists, along with studio recordings of the compositional processes available in Genesis are presented in audiovisual examples contained in the accompanying DVD. The evaluation of the Genesis system’s capability to form a real-time compositional process, thereby maintaining real-time interaction between the sonic features of real-time sound objects and its selected algorithmic compositional methods, focuses on existing evaluation techniques founded in HCI and the qualitative issues such evaluation methods present. In terms of the compositional products generated by Genesis, the challenges in quantifying and qualifying its compositional outputs are identified, demonstrating the intricacies of assessing generative methods of compositional processes, and their impact on a resulting compositional product. The thesis concludes by considering further advances and applications of Genesis, and inviting further dissemination of the Genesis system and promotion of research into evaluative methods of generative techniques, with the hope that this may provide additional insight into the relative success of products generated by real-time algorithmic compositional processes

Advanced Knowledge Application in Practice

The integration and interdependency of the world economy leads towards the creation of a global market that offers more opportunities, but is also more complex and competitive than ever before. Therefore widespread research activity is necessary if one is to remain successful on the market. This book is the result of research and development activities from a number of researchers worldwide, covering concrete fields of research

Exploiting multiple levels of parallelism of Convergent Cross Mapping

Identifying causal relationships between variables remains an essential problem across various scientific fields. Such identification is particularly important but challenging in complex systems, such as those involving human behaviour, sociotechnical contexts, and natural ecosystems. By exploiting state space reconstruction via lagged embeddings of time series, convergent cross mapping (CCM) serves as an important method for addressing this problem. While powerful, CCM is computationally costly; moreover, CCM results are highly sensitive to several parameter values. Current best practice involves performing a systematic search on a range of parameters, but results in high computational burden, which mainly raises barriers to practical use. In light of both such challenges and the growing size of commonly encountered datasets from complex systems, inferring the causality with confidence using CCM in a reasonable time becomes a biggest challenge. In this thesis, I investigate the performance associated with a variety of parallel techniques (CUDA, Thrust, OpenMP, MPI and Spark, etc.,) to accelerate convergent cross mapping. The performance of each method was collected and compared across multiple experiments to further evaluate potential bottlenecks. Moreover, the work deployed and tested combinations of these techniques to more thoroughly exploit available computation resources. The results obtained from these experiments indicate that GPUs can only accelerate the CCM algorithm under certain circumstances and requirements. Otherwise, the overhead of data transfer and communication can become the limiting bottleneck. On the other hand, in cluster computing, the MPI/OpenMP framework outperforms the Spark framework by more than one order of magnitude in terms of processing speed and provides more consistent performance for distributed computing. This also reflects the large size of the output from the CCM algorithm. However, Spark shows better cluster infrastructure management, ease of software engineering, and more ready handling of other aspects, such as node failure and data replication. Furthermore, combinations of GPU and cluster frameworks are deployed and compared in GPU/CPU clusters. An apparent speedup can be achieved in the Spark framework, while extra time cost is incurred in the MPI/OpenMP framework. The underlying reason reflects the fact that the code complexity imposed by GPU utilization cannot be readily offset in the MPI/OpenMP framework. Overall, the experimental results on parallelized solutions have demonstrated a capacity for over an order of magnitude performance improvement when compared with the widely used current library rEDM. Such economies in computation time can speed learning and robust identification of causal drivers in complex systems. I conclude that these parallel techniques can achieve significant improvements. However, the performance gain varies among different techniques or frameworks. Although the use of GPUs can accelerate the application, there still exists constraints required to be taken into consideration, especially with regards to the input data scale. Without proper usage, GPUs use can even slow down the whole execution time. Convergent cross mapping can achieve a maximum speedup by adopting the MPI/OpenMP framework, as it is suitable to computation-intensive algorithms. By contrast, the Spark framework with integrated GPU accelerators still offers low execution cost comparing to the pure Spark version, which mainly fits in data-intensive problems

Humanoid Robots

For many years, the human being has been trying, in all ways, to recreate the complex mechanisms that form the human body. Such task is extremely complicated and the results are not totally satisfactory. However, with increasing technological advances based on theoretical and experimental researches, man gets, in a way, to copy or to imitate some systems of the human body. These researches not only intended to create humanoid robots, great part of them constituting autonomous systems, but also, in some way, to offer a higher knowledge of the systems that form the human body, objectifying possible applications in the technology of rehabilitation of human beings, gathering in a whole studies related not only to Robotics, but also to Biomechanics, Biomimmetics, Cybernetics, among other areas. This book presents a series of researches inspired by this ideal, carried through by various researchers worldwide, looking for to analyze and to discuss diverse subjects related to humanoid robots. The presented contributions explore aspects about robotic hands, learning, language, vision and locomotion