On the Application of PSpice for Localised Cloud Security

The work reported in this thesis commenced with a review of methods for creating random binary sequences for encoding data locally by the client before storing in the Cloud. The first method reviewed investigated evolutionary computing software which generated noise-producing functions from natural noise, a highly-speculative novel idea since noise is stochastic. Nevertheless, a function was created which generated noise to seed chaos oscillators which produced random binary sequences and this research led to a circuit-based one-time pad key chaos encoder for encrypting data. Circuit-based delay chaos oscillators, initialised with sampled electronic noise, were simulated in a linear circuit simulator called PSpice. Many simulation problems were encountered because of the nonlinear nature of chaos but were solved by creating new simulation parts, tools and simulation paradigms. Simulation data from a range of chaos sources was exported and analysed using Lyapunov analysis and identified two sources which produced one-time pad sequences with maximum entropy. This led to an encoding system which generated unlimited, infinitely-long period, unique random one-time pad encryption keys for plaintext data length matching. The keys were studied for maximum entropy and passed a suite of stringent internationally-accepted statistical tests for randomness. A prototype containing two delay chaos sources initialised by electronic noise was produced on a double-sided printed circuit board and produced more than 200 Mbits of OTPs. According to Vladimir Kotelnikov in 1941 and Claude Shannon in 1945, one-time pad sequences are theoretically-perfect and unbreakable, provided specific rules are adhered to. Two other techniques for generating random binary sequences were researched; a new circuit element, memristance was incorporated in a Chua chaos oscillator, and a fractional-order Lorenz chaos system with order less than three. Quantum computing will present many problems to cryptographic system security when existing systems are upgraded in the near future. The only existing encoding system that will resist cryptanalysis by this system is the unconditionally-secure one-time pad encryption

Towards computer-automated mechatronic design and optimization using linear graphs and evolutionary computing

A comparison of literature between the linear graph (LG) and bond graph (BG) approaches shows that the surpassing of BGs to LGs in applications related to the modeling of mechatronic systems is driven primarily by a lack of available LG-based software. As a result, a robust software toolbox called LGtheory has been developed for automating the evaluation of LG models in the MATLAB programming environment. This thesis details the development of LGtheory, and the algorithms and procedures employed for the evaluation of LG models. In addition, demonstrations of this toolbox to a process for automating the design of electronic filter circuits, as well as, the modeling and simulation of the dynamics of a mobile robotic system are presented. The results of these demonstrations validate the accuracy of the LGtheory toolbox for modeling complex multi-domain systems, and provides the methodological basis for the automated design of mechatronic systems using LGs

Tools for Modelling and Identification with Bond Graphs and Genetic Programming

The contributions of this work include genetic programming grammars for bond graph modelling and for direct symbolic regression of sets of differential equations; a bond graph modelling library suitable for programmatic use; a symbolic algebra library specialized to this use and capable of, among other things, breaking algebraic loops in equation sets extracted from linear bond graph models. Several non-linear multi-body mechanics examples are pre- sented, showing that the bond graph modelling library exhibits well-behaved simulation results. Symbolic equations in a reduced form are produced au- tomatically from bond graph models. The genetic programming system is tested against a static non-linear function identification problem using type- less symbolic regression. The direct symbolic regression grammar is shown to have a non-deceptive fitness landscape: perturbations of an exact pro- gram have decreasing fitness with increasing distance from the ideal. The planned integration of bond graphs with genetic programming for use as a system identification technique was not successfully completed. A catego- rized overview of other modelling and identification techniques is included as context for the choice of bond graphs and genetic programming

Digital Filters

The new technology advances provide that a great number of system signals can be easily measured with a low cost. The main problem is that usually only a fraction of the signal is useful for different purposes, for example maintenance, DVD-recorders, computers, electric/electronic circuits, econometric, optimization, etc. Digital filters are the most versatile, practical and effective methods for extracting the information necessary from the signal. They can be dynamic, so they can be automatically or manually adjusted to the external and internal conditions. Presented in this book are the most advanced digital filters including different case studies and the most relevant literature

Investigation of a novel multiresonant beam energy harvester and a complex conjugate matching circuit

The aim of the work described in this thesis is firstly to improve the collection of vibration energy for piezoelectric cantilever harvesters, by a mechanical technique, so that the devices can harvest energy over a wider bandwidth. Secondly to investigate a new circuit topology for achieving complex conjugate load matching to the piezoelectric harvester. The thesis has been divided into two parts - the mechanical approach and the electrical approach. For the mechanical approach, a novel multiresonant beam, comprising piezoelectric fiber composites on a clamped-clamped beam and side mounted cantilevers, was proposed. The side cantilevers are tuned by tip masses to be resonant at different frequencies. A Rayleigh-Ritz model was developed to predict the vibration response of the proposed model multiresonant beam. This model showed that the bandwidth of the multiresonant beam was increased over that of a single cantilever harvester. A multiresonant beam for energy harvesting was experimentally tested and compared with a single cantilever energy harvester. The transmissibility and voltage responses were investigated, the beam showed a wide frequency response between 14.5Hz and 31Hz, whereas the single cantilever only showed one resonant frequency. Therefore the multiresonant beam system is feasible for wide band energy harvesting. For the electrical approach, the task was to investigate complex conjugate impedance matching for the piezoelectric energy harvesters, so that the output impedance from the piezoelectric harvester can be reduced, and maximum energy extracted from the device with a possibility of frequency tuning. A new amplified inductor circuit was proposed to enable the capacitive output impedance of the piezoelectric device to be cancelled. Experimental and software simulations are provided to verify the theoretical predictions. A prototype amplified inductor circuit was simulated and tested. The results showed that a variable effective inductance was achieved. However the circuit is lossy due to imperfections within the system, and needs further work to eliminate these imperfections.EThOS - Electronic Theses Online ServiceGBUnited Kingdo

Digital Filter Design Using Improved Artificial Bee Colony Algorithms

Digital filters are often used in digital signal processing applications. The design objective of a digital filter is to find the optimal set of filter coefficients, which satisfies the desired specifications of magnitude and group delay responses. Evolutionary algorithms are population-based meta-heuristic algorithms inspired by the biological behaviors of species. Compared to gradient-based optimization algorithms such as steepest descent and Newton’s like methods, these bio-inspired algorithms have the advantages of not getting stuck at local optima and being independent of the starting point in the solution space. The limitations of evolutionary algorithms include the presence of control parameters, problem specific tuning procedure, premature convergence and slower convergence rate. The artificial bee colony (ABC) algorithm is a swarm-based search meta-heuristic algorithm inspired by the foraging behaviors of honey bee colonies, with the benefit of a relatively fewer control parameters. In its original form, the ABC algorithm has certain limitations such as low convergence rate, and insufficient balance between exploration and exploitation in the search equations. In this dissertation, an ABC-AMR algorithm is proposed by incorporating an adaptive modification rate (AMR) into the original ABC algorithm to increase convergence rate by adjusting the balance between exploration and exploitation in the search equations through an adaptive determination of the number of parameters to be updated in every iteration. A constrained ABC-AMR algorithm is also developed for solving constrained optimization problems.There are many real-world problems requiring simultaneous optimizations of more than one conflicting objectives. Multiobjective (MO) optimization produces a set of feasible solutions called the Pareto front instead of a single optimum solution. For multiobjective optimization, if a decision maker’s preferences can be incorporated during the optimization process, the search process can be confined to the region of interest instead of searching the entire region. In this dissertation, two algorithms are developed for such incorporation. The first one is a reference-point-based MOABC algorithm in which a decision maker’s preferences are included in the optimization process as the reference point. The second one is a physical-programming-based MOABC algorithm in which physical programming is used for setting the region of interest of a decision maker. In this dissertation, the four developed algorithms are applied to solve digital filter design problems. The ABC-AMR algorithm is used to design Types 3 and 4 linear phase FIR differentiators, and the results are compared to those obtained by the original ABC algorithm, three improved ABC algorithms, and the Parks-McClellan algorithm. The constrained ABC-AMR algorithm is applied to the design of sparse Type 1 linear phase FIR filters of filter orders 60, 70 and 80, and the results are compared to three state-of-the-art design methods. The reference-point-based multiobjective ABC algorithm is used to design of asymmetric lowpass, highpass, bandpass and bandstop FIR filters, and the results are compared to those obtained by the preference-based multiobjective differential evolution algorithm. The physical-programming-based multiobjective ABC algorithm is used to design IIR lowpass, highpass and bandpass filters, and the results are compared to three state-of-the-art design methods. Based on the obtained design results, the four design algorithms are shown to be competitive as compared to the state-of-the-art design methods

Minimally intrusive strategies for fault detection and energy monitoring

Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2006.Includes bibliographical references (p. 185-196).This thesis addresses the need for automated monitoring systems that rely on minimally intrusive sensor arrays. The monitoring techniques employed in this thesis require fewer sensors because they take a different approach to the measurement problem. Specifically, these techniques use the power distribution network in the target system as a power source, a sensor array, and a communications channel. In this "multi-use" approach, the only measurement sources are a set of centrally located electrical transducers (i.e. voltage and current sensors) and a set of remotely located sensors that communicate with a central processing unit via power line modems. In general, these systems determine the status of critical loads or systems using only electrical data. Thus, remotely located sensors are only employed in order to gather information that would be difficult, if not impossible, to obtain electrically. Examples of such quantities include air exchange rates and occupancy levels in individual rooms. This thesis describes the development and application of several critical features of the minimally intrusive monitoring systems described above. First, it presents several model-based methods that make it possible to use electrical data to detect faults in certain mechanical systems.(cont.) In particular, two such models are described. The first of these is intended to be applied in systems in which an electromechanical actuator cycles its operation according to the value of some other variable, such as a pressure or a temperature. Examples include compressed air and vacuum systems. The other model is used to diagnose the impending failure of the mechanical coupling through which a motor drives an inertial load such as a pump impeller. This thesis also describes the development of a minimally intrusive airflow monitoring system that uses ozone as a tracer gas. This system fits easily into the "multi-use" framework because it relies on a network of distributed ozone generators and detectors whose operation is coordinated via power line communications. Finally, this thesis also presents and demonstrates a method for detecting the operation of various electrical loads using transient changes in the measured line voltage. This technique makes it possible to use "plug-in" sensors to determine the operating schedule of each of the various loads in a home or commercial facility. All of the techniques and methods described here are demonstrated experimentally.by Robert Williams Cox, IV.Ph.D