Relative entropy rate based multiple hidden Markov Model Approximation

This paper proposes a novel relative entropy rate (RER) based approach for multiple HMM (MHMM) approximation of a class of discrete-time uncertain processes. Under different uncertainty assumptions, the model design problem is posed either as a min-max optimisation problem or stochastic minimisation problem on the RER between joint laws describing the state and output processes (rather than the more usual RER between output processes). A suitable filter is proposed for which performance results are established which bound conditional mean estimation performance and show that estimation performance improves as the RER is reduced. These filter consistency and convergence bounds are the first results characterising multiple HMM approximation performance and suggest that joint RER concepts provide a useful model selection criteria. The proposed model design process and MHMM filter are demonstrated on an important image processing dim-target detection problem.

Understanding how to enhance business creativity

This PhD study examines some of what happens in an individual’s mind regarding creativity during problem solving within an organisational context. It presents innovations related to creative motivation, cognitive style and framing effects that can be applied by managers to enhance individual employee creativity within the organisation and thereby assist organisations to become more innovative. The project delivers an understanding of how to leverage natural changes in creative motivation levels during problem solving. This pattern of response is called Creative Resolve Response (CRR). The project also presents evidence of how framing effects can be used to influence decisions involving creative options in order to enhance the potential for managers get employees to select creative options more often for implementation. The study’s objectives are to understand: • How creative motivation changes during problem solving • How cognitive style moderates these creative motivation changes • How framing effects apply to decisions involving creative options to solve problems • How cognitive style moderate these framing effects The thesis presents the findings from three controlled experiments based around self reports during contrived problem solving and decision making situations. The first experiment suggests that creative motivation varies in a predictable and systematic way during problem solving as a function of the problem solver’s perception of progress. The second experiment suggests that there are specific framing effects related to decisions involving creativity. It seems that simply describing an alternative as innovative may activate perceptual biases that overcome risk based framing effects. The third experiment suggests that cognitive style moderates decisions involving creativity in complex ways. It seems that in some contexts, decision makers will prefer a creative option, regardless of their cognitive style, if this option is both outside the bounds of what is officially allowed and yet ultimately safe. The thesis delivers innovation on three levels: theoretical, methodological and empirical. The highlights of these findings are outlined below: 1. Theoretical innovation with the conceptualisation of Creative Resolve Response based on an extension of Amabile’s research regarding creative motivation. 2. Theoretical innovation linking creative motivation and Kirton’s research on cognitive style. 3. Theoretical innovation linking both risk based and attribute framing effects to cognitive style. 4. Methodological innovation for defining and testing preferences for creative solution implementation in the form of operationalised creativity decision alternatives. 5. Methodological innovation to identify extreme decision options by applying Shafir’s findings regarding attribute framing effects in reverse to create a test. 6. Empirical innovation with statistically significant research findings which indicate creative motivation varies in a systematic way. 7. Empirical innovation with statistically significant research findings which identify innovation descriptor framing effects 8. Empirical innovation with statistically significant research findings which expand understanding of Kirton’s cognitive style descriptors including the importance of safe rule breaking. 9. Empirical innovation with statistically significant research findings which validate how framing effects do apply to decisions involving operationalised creativity. Drawing on previous research related to creative motivation, cognitive style, framing effects and supervisor interactions with employees, this study delivers insights which can assist managers to increase the production and implementation of creativity in organisations. Hopefully this will result in organisations which are more innovative. Such organisations have the potential to provide ongoing economic and social benefits.

A Numerical Solution Using an Adaptively Preconditioned Lanczos Method for a Class of Linear Systems Related with the Fractional Poisson Equation

This study considers the solution of a class of linear systems related with the fractional Poisson equation (FPE) (−∇2)α/2φ=g(x,y) with nonhomogeneous boundary conditions on a bounded domain. A numerical approximation to FPE is derived using a matrix representation of the Laplacian to generate a linear system of equations with its matrix A raised to the fractional power α/2. The solution of the linear system then requires the action of the matrix function f(A)=A−α/2 on a vector b. For large, sparse, and symmetric positive definite matrices, the Lanczos approximation generates f(A)b≈β0Vmf(Tm)e1. This method works well when both the analytic grade of A with respect to b and the residual for the linear system are sufficiently small. Memory constraints often require restarting the Lanczos decomposition; however this is not straightforward in the context of matrix function approximation. In this paper, we use the idea of thick-restart and adaptive preconditioning for solving linear systems to improve convergence of the Lanczos approximation. We give an error bound for the new method and illustrate its role in solving FPE. Numerical results are provided to gauge the performance of the proposed method relative to exact analytic solutions.

Type Test Elimination using Typeflow Analysis

Programs written in languages of the Oberon family usually contain runtime tests on the dynamic type of variables. In some cases it may be desirable to reduce the number of such tests. Typeflow analysis is a static method of determining bounds on the types that objects may possess at runtime. We show that this analysis is able to reduce the number of tests in certain plausible circumstances. Furthermore, the same analysis is able to detect certain program errors at compile time, which would normally only be detected at program execution. This paper introduces the concepts of typeflow analysis and details its use in the reduction of runtime overhead in Oberon-2.

Numerical investigations of linear least squares methods for derivative estimation

The results of a numerical investigation into the errors for least squares estimates of function gradients are presented. The underlying algorithm is obtained by constructing a least squares problem using a truncated Taylor expansion. An error bound associated with this method contains in its numerator terms related to the Taylor series remainder, while its denominator contains the smallest singular value of the least squares matrix. Perhaps for this reason the error bounds are often found to be pessimistic by several orders of magnitude. The circumstance under which these poor estimates arise is elucidated and an empirical correction of the theoretical error bounds is conjectured and investigated numerically. This is followed by an indication of how the conjecture is supported by a rigorous argument.

A point interpolation method with locally smoothed strain field (PIM-LS2) for mechanics problems using triangular mesh

A point interpolation method with locally smoothed strain field (PIM-LS2) is developed for mechanics problems using a triangular background mesh. In the PIM-LS2, the strain within each sub-cell of a nodal domain is assumed to be the average strain over the adjacent sub-cells of the neighboring element sharing the same field node. We prove theoretically that the energy norm of the smoothed strain field in PIM-LS2 is equivalent to that of the compatible strain field, and then prove that the solution of the PIM- LS2 converges to the exact solution of the original strong form. Furthermore, the softening effects of PIM-LS2 to system and the effects of the number of sub-cells that participated in the smoothing operation on the convergence of PIM-LS2 are investigated. Intensive numerical studies verify the convergence, softening effects and bound properties of the PIM-LS2, and show that the very ‘‘tight’’ lower and upper bound solutions can be obtained using PIM-LS2.

On the optimal capacity expansion of electric power systems

The analysis of investment in the electric power has been the subject of intensive research for many years. The efficient generation and distribution of electrical energy is a difficult task involving the operation of a complex network of facilities, often located over very large geographical regions. Electric power utilities have made use of an enormous range of mathematical models. Some models address time spans which last for a fraction of a second, such as those that deal with lightning strikes on transmission lines while at the other end of the scale there are models which address time horizons consisting of ten or twenty years; these usually involve long range planning issues. This thesis addresses the optimal long term capacity expansion of an interconnected power system. The aim of this study has been to derive a new, long term planning model which recognises the regional differences which exist for energy demand and which are present in the construction and operation of power plant and transmission line equipment. Perhaps the most innovative feature of the new model is the direct inclusion of regional energy demand curves in the nonlinear form. This results in a nonlinear capacity expansion model. After review of the relevant literature, the thesis first develops a model for the optimal operation of a power grid. This model directly incorporates regional demand curves. The model is a nonlinear programming problem containing both integer and continuous variables. A solution algorithm is developed which is based upon a resource decomposition scheme that separates the integer variables from the continuous ones. The decompostion of the operating problem leads to an interactive scheme which employs a mixed integer programming problem, known as the master, to generate trial operating configurations. The optimum operating conditions of each trial configuration is found using a smooth nonlinear programming model. The dual vector recovered from this model is subsequently used by the master to generate the next trial configuration. The solution algorithm progresses until lower and upper bounds converge. A range of numerical experiments are conducted and these experiments are included in the discussion. Using the operating model as a basis, a regional capacity expansion model is then developed. It determines the type, location and capacity of additional power plants and transmission lines, which are required to meet predicted electicity demands. A generalised resource decompostion scheme, similar to that used to solve the operating problem, is employed. The solution algorithm is used to solve a range of test problems and the results of these numerical experiments are reported. Finally, the expansion problem is applied to the Queensland electricity grid in Australia.

On the optimal capacity expansion of electric power systems

The analysis of investment in the electric power has been the subject of intensive research for many years. The efficient generation and distribution of electrical energy is a difficult task involving the operation of a complex network of facilities, often located over very large geographical regions. Electric power utilities have made use of an enormous range of mathematical models. Some models address time spans which last for a fraction of a second, such as those that deal with lightning strikes on transmission lines while at the other end of the scale there are models which address time horizons consisting of ten or twenty years; these usually involve long range planning issues. This thesis addresses the optimal long term capacity expansion of an interconnected power system. The aim of this study has been to derive a new, long term planning model which recognises the regional differences which exist for energy demand and which are present in the construction and operation of power plant and transmission line equipment. Perhaps the most innovative feature of the new model is the direct inclusion of regional energy demand curves in the nonlinear form. This results in a nonlinear capacity expansion model. After review of the relevant literature, the thesis first develops a model for the optimal operation of a power grid. This model directly incorporates regional demand curves. The model is a nonlinear programming problem containing both integer and continuous variables. A solution algorithm is developed which is based upon a resource decomposition scheme that separates the integer variables from the continuous ones. The decompostion of the operating problem leads to an interactive scheme which employs a mixed integer programming problem, known as the master, to generate trial operating configurations. The optimum operating conditions of each trial configuration is found using a smooth nonlinear programming model. The dual vector recovered from this model is subsequently used by the master to generate the next trial configuration. The solution algorithm progresses until lower and upper bounds converge. A range of numerical experiments are conducted and these experiments are included in the discussion. Using the operating model as a basis, a regional capacity expansion model is then developed. It determines the type, location and capacity of additional power plants and transmission lines, which are required to meet predicted electicity demands. A generalised resource decompostion scheme, similar to that used to solve the operating problem, is employed. The solution algorithm is used to solve a range of test problems and the results of these numerical experiments are reported. Finally, the expansion problem is applied to the Queensland electricity grid in Australia

The patentability of non-physical inventions : lessons from the United States

Patent systems around the world are being pressed to recognise and protect challengingly new and exciting subject matter in order to keep pace with the rapid technological advancement of our age and the fact we are moving into the era of the ‘knowledge economy’. This rapid development and pressure to expand the bounds of what has traditionally been recognised as patentable subject matter has created uncertainty regarding what it is that the patent system is actually supposed to protect. Among other things, the patent system has had to contend with uncertainty surrounding claims to horticultural and agricultural methods, artificial living micro-organisms, methods of treating the human body, computer software and business methods. The contentious issue of the moment is one at whose heart lies the important distinction between what is a mere abstract idea and what is properly an invention deserving of the monopoly protection afforded by a patent. That question is whether purely intangible inventions, being methods that do not involve a physical aspect or effect or cause a physical transformation of matter, constitute patentable subject matter. This paper goes some way to addressing these uncertainties by considering how the Australian approach to the question can be informed by developments arising in the United States of America, and canvassing some of the possible lessons we in Australia might learn from the approaches taken thus far in the United States.

Adaptive instantaneous frequency estimation: Techniques and algorithms

This thesis deals with the problem of the instantaneous frequency (IF) estimation of sinusoidal signals. This topic plays significant role in signal processing and communications. Depending on the type of the signal, two major approaches are considered. For IF estimation of single-tone or digitally-modulated sinusoidal signals (like frequency shift keying signals) the approach of digital phase-locked loops (DPLLs) is considered, and this is Part-I of this thesis. For FM signals the approach of time-frequency analysis is considered, and this is Part-II of the thesis. In part-I we have utilized sinusoidal DPLLs with non-uniform sampling scheme as this type is widely used in communication systems. The digital tanlock loop (DTL) has introduced significant advantages over other existing DPLLs. In the last 10 years many efforts have been made to improve DTL performance. However, this loop and all of its modifications utilizes Hilbert transformer (HT) to produce a signal-independent 90-degree phase-shifted version of the input signal. Hilbert transformer can be realized approximately using a finite impulse response (FIR) digital filter. This realization introduces further complexity in the loop in addition to approximations and frequency limitations on the input signal. We have tried to avoid practical difficulties associated with the conventional tanlock scheme while keeping its advantages. A time-delay is utilized in the tanlock scheme of DTL to produce a signal-dependent phase shift. This gave rise to the time-delay digital tanlock loop (TDTL). Fixed point theorems are used to analyze the behavior of the new loop. As such TDTL combines the two major approaches in DPLLs: the non-linear approach of sinusoidal DPLL based on fixed point analysis, and the linear tanlock approach based on the arctan phase detection. TDTL preserves the main advantages of the DTL despite its reduced structure. An application of TDTL in FSK demodulation is also considered. This idea of replacing HT by a time-delay may be of interest in other signal processing systems. Hence we have analyzed and compared the behaviors of the HT and the time-delay in the presence of additive Gaussian noise. Based on the above analysis, the behavior of the first and second-order TDTLs has been analyzed in additive Gaussian noise. Since DPLLs need time for locking, they are normally not efficient in tracking the continuously changing frequencies of non-stationary signals, i.e. signals with time-varying spectra. Nonstationary signals are of importance in synthetic and real life applications. An example is the frequency-modulated (FM) signals widely used in communication systems. Part-II of this thesis is dedicated for the IF estimation of non-stationary signals. For such signals the classical spectral techniques break down, due to the time-varying nature of their spectra, and more advanced techniques should be utilized. For the purpose of instantaneous frequency estimation of non-stationary signals there are two major approaches: parametric and non-parametric. We chose the non-parametric approach which is based on time-frequency analysis. This approach is computationally less expensive and more effective in dealing with multicomponent signals, which are the main aim of this part of the thesis. A time-frequency distribution (TFD) of a signal is a two-dimensional transformation of the signal to the time-frequency domain. Multicomponent signals can be identified by multiple energy peaks in the time-frequency domain. Many real life and synthetic signals are of multicomponent nature and there is little in the literature concerning IF estimation of such signals. This is why we have concentrated on multicomponent signals in Part-H. An adaptive algorithm for IF estimation using the quadratic time-frequency distributions has been analyzed. A class of time-frequency distributions that are more suitable for this purpose has been proposed. The kernels of this class are time-only or one-dimensional, rather than the time-lag (two-dimensional) kernels. Hence this class has been named as the T -class. If the parameters of these TFDs are properly chosen, they are more efficient than the existing fixed-kernel TFDs in terms of resolution (energy concentration around the IF) and artifacts reduction. The T-distributions has been used in the IF adaptive algorithm and proved to be efficient in tracking rapidly changing frequencies. They also enables direct amplitude estimation for the components of a multicomponent

On the data consumption benefits of accepting increased uncertainty

In the context of learning paradigms of identification in the limit, we address the question: why is uncertainty sometimes desirable? We use mind change bounds on the output hypotheses as a measure of uncertainty, and interpret ‘desirable’ as reduction in data memorization, also defined in terms of mind change bounds. The resulting model is closely related to iterative learning with bounded mind change complexity, but the dual use of mind change bounds — for hypotheses and for data — is a key distinctive feature of our approach. We show that situations exists where the more mind changes the learner is willing to accept, the lesser the amount of data it needs to remember in order to converge to the correct hypothesis. We also investigate relationships between our model and learning from good examples, set-driven, monotonic and strong-monotonic learners, as well as class-comprising versus class-preserving learnability.

Mind change complexity of learning logic programs

The present paper motivates the study of mind change complexity for learning minimal models of length-bounded logic programs. It establishes ordinal mind change complexity bounds for learnability of these classes both from positive facts and from positive and negative facts. Building on Angluin’s notion of finite thickness and Wright’s work on finite elasticity, Shinohara defined the property of bounded finite thickness to give a sufficient condition for learnability of indexed families of computable languages from positive data. This paper shows that an effective version of Shinohara’s notion of bounded finite thickness gives sufficient conditions for learnability with ordinal mind change bound, both in the context of learnability from positive data and for learnability from complete (both positive and negative) data. Let Omega be a notation for the first limit ordinal. Then, it is shown that if a language defining framework yields a uniformly decidable family of languages and has effective bounded finite thickness, then for each natural number m >0, the class of languages defined by formal systems of length <= m: • is identifiable in the limit from positive data with a mind change bound of Omega (power)m; • is identifiable in the limit from both positive and negative data with an ordinal mind change bound of Omega × m. The above sufficient conditions are employed to give an ordinal mind change bound for learnability of minimal models of various classes of length-bounded Prolog programs, including Shapiro’s linear programs, Arimura and Shinohara’s depth-bounded linearly covering programs, and Krishna Rao’s depth-bounded linearly moded programs. It is also noted that the bound for learning from positive data is tight for the example classes considered.

Securing IEEE 802.11 wireless LANs

As the acceptance and popularity of wireless networking technologies has proliferated, the security of the IEEE 802.11 wireless local area network (WLAN) has advanced in leaps and bounds. From tenuous beginnings, where the only safe way to deploy a WLAN was to assume it was hostile and employ higherlayer information security controls, to the current state of the art, all manner of improvements have been conceived and many implemented. This work investigates some of the remaining issues surrounding IEEE 802.11 WLAN operation. While the inherent issues in WLAN deployments and the problems of the original Wired Equivalent Privacy (WEP) provisions are well known and widely documented, there still exist a number of unresolved security issues. These include the security of management and control frames and the data link layer protocols themselves. This research introduces a novel proposal to enhance security at the link layer of IEEE 802.11 WLANs and then conducts detailed theoretical and empirical investigation and analysis of the eects of such proposals. This thesis �rst de�nes the state of the art in WLAN technology and deployment, including an overview of the current and emerging standards, the various threats, numerous vulnerabilities and current exploits. The IEEE 802.11i MAC security enhancements are discussed in detail, along with the likely outcomes of the IEEE 802.11 Task Group W1, looking into protected management frames. The problems of the remaining unprotected management frames, the unprotected control frames and the unprotected link layer headers are reviewed and a solution is hypothesised, to encrypt the entire MAC Protocol Data Unit (MPDU), including the MAC headers, not just the MAC Service Data Unit (MSDU) commonly performed by existing protocols. The proposal is not just to encrypt a copy of the headers while still using cleartext addresses to deliver the frame, as used by some existing protocols to support the integrity and authenticity of the headers, but to pass the entire MPDU only as ciphertext to also support the con�dentiality of the frame header information. This necessitates the decryption of every received frame using every available key before a station can determine if it is the intended recipient. As such, this raises serious concerns as to the viability of any such proposal due to the likely impact on throughput and scalability. The bulk of the research investigates the impacts of such proposals on the current WLAN protocols. Some possible variations to the proposal are also provided to enhance both utility and speed. The viability this proposal with respect to the eect on network throughput is then tested using a well known and respected network simulation tool, along with a number of analysis tools developed speci�cally for the data generated here. The simulator's operation is �rst validated against recognised test outputs, before a comprehensive set of control data is established, and then the proposal is tested and and compared against the controls. This detailed analysis of the various simulations should be of bene�t to other researchers who need to validate simulation results. The analysis of these tests indicate areas of immediate improvement and so the protocols are adjusted and a further series of experiments conducted. These �nal results are again analysed in detail and �nal appraisals provided.

Output feedback control of discrete-time systems in networked environments

This correspondence paper addresses the problem of output feedback stabilization of control systems in networked environments with quality-of-service (QoS) constraints. The problem is investigated in discrete-time state space using Lyapunov’s stability theory and the linear inequality matrix technique. A new discrete-time modeling approach is developed to describe a networked control system (NCS) with parameter uncertainties and nonideal network QoS. It integrates a network-induced delay, packet dropout, and other network behaviors into a unified framework. With this modeling, an improved stability condition, which is dependent on the lower and upper bounds of the equivalent network-induced delay, is established for the NCS with norm-bounded parameter uncertainties. It is further extended for the output feedback stabilization of the NCS with nonideal QoS. Numerical examples are given to demonstrate the main results of the theoretical development.

Meshless TL and UL approaches for large deformation analysis

To accurately and effectively simulate large deformation is one of the major challenges in numerical modeling of metal forming. In this paper, an adaptive local meshless formulation based on the meshless shape functions and the local weak-form is developed for the large deformation analysis. Total Lagrangian (TL) and the Updated Lagrangian (UL) approaches are used and thoroughly compared each other in computational efficiency and accuracy. It has been found that the developed meshless technique provides a superior performance to the conventional FEM in dealing with large deformation problems for metal forming. In addition, the TL has better computational efficiency than the UL. However, the adaptive analysis is much more efficient using the UL approach than using in the TL approach.