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Secret sharing is a fundamental notion for secure cryptographic design. In a secret sharing scheme, a set of participants shares a secret among them such that only pre-specified subsets of these shares can get together to recover the secret. This dissertation introduces a neural network approach to solve the problem of secret sharing for any given access structure. Other approaches have been used to solve this problem. However, the yet known approaches result in exponential increase in the amount of data that every participant need to keep. This amount is measured by the secret sharing scheme information rate. This work is intended to solve the problem with better information rate.

<p>The aim of this thesis is to describe how a statisticallybased neural network technology, here named BCPNN (BayesianConfidence Propagation Neural Network), which may be identifiedby rewriting Bayes' rule, can be used within a fewapplications, data mining and classification with credibilityintervals as well as unsupervised pattern recognition.</p><p>BCPNN is a neural network model somewhat reminding aboutBayesian decision trees which are often used within artificialintelligence systems. It has previously been success- fullyapplied to classification tasks such as fault diagnosis,supervised pattern recognition, hiearchical clustering and alsoused as a model for cortical memory. The learning paradigm usedin BCPNN is rather different from many other neural networkarchitectures. The learning in, e.g. the popularbackpropagation (BP) network, is a gradient method on an errorsurface, but learning in BCPNN is based upon calculations ofmarginal and joint prob- abilities between attributes. This isa quite time efficient process compared to, for instance,gradient learning. The interpretation of the weight values inBCPNN is also easy compared to many other networkarchitechtures. The values of these weights and theiruncertainty is also what we are focusing on in our data miningapplication. The most important results and findings in thisthesis can be summarised in the following points:</p><p>    We demonstrate how BCPNN (Bayesian Confidence PropagationNeural Network) can be extended to model the uncertainties incollected statistics to produce outcomes as distributionsfrom two different aspects: uncertainties induced by sparsesampling, which is useful for data mining; uncertainties dueto input data distributions, which is useful for processmodelling.</p><p>    We indicate how classification with BCPNN gives highercertainty than an optimal Bayes classifier and betterprecision than a naïve Bayes classifier for limited datasets.</p><p>    We show how these techniques have been turned into auseful tool for real world applications within the drugsafety area in particular.</p><p>    We present a simple but working method for doingautomatic temporal segmentation of data sequences as well asindicate some aspects of temporal tasks for which a Bayesianneural network may be useful.</p><p>    We present a method, based on recurrent BCPNN, whichperforms a similar task as an unsupervised clustering method,on a large database with noisy incomplete data, but muchquicker, with an efficiency in finding patterns comparablewith a well known (Autoclass) Bayesian clustering method,when we compare their performane on artificial data sets.Apart from BCPNN being able to deal with really large datasets, because it is a global method working on collectivestatistics, we also get good indications that the outcomefrom BCPNN seems to have higher clinical relevance thanAutoclass in our application on the WHO database of adversedrug reactions and therefore is a relevant data mining toolto use on the WHO database.</p><p>Artificial neural network, Bayesian neural network, datamining, adverse drug reaction signalling, classification,learning.</p>

We use geostatistical and pedotrasnfer functions to estimate the three-dimensional distributions of soil types and hydraulic properties in a relatively large volume of vadose zone underlying the Maricopa Agriculture Center near Phoenix, Arizona. Soil texture and bulk density data from the site are analyzed geostatistically to reveal the underlying stratigraphy as well as finer features of their three-dimensional variability in space. Such fine features are revealed by cokriging soil texture and water content measured prior to large-scale long-term infiltration experiments. Resultant estimates of soil texture and bulk density data across the site are then used as input into a pedotransfer function to produce estimates of soil hydraulic parameter (saturated and residual water content θs and θr, saturated hydraulic conductivity Ks, van Genuchten parameters αand n) distributions across the site in three dimensions. We compare these estimates with laboratory-measured values of these same hydraulic parameters and find the estimated parameters match the measured well for θs, n and Ks but not well for θr nor α, while some measured extreme values are not captured. Finally the estimated soil hydraulic parameters are put into a numerical simulator to test the reliability of the models. Resultant simulated water contents do not agree well with those observed, indicating inverse calibration is required to improve the modeling performance. The results of this research conform to a previous work by Wang et al. at 2003. Also this research covers the gaps of Wang’s work in sense of generating 3-D heterogeneous fields of soil texture and bulk density by cokriging and providing comparisons between estimated and measured soil hydraulic parameters with new field and laboratory measurements of water retentions datasets.

To obtain a suitable mathematical model of the input-output behavior of highly nonlinear, multi-scale, nonparametric phenomena, we introduce an adaptive radial basis function approximation approach. We use this approach to estimate the discrepancy between traditional model areas and the multiscale physics of systems involving distributed sensing and technology. Radial Basis Function Networks offers the possible approach to nonparametric multi-scale modeling for dynamical systems like the adaptive wing with the Synthetic Jet Actuator (SJA). We use the Regularized Orthogonal Least Square method (Mark, 1996) and the RAN-EKF (Resource Allocating Network-Extended Kalman Filter) as a reference approach. The first part of the algorithm determines the location of centers one by one until the error goal is met and regularization is achieved. The second process includes an algorithm for the adaptation of all the parameters in the Radial Basis Function Network, centers, variances (shapes) and weights. To demonstrate the effectiveness of these algorithms, SJA wind tunnel data are modeled using this approach. Good performance is obtained compared with conventional neural networks like the multi layer neural network and least square algorithm. Following this work, we establish Model Reference Adaptive Control (MRAC) formulations using an off-line Radial Basis Function Networks (RBFN). We introduce the adaptive control law using a RBFN. A theory that combines RBFN and adaptive control is demonstrated through the simple numerical simulation of the SJA wing. It is expected that these studies will provide a basis for achieving an intelligent control structure for future active wing aircraft.

A self-­‐learning Audio Player was built to learn users habits by analyzing operations the user does when listening to music. The self-­‐learning component is intended to provide a better music experience for the user by generating a special playlist based on the prediction of users favorite songs. The rough set core characteristics are used throughout the learning process to capture the dynamics of changing user interactions with the audio player. The engine is evaluated by simulation data. The simulation process ensures the data contain specific predetermined patterns. Evaluation results show the predictive power and stability of the hybrid engine for learning a users habits and the increased intelligence achieved by combining rough sets and NN when compared with using NN by itself.

Control of the contact force between a robot manipulator and a workpiece is critical for successful execution of tasks where the robot’s end effector must perform a contact operation along the contour of a workpiece. Representative tasks include polishing, grinding and deburring. Considerable research has been conducted on force control with electric robots. By contrast, little research has been conducted on force control with pneumatic robots. The later has the potential to be considerably cheaper. However, the compressible nature of air as the working fluid and relatively high friction means pneumatic robots are more difficult to control. The subject of this thesis is the design and testing of a controller that regulates the normal contact force and tangential velocity of the end effector of a pneumatic gantry robot while tracking the contour of a planar workpiece. Both experimental and simulation results are presented. A PI Force Velocity (FV) controller for contour tracking was designed and tested experimentally. Three different workpiece edge geometries were studied: straight, inclined and curved. The tracking performance with the PI FV controller was comparable to the performance reported by other researchers with a similar controller implemented with an electric robot. This result confirms the potential of pneumatically actuated robots in force control applications. A system model was developed and validated in order to investigate the parameters that affect performance. A good match between experiment and simulation was achieved when the friction of the z-axis cylinder was modeled with a Displacement Dependent Friction Model (DDFM) instead of a Velocity Dependent Friction Model (VDFM). Subsequently, a DDFM based friction compensator was designed and tested. However, it was found that performance could not be improved even with perfect friction compensation, due to the effects of system lag. Two Neural Network (NN) compensators were designed to compensate for both the lag and friction in the system. Simulation results for straight and curved edges were used to examine the effectiveness of the NN compensators. The performance of the PI FV controller was found to improve significantly when a NN compensator was added. This result confirms the value of NN’s in control compensation for tracking applications with pneumatic actuation. / Thesis (Ph.D, Mechanical and Materials Engineering) -- Queen's University, 2008-09-16 12:29:44.679

Considerable research has been conducted on the control of pneumatic systems due to their potential as a low-cost, clean, high power-to-weight ratio actuators. However, nonlinearities such as those due to compressibility of air continue to limit their accuracy. Among the nonlinearities in a pneumatic system, friction can have a significant effect on tracking performance, especially in applications that use rodless cylinders which have higher Coulomb friction than rodded cylinders. Compensation for nonlinearities in pneumatic systems has been a popular area of research in pneumatic system control. Most advanced nonlinear control strategies are based on a detailed mathematical model of the system. If a simplified mathematical model is used, then performance is sensitive to uncertainties and parameter variations in the robot. Although they show relatively good results, the requirement for model parameter identification has made these methods difficult to implement. This highlights the need for an adaptive controller that is not based on a mathematical model. The objective of this thesis was to design and evaluate a position and velocity controller for application to a pneumatic gantry robot. An Adaptive Neural Network (ANN) structure was implemented as both a controller and as a compensator. The implemented ANN had online training as this was considered to be the algorithm that had the greatest potential to enhance the performance of the pneumatic system. One axis of the robot was used to obtain results for the cases of velocity and position control. Seven different velocity controllers were tested and their performance compared. For position control, only two controllers were examined: conventional PID and PID with an ANN Compensator (ANNC). The position controllers were tuned for step changes in the setpoint. Their performance was evaluated as applied to sinusoid tracking. It was shown that the addition of ANN as a compensator could improve the performance of both position and velocity control. For position control, the ANNC improved the tracking performance by over 20%. Although performance was better than with conventional PID control, it was concluded that the level of improvement with ANNC did not warrant the extra effort in tuning and implementation. / Thesis (Master, Mechanical and Materials Engineering) -- Queen's University, 2010-09-08 15:02:28.177

Real-time tracking and object recognition is a large field with many possible applications. In this thesis we present a technical demo of a stereoscopic tracking system using artificial neural networks (ANN) and also an overview of the entire system, and its core functions. We have implemented a system able of tracking an object in real time at 60 frames per second. Using stereo matching we can extract the object coordinates in each camera, and calculate a distance estimate from the cameras to the object. The system is developed around the Xilinx ZC-706 evaluation board featuring a Zynq XC7Z045 SoC. Performance critical functions are implemented in the FPGA fabric. A dual-core ARM processor, integrated on the chip, is used for support and communication with an external PC. The system runs at moderate clock speeds to decrease power consumption and provide headroom for higher resolutions. A toolbox has been developed for prototyping and the aim has been to run the system with a one-push-button approach. The system can be taught to track any kind of object using an eight bit 32 × 16 pixel pattern generated by the user. The system is controlled over Ethernet from a regular workstation PC, which enables it to be very user-friendly.

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