<p>Artificial intelligence is a broad research area and there are many different reasons why it is interesting to study artificial intelligence. One of the main reasons is to understand how information might be represented in the human brain. The Recursive Auto Associative Memory (RAAM) is a connectionist architecture that with some success has been used for that purpose since it develops compact distributed representations for compositional structures.</p><p>A lot of extensions to the RAAM architecture have been developed through the years in order to improve the performance of RAAM; Bi coded RAAM (B-RAAM) is one of those extensions. In this work a modified B-RAAM architecture is tested and compared to RAAM regarding: Training speed, ability to learn with smaller internal representations and generalization ability. The internal representations of the two network models are also analyzed and compared. This dissertation also includes a discussion of some theoretical aspects of B-RAAM.</p><p>It is found here that the training speed for B-RAAM is considerably lower than RAAM, on the other hand, RAAM learns better with smaller internal representations and is better at generalize than B-RAAM. It is also shown that the extracted internal representation of RAAM reveals more structural information than it does for B-RAAM. This has been shown by hieratically cluster the internal representation and analyse the tree structure. In addition to this a discussion is added about the justifiability to label B-RAAM as an extension to RAAM.</p>

Over 300,000 individuals are injured every year by vehicle related accidents in the United Kingdom alone. Government and the vehicle manufacturers are not only bringing new legislation but are also investing in vehicle safety research to bring this figure down. A private self-driven car is an important factor in maintaining the independence and quality of life of the third age individuals. However, since older people brings deterioration of cognitive, physical and visual abilities, resulting in slower reaction times and lapses while driving. The third age individuals are involved in more vehicle related accidents than middle aged individuals. This scenario is corrected by the fact that the number of third age individuals is increasing, especially in developed countries. It is expected that the percentage of third age individuals in the United Kingdom will increase to 20% of the total population by 2010. Several safety systems have been developed by the automotive industry including intelligent airbags, Electronic Stability Control (ESC) and pre-tensioned seat belts, but nothing has been specifically developed for the third age related problems. This thesis proposes a driver posture identification system using low resolution infrared imaging. The use of a low resolution thermal imager provides a reliable non-contact based posture identification system at a relatively low cost and is shown to provide robust performance over a wide range of conditions. The low resolution also protects the privacy of the driver. In order to develop the proposed safety system an Artificial Intelligent Thermal Imaging algorithm (AITl) is created in MatLAB. Experimentation is conducted in real and simulated environment, with human subjects, to evaluate the results of the algorithm. The result shows that the safety system is able to identify eighteen different driving postures. The system also provides other valuable information about the driver such as driver physical built, fatigue, smoking, mobile phone usage, eye-height and trunk stability. It is clear that in incorporating this safety system in the overall automotive central strategy, better safety for third age individual can be achieved. This thesis provides various contributions to knowledge including a novel neural network design, a safety system using low resolution infrared imager and an algorithm that can identify driver posture.

Manual sleep scoring, executed by visual inspection of the EEG, is a very time consuming activity, with an inherent subjective decisional component. Automatic sleep scoring could ease the job of the technicians, because faster and more accurate. Frequency information characterizing the main brain rhythms, and consequently the sleep stages, needs to be extracted from the EEG data. The approach used in this study involves a wavelet filter bank for the EEG frequency features extraction. The wavelet packet analysis tool in MATLAB has been employed and the frequency information subsequently used for the automatic sleep scoring by means of an artificial neural network. Finally, the automatic sleep scoring has been employed for epoching the fMRI data, thus allowing for studying brain resting state networks during sleep. Three resting state networks have been inspected; the Default Mode Network, The Attentional Network and the Salience Network. The networks functional connectivity variations have been inspected in both healthy and narcoleptic subjects. Narcolepsy is a neurobiological disorder characterized by an excessive daytime sleepiness, whose aetiology may be linked to a loss of neurons in the hypothalamic region.

No description available.

Šiame darbe yra nagrinėjama kliūčių atpažinimo vaizde problema. Ši problema susideda iš skirtumų vaizdo formavimo, vaizdo paruošimo dirbtiniam neuroniniam tinklui ir objektų klasifikavimo, naudojant dirbtinį neuroninį tinklą, uždavinių. Darbe siekiama išnagrinėti esamus vaizdo formavimo, apdorojimo ir dirbtinio neuroninio tinklo klasifikavimo būdus ir pateikti uždavinių sprendimo variantą kaip tai galima padaryti geriau. Remiantis autoriaus siūlomais sprendimais yra sukurta programinė įrangą, kuri sudaryta iš trijų modulių: skirtumų žemėlapio vaizdo formavimo, sukurtojo skirtumų žemėlapio vaizdo pirminio apdorojimo ir DNT kliūčių identifikavimo apdorotame skirtumų žemėlapio vaizde. / The problem of obstacle recognition on way is analyzed by author in this work. This problem consists of view formation, view preparation for Artificial Neural Network and object classification using neural networks tasks. It is striving to analyze the formation of view, processing of view and ways of ANN classification, and suggest the better way of task solutions in this thesis. It is compiled software using authors suggested solutions which consists of three modules: disparity map formation, filtering preparation of created one and obstacle recognition using ANN.

This thesis proposes an integrated artificial neural network based approach to classify the position of a wireless device in an indoor protected area. Our experiments are conducted in two different types of interference affected indoor locations. We found that the environment greatly influences the received signal strength. We realized the need of incorporating a complexity measure of the Wi-Fi signal as additional information in our localization algorithm. The inputs to the integrated artificial neural network were comprised of an integer dimension representation and a fractional dimension representation of the Wi-Fi signal. The integer dimension representation consisted of the raw signal strength, whereas the fractional dimension consisted of a variance fractal dimension of the Wi-Fi signal. The results show that the proposed approach performed 8.7% better classification than the “one dimensional input” ANN approach, achieving an 86% correct classification rate. The conventional Trilateration method achieved only a 47.97% correct classification rate.

The work described in this thesis contributes to the development of a system to evaluate sailing performance. This work was motivated by the lack of tools available to evaluate sailing performance. The goal of the work presented is to detect and classify the turns of a sailing yacht. Data was collected using a BlackBerry PlayBook affixed to a J/24 sailing yacht. This data was manually annotated with three types of turn: tack, gybe, and mark rounding. This manually annotated data was used to train classification methods. Classification methods tested were multi-layer perceptrons (MLPs) of two sizes in various committees and nearest- neighbour search. Pre-processing algorithms tested were Kalman filtering, categorization using quantiles, and residual normalization. The best solution was found to be an averaged answer committee of small MLPs, with Kalman filtering and residual normalization performed on the input as pre-processing.

<p>This thesis presents a method for solving partial differential equations (PDEs) using articial neural networks. The method uses a constrained backpropagation (CPROP) approach for preserving prior knowledge during incremental training for solving nonlinear elliptic and parabolic PDEs adaptively, in non-stationary environments. Compared to previous methods that use penalty functions or Lagrange multipliers,</p><p>CPROP reduces the dimensionality of the optimization problem by using direct elimination, while satisfying the equality constraints associated with the boundary and initial conditions exactly, at every iteration of the algorithm. The effectiveness of this method is demonstrated through several examples, including nonlinear elliptic</p><p>and parabolic PDEs with changing parameters and non-homogeneous terms. The computational complexity analysis shows that CPROP compares favorably to existing methods of solution, and that it leads to considerable computational savings when subject to non-stationary environments.</p><p>The CPROP based approach is extended to a constrained integration (CINT) method for solving initial boundary value partial differential equations (PDEs). The CINT method combines classical Galerkin methods with CPROP in order to constrain the ANN to approximately satisfy the boundary condition at each stage of integration. The advantage of the CINT method is that it is readily applicable to PDEs in irregular domains and requires no special modification for domains with complex geometries. Furthermore, the CINT method provides a semi-analytical solution that is infinitely differentiable. The CINT method is demonstrated on two hyperbolic and one parabolic initial boundary value problems (IBVPs). These IBVPs are widely used and have known analytical solutions. When compared with Matlab's nite element (FE) method, the CINT method is shown to achieve significant improvements both in terms of computational time and accuracy. The CINT method is applied to a distributed optimal control (DOC) problem of computing optimal state and control trajectories for a multiscale dynamical system comprised of many interacting dynamical systems, or agents. A generalized reduced gradient (GRG) approach is presented in which the agent dynamics are described by a small system of stochastic dierential equations (SDEs). A set of optimality conditions is derived using calculus of variations, and used to compute the optimal macroscopic state and microscopic control laws. An indirect GRG approach is used to solve the optimality conditions numerically for large systems of agents. By assuming a parametric control law obtained from the superposition of linear basis functions, the agent control laws can be determined via set-point regulation, such</p><p>that the macroscopic behavior of the agents is optimized over time, based on multiple, interactive navigation objectives.</p><p>Lastly, the CINT method is used to identify optimal root profiles in water limited ecosystems. Knowledge of root depths and distributions is vital in order to accurately model and predict hydrological ecosystem dynamics. Therefore, there is interest in accurately predicting distributions for various vegetation types, soils, and climates. Numerical experiments were were performed that identify root profiles that maximize transpiration over a 10 year period across a transect of the Kalahari. Storm types were varied to show the dependence of the optimal profile on storm frequency and intensity. It is shown that more deeply distributed roots are optimal for regions where</p><p>storms are more intense and less frequent, and shallower roots are advantageous in regions where storms are less intense and more frequent.</p> / Dissertation

The formation of disinfection by-products (DBPs) in drinking water has become an issue of greater concern in recent years. Bench-scale jar tests were conducted on a surface water to evaluate the impact of enhanced coagulation on the removal of organic DBP precursors and the formation of trihalomethanes (THMs) and haloacetic acids (HAAs). The results of this testing indicate that enhanced coagulation practices can improve treated water quality without increasing coagulant dosage. The data generated were also used to develop artificial neural networks (ANNs) to predict THM and HAA formation. Testing of these models showed high correlations between the actual and predicted data. In addition, an experimental plan was developed to use ANNs for treatment optimization at the Peterborough pilot plant.

The formation of disinfection by-products (DBPs) in drinking water has become an issue of greater concern in recent years. Bench-scale jar tests were conducted on a surface water to evaluate the impact of enhanced coagulation on the removal of organic DBP precursors and the formation of trihalomethanes (THMs) and haloacetic acids (HAAs). The results of this testing indicate that enhanced coagulation practices can improve treated water quality without increasing coagulant dosage. The data generated were also used to develop artificial neural networks (ANNs) to predict THM and HAA formation. Testing of these models showed high correlations between the actual and predicted data. In addition, an experimental plan was developed to use ANNs for treatment optimization at the Peterborough pilot plant.