Human interfaces to computers continue to be an active area of research. The keyboard is considered the basic interface for editing control as well as text input. Problems of correct typing and typing speed have urged research for alternative means for keyboard replacement, or at least "resizing" its monopoly. Pointing devices (e.g. a mouse) have been developed, and supporting software with icons is now widely used. Two other means are being developed and operationally tested, namely, the pen for handwriting text, commands and drawings, and spoken language interface, which is the subject of this thesis. Human/computer interface is an interactive man-machine communication facility that enjoys the following advantages. • High input speed: some experiments reveal that the rate of information input by speech is three times faster than keyboard input and eight times faster than inputting characters by hand. • No training needed: because the generation of speech is a very natural human action, it requires no special training. • Parallel processing with other information: production of speech works quite well in conjunction with gestures of hands and feet for visual perception of information. • Simple and economical input sensor: microphones are inexpensive and are readily available. • Coping with handicaps: these interfaces can be used in unusual circumstances of darkness, blindness, or other visual handicap. This dissertation presents a design of a Human Computer Interface (HCI) system that can be trained to work with an individual speaker. A new approach is introduced to extract key voice features, called Median Linear Predictive Coding (MLPC). MLPC reduces the HCI calculation time and gives an improved recognition rate. This design eliminated the typical Multi-layer Perceptron (MLP) problems of complexity growth with vocabulary size, the large training times required and the need for complete re-training whenever the vocabulary is extended. A novel modular neural network architecture, called a Pyramidal Modular Neural Network (PMNN), is introduced for recursive speech identification. In addition, many other system algorithms/components, such as speech endpoint detection, automatic noise thresholding, etc., must be tailored correctly in order to achieve high recognition accuracy. / Ph. D.

In this dissertation, it is aimed to develop an approach for the trip distribution element which is one of the important phases of four-step travel demand modelling. The trip distribution problem using back-propagation artificial neural networks has been researched in a limited number of studies and, in a critically evaluated study it has been concluded that the artificial neural networks underperform when compared to the traditional models. The underperformance of back-propagation artificial neural networks appears to be due to the thresholding the linearly combined inputs from the input layer in the hidden layer as well as thresholding the linearly combined outputs from the hidden layer in the output layer. In the proposed neural trip distribution model, it is attempted not to threshold the linearly combined outputs from the hidden layer in the output layer. Thus, in this approach, linearly combined iv inputs are activated in the hidden layer as in most neural networks and the neuron in the output layer is used as a summation unit in contrast to other neural networks. When this developed neural trip distribution model is compared with various approaches as modular, gravity and back-propagation neural models, it has been found that reliable trip distribution predictions are obtained.

Maintaining quality outcomes is an essential task for any manufacturing organization. Visual inspections have long been an avenue to detect defects in manufactured products, and recent advances within the field of deep learning has led to a surge of research in how technologies like convolutional neural networks can be used to perform these quality inspections automatically. An alternative to these often large and deep network structures is the modular neural network, which can instead divide a classification task into several sub-tasks to decrease the overall complexity of a problem. To investigate how these two approaches to image classification compare in a quality inspection task, a case study was performed at AR Packaging, a manufacturer of food containers. The many different colors, prints and geometries present in the AR Packaging product family served as a natural occurrence of complexity for the quality classification task. A modular network was designed, being formed by one routing module to classify variant type which is subsequently used to delegate the quality classification to an expert module trained for that specific variant. An image dataset was manually generated from within the production environment portraying a range of product variants in both defective and non-defective form. An image processing algorithm was developed to minimize image background and align the products in the pictures. To evaluate the adaptability of the two approaches, the networks were initially trained on same data from five variants, and then retrained with added data from a sixth variant. The modular networks were found to be overall less accurate and slower in their classification than the conventional single networks were. However, the modular networks were more than six times smaller and required less time to train initially, though the retraining times were roughly equivalent in both approaches. The retraining of the single network did also cause some fluctuation in the predictive accuracy, something which was not noted in the modular network. / <p>Det finns övrigt digitalt material (t.ex. film-, bild- eller ljudfiler) eller modeller/artefakter tillhörande examensarbetet som ska skickas till arkivet.</p>