The researchers at the Department of Electricity at Uppsala University has recently entered the field of electric motor design, however no real knowledge of motor control of salient pole permanent magnet motors exists in the department. This thesis will present a general description of the control method of motors that exist today, this has been done by reviewing existing literature. The literature review has shown that there are at least three control methods with a significant different in their control approach, Scalar-, Field Oriented- and Direct Torque- Control. The two last methods were chosen by the author as the most useful and was implemented and simulated together with the newly developed motor in MATLAB Simulink to evaluate their performance. The simulation results show that there is no difference in performance of the two methods, but they show a difference in efficiency. The results show that it's worth to develop both methods further, mainly for reducing the torque and current ripple. This result was not expected according to literature, which suggests that the Field Oriented Control has a lower torque ripple. This could be caused by the choice of hysteresis control for inverter switching, instead of more sophisticated methods with a proportional integral derivative controller (PID) together with Sinusoidal Pulse Width Modulation (SPWM) or Space Vector Modulation (SVM).

A major feature of the human eye is the compressed sensitiveness of the retina. An image coder, which makes use of this, can heavily encode the parts of the image which is not close to the focus of our eyes. Existing image coding schemes require that the gaze direction of the viewer is measured. However, a great advantage would be if an estimator predicts the focus of attention (FOA) regions in the image. This report presents such an implementation, which is based on a model that mimics many of the biological features of the human visual system (HVS). For example, it uses a center-surround mechanism, which is a replica of the receptive fields of the neurons in the HVS. An extra feature of the implementation is the extension to handle video sequences, and the expansion of the FOA:s. The test results of the system show good results from a large variety of images.

Human vision is remarkable. By limiting the main concentration of high-acuity photoreceptors to the eye's central fovea region, we efficiently view the world by redirecting the fovea between points of interest using eye movements called <em>saccades</em>. <br /><br /> Part I describes a saccadic vision system prototype design. The dual-resolution saccadic camera detects objects of interest in a scene by processing low-resolution image information; it then revisits salient regions in high-resolution. The end product is a dual-resolution image in which background information is displayed in low-resolution, and salient areas are captured in high-acuity. This lends to a resource-efficient active vision system. <br /><br />Part II describes CMOS image sensor designs for active vision. Specifically, this discussion focuses on methods to determine regions of interest and achieve high dynamic range on the sensor.

Visual Attention is an interesting concept that constantly widens its application areas in the field of image processing and computer vision. The main idea of visual attention is to find the locations on the image that are visually attractive. In this thesis, the visually attractive regions are extracted and tracked in video sequences coming from the vision systems of mobile platforms. First, the salient regions are extracted in each frame and a feature vector is constructed for each one. Then Scale Invariant Feature Transform (SIFT) is applied only to the salient regions to extract more stable features. The tracking is achieved by matching the salient regions of consecutive frames by comparing their feature vectors. Then the SIFT points of salient regions are matched to calculate the shift values for the matched pairs. Limiting the SIFT application to only the salient regions results in significantly reduced computational cost. Moreover, the salient region detection procedure is also limited to the predetermined regions throughout the video sequence in order to increase the efficiency. In addition, the visual attention channels are limited to the most dominant features of the regions. Experimental results that compare the algorithm outputs with ground-truth data reveal that, the proposed algorithm has fine tracking performance together with acceptable computational cost. Promising results are obtained even with blurred video sequences typical of ground vehicles and robots and in an uncontrolled environment.

We describe an ensemble approach to learning salient spatial regions from arbitrarily partitioned simulation data. Ensemble approaches for anomaly detection are also explored. The partitioning comes from the distributed processing requirements of large-scale simulations. The volume of the data is such that classifiers can train only on data local to a given partition. Since the data partition reflects the needs of the simulation, the class statistics can vary from partition to partition. Some classes will likely be missing from some or even most partitions. We combine a fast ensemble learning algorithm with scaled probabilistic majority voting in order to learn an accurate classifier from such data. Since some simulations are difficult to model without a considerable number of false positive errors, and since we are essentially building a search engine for simulation data, we order predicted regions to increase the likelihood that most of the top-ranked predictions are correct (salient). Results from simulation runs of a canister being torn and from a casing being dropped show that regions of interest are successfully identified in spite of the class imbalance in the individual training sets. Lift curve analysis shows that the use of data driven ordering methods provides a statistically significant improvement over the use of the default, natural time step ordering. Significant time is saved for the end user by allowing an improved focus on areas of interest without the need to conventionally search all of the data. We have also found that using random forests weighted and distance-based outlier ensemble methods for supervised learning of anomaly detection provide significant accuracy improvements when compared to existing methods on the same dataset. Further, distance-based outlier and local outlier factor ensemble methods for unsupervised learning of anomaly detection also compare favorably to existing methods.

Recently concern about aviation security has focused on the work of airport security screeners who detect threat items in passengers' luggage. An effective method of training and screening is required for improving screeners' detection abilities and performance to cope with the unreliable human performance of screening. The overall aim of this thesis is to understand and define the potential visual and cognitive factors in the task of inspecting airport passengers' X-ray luggage images, examine usability of perceptual feedback in this demanding task and develop a new method of salient regions which assist screeners to detect targets. The result of this work would obtain knowledge and skills of X-ray luggage images examination, provide insight into the design of training system and develop a method to significantly enhance screeners' detection ability. A questionnaire was developed for screeners to extract the expertise of the screening task and investigate the effect of image features on visual attention. A series of experiments were designed to understand the screening task and explore how knowledge and skills are developed with practice. Results indicated that training under time stressed conditions is recommended for ensuring adequate high detection ability in real life situation as screeners have to balance accuracy and speed in time pressure. The advantages of screeners are better detection ability and search skills which were gained by experience of the search task. Hit rate of naive people was improved with the perceptual exposure of images of threat items. However, scanning did not become efficient. It has demonstrated that detection performance and search skills are improved by the practice of frequency exposure targets in the search task and such ability partly transfer to novel targets. Learning in visual search of threat items is stimuli specific such that familiarity with stimulus and task is the source of performance enhancement. Threat items should be updated constantly and massive amount of X-ray threat objects should be employed for airport security screeners training so as to enlarge object knowledge and enhance recognition ability. Perceptual feedback of circling areas with dwell duration longer than 1000ms does not Significantly improve observers' detection ability in the airport screening task. Features of bags and threat items influence initial attention and attention allocation in the search process. Salient regions, based on the pure stimulus properties, not only contain most of targets in X-ray images but also improve observers' detection performance of high hit rate by forcing observers to scrutinize these areas carefully.

This thesis has presented a computational model for the combination of bottom-up and top-down attentional mechanisms. Furthermore, the use for this model has been demonstrated in a variety of applications of machine and robotic vision. We have observed that an attentional mechanism is imperative in any active vision system, machine as well as biological, since it not only reduces the amount of information that needs to be further processed (for say recognition, action), but also by only processing the attended image regions, such tasks become more robust to large amounts of clutter and noise in the visual field. Using various feature channels such as color, orientation, texture, depth and symmetry, as input, the presented model is able with a pre-trained artificial neural network to modulate a saliency map for a particular top-down goal, e.g. visual search for a target object. More specifically it dynamically combines the unmodulated bottom-up saliency with the modulated top-down saliency, by means of a biologically and psychophysically motivated temporal differential equation. This way the system is for instance able to detect important bottom-up cues, even while in visual search mode (top-down) for a particular object. All the computational steps for yielding the final attentional map, that ranks regions in images according to their importance for the system, are shown to be biologically plausible. It has also been demonstrated that the presented attentional model facilitates tasks other than visual search. For instance, using the covert attentional peaks that the model returns, we can improve scene understanding and segmentation through clustering or scattering of the 2D/3D components of the scene, depending on the configuration of these attentional peaks and their relations to other attributes of the scene. More specifically this is performed by means of entropy optimization of the scence under varying cluster-configurations, i.e. different groupings of the various components of the scene. Qualitative experiments demonstrated the use of this attentional model on a robotic humanoid platform and in a real-time manner control the overt attention of the robot by specifying the saccadic movements of the robot head. These experiments also exposed another highly important aspect of the model; its temporal variability, as opposed to many other attentional (saliency) models that exclusively deal with static images. Here the dynamic aspects of the attentional mechanism proved to allow for a temporally varying trade-off between top-down and bottom-up influences depending on changes in the environment of the robot. The thesis has also lay forward systematic and quantitative large scale experiments on the actual benefits and uses of this kind of attentional model. To this end a simulated 2D environment was implemented, where the system could not “see” the entire environment and needed to perform overt shifts of attention (a simulated saccade) in order to perfom a visual search task for a pre-defined sought object. This allowed for a simple and rapid substitution of the core attentional-model of the system with comparative computational models designed by other researchers. Nine such contending models were tested and compared with the presented model, in a quantitative manner. Given certain asumptions these experiments showed that the attentional model presented in this work outperforms the other models in simple visualsearch tasks. / QC 20111228

By visual attention process biological and machine vision systems are able to select the most relevant regions from a scene. The relevancy process is achieved either by top-down factors, driven by task, or bottom-up factors, the visual saliency, which distinguish a scene region that are different from its surrounding. During the past 20 years numerous research efforts have aimed to model bottom-up visual saliency with many successful applications in computer vision and robotics.In this thesis we have performed a comparison between a state-of-the-art saliency model and subjective test (human eye tracking) using different evaluation methods over three generated dataset of synthetic patterns and natural images. Our results showed that the objective model is partially valid and highly center-biased.By using empirical data obtained from subjective experiments we propose a special function, the Probability of Characteristic Radially Dependency Function, to model the lateral distribution of visual attention process.

The following research was conducted to contribute to the greater understanding of the impact that most often utilized methods of public awareness and education have on behaviors relative to sickness and disease for the general public in terms of action toward prevention behaviors within a healthcare setting. The psycho educational approach is often considered an effective means to promote behavior change as it relates to preventative behavior, and in the clinical therapeutic setting has shown some relevance as an effective procedure. Unfortunately, no research as of yet speaks to the comparative effectiveness this approach may have over other approaches often thought to enhance preventative behavior, such as the more empirically based behavior analytic methods. This study provides such an analysis of the effectiveness each methodology has on changing the behavior of the public at large. Based on a study conducted in the academic setting to increase hand-sanitizing behavior of facility patrons (Loukus & Dixon, in review), this study utilizes the most effective form of prompting found to increase sanitizer use in a public facility. Because healthcare facilities often rely on psycho educational methodologies to influence sanitizer use amongst visitors and employees by placing "sanitizing stations" at the main entrance to the facility, this setting provides an appropriate venue for scientific manipulation of prompting variables to determine effectiveness on public preventative behavior towards sickness and disease, while a simple reversal design enhances the comparative value of effects obtained on behavior through observation and implementation of the two approaches within the setting.

Les études menées dans cette thèse portent sur le rôle des visages dans l'attention visuelle. Nous avons cherché à mieux comprendre l'influence des visages dans les vidéos sur les mouvements oculaires, afin de proposer un modèle de saillance visuelle pour la prédiction de la direction du regard. Pour cela, nous avons analysé l'effet des visages sur les fixations oculaires d'observateurs regardant librement (sans consigne ni tâche particulière) des vidéos. Nous avons étudié l'impact du nombre de visages, de leur emplacement et de leur taille. Il est apparu clairement que les visages dans une scène dynamique (à l'instar de ce qui se passe sur les images fixes) modifie fortement les mouvements oculaires. En nous appuyant sur ces résultats, nous avons proposé un modèle de saillance visuelle, qui combine des caractéristiques classiques de bas-niveau (orientations et fréquences spatiales, amplitude du mouvement des objets) avec cette caractéristique importante de plus haut-niveau que constitue les visages. Enfin, afin de permettre des traitements plus proches du temps réel, nous avons développé une implémentation parallèle de ce modèle de saillance visuelle sur une plateforme multi-GPU. Le gain en vitesse est d'environ 130 par rapport à une implémentation sur un processeur multithread. / Studies conducted in this thesis focuses on faces and visual attention. We are interested to better understand the influence and perception of faces, to propose a visual saliency model with face features. Throughout the thesis, we concentrate on the question, "How people explore dynamic visual scenes, how the different visual features are modeled to mimic the eye movements of people, in particular, what is the influence of faces?" To answer these questions we analyze the influence of faces on gaze during free-viewing of videos, as well as the effects of the number, location and size of faces. Based on the findings of this work, we propose model with face as an important information feature extracted in parallel alongside other classical visual features (static and dynamic features). Finally, we propose a multi-GPU implementation of the visual saliency model, demonstrating an enormous speedup of more than 132 times compared to a multithreaded CPU.