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Patients with SCI sometimes recover lost function after using FES. This phenomenon, known as the carry-over effect, is not fully understood. One theory used to explain this mechanism is that electrical stimulation of the peripheral nerve causes antidromic action potentials to reach the anterior horn cells in time with the patient’s voluntary effort. This may reinforce the motor pathways and consequently restore voluntary control. However, the theory has never been properly tested and testing requires a method of measuring the voluntary drive. This project aims to find out whether it is possible to estimate the voluntary drive from measured myoelectric signals. The project is based on an FES cycling system with the ability to adjust the stimulation intensity relating to the corresponding voluntary drive. In paretic muscles, the weak voluntary contraction produces an EMG response. The EMG signal cannot be used directly as an indication of the voluntary drive because of the presence of stimulus artefact and reflexes. Two methods were investigated to estimate the voluntary drive. A time domain method was tested using RMS EMG extracted from a range of time windows following the stimulation pulse. This approach was unsatisfactory because the large variations seen in the RMS EMG amplitudes for the same power output as well as the low sensitivity of it to the change of power output. A frequency domain approach was then tested using coherence between co-contracting muscles. It was encouraging to see that the area under the coherence curve in the β band reflected changes in the power output level. However, further tests showed that this area was also greatly influenced by exercise time, becoming unpredictable after 3 minutes. In conclusion, neither of the two methods of using the myoelectric signal from muscles under stimulation is practical for the estimation of voluntary drive.

Enterprise risk management (ERM) has received significant attention in recent years in order to establish a comprehensive approach for managing different types of events (risks and opportunities) relevant to organizations' strategic objectives. Although the number of ERM implementations among Saudi organizations has grown significantly, there is a lack of knowledge of key factors to be considered when implementing ERM. Therefore, the purpose of this study is to empirically determine the factors affecting ERM implementation based on how the 44 ERM dimensions, derived from the Committee of Sponsoring Organizations ERM Integrated Framework, were actually implemented in a sample of Saudi organizations. The exploratory factor analysis method is used to analyze a sample of 103 responses received through an online survey questionnaire. The results of the exploratory analysis support the retention of three factors that require specific considerations when implementing ERM, namely, (1) the ERM structure and standards, (2) the enterprise's portfolio of risks and opportunities and (3) risk oversight and corporate governance. This study differs from previous research because it is the first to explore ERM implementation among Saudi organizations and because its findings provide a foundational understanding of the key factors affecting ERM implementation. This study also suggests an ERM implementation framework that addresses identified factors and guides organizations on how to enhance ERM implementation. Reflecting this suggested framework in practice helped the researcher's organization to identify gaps in its ERM program and develop actions to improve ERM implementation. The actionable knowledge generated focuses more on developing integrated ERM and strategic planning processes to have a better view of the enterprise's key risks and opportunities relevant to the strategic objectives and defining responsibilities of the leadership team to establish clear accountability for risk oversight.

The thesis considers customer security in a major European bank Bank X. The action research focus is to strengthen the processes and the overall solution for online Bank customers which experience a significant growth and increase in use. There seem to be a common misperception between the internal staff of the bank and its customers regarding what security is, and the research considers several new security situations in the banking business. Moving from branch-oriented business to a digital orientation within the time space of just a few years has challenged banks to create new solutions for their customers. Such solutions must be secure, but technical security need separation from human security since new approaches as for example social engineering easily can be a source for fraud and losses for customers in a modern digital bank. This thesis uses action research by interviewing leaders and team members in one security team. The internal researcher role also introduce proposals and suggestions to the team during the research, which locate the action. A connection between stakeholders’ security definitions, re-organisations and the end solutions is important to understand, to improve the situation. Findings reveal that internal communications have an external customer’s communications effect and that re-organisational tasks should focus to adjust the external factors, not only internal benefits. Re-organisational planning does not increase customer security instantly, but some participants can see long-term benefits. The problem is, however, that the long-term world-view changes continuously, and without adjustments organisations depending on cyber security fall behind in the new, but ongoing battle between usability vs security and cost. A modern digital bank should no longer act as a traditional bank, but more as an IT company. This claim opens the way for new ideas and solutions, but blending the banking business and the computer industry can be challenging due to the professional distance between the groups. Future re-organisations should therefore aim for cross-divisional collaboration, increasing communication internally and opening routes for a dualistic contact with customers whereby information and awareness campaigns can be just as effective as technical and physical firewalls. The computer and banking industries are now working towards a remote market with a distance to the online-customers, and while the computer industry has experience to perform business with less customer contact, the banks have now also moved from close contact to digital contact. For this reason, modern banks must think differently and understand what the business is and how to secure it, but never forget the major resource and security problem – the human being.

Most consumer images serve emotional functions as well as informational ones. The impression of an image can be affected not only by physical properties e. g. size, colour and media but also by the context of images, aesthetic properties and social/personal backgrounds of observers. However in traditional frameworks in image quality studies, the impacts of colour-appearance attributes on image quality have focused on maximising the informational functions of images, considering an image as a reproduced copy of a real scene. Thus, a new approach was adopted in this study in an attempt to investigate the emotional aspect of an image. The goal of this research is to study the impact of colour-appearance attributes of an image on emotional responses, and to develop quantitative models for predicting emotional response considering the context of the image. To achieve this goal, three sets of psychophysical and physiological experiments have been conducted. First, the relationship between colour-appearance attributes and overall affective response to images was investigated for four different types of image contents. It was found that image colourfulness and lightness contrast had a consistent influence on these relationships for all types of images. The relationships between emotional responses of image pleasantness and excitement were significantly different between positive images and negative images. Accordingly, quantitative models of image pleasantness and excitement were developed as a function of image colourfulness and contrast separately for the two groups of images. Finally, models of image pleasantness and excitement for positive and negative images were developed as a linear equation based on models developed for each colour attribute. The relationships between colour-appearance attributes and responses on colour-emotion scales, active-passive, heavy-light and warm-cool, were also studied for four different types of image content. Quantitative models of the three colour-emotion scales were developed as a function of colour attributes of images such as lightness, colourfulness and lightness contrast. As an application of using the colour-emotion model developed for images, the relationships between colouremotion scales and image emotion were investigated and quantitative models of image pleasantness and excitement were developed as functions of three colouremotion scales for two groups of images: positive and negative. The model performance based on the colour-emotion scales was compared with the performance of models based on the colour attributes. As a result, the latter model performed better than former. The impact of image content and colour attributes of an image on emotional responses to images was investigated by measuring physiological responses to images which were compared with the psychophysical responses. It was found that the activities in skin conductance and heart rate showed significantly greater responses for the images with personal meanings and significances. For the effect of colour attributes in images, it was found that more chromatic images generated higher activity in skin conductance responses. It was also found that lower contrast images generated higher activity in corrugator EMG responses

The memory colour reproduction is an important factor in judging image quality of photographic images of real life scenes. As the most important memory colour category, skin tone was extensively studied for preferred colour reproduction in this research. The methodology to study skin colour preference was then applied to study the colour preference of two other important colour categories: green foliage and blue sky. There are three essential parts for preferred skin colour enhancement: 1) building a skin colour model to detect skin colours or skin pixels; 2) finding a preferred skin colour region or a preferred skin colour centre; and 3) developing an algorithm to morph skin colours toward the preferred skin colour region. This study for skin colour enhancement started with the mathematical modelling of the skin colour region for skin colour detection. The modelling of skin colours was then applied to adjust skin colours of test images for psychophysical experiments that were to determine a preferred skin colour region. Finally, the skin colour modelling and the preferred skin colour centres were applied to adjust skin colours of digital photographic images for preferred colour reproduction. Two approaches were developed to model the skin colour distribution for skin colour detection. The first approach was to model a local colour region for general applications. A convex hull is constructed to fit the geometrical shape of a local region, and then the convex hull is approximated with mathematical formulae. The formulations and data fitting are adjusted with interactive 3-D visualization. The approach is flexible for fitting data gamut with various mathematical forms for different purposes. The other approach was to model skin colours with elliptical shapes. Three elliptical skin colour models were developed for skin colour detection. The first one is to model the skin colour cluster using a single ellipse ignoring the lightness (or luminance) dependency. It is simple and efficient, and the skin colour detection accuracy may be adequate for many applications. In the second model, the skin colour ellipse is adapted to different lightness so that the shape of the ellipse fits the skin colour cluster more accurately. The model is more complex to train and is less efficient in computation, but it is more accurate in skin colour detection. In the third method, an ellipsoid is trained to fit the skin colour cluster. It is almost as simple to train as the first model, but the skin colour detection accuracy is improved. Finally, these models were applied to train mixed skin colours, African skin colours, Caucasian skin colours, and Asian skin colours.