The introduction of optical technology has brought forth the age of high speed communications. Emerging high bandwidth applications, coupled with existing applications are providing a strong push for the realization of integrated high speed networks. The rapid development of lightwave technology offers the network architect several terahertz of bandwidth in a single optical fiber. However, we do not expect to see the implementation of all-optical networks in the near future, so we are forced to consider hybrid networks that employ a combination of both lightwave and electronic technologies. One approach, called wavelength division multiplexing (WDM), could provide a large number of high speed channels on a single optical fiber by independently modulating different wavelengths of light in the spectrum passed by the fiber. This dissertation presents a new approach to the emerging areas of satellite communication and high speed local area networks. These areas generated new and challenging research problems that have to be solved for efficient system operation. For the satellite cluster with optical intersatellite links, the first slot assignment algorithm is developed. The first approach for the time-wavelength allocation algorithms that incorporate the unique aspects of WDM communication for the single star based and multiple star based systems is also presented in this dissertation. The virtual topology design in multihop lightwave networks is investigated to minimize the number of wavelengths in the system.

Thesis (Ph.D.)--Aberdeen University, 2009. / With: Clustering with self-organised spiking neural network / Muhamad K. Amin ... et al. Joint 4th International Conference on Soft Computing and Intelligent Systems (SCIS) and 9th International Symposium on Advanced Intelligent Systems (SIS) Sept. 17-21, 2008, Nagoya. Japan. Includes bibliographical references.

This thesis presents a Multiple Self-Organised Spiking Neural Networks (MSOSNN). The aim of this architecture is to achieve a more biologically plausible artificial neural network. Spiking neurons with delays are proposed to encode the information and perform computations. The proposed method is further implemented to enable unsupervised competitive and self-organising learning. The method is evaluated by application to real world datasets. Computer simulation results show that the proposed method is able to function similarly to conventional neural networks i.e. the Kohonen Self-Organising Maps. The SOSNN are further combined to form multiple networks of the Self-Organised Spiking Neural Networks. This network architecture is structured into <i>n</i> component modules with each module providing a solution to the sub-task and then combined with other modules to solve the main task. The training is made in such a way that a module becomes a winner at each step of the learning phase. The evaluation using different data sets as well as comparing the network to a single unity network showed that the proposed architecture is very useful for high dimensional input vectors. The Multiple SOSNN architecture thus provides a guideline for a complex large-scale network solution.

Thesis (Ph.D.)--Stanford University, 2005. / (UnM)AAI3187317. Source: Dissertation Abstracts International, Volume: 66-08, Section: A, page: 3044. Adviser: Benjamin Van Roy.

Thesis (M.S.)--Ohio University, June, 1994. / Title from PDF t.p.

This thesis is about students’ approaches to learning (SAL) in computer science education. Since the initial development of SAL instruments and inventories in the 70’s, they have been used as a means to understand students’ approaches to learning better, as well as to measure and predict academic achievement (such as retention, grades and credits taken) and other correlating factors. It is an instrument to measure a student’s study strategies – not how “good” a student is. A Swedish short version of Approaches and Study Skills Inventory for Students (ASSIST) was used to gather information on whether we, through context and content, encouraged sustainable study behaviour among our students. ASSIST was used in two distinct situations: 1) Evaluation and evolvement of an online programming course design, and 2) Engineering education in media technology and computer science in a campus environment where approaches to learning has been evaluated and studied over time during the five year long programmes. Repeated measurements have been analysed against factors predicting academic achievement, and have been evaluated on a cohort level (not individual) in order to clarify patterns rather than individual characteristics. Significant for both projects was that a surface approach to learning correlated negatively with retention. Students who adopted a combination of deep and strategic approach to learning performed better in terms of grades, ECTS credits completed and perceived value of the education. As part of developmental tools it can be beneficial to use ASSIST at a group level in order to see what kind of approach a course design or a programme supports among the students. / <p>QC 20161028</p>

<p>Computer programming is a difficult undertaking for novices, requiring a lot of patience and persistence. Hence, in a programming class, students experience an array of emotions that may promote or thwart their performance and learning. For instance, frustration may reduce students' motivation to learn programming. In extreme cases, continued frustration may convince students to abandon plans for engineering or computing careers. Even though emotions are crucial for learning, very little is known about how students experience emotions in an introductory programming class. </p><p>In this dissertation, I report my investigation of emotions experienced by first-year engineering students during programming tasks, the reasons for experiencing those emotions, and the self-regulation strategies they adopted to cope with those emotions.</p><p>I recruited 17 novice first-year engineering students taking an introductory programming class for the first time. Each participant took part in two sessions, which collected multi-modal data: programming task and retrospective think-aloud interview. During the programming task, participants worked on four programming problems for thirty minutes. In this session, I collected five types of data: screen capture, facial expressions, eye-gaze data, electrodermal activity, and survey instruments that assessed emotions and neuroticism. After the programming task, I conducted a short post-task interview to ask follow-up questions. The participants returned three to seven days after the programming task for a retrospective think-aloud interview. During this session, participants viewed a video of their actions during the programming task. After every two minutes of viewing, I paused the video and asked about the emotions they experienced during that segment.</p><p>The overarching findings from this study suggest the students experienced frustration most frequently while working on programming problems. Students also experienced multiple emotions because of the same event. For instance, one student reported feeling annoyed because she had made a mistake, but she also experienced joy when she was able to fix the mistake. Findings of this study also suggest that most students tended to persevere despite encountering errors. When they overcame the errors, they experienced joy and pride.</p><p>A better understanding of student emotions may help educators design curriculum and pedagogy to help mitigate the effects of negative emotions, and to promote positive emotions. This improved curriculum and pedagogy may eventually help students maximize their learning and performance in programming courses. Subsequently, student motivation and interest in programming may also be increased by using this improved and enhanced curriculum and pedagogy.</p>