Thesis (M.A.)--University of Toledo, 2009. / Typescript. "Submitted as partial fulfillment of the requirements for The Master of Arts in Philosophy." "A thesis entitled"--at head of title. Bibliography: leaves 80-83.

Thesis (M.S.)--University of Wisconsin--Madison, 1984. / Typescript. eContent provider-neutral record in process. Description based on print version record. Includes bibliographical references (leaves 75-80).

Thesis (M. Sc.)--University of Hong Kong, 1997. / Includes bibliographical references (leaf 98-102).

Thesis (M. Phil.)--University of Hong Kong, 2002. / Includes bibliographical references (leaves 117-121).

Thesis (M.S.)--University of Wisconsin--Madison, 1980. / Typescript. eContent provider-neutral record in process. Description based on print version record. Includes bibliographical references (leaves 83-84).

Thesis (Ph. D.)--Case Western Reserve University, 2010. / [School of Medicine] Department of Biomedical Engineering. Includes bibliographical references.

Thesis (Ph.D.)--Aberdeen University, 2009. / Title from web page (viewed on July 20, 2009). Includes bibliographical references.

Deep-sea systems are understudied compared to any other ecological system on Earth, but they are important for ecosystem functioning and services. The deep sea is important in the climatic regulation of Earth, and it is a new frontier for resource provisioning for humanity. Impacts, such as increased carbon emissions and deep-sea fishing and mining will likely influence the system, but these effects are not well understood. To recognise these impacts, common patterns in community structure need to be understood. This study aims to assess community structure in the deep sea by looking at patterns in body size and biodiversity. It uses polychaetes (bristle worms) as a study group as they are the most abundant group in the benthic macrofauna in terms of density and play key roles in the food web. Body size is an important component of the community structure, as body size is correlated with many other traits of the organism, from physiological rates (e.g. heart or breathing rates) to population dynamics (e.g. production rates or population abundances) and species richness. It is thought that body size of deep-sea (endo)benthic organisms declines with increasing depth, which is often related to food availability which itself declines with increasing depth. Many contradictory results on body-size change with increasing depth, however, have been reported, including no change, increasing, or a parabolic relationship. It is demonstrated here (Chapter 2) that there is much variety in body-size estimates between different geographic regions and taxonomic groups. These differences can ultimately influence the predictions of other traits, and might hint at what might happen in changing climatic conditions. It sets the basis to argue that there should be a focus on explaining why there are differences, instead of focusing on finding a general trend for organisms in all geographical regions. Furthermore, it is unlikely that food availability alone can explain a change in body size. An alternative explanation is offered (Chapter 3), where habitat complexity is shown to influence body size. Sponge density, in the form of habitat complexity, can have a structuring effect on the community potentially through the loss of spicules that add complexity to soft-sediments, and this in turn can influence body size of organisms. Deep-sea community structure in terms of family richness has been studied at local spatial scale. Fewer studies have been performed on regional spatial scale and these studies lack extensive sampling coverage of environmental gradients. Here (Chapter 4), the first study is presented on the maintenance of deep-sea family composition on regional scale with high sampling coverage along a variety of environmental gradients. It is shown that energy (food) availability, habitat complexity, and long-term temperature are important in influencing the polychaete distribution in this region. It is shown that there is an unusual high proportion of an opportunistic group, the Capitellidae, present in the study area. Biodiversity is important for the maintenance of ecosystem functioning, but human impacts result in the restructuring of biodiversity. The first deep-sea biodiversity - ecosystem functioning relationship for macrofauna is presented (Chapter 5). It is shown that there is a positive and saturating relationship between biodiversity and ecosystem functioning. However, fishing intensity seems to influence this relationship by potentially affecting secondary biomass production, abundance and taxonomic and functional diversity measures. It is suggested that as the disturbance of fishing negatively impacts taxonomic and functional evenness, a system is created where opportunistic species are dominant, like communities found in disturbed areas such as under fish farms. This will have consequences for the state of the system and energy transfer to trophic levels higher up.

Face recognition, although being a popular area of research for over a decade has still many open research challenges. Some of these challenges include the recognition of poorly illuminated faces, recognition under pose variations and also the challenge of capturing sufficient training data to enable recognition under pose/viewpoint changes. With the appearance of cheap and effective multimodal image capture hardware, such as the Microsoft Kinect device, new possibilities of research have been uncovered. One opportunity is to explore the potential use of the depth maps generated by the Kinect as an additional data source to recognize human faces under low levels of scene illumination, and to generate new images through creating a 3D model using the depth maps and visible-spectrum/RGB images that can then be used to enhance face recognition accuracy by improving the training phase of a classification task. With the goal of enhancing face recognition, this research first investigated how depth maps, since not affected by illumination, can improve face recognition, if algorithms traditionally used in face recognition were used. To this effect a number of popular benchmark face recognition algorithms are tested. It is proved that algorithms based on LBP and Eigenfaces are able to provide high level of accuracy in face recognition due to the significantly high resolution of the depth map images generated by the latest version of the Kinect device. To complement this work a novel algorithm named the Dense Feature Detector is presented and is proven to be effective in face recognition using depth map images, in particular under wellilluminated conditions. Another technique that was presented for the goal of enhancing face recognition is to be able to reconstruct face images in different angles, through the use of the data of one frontal RGB image and the corresponding depth map captured by the Kinect, using faster and effective 3D object reconstruction technique. Using the Overfeat network based on Convolutional Neural Networks for feature extraction and a SVM for classification it is shown that a technically unlimited number of multiple views can be created from the proposed 3D model that consists features of the face if captured real at similar angles. Thus these images can be used as real training images, thus removing the need to capture many examples of a facial image from different viewpoints for the training of the image classifier. Thus the proposed 3D model will save significant amount of time and effort in capturing sufficient training data that is essential in recognition of the human face under variations of pose/viewpoint. The thesis argues that the same approach can also be used as a novel approach to face recognition, which promises significantly high levels of face recognition accuracy base on depth images. Finally following the recent trends in replacing traditional face recognition algorithms with the effective use of deep learning networks, the thesis investigates the use of four popular networks, VGG-16, VGG-19, VGG-S and GoogLeNet in depth maps based face recognition and proposes the effective use of Transfer Learning to enhance the performance of such Deep Learning networks.

A new data capturing technique for a potentially coupled bus of lines is proposed that always accommodates fast operation. The proposed method utilizes multiple reference voltages available within a line's receiving logic and the initial conditions of the involved wires in order to determine early and accurately the transmitted data in the current cycle. The presented data reading technique rarely requires repeater insertion and it can considerably accelerate signal propagation. The introduced logic at the receiver-end of a victim wire entails an affordable area overhead. Experimental results are given in the 65nm CMOS process for interconnects of various lengths. An architecture is proposed that allows for data reading with fault detection capability on lines which are likely to operate under a potentially wide range of capacitive coupling. In order to develop such a methodology, multiple reference or threshold voltages in the receiving logic of the lines are considered instead of typically one. The proposed technique utilizes the additional reference voltages to evaluate whether an intermittent fault has occurred during the capture of the transmitted data. Some combinational logic is introduced on the receiver side to accomplish this task. The mechanism is initially illustrated on a line with one adjacent aggressor. Subsequently, the case of a line with two adjacent aggressors is discussed and it is shown how to generalize the technique for wide buses. In this work the efficiency of the detection mechanism is evaluated for both single and multiple faulty occurrences. A novel circuit to treat crosstalk induced glitches on local interconnects is presented. Design irregularities and manufacturing defects on wires may result in spurious electrical events that impact the reliability of the interconnect infrastructure. The proposed methods act by dynamically adjusting the threshold voltage of the receiving gate on the victim line. The proposed technique can be used in combination with encoding algorithms on data buses. A comparative study in the 180nm CMOS process is presented that supports the applicability of the approach. Transient faults due to radiation have become increasingly observable in combinational logic. This is due to the weakening of inherent protective mechanisms that logic traditionally held against such flawed spurious events. Further boosting of such effects is increasingly probable due to the interaction of transients appearing at the inputs of logic gates. Such multiple instances of transients can arise either because of re-convergent circuit paths or because of significant reduction in the critical charge of modern technologies. The latter, in particular, makes more than one circuit nodes susceptible to the same high energy ions. A static transient propagation is employed to address possible transient interaction and to compute its worst-case effects in logic. The quantified effects of interest are the maximum duration and slope of the resulting hazards at the circuit outputs. A hardening methodology is also proposed to protect combinational logic from such events. For this purpose, filtering circuits are inserted in logic and several placement algorithms are developed and evaluated.