With the increase in health consciousness, noninvasive body monitoring has aroused interest among researchers. As one of the most important pieces of physiological information, researchers have remotely estimated heart rates from facial videos in recent years. Although progress has been made over the past few years, there are still some limitations, such as the increase in processing time with accuracy and the lack of comprehensive and challenging datasets for use and comparison. Recently, it was shown that heart rate information can be extracted from facial videos by spatial decomposition and temporal filtering. Inspired by this, a new framework is introduced in this thesis for remotely estimating the heart rate under realistic conditions by combining spatial and temporal filtering and a convolutional neural network. Our proposed approach exhibits better performance compared with that of the benchmark on the MMSE-HR dataset in terms of both the average heart rate estimation and short-term heart rate estimation. High consistency in short-term heart rate estimation is observed between our method and the ground truth.

abstract: Due to restructuring and open access to the transmission system, modern electric power systems are being operated closer to their operational limits. Additionally, the secure operational limits of modern power systems have become increasingly difficult to evaluate as the scale of the network and the number of transactions between utilities increase. To account for these challenges associated with the rapid expansion of electric power systems, dynamic equivalents have been widely applied for the purpose of reducing the computational effort of simulation-based transient security assessment. Dynamic equivalents are commonly developed using a coherency-based approach in which a retained area and an external area are first demarcated. Then the coherent generators in the external area are aggregated and replaced by equivalenced models, followed by network reduction and load aggregation. In this process, an improperly defined retained area can result in detrimental impacts on the effectiveness of the equivalents in preserving the dynamic characteristics of the original unreduced system. In this dissertation, a comprehensive approach has been proposed to determine an appropriate retained area boundary by including the critical generators in the external area that are tightly coupled with the initial retained area. Further-more, a systematic approach has also been investigated to efficiently predict the variation in generator slow coherency behavior when the system operating condition is subject to change. Based on this determination, the critical generators in the external area that are tightly coherent with the generators in the initial retained area are retained, resulting in a new retained area boundary. Finally, a novel hybrid dynamic equivalent, consisting of both a coherency-based equivalent and an artificial neural network (ANN)-based equivalent, has been proposed and analyzed. The ANN-based equivalent complements the coherency-based equivalent at all the retained area boundary buses, and it is designed to compensate for the discrepancy between the full system and the conventional coherency-based equivalent. The approaches developed have been validated on a large portion of the Western Electricity Coordinating Council (WECC) system and on a test case including a significant portion of the eastern interconnection. / Dissertation/Thesis / Ph.D. Electrical Engineering 2011

Identification of individuals has been solved with many different solutions around the world, either using biometric data or external means of verification such as id cards or RFID tags. The advantage of using biometric measurements is that they are directly tied to the individual and are usually unalterable. Acquiring dependable measurements is however challenging when the individuals are uncooperative. A dependable system should be able to deal with this and produce reliable identifications. The system proposed in this thesis can autonomously classify uncooperative specimens from depth data. The data is acquired from a depth camera mounted in an uncontrolled environment, where it was allowed to continuously record for two weeks. This requires stable data extraction and normalization algorithms to produce good representations of the specimens. Robust descriptors can therefore be extracted from each sample of a specimen and together with different classification algorithms, the system can be trained or validated. Even with as many as 138 different classes the system achieves high recognition rates. Inspired by the research field of face recognition, the best classification algorithm, the method of fisherfaces, was able to accurately recognize 99.6% of the validation samples. Followed by two variations of the method of eigenfaces, achieving recognition rates of 98.8% and 97.9%. These results affirm that the capabilities of the system are adequate for a commercial implementation.

Fractal was first used in measuring the length of the coastline, with the fractal research and development, not only to break the traditional Archimedean geometry, but also to explain many scientific to ignore the complexity and nature of nonlinear phenomena structure .Fractal has been widely applied to such as physics, astronomy, geography and sociology and other fields, as a wave of interdisciplinary research in recent years. Coastal areas has always been cultural, economic and activities areas since ancient times. Coastal zone was land and sea for the interaction region by a variety of factors (ex: waves, tides, currents and wind, etc.) continue to function, derived from different coastal terrain. Therefore changes in the coast of the deep impact of humanity. Under the principle of the conservation and development, Coastal areas should be use of modern technology to prediction, analysis, assessment, planning, and management, so that a sustainable preservation of coastal resources. In this study, static and dynamic predict and simulation the coast shape base on fractal. The static part is observation of 29 beaches in South China coast. And collect and calculate the parameters and fractal dimensions of the coast. Through the shape of image processing and analysis of information, to find two generators of the coast. Through the data mining technology to identify the criteria for classification, and to simulation the coastline by generate iterations method. The dynamic part is based on hydraulic model¡¦s results, the use of traditional multiple linear regression and neural network to compare the dynamic prediction of the coastline. The results show that the use of neural networks to predict than the use of multiple linear regression, and effect of use difference angle (£c) to predict sub-coastlines than the effect of not use difference angle (£c) to predict, and add fractal dimension can effectively reduce the predict error and increase the degree of interpretation.

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