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Radar-based respiration monitoring has been increasingly popular among researchers in biomedical fields during the last decades since it is a contactless monitoring technique. It is very convenient for subjects because it does not impose any restrictions on subjects or require their cooperation. Meanwhile, recognizing alternations in respiratory patterns is an important early clue of the diagnosis of several cardiorespiratory diseases. Thus, a study of biomedical radar-based respiration monitoring and respiratory pattern classification is carried out in this thesis. Radar-based respiration monitoring technology has a shortcoming that the collected respiratory signal will be easily distorted by the body movement of the monitoring subjects or disturbed by environment noise because of the contactless measurement attribute. This shortcoming limits the application of the respiratory pattern classification model, that is, the existing models cannot be applied automatically since the distorted respiratory signal needs to be manually filtered out ahead of the classification. In this study, a new respiratory pattern classification strategy, which can be implemented full-automatic, is proposed. In this strategy, a class “moving” is introduced to classify the distorted signal, and the sampling window length is shortened to reduce the effect caused by the signal distortion. A performance requirement for the continuous respiratory pattern classification is also proposed based on its expected function that can alert the occurrence of the abnormal breathing patterns. Several models which can meet the proposed performance requirement are developed in this thesis based on the state-of-the-art pattern classification technique and the time-series-based shapelet transform algorithm. The proposed models can classify four breathing patterns including eupnea, Cheyne Stokes respiration, Kussmaul breathing and apnea. A radar-collected respiratory signal database is built in this study, and a respiration simulation model which can generate breath samples for pattern classification is developed in this thesis. The proposed models were tested and validated in batch and stream processing manner with independently collected data and continuously collected data, respectively.

Researchers and GIS analysts have used Aerial Laser Scanning (ALS) data to generate Digital Terrain Models (DTM) since the 1990s, and various algorithms developed for ground point extraction have been proposed based on the characteristics of ALS data. However, Terrestrial Laser Scanning (TLS) data, which might be a better indicator of ground morphological features under dense tree canopies and more accessible for small areas, have been long ignored. In this research, the aim was to evaluate if TLS data were as qualified as ALS to serve as a source of a DTM. To achieve this goal, there were three steps: acquiring and aligning ALS and TLS of the same region, applying ground filters on both of the data sets, and comparing the results. Our research area was a 100m by 140m region of grass, weeds and small trees along Strouble's Creek on the Virginia Tech campus. Four popular ground filter tools (ArcGIS, LASTools, PDAL, MCC) were applied to both ALS and TLS data. The output ground point clouds were then compared with a DTM generated from ALS data of the same region. Among the four ground filter tools employed in this research, the distances from TLS ground points to the ALS ground surface were no more than 0.06m with standard deviations less than 0.3m. The results indicated that the differences between the ground extracted from TLS and that extracted from ALS were subtle. The conclusion is that Digital Terrain Models (DTM) generated from TLS data are valid. / Master of Science / Elevation is one of the most basic data for researches such as flood prediction and land planning in the field of geography, agriculture, forestry, etc. The most common elevation data that could be downloaded from the internet were acquired from field measurements or satellites. However, the finest grained of that kind of data is 1/3m and errors can be introduced by ground objects such as trees and buildings. To acquire more accurate and pure-ground elevation data (also called Digital Terrain Models (DTM)), Researchers and GIS analysts introduced laser scanners for small area geographical research. For land surface data collection, researchers usually fly a drone with laser scanner (ALS) to derive the data underneath, which could be blocked by ground objects. An alternative way is to place the laser scanner on a tripod on the ground (TLS), which provides more data for ground morphological features under dense tree canopies and better precision. As ALS and TLS collect data from different perspectives, the coverage of a ground area can be different. As most of the ground extraction algorithm were designed for ALS data, their performance on TLS data hasn’t been fully tested yet. Our research area was a 100m by 140m region of grass, weeds and small trees along Strouble’s Creek on the Virginia Tech campus. Four popular ground filter tools (ArcGIS, LASTools, PDAL, MCC) were applied to both ALS and TLS data. The output ground point clouds were then compared with a ground surface generated from ALS data of the same region. Among the four ground filter tools employed in this research, the distances from TLS ground points to the ALS ground surface were no more than 0.06m with standard deviations less than 0.3m. The results indicated that the differences between the ground extracted from TLS and that extracted from ALS were subtle. The conclusion is that Digital Terrain Models (DTM) generated from TLS data are valid.

Most mineral and coal processing plants are forced to size their particulate streams in order to maximize the efficiency of their unit operations. Classifiers are generally considered to be more practical than screens for fine sizing, but the separation efficiency decreases dramatically for particles smaller than approximately 150 μm. In addition, classifiers commonly suffer from bypass, which occurs when a portion of the ultrafine particles (slimes) are misplaced by hydraulic carryover into the oversize product. The unwanted misplacement can have a large adverse impact on downstream separation processes. One method of reducing bypass is to inject water into the cyclone apex. Unfortunately, existing water injection systems tend to substantially increase the particle cut size, which makes it unacceptable for ultrafine sizing applications. A new apex washing technology was developed to reduce the bypass of ultrafine material to the hydrocyclone underflow while maintaining particle size cuts in the 25-50 m size range. Another method of reducing bypass is to retreat the cyclone underflow using multiple stages of classifiers. However, natural variations in the physical properties of the feed make it difficult to calculate the exact improvement offered by multistage classification in experimental studies. Therefore, several mathematical equations for multistage classification circuits were evaluated using mathematical tools to calculate the expected impact of multistage hydrocyclone circuits on overall cut size, separation efficiency and bypass. These studies suggest that a two-stage circuit which retreats primary underflow and recycles secondary overflow offers the best balance between reducing bypass and maintaining a small cut size and high efficiency. / Master of Science

<p> This project studies the applicability of two ordination methods, principle component analysis and correspondence analysis, and one classification method, mode analysis, for a specific ecological data set. The differences between techniques are discussed and the results are compared.</p> / Thesis / Master of Science (MSc)

Hidden Markov models (HMM) with Gaussian mixture observation densities are the dominant approach in speech recognition. These systems typically use a representational model for acoustic modeling which can often be prone to overfitting and does not translate to improved discrimination. We propose a new paradigm centered on principles of structural risk minimization using a discriminative framework for speech recognition based on support vector machines (SVMs). SVMs have the ability to simultaneously optimize the representational and discriminative ability of the acoustic classifiers. We have developed the first SVM-based large vocabulary speech recognition system that improves performance over traditional HMM-based systems. This hybrid system achieves a state-of-the-art word error rate of 10.6% on a continuous alphadigit task ? a 10% improvement relative to an HMM system. On SWITCHBOARD, a large vocabulary task, the system improves performance over a traditional HMM system from 41.6% word error rate to 40.6%. This dissertation discusses several practical issues that arise when SVMs are incorporated into the hybrid system.

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