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The revolutionary movement in the colonial countries: Speech, revised and augments, delivered August 7, 1935
Title
The revolutionary movement in the colonial countries: Speech, revised and augments, delivered August 7, 1935.
Creator
Ch'ên, Shao yü, Wang Ming [pseud.]
Date Issued
1935
Identifier
367845, CFDT367845, ucf:5352
Format
Document (PDF)
PURL
http://purl.flvc.org/FCLA/DT/367845
A NEW DEVELOPMENT OF FEEDBACK CONTROLLER FOR LEFT VENTRICULAR ASSIST DEVICE
Title
A NEW DEVELOPMENT OF FEEDBACK CONTROLLER FOR LEFT VENTRICULAR ASSIST DEVICE.
Creator
Wang, Yu, Simaan, Marwan, University of Central Florida
Abstract / Description
The rotary Left Ventricular Assist Device (LVAD) is a mechanical pump surgically implanted in patients with end-stage congestive heart failure to help maintain the flow of blood from the sick heart. The rotary type pumps are controlled by varying the impeller speed to control the amount of blood flowing through the LVAD. One important challenge in using these devices is to prevent the occurrence of excessive pumping of blood from the left ventricle (known as suction) that may cause it to...
Show moreThe rotary Left Ventricular Assist Device (LVAD) is a mechanical pump surgically implanted in patients with end-stage congestive heart failure to help maintain the flow of blood from the sick heart. The rotary type pumps are controlled by varying the impeller speed to control the amount of blood flowing through the LVAD. One important challenge in using these devices is to prevent the occurrence of excessive pumping of blood from the left ventricle (known as suction) that may cause it to collapse due to the high pump speed. The development of a proper feedback controller for the pump speed is therefore crucial to meet this challenge. In this thesis, some theoretical and practical issues related to the development of such a controller are discussed. First, a basic nonlinear, time-varying cardiovascular-LVAD circuit model that will be used to develop the controller is reviewed. Using this model, a suction index is tested to detect suction. Finally we propose a feedback controller that uses the pump flow signal to regulate the pump speed based on the suction index and an associated threshold. The objective of this controller is to continuously update the pump speed to adapt to the physiological changes of the patient while at the same time avoiding suction. Simulation results are presented under different conditions of the patient activities. Robustness of the controller to measurement noise is also discussed.
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Date Issued
2010
Identifier
CFE0003296, ucf:48497
Format
Document (PDF)
PURL
http://purl.flvc.org/ucf/fd/CFE0003296
Stochastic-Based Computing with Emerging Spin-Based Device Technologies
Title
Stochastic-Based Computing with Emerging Spin-Based Device Technologies.
Creator
Bai, Yu, Lin, Mingjie, DeMara, Ronald, Wang, Jun, Jin, Yier, Dong, Yajie, University of Central Florida
Abstract / Description
In this dissertation, analog and emerging device physics is explored to provide a technology plat- form to design new bio-inspired system and novel architecture. With CMOS approaching the nano-scaling, their physics limits in feature size. Therefore, their physical device characteristics will pose severe challenges to constructing robust digital circuitry. Unlike transistor defects due to fabrication imperfection, quantum-related switching uncertainties will seriously increase their sus-...
Show moreIn this dissertation, analog and emerging device physics is explored to provide a technology plat- form to design new bio-inspired system and novel architecture. With CMOS approaching the nano-scaling, their physics limits in feature size. Therefore, their physical device characteristics will pose severe challenges to constructing robust digital circuitry. Unlike transistor defects due to fabrication imperfection, quantum-related switching uncertainties will seriously increase their sus- ceptibility to noise, thus rendering the traditional thinking and logic design techniques inadequate. Therefore, the trend of current research objectives is to create a non-Boolean high-level compu- tational model and map it directly to the unique operational properties of new, power efficient, nanoscale devices.The focus of this research is based on two-fold: 1) Investigation of the physical hysteresis switching behaviors of domain wall device. We analyze phenomenon of domain wall device and identify hys- teresis behavior with current range. We proposed the Domain-Wall-Motion-based (DWM) NCL circuit that achieves approximately 30x and 8x improvements in energy efficiency and chip layout area, respectively, over its equivalent CMOS design, while maintaining similar delay performance for a one bit full adder. 2) Investigation of the physical stochastic switching behaviors of Mag- netic Tunnel Junction (MTJ) device. With analyzing of stochastic switching behaviors of MTJ, we proposed an innovative stochastic-based architecture for implementing artificial neural network (S-ANN) with both magnetic tunneling junction (MTJ) and domain wall motion (DWM) devices, which enables efficient computing at an ultra-low voltage. For a well-known pattern recognition task, our mixed-model HSPICE simulation results have shown that a 34-neuron S-ANN imple- mentation, when compared with its deterministic-based ANN counterparts implemented with dig- ital and analog CMOS circuits, achieves more than 1.5 ? 2 orders of magnitude lower energy consumption and 2 ? 2.5 orders of magnitude less hidden layer chip area.
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Date Issued
2016
Identifier
CFE0006680, ucf:51921
Format
Document (PDF)
PURL
http://purl.flvc.org/ucf/fd/CFE0006680
Techniques for boosting the performance in Content-Based Image Retrieval Systems
Title
Techniques for boosting the performance in Content-Based Image Retrieval Systems.
Creator
Yu, Ning, Hua, Kien, Hughes, Charles, Dutton, Ronald, Wang, Chung-Ching, University of Central Florida
Abstract / Description
Content-Based Image Retrieval has been an active research area for decades. In a CBIR system, one or more images are used as query to search for similar images. The similarity is measured on the low level features, such as color, shape, edge, texture. First, each image is processed and visual features are extracted. Therefore each image becomes a point in the feature space. Then, if two images are close to each other in the feature space, they are considered similar. That is, the k nearest...
Show moreContent-Based Image Retrieval has been an active research area for decades. In a CBIR system, one or more images are used as query to search for similar images. The similarity is measured on the low level features, such as color, shape, edge, texture. First, each image is processed and visual features are extracted. Therefore each image becomes a point in the feature space. Then, if two images are close to each other in the feature space, they are considered similar. That is, the k nearest neighbors are considered the most similar images to the query image. In this K-Nearest Neighbor (k-NN) model, semantically similar images are assumed to be clustered together in a single neighborhood in the high-dimensional feature space. Unfortunately semantically similar images with different appearances are often clustered into distinct neighborhoods, which might scatter in the feature space. Hence, confinement of the search results to a single neighborhood is the latent reason of the low recall rate of typical nearest neighbor techniques. In this dissertation, a new image retrieval technique - the Query Decomposition (QD) model is introduced. QD facilitates retrieval of semantically similar images from multiple neighborhoods in the feature space and hence bridges the semantic gap between the images' low-level feature and the high-level semantic meaning. In the QD model, a query may be decomposed into multiple subqueries based on the user's relevance feedback to cover multiple image clusters which contain semantically similar images. The retrieval results are the k most similar images from multiple discontinuous relevant clusters. To apply the benefit from QD study, a mobile client-side relevance feedback study was conducted. With the proliferation of handheld devices, the demand of multimedia information retrieval on mobile devices has attracted more attention. A relevance feedback information retrieval process usually includes several rounds of query refinement. Each round incurs exchange of tens of images between the mobile device and the server. With limited wireless bandwidth, this process can incur substantial delay making the system unfriendly to use. The Relevance Feedback Support (RFS) structure that was designed in QD technique was adopted for Client-side Relevance Feedback (CRF). Since relevance feedback is done on client side, system response is instantaneous significantly enhancing system usability. Furthermore, since the server is not involved in relevance feedback processing, it is able to support thousands more users simultaneously. As the QD technique improves on the accuracy of CBIR systems, another study, which is called In-Memory relevance feedback is studied in this dissertation. In the study, we improved the efficiency of the CBIR systems. Current methods rely on searching the database, stored on disks, in each round of relevance feedback. This strategy incurs long delay making relevance feedback less friendly to the user, especially for very large databases. Thus, scalability is a limitation of existing solutions. The proposed in-memory relevance feedback technique substantially reduce the delay associated with feedback processing, and therefore improve system usability. A data-independent dimensionality-reduction technique is used to compress the metadata to build a small in-memory database to support relevance feedback operations with minimal disk accesses. The performance of this approach is compared with conventional relevance feedback techniques in terms of computation efficiency and retrieval accuracy. The results indicate that the new technique substantially reduces response time for user feedback while maintaining the quality of the retrieval. In the previous studies, the QD technique relies on a pre-defined Relevance SupportSupport structure. As the result and user experience indicated that the structure might confine the search range and affect the result. In this dissertation, a novel Multiple Direction Search framework for semi-automatic annotation propagation is studied. In this system, the user interacts with the system to provide example images and the corresponding annotations during the annotation propagation process. In each iteration, the example images are dynamically clustered and the corresponding annotations are propagated separately to each cluster: images in the local neighborhood are annotated. Furthermore, some of those images are returned to the user for further annotation. As the user marks more images, the annotation process goes into multiple directions in the feature space. The query movements can be treated as multiple path navigation. Each path could be further split based on the user's input. In this manner, the system provides accurate annotation assistance to the user - images with the same semantic meaning but different visual characteristics can be handled effectively. From comprehensive experiments on Corel and U. of Washington image databases, the proposed technique shows accuracy and efficiency on annotating image databases.
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Date Issued
2011
Identifier
CFE0004182, ucf:49058
Format
Document (PDF)
PURL
http://purl.flvc.org/ucf/fd/CFE0004182
Real-time traffic safety evaluation models and their application for variable speed limits
Title
Real-time traffic safety evaluation models and their application for variable speed limits.
Creator
Yu, Rongjie, Abdel-Aty, Mohamed, Radwan, Ahmed, Madani Larijani, Kaveh, Ahmed, Mohamed, Wang, Xuesong, University of Central Florida
Abstract / Description
Traffic safety has become the first concern in the transportation area. Crashes have cause extensive human and economic losses. With the objective of reducing crash occurrence and alleviating crash injury severity, major efforts have been dedicated to reveal the hazardous factors that affect crash occurrence at both the aggregate (targeting crash frequency per segment, intersection, etc.,) and disaggregate levels (analyzing each crash event). The aggregate traffic safety studies, mainly...
Show moreTraffic safety has become the first concern in the transportation area. Crashes have cause extensive human and economic losses. With the objective of reducing crash occurrence and alleviating crash injury severity, major efforts have been dedicated to reveal the hazardous factors that affect crash occurrence at both the aggregate (targeting crash frequency per segment, intersection, etc.,) and disaggregate levels (analyzing each crash event). The aggregate traffic safety studies, mainly developing safety performance functions (SPFs), are being conducted for the purpose of unveiling crash contributing factors for the interest locations. Results of the aggregate traffic safety studies can be used to identify crash hot spots, calculate crash modification factors (CMF), and improve geometric characteristics. Aggregate analyses mainly focus on discovering the hazardous factors that are related to the frequency of total crashes, of specific crash type, or of each crash severity level. While disaggregate studies benefit from the reliable surveillance systems which provide detailed real-time traffic and weather data. This information could help in capturing microlevel influences of the hazardous factors which might lead to a crash. The disaggregate traffic safety models, also called real-time crash risk evaluation models, can be used in monitoring crash hazardousness with the real-time field data fed in. One potential use of real-time crash risk evaluation models is to develop Variable Speed Limits (VSL) as a part of a freeway management system. Models have been developed to predict crash occurrence to proactively improve traffic safety and prevent crash occurrence.In this study, first, aggregate safety performance functions were estimated to unveil the different risk factors affecting crash occurrence for a mountainous freeway section. Then disaggregate real-time crash risk evaluation models have been developed for the total crashes with both the machine learning and hierarchical Bayesian models. Considering the need for analyzing both aggregate and disaggregate aspects of traffic safety, systematic multi-level traffic safety studies have been conducted for single- and multi-vehicle crashes, and weekday and weekend crashes. Finally, the feasibility of utilizing a VSL system to improve traffic safety on freeways has been investigated. This research was conducted based on data obtained from a 15-mile mountainous freeway section on I-70 in Colorado. The data contain historical crash data, roadway geometric characteristics, real-time weather data, and real-time traffic data. Real-time weather data were recorded by 6 weather stations installed along the freeway section, while the real-time traffic data were obtained from the Remote Traffic Microwave Sensor (RTMS) radars and Automatic Vechicle Identification (AVI) systems. Different datasets have been formulated from various data sources, and prepared for the multi-level traffic safety studies. In the aggregate traffic safety investigation, safety performance functions were developed to identify crash occurrence hazardous factors. For the first time real-time weather and traffic data were used in SPFs. Ordinary Poisson model and random effects Poisson models with Bayesian inference approach were employed to reveal the effects of weather and traffic related variables on crash occurrence. Two scenarios were considered: one seasonal based case and one crash type based case. Deviance Information Criterion (DIC) was utilized as the comparison criterion; and the correlated random effects Poisson models outperform the others. Results indicate that weather condition variables, especially precipitation, play a key role in the safety performance functions. Moreover, in order to compare with the correlated random effects Poisson model, Multivariate Poisson model and Multivariate Poisson-lognormal model have been estimated. Conclusions indicate that, instead of assuming identical random effects for the homogenous segments, considering the correlation effects between two count variables would result in better model fit. Results from the aggregate analyses shed light on the policy implication to reduce crash frequencies. For the studied roadway segment, crash occurrence in the snow season have clear trends associated with adverse weather situations (bad visibility and large amount of precipitation); weather warning systems can be employed to improve road safety during the snow season. Furthermore, different traffic management strategies should be developed according to the distinct seasonal influence factors. In particular, sites with steep slopes need more attention from the traffic management center and operators especially during snow seasons to control the excess crash occurrence. Moreover, distinct strategy of freeway management should be designed to address the differences between single- and multi-vehicle crash characteristics.In addition to developing safety performance functions with various modeling techniques, this study also investigates four different approaches of developing informative priors for the independent variables. Bayesian inference framework provides a complete and coherent way to balance the empirical data and prior expectations; merits of these informative priors have been tested along with two types of Bayesian hierarchical models (Poisson-gamma and Poisson-lognormal models). Deviance Information Criterion, R-square values, and coefficients of variance for the estimations were utilized as evaluation measures to select the best model(s). Comparisons across the models indicate that the Poisson-gamma model is superior with a better model fit and it is much more robust with the informative priors. Moreover, the two-stage Bayesian updating informative priors provided the best goodness-of-fit and coefficient estimation accuracies.In addition to the aggregate analyses, real-time crash risk evaluation models have been developed to identify crash contributing factors at the disaggregate level. Support Vector Machine (SVM), a recently proposed statistical learning model and Hierarchical Bayesian logistic regression models were introduced to evaluate real-time crash risk. Classification and regression tree (CART) model has been developed to select the most important explanatory variables. Based on the variable selection results, Bayesian logistic regression models and SVM models with different kernel functions have been developed. Model comparisons based on receiver operating curves (ROC) demonstrate that the SVM model with Radial basis kernel function outperforms the others. Results from the models demonstrated that crashes are likely to happen during congestion periods (especially when the queuing area has propagated from the downstream segment); high variation of occupancy and/or volume would increase the probability of crash occurrence.Moreover, effects of microscopic traffic, weather, and roadway geometric factors on the occurrence of specific crash types have been investigated. Crashes have been categorized as rear-end, sideswipe, and single-vehicle crashes. AVI segment average speed, real-time weather data, and roadway geometric characteristics data were utilized as explanatory variables. Conclusions from this study imply that different active traffic management (ATM) strategies should be designed for three- and two-lane roadway sections and also considering the seasonal effects. Based on the abovementioned results, real-time crash risk evaluation models have been developed separately for multi-vehicle and single-vehicle crashes, and weekday and weekend crashes. Hierarchical Bayesian logistic regression models (random effects and random parameter logistic regression models) have been introduced to address the seasonal variations, crash unit level's diversities, and unobserved heterogeneity caused by geometric characteristics. For the multi-vehicle crashes: congested conditions at downstream would contribute to an increase in the likelihood of multi-vehicle crashes; multi-vehicle crashes are more likely to occur during poor visibility conditions and if there is a turbulent area that exists downstream. Drivers who are unable to reduce their speeds timely are prone to causing rear-end crashes. While for the single-vehicle crashes: slow moving traffic platoons at the downstream detector of the crash occurrence locations would increase the probability of single-vehicle crashes; large variations of occupancy downstream would also increase the likelihood of single-vehicle crash occurrence.Substantial efforts have been dedicated to revealing the hazardous factors that affect crash occurrence from both the aggregate and disaggregate level in this study, however, findings and conclusions from these research work need to be transferred into applications for roadway design and freeway management. This study further investigates the feasibility of utilizing Variable Speed Limits (VSL) system, one key part of ATM, to improve traffic safety on freeways. A proactive traffic safety improvement VSL control algorithm has been proposed. First, an extension of the traffic flow model METANET was employed to predict traffic flow while considering VSL's impacts on the flow-density diagram; a real-time crash risk evaluation model was then estimated for the purpose of quantifying crash risk; finally, the optimal VSL control strategies were achieved by employing an optimization technique of minimizing the total predicted crash risks along the VSL implementation area. Constraints were set up to limit the increase of the average travel time and differences between posted speed limits temporarily and spatially. The proposed VSL control strategy was tested for a mountainous freeway bottleneck area in the microscopic simulation software VISSIM. Safety impacts of the VSL system were quantified as crash risk improvements and speed homogeneity improvements. Moreover, three different driver compliance levels were modeled in VISSIM to monitor the sensitivity of VSL's safety impacts on driver compliance levels. Conclusions demonstrate that the proposed VSL system could effectively improve traffic safety by decreasing crash risk, enhancing speed homogeneity, and reducing travel time under both high and moderate driver compliance levels; while the VSL system does not have significant effects on traffic safety enhancement under the low compliance scenario. Future implementations of VSL control strategies and related research topics were also discussed.
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Date Issued
2013
Identifier
CFE0005283, ucf:50556
Format
Document (PDF)
PURL
http://purl.flvc.org/ucf/fd/CFE0005283
Suction Detection and Feedback Control for the Rotary Left Ventricular Assist Device
Title
Suction Detection and Feedback Control for the Rotary Left Ventricular Assist Device.
Creator
Wang, Yu, Simaan, Marwan, Qu, Zhihua, Haralambous, Michael, Kassab, Alain, Divo, Eduardo, University of Central Florida
Abstract / Description
The Left Ventricular Assist Device (LVAD) is a rotary mechanical pump that is implanted in patients with congestive heart failure to help the left ventricle in pumping blood in the circulatory system. The rotary type pumps are controlled by varying the pump motor current to adjust the amount of blood flowing through the LVAD. One important challenge in using such a device is the desire to provide the patient with as close to a normal lifestyle as possible until a donor heart becomes available...
Show moreThe Left Ventricular Assist Device (LVAD) is a rotary mechanical pump that is implanted in patients with congestive heart failure to help the left ventricle in pumping blood in the circulatory system. The rotary type pumps are controlled by varying the pump motor current to adjust the amount of blood flowing through the LVAD. One important challenge in using such a device is the desire to provide the patient with as close to a normal lifestyle as possible until a donor heart becomes available. The development of an appropriate feedback controller that is capable of automatically adjusting the pump current is therefore a crucial step in meeting this challenge. In addition to being able to adapt to changes in the patient's daily activities, the controller must be able to prevent the occurrence of excessive pumping of blood from the left ventricle (a phenomenon known as ventricular suction) that may cause collapse of the left ventricle and damage to the heart muscle and tissues.In this dissertation, we present a new suction detection system that can precisely classify pump flow patterns, based on a Lagrangian Support Vector Machine (LSVM) model that combines six suction indices extracted from the pump flow signal to make a decision about whether the pump is not in suction, approaching suction, or in suction. The proposed method has been tested using in vivo experimental data based on two different LVAD pumps. The results show that the system can produce superior performance in terms of classification accuracy, stability, learning speed, and good robustness compared to three other existing suction detection methods and the original SVM-based algorithm. The ability of the proposed algorithm to detect suction provides a reliable platform for the development of a feedback control system to control the current of the pump (input variable) while at the same time ensuring that suction is avoided.Based on the proposed suction detector, a new control system for the rotary LVAD was developed to automatically regulate the pump current of the device to avoid ventricular suction. The control system consists of an LSVM suction detector and a feedback controller. The LSVM suction detector is activated first so as to correctly classify the pump status as No Suction (NS) or Suction (S). When the detection is (")No Suction("), the feedback controller is activated so as to automatically adjust the pump current in order that the blood flow requirements of the patient's body at different physiological states are met according to the patient's activity level. When the detection is (")Suction("), the pump current is immediately decreased in order to drive the pump back to a normal No Suction operating condition. The performance of the control system was tested in simulations over a wide range of physiological conditions.
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Date Issued
2013
Identifier
CFE0005070, ucf:49956
Format
Document (PDF)
PURL
http://purl.flvc.org/ucf/fd/CFE0005070