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DETECTION OF THE R-WAVE IN ECG SIGNALS
Title
DETECTION OF THE R-WAVE IN ECG SIGNALS.
Creator
Valluri, Sasanka, Weeks, Arthur, University of Central Florida
Abstract / Description
This thesis aims at providing a new approach for detecting R-waves in the ECG signal and generating the corresponding R-wave impulses with the delay between the original R-waves and the R-wave impulses being lesser than 100 ms. The algorithm was implemented in Matlab and tested with good results against 90 different ECG recordings from the MIT-BIH database. The Discrete Wavelet Transform (DWT) forms the heart of the algorithm providing a multi-resolution analysis of the ECG signal. The...
Show moreThis thesis aims at providing a new approach for detecting R-waves in the ECG signal and generating the corresponding R-wave impulses with the delay between the original R-waves and the R-wave impulses being lesser than 100 ms. The algorithm was implemented in Matlab and tested with good results against 90 different ECG recordings from the MIT-BIH database. The Discrete Wavelet Transform (DWT) forms the heart of the algorithm providing a multi-resolution analysis of the ECG signal. The wavelet transform decomposes the ECG signal into frequency scales where the ECG characteristic waveforms are indicated by zero crossings. The adaptive threshold algorithms discussed in this thesis search for valid zero crossings which characterize the R-waves and also remove the Preventricular Contractions (PVC's). The adaptive threshold algorithms allow the decision thresholds to adjust for signal quality changes and eliminate the need for manual adjustments when changing from patient to patient. The delay between the R-waves in the original ECG signal and the R-wave impulses obtained from the algorithm was found to be less than 100 ms.
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Date Issued
2005
Identifier
CFE0000498, ucf:46369
Format
Document (PDF)
PURL
http://purl.flvc.org/ucf/fd/CFE0000498
The Effect of Repeated Sprint Training in Hypoxia and Beta-Alanine Supplementation On Exercise Performance
Title
The Effect of Repeated Sprint Training in Hypoxia and Beta-Alanine Supplementation On Exercise Performance.
Creator
Wang, Ran, Hoffman, Jay, Fukuda, David, Stout, Jeffrey, Kang, Jie, University of Central Florida
Abstract / Description
The primary objective of this study was to evaluate the synergistic effects of repeated sprint training in hypoxia (RSH) and beta-alanine supplementation on performance in recreationally active men. Participants were randomly assigned to one of the following groups: hypoxia + beta-alanine (HB, n = 10), hypoxia + placebo (HP, n = 9), normoxia + beta-alanine (NB, n = 11) and normoxia + placebo (NP, n = 8). All participants completed a total of 8 training sessions (each consisting of 3 sets of 5...
Show moreThe primary objective of this study was to evaluate the synergistic effects of repeated sprint training in hypoxia (RSH) and beta-alanine supplementation on performance in recreationally active men. Participants were randomly assigned to one of the following groups: hypoxia + beta-alanine (HB, n = 10), hypoxia + placebo (HP, n = 9), normoxia + beta-alanine (NB, n = 11) and normoxia + placebo (NP, n = 8). All participants completed a total of 8 training sessions (each consisting of 3 sets of 5 (&)#215; 10-s sprints at a resistance of 7.5% of body mass, with 20-s rest intervals between sprints) over 4 weeks on a cycle ergometer either in hypoxia (Oxygen fraction: FiO2 = 14.2%) or normoxia (FiO2 = 20.9%). Participants were instructed to consume a daily dosage of 6.4g (two 800 mg tablets ingested 4 times per day at 3-4 hour intervals) of either beta-alanine or placebo. Changes in performance in a graded exercise test (GXT), repeated sprint test (RST) and 3-min all-out test (3MT) were examined before and after 28-days of training and supplementation. Aerobic performance was measured by maximal oxygen consumption (VO2max), peak power output (PPO). Exercise intolerance was assessed from critical power (CP), oxygen consumption (VO2RCP) and power output (PRCP) at respiratory compensation point. Exercise capacity was measured by total work (TW) during 3MT. Anaerobic capacity was evaluated via anaerobic working capacity (AWC), heart rate response to RST (RST_HR60) and lactate responses to RST (RST_La) and 3MT (3MT_La). Repeated sprint performance was estimated through average power output of the last sprint (RST_AP5) and all sprints (RST_AP). No between-group differences were observed for training volume or supplementation compliance. Anthropometric and hematological measures remain unchanged before and after intervention in all groups. A main effect of altitude was shown for VO2RCP, PRCP, RST_AP5, RST_HR60, and TW, with post-intervention values in the hypoxia groups significantly (p (<) 0.05) higher (lower for RST_HR60) than the normoxia groups. A main effect of beta-alanine was detected in AWC, with post-intervention values in the beta-alanine groups being significantly (p (<) 0.05) higher than the placebo groups. Results of this investigation demonstrated that RSH and beta-alanine benefit performance from different perspectives. RSH improved aerobic performance, exercise tolerance, cardiovascular recovery and exercise capacity, while beta-alanine supplementation maintained anaerobic working capacity in recreationally-trained men during the four-week repeated sprint training intervention.
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Date Issued
2017
Identifier
CFE0006961, ucf:51633
Format
Document (PDF)
PURL
http://purl.flvc.org/ucf/fd/CFE0006961
Characterization of a Spiking Neuron Model via a Linear Approach
Title
Characterization of a Spiking Neuron Model via a Linear Approach.
Creator
Jabalameli, Amirhossein, Behal, Aman, Hickman, James, Haralambous, Michael, University of Central Florida
Abstract / Description
In the past decade, characterizing spiking neuron models has been extensively researched as anessential issue in computational neuroscience. In this thesis, we examine the estimation problemof two different neuron models. In Chapter 2, We propose a modified Izhikevich model withan adaptive threshold. In our two-stage estimation approach, a linear least squares method anda linear model of the threshold are derived to predict the location of neuronal spikes. However,desired results are not...
Show moreIn the past decade, characterizing spiking neuron models has been extensively researched as anessential issue in computational neuroscience. In this thesis, we examine the estimation problemof two different neuron models. In Chapter 2, We propose a modified Izhikevich model withan adaptive threshold. In our two-stage estimation approach, a linear least squares method anda linear model of the threshold are derived to predict the location of neuronal spikes. However,desired results are not obtained and the predicted model is unsuccessful in duplicating the spikelocations. Chapter 3 is focused on the parameter estimation problem of a multi-timescale adaptivethreshold (MAT) neuronal model. Using the dynamics of a non-resetting leaky integrator equippedwith an adaptive threshold, a constrained iterative linear least squares method is implemented tofit the model to the reference data. Through manipulation of the system dynamics, the thresholdvoltage can be obtained as a realizable model that is linear in the unknown parameters. This linearlyparametrized realizable model is then utilized inside a prediction error based framework to identifythe threshold parameters with the purpose of predicting single neuron precise firing times. Thisestimation scheme is evaluated using both synthetic data obtained from an exact model as well asthe experimental data obtained from in vitro rat somatosensory cortical neurons. Results show theability of this approach to fit the MAT model to different types of reference data.
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Date Issued
2015
Identifier
CFE0005958, ucf:50803
Format
Document (PDF)
PURL
http://purl.flvc.org/ucf/fd/CFE0005958
Adaptive Architectural Strategies for Resilient Energy-Aware Computing
Title
Adaptive Architectural Strategies for Resilient Energy-Aware Computing.
Creator
Ashraf, Rizwan, DeMara, Ronald, Lin, Mingjie, Wang, Jun, Jha, Sumit, Johnson, Mark, University of Central Florida
Abstract / Description
Reconfigurable logic or Field-Programmable Gate Array (FPGA) devices have the ability to dynamically adapt the computational circuit based on user-specified or operating-condition requirements. Such hardware platforms are utilized in this dissertation to develop adaptive techniques for achieving reliable and sustainable operation while autonomously meeting these requirements. In particular, the properties of resource uniformity and in-field reconfiguration via on-chip processors are exploited...
Show moreReconfigurable logic or Field-Programmable Gate Array (FPGA) devices have the ability to dynamically adapt the computational circuit based on user-specified or operating-condition requirements. Such hardware platforms are utilized in this dissertation to develop adaptive techniques for achieving reliable and sustainable operation while autonomously meeting these requirements. In particular, the properties of resource uniformity and in-field reconfiguration via on-chip processors are exploited to implement Evolvable Hardware (EHW). EHW utilize genetic algorithms to realize logic circuits at runtime, as directed by the objective function. However, the size of problems solved using EHW as compared with traditional approaches has been limited to relatively compact circuits. This is due to the increase in complexity of the genetic algorithm with increase in circuit size. To address this research challenge of scalability, the Netlist-Driven Evolutionary Refurbishment (NDER) technique was designed and implemented herein to enable on-the-fly permanent fault mitigation in FPGA circuits. NDER has been shown to achieve refurbishment of relatively large sized benchmark circuits as compared to related works. Additionally, Design Diversity (DD) techniques which are used to aid such evolutionary refurbishment techniques are also proposed and the efficacy of various DD techniques is quantified and evaluated.Similarly, there exists a growing need for adaptable logic datapaths in custom-designed nanometer-scale ICs, for ensuring operational reliability in the presence of Process, Voltage, and Temperature (PVT) and, transistor-aging variations owing to decreased feature sizes for electronic devices. Without such adaptability, excessive design guardbands are required to maintain the desired integration and performance levels. To address these challenges, the circuit-level technique of Self-Recovery Enabled Logic (SREL) was designed herein. At design-time, vulnerable portions of the circuit identified using conventional Electronic Design Automation tools are replicated to provide post-fabrication adaptability via intelligent techniques. In-situ timing sensors are utilized in a feedback loop to activate suitable datapaths based on current conditions that optimize performance and energy consumption. Primarily, SREL is able to mitigate the timing degradations caused due to transistor aging effects in sub-micron devices by reducing the stress induced on active elements by utilizing power-gating. As a result, fewer guardbands need to be included to achieve comparable performance levels which leads to considerable energy savings over the operational lifetime.The need for energy-efficient operation in current computing systems has given rise to Near-Threshold Computing as opposed to the conventional approach of operating devices at nominal voltage. In particular, the goal of exascale computing initiative in High Performance Computing (HPC) is to achieve 1 EFLOPS under the power budget of 20MW. However, it comes at the cost of increased reliability concerns, such as the increase in performance variations and soft errors. This has given rise to increased resiliency requirements for HPC applications in terms of ensuring functionality within given error thresholds while operating at lower voltages. My dissertation research devised techniques and tools to quantify the effects of radiation-induced transient faults in distributed applications on large-scale systems. A combination of compiler-level code transformation and instrumentation are employed for runtime monitoring to assess the speed and depth of application state corruption as a result of fault injection. Finally, fault propagation models are derived for each HPC application that can be used to estimate the number of corrupted memory locations at runtime. Additionally, the tradeoffs between performance and vulnerability and the causal relations between compiler optimization and application vulnerability are investigated.
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Date Issued
2015
Identifier
CFE0006206, ucf:52889
Format
Document (PDF)
PURL
http://purl.flvc.org/ucf/fd/CFE0006206