Dissertations / Theses on the topic 'Signal analysis'
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Haghighi-Mood, Ali. "Analysis of phonocardiographic signals using advanced signal processing techniques." Thesis, University of Sussex, 1996. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.321465.
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Mishin, A. "Biomagnetic signal analysis." Thesis, Swansea University, 2003. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.638202.
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Most of this thesis is an account of the effort to develop new methods for biomagnetic data analysis. Variations of the heart rate reflect the neural heart control mechanisms which are performed via the electrical modulation of the sinoatrial node by the autonomic nervous system. This modulation involves the interaction of several physiological mechanisms that operate on differing time scales. Using SQUID (superconducting quantum interference device) instrumentation, the fetal cardiogram can be measured with great accuracy and a high temporal resolution, thereby providing the opportunity to assess the neural function in the fetus non-invasively by analysing heart rate variability (HRV). However, a quantitative analysis of HRV requires several other physiological parameters such as blood pressure, respiration etc. to be analysed simultaneously with HRV. These parameters are obviously inaccessible in the fetus although they are routinely recorded in premature neonates treated in the intensive care units. Using a time domain correlation method, the behaviour of different HRV components was quantitatively studied for both fetuses and premature neonates and a number of consistent features were found. The correlation between neonatal HRV, respiration and arterial blood pressure was studied with the ultimate goal of constructing a numerical model of HRV. It was also observed that different types of ventilation equipment used in neonatal intensive care cause different patterns of respiration/HRV correlation, which may be indicative of the efficacy of the ventilator. Investigation of the spontaneous activity of the human brain and in particular alpha rhythm is another area where SQUID-based biomagnetic techniques can make an important contribution. In the final chapter of this work the multichannel alpha magnetoencephalogram (MEG) is considered as a sequences of MEG maps. A neural-net based algorithm for segmentation of MEG records into words is presented. Using this method three recurring words were found in an eight-second magnetoecephalogram. This could be of value for active testing of the functional role of the cortex in neurological experiments.
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Vaizurs, Raja Sarath Chandra Prasad. "Atrial Fibrillation Signal Analysis." Scholar Commons, 2011. http://scholarcommons.usf.edu/etd/3386.
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Atrial fibrillation (AF) is the most common type of cardiac arrhythmia encountered in clinical practice and is associated with an increased mortality and morbidity. Identification of the sources of AF has been a goal of researchers for over 20 years. Current treatment procedures such as Cardio version, Radio Frequency Ablation, and multiple drugs have reduced the incidence of AF. Nevertheless, the success rate of these treatments is only 35-40% of the AF patients as they have limited effect in maintaining the patient in normal sinus rhythm. The problem stems from the fact that there are no methods developed to analyze the electrical activity generated by the cardiac cells during AF and to detect the aberrant atrial tissue that triggers it.
In clinical practice, the sources triggering AF are generally expected to be at one of the four pulmonary veins in the left atrium. Classifying the signals originated from four pulmonary veins in left atrium has been the mainstay of signal analysis in this thesis which ultimately leads to correctly locating the source triggering AF. Unlike many of the current researchers where they use ECG signals for AF signal analysis, we collect intra cardiac signals along with ECG signals for AF analysis. AF Signal collected from catheters placed inside the heart gives us a better understanding of AF characteristics compared to the ECG.
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In recent years, mechanisms leading to AF induction have begun to be explored but the current state of research and diagnosis of AF is mainly about the inspection of 12 lead ECG, QRS subtraction methods, spectral analysis to find the fibrillation rate and limited to establishment of its presence or absence. The main goal of this thesis research is to develop methodology and algorithm for finding the source of AF. Pattern recognition techniques were used to classify the AF signals originated from the four pulmonary veins. The classification of AF signals recorded by a stationary intra-cardiac catheter was done based on dominant frequency, frequency distribution and normalized power. Principal Component Analysis was used to reduce the dimensionality and further, Linear Discriminant Analysis was used as a classification technique. An algorithm has been developed and tested during recorded periods of AF with promising results.
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Ajayi, A. A. "Turbine flowmeter signal analysis." Thesis, University of Bradford, 1988. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.381420.
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Krishnan, Sridhar. "Adaptive signal processing techniques for analysis of knee joint vibroarthrographic signals." Thesis, National Library of Canada = Bibliothèque nationale du Canada, 1999. http://www.collectionscanada.ca/obj/s4/f2/dsk1/tape8/PQDD_0016/NQ47897.pdf.
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Alsop, Stephen A. "Defeating signal analysis aliasing problems." Thesis, University of Strathclyde, 2002. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.248868.
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Bienvenu, Kirk Jr. "Underwater Acoustic Signal Analysis Toolkit." ScholarWorks@UNO, 2017. https://scholarworks.uno.edu/td/2398.
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This project started early in the summer of 2016 when it became evident there was a need for an effective and efficient signal analysis toolkit for the Littoral Acoustic Demonstration Center Gulf Ecological Monitoring and Modeling (LADC-GEMM) Research Consortium. LADC-GEMM collected underwater acoustic data in the northern Gulf of Mexico during the summer of 2015 using Environmental Acoustic Recording Systems (EARS) buoys. Much of the visualization of data was handled through short scripts and executed through terminal commands, each time requiring the data to be loaded into memory and parameters to be fed through arguments. The vision was to develop a graphical user interface (GUI) that would increase the productivity of manual signal analysis. It has been expanded to make several calculations autonomously for cataloging and meta data storage of whale clicks. Over the last year and a half, a working prototype has been developed with MathWorks matrix laboratory (MATLAB), an integrated development environment (IDE). The prototype is now very modular and can accept new tools relatively quickly when development is completed. The program has been named Banshee, as the mythical creatures are known to “wail”. This paper outlines the functionality of the GUI, explains the benefits of frequency analysis, the physical models that facilitate these analytics, and the mathematics performed to achieve these models.
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Lei, Chi-un, and 李志遠. "VLSI macromodeling and signal integrity analysis via digital signal processing techniques." Thesis, The University of Hong Kong (Pokfulam, Hong Kong), 2011. http://hub.hku.hk/bib/B45700588.
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Ashraf, Pouya, Linnar Billman, and Adam Wendelin. "Teaching Signals to Students: a Tool for Visualizing Signal, Filter and DSP Concepts." Thesis, Uppsala universitet, Institutionen för informationsteknologi, 2016. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-297168.
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Students at Uppsala University have for some years been given the opportunity to take courses in subjects directly, or indirectly, related to the fields of signal processing and signal analysis. According to the directors of these courses, a considerable number of students are recurringly having difficulties grasping different concepts related to this field of study. This report covers a tool that easily allows teachers to visualize and listen to different manipulations of signals, which should help students get an intuitive understanding of the subject. Features of the system include multiple kinds of analog filters, sampling with variable settings and zero-order hold reconstruction. The finished system is flexible, tunable and modifiable to the teachers every need, making it usable for a wide variety of courses involving signal processing. The system meets its requirements even though individual components’ results de- viate slightly from ideal values.Studenter vid Uppsala Universitet har, under ett antal år, givits möjligheten att läsa kurser inom ämnen direkt, eller indirekt, relaterade till signalbehandling/signalanalys. Enligt kursansvariga för dessa kurser har en ansenlig andel av studenterna svårigheter med att förstå en del av de begrepp och fenomen som förekommer under kurserna. Denna rapport behandlar ett verktyg som ger lärare i dessa kurser möjlighet att på ett enkelt sätt visualisera och lyssna på olika manipulationer av signaler, vilket bör hjälpa studenterna bygga en intuition för ämnet. Systemets olika funktioner inkluderar flera olika typer av analoga filter, sampling med olika inställningar, och så kallad ’Zero-Order-Hold’ rekonstruktion. Det resulterande systemet är flexibelt, inställbart och modifierbart till användarens behov, vilket gör det applicerbart i flera kurser som innefattar signalbehandling/analys. Systemet möter kraven som ställs, även fast resultaten hos individuella komponenter avviker aningen från ideala värden.
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Purahoo, K. "Maximum entropy data analysis." Thesis, Cranfield University, 1994. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.260038.
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Lehtomäki, J. (Janne). "Analysis of energy based signal detection." Doctoral thesis, University of Oulu, 2005. http://urn.fi/urn:isbn:9514279255.
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The focus of this thesis is on the binary signal detection problem, i.e., if a signal or signals are present or not. Depending on the application, the signal to be detected can be either unknown or known. The detection is based on some function of the received samples which is compared to a threshold. If the threshold is exceeded, it is decided that signal(s) is (are) present. Energy detectors (radiometers) are often used due to their simplicity and good performance. The main goal here is to develop and analyze energy based detectors as well as power-law based detectors.
Different possibilities for setting the detection threshold for a quantized total power radiometer are analyzed. The main emphasis is on methods that use reference samples. In particular, the cell-averaging (CA) constant false alarm rate (CFAR) threshold setting method is analyzed. Numerical examples show that the CA strategy offers the desired false alarm probability, whereas a more conventional strategy gives too high values, especially with a small number of reference samples.
New performance analysis of a frequency sweeping channelized radiometer is presented. The total power radiometer outputs from different frequencies are combined using logical-OR, sum and maximum operations. An efficient method is presented for accurately calculating the likelihood ratio used in the optimal detection. Also the effects of fading are analyzed. Numerical results show that although sweeping increases probability of intercept (POI), the final probability of detection is not increased if the number of observed hops is large.
The performance of a channelized radiometer is studied when different CFAR strategies are used to set the detection threshold. The proposed iterative methods for setting the detection threshold are the forward consecutive mean excision (FCME) method with the CA scaling factors in final detection decision (FCME+CA), the backward consecutive mean excision (BCME) method with the CA scaling factors in detection (BCME+CA) and a method that uses the CA scaling factors for both censoring and detection (CA+CA). Numerical results show that iterative CFAR methods may improve detection performance compared to baseline methods.
Finally, a method to set the threshold of a power-law detector that uses a nonorthogonal transform is presented. The mean, variance and skewness of the decision variable in the noise-only case are derived and these are used to find a shifted log-normal approximation for the distribution of the decision variable. The accuracy of this method is verified through simulations.
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Bousfield, Bruce M. "Real time aero engine signal analysis." Thesis, Loughborough University, 1988. https://dspace.lboro.ac.uk/2134/10425.
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For many years the analysis of dynamic signals obtained from aero engine mounted transducers has been performed either by using real time spectrum analysers within the test facility, or by making high quality tape recordings during engine tests and analysing the data via an off-line main frame computer. Although real time analysers produce the information where it is most needed, they provide no history of events and the results are operator dependent. Analysis from tape recordings enables information extraction algorithms to be performed and tables and graphs of notable events to be printed. However much of this information could be more effectively utilised if produced within the test facility and in real time. This thesis describes the design and development of a real time data acquisition, signal processing and information extraction system ideally suited for engine health and performance monitoring within test facilities. The thesis begins with a detailed description of the problems encountered in dynamic signal analysis in the field of aero engine performance testing, and with an overview of digital signal processing and the latest technology signal processing micro processors that have made this project possible. It then describes the problems encountered and the subsequent solutions found during the design and development of the hardware and software needed for the·high bandwidth data acquisition and fast signal processing algorithms. The fast Fourier transform has been used for very many years in the field of spectrum analysis, ... however this technique has limitations which are overcome by some of the more modern spectrum estimation techniques. This thesis makes an assessment of some of these techniques, noting particularly their performance on aero engine type signals. The results of these tests are recorded and the possible use of the techniques in aero engine analysis is discussed.
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Chuang, Ming-Fei. "Interactive tools for sound signal analysis." Thesis, Monterey, California. Naval Postgraduate School, 1997. http://hdl.handle.net/10945/8550.
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Approved for public release; distribution is unlimitedThis thesis develops a series of programs that implement the sinusoidal representation model for speech and sound waveform analysis and synthesis. This sinusoidal representation model can also be used for a variety of sound signal transformations such as time-scale modification and frequency scaling. The above sound analysis/synthesis sinusoidal representations and transformations were developed as two interactive tools-with Graphical User Interface (GUI) using MATLAB. In addition, an interactive tool for signal frequency component editing based on the sinusoidal model is also presented in this thesis.
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Lam, Wa-Kwai. "Risk analysis and traffic signal design." Thesis, University of Southampton, 1989. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.328239.
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Crane, Nicola. "Debiasing reasoning : a signal detection analysis." Thesis, Lancaster University, 2016. http://eprints.lancs.ac.uk/82265/.
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This thesis focuses on deductive reasoning and how the belief bias effect can be reduced or ameliorated. Belief bias is a phenomenon whereby the evaluation of the logical validity of an argument is skewed by the degree to which the reasoner believes the conclusion. There has been little research examining ways of reducing such bias and whether there is some sort of effective intervention which makes people reason more on the basis of logic. Traditional analyses of this data has focussed on simple measures of accuracy, typically deducting the number of incorrect answers from the number of correct answers to give an accuracy score. However, recent theoretical developments have shown that this approach fails to separate reasoning biases and response biases. A reasoning bias, is one which affects individuals’ ability to discriminate between valid and invalid arguments, whereas a response bias is simply the individual’s tendency to give a particular answer, independent of reasoning. A Signal Detection Theory (SDT) approach is used to calculate measures of reasoning accuracy and response bias. These measures are then analysed using mixed effects models. Chapter 1 gives a general introduction to the topic, and outlines the content of subsequent chapters. In Chapter 2, I review the psychological literature around belief bias, the growth of the use of SDT models, and approaches to reducing bias. Chapter 3 covers the methodology, and includes a a thorough description of the calculation of the SDT measures, and an explanation of the mixed effects models I used to analyse these. Chapter 4 presents an experiment in which the effects of feedback on reducing belief bias is examined. In Chapter 5, the focus shifts in the direction of individual differences, and looks at the effect of different instructions given to participants, and Chapter 6 examines the effects of both feedback and specific training. Chapter 7 provides a general discussion of the implications of the previous three chapters.
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Salma, Nabila. "EEG Signal Analysis in Decision Making." Thesis, University of North Texas, 2017. https://digital.library.unt.edu/ark:/67531/metadc984237/.
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Decision making can be a complicated process involving perception of the present situation, past experience and knowledge necessary to foresee a better future. This cognitive process is one of the essential human ability that is required from everyday walk of life to making major life choices. Although it may seem ambiguous to translate such a primitive process into quantifiable science, the goal of this thesis is to break it down to signal processing and quantifying the thought process with prominence of EEG signal power variance. This paper will discuss the cognitive science, the signal processing of brain signals and how brain activity can be quantifiable through data analysis. An experiment is analyzed in this thesis to provide evidence that theta frequency band activity is associated with stress and stress is negatively correlated with concentration and problem solving, therefore hindering decision making skill. From the results of the experiment, it is seen that theta is negatively correlated to delta and beta frequency band activity, thus establishing the fact that stress affects internal focus while carrying out a task.
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Yan, Xie. "CHEMICAL SIGNAL ANALYSIS WITH FOURIER MICROFLUIDICS." Case Western Reserve University School of Graduate Studies / OhioLINK, 2008. http://rave.ohiolink.edu/etdc/view?acc_num=case1216058414.
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Dawber, W. N. "Radiofrequency analysis using optical signal processing." Thesis, University of St Andrews, 1991. http://hdl.handle.net/10023/15035.
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The basic form of conventional electronic and acoustooptic radiofrequency spectrum analysers is described. The advantages and disadvantages of the various systems are discussed with particular reference to radar signal processing in a hostile environment. Acoustooptic interaction is described using electromagnetic wave theory and also in terms of particle dynamics. A discussion of the various factors which effect Bragg-cell performance is presented, together with experimental results from the characterisation of acoustooptic cells. Coherent light detection is described when used in conjunction with a Bragg-cell spectrum analyser. Using this approach the dynamic range of the device may be dramatically increased. A novel approach is described which uses optical fibres in the Fourier transform plane and fusion spliced couplers to combine the signal and local oscillator beams. Experimental results are presented using single-mode fibres. Improvements in diffraction efficiency, reduced material intermodulation and increased frequency resolution are possible in an acoustooptic spectrum analyser if a Bragg-cell with a long transducer is used. However this leads to reduced instantaneous bandwidth in a conventional configuration. Two new approaches are described which allow a long transducer to be used without loss of bandwidth. An analysis of Bragg-cell diffraction within active and passive resonant optical cavities shows the diffraction efficiency per watt of a Bragg-cell may be increased by orders of magnitude by placing it within a passive cavity. Various cavity configurations are analysed and experimental results are given. A temporal analysis of light diffracted from radiofrequency pulses within an acoustooptic Bragg-cell is presented. Experimental evidence backs up the theory, which shows a possible means of eliminating the "Rabbit's Ears" phenomenon. Conventional acoustooptic Bragg cells have bandwidths limited by the acoustic losses in the crystals used for the cells and impedance matching of the transducer to the driver and crystal. Commercial cells are available with bandwidths of several gigahertz. Many applications require significantly larger bandwidths than are offered by conventional Bragg cells. We describe a new kind of diffraction cell with a potential bandwidth in excess of fifty gigahertz. The theory of operation and an example design are presented. A novel ultra-high data rate optical communication link is described. This makes use of the temporal distribution produced by light diffracted from radiofrequency pulses within a Bragg-cell. Also a covert, free-space link is described. A two channel system is demonstrated using acoustooptic cells.
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Bottrell, Nathaniel. "Small-signal analysis of active loads and large-signal analysis of faults in inverter interfaced microgrid applications." Thesis, Imperial College London, 2013. http://hdl.handle.net/10044/1/24658.
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Rectifiers and voltage regulators that have characteristics of constant power loads may form a significant percentage of a microgrid's total loads. The real part of the input impedance of a constant power load is negative and it may have control loop dynamics in a similar frequency range to the inverters that are supplying the microgrid. This thesis examines the interactions between an active constant power load and a microgrid for the impact on stability. Participation analysis of the eigenvalues that result from the model of the combined microgrid and active load identified that the low-frequency modes are associated with the voltage controller of the active rectifier and the droop-controllers of the inverters. The analysis also revealed that when the active load dc-voltage controller is designed with large gains, the voltage controller of the inverter becomes unstable but the low frequency modes associated with the droop controller of the inverter remain stable. The transient stability of a microgrid may require that the inverter-interfaced generation remain connected during a fault and return to normal power export once a fault is cleared. For an inverter to supply fault current, the controller of the inverter must current-limit the output and the fault strategy chosen must ensure that the current and voltage limiter do not latch-up and that the controller integrators do not wind-up. This thesis analyses different limiting and reset strategies and concluded that that it is not possible to successfully reset a limiter when using a reset signal from a closed-loop controller within the inverter. In a system where there are cascaded limiters, successful operation is obtained when the inner limit is a saturation limiter and the output limiter is a set-reset limiter. It was found that the transient stability of an inverter interfaced microgrid using a droop control algorithm is dependent on the current limiter and inductance of the network.
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Zheng, Li-Rong. "Design, analysis and integration of mixed-signal systems for signal and power industry /." Stockholm, 2001. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-3233.
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Uhlin, Jakob. "CAN signal quality analysis and development of the signal processing on a FPGA." Thesis, Linköpings universitet, Fysik och elektroteknik, 2014. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-108366.
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This master thesis report is a part of the thesis project conducted by Jakob Uhlin at Syntronic R R and D, Stockholm Sweden. The objective of this thesis is to develop a way to process the signal being sent on a CAN-bus and subsequently analyse its quality and its source in the network. A process of gathering appropriate theories and data has been done, parallel with the development of the analyzer module. The intelligence is implemented in an FPGA through the hardware description language VHDL. In this way, the algorithms can process the data in a real-time domain. The central findings and conclusions have been that is possible to analyze the signal quality of a CAN message properly on a FPGA.
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Prabhakaran, Anand. "Power Signal Analysis of Channel Current Signal Using HMM-EM and Time Domain FSA." ScholarWorks@UNO, 2006. http://scholarworks.uno.edu/td/321.
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The Nanopore Detector using á-hemolysin channel transcribes kinetics of a single molecule along the nanometer-scale pore. The transcribed data is represented by electrical measurements. We present accurate and computationally inexpensive tools to analyze single molecule kinetics. The HMM-EM level projection method de-noises data, retaining the transitions with very high precision. This approach doesn't require input number of levels. Another advantage is the minimal tuning required. The levels are then identified using Finite State Automata (FSAs). Spike Detector algorithm analyzes spikes characterizing behavior of molecule in pore. No commercial tools available are capable of analyzing spikes in presence of noise. The formulation of HMM-EM, FSAs and Spike Detector together provides a robust method for analysis of channel current data. Application of these methods is described for Vercoutere channel blockade dataset which contains signals of radiated and non-radiated molecules. The tools developed were used successfully to differentiate between these two molecules.
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Patton, Kevin Bernard. "Analysis of parametric model signal processing techniques for signature analysis." Thesis, Virginia Tech, 1988. http://hdl.handle.net/10919/43264.
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Five parametric modeling techniques have been identified to be possible alternatives to the Fast Fourier Transform (FFT) for signature analyses involving short data records. The developments in signal processing that have lead to these techniques are reviewed. Mathematical definitions for parametric models are provided in terms of time-domain stochastic difference equations as well as in terms of frequency-domain rational transfer functions. Computer programs are developed for implementation of each of the five parametric modeling techniques. Results are presented from studies conducted on simulated stochastic signals to characterize the performance of the parametric modeling techniques and to compare the performance of these techniques to the FFT. One of the parametric modeling techniques, Pisarenko Harmonic Decomposition shows outstanding performance in comparison to the FFT and to the other parametric modeling techniques.Master of Science
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Karlholm, Jörgen. "Local Signal Models for Image Sequence Analysis." Doctoral thesis, Linköpings universitet, Bildbehandling, 1998. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-54327.
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The thesis describes novel methods for image motion computation and template matching. A multiscale algorithm for energy-based estimation and representation of local spatiotemporal structure by second order symmetric tensors is presented. An efficient spatiotemporal implementation of a signalmodellingmethod called normalized convolution is described. This provides a means to handle signals with varying degree of reliability. As an application of the above results, a smooth pursuit motion tracking algorithm that uses observations of both targetmotion and position for camera head control and motion prediction is described. The target is detected using a novel motion field segmentation algorithm which assumes that the motion fields of the target and its immediate vicinity, at least occasionally, each can be modelled by a single parameterized motion model. A method to eliminate camera-induced background motion in the case of a pan/tilt rotating camera is suggested. In a second application, a high-precision image motion estimation algorithm performing clustering in motion parameter space is developed. The algorithm, which can handle multiple motions by simultaneous motion parameter estimation and image segmentation, iteratively maximizes the posterior probability of the motion parameter set given the observed local spatiotemporal structure tensor field. The probabilistic formulation provides a natural way to incorporate additional prior information about the segmentation of the scene into the objective function. A simple homotopy continuation method (embedding algorithm) is used to increase the likelihood of convergence to a nearoptimal solution. The final part of the thesis is concerned with tracking of (partially) occluded targets. An algorithm for target tracking in head-up display sequences is presented. The method generalizes cross-correlation coefficient matching by introducing a signal confidencebased distance metric. To handle target shape changes, a method for template mask shape-adaptation based on geometric transformation parameter optimisation is introduced. The presence of occluding objects makes local structure descriptors (e.g., the gradient) unreliable, which means that only pixelwise comparisons of target and template can be made, unless the local structure operators are modified to take into account the varying signal certainty. Normalized convolution provides the means for such a modification. This is demonstrated in a section on phase-based target tracking, which also contains a presentation of a generic method for tracking of occluded targets by combining normalized convolution with iterative reweighting.
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Ponnala, Lalit. "Analysis of Genetic Translation using Signal Processing." NCSU, 2007. http://www.lib.ncsu.edu/theses/available/etd-02072007-174200/.
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A series of free energy estimates can be calculated from the ribosome's progressive interaction with mRNA sequences during the process of translation elongation in eubacteria. A sinusoidal pattern of roughly constant phase has been detected in these free energy signals. Frameshifts of the +1 type occur when the ribosome skips an mRNA base in the 5'-3' direction, and can be associated with local phase-shifts in the free energy signal. We propose a mathematical model that captures the mechanism of frameshift based on the information content of the signal parameters and the relative abundance of tRNA in the bacterial cell. The model shows how translational speed can modulate translational accuracy to accomplish programmed +1 frameshifts and could have implications for the regulation of translational efficiency. Results are presented using experimentally verified frameshift genes across eubacteria.
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Holdsworth, David A. "Signal analysis with applications to atmospheric radars /." Title page, abstract and contents only, 1995. http://web4.library.adelaide.edu.au/theses/09PH/09phh728.pdf.
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Valejev, Najl V. "In silico analysis of signal transduction proteins." Thesis, University of Oxford, 2006. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.432258.
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Roberts, G. "Some aspects seismic signal processing and analysis." Thesis, Bangor University, 1987. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.379692.
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Gorcin, Ali. "Multidimensional Signal Analysis for Wireless Communications Systems." Scholar Commons, 2013. http://scholarcommons.usf.edu/etd/4680.
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Wireless communications systems underwent an evolution as the voice oriented applications evolved to data and multimedia based services. Furthermore, current wireless technologies, regulations and the un-
derstanding of the technology are insufficient for the requirements of future wireless systems. Along with the rapid rise at the number of users, increasing demand for more communications capacity to deploy multimedia applications entail effective utilization of communications resources. Therefore, there is a need for effective spectrum allocation, adaptive and complex modulation, error recovery, channel estimation, diversity and code design techniques to allow high data rates while maintaining desired quality of service, and reconfigurable and flexible air interface technologies for better interference and fading management. However, traditional communications system design is based on allocating fixed amounts of resources to the user and does not
consider adaptive spectrum utilization.
Technologies which will lead to adaptive, intelligent, and aware wireless communications systems are expected to come up with consistent methodologies to provide solutions for the capacity, interference, and
reliability problems of the wireless networks. Spectrum sensing feature of cognitive radio systems are a step forward to better recognize the problems and to achieve efficient spectrum allocation. On the other hand, even though spectrum sensing can constitute a solid base to achieve the reconfigurability and awareness goals of next generation networks, a new perspective is required to benefit from the whole dimensions of the available electro hyperspace. Therefore, spectrum sensing should evolve to a more general and comprehensive awareness providing a mechanism, not only as a part of CR systems which provide channel occupancy information but also as a communication environment awareness component of dynamic spectrum access paradigm which
can adapt sensing parameters autonomously to ensure robust identification and parameter estimation for the signals over the monitored spectrum. Such an approach will lead to recognition of communications opportunities in different dimensions of spectrum hyperspace, and provide necessary information about the air interfaces, access techniques and waveforms that are deployed over the monitored spectrum to accomplish adaptive resource management and spectrum access.
We define multidimensional signal analysis as a methodology, which not only provides the information that the spectrum hyperspace dimension in interest is occupied or not, but also reveals the underlaying information regarding to the parameters, such as employed channel access methods, duplexing techniques and other parameters related to the air interfaces of the signals accessing to the monitored channels and more. To achieve multidimensional signal analysis, a comprehensive sensing, classification, and a detection approach is required at the initial stage. In this thesis, we propose the multidimensional signal analysis procedures
under signal identification algorithms in time, frequency. Moreover, an angle of arrival estimation system for wireless signals, and a spectrum usage modeling and prediction method are proposed as multidimensional signal analysis functionalities.
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Mgdob, Hosam Mohamed. "Heart sound acquisition system and signal analysis." Thesis, University of Sussex, 2004. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.400032.
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Brown, Allen David Evans. "Parametric spectral analysis using digital signal microprocessors." Thesis, University of Hertfordshire, 1993. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.387144.
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Page-Jones, Michael Andrew. "Components for optical signal analysis and routing." Thesis, King's College London (University of London), 1995. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.339096.
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Fuster, García Elíes. "Biomedical signal analysis in automatic classification problems." Doctoral thesis, Editorial Universitat Politècnica de València, 2012. http://hdl.handle.net/10251/17176.
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A lo largo de la última década hemos asistido a un desarrollo sin precedentes de las tecnologías de la salud. Los avances en la informatización, la creación de redes, las técnicas de imagen, la robótica, las micro/nano tecnologías, y la genómica, han contribuido a aumentar significativamente la cantidad y diversidad de información al alcance del personal clínico para el diagnóstico, pronóstico, tratamiento y seguimiento de los pacientes. Este aumento en la cantidad y diversidad de datos clínicos requiere del continuo desarrollo de técnicas y metodologías capaces de integrar estos datos, procesarlos, y dar soporte en su interpretación de una forma robusta y eficiente.
En este contexto, esta Tesis se focaliza en el análisis y procesado de señales biomédicas y su uso en problemas de clasificación automática. Es decir, se focaliza en: el diseño e integración de algoritmos para el procesado automático de señales biomédicas, el desarrollo de nuevos métodos de extracción de características para señales, la evaluación de compatibilidad entre señales biomédicas, y el diseño de modelos de clasificación para problemas clínicos específicos. En la mayoría de casos contenidos en esta Tesis, estos problemas se sitúan en el ámbito de los sistemas de apoyo a la decisión clínica, es decir, de sistemas computacionales que proporcionan conocimiento experto para la decisión en el diagnóstico, pronóstico y tratamiento de los pacientes.
Una de las principales contribuciones de esta tesis consiste en la evaluación de la compatibilidad entre espectros de resonancia magnética (ERM) obtenidos mediante dos tecnologías de escáneres de resonancia magnética coexistentes en la actualidad (escáneres de 1.5T y de 3T). Esta compatibilidad se evalúa en el contexto de clasificación automática de tumores cerebrales. Los resultados obtenidos en este trabajo sugieren que los clasificadores existentes basados en datos de ERM de 1.5T pueden ser aplicables a casos obtenidos con la nueva tecnologFuster García, E. (2012). Biomedical signal analysis in automatic classification problems [Tesis doctoral]. Editorial Universitat Politècnica de València. https://doi.org/10.4995/Thesis/10251/17176
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Shi, Rui. "Off-chip wire distribution and signal analysis." Diss., Connect to a 24 p. preview or request complete full text in PDF format. Access restricted to UC campuses, 2008. http://wwwlib.umi.com/cr/ucsd/fullcit?p3336647.
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Thesis (Ph. D.)--University of California, San Diego, 2008.Title from first page of PDF file (viewed Jan. 6, 2009). Available via ProQuest Digital Dissertations. Vita. Includes bibliographical references (p. 91-93).
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Amanzholov, Anuar. "Analysis of off-peak traffic signal operations." Access to citation, abstract and download form provided by ProQuest Information and Learning Company; downloadable PDF file, 129 p, 2008. http://proquest.umi.com/pqdweb?did=1605156311&sid=6&Fmt=2&clientId=8331&RQT=309&VName=PQD.
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Kang, Chunmei. "Meteor radar signal processing and error analysis." Connect to online resource, 2008. http://gateway.proquest.com/openurl?url_ver=Z39.88-2004&rft_val_fmt=info:ofi/fmt:kev:mtx:dissertation&res_dat=xri:pqdiss&rft_dat=xri:pqdiss:3315846.
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Mirmojarabian, S. (Seyed). "Signal analysis tool to investigate walking abnormalities." Master's thesis, University of Oulu, 2018. http://jultika.oulu.fi/Record/nbnfioulu-201809062748.
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Abstract. This thesis presents a signal analysis tool, which has been designed to investigate walking abnormalities which are related to foot rolling movements during walking; interaction of foot with ground which is called stance phase. They would cause a wide range of severe anatomical damages such as ankle, leg, heel and back pain in the long-term.
Comparing to the conventional data acquisition setups of biomechanical researches, inertial measurement sensors (IMU), which are being used widely as an appropriate alternative setup recently, facilitate monitoring human movement for a long-term period out of laboratory. This justifies the growing trend of improving the IMU-based algorithms which are designed for events detection, position calculation, and rotation estimation. Therefore, a set of 4 IMUs, placed on shank and foot of both legs, has been used for data collection.
In data processing stage, two novel algorithms have been developed and implemented as the backbone of the designed software aiming to detect and integrate stance phases. The first algorithm was developed to detect stance phases in gait cycle data. Even though the detection of events in gait cycles has been the topic of a majority of biomechanical researches, stance phase as the interval between two consecutive events has not been studied sufficiently. The second algorithm, sensor alignment, generates a rotation matrix which is used to align IMU sensors placed on the same foot and shank. This alignment of the two sensors enables us to add or subtract the data point-wisely to make a more meaningful interpretation of the data regarding thought-out walking abnormalities during phase stances.
The visualized results of the thesis can be considered as an early stage of a more comprehensive research which might lead to quantitative results corresponding to different walking abnormalities.
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Liljekvist, Erika, and Oscar Hedlund. "Uncovering Signal : Simplifying Forensic Investigations of the Signal Application." Thesis, Högskolan i Halmstad, Akademin för informationsteknologi, 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:hh:diva-44835.
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The increasing availability of easy-to-use end-to-end encrypted messaging applications has made it possible for more people to conduct their conversations privately. This is something that criminals have taken advantage of and it has proven to make digital forensic investigations more difficult as methods of decrypting the data are needed. In this thesis, data from iOS and Windows devices is extracted and analysed, with focus on the application Signal. Even though other operating systems are compatible with the Signal application, such as Android, it is outside the scope of this thesis. The results of this thesis provide access to data stored in the encrypted application Signalwithout the need for expensive analysis tools. This is done by developing and publishing the first open-source script for decryption and parsing of the Signal database. The script is available for anyone at https://github.com/decryptSignal/decryptSignal.
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Manning, George Keith. "Signal processing for ultrasonic foetal monitoring." Thesis, University of Edinburgh, 1987. http://hdl.handle.net/1842/12559.
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Ravirala, Narayana. "Device signal detection methods and time frequency analysis." Diss., Rolla, Mo. : University of Missouri-Rolla, 2007. http://scholarsmine.umr.edu/thesis/pdf/Ravirala_09007dcc803fea67.pdf.
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Thesis (M.S.)--University of Missouri--Rolla, 2007.Vita. The entire thesis text is included in file. Title from title screen of thesis/dissertation PDF file (viewed March 18, 2008) Includes bibliographical references (p. 89-90).
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Wang, Fa-Yu, and 王法禹. "Deterministic Blind Extraction of Signals for Biomedical Signal Analysis." Thesis, 2011. http://ndltd.ncl.edu.tw/handle/17231863721317468788.
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博士國立清華大學
通訊工程研究所
99
Although significant efforts have been made in developing blind source separation (BSS) techniques, most of the existing methods rely on the foundational assumption that the sources are statistically independent or uncorrelated. However, in many biomedical applications the source signals, which represent interactions of specific proteins and molecules in living cells, may be mutually correlated, leading to the conventional independent component analysis (ICA) not applicable. In view of this, we focus on the blind extraction of correlated signals with specific deterministic properties. Two classes of BSS problems are studied. In the first part of the thesis, the separation of non-negative sources is considered, which could appear in biomedical imaging modalities including dynamic contrast enhanced magnetic resonance imaging (DCE-MRI), X-ray imaging, ultrasonic imaging, and fluorescence microscope imaging, or appear in spectrum signals including nuclear magnetic resonance (NMR) spectrum and infrared (IR) spectrum. By minimizing the correlation coefficient of two non-negative sources, a non-negative least-correlated component analysis ($n$LCA) method is proposed to design the unmixing matrix. We show that a closed-form solution is available for unmixing two mixtures of two sources. For extracting more than two sources, a joint correlation function of multiple signals is proposed to determine the unmixing matrix. Based on minimizing the joint correlation function among the estimated non-negative sources, we propose an iterative volume maximization (IVM) principle which involves solving linear programming problem only for non-negative source extraction. The source identifiability is further discussed and analyzed. Both simulation data and real biomedical data were used to demonstrate its superior performance of the proposed nLCA method over some existing benchmark algorithms. In the second part of the thesis, the exponential signal analysis for biomedical applications is studied. The exponential signal extraction problem arises in many applications including ultrasonic sensor array processing, blood flow imaging, and fluorescence cellular imaging. Depending on the applications, the sources have specific properties that can be used as constraints for source separation. Based on this idea, we propose a multiple rooting technique for multiple signal classification (MR-MUSIC) algorithm, which can integrate the prior information of signals for improving the source extraction performance. Moreover, for fluorescence decay signals, a subspace distance data segmentation (SDDS) is proposed to identify the region of interest (ROI) with the same characteristics. By using principal component analysis (PCA) on all pixel data in the ROI and MR-MUSIC algorithm, an accurate estimation of image signatures, i.e., the decay constants, can be obtained. These proposed methods were evaluated with simulation data to demonstrate their superior performance over several existing benchmark methods.
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"Speech signal analysis." 1997. http://library.cuhk.edu.hk/record=b5896236.
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by Bill, Kan Shek Chow.Thesis (M.Phil.)--Chinese University of Hong Kong, 1997.
Includes bibliograhical references (leaves 39-40).
Chapter Chapter 1. --- Introduction --- p.1
Chapter Chapter 2. --- The spectrogram --- p.4
Chapter 2.1 --- Speech signal background --- p.4
Chapter 2.2 --- Windowed Fourier transform --- p.4
Chapter 2.3 --- Kernel function --- p.6
Chapter 2.4 --- Spectrum analysis --- p.7
Chapter 2.5 --- Spectrogram --- p.9
Chapter 2.6 --- Reducing dimension of the spectrogram 一 Filter banks --- p.12
Chapter 2.7 --- Recent experiment on filter banks --- p.12
Chapter Chapter 3. --- Spectrogram compression --- p.15
Chapter 3.1 --- Capturing the movement of the spectrum along time --- p.16
Chapter 3.2 --- Informative statistics ´ؤ peak distance --- p.18
Chapter 3.3 --- Estimated spectrogram --- p.21
Chapter 3.4 --- Relationship between spectrogram and the speech signal --- p.22
Chapter Chapter 4. --- The phase problem --- p.27
Chapter 4.1 --- The role of the Fourier phase --- p.27
Chapter 4.2 --- Iteration scheme --- p.27
Chapter 4.3 --- Smoothing on the noise ´ؤ interpolation --- p.34
Chapter Chapter 5. --- Conclusion and further discussion --- p.37
Chapter 5.1 --- Conclusion --- p.37
Chapter 5.2 --- Further discussion --- p.38
References --- p.39
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Pradhan, Alok Kumar. "Analysis of partial discharge signals using digital signal processing techniques." Thesis, 2012. http://ethesis.nitrkl.ac.in/4124/1/Alok_Thesis.pdf.
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Partial discharge (PD) measurements have emerged as a powerful diagnostic tool for condition monitoring of insulation in high voltage equipments. Study of PD patterns reveals the nature and severity of the defects present in the insulation. Nowadays online and onsite PD measurements are preferred to keep the equipment in service while assessing its condition. A major difficulty in such measurements is to extract the PD signal from severe noise and interferences. Various time and frequency domain de-noising techniques are adopted for the extraction of PD signal. Recent research shows that wavelet analysis is a powerful tool in de-noising PD signals. Wavelet analysis can be performed using discrete wavelet transform (DWT) and second generation wavelet transform (SGWT) (also called lifting wavelet transform). In the wavelet analysis based de-noising of PD signals, selection of mother wavelet, maximum decomposition level, and thresholding rule are some of the important issues that affect the de-noising results. Further, time-frequency analysis of PD signal can be performed using S-transform. S-transform is an effective tool for the time-frequency analysis of a signal. This work applies different wavelet based de-noising techniques to five noisy PD signals having different characteristics. Among the five signals four signals are numerically simulated and one is a practical signal. The signals are de-noised using DWT and SGWT based de-noising schemes. The de-noising schemes adopt different types of mother wavelet selection methods, and thresholding rules. Based on the de-noising techniques, varied de-noising results are obtained. A comparative analysis of the de-noising results is made using various de-noising performance indices. Then the time-frequency analysis of the de-noised practical signal is made using S-transform. From the results of the work it emerges that wavelet analysis is a superior tool for the extraction of PD signals. And selection of mother wavelet and thresholding rule for the wavelet based de-noising, depends on the type of signal and the severity of noise and interferences.
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葉鎮國. "Electromagnetic wave detection signal and applications signal of analysis." Thesis, 2013. http://ndltd.ncl.edu.tw/handle/72887924228634133734.
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碩士國立彰化師範大學
機電工程學系
102
The antenna of the study is a portable coil which is used to sense the orientation of the electromagnetic pulse (EMP). A peak detector with feedback is employed to catch the information by detecting the peak voltages in the signal and the resonant circuits are engaged to select specific broadbands. Through the Hilbert–Huang transform (HHT) and Ensemble EMD(EEMD), the information and signal caught by the peak detector and resonant circuits are extracted and decomposed into a number of intrinsic mode functions(IMFs). The decomposed IMFs representing the data set are further defined as shown in figures.
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Lin, Wei-Chih, and 林威志. "Analysis of the EEG Signals in Response to Musical Signal Stimuli." Thesis, 2005. http://ndltd.ncl.edu.tw/handle/22130193189578350916.
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碩士臺北醫學大學
醫學資訊研究所
93
In recent years, many researches have focused on the physiological effects of music. The electroencephalographic (EEG) is often used to verify the influences of music on human brain activity. In this study, we attempted to apply the spectral analysis and the independent component analysis (ICA) to analyze and to discover the EEG responses of subjects with different musical signal stimuli. It is expected that some features on EEG can be demonstrated to reflect the different musical signal stimuli. The EEGs of thirty-two healthy volunteers listening to different music was acquired. Musical signal stimuli are categorized into metal music, sonata music, no music and the favorite music selected by subjects. Spectral analysis wase applied to obtain the Alpha, Beta, Theta and Gamma band power of EEG signal under different music stimuli. The power at each band of each channel was used as the features of EEG. The correlation of the features between different situations and subjects was used to show which channel displays the difference of EEG signals. The results show that minimum alpha power was recorded in listening to metal music and the power of gamma band is lower when listening to no music, which imply that gamma band appears during music listening process, and reduction of alpha band occurs when listening to metal music. Regarding the difference between each individual, we found that the similarity between individuals is high when listening to metal music, and it is low when listening to favorite music. Besides, the similarity between each individual is high in the channel at the left of anterior cranial is highly different. When listening to metal music, sonata music and favorite music, which implies that this section may be sensitive to musical signal stimuli. Besides, the study discovers that the difference between individual is greater than the difference between musical signal stimuli. So how to eliminate the difference of EEG data caused by the difference of individual is important to obtain the accurate analysis results. In the study of independent component analysis, we discovered that some independent components of EEG can display the difference of spectral power in listening different music. But not every subject showed this phenomenon.
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Lin, Pei-Feng, and 林佩芬. "Correlation analysis between ECG and EEG signals based on signal complexity." Thesis, 2015. http://ndltd.ncl.edu.tw/handle/05363265560635320629.
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博士國立臺灣大學
生醫電子與資訊學研究所
103
Introduction The secret of life remains extremely concealed. There are all sorts of rhythms in human bodies and they are central to life. The rhythms interact with each other as well as the outside fluctuating, noisy environment under the control of innumerable feedback systems. They provide an orderly function that enables life. The heart has been considered the source of emotional experience and wisdom in many cultures throughout the ages. Most neuroscientists consider consciousness or even thought is merely an epiphenomenon of the human brain function and its associated neurophysiology. However, the heart begins to beat before the brain is formed. Conventionally, both neural and humoral pathways connect the heart with the brain. Whether the interplay between the heart and brain could be explored through their rhythms is the question. Heart rate variability is recognized as the indicator of cardiac autonomic function. The dynamics of human electroencephalography (EEG) dynamics has been proved to be related to cognitive activities. This dissertation starts with reviewing the nonlinear methods in analyzing biological rhythms, which are multiscale, nonlinear and non-stationary. Regardless of whether chaos is present, deterministic complexity exists in biological rhythms. Regularity based complexity was chosen after comparisons. The goal is to find correlations between EEG and electrocardiography (ECG) through regularity based complexity analysis. Both simultaneous and non-simultaneous data would be examined. The experimental subjects are from a geriatric sample with varied cognitive abilities and basically healthy hearts. The electromagnetic activity of the brain works at an extremely fast speed, and the quasi-stationary epochs of EEG are, in general, short lasting, in the order of tens of seconds. Therefore symbolic techniques were introduced when exploring the very short simultaneous EEG and R-R interval (RRI) data. The origin of EEG remains unknown. Slow cortical potential (SCP), one component of EEG, is in the frequency range similar to that of the heart, and would be explored in an intuitive nonlinear way. In addition, the amplitude and instantaneous frequency of EEG would be separately approached. Methods The sample consisted of 89 geriatric outpatients in three patient groups: 38 fresh cases of vascular dementia (VD), 22 fresh cases of Alzheimer’s disease (AD) and 29 controls. Multiscale entropy (MSE) analysis was applied to the non-simultaneous EEG and RRI data. Symbolic analysis was applied to the simultaneous EEG and RRI data. Discrete events (local peaks) of EEG were extracted to separate the amplitude and instantaneous frequency. The low-to-high frequency power (LF/HF) ratio of RRI was calculated to represent sympatho-vagal balance. Results and Discussions MSE revealed correlations between the signal complexity of brain and cardiac activities in non-simultaneous data. Linear correlation between the MSE value and the score of the mini-mental state examination was first found. Symbolic dynamics failed to correlate the heart to the brain. This is due to that the RRI is too short to represent the characteristics of a subject. The symbolic analysis revealed important information that the EEG dynamics which relates to either the cognitive functions or the underlying pathologies of dementia are embedded within the dynamics of the amount of but not the interval between each synchronized firing of adjacent cerebral neurons. Just like RRI of ECG, discrete events of EEG also provided important information. The relative value of complexity does not indicate health condition straightly. It depends on the method and the scale or dimension that particular method measures. Discrete events provide no less information than continuous waveforms of EEG. Pathological condition is continuous rather than stepwise.
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Chen, Yu. "Digital Signal Processing with Signal-Derived Timing: Analysis and Implementation." Thesis, 2017. https://doi.org/10.7916/D8PR81KW.
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This work investigates two different digital signal processing (DSP) approaches that rely on signal-derived timing: continuous-time (CT) DSP and variable-rate DSP. Both approaches enable designs of energy-efficient signal processing systems by relating their operation rates to the input activity.
The majority of this thesis focuses on CT-DSP, whose operations are completely digital in CT, without the use of a clock. The spectral features of CT digital signals are analyzed first, demonstrating a general pattern of the quantization noise spectrum added in CT amplitude quantization. Then the focus is narrowed to the investigations of the system characteristics and architecture of CT digital infinite-impulse-response (IIR) filters, which are barely studied in the previous work on this topic. This thesis discusses and addresses previously unreported stability issue in CT digital IIR filters with the presence of delay-line mismatches and proposes an innovative method to design high-order CT digital IIR filters with only two tap delays. Introducing an event detector allows the operation rate of a CT digital IIR filter to closely track the input activity even though it is a feedback system. For the first time, the filtered CT digital signal is converted to a synchronous digital signal. This facilitates integrating the CT digital filter and conventional discrete-time systems and expands the applications of the former. This discussion uses a computationally efficient interpolation filter to improve the signal accuracy of the synchronous digital output. On the circuit level, a new delay-cell design is introduced. It ensures low jitter, good matching, robust communication with adjacent circuits and event-independent delay.
An integrated circuit (IC) with all these ideas adopted was fabricated in a TSMC 65 nm LP CMOS process. It is the first IC implementation of a CT digital IIR filter. It can process signals with a data rate up to 20 MHz. Thanks to the IIR response and the 16-bit resolution used in the system, the implemented filter can achieve a frequency response much more versatile and accurate than the CT digital filters in prior art. The implemented system features an agile power adaptive to input activity, varying from 2.32mW (full activity) to 40μW (idle) with no power-management circuitry.
The second part of the thesis discusses a variable-rate DSP capable of processing samples with a variable sampling rate. The clock rate in the variable-rate DSP tracks the input sampling rate. Compared to a fixed-rate DSP, the proposed system has a lower output data rate and hence is more computationally efficient. A reconstruction filter with a variable cutoff frequency is used to reconstruct the output. The signal-to-noise ratio remains fixed when the sampling rate changes.
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蔡宛純. "Analysis and Design of Signal Integrity High Speed Signal Connector." Thesis, 2015. http://ndltd.ncl.edu.tw/handle/keqp3r.
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Lee, Wei-Yang, and 李威揚. "Dynamics Analysis of Chaotic Signal." Thesis, 1995. http://ndltd.ncl.edu.tw/handle/03985814920237239491.
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碩士國立成功大學
化學工程研究所
83
In recent years, it has recognized that chaotic dynamics are inherent in all of the nonlinear physical phenomena that have created a sense of revolution in science today. As a researcher in engineering, we have to determine if the chaotic phenomena exist in a nonlinear system. The objective of this paper is to identify the chaotic dyna- mics of a system by the use of new dynamical tools, such as po- wer spectrum, fractal dimension, Poincare map and Lyapunov exp- onent. The Lyapunov exponent has been shown to be the most sig- nificant tool developed so far. As a result, we describe the calculation of Lyapunov exponent in detail. Finally, we apply the Lyapunov exponent to characterize the chaotic behavior of some deterministic dynamics arising from Lorenz equation, Rossler equation, Duffing equation and discuss the practical effectiveness of the method.
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Lin, Yenn-Jiang, and 林彥璋. "Signal Analysis of Atrial Fibrillation." Thesis, 2009. http://ndltd.ncl.edu.tw/handle/00875485900031306551.
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博士國立陽明大學
臨床醫學研究所
98
Atrial fibrillation (AF) is the most common cardiac arrhythmia in clinical practice. It is well known that AF depends on the interaction between the triggers and substrate. The substrate mapping data helped us to learn more about the mechanism of AF maintenance and to identify the critical substrate for catheter ablation. However, it is difficult to analyze the varied and complex fibrillation signals throughout the atria during AF. The signal analysis of the fibrillation signals provided a feasible method to analyze the atrial substrate. The aim of this study was to explore the feasibility of the computer-assisted signal analysis in the intracardiac mapping of AF. In this study, the application of a frequency domain analysis and time domain analysis of fractionation electrogram analysis was summarized as follows. (1) Frequency Domain Analysis The frequency analysis provided a feasible method to analyze the temporal and spatial distribution of the atrial activation during AF. Technologically, we first characterize the effect of the QRS wave on the reliability of the frequency spectra obtained from intracardiac atrial signals. The requirement of the QRS-T wave subtraction was confirmed in animal models of AF, and the accuracy of the frequency spectra obtained from the 3D non-contact mapping system was validated by using a coherence analysis. Therefore, the clinical implication of simultaneous and global high-density frequency mapping during AF is reliable. During AF, the highest DF drivers were located within the arrhythmogenic PV or its ostium with a LA-to-RA DF gradient. In patients with SVC-AF and persistent AF, the LA-to-RA DF gradient was not evident. In substrate dominant or persistent AF patients, one or multiple atrial sources could participate in the maintenance of AF. The highest DF could be identified around the stationary reentrant circuits in the atrial substrate. The ablation strategy guided by frequency analysis may be a better way for finding the critical substrate in the maintenance of AF. During SR, a spectral analysis also could detect an abnormal atrial substrate. Previous studies showed that regions with an abnormal atrial substrate could be identified by multiple rapid deflections, fractionated and low voltage bipolar intracardiac electrograms during SR. Those sites were characterized by higher frequencies than the other surrounding normal atrial substrates with the spectral analysis during SR, and are known as AF nests. We observed that the distribution of the AF nests could predict the substrate characteristics and efficacy of the catheter ablation for PV isolation. Therefore, those areas may play a role in the perpetuation of AF and indicate an atrial substrate abnormality. (2) Time Domain Analysis The signal analysis based on the DF gradients provided limited value in patients with long-lasting AF, because the regional DF gradient was limited. In patients with non-paroxysmal AF, an atrial substrate with complex fractionated electrograms (CFEs) was considered to be the maintainers of AF. However, a temporal and spatial variation in the CFEs exists. Further, CFEs are usually observed in many regions of the atria, making identification of a critical atrial substrate difficult. Technologically, we quantified the degree of CFEs by using an automatic algorithm, which enabled a more precise identification of the critical substrate for maintaining AF. Our study indicated that a consistent presence of continuous CFEs (longer than 5 seconds) before and after pulmonary vein isolation correlated with procedural termination sites. A greater temporal and regional variation in CFEs is seen in sites with lower degrees of fractionation. The consistency of the fibrillation waves in the high DF region indicates its active role in patients with non-paroxysmal AF. High density mapping demonstrated that the pattern of spatiotemporal activity in the LA was related to the location of the high DF sites, the degree of atrial remodeling. Clinically, we evaluated the spatiotemporal organization before and after pulmonary vein isolation. The fibrillatory activities in the LA was more organized and frequency distribution more homogeneous after a complete pulmonary vein isolation. A persistent presence of continuous CFEs before and after PVI in the vicinity of the high frequency sites was important for the maintenance of AF. In conclusion, using bioengineering techniques, the nature of the fibrillatory electrograms can be characterized, and the atrial substrate property can be assessed. Individual knowledge of the spatial and temporal organization of fibrillation waves during AF and SR in each patient may help in the identification of the different mechanisms of AF.