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Relevant bibliographies by topics / Signals classification / Dissertations / Theses

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Dissertations / Theses on the topic 'Signals classification'

Author: Grafiati

Published: 7 September 2022

Last updated: 31 July 2025

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1

Rida, Imad. "Temporal signals classification." Thesis, Normandie, 2017. http://www.theses.fr/2017NORMIR01/document.

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De nos jours, il existe de nombreuses applications liées à la vision et à l’audition visant à reproduire par des machines les capacités humaines. Notre intérêt pour ce sujet vient du fait que ces problèmes sont principalement modélisés par la classification de signaux temporels. En fait, nous nous sommes intéressés à deux cas distincts, la reconnaissance de la démarche humaine et la reconnaissance de signaux audio, (notamment environnementaux et musicaux). Dans le cadre de la reconnaissance de la démarche, nous avons proposé une nouvelle méthode qui apprend et sélectionne automatiquement les p

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2

楊永生 and Yongsheng Yang. "Fuzzy classification of biomedical signals." Thesis, The University of Hong Kong (Pokfulam, Hong Kong), 1996. http://hub.hku.hk/bib/B31213832.

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3

Alty, Stephen Robert. "The classification of voiceband signals." Thesis, Liverpool John Moores University, 1998. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.242312.

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4

Proper, Ethan R. (Ethan Richard). "Automated classification of power signals." Thesis, Massachusetts Institute of Technology, 2008. http://hdl.handle.net/1721.1/44842.

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Thesis (Nav. E.)--Massachusetts Institute of Technology, Dept. of Mechanical Engineering; and, (S.M.)--Massachusetts Institute of Technology, System Design and Management Program, 2008.<br>Includes bibliographical references (p. 106-107).<br>The Non-Intrusive Load Monitor (NILM) is a device that utilizes voltage and current measurements to monitor an entire system from a single reference point. The NILM and associated software convert the V/I signal to spectral power envelopes that can be searched to determine when a transient occurs. The identification of this signal can then be determined by

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5

Proper, Ethan R. "Automated classification of power signals." Thesis, (7 MB), 2008. http://handle.dtic.mil/100.2/ADA488187.

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Thesis (Degrees of Naval Engineer and M.S. in Engineering and Management)--Massachusetts Institute of Technology, June 2008.<br>"June 2008." Description based on title screen as viewed on August 26, 2009. DTIC Descriptor(s): Reverse Osmosis, Shipboard, Electronic Equipment, Electronics, Waste Disposal, Voltage, Graphical User Interface, Electromagnetic Radiation, Computer Programs, Classification, Measurement, Expert Systems, Transients, Waste Collection. DTIC Identifier(s): Non-Intrusive Load Monitors, Electromagnetic Systems, Electronic Systems, Power Signals, NILM (Non-Intrusive Load Monito

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6

Yang, Yongsheng. "Fuzzy classification of biomedical signals /." Hong Kong : University of Hong Kong, 1996. http://sunzi.lib.hku.hk/hkuto/record.jsp?B19669549.

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7

VanDerKamp, Martha M. "Modeling and classification of biological signals." Thesis, Monterey, Calif. : Springfield, Va. : Naval Postgraduate School; Available from the National Technical Information Service, 1992. http://edocs.nps.edu/npspubs/scholarly/theses/1992/Dec/92Dec_VanDerKamp.pdf.

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8

Ramakonar, Visalakshi S. "Modulation classification of digital communication signals." Thesis, Edith Cowan University, Research Online, Perth, Western Australia, 2002. https://ro.ecu.edu.au/theses/752.

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Modulation classification of digital communications signals plays an important role in both military and civilian sectors. It has the potential of replacing several receivers with one universal receiver. An automatic modulation classifier can be defined as a system that automatically identifies the modulation type of the received signal given that the signal exists and its parameters lie in a known range. This thesis addresses the need for a universal modulation classifier capable of classifying a comprehensive list of digital modulation schemes. Two classification approaches are presented: a

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9

Atsma, Willem Jentje. "Classification of myoelectric signals using neural networks." Thesis, National Library of Canada = Bibliothèque nationale du Canada, 1997. http://www.collectionscanada.ca/obj/s4/f2/dsk2/ftp01/MQ29968.pdf.

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10

Ju, Peter M. (Peter Ming-Wei) 1977. "Classification of finger gestures from myoelectric signals." Thesis, Massachusetts Institute of Technology, 2000. http://hdl.handle.net/1721.1/9074.

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Thesis (S.B. and M.Eng.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2000.<br>Includes bibliographical references (p. 73-75).<br>Electromyographic signals may provide an important new class of user interface for consumer electronics. In order to make such interfaces effective, it will be crucial to map EMG signals to user gestures in real time. The mapping from signals to gestures will vary from user to user, so it must be acquired adaptively. In this thesis, I describe and compare three methods for static classification of EMG signals. I then

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11

Hannaske, Roland. "Fast Digitizing and Digital Signal Processing of Detector Signals." Forschungszentrum Dresden-Rossendorf, 2009. https://hzdr.qucosa.de/id/qucosa%3A21615.

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A fast-digitizer data acquisition system recently installed at the neutron time-of-flight experiment nELBE, which is located at the superconducting electron accelerator ELBE of Forschungszentrum Dresden-Rossendorf, is tested with two different detector types. Preamplifier signals from a high-purity germanium detector are digitized, stored and finally processed. For a precise determination of the energy of the detected radiation, the moving-window deconvolution algorithm is used to compensate the ballistic deficit and different shaping algorithms are applied. The energy resolution is determined

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12

Duzenli, Ozhan. "Classification of underwater signals using wavelet-based decompositions." Thesis, Monterey, Calif. : Springfield, Va. : Naval Postgraduate School ; Available from National Technical Information Service, 1998. http://handle.dtic.mil/100.2/ADA349588.

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Thesis (M.S. in Electrical Engineering) Naval Postgraduate School, June 1998.<br>"June 1998." Thesis advisor(s): Monique P. Farques, Ralph D. Hippenstiel. Includes bibliographical references (p. 161-163). Also available online.

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13

Yagci, Tayfun. "Target Classification And Recognition Using Underwater Acoustic Signals." Master's thesis, METU, 2005. http://etd.lib.metu.edu.tr/upload/3/12606373/index.pdf.

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Nowadays, fulfillment of the tactical operations in secrecy has great importance for especially subsurface and surface warfare platforms as a result of improvements in weapon technologies. Spreading out of the tactical operations to the larger areas has made discrimination of targets unavoidable. Due to enlargement of the weapon ranges and increasing subtle hostile threats as a result of improving technology, &ldquo<br>visual&rdquo<br>target detection methods left the stage to the computerized acoustic signature detection and evaluation methods. Despite this, the research projects have not s

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14

Clemedson, Johan. "Audio Generation from Radar signals, for target classification." Thesis, KTH, Optimeringslära och systemteori, 2017. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-215502.

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Classification in radar application are often of great interest, since one does not only want to know where a target is, but also what type of target it is. This thesis focus on transforming the radar return from a target into a audio signal. So that the classification can be done by human perception, in this case human hearing. The aim of these classification methods is to be able to distinguish between two types of targets of roughly the same size, namely birds and smaller Unmanned Aerial Vehicles (UAV). It is possible with the radar to measure the targets velocity by using the Doppler effec

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15

Bissinger, Brett Bose N. K. Culver R. Lee. "Minimum hellinger distance classification of underwater acoustic signals." [University Park, Pa.] : Pennsylvania State University, 2009. http://etda.libraries.psu.edu/theses/approved/WorldWideIndex/ETD-4677/index.html.

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16

Malfante, Marielle. "Automatic classification of natural signals for environmental monitoring." Thesis, Université Grenoble Alpes (ComUE), 2018. http://www.theses.fr/2018GREAU025/document.

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Ce manuscrit de thèse résume trois ans de travaux sur l’utilisation des méthodes d’apprentissage statistique pour l’analyse automatique de signaux naturels. L’objectif principal est de présenter des outils efficaces et opérationnels pour l’analyse de signaux environnementaux, en vue de mieux connaitre et comprendre l’environnement considéré. On se concentre en particulier sur les tâches de détection et de classification automatique d’événements naturels.Dans cette thèse, deux outils basés sur l’apprentissage supervisé (Support Vector Machine et Random Forest) sont présentés pour (i) la classif

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17

Kanneganti, Raghuveer. "CLASSIFICATION OF ONE-DIMENSIONAL AND TWO-DIMENSIONAL SIGNALS." OpenSIUC, 2014. https://opensiuc.lib.siu.edu/dissertations/892.

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This dissertation focuses on the classification of one-dimensional and two-dimensional signals. The one-dimensional signal classification problem involves the classification of brain signals for identifying the emotional responses of human subjects under given drug conditions. A strategy is developed to accurately classify ERPs in order to identify human emotions based on brain reactivity to emotional, neutral, and cigarette-related stimuli in smokers. A multichannel spatio-temporal model is employed to overcome the curse of dimensionality that plagues the design of parametric multivariate c

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18

Bertoncini, Crystal Ann. "Applications of pattern classification to time-domain signals." W&M ScholarWorks, 2010. https://scholarworks.wm.edu/etd/1539623559.

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Many different kinds of physics are used in sensors that produce time-domain signals, such as ultrasonics, acoustics, seismology, and electromagnetics. The waveforms generated by these sensors are used to measure events or detect flaws in applications ranging from industrial to medical and defense-related domains. Interpreting the signals is challenging because of the complicated physics of the interaction of the fields with the materials and structures under study. often the method of interpreting the signal varies by the application, but automatic detection of events in signals is always use

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19

Ojo, Catherine A. "Analysis & automatic classification of nuclear magnetic resonance signals." Thesis, University of Edinburgh, 2010. http://hdl.handle.net/1842/4109.

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The human brain consists of a myriad of chemical compounds critical to its functioning. A group of these compounds, collectively known as metabolites, have been a research interest for years because the pathogenesis of neurodegenerative diseases, a tumours classification, the effectiveness of a drug, etc., can be investigated via variations in brain metabolite concentration levels. Nuclear Magnetic Resonance Spectroscopy (NMRS) enables investigators to conduct non-invasive in vivo studies of metabolites in the human brain and the rest of the body. However a number of problems have hindered the

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20

Idowu, Ibrahim Olatunji. "Classification techniques using EHG signals for detecting preterm births." Thesis, Liverpool John Moores University, 2017. http://researchonline.ljmu.ac.uk/7062/.

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Premature birth is defined as an infant born before 37 weeks of gestation and can be sub-categorized into three phrases; late preterm delivery between 34 and 36 weeks of gestation; moderately preterm between 32 and 34 weeks, and extreme preterm less than 28 weeks of gestation. Globally, the rate of preterm births is increasing, thus resulting in significant health, development and economic problems. The current methods for the detection of preterm birth are inadequate due to the fact that the exact cause of premature uterine contractions leading to delivery is mostly unknown. Another problem i

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21

Brown, Elliot Morgan. "The Application of Synthetic Signals for ECG Beat Classification." BYU ScholarsArchive, 2019. https://scholarsarchive.byu.edu/etd/8116.

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A brief overview of electrocardiogram (ECG) properties and the characteristics of various cardiac conditions is given. Two different models are used to generate synthetic ECG signals. Domain knowledge is used to create synthetic examples of 16 different heart beat types with these models. Other techniques for synthesizing ECG signals are explored. Various machine learning models with different combinations of real and synthetic data are used to classify individual heart beats. The performance of the different methods and models are compared, and synthetic data is shown to be useful in beat cla

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22

Arafat, Samer M. "Uncertainty modeling for classification and analysis of medical signals /." free to MU campus, to others for purchase, 2003. http://wwwlib.umi.com/cr/mo/fullcit?p3115520.

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23

Bennett, Richard Campbell. "Classification of underwater signals using a back-propagation neural network." Monterey, Calif. : Springfield, Va. : Naval Postgraduate School ; Available from National Technical Information Service, 1997. http://handle.dtic.mil/100.2/ADA331774.

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Thesis (M.S. in Electrical Engineering) Naval Postgraduate School, June 1997.<br>Thesis advisors, Monique P. Fargues, Roberto Cristi. Includes bibliographical references (p. 95). Also available online.

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24

Larsen, Erik Andreas. "Classification of EEG Signals in a Brain-Computer Interface System." Thesis, Norges teknisk-naturvitenskapelige universitet, Institutt for datateknikk og informasjonsvitenskap, 2011. http://urn.kb.se/resolve?urn=urn:nbn:no:ntnu:diva-13578.

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Electroencephalography (EEG) equipment are becoming more available on thepublic market, which enables more diverse research in a currently narrow field.The Brain-Computer Interface (BCI) community recognize the need for systemsthat makes BCI more user-friendly, real-time, manageable and suited for peoplethat are not forced to use them, like clinical patients, and those who are disabled.Thus, this project is an effort to seek such improvements, having a newly availablemarket product to experiment with: a single channel brain wave reader. However,it is important to stress that this shift in BCI,

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25

Gustavsson, Jan-Olof. "Estimation in non-gaussian noise and classification of welding signals." Licentiate thesis, Luleå tekniska universitet, Signaler och system, 1991. http://urn.kb.se/resolve?urn=urn:nbn:se:ltu:diva-26514.

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26

Daura, Ashiru Sani. "A wavelet-based method for the classification of PCG signals." Thesis, University of Newcastle Upon Tyne, 1998. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.244474.

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27

Burger, Christiaan. "A novel method of improving EEG signals for BCI classification." Thesis, Stellenbosch : Stellenbosch University, 2014. http://hdl.handle.net/10019.1/95984.

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Thesis (MEng)--Stellenbosch University, 2014.<br>ENGLISH ABSTRACT: Muscular dystrophy, spinal cord injury, or amyotrophic lateral sclerosis (ALS) are injuries and disorders that disrupts the neuromuscular channels of the human body thus prohibiting the brain from controlling the body. Brain computer interface (BCI) allows individuals to bypass the neuromuscular channels and interact with the environment using the brain. The system relies on the user manipulating his neural activity in order to control an external device. Electroencephalography (EEG) is a cheap, non-invasive, real time ac

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28

Bond, Zachary. "Unsupervised Classification of Music Signals: Strategies Using Timbre and Rhythm." Thesis, Virginia Tech, 2006. http://hdl.handle.net/10919/36469.

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This thesis describes the ideal properties of an adaptable music classification system based on unsupervised machine learning, and argues that such a system should be based on the fundamental musical properties of timbre, rhythm, melody and harmony. The first two properties and the signal features associated with them are then explored in more depth. In the area of timbre, the relationship between musical style and commonly-extracted signal features within a broad range of piano music is explored, in an effort to identify features which are consistent among all piano music but different for

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29

Jouny, Ismail. "Bispectral analysis of radar signals with application to target classification /." The Ohio State University, 1990. http://rave.ohiolink.edu/etdc/view?acc_num=osu1487683049375875.

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30

Hong, Liang. "Advanced techniques for automatic classification of digitally modulated communication signals /." free to MU campus, to others for purchase, 2002. http://wwwlib.umi.com/cr/mo/fullcit?p3074408.

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31

Evans, Naoko. "Automated vehicle detection and classification using acoustic and seismic signals." Thesis, University of York, 2010. http://etheses.whiterose.ac.uk/1151/.

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Security threats to important infrastructure cause problems to not only those who live nearby but also in a much wider sense. It is therefore desirable to consider the use of automated systems capable of detection and identification of potential threats. This thesis describes an investigation into acoustic and seismic methods for achieving such a system specifically for commercial road vehicles. Accurate algorithms have been developed for recognition of moving vehicles using fusion of acoustic and seismic signals. It has been found that seismic signals are less susceptible to interfering signa

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32

Sapiano, Philip Charles. "The automatic classification of the modulation type of communication signals." Thesis, University of Bath, 1997. https://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.362152.

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33

Ohrnberger, Matthias, Joachim Wassermann, and Gudrun Richter. "Automatic detection and classification of seismic signals for monitoring purposes : [Poster]." Universität Potsdam, 2006. http://www.uni-potsdam.de/imaf/events/ge_work0602.html.

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34

Bekiroglu, Yasemi. "Nonstationary feature extraction techniques for automatic classification of impact acoustic signals." Thesis, Högskolan Dalarna, Datateknik, 2008. http://urn.kb.se/resolve?urn=urn:nbn:se:du-3592.

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Condition monitoring of wooden railway sleepers applications are generallycarried out by visual inspection and if necessary some impact acoustic examination iscarried out intuitively by skilled personnel. In this work, a pattern recognition solutionhas been proposed to automate the process for the achievement of robust results. Thestudy presents a comparison of several pattern recognition techniques together withvarious nonstationary feature extraction techniques for classification of impactacoustic emissions. Pattern classifiers such as multilayer perceptron, learning cectorquantization and g

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35

Marvasti, Salman Alim. "Automated Detection, Classification and Denoising of Embolic Signals in TCD Ultrasound." Thesis, Imperial College London, 2009. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.502920.

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36

Ramaswamy, Ganesh Nachiappa. "Adaptive classification of interfering signals in a shared radio frequency environment." Thesis, Massachusetts Institute of Technology, 1992. http://hdl.handle.net/1721.1/28009.

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Thesis (M.S.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 1992.<br>Includes bibliographical references (leaves 107-114).<br>by Ganesh Nachiappa Ramaswamy.<br>M.S.

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37

Khodjet-Kesba, Mahmoud. "Automatic target classification based on radar backscattered ultra wide band signals." Thesis, Clermont-Ferrand 2, 2014. http://www.theses.fr/2014CLF22506/document.

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L’objectif de cette thèse est la classification automatique des cibles (ATC) en utilisant les signaux rétrodiffusés par un radar ultra large bande (UWB). La classification des cibles est réalisée en comparant les signatures des cibles et les signatures stockées dans une base de données. Premièrement, une étude sur la théorie de diffusion nous a permis de comprendre le sens physique des paramètres extraits et de les exprimer mathématiquement. Deuxièmement, des méthodes d’extraction de paramètres sont appliquées afin de déterminer les signatures des cibles. Un bon choix des paramètres est import

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38

Miller, Corey Alexander. "Intelligent Feature Selection Techniques for Pattern Classification of Time-Domain Signals." W&M ScholarWorks, 2013. https://scholarworks.wm.edu/etd/1539623620.

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Time-domain signals form the basis of analysis for a variety of applications, including those involving variable conditions or physical changes that result in degraded signal quality. Typical approaches to signal analysis fail under these conditions, as these types of changes often lie outside the scope of the domain's basic analytic theory and are too complex for modeling. Sophisticated signal processing techniques are required as a result. In this work, we develop a robust signal analysis technique that is suitable for a wide variety of time-domain signal analysis applications. Statistical p

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39

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, proces

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40

Axelsson, Oskar. "Automatic Classification of surface ships based on signals from passive underwater sensors." Thesis, Luleå tekniska universitet, Institutionen för system- och rymdteknik, 2018. http://urn.kb.se/resolve?urn=urn:nbn:se:ltu:diva-69335.

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41

Chiappa, Silvia. "Analysis and classification of EEG signals using probabilistic models for brain computer interfaces /." [S.l.] : [s.n.], 2006. http://library.epfl.ch/theses/?nr=3547.

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42

Day-Williams, Hugh C. "Effects of training methods on classification on surface electromyographic signals for myoelectric control." Thesis, Massachusetts Institute of Technology, 2018. http://hdl.handle.net/1721.1/119965.

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Thesis: S.B., Massachusetts Institute of Technology, Department of Mechanical Engineering, 2018.<br>Cataloged from PDF version of thesis.<br>Includes bibliographical references (page 25).<br>Myoelectric devices, devices which use the electric signals from human muscles as a control scheme, have shown promise in their potential to aid in human movement augmentation and assistance for those that have suffered injury. Previous studies involving myoelectric devices and the classification of surface electromyographic (sEMG) signals, electrical impulses obtained from muscles from sensors on the skin

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43

Marques, JoÃo Alexandre LÃbo. "SISCTG- an intelligent systems for classification of cardiotocography signals for help diagnosis doctor." Universidade Federal do CearÃ, 2007. http://www.teses.ufc.br/tde_busca/arquivo.php?codArquivo=2046.

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nÃo hÃ<br>The accurate analysis of the fetal heart rate (FHR) and its correlation with uterine contractions (UC) allows the diagnostic and the anticipation of many problems related to fetal distress and the preservation of his life. This dissertation presents the results of an hibrid system based on a set of deterministic rules and fuzzy inference system developed to analyze FHR and UC signals collected by cardiotocography (CTG) exams. The studied variables are basal FHR, short and long term FHR variability, transitory accelerations and decelerations, these lasts classified by their type and n

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44

Jonsson, Maja, and Jennifer Brown. "Deep Learning for Driver Sleepiness Classification using Bioelectrical Signals and Karolinska Sleepiness Scale." Thesis, Linköpings universitet, Institutionen för medicinsk teknik, 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-178082.

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Driver sleepiness contributes to a large amount of all road traffic crashes. Developing an objective measurement of driver sleepiness in order to prevent eventual traffic accidents is desirable. The aim of this master thesis was to investigate if deep learning can be used to provide a driver sleepiness classification from brain activity signals obtained by electroencephalography (EEG). The intention was to study the classification performance when using different representations of the input data and to examine how various deep neural network architectures and class weighting during training a

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45

Bsoul, Abed Al-Raoof. "PROCESSING AND CLASSIFICATION OF PHYSIOLOGICAL SIGNALS USING WAVELET TRANSFORM AND MACHINE LEARNING ALGORITHMS." VCU Scholars Compass, 2011. http://scholarscompass.vcu.edu/etd/258.

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Over the last century, physiological signals have been broadly analyzed and processed not only to assess the function of the human physiology, but also to better diagnose illnesses or injuries and provide treatment options for patients. In particular, Electrocardiogram (ECG), blood pressure (BP) and impedance are among the most important biomedical signals processed and analyzed. The majority of studies that utilize these signals attempt to diagnose important irregularities such as arrhythmia or blood loss by processing one of these signals. However, the relationship between them is not yet fu

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46

Crysandt, Holger. "Hierarchical classification of sound signals with class separation based on information theoretic criteria /." Aachen : Shaker, 2008. http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&doc_number=017071704&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA.

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47

Persson, Christer N. E. "Classification and analysis of low probability of intercept radar signals using image processing." Thesis, Monterey, Calif. : Springfield, Va. : Naval Postgraduate School ; Available from National Technical Information Service, 2003. http://library.nps.navy.mil/uhtbin/hyperion-image/03sep%5FPersson.pdf.

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Thesis (M.S. in Systems Engineering and M.S. in Engineering Science (Electrical Engineering))--Naval Postgraduate School, September 2003.<br>Thesis advisor(s): Phillip E. Pace, D. Curtis Schleher. Includes bibliographical references (p. 125-126). Also available online.

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48

Nakisa, Bahareh. "Emotion classification using advanced machine learning techniques applied to wearable physiological signals data." Thesis, Queensland University of Technology, 2019. https://eprints.qut.edu.au/129875/9/Bahareh%20Nakisa%20Thesis.pdf.

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This research contributed to the development of advanced feature selection model, hyperparameter optimization and temporal multimodal deep learning model to improve the performance of dimensional emotion recognition. This study adopts different approaches based on portable wearable physiological sensors. It identified best models for feature selection and best hyperparameter values for Long Short-Term Memory network and how to fuse multi-modal sensors efficiently for assessing emotion recognition. All methods of this thesis collectively deliver better algorithms and maximize the use of miniatu

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49

Ohrnberger, Matthias. "Continuous automatic classification of seismic signals of volcanic origin at Mt. Merapi, Java, Indonesia." Phd thesis, [S.l. : s.n.], 2001. http://pub.ub.uni-potsdam.de/2001/0016/ohrnberg.pdf.

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50

Narayanan, Bharath Krishnan. "A new strategy for speech recognition through the parametric classification of ear pressure signals /." Available to subscribers only, 2005. http://proquest.umi.com/pqdweb?did=1079666561&sid=6&Fmt=2&clientId=1509&RQT=309&VName=PQD.

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