Дисертації з теми "Signals classification"
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1
Rida, Imad. "Temporal signals classification." Thesis, Normandie, 2017. http://www.theses.fr/2017NORMIR01/document.
Повний текст джерелаАнотація:
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 parties dynamiques du corps humain. Ceci permet de résoudre le problème des variations intra-classe de façon dynamique; les méthodes à l’état de l’art se basant au contraire sur des connaissances a priori. Dans le cadre de la reconnaissance audio, aucune représentation de caractéristiques conventionnelle n’a montré sa capacité à s’attaquer indifféremment à des problèmes de reconnaissance d’environnement ou de musique : diverses caractéristiques ont été introduites pour résoudre chaque tâche spécifiquement. Nous proposons ici un cadre général qui effectue la classification des signaux audio grâce à un problème d’apprentissage de dictionnaire supervisé visant à minimiser et maximiser les variations intra-classe et inter-classe respectivementNowadays, there are a lot of applications related to machine vision and hearing which tried to reproduce human capabilities on machines. These problems are mainly amenable to a temporal signals classification problem, due our interest to this subject. In fact, we were interested to two distinct problems, humain gait recognition and audio signal recognition including both environmental and music ones. In the former, we have proposed a novel method to automatically learn and select the dynamic human body-parts to tackle the problem intra-class variations contrary to state-of-art methods which relied on predefined knowledge. To achieve it a group fused lasso algorithm is applied to segment the human body into parts with coherent motion value across the subjects. In the latter, while no conventional feature representation showed its ability to tackle both environmental and music problems, we propose to model audio classification as a supervised dictionary learning problem. This is done by learning a dictionary per class and encouraging the dissimilarity between the dictionaries by penalizing their pair- wise similarities. In addition the coefficients of a signal representation over these dictionaries is sought as sparse as possible. The experimental evaluations provide performing and encouraging results
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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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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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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Proper, Ethan R. "Automated classification of power signals." Thesis, (7 MB), 2008. http://handle.dtic.mil/100.2/ADA488187.
Повний текст джерелаАнотація:
Thesis (Degrees of Naval Engineer and M.S. in Engineering and Management)--Massachusetts Institute of Technology, June 2008."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 Monitor), Shipboard Systems, Spectral Power Envelopes, Prep (Preprocessed Power Data), CHT (Classification Of Waste Disposal). Includes bibliographical references. Also available in CR-ROM format.
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Proper, Ethan R. (Ethan Richard). "Automated classification of power signals." Thesis, Massachusetts Institute of Technology, 2008. http://hdl.handle.net/1721.1/44842.
Повний текст джерелаАнотація:
Thesis (Nav. E.)--Massachusetts Institute of Technology, Dept. of Mechanical Engineering; and, (S.M.)--Massachusetts Institute of Technology, System Design and Management Program, 2008.Includes bibliographical references (p. 106-107).
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 an expert classifier and a series of these classifications can be used to diagnose system failures or improper operation. Current NILM research conducted at Massachusetts Institute of Technology's Laboratory for Electromagnetic and Electronic Systems (LEES) is exploring the application and expansion of NILM technology for the use of monitoring shipboard systems. This thesis presents the ginzu application that implements a detect-classify-verify loop that locates the indexes of transients, identifies them using a decision-tree based expert classifier, and then generates a summary event file containing relevant information. The ginzu application provides a command-line interface between streaming preprocessed power data (PREP) and an included graphical user interface. This software was developed using thousands of hours of archived data from the Coast Guard Cutters ESCANABA (WMEC-907) and SENECA (WMEC-906). A validation of software effectiveness was conducted as the software was installed onboard ESCANABA.
by Ethan R. Proper.
S.M.
Nav.E.
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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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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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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.
Повний текст джерелаАнотація:
Thesis (S.B. and M.Eng.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2000.Includes bibliographical references (p. 73-75).
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 go on to explore methods for adapting the classifiers over time and for sequential analysis of the gesture stream by combining the static classification algorithm with a hidden Markov model. I conclude with an evaluation of the combined model on an unsegmented stream of gestures.
by Peter M. Ju.
S.B.and M.Eng.
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Hannaske, Roland. "Fast Digitizing and Digital Signal Processing of Detector Signals." Forschungszentrum Dresden-Rossendorf, 2009. https://hzdr.qucosa.de/id/qucosa%3A21615.
Повний текст джерелаАнотація:
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 in an experiment with γ-rays from a 22Na source and is compared to the energy resolution achieved with analogously processed signals. On the other hand, signals from the photomultipliers of barium fluoride and plastic scintillation detectors are digitized. These signals have risetimes of a few nanoseconds only. The moment of interaction of the radiation with the detector is determined by methods of digital signal processing. Therefore, different timing algorithms are implemented and tested with data from an experiment at nELBE. The time resolutions achieved with these algorithms are compared to each other as well as to reference values coming from analog signal processing. In addition to these experiments, some properties of the digitizing hardware are measured and a program for the analysis of stored, digitized data is developed. The analysis of the signals shows that the energy resolution achieved with the 10-bit digitizer system used here is not competitive to a 14-bit peak-sensing ADC, although the ballistic deficit can be fully corrected. However, digital methods give better result in sub-ns timing than analog signal processing.
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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.
Повний текст джерелаАнотація:
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, &ldquovisual&rdquo
target detection methods left the stage to the computerized acoustic signature detection and evaluation methods. Despite this, the research projects have not sufficiently addressed in the field of acoustic signature evaluation. This thesis work mainly investigates classification and recognition techniques with TRN / LOFAR signals, which are emitted from surface and subsurface platforms and proposes possible adaptations of existing methods that may give better results if they are used with these signals. Also a detailed comparison has been made about the experimental results with underwater acoustic signals.
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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.
Повний текст джерелаАнотація:
Thesis (M.S. in Electrical Engineering) Naval Postgraduate School, June 1998."June 1998." Thesis advisor(s): Monique P. Farques, Ralph D. Hippenstiel. Includes bibliographical references (p. 161-163). Also available online.
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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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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.
Повний текст джерелаАнотація:
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 effect. To be able to distinguish in which direction the target is moving are a so called I/Q representation of the radar return used, which is a complex representation of the signal. Using signal processing techniques, we extract radar signals generated from the target. By spectral transforms it is possible to generate real valued signals from the extracted target signals. It is required to extend these signals to be able to use them as audio signals, this is done with an extrapolation technique based on Autoregressive (AR) processes. The extrapolated signals are the signals used as the audio output, it is possible to perform the audio classification in most of the cases. This project is done in collaboration with Sebastian Edman [7], where different perspectives of radar classification has been investigated. As mentioned this thesis focus on transforming the radar return into an audio signal. While Edman in his thesis [7] making use of a machine learning approach to classify the targets from the generated audio signal.Klassificering är ofta av stort intresse inom radarapplikation, eftersom man inte bara vill veta var ett mål befinner sig men också vad för typ av mål det är. Denna uppsats fokuserar på att omvandla radarekot från ett mål till en ljudsignal. Så att klassificeringen kan ske med mänskliga sinnen, i detta fall hörseln. Syftet med dessa klassificeringsmetoder är att kunna klassificera två typer av mål med ungefär samma storlek, nämligen fåglar och mindre obemannade flygfordon (UAV). Det är möjligt att med radarn mäta målets hastighet med hjälp av Doppler-effekten. För att kunna avgöra i vilken riktning målet rör sig används en I/Q-representation, som är en komplex representation av radar signalen. Med signalbehandling är det möjligt att extrahera radar signaler som målet generar. Genom att använda spektrala transformationer är det möjligt att generera reellvärda signaler från de extraherade målsignalerna. Det är nödvändigt att förlänga dessa signaler för att kunna använda dem som ljudsignaler, detta görs med en extrapoleringsteknik baserad på Autoregressiva (AR) -processer. De ljudsignaler som används är dessa extrapolerade signalerna, det är i det flesta fall möjligt att utifrån ljudet genomföra klassificeringen. Detta projekt är utfört i samarbete med Sebastian Edman [7], där olika inriktningar av radarklassificering har undersökts. Som nämnts ovan fokuserar denna uppsats på att omvandla
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Malfante, Marielle. "Automatic classification of natural signals for environmental monitoring." Thesis, Université Grenoble Alpes (ComUE), 2018. http://www.theses.fr/2018GREAU025/document.
Повний текст джерелаАнотація:
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 classification automatique d’événements, et (ii) pour la détection et classification automatique d’événements. La robustesse des approches proposées résulte de l’espace des descripteurs dans lequel sont représentés les signaux. Les enregistrements y sont en effet décrits dans plusieurs espaces: temporel, fréquentiel et quéfrentiel. Une comparaison avec des descripteurs issus de réseaux de neurones convolutionnels (Deep Learning) est également proposée, et favorise les descripteurs issus de la physique au détriment des approches basées sur l’apprentissage profond.Les outils proposés au cours de cette thèse sont testés et validés sur des enregistrements in situ de deux environnements différents : (i) milieux marins et (ii) zones volcaniques. La première application s’intéresse aux signaux acoustiques pour la surveillance des zones sous-marines côtières : les enregistrements continus sont automatiquement analysés pour détecter et classifier les différents sons de poissons. Une périodicité quotidienne est mise en évidence. La seconde application vise la surveillance volcanique : l’architecture proposée classifie automatiquement les événements sismiques en plusieurs catégories, associées à diverses activités du volcan. L’étude est menée sur 6 ans de données volcano-sismiques enregistrées sur le volcan Ubinas (Pérou). L’analyse automatique a en particulier permis d’identifier des erreurs de classification faites dans l’analyse manuelle originale. L’architecture pour la classification automatique d’événements volcano-sismiques a également été déployée et testée en observatoire en Indonésie pour la surveillance du volcan Mérapi. Les outils développés au cours de cette thèse sont rassemblés dans le module Architecture d’Analyse Automatique (AAA), disponible en libre accèsThis manuscript summarizes a three years work addressing the use of machine learning for the automatic analysis of natural signals. The main goal of this PhD is to produce efficient and operative frameworks for the analysis of environmental signals, in order to gather knowledge and better understand the considered environment. Particularly, we focus on the automatic tasks of detection and classification of natural events.This thesis proposes two tools based on supervised machine learning (Support Vector Machine, Random Forest) for (i) the automatic classification of events and (ii) the automatic detection and classification of events. The success of the proposed approaches lies in the feature space used to represent the signals. This relies on a detailed description of the raw acquisitions in various domains: temporal, spectral and cepstral. A comparison with features extracted using convolutional neural networks (deep learning) is also made, and favours the physical features to the use of deep learning methods to represent transient signals.The proposed tools are tested and validated on real world acquisitions from different environments: (i) underwater and (ii) volcanic areas. The first application considered in this thesis is devoted to the monitoring of coastal underwater areas using acoustic signals: continuous recordings are analysed to automatically detect and classify fish sounds. A day to day pattern in the fish behaviour is revealed. The second application targets volcanoes monitoring: the proposed system classifies seismic events into categories, which can be associated to different phases of the internal activity of volcanoes. The study is conducted on six years of volcano-seismic data recorded on Ubinas volcano (Peru). In particular, the outcomes of the proposed automatic classification system helped in the discovery of misclassifications in the manual annotation of the recordings. In addition, the proposed automatic classification framework of volcano-seismic signals has been deployed and tested in Indonesia for the monitoring of Mount Merapi. The software implementation of the framework developed in this thesis has been collected in the Automatic Analysis Architecture (AAA) package and is freely available
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Kanneganti, Raghuveer. "CLASSIFICATION OF ONE-DIMENSIONAL AND TWO-DIMENSIONAL SIGNALS." OpenSIUC, 2014. https://opensiuc.lib.siu.edu/dissertations/892.
Повний текст джерелаАнотація:
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 classifiers for multi-channel ERPs. The strategy is tested on the ERPs of 156 smokers who participated in a smoking cessation program. One half of the subjects were given nicotine patches and the other half were given placebo patches. ERPs were collected from 29 channel in response to the presentation of the pictures with emotional (pleasant and unpleasant), neutral/boring, and cigarette-related content. It is shown that human emotions can be classified accurately and the results also show that smoking cessation causes a drop in the classification accuracies of emotions in the placebo group, but not in the nicotine patch group. Given that individual brain patterns were compared with group average brain patterns, the findings support the view that individuals tend to have similar brain reactions to different types of emotional stimuli. Overall, this new classification approach to identify differential brain responses to different emotional types could lead to new knowledge concerning brain mechanisms associated with emotions common to most or all people. This novel classification technique for identifying emotions in the present study suggests that smoking cessation without nicotine replacement results in poorer differentiation of brain responses to different emotional stimuli. Future, directions in this area would be to use these methods to assess individual differences in responses to emotional stimuli and to different drug treatments. Advantages of this and other brain-based assessment include temporal precision (e.g, 400-800 ms post stimulus), and the elimination of biases related to self-report measures. The two-dimensional signal classification problems include the detection of graphite in testing documents and the detection of fraudulent bubbles in test sheets. A strategy is developed to detect graphite responses in optical mark recognition (OMR) documents using inexpensive visible light scanners. The main challenge in the formulation of the strategy is that the detection should be invariant to the numerous background colors and artwork in typical optical mark recognition documents. A test document is modeled as a superposition of a graphite response image and a background image. The background image in turn is modeled as superposition of screening artwork, lines, and machine text components. A sequence of image processing operations and a pattern recognition algorithm are developed to estimate the graphite response image from a test document by systematically removing the components of the background image. The proposed strategy is tested on a wide range of scanned documents and it is shown that the estimated graphite response images are visually similar to those scanned by very expensive infra-red scanners currently employed for optical mark recognition. The robustness of the detection strategy is also demonstrated by testing a large number of simulated test documents. A procedure is also developed to autonomously determine if cheating has occurred by detecting the presence of aberrant responses in scanned OMR test books. The challenges introduced by the significant imbalance in the numbers of typical and aberrant bubbles were identified. The aberrant bubble detection problem is formulated as an outlier detection problem. A feature based outlier detection procedure in conjunction with a one-class SVM classifier is developed. A multi-criteria rank-of-rank-sum technique is introduced to rank and select a subset of features from a pool of candidate features. Using the data set of 11 individuals, it is shown that a detection accuracy of over 90% is possible. Experiments conducted on three real test books flagged for suspected cheating showed that the proposed strategy has the potential to be deployed in practice.
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Bertoncini, Crystal Ann. "Applications of pattern classification to time-domain signals." W&M ScholarWorks, 2010. https://scholarworks.wm.edu/etd/1539623559.
Повний текст джерелаАнотація:
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 useful in order to attain results quickly with less human error. One method of automatic interpretation of data is pattern classification, which is a statistical method that assigns predicted labels to raw data associated with known categories. In this work, we use pattern classification techniques to aid automatic detection of events in signals using features extracted by a particular application of the wavelet transform, the Dynamic Wavelet Fingerprint (DWFP), as well as features selected through physical interpretation of the individual applications. The wavelet feature extraction method is general for any time-domain signal, and the classification results can be improved by features drawn for the particular domain. The success of this technique is demonstrated through four applications: the development of an ultrasonographic periodontal probe, the identification of flaw type in Lamb wave tomographic scans of an aluminum pipe, prediction of roof falls in a limestone mine, and automatic identification of individual Radio Frequency Identification (RFID) tags regardless of its programmed code. The method has been shown to achieve high accuracy, sometimes as high as 98%.
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Ojo, Catherine A. "Analysis & automatic classification of nuclear magnetic resonance signals." Thesis, University of Edinburgh, 2010. http://hdl.handle.net/1842/4109.
Повний текст джерелаАнотація:
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 usage of NMRS as a clinical diagnostic tool. One is the non-uniqueness of the most widely used analysis methods, i.e. as the parameters and/or prior knowledge data of an analysis method are changed, the results also change. A second problem is the lack of a method that can automatically classify the signal components estimated via signal decomposition based signal analysis methods. Additionally, some of the most widely used analysis methods, by virtue of their algorithms, intrinsically assume the nature of NMRS signals, e.g. stationary, linear, Lorentzian, etc. Hence, this thesis explores a new analysis approach, based on a theoretical and practical understanding of NMRS, that (a) avoids making assumptions about the nature of experimentally acquired NMRS signals, (b) relies on a unique decomposition analysis method, and (c) automatically classifies the estimated peaks of an analysis. Unique decomposition analysis was conducted via the rarely used unique and non-linear signal decomposition method − the Fast Pad´e Transform (FPT). The FPT is compared with the main decomposition based NMRS analysis methods via a detailed mathematical analysis, and a comparative analysis. Automatic classification was conducted via a novel classification method, which is introduced herein, and which is based on quantum mechanical predictions of metabolite NMRS behaviour.
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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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Idowu, Ibrahim Olatunji. "Classification techniques using EHG signals for detecting preterm births." Thesis, Liverpool John Moores University, 2017. http://researchonline.ljmu.ac.uk/7062/.
Повний текст джерелаАнотація:
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 is the interpretation of temporal and spectral characteristics of Electromyography (EMG), which is an electrodiagnostic medicine technique for recording and evaluating the electrical activity produced by uterine muscles during pregnancy and parturition – significant variability exists among obstetric care practitioners. Apart from a small number of potential causes for preterm birth, such as medication, uterine over-distension, preterm premature rupture of membranes (PPROM), intrauterine inflammation, precocious foetal endocrine activation, surgery, ethnicity and lifestyle, there is still a large amount of uncertainty about their specific risks. Hence, it is currently very difficult to make reliable predictions about preterm delivery risk. There has also been some evidence that the analysis of uterine electrical signals, collected from the abdominal surface, could provide an independent and easier way to diagnose true labour and detect the onset of preterm delivery. Early detection opens up new avenues for the development of an automated ambulatory system, based on uterine EMG, for patient monitoring during pregnancy. This can be made possible through the use of machine learning. The essence of machine learning is the utilisation of previously recorded data outcomes to train algorithms to ii stimulate software learning elements. Such learned models can, as a result, be used to detect and predict the early signs associated with the onset of preterm birth. Therefore in this thesis, Electrohysterography signals are used to classify uterine activity associated with preterm birth. This is achieved using an open dataset, which contains 262 records for women who delivered at term and 38 who delivered prematurely. Several new features from Electromyography studies are utilized, as well as feature-ranking techniques to determine their discriminative capabilities in detecting term and preterm records. The results illustrate that the combination of the Levenberg-Marquardt trained Feed-Forward Neural Network, Radial Basis Function Neural Network and the Random Neural Network classifiers performed the best, with 91% for sensitivity, 84% for specificity, 94% for the area under the curve and 12% for the mean error rate. Applying advanced machine learning algorithms, in conjunction with innovative signal processing techniques and the analysis of Electrohysterography signals shows significant benefits for use in clinical interventions for preterm birth assessments.
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Brown, Elliot Morgan. "The Application of Synthetic Signals for ECG Beat Classification." BYU ScholarsArchive, 2019. https://scholarsarchive.byu.edu/etd/8116.
Повний текст джерелаАнотація:
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 classification.
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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.
Повний текст джерелаАнотація:
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, from patients to healthy and ordinaryusers, should ultimately be beneficial for those who really need it, indeed.The main focus have been building a system which enables usage of the availableEEG device, and making a prototype that incorporates all parts of a functioningBCI system. These parts are 1) acquiring the EEG signal 2) process and classify theEEG signal and 3) use the signal classification to control a feature in a game. Thesolution method in the project uses the NeuroSky mindset for part 1, the Fouriertransform and an Artificial Neural Network for classifying brain wave patterns inpart 2, and a game of Snake uses the classification results to control the characterin part 3.This report outlines the step-by-step implementation and testing for this system,and the result is a functional prototype that can use user EEG to control the snakein the game with over 90% accuracy. Two mental tasks have been used to separatebetween turning the snake left or right, baseline (thinking nothing in particular)and mental counting. The solution differentiates from other appliances of the NeuroSkymindset that it does not require any pre-training for the user, and it is onlypartially real-time.
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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.
Повний текст джерелаАнотація:
Thesis (M.S. in Electrical Engineering) Naval Postgraduate School, June 1997.Thesis advisors, Monique P. Fargues, Roberto Cristi. Includes bibliographical references (p. 95). Also available online.
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Burger, Christiaan. "A novel method of improving EEG signals for BCI classification." Thesis, Stellenbosch : Stellenbosch University, 2014. http://hdl.handle.net/10019.1/95984.
Повний текст джерелаАнотація:
Thesis (MEng)--Stellenbosch University, 2014.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 acquisition device used in BCI applications to record neural activity. However, noise, known as artifacts, can contaminate the recording, thus distorting the true neural activity. Eye blinks are a common source of artifacts present in EEG recordings. Due to its large amplitude it greatly distorts the EEG data making it difficult to interpret data for BCI applications. This study proposes a new combination of techniques to detect and correct eye blink artifacts to improve the quality of EEG for BCI applications. Independent component analysis (ICA) is used to separate the EEG signals into independent source components. The source component containing eye blink artifacts are corrected by detecting each eye blink within the source component and using a trained wavelet neural network (WNN) to correct only a segment of the source component containing the eye blink artifact. Afterwards, the EEG is reconstructed without distorting or removing the source component. The results show a 91.1% detection rate and a 97.9% correction rate for all detected eye blinks. Furthermore for channels located over the frontal lobe, eye blink artifacts are corrected preserving the neural activity. The novel combination overall reduces EEG information lost, when compared to existing literature, and is a step towards improving EEG pre-processing in order to provide cleaner EEG data for BCI applications.
AFRIKAANSE OPSOMMING: Spierdistrofie, ’n rugmurgbesering, of amiotrofiese laterale sklerose (ALS) is beserings en steurnisse wat die neuromuskulêre kanale van die menslike liggaam ontwrig en dus verhoed dat die brein die liggaam beheer. ’n Breinrekenaarkoppelvlak laat toe dat die neuromuskulêre kanale omlei word en op die omgewing reageer deur die brein. Die BCI-stelsel vertrou op die gebruiker wat sy eie senuwee-aktiwiteit manipuleer om sodoende ’n eksterne toestel te beheer. Elektro-enkefalografie (EEG) is ’n goedkoop, nie-indringende, intydse dataverkrygingstoestel wat gebruik word in BCI toepassings. Nie net senuwee aktiwiteit nie, maar ook geraas , bekend as artefakte word opgeneem, wat dus die ware senuwee aktiwiteit versteur. Oogknip artefakte is een van die algemene artefakte wat teenwoordig is in EEG opnames. Die groot omvang van hierdie artefakte verwring die EEG data wat dit moeilik maak om die data te ontleed vir BCI toepassings. Die studie stel ’n nuwe kombinasie tegnieke voor wat oogknip artefakte waarneem en regstel om sodoende die kwaliteit van ’n EEG vir BCI toepassings te verbeter. Onafhanklike onderdeel analise (Independent component analysis (ICA)) word gebruik om die EEG seine te skei na onafhanklike bron-komponente. Die bronkomponent wat oogknip artefakte bevat word reggestel binne die komponent en gebruik ’n ervare/geoefende golfsenuwee-netwerk om slegs ’n deel van die komponent wat die oogknip artefak bevat reg te stel. Daarna word die EEG hervorm sonder verwringing of om die bron-komponent te verwyder. Die resultate toon ’n 91.1% opsporingskoers en ’n 97.9% regstellingskoers vir alle waarneembare oogknippe. Oogknip artefakte in kanale op die voorste lob word reggestel en behou die senuwee aktiwiteit wat die oorhoofse EEG kwaliteit vir BCI toepassings verhoog.
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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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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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Evans, Naoko. "Automated vehicle detection and classification using acoustic and seismic signals." Thesis, University of York, 2010. http://etheses.whiterose.ac.uk/1151/.
Повний текст джерелаАнотація:
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 signals, making them optimal for detection of vehicles. Their much narrower bandwidth also increases processing efficiency and speed. Thus, the algorithm developed utilises firstly only seismic signals to detect vehicle presence, and then employs both acoustic and seismic signals for classifying type of the vehicle. The detection algorithm is purely time domain and uses seismic Log Energy together with a modification of Time Domain Signal Coding. The best detection accuracy obtained was 97.71 % with Support Vector Machine and 99.02 % with Learning Vector Quantisation Neural Networks. The classification algorithm to distinguish between trucks and cars utilises three relatively simple time domain methods: Zero-Crossing Rate, Log Energy and Autocorrelation of seismic signals; combined with LPC coefficients collected from acoustic signals. Classification with either SVM or LVQ reached 93.30 % or 80.80 % respectively. This study therefore has demonstrated it is possible to detect an approaching vehicle and classify its type by using acoustic and seismic signal processing.
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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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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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Bond, Zachary. "Unsupervised Classification of Music Signals: Strategies Using Timbre and Rhythm." Thesis, Virginia Tech, 2006. http://hdl.handle.net/10919/36469.
Повний текст джерелаАнотація:
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 other instruments. The effect of lossy compression on these same timbre features is also investigated. In the area of rhythm, a new tempo tracking tool is provided which produces a series of histograms containing beat and sub-beat information throughout the course of a musical recording. These histograms are then shown to be useful in the analysis of synthesized rhythms and real music. Additionally, a novel method based on the Expectation-Maximization algorithm is used to extract features for classification from the histograms.Master of Science
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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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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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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.
Повний текст джерелаАнотація:
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 gaussian mixture models, are combined with nonstationary featureextraction techniques such as Short Time Fourier Transform, Continuous WaveletTransform, Discrete Wavelet Transform and Wigner-Ville Distribution. Due to thepresence of several different feature extraction and classification technqies, datafusion has been investigated. Data fusion in the current case has mainly beeninvestigated on two levels, feature level and classifier level respectively. Fusion at thefeature level demonstrated best results with an overall accuracy of 82% whencompared to the human operator.
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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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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.
Повний текст джерелаАнотація:
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 important afin de distinguer les différentes cibles. Différentes méthodes d’extraction de paramètres sont comparées notamment : méthode de Prony, Racine-classification des signaux multiples (Root-MUSIC), l’estimation des paramètres des signaux par des techniques d’invariances rotationnels (ESPRIT), et la méthode Matrix Pencil (MPM). Troisièmement, une méthode efficace de classification supervisée est nécessaire afin de classer les cibles inconnues par l’utilisation de leurs signatures extraites. Différentes méthodes de classification sont comparées notamment : Classification par la distance de Mahalanobis (MDC), Naïve Bayes (NB), k-plus proches voisins (k-NN), Machines à Vecteurs de Support (SVM). Une bonne technique de classification doit avoir une bonne précision en présence de signaux bruités et quelques soit l’angle d’émission. Les différents algorithmes ont été validés en utilisant les simulations des données rétrodiffusées par des objets canoniques et des cibles de géométries complexes modélisées par des fils minces et parfaitement conducteurs. Une méthode de classification automatique de cibles basée sur l’utilisation de la méthode Matrix Pencil dans le domaine fréquentiel (MPMFD) pour l’extraction des paramètres et la classification par la distance de Mahalanobis est proposée. Les résultats de simulation montrent que les paramètres extraits par MPMFD présentent une solution plausible pour la classification automatique des cibles. En outre, nous avons prouvé que la méthode proposée a une bonne tolérance aux bruits lors de la classification des cibles. Enfin, les différents algorithmes sont validés sur des données expérimentales et cibles réellesThe objective of this thesis is the Automatic Target Classification (ATC) based on radar backscattered Ultra WideBand (UWB) signals. The classification of the targets is realized by making comparison between the deduced target properties and the different target features which are already recorded in a database. First, the study of scattering theory allows us to understand the physical meaning of the extracted features and describe them mathematically. Second, feature extraction methods are applied in order to extract signatures of the targets. A good choice of features is important to distinguish different targets. Different methods of feature extraction are compared including wavelet transform and high resolution techniques such as: Prony’s method, Root-Multiple SIgnal Classification (Root-MUSIC), Estimation of Signal Parameters via Rotational Invariance Techniques (ESPRIT) and Matrix Pencil Method (MPM). Third, an efficient method of supervised classification is necessary to classify unknown targets by using the extracted features. Different methods of classification are compared: Mahalanobis Distance Classifier (MDC), Naïve Bayes (NB), k-Nearest Neighbors (k-NN) and Support Vector Machine (SVM). A useful classifier design technique should have a high rate of accuracy in the presence of noisy data coming from different aspect angles. The different algorithms are demonstrated using simulated backscattered data from canonical objects and complex target geometries modeled by perfectly conducting thin wires. A method of ATC based on the use of Matrix Pencil Method in Frequency Domain (MPMFD) for feature extraction and MDC for classification is proposed. Simulation results illustrate that features extracted with MPMFD present a plausible solution to automatic target classification. In addition, we prove that the proposed method has better ability to tolerate noise effects in radar target classification. Finally, the different algorithms are validated on experimental data and real targets
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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.
Повний текст джерелаАнотація:
Thesis (M.S.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 1992.Includes bibliographical references (leaves 107-114).
by Ganesh Nachiappa Ramaswamy.
M.S.
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Miller, Corey Alexander. "Intelligent Feature Selection Techniques for Pattern Classification of Time-Domain Signals." W&M ScholarWorks, 2013. https://scholarworks.wm.edu/etd/1539623620.
Повний текст джерелаАнотація:
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 pattern classification routines are applied to problems of interest involving a physical change in the domain of the problem that translate into changes in the signal characteristics. The basis of this technique involves a signal transformation known as the Dynamic Wavelet Fingerprint, used to generate a feature space in addition to features related to the physical domain of the individual application. Feature selection techniques are explored that incorporate the context of the problem into the feature space reduction in an attempt to identify optimal representations of these data sets.
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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.
Повний текст джерелаАнотація:
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
Palancia
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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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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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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.
Повний текст джерелаАнотація:
Thesis (M.S. in Systems Engineering and M.S. in Engineering Science (Electrical Engineering))--Naval Postgraduate School, September 2003.Thesis advisor(s): Phillip E. Pace, D. Curtis Schleher. Includes bibliographical references (p. 125-126). Also available online.
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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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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.
Повний текст джерелаАнотація:
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 fully studied using computational methods. Therefore, a system that extract and combine features from all physiological signals representative of states such as arrhythmia and loss of blood volume to predict the presence and the severity of such complications is of paramount importance for care givers. This will not only enhance diagnostic methods, but also enable physicians to make more accurate decisions; thereby the overall quality of care provided to patients will improve significantly. In the first part of the dissertation, analysis and processing of ECG signal to detect the most important waves i.e. P, QRS, and T, are described. A wavelet-based method is implemented to facilitate and enhance the detection process. The method not only provides high detection accuracy, but also efficient in regards to memory and execution time. In addition, the method is robust against noise and baseline drift, as supported by the results. The second part outlines a method that extract features from ECG signal in order to classify and predict the severity of arrhythmia. Arrhythmia can be life-threatening or benign. Several methods exist to detect abnormal heartbeats. However, a clear criterion to identify whether the detected arrhythmia is malignant or benign still an open problem. The method discussed in this dissertation will address a novel solution to this important issue. In the third part, a classification model that predicts the severity of loss of blood volume by incorporating multiple physiological signals is elaborated. The features are extracted in time and frequency domains after transforming the signals with Wavelet Transformation (WT). The results support the desirable reliability and accuracy of the system.
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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.
Повний текст джерелаАнотація:
nÃo hÃThe 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 number of ocurrencies. The system output is a first level diagnostics based on those input variables. The SISCTG system is developed using the Matlab version 7 script language. Tests and modeling issues used the Matlab Fuzzy Toolbox. The project also supports a multi-institutional agreement between Brazil and Germany, among the DETI - Departamento de Engenharia de TeleinformÂatica of the UFC â Universidade Federal do CearÂa, the MEAC - Maternidade-Escola Assis Chateaubriand), the TUM - Technische UniversitÃt MÃnchen, the Bundeswehr UniversitÃt MÃnchen and the Trium Analysis Online GmbH. The SISCTG results are very promising, correctly classifying all normal exams. This is the expected behavior, once CTG exams are classified as of low specificity, with the most interest focused in finding pathologies aspects, but not precisely identifying them. These results allow the projection of improvements to the proposed system, inserting new input variables, for example. The system validation methodology was based on the knowledge of Brazilian and German obstetricians.
A anÃlise acurada da freqÃÃncia cardÃaca fetal (FCF ou FHR - Fetal Heart Rate) correlacionada com as contraÃÃes uterinas maternas (UC - Uterine Contractions) permite gerar diagnÃsticos e a conseqÃente antecipaÃÃo de problemas diversos relativos ao bem estar fetal e a preservaÃÃo de sua vida. O presente trabalho apresenta os resultados de um sistema hibrido baseado em regras determinÃsticas e em um mÃdulo de inferÃncia nebuloso (fuzzy) para anÃlise de sinais de FCF e UC coletados atravÃs de exames denominados cardiotocografias (CTG). As variÃveis analisadas sÃo o valor basal da FCF, sua variabilidade de curto e de longo prazo, aceleraÃÃes transitÃrias e desaceleraÃÃes, sendo estas classificadas por seu tipo e pelo nÃmero de ocorrÃncias. A saÃda do sistema à o diagnÃstico em primeiro nÃvel, baseado nas informaÃÃes das variÃveis de entrada definidas. O sistema SISCTG à desenvolvido na linguagem de scripts do programa Matlab versÃo 7. Modelagens e testes sÃo realizados utilizando-se o Fuzzy Toolbox do programa Matlab. O projeto tambÃm conta com uma parceria multi-institucional entre o Brasil e a Alemanha, envolvendo a Universidade Federal do Cearà (UFC), atravÃs do Departamento de Engenharia de TeleinformÃtica (DETI) e da Maternidade-Escola Assis Chateaubriand (MEAC), a Technische UniversitÃt MÃnchen (TUM), a Bundeswehr UniversitÃt MÃnchen e a empresa Trium Analysis Online GmbH. Os resultados obtidos pelo SISCTG sÃo bastante promissores, classificando todos os exames normais corretamente. Este à o comportamento esperado, uma vez que CTGs sÃo exames de baixa especificidade, tendo como interesse maior encontrar indÃcios de patologias, sem a necessidade de identificÃ-las precisamente. Estes resultados permitem projetar o aperfeiÃoamento deste sistema com a inserÃÃo, por exemplo, de novas variÃveis de entrada. SÃo realizados procedimentos de validaÃÃo com mÃltiplos especialistas na Ãrea obstÃtrica tanto no Brasil quanto na Alemanha.
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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.
Повний текст джерелаАнотація:
Thesis: S.B., Massachusetts Institute of Technology, Department of Mechanical Engineering, 2018.Cataloged from PDF version of thesis.
Includes bibliographical references (page 25).
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, have sought to use various types of machine learning models for sEMG pattern recognition. This technique shows promise in being able to accurately classify human sEMG signals and map them to certain movements, which can then be used as a method of myoelectric control. In this study we explored how two methods of training a K-Nearest Neighbor (KNN) classifier, used to control a MyoPro arm orthosis, affect two subjects' performance on various experimental tasks and their measured sEMG activation throughout the tasks. It was found that for subject 1, the assisted training method, where another individual helps move the orthosis while training the KNN, resulted in a lower variance in the measured mean sEMG values, and reduced the cross validation accuracy of the controller, but did not reduce subjects' performance of the experimental trials, as compared to the KNN controller trained without assistance. For subject 2, the assisted controller reduced the performance on three out of the four tests performed compared to the unassisted controller.
by Hugh C. Day-Williams.
S.B.
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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.
Повний текст джерелаАнотація:
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 affect the classification. The data was collected from 12 experiments, where 269 participants (1187 driving sessions) were driving either on real roads or in a moving-base driving simulator, while electrophysiological data was recorded. Several deep neural network architectures were developed, depending on the representation of the input data. Regardless of which data representation that was used as input to the network, the datawas divided into three datasets: Training 60%, validation 20% and test 20%. The data from each participant, with associated driving sessions, were randomly assigned to the different datasets according to the given percentage, which resulted in a subject-independent sleepiness detection. The output was in the form of continuous regression further rounded to the closest integer and divided into five classes according to Karolinska Sleepiness Scale (KSS = 1-5, 6, 7, 8, 9). The best performance was obtained with a convolutional neural network (CNN) combined with Long Short-Term Memory (LSTM) architecture, with time series data as input. This gave an accuracy of 41.44%, a mean absolute error of 0.94 and a macro F1-score of 0.37. Overall, the models with time series data showed better classification results compared to those with time-frequency data. Class weighting, giving all classes inverse proportional weight to their appearance, compensated slightly for class imbalance, but all networks had in general difficulties with generalizing to new data.
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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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Kolb, Dirk [Verfasser], and Elmar [Akademischer Betreuer] Nöth. "Efficient and Trainable Detection and Classification of Radio Signals / Dirk Kolb. Betreuer: Elmar Nöth." Erlangen : Universitätsbibliothek der Universität Erlangen-Nürnberg, 2012. http://d-nb.info/1025963725/34.
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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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Lucking, Walter. "The application of time encoded signals to automated machine condition classification using neural networks." Thesis, University of Hull, 1997. http://hydra.hull.ac.uk/resources/hull:3766.
Повний текст джерелаАнотація:
This thesis considers the classification of physical states in a simplified gearbox using acoustical data and simple time domain signal shape characterisation techniques allied to a basic feedforward multi-layer perceptron neural network. A novel extension to the signal coding scheme (TES), involving the application of energy based shape descriptors, was developed. This sought specifically to improve the techniques suitability to the identification of mechanical states and was evaluated against the more traditional minima based TES descriptors. The application of learning based identification techniques offers potential advantages over more traditional programmed techniques both in terms of greater noise immunity and in the reduced requirement for highly skilled operators. The practical advantages accrued by using these networks are studied together with some of the problems associated in their use within safety critical monitoring systems.Practical trials were used as a means of developing the TES conversion mechanism and were used to evaluate the requirements of the neural networks being used to classify the data. These assessed the effects upon performance of the acquisition and digital signal processing phases as well as the subsequent training requirements of networks used for accurate condition classification. Both random data selection and more operator intensive performance based selection processes were evaluated for training. Some rudimentary studies were performed on the internal architectural configuration of the neural networks in order to quantify its influence on the classification process, specifically its effect upon fault resolution enhancement.The techniques have proved to be successful in separating several unique physical states without the necessity for complex state definitions to be identified in advance. Both the computational demands and the practical constraints arising from the use of these techniques fall within the bounds of a realisable system.