Dissertations / Theses on the topic 'Independent Component Analysis (ICA)'
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Harmeling, Stefan. "Independent component analysis and beyond." Phd thesis, [S.l. : s.n.], 2004. http://deposit.ddb.de/cgi-bin/dokserv?idn=973631805.
Full textLai, Di. "Independent component analysis (ICA) applied to ultrasound image processing and tissue characterization /." Online version of thesis, 2009. http://hdl.handle.net/1850/11367.
Full textAbou, Elseoud A. (Ahmed). "Exploring functional brain networks using independent component analysis:functional brain networks connectivity." Doctoral thesis, Oulun yliopisto, 2013. http://urn.fi/urn:isbn:9789526201597.
Full textTiivistelmä Toiminnallisten aivoalueiden välinen viestintä on todennäköisesti avainasemassa kognitiivisissa prosesseissa, jotka edellyttävät jatkuvaa tiedon integraatiota aivojen eri alueiden välillä. Tämä tekee ihmisaivojen toiminnallisen kytkennällisyyden tutkimuksesta erittäin tärkeätä. Kytkennälllisyyden tutkiminen antaa myös uutta tietoa ihmisaivojen osa-alueiden välisestä hierarkiasta. Aivojen hermoverkot voidaan luotettavasti ja toistettavasti havaita lepotilan toiminnasta yksilö- ja ryhmätasolla käyttämällä itsenäisten komponenttien analyysia (engl. Independent component analysis, ICA). Yhä useammat ICA-tutkimukset ovat raportoineet poikkeuksellisia toiminnallisen konnektiviteetin muutoksia kliinisissä populaatioissa. Tässä tutkimuksessa hypotetisoitiin, että ICA:lla laskettaujen komponenttien lukumäärä (l. asteluku) vaikuttaa tuloksena saatujen hermoverkkojen ominaisuuksiin kuten tilavuuteen ja kytkennällisyyteen. Lisäksi oletettiin, että korkea ICA-asteluku voisi olla herkempit tuottamaan yksityiskohtaisia toiminnallisen jaottelun tuloksia. Aivojen lepotilan hermoverkkojen ominaisuudet, kuten anatominen jakautuminen, volyymi ja lepohermoverkkojen havainnoinnin toistettavuus evaluoitin. Myös toiminnallisen kytkennällisyyden erot tutkitaan eri ICA-asteluvuilla. Havaittiin että asteluvulla on huomattava vaikutus aivojen lepotilan hermoverkkojen tilaominaisuuksiin sekä niiden jakautumiseen alaverkoiksi. Pienillä asteluvuilla hermoverkojen neuroanatomisesti erilliset yksiköt pyrkivät keräytymään laajoiksi yksittäisiksi komponenteiksi, kun taas korkeammilla asteluvuilla ne havaitaan erillisinä. Sairauksien aiheuttamat muutokset toiminnallisessa kytkennällisyydessä näyttävät muuttuvan myös ICA asteluvun mukaan saavuttaen maksiminsa korkeilla asteluvuilla. Korkeilla asteluvuilla voidaan havaita yksityiskohtaisia, sairaudelle ominaisia toiminnallisen konnektiviteetin muutoksia. Korkeisiin ICA asteluvun liittyvän tilastollisen monivertailuongelman ratkaisemiseksi kehitimme uuden menetelmän, jossa permutaatiotestejä edeltävien itsenäisten IC-karttoja yhdistämällä voidaan tehdä luotettava tilastollinen arvio yhtä aikaa lukuisista hermoverkoista. Kaamosmasennuspotilailla esimerkiksi kehittämämme korjaus paljastaa merkittävästi lisääntynyttä toiminnallista kytkennällisyyttä yhdessätoista hermoverkossa
Rodeia, José Pedro dos Santos. "Analysis and recognition of similar environmental sounds." Master's thesis, FCT - UNL, 2009. http://hdl.handle.net/10362/2305.
Full textHumans have the ability to identify sound sources just by hearing a sound. Adapting the same problem to computers is called (automatic) sound recognition. Several sound recognizers have been developed throughout the years. The accuracy provided by these recognizers is influenced by the features they use and the classification method implemented. While there are many approaches in sound feature extraction and in sound classification, most have been used to classify sounds with very different characteristics. Here, we implemented a similar sound recognizer. This recognizer uses sounds with very similar properties making the recognition process harder. Therefore, we will use both temporal and spectral properties of the sound. These properties will be extracted using the Intrinsic Structures Analysis (ISA) method, which uses Independent Component Analysis and Principal Component Analysis. We will implement the classification method based on k-Nearest Neighbor algorithm. Here we prove that the features extracted in this way are powerful in sound recognition. We tested our recognizer with several sets of features the ISA method retrieves, and achieved great results. We, finally, did a user study to compare human performance distinguishing similar sounds against our recognizer. The study allowed us to conclude the sounds are in fact really similar and difficult to distinguish and that our recognizer has much more ability than humans to identify them.
Nichele, Cristina. "Independent Component Analysis of GPS time series in the Altotiberina fault region in the Northern Apennines (Italy)." Master's thesis, Alma Mater Studiorum - Università di Bologna, 2016. http://amslaurea.unibo.it/12437/.
Full textWiedemeyer, Christian [Verfasser], and Carsten [Akademischer Betreuer] Konrad. "Anwendungsmöglichkeiten und Praktikabilität der Independent Component Analysis (ICA) in der funktionellen Magnetresonanztomographie (fMRT) / Christian Wiedemeyer. Betreuer: Carsten Konrad." Marburg : Philipps-Universität Marburg, 2011. http://d-nb.info/1013288475/34.
Full textNaik, Ganesh Ramachandra, and ganesh naik@rmit edu au. "Iterative issues of ICA, quality of separation and number of sources: a study for biosignal applications." RMIT University. Electrical and Computer Engineering, 2009. http://adt.lib.rmit.edu.au/adt/public/adt-VIT20090320.115103.
Full textWhinnett, Mark. "Analysis of face specific visual processing in humans by applying independent components analysis(ICA) to magnetoencephalographic (MEG) data." Thesis, Open University, 2014. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.607160.
Full textFontes, Nayanne Maria Garcia Rego. "Monitoramento e avaliação de desempenho de sistemas MPC utilizando métodos estatísticos multivariados." Universidade Federal de Sergipe, 2017. http://ri.ufs.br:8080/xmlui/handle/123456789/5037.
Full textMonitoring of process control systems is extremely important for industries to ensure the quality of the product and the safety of the process. Predictive controllers, also known by MPC (Model Predictive Control), usually has a well performance initially. However, after a period, many factors contribute to the deterioration of its performance. This highlights the importance of monitoring the MPC control systems. In this work, tools based on multivariate statistical methods are discussed and applied to the problem of monitoring and Performance Assessment of predictive controllers. The methods presented here are: PCA (Principal Component Analysis) and ICA (Independent Component Analysis). Both are techniques that use data collected directly from the process. The first is widely used in Performance Assessment of predictive controllers. The second is a more recent technique that has arisen, mainly in order to be used in fault detection systems. The analyzes are made when applied in simulated processes characteristic of the petrochemical industry operating under MPC control.
O monitoramento de sistemas de controle de processos é extremamente importante no que diz respeito às indústrias, para garantir a qualidade do que é produzido e a segurança do processo. Os controladores preditivos, também conhecidos pela sigla em inglês MPC (Model Predictive Control), costumam ter um bom desempenho inicialmente. Entretanto, após um certo período, muitos fatores contribuem para a deterioração de seu desempenho. Isto evidencia a importância do monitoramento dos sistemas de controle MPC. Neste trabalho aborda-se ferramentas, baseada em métodos estatísticos multivariados, aplicados ao problema de monitoramento e avaliação de desempenho de controladores preditivos. Os métodos aqui apresentados são: o PCA (Análise por componentes principais) e o ICA (Análise por componentes independentes). Ambas são técnicas que utilizam dados coletados diretamente do processo. O primeiro é largamente utilizado na avaliação de desempenho de controladores preditivos. Já o segundo, é uma técnica mais recente que surgiu, principalmente, com o intuito de ser utilizado em sistemas de detecção de falhas. As análises são feitas quando aplicadas em processos simulados característicos da indústria petroquímica operando sob controle MPC.
Rafique, Muhammad T. "Monitoring, diagnostics and improvement of process performance." Thesis, Curtin University, 2009. http://hdl.handle.net/20.500.11937/1333.
Full textMahadevan, Aparna. "Separating a Gas Mixture Into Its Constituent Analytes Using Fica." Thesis, Virginia Tech, 2009. http://hdl.handle.net/10919/33312.
Full textMaster of Science
TAKEDA, Kazuya, Takanori NISHINO, and Kenta NIWA. "Selective Listening Point Audio Based on Blind Signal Separation and Stereophonic Technology." Institute of Electronics, Information and Communication Engineers, 2009. http://hdl.handle.net/2237/15055.
Full textGuimaraes, figueroa pralon Leandro. "Scene Analysis and Interpretation by ICA Based Polarimetric Incoherent Target Decomposition for Polarimetric SAR Data." Thesis, Université Grenoble Alpes (ComUE), 2016. http://www.theses.fr/2016GREAT100/document.
Full textThis thesis comprises two research axes. First, a new methodological framework to assess the conformity of multivariate high-resolution Synthetic Aperture Radar (SAR) data with respect to the Spherically Invariant Random Vector model in terms of asymptotic statistics is proposed. More precisely, spherical symmetry is investigated by applying statistical hypotheses testing on the structure of the quadricovariance matrix. Both simulated and real data are taken into consideration to investigate the performance of the derived test by a detailed qualitative and quantitative analysis. The most important conclusion drawn, regarding the methodology employed in analysing SAR data, is that, depending on the scenario under study, a considerable portion of high heterogeneous data may not fit the aforementioned model. Therefore, traditional detection and classification algorithms developed based on the latter become sub-optimal when applied in such kind of regions. This assertion highlights for the need of the development of model independent algorithms, like the Independent Component Analysis, what leads to the second part of the thesis. A Monte Carlo approach is performed in order to investigate the bias in estimating the Touzi's Target Scattering Vector Model (TSVM) parameters when ICA is employed using a sliding window approach under different scenarios. Finally, the performance of the algorithm is also evaluated under Gaussian clutter assumption and when spatial correlation is introduced in the model. These theoretical assessment of ICA based ICTD enables a more efficient analysis of the potential new information provided by the ICA based ICTD. Both Touzi TSVM as well as Cloude and Pottier H/alpha feature space are then taken into consideration for that purpose. The combined use of ICA and Touzi TSVM is straightforward, indicating new, but not groundbreaking information, when compared to the Eigenvector approach. Nevertheless, the analysis of the combined use of ICA and Cloude and Pottier H/alpha feature space revealed a potential aspect of the Independent Component Analysis based ICTD, which can not be matched by the Eigenvector approach. ICA does not introduce any unfeasible region in the H/alpha plane, increasing the range of possible natural phenomenons depicted in the aforementioned feature space
Ghanadian, Hamideh. "A Machine Learning Method to Improve Non-Contact Heart Rate Monitoring Using RGB Camera." Thesis, Université d'Ottawa / University of Ottawa, 2018. http://hdl.handle.net/10393/38563.
Full textBayik, Tuba Makbule. "Automatic Target Recognition In Infrared Imagery." Master's thesis, METU, 2004. http://etd.lib.metu.edu.tr/upload/2/12605388/index.pdf.
Full textMoragues, Escrivá Jorge. "New energy detector extensions with application in sound based surveillance systems." Doctoral thesis, Universitat Politècnica de València, 2011. http://hdl.handle.net/10251/11520.
Full textMoragues Escrivá, J. (2011). New energy detector extensions with application in sound based surveillance systems [Tesis doctoral no publicada]. Universitat Politècnica de València. https://doi.org/10.4995/Thesis/10251/11520
Palancia
Orbán, Levente L. "Behavioural Studies and Computational Models Exploring Visual Properties that Lead to the First Floral Contact by Bumblebees." Thèse, Université d'Ottawa / University of Ottawa, 2014. http://hdl.handle.net/10393/30917.
Full textMahadevan, Anandi. "Real Time Ballistocardiogram Artifact Removal in EEG-fMRI Using Dilated Discrete Hermite Transform." University of Akron / OhioLINK, 2008. http://rave.ohiolink.edu/etdc/view?acc_num=akron1226235813.
Full textAygar, Alper. "Doppler Radar Data Processing And Classification." Master's thesis, METU, 2008. http://etd.lib.metu.edu.tr/upload/12609890/index.pdf.
Full textAlghoul, Karim. "Heart Rate Variability Extraction from Video Signals." Thesis, Université d'Ottawa / University of Ottawa, 2015. http://hdl.handle.net/10393/33003.
Full textDe, Sanctis Silvia [Verfasser], and Hans Robert [Akademischer Betreuer] Kalbitzer. "Application of Singular Spectrum Analysis (SSA), Independent Component Analysis (ICA) and Empirical Mode Decomposition (EMD) for automated solvent suppression and automated baseline and phase correction from multi-dimensional NMR spectra / Silvia De Sanctis. Betreuer: Hans Robert Kalbitzer." Regensburg : Universitätsbibliothek Regensburg, 2011. http://d-nb.info/1030178941/34.
Full textZitka, Adam. "Vícekanálové metody zvýrazňování řeči." Master's thesis, Vysoké učení technické v Brně. Fakulta elektrotechniky a komunikačních technologií, 2008. http://www.nusl.cz/ntk/nusl-217524.
Full textBartůšek, Jan. "Time Frequency Analysis of ERP Signals." Master's thesis, Vysoké učení technické v Brně. Fakulta informačních technologií, 2007. http://www.nusl.cz/ntk/nusl-412769.
Full textZinchenko, Tetiana. "Indendent component analysis (ICA)." Thesis, Київський національний університет технологій та дизайну, 2015. https://er.knutd.edu.ua/handle/123456789/17313.
Full textZiehe, Andreas. "Blind source separation based on joint diagonalization of matrices with applications in biomedical signal processing." Phd thesis, [S.l. : s.n.], 2005. http://deposit.ddb.de/cgi-bin/dokserv?idn=976710331.
Full textJaneček, David. "Sdružená EEG-fMRI analýza na základě heuristického modelu." Master's thesis, Vysoké učení technické v Brně. Fakulta elektrotechniky a komunikačních technologií, 2015. http://www.nusl.cz/ntk/nusl-221334.
Full textGao, Pei. "Nonlinear independent component analysis." Thesis, University of Newcastle Upon Tyne, 2007. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.437979.
Full textSadovský, Petr. "Analýza spánkového EEG." Doctoral thesis, Vysoké učení technické v Brně. Fakulta elektrotechniky a komunikačních technologií, 2007. http://www.nusl.cz/ntk/nusl-233411.
Full textBlaschke, Tobias. "Independent component analysis and slow feature analysis." Doctoral thesis, Humboldt-Universität zu Berlin, Mathematisch-Naturwissenschaftliche Fakultät I, 2005. http://dx.doi.org/10.18452/15270.
Full textWithin this thesis, we focus on the relation between independent component analysis (ICA) and slow feature analysis (SFA). To allow a comparison between both methods we introduce CuBICA2, an ICA algorithm based on second-order statistics only, i.e.\ cross-correlations. In contrast to algorithms based on higher-order statistics not only instantaneous cross-correlations but also time-delayed cross correlations are considered for minimization. CuBICA2 requires signal components with auto-correlation like in SFA, and has the ability to separate source signal components that have a Gaussian distribution. Furthermore, we derive an alternative formulation of the SFA objective function and compare it with that of CuBICA2. In the case of a linear mixture the two methods are equivalent if a single time delay is taken into account. The comparison can not be extended to the case of several time delays. For ICA a straightforward extension can be derived, but a similar extension to SFA yields an objective function that can not be interpreted in the sense of SFA. However, a useful extension in the sense of SFA to more than one time delay can be derived. This extended SFA reveals the close connection between the slowness objective of SFA and temporal predictability. Furthermore, we combine CuBICA2 and SFA. The result can be interpreted from two perspectives. From the ICA point of view the combination leads to an algorithm that solves the nonlinear blind source separation problem. From the SFA point of view the combination of ICA and SFA is an extension to SFA in terms of statistical independence. Standard SFA extracts slowly varying signal components that are uncorrelated meaning they are statistically independent up to second-order. The integration of ICA leads to signal components that are more or less statistically independent.
Labounek, René. "Fúze simultánních EEG-FMRI dat za pomoci zobecněných spektrálních vzorců." Doctoral thesis, Vysoké učení technické v Brně. Fakulta elektrotechniky a komunikačních technologií, 2018. http://www.nusl.cz/ntk/nusl-371799.
Full textBrock, James L. "Acoustic classification using independent component analysis /." Link to online version, 2006. https://ritdml.rit.edu/dspace/handle/1850/2067.
Full textPapathanassiou, Christos. "Independent component analysis of magnetoencephalographic signals." Thesis, University of Surrey, 2003. http://epubs.surrey.ac.uk/771941/.
Full textMiskin, James William. "Ensemble learning for independent component analysis." Thesis, University of Cambridge, 2001. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.621116.
Full textGarvey, Jennie Hill. "Independent component analysis by entropy maximization (infomax)." Thesis, Monterey, Calif. : Naval Postgraduate School, 2007. http://bosun.nps.edu/uhtbin/hyperion-image.exe/07Jun%5FGarvey.pdf.
Full textThesis Advisor(s): Frank E. Kragh. "June 2007." Includes bibliographical references (p. 103). Also available in print.
Mitianoudis, Nikolaos. "Audio source separation using independent component analysis." Thesis, Queen Mary, University of London, 2004. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.406171.
Full textChoudrey, Rizwan A. "Variational methods for Bayesian independent component analysis." Thesis, University of Oxford, 2002. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.275566.
Full textKalkan, Olcay Altınkaya Mustafa Aziz. "Independent component analysis applications in CDMA systems/." [s.l.]: [s.n.], 2004. http://library.iyte.edu.tr/tezler/master/elektronikvehaberlesme/T000473.rar.
Full textBjörling, Robin. "Denoising of Infrared Images Using Independent Component Analysis." Thesis, Linköping University, Department of Electrical Engineering, 2005. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-4954.
Full textDenna uppsats syftar till att undersöka användbarheten av metoden Independent Component Analysis (ICA) för brusreducering av bilder tagna av infraröda kameror. Speciellt fokus ligger på att reducera additivt brus. Bruset delas upp i två delar, det Gaussiska bruset samt det sensorspecifika mönsterbruset. För att reducera det Gaussiska bruset används en populär metod kallad sparse code shrinkage som bygger på ICA. En ny metod, även den byggandes på ICA, utvecklas för att reducera mönsterbrus. För varje sensor utförs, i den nya metoden, en analys av bilddata för att manuellt identifiera typiska mönsterbruskomponenter. Dessa komponenter används därefter för att reducera mönsterbruset i bilder tagna av den aktuella sensorn. Det visas att metoderna ger goda resultat på infraröda bilder. Algoritmerna testas både på syntetiska såväl som på verkliga bilder och resultat presenteras och jämförs med andra algoritmer.
The purpose of this thesis is to evaluate the applicability of the method Independent Component Analysis (ICA) for noise reduction of infrared images. The focus lies on reducing the additive uncorrelated noise and the sensor specific additive Fixed Pattern Noise (FPN). The well known method sparse code shrinkage, in combination with ICA, is applied to reduce the uncorrelated noise degrading infrared images. The result is compared to an adaptive Wiener filter. A novel method, also based on ICA, for reducing FPN is developed. An independent component analysis is made on images from an infrared sensor and typical fixed pattern noise components are manually identified. The identified components are used to fast and effectively reduce the FPN in images taken by the specific sensor. It is shown that both the FPN reduction algorithm and the sparse code shrinkage method work well for infrared images. The algorithms are tested on synthetic as well as on real images and the performance is measured.
Sahambi, Harkirat S. "Appearance based object recognition using independent component analysis." Thesis, National Library of Canada = Bibliothèque nationale du Canada, 2000. http://www.collectionscanada.ca/obj/s4/f2/dsk1/tape4/PQDD_0017/MQ54320.pdf.
Full textWu, Hao-cun. "Independent component analysis and its applications in finance." Click to view the E-thesis via HKUTO, 2007. http://sunzi.lib.hku.hk/HKUTO/record/B39559099.
Full textBeckmann, Christian F. "Independent component analysis for functional magnetic resonance imaging." Thesis, University of Oxford, 2004. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.404108.
Full text吳浩存 and Hao-cun Wu. "Independent component analysis and its applications in finance." Thesis, The University of Hong Kong (Pokfulam, Hong Kong), 2007. http://hub.hku.hk/bib/B39559099.
Full textRobila, Stefan Alexandru. "Independent component analysis based feature extraction for hyperspectral images." Related electronic resource: Current Research at SU : database of SU dissertations, recent titles available full text, 2002. http://wwwlib.umi.com/cr/syr/main.
Full textBaylor, Martha-Elizabeth. "Analog optoelectronic independent component analysis for radio frequency signals." Connect to online resource, 2007. http://gateway.proquest.com/openurl?url_ver=Z39.88-2004&rft_val_fmt=info:ofi/fmt:kev:mtx:dissertation&res_dat=xri:pqdiss&rft_dat=xri:pqdiss:3288865.
Full textE, Okwelume Gozie, and Ezeude Anayo Kingsley. "BLIND SOURCE SEPARATION USING FREQUENCY DOMAIN INDEPENDENT COMPONENT ANALYSIS." Thesis, Blekinge Tekniska Högskola, Avdelningen för signalbehandling, 2007. http://urn.kb.se/resolve?urn=urn:nbn:se:bth-1312.
Full textGozie: modebelu2001@yahoo.com Anayo: ezeudea@yahoo.com
Herrmann, Frank. "Independent component analysis with applications to blind source separation." Thesis, University of Liverpool, 2000. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.399147.
Full textRezaee, Sayed Majid. "A study on independent component analysis over galois fields." reponame:Repositório Institucional da UnB, 2015. http://dx.doi.org/10.26512/2015.12.D.20421.
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Nas últimas décadas, o problema de separação cega de fontes (BSS, do inglês Blind Source Separation) – que trata de estimar um conjunto desconhecido de fontes de sinais a partir de versões misturadas destes – tornou-se relevante em vários campos da engenharia, incluindo o processamento matricial, comunicações sem fio, processamento de sinais médicos, processamento de voz e engenharia biomédica. A fim de resolver o problema de BSS no contexto de modelos lineares, considerando-se várias técnicas possíveis, a Análise de Componentes Independentes (ICA, do inglês Independent Component Analysis) – que utiliza a independência estatística das fontes como uma premissa – demonstrou ser uma das mais importantes estratégias de solução. Além disso, embora o modelo de BSS/ICA para sinais reais ou complexos esteja bem estabelecido, a recente perspectiva de uma formulação do problema com sinais e modelos definidos em corpos de Galois oferece várias possibilidades de análise e contribuições. Esta dissertação de mestrado realiza um estudo da Análise de Componentes Independentes em corpos de Galois, considerando os conceitos teóricos e abordagens para o problema, assim como dos algoritmos estado-da-arte até agora propostos, em termos de suas capacidades de separação e custo computacional. Especificamente, as técnicas dos algoritmos AMERICA e MEXICO são estudadas juntamente com o algoritmo cobICA. Como as simulações experimentais indicam, devido à sua complexidade computacional menor e uma qualidade de desempenho satisfatório, o algoritmo cobICA apresenta-se como uma solução de compromisso entre os algoritmos AMERICA e MEXICO para executar BSS/ICA em corpos de Galois.
Over the past decades, the Blind Source Separation (BSS) problem – which deals with estimating an unknown set of source signals from their measured mixtures –has become prevalent in several engineering fields, including array processing, wireless communications, medical signal processing, speech processing and biomedical engineering. In order to solve the BSS problem in the context of linear models, considering several possible techniques, Independent Component Analysis (ICA) – which uses statistical independence of the source signals as a premise – has been shown to be one of the most important approaches. Furthermore, although the BSS/ICA framework for real- or complex-valued signals is firmly established, the recent perspective of a BSS/ICA formulation where the signals and models are defined over Galois fields gives several possibilities of analyzes and contributions. This Master’s thesis performs a study on Independent Component Analysis over Galois fields, considering the theoretical concepts and aspects of the problem and the investigation, in terms of capability and efficiency, of the state-of-the-art algorithms so far introduced. In this context, AMERICA and MEXICO techniques are studied, along with cobICA algorithm – a bioinspired framework based on cob-aiNet[C] immune-inspired algorithm –, mainly focusing on comparing the quality of separation and on discussing the computational burden of each technique. As the experimental simulations indicate, due to its lower computational complexity and a satisfactory performance quality, cobICA takes place as a compromise solution between AMERICA and MEXICO algorithms, to perform BSS/ICA over Galois fields.
Wang, Shun Ping, and 王舜平. "A Low-Cost Independent Component Analysis (ICA) Chip Design." Thesis, 2019. http://ndltd.ncl.edu.tw/cgi-bin/gs32/gsweb.cgi/login?o=dnclcdr&s=id=%22107CGU05428011%22.&searchmode=basic.
Full textMarcynuk, Kathryn L. "Independent component analysis for maternal-fetal electrocardiography." 2015. http://hdl.handle.net/1993/30181.
Full textWei, Min Xian, and 韋旻賢. "A VLSI implementation of Independent Component Analysis (ICA) for Biomedical Signal Separation." Thesis, 2017. http://ndltd.ncl.edu.tw/handle/3vxrgv.
Full text長庚大學
電子工程學系
105
Independent component analysis (ICA) is a new algorithm developed in recent years to solve the problem of shielding source separation (BSS). The characteristic of the algorithm is that it allows a direct separation for a number of mixed signals with unknown coefficients of mixture. Thus the ICA algorithm becomes important in the field of the digital signal processing, especially in biomedical signal. This study aims to design and implement a real-time hardware architecture of the extend infomax ICA algorithm which can separate the super-Gaussian signal sources by using integrated circuit (IC). Implemented using the TSMC 0.18-μm CMOS process, the proposed ICA circuit achieved an operation frequency of 62.5 MHz and a gate count of 141.5 k.