Keyword Spotting in general means identifying a keyword in a verbal or written document. In this research a novel approach in designing a simple spoken Keyword Spotting/Recognition system based on Template Matching is proposed, which is different from the Hidden Markov Model based systems that are most widely used today. The system can be used equally efficiently on any language as it does not rely on an underlying language model or grammatical constraints. The proposed method for keyword spotting is based on a modified version of classical Dynamic Time Warping which has been a primary method for measuring the similarity between two sequences varying in time. For processing, a speech signal is divided into small stationary frames. Each frame is represented in terms of a quantized feature vector. Both the keyword and the  speech  utterance  are  represented  in  terms  of  1‐dimensional  codebook  indices.  The  utterance is divided into segments and the warped distance is computed for each segment and compared against the test keyword. A distortion score for each segment is computed as likelihood measure of the keyword. The proposed algorithm is designed to take advantage of multiple instances of test keyword (if available) by merging the score for all keywords used.   The training method for the proposed system is completely unsupervised, i.e., it requires neither a language model nor phoneme model for keyword spotting. Prior unsupervised training algorithms were based on computing Gaussian Posteriorgrams making the training process complex but the proposed algorithm requires minimal training data and the system can also be trained to perform on a different environment (language, noise level, recording medium etc.) by  re‐training the original cluster on additional data.  Techniques for designing a model keyword from multiple instances of the test keyword are discussed. System performance over variations of different parameters like number of clusters, number of instance of keyword available, etc were studied in order to optimize the speed and accuracy of the system. The system performance was evaluated for fourteen different keywords from the Call - Home and the Switchboard speech corpus. Results varied for different keywords and a maximum accuracy of 90% was obtained which is comparable to other methods using the same time warping algorithms on Gaussian Posteriorgrams. Results are compared for different parameters variation with suggestion of possible improvements. / Electrical and Computer Engineering

Noise pollution is a big health hazard for people living in urban areas, and its effects on humans is a growing field of research. One of the major contributors to urban noise pollution is the noise generated by traffic. Noise simulations can be made in order to build noise maps used for noise management action plans, but in order to test their accuracy real measurements needs to be done, in this case in the form of noise measurements taken adjacent to a road. The aim of this project is to test machine learning based methods in order to develop a robust way of detecting and classifying vehicle noise in sparse traffic conditions. The primary focus is to detect traffic noise events, and the secondary focus is to classify what kind of vehicle is producing the noise. The data used in this project comes from sensors installed on a testbed at a street in southern Stockholm. The sensors include a microphone that is continuously measuring the local noise environment, a radar that detects each time a vehicle is passing by, and a camera that also detects a vehicle by capturing its license plate. Only sparse traffic noises are considered for this thesis, as such the audio recordings used are those where the radar has only detected one vehicle in a 40 second window. This makes the data gathered weakly labeled. The resulting detection method is a two-step process: First, the unsupervised learning method k-means is implemented for the generation of strong labels. Second, the supervised learning method random forest or support vector machine uses the strong labels in order to classify audio features. The detection system of sparse traffic noise achieved satisfactory results. However, the unsupervised vehicle classification method produced inadequate results and the clustering could not differentiate different vehicle classes based on the noise data. / Buller är en stor hälsorisk för människor som bor i stadsområden, och dess effekter på människor är ett växande forskningsfält. En av de största bidragen till stadsbuller är oljud som genereras av trafiken. Man kan utföra simuleringar i syfte att skapa bullerkartor som kan användas till planer för att minska dessa ljud. För att testa deras noggrannhet måste verkliga mätningar tas, i detta fall i formen av ljudmätningar tagna intill en väg. Syftet med detta projekt är att testa maskininlärningsmetoder för att utveckla ett robust sätt att detektera och klassificera fordonsljud i glesa trafikförhållanden. Primärt fokus ligger på att detektera bullerhändelser från trafiken, och sekundärt fokus är att försöka klassificera vilken typ av fordon som producerade ljudet. Datan som används i detta projekt kommer från sensorer installerade på en testbädd på en gata i södra Stockholm. Sensorerna inkluderar en mikrofon som kontinuerligt mäter den lokala ljudmiljön, en radar som detekterar varje gång ett fordon passerar, och en kamera som också detekterar ett fordon genom att ta bild på dess registreringsskylt. Endast ljud från gles trafik kommer att beaktas och användas i detta arbete, och därför används bara de ljudinspelningar där radarn har upptäckt ett enskilt fordon under ett 40 sekunders intervall. Detta gör att den insamlade datan har svaga etiketter. Den resulterande detekteringsmetoden är en tvåstegsprocess: För det första används den oövervakade inlärningsmetoden k-means för att generera starka etiketter. För det andra används de starka etiketterna av den övervakade inlärningsmetoden slumpmässig beslutsskog eller stödvektormaskin i syfte att klassificera ljudegenskaper. Detekteringssystemet av glest trafikljud uppnådde tillfredsställande resultat. Däremot producerade den oövervakade klassificeringsmetoden för fordonsljud otillräckliga resultat, och klustringen kunde inte urskilja mellan olika fordonsklasser baserat på ljuddatan.

Microstructure reconstruction is a necessary tool for use in multi-scale modeling, as it allows for the analysis of the microstructure of a material without the cost of measuring all of the required data for the analysis. For microstructure reconstruction to be effective, the synthetic microstructure needs to predict what a small sample of measured data would look like on a larger domain. The Markov Random Field (MRF) algorithm is a method of generating statistically similar microstructures for this process. In this work, two key factors of the MRF algorithm are analyzed. The first factor explored is how the base features of the microstructure related to orientation and grain/phase topology information influence the selection of the MRF parameters to perform the reconstruction. The second focus is on the analysis of the numerical uncertainty (epistemic uncertainty) that arises from the use of the MRF algorithm. This is done by first removing the material uncertainty (aleatoric uncertainty), which is the noise that is inherent in the original image representing the experimental data. The epistemic uncertainty that arises from the MRF algorithm is analyzed through the study of the percentage of isolated pixels and the difference in average grain sizes between the initial image and the reconstructed image. This research mainly focuses on two different microstructures, B4C-TiB2 and Ti-7Al, which are a ceramic composite and a metallic alloy, respectively. Both of them are candidate materials for many aerospace systems owing to their desirable mechanical performance under large thermo-mechanical stresses. / Master of Science / Microstructure reconstruction is a necessary tool for use in multi-scale modeling, as it allows for the analysis of the microstructure of a material without the cost of measuring all of the required data for the analysis. For microstructure reconstruction to be effective, the synthetic microstructure needs to predict what a small sample of measured data would look like on a larger domain. The Markov Random Field (MRF) algorithm is a method of generating statistically similar microstructures for this process. In this work, two key factors of the MRF algorithm are analyzed. The first factor explored is how the base features of the microstructures related to orientation and grain/phase topology information influence the selection of the MRF parameters to perform the reconstruction. The second focus is on the analysis of the numerical uncertainty that arises from the use of the MRF algorithm. This is done by first removing the material uncertainty, which is the noise that is inherent in the original image representing the experimental data. This research mainly focuses on two different microstructures, B4C-TiB2 and Ti-7Al, which are a ceramic composite and a metallic alloy, respectively. Both of them are candidate materials for many aerospace systems owing to their desirable mechanical performance under large thermo-mechanical stresses.

This thesis investigates the possibilities of Software-defined radios as a surveillance system by monitoring the electromagnetic spectrum. The surveillance system monitors the Bluetooth bandwidth with a multi-channel receiver that passively listens to Bluetooth packets. Furthermore, this thesis investigates the possibility of implementing an automatic k-means clustering algorithm to count unique devices in the vicinity. The Background explains the fundamental technologies used in Bluetooth and explains how devices communicate with each other. The Background also explains the proposed receiver architecture and its technologies. Section Related work and similar products investigate different approaches to detecting mobile devices and the effectiveness of the k-means algorithm. The Method explains how the receiver is modeled, how the Python script identifies Bluetooth packets in the bit stream, and how the physical imperfections are collected for the automatic k-means algorithm. Lastly, the Method explains how the labs were conducted. The Result section shows the performance of the receiver and the k-means algorithm. The Discussion section analyzes the results and discusses some design flaws and how to fix them potentially. Lastly, the Conclusion section compares the goals with the results and presents future work for further development.

Cette thèse est consacrée à l'évaluation des familles exponentielles pour les problèmes de la modélisation des bruits et de la segmentation des images couleurs. Dans un premier temps, nous avons développé une nouvelle caractérisation des familles exponentielles naturelles infiniment divisible basée sur la fonction trace de la matrice de variance covariance associée. Au niveau application, cette nouvelle caractérisation a permis de détecter la nature de la loi d'un bruit additif associé à un signal où à une image couleur. Dans un deuxième temps, nous avons proposé un nouveau modèle statistique paramétrique mulltivarié basé sur la loi de Riesz. La loi de ce nouveau modèle est appelée loi de la diagonale modifiée de Riesz. Ensuite, nous avons généralisé ce modèle au cas de mélange fini de lois. Enfin, nous avons introduit un algorithme de segmentation statistique d'image ouleur, à travers l'intégration de la méthode des centres mobiles (K-means) au niveau de l'initialisation pour une meilleure définition des classes de l'image et l'algorithme EM pour l'estimation des différents paramètres de chaque classe qui suit la loi de la diagonale modifiée de la loi de Riesz. / This thesis is dedicated to the evaluation of the exponential families for the problems of the noise modeling and the color images segmentation. First, we developed a new characterization of the infinitely divisible natural exponential families based on the trace function of the associated variance-covariance matrix. At the application level, this new characterization allowed to detect the nature of the law of an additive noise associated with a signal or with a color image. Second, we proposed a new parametric multivariate statistical model based on Riesz's distribution. The law of this new model is called the modified diagonal Riesz distribution. Then we generalized this model in the case of a finished mixture of distibution. Finally we introduced an algorithm of statistical segmentation of color images through the integration of the k-means method at the level of the initialization for a better definition of the image classes and the algorithm EM for the estimation of the different parameters of every class which follows the modified diagonal Riesz distribution.

Submitted by Maria Aparecida (cidazen@gmail.com) on 2017-08-21T14:59:23Z No. of bitstreams: 1 Andre Pereira.pdf: 3105574 bytes, checksum: 06e2fe68d48179a3c62a46e447b82513 (MD5) / Made available in DSpace on 2017-08-21T14:59:23Z (GMT). No. of bitstreams: 1 Andre Pereira.pdf: 3105574 bytes, checksum: 06e2fe68d48179a3c62a46e447b82513 (MD5) Previous issue date: 2009-02-20 / Breast cancer is one of the major causes of mortality among women throughout the world. Presently, the analysis of breast radiography is the most used method to early detection of this kind of cancer. It enables the identification of anomalies at their initial stage, which is a fundamental factor for success in the treatment. The sensitivity of this kind of exam, although, depends on several factors, such as the size and the location of the abnormalities, density of the breast tissue, quality of the technical resources and radiologist's ability. This work presents a methodology that uses the K-Means clustering algorithm and the Template Matching technique for segmentation of suspicious regions. Next, geometry and texture features are extracted from each of these regions, being the texture described by the Simpson's Diversity Index, a statistic used in Ecology to measure the biodiversity of an ecosystem. Finally, this information is submitted to a Support Vector Machine so that the suspicious regions are classified into masses and non-masses. The methodology was tested with 650 mammographic images from the DDSM database, achieving 83.94% of accuracy, 83.24% of sensibility and 84.14% of specificity in average. / O câncer de mama é uma das maiores causas de mortalidade entre as mulheres no mundo todo. Atualmente, a análise da radiografia da mama é o recurso mais utilizado na detecção precoce desse tipo de câncer, pois possibilita a identificação de anomalias em sua fase inicial, fator fundamental para o sucesso do tratamento. A sensibilidade desse tipo de exame, no entanto, depende de diversos fatores, tais como tamanho e localização das anomalias, densidade do tecido mamário, qualidade dos recursos técnicos e habilidade do radiologista. Este trabalho apresenta uma metodologia para detecção de massas em imagens digitais de mamografias que poderá auxiliar o especialista em sua análise. O método proposto utiliza o algoritmo de agrupamento K-Means e a técnica de Template Matching para segmentar as regiões suspeitas de conterem massas. Em seguida, medidas de geometria e textura são extraídas de cada uma dessas regiões, sendo a textura descrita através do Índice de Diversidade de Simpson, uma estatística usada na Ecologia para mensurar a biodiversidade de um ecossistema. Finalmente, essas informações são submetidas a uma Máquina de Vetores de Suporte para que as regiões suspeitas sejam classificadas em massas ou não massas. A metodologia foi testada com 650 imagens mamográficas obtidas da base de dados DDSM, atingindo 83,94% de acurácia, 83,24% de sensibilidade, e 84,14% de especificidade em média.

Fundação de Amparo a Pesquisa do Estado de Minas Gerais / Skeletal muscle consists of muscle fiber types that have different physiological and biochemical characteristics. Basically, the muscle fiber can be classified into type I and type II, presenting, among other features, contraction speed and sensitivity to fatigue different for each type of muscle fiber. These fibers coexist in the skeletal muscles and their relative proportions are modulated according to the muscle functionality and the stimulus that is submitted. To identify the different proportions of fiber types in the muscle composition, many studies use biopsy as standard procedure. As the surface electromyography (EMGs) allows to extract information about the recruitment of different motor units, this study is based on the assumption that it is possible to use the EMG to identify different proportions of fiber types in a muscle. The goal of this study was to identify the characteristics of the EMG signals which are able to distinguish, more precisely, different proportions of fiber types. Also was investigated the combination of characteristics using appropriate mathematical models. To achieve the proposed objective, simulated signals were developed with different proportions of motor units recruited and with different signal-to-noise ratios. Thirteen characteristics in function of time and the frequency were extracted from emulated signals. The results for each extracted feature of the signals were submitted to the clustering algorithm k-means to separate the different proportions of motor units recruited on the emulated signals. Mathematical techniques (confusion matrix and analysis of capability) were implemented to select the characteristics able to identify different proportions of muscle fiber types. As a result, the average frequency and median frequency were selected as able to distinguish, with more precision, the proportions of different muscle fiber types. Posteriorly, the features considered most able were analyzed in an associated way through principal component analysis. Were found two principal components of the signals emulated without noise (CP1 and CP2) and two principal components of the noisy signals (CP1 and CP2 ). The first principal components (CP1 and CP1 ) were identified as being able to distinguish different proportions of muscle fiber types. The selected characteristics (median frequency, mean frequency, CP1 and CP1 ) were used to analyze real EMGs signals, comparing sedentary people with physically active people who practice strength training (weight training). The results obtained with the different groups of volunteers show that the physically active people obtained higher values of mean frequency, median frequency and principal components compared with the sedentary people. Moreover, these values decreased with increasing power level for both groups, however, the decline was more accented for the group of physically active people. Based on these results, it is assumed that the volunteers of the physically active group have higher proportions of type II fibers than sedentary people. Finally, based on these results, we can conclude that the selected characteristics were able to distinguish different proportions of muscle fiber types, both for the emulated signals as to the real signals. These characteristics can be used in several studies, for example, to evaluate the progress of people with myopathy and neuromyopathy due to the physiotherapy, and also to analyze the development of athletes to improve their muscle capacity according to their sport. In both cases, the extraction of these characteristics from the surface electromyography signals provides a feedback to the physiotherapist and the coach physical, who can analyze the increase in the proportion of a given type of fiber, as desired in each case. / A musculatura esquelética é constituída por tipos de fibras musculares que possuem características fisiológicas e bioquímicas distintas. Basicamente, elas podem ser classificadas em fibras do tipo I e fibras do tipo II, apresentando, dentre outras características, velocidade de contração e sensibilidade à fadiga diferentes para cada tipo de fibra muscular. Estas fibras coexistem na musculatura esquelética e suas proporções relativas são moduladas de acordo com a funcionalidade do músculo e com o estímulo a que é submetido. Para identificar as diferentes proporções de tipos de fibra na composição muscular, muitos estudos utilizam a biópsia como procedimento padrão. Como a eletromiografia de superfície (EMGs) nos permite extrair informações sobre o recrutamento de diferentes unidades motoras, este estudo parte da hipótese de que seja possível utilizar a EMGs para identificar diferentes proporções de tipos de fibras em uma musculatura. O objetivo deste estudo foi identificar as características dos sinais EMGs que sejam capazes de distinguir, com maior precisão, diferentes proporções de tipos de fibras. Também foi investigado a combinação de características por meio de modelos matemáticos apropriados. Para alcançar o objetivo proposto, sinais emulados foram desenvolvidos com diferentes proporções de unidades motoras recrutadas e diferentes razões sinal-ruído. Treze características no domínio do tempo e da frequência foram extraídas do sinais emulados. Os resultados de cada característica extraída dos sinais emulados foram submetidos ao algorítimo de agrupamento k-means para separar as diferentes proporções de unidades motoras recrutadas nos sinais emulados. Técnicas matemáticas (matriz confusão e técnica de capabilidade) foram implementadas para selecionar as características capazes de identificar diferentes proporções de tipos de fibras musculares. Como resultado, a frequência média e a frequência mediana foram selecionadas como capazes de distinguir com maior precisão as diferentes proporções de tipos de fibras musculares. Posteriormente, as características consideradas mais capazes foram analisadas de forma associada por meio da análise de componentes principais. Foram encontradas duas componentes principais para os sinais emulados sem ruído (CP1 e CP2) e duas componentes principais para os sinais com ruído (CP1 e CP2 ), sendo as primeiras componentes principais (CP1 e CP1 ) identificadas como capazes de distinguirem diferentes proporções de fibras. As características selecionadas (frequência mediana, frequência média, CP1 e CP1 ) foram utilizadas para analisar sinais EMGs reais, comparando pessoas sedentárias com pessoas fisicamente ativas praticantes de treinamentos físicos de força (musculação). Os resultados obtidos com os diferentes grupos de voluntários mostram que as pessoas fisicamente ativas obtiveram valores mais elevados de frequência média, frequência mediana e componentes principais em comparação com as pessoas sedentárias. Além disto, estes valores decaíram com o aumento do nível de força para ambos os grupo, entretanto, o decaimento foi mais acentuado para o grupo de pessoas fisicamente ativas. Com base nestes resultados, presume-se que os voluntários do grupo fisicamente ativo apresentam maiores proporções de fibras do tipo II, se comparado com as pessoas sedentárias. Por fim, com base nos resultados obtidos, pode-se concluir que as características selecionadas foram capazes de distinguir diferentes proporções de tipos de fibras musculares, tanto para os sinais emulados quanto para os sinais reais. Estas características podem ser utilizadas em vários estudos, como por exemplo, para avaliar a evolução de pessoas com miopatias e neuromiopatia em decorrência da reabilitação fisioterápica, e também para analisar o desenvolvimento de atletas que visam melhorar sua capacidade muscular de acordo com sua modalidade esportiva. Em ambos os casos, a extração destas características dos sinais de eletromiografia de superfície proporciona um feedback ao fisioterapeuta e ao treinador físico, que podem analisar o aumento na proporção de determinado tipo de fibra, conforme desejado em cada caso. / Mestre em Ciências

Made available in DSpace on 2016-03-15T19:37:30Z (GMT). No. of bitstreams: 1 Mauro Ulisses Schneider.pdf: 1455677 bytes, checksum: 6eba28391f8f6910fbf5457a57119bd3 (MD5) Previous issue date: 2010-08-23 / Fundo Mackenzie de Pesquisa / The use of biometric systems has been widely stimulated by both the government and private entities to replace or improve traditional security systems. Biometric systems are becoming increasingly indispensable to protecting life and property, mainly due to its robustness, reliability, difficult to counterfeit and fast authentication. In real world applications, the devices for image acquisition and the environment are not always controlled and may under certain circumstances produce noisy images or with large variations in tonality, texture, geometry, hindering segmentation and consequently the authentication of the an individual. To deal effectively with such problems, this dissertation investigates the possibility of using committee machines combined with digital image processing techniques for iris segmentation. The components employed in the composition of the committee machines are support vector clustering, k-means and self organizing maps. In order to evaluate the performance of the tools developed in this dissertation, the experimental results obtained are compared with related works reported in the literature. Experiments on publicity available UBIRIS database indicate that committee machine can be successfully applied to the iris segmentation. / A utilização de sistemas biométricos vem sendo amplamente; incentivados pelo governo e entidades privadas a fim de substituir ou melhorar os sistemas de segurança tradicionais. Os sistemas biométricos são cada vez mais indispensáveis para proteger vidas e bens, sendo robustos, confiáveis, de difícil falsificação e rápida autenticação. Em aplicações de mundo real, os dispositivos de aquisição de imagem e o ambiente nem sempre são controlados, podendo em certas circunstâncias produzir imagens ruidosas ou com grandes variações na tonalidade, textura, geometria, dificultando a sua segmentação e por conseqüência a autenticação do indivíduo. Para lidar eficazmente com tais problemas, nesta dissertação é estudado o emprego de comitês de máquinas em conjunto com técnicas de processamento de imagens digitais para a segmentação da íris. Os componentes estudados na composição do comitê de máquinas são agrupamento por vetores-suporte, k-means e mapas auto- organizáveis. Para a avaliação do desempenho das ferramentas desenvolvidas neste trabalho, os resultados obtidos são comparados com trabalhos relacionados na literatura. Foi utilizada a base de dados pública UBIRIS disponível na internet.

[pt] Exames de ultrassom são uma ferramenta popular de aquisição de imagens na medicina atual por ser um procedimento não-invasivo, seguro e barato. Entretanto, inerente a qualquer exame de ultrassom encontra-se o ruído speckle, responsável pela degradação da imagem e dificultando tanto sua interpretação por parte de médicos e pacientes, quanto prejudicando a acurácia de métodos computacionais de pós processamento, como classificação, reconstrução, caracterização de tecidos e segmentação, entre outros. Portanto, métodos de remoção ou suavização deste ruído que preservem as principais características do conteúdo observado se fazem fundamentais para um avanço nestes processos. Definido como um ruído multiplicativo, que segue estatísticas não-Gaussianas e como fortemente correlacionado, sua solução ainda hoje é tema de debates e estudos. Neste trabalho apresentaremos diversos métodos de filtragem 2D que se propõem a reduzir ou solucionar o ruído speckle bem como métodos qualitativos para avaliar seus desempenhos e técnicas para escolher os melhores parâmetros de cada filtro a fim de eleger quais métodos melhor solucionam este ruído. / [en] Ultrasound exams are a popular tool for image acquisition in day-to-day medicine, since it is a noninvasive, safe and cheap procedure. However, speckle noise is intrinsic to any ultrasound exam, and it is responsible for image quality degradation and for hindering its interpretation by doctors and patients alike, while also impairing the accuracy of post processing computational methods, such as classification, reconstruction, tissue characterization and segmentation, among others. Hence, smoothing or denoising methods that preserves the observed content core attributes are essential for those processes. Defined as a multiplicative noise, following non-Gaussian statistics and as strongly correlated, its solution today is still a matter of debates and research. In this work, several 2D filters that aim to smooth or remove speckle noise along with qualitative methods to evaluate their performances and means of choosing their best parameters are presented.

De nouvelles architectures de traction (hybride, électrique) entrent en concurrence avec les motorisations thermiques conventionnelles. Des batteries Lithium-ion équipent ces véhicules innovants. La durabilité de ces batteries constitue un enjeu majeur mais dépend de nombreux paramètres environnementaux externes. Les outils de prédiction de durée de vie actuellement utilisés sont souvent trop simplificateurs dans leur approche. L’objet de ces travaux consiste à caractériser les conditions d’usage de ces batteries (température, tension, courant, SOC et DOD) afin d’étudier avec précision la durée de vie que l’on peut en attendre en fonction de l’application visée. Différents types de véhicules électrifiés (vélos à assistance électrique, voitures électriques, voitures hybrides, et trolleybus) ont été instrumentés afin de documenter les conditions d’usage réel des batteries. De larges volumes de données ont été recueillis puis analysés au moyen d’une méthode innovante qui s’appuie sur la classification d’impulsions de courant par l’algorithme des K-means et la génération de cycles synthétiques par modélisation par chaine de Markov. Les cycles synthétiques ainsi obtenus présentent des caractéristiques très proches de l’échantillon complet de données récoltées et permettent donc de représenter fidèlement l’usage réel. Utilisés lors de campagnes de vieillissement de batteries, ils sont susceptibles de permettre l’obtention d’une juste prédiction de la durée de vie des batteries pour l’application considérée. Plusieurs résultats expérimentaux sont présentés afin d’étayer la pertinence de cette approche / Lithium-ion batteries are being used as energy storage systems in recent electric and hybrid electric vehicles coming to market. Current cycle-life estimation techniques show evidence of discrepancy between laboratory results and real-world results. This work is aimed at characterizing actual battery usage in electrified transportation applications. Factors such as temperature, State Of Charge, Depth Of Discharge, current and voltage have to be carefully considered for accurate cycle-life prediction within a given application. Five electrified vehicles have been studied (two electric bicycles, one light EV, one mainstream HEV and one Heavy-Duty trolleybus). These vehicles have been equipped with sensors and data-logger and then test-driven on open roads under real-world conditions. Large amounts of data have been stored and later processed through an innovative method for analysis of actual usage. This method relies on data mining based on K-means clustering and synthetic duty cycle generation based on Markov chain modeling. Resulting synthetic cycles exhibit features similar to those observed on the large original datasets. This enables accurate prediction of cycle-life through realistic ageing trials of Lithium-ion batteries. Several experimental results are presented in order to assess the fitness of this method