Dans l’histoire de la musique occidentale, de nombreuses formes orchestrales se sont imposées de l’orchestre symphonique aux ensembles de musique de chambre. L’intérêt pour ces formes s’est rarement porté sur les ensembles d’instruments à vent particulièrement adaptés à la musique de plein air. Le choix de la longue durée historique laisse entendre tous les possibles d’une histoire politique, culturelle et sociale de 1700 à 1914. Les conditions dans lesquelles sont nés et se sont développés ces ensembles en France, permet d’en voir leur usage, leur répertoire, leur sociabilité et leur impact sur la vie musicale.L’histoire de l’ensemble à vent est complexe. Musique ostentatoire ou militaire il est pendant longtemps l’objet sonore du pouvoir. L’étude des conditions de la création musicale spécifique pour ces ensembles fait apparaître des usages musicaux particuliers et permet d’établir un premier recensement d’oeuvres originales et de compositeurs, tout en montrant les relations entre création musicale savante et usage utilitaire ou populaire.La pratique instrumentale évolue avec les innovations de la facture tout comme l’art de l’orchestration. Le statut des musiciens évolue du professionnalisme à l’amateurisme orphéonique. La modélisation de la musique d’harmonie ou de fanfare et son instrumentalisation par le pouvoir lui confère une fonction représentative officielle. Le modèle « français » se répand sur tous les continents. Parallèlement, d’un usage ostentatoire et privé, l’ensemble à vent devient musique du peuple et sa pratique se diffuse sur l’ensemble du territoire national imposant un mode original de transmission musicale en marge des enseignements officiels et en quête de reconnaissance.Entre unité et diversité cette étude invite à redécouvrir la part patrimoniale des ensembles d’instruments à vent dans les champs historiques, sociologiques et musicologiques, à l’heure où, pour de multiples raisons, ces ensembles sont menacés. / In the history of western music, a lot of different shapes of bands grew on, from the symphony orchestra to the chamber music ensembles. But among these bands, the wind bands hardly attracted interest to perform outdoor music. With the choice of a historically long time period we are able to imagine all that their political, cultural and social history could be, from 1700 to 1914. The conditions of birth and development of these ensembles allow us to observe what their uses, repertoire and sociability were and what was their impact on musical life.The history of the wind band is complicated. For a long time period, being either conspicuous music or military music, it was used by the political power as an object of expression. Studying the conditions of musical creation of these ensembles reveals specific musical uses. It enables to establish a first inventory of original works and composers, while linking scholar musical creation and practical or popular use.The instrumental practice changes according to the innovations in the making process, as the orchestration art. The musicians' status evolves from professionalism to band amateurism. The establishment of a model of concert band music or brass band music and its exploitation by the political power leads to an official representative function. France becomes all over the world a country to look up to in this matter. At the same time the wind ensemble, for a conspicuous and private use, happens to become the music of the people. Its practice spreads throughout France, setting a specific way to pass music on, detached from the official teachings and in search for recognition.Between unity and diversity, this study is an invitation to discover again the cultural heritage of the wind bands in the history, sociology and musicology fields, when today these bands are threatened.

En France, depuis les années 2000, des émeutes urbaines hebdomadaires, et quasi quotidiennes parfois, signent l'accentuation tant quantitative que qualitative de la ghettoïsation des secteurs marginalisés des villes. C'est notamment dans les grands ensembles de l'urbanisation massive des années 1950-1970 que le phénomène de ghetto moderne peut se définir. Malgré des qualités indéniables de confort et de taille des logements, par rapport aux normes d'avant-guerre, de nombreuses caractéristiques de production, de peuplement et de gestion ont engendré un habitat défectueux. Celui-ci a pu d'abord susciter l?ennui, le stress et la marginalisation par rapport à l'environnement. À partir des années 1970, des tensions sociales croissantes se sont développées avec la concentration spatiale des ménages les plus en difficultés socio-économiques. L'analyse du destin de territoires de grands ensembles à une échelle plus large que celle de secteurs internes les plus dégradés, comme celui de leurs communes d'appartenance (sept communes de grands ensembles étudiées), montre que les divers aspects de la ghettoïsation se mesurent sous des formes convergentes à ces secteurs malgré des attributs urbains plus élevés (activités, aménagements et équipements divers...). Les processus de dégradation matérielle, économique, sociale et symbolique que connaissent ces petites villes évoquent un déclin social urbain, notion à partir de laquelle est abordée la ségrégation sociale qui en est un phénomène causal multiforme. L?élargissement du périmètre d'appréhension de la dégradation sociale des espaces en permet une analyse avancée, entre son cadre idéologique et politique, ses ressorts psychosociologiques et la multiplicité de ses manifestations, au niveau institutionnel et des pratiques sociales. La ségrégation sociale et urbaine des catégories les moins qualifiées se traduit alors par leur marginalisation du système socio-économique, par leur relégation spatiale dans des zones peu valorisées et mal gérées, par leur inégal accès aux équipements d'intégration et de promotion sociale ainsi que par la stigmatisation de leur habitat et leur évitement par les catégories supérieures, notamment du privé, en recherche d'entre-soi pour se préserver du déclassement social. Ce qui contribue à étendre le champ des manifestations des inégalités sociales de l'espace, tant que le déclin social des espaces résidentiels les moins valorisés continuera à se produire en raison de la hausse des conduites ségrégatives en milieu urbain. / In France, since the 2000s, weekly urban riots, and almost daily sometimes, sign accentuation so quantitative as qualitative of the ghettoization of marginalized sectors of cities. It is in particular in the complexes of the massive urbanization of the years 1950-1970, the grands ensembles d'habitat, that the phenomenon of modern ghetto can define itself. In spite of undeniable qualities of comfort and size of housing, with regard to the pre-war standards, numerous characteristics of production, populating and management engendered a defective housing environment. This one was able at first to arouse the boredom, the stress and the marginalization compared with the environment. From 1970s, increasing social tensions developed with the spatial concentration of the households most in socioeconomic difficulties. The analysis of the fate of territories of complexes in a scale wider than that of the most degraded internal sectors, as that of their little town of membership (seven studied little towns of grands ensembles), shows that the diverse aspects of the ghettoization confront under convergent forms in these sectors in spite of higher urban attributes (activities, developments and diverse equipments). The processes of material, economic, social and symbolic degradation which know these towns evoke an urban social decline, a notion from which is approached the social segregation which is a multi-form causal phenomenon. The extension of the scale of apprehension of the social degradation of spaces allows an advanced analysis, between its ideological and political frame, its social motivations and the multiplicity of its appearances, at the institutional level and the social practices. The social and urban segregation of the least qualified categories is then translated by their marginalization of the socioeconomic system, by their spatial banishment in little valued and badly managed zones, by their uneven access to the equipments of integration and social advancement as well as by the stigmatization of their housing environment and their avoidance by the superior categories, in particular of the private, in search of one to protect itself from the loss of social position. What contributes to widen the field of the social inequalities of the space, as long as the social decline of the least valued residential spaces will continue to occur because of the increase of the segregationist conducts in the urban environment.

Evolutionary algorithms are one of the most successful methods for solving non-traditional optimization problems. As they employ only function values of the objective function, evolutionary algorithms converge much more slowly than optimization methods for smooth functions. This property of evolutionary algorithms is particularly disadvantageous in the context of costly and time-consuming empirical way of obtaining values of the objective function. However, evolutionary algorithms can be substantially speeded up by employing a sufficiently accurate regression model of the empirical objective function. This thesis provides a survey of utilizability of regression trees and their ensembles as a surrogate model to accelerate convergence of evolutionary optimization.

This thesis extends the methodology for extracting evaluations of players from samples of Go game records originally presented in (Baudiš - Moudřík, 2012). Firstly, this work adds more features and lays out a methodology for their comparison. Secondly, we develop a robust machine-learning framework, which is able to capture dependencies between the evaluations and general target variable using ensemble meta-learning with a genetic algorithm. We apply this framework to two domains, estimation of strength and styles. The results show that the inference of the target variables in both cases is viable and reasonably precise. Finally, we present a web application, which realizes the methodology, while presenting a prototype teaching aid for the Go players and gathering more data. Powered by TCPDF (www.tcpdf.org)

Ruído pode ser definido como um exemplo em um conjunto de dados que aparentemente é inconsistente com o restante dos dados existentes, pois não segue o mesmo padrão dos demais. Ruídos em conjuntos de dados podem reduzir o desempenho das técnicas de Aprendizado de Máquina (AM) empregadas e aumentar o tempo de construção da hipótese induzida, assim como sua complexidade. Dados são geralmente coletados por meio de medições realizadas em um domínio de interesse. Nesse sentido, nenhum conjunto de dados é perfeito. Erros de medições, dados incompletos, errados, corrompidos ou distorcidos, falhas humanas ou dos equipamentos utilizados, dentre muitos outros fatores, contribuem para a contaminação dos dados, e isso é particularmente verdadeiro para dados com elevada dimensionalidade. Sendo assim, a detecção de ruídos é uma tarefa crítica, principalmente em ambientes que exigem segurança e confiabilidade, uma vez que a presença desses pode indicar situações que degradam o desempenho do sistema ou a segurança e confiabilidade das informações. Algoritmos para a detecção e remoção de ruídos podem aumentar a confiabilidade de conjuntos de dados ruidosos. Nesse âmbito, esse trabalho investiga técnicas de detecção de ruído baseadas em distância, em que a remoção de ruídos é feita em uma etapa de pré-processamento, aplicadas a problemas de classificação de dados de Expressão Gênica, caracterizados pela presença de ruídos, elevada dimensionalidade e complexidade. O objetivo é melhorar o desempenho das técnicas de AM empregadas para solucioná-los. Por fim, combinações de técnicas de detecção de ruído são implementadas de modo a analisar a possibilidade de melhorar, ainda mais, o desempenho obtido. / Noise can be defined as an example which seems to be inconsistent with the remaining ones in a data set. The presence of noise in data sets can decrease the performance of Machine Learning (ML) techniques in the problem analysis and also increase the time taken to build the induced hypothesis and its complexity. Data are collected from measurements made which represent a given domain of interest. In this sense, no data set is perfect. Measurement errors, incomplete, corrupted, wrong or distorted examples, equipment problems or human fails, besides many other related factors, help contaminating the data, and this is particularly true for data sets with high dimensionality. For this reason, noise detection is a critical task, specially in domains which demand security and trustworthiness, since the presence of noise can lead to situations which degrade the system performance or the security and trustworthiness of the involved information. Algorithms to detect and remove noise may increase trustworthiness of noisy data sets. Based on that, this work evaluates distance-based noise detection techniques, in which noise removal is done by a pre-processing phase, in gene expression classification problems, characterized by the presence of noise, high dimensionality and complexity. The objective is to improve the performance of ML techniques used to solve these problems. Next, ensembles of noise detection techniques are developed in order to analyze the possibility to further improve the performance obtained.

Dada a grande quantidade de dados gerados atualmente, apenas uma pequena porção dos mesmos pode ser rotulada manualmente por especialistas humanos. Isso é um desafio comum para aplicações de aprendizagem de máquina. Aprendizado semi-supervisionado aborda este problema através da manipulação dos dados não rotulados juntamente aos dados rotulados. Entretanto, se apenas uma quantidade limitada de exemplos rotulados está disponível, o desempenho da tarefa de aprendizagem de máquina (e.g., classificação) pode ser não satisfatória. Diversas soluções abordam este problema através do uso de uma ensemble de classificadores, visto que essa abordagem aumenta a diversidade dos classificadores. Algoritmos como o co-training e o tri-training utilizam múltiplas partições de dados ou múltiplos algoritmos de aprendizado para melhorar a qualidade da classificação de instâncias não rotuladas através de concordância por maioria simples. Além disso, existem abordagens que estendem esta ideia e adotam processos de votação menos triviais para definir os rótulos, como eleição por maioria ponderada, por exemplo. Contudo, estas soluções requerem que os rótulos possuam um certo nível de confiança para serem utilizados no treinamento. Consequentemente, nem toda a informação disponível é utilizada. Por exemplo: informações associadas a níveis de confiança baixos são totalmente ignoradas. Este trabalho propõe uma abordagem chamada social-training, que utiliza toda a informação disponível na tarefa de aprendizado semi-supervisionado. Para isto, múltiplos classificadores heterogêneos são treinados com os dados rotulados e geram diversas classificações para as mesmas instâncias não rotuladas. O social-training, então, agrega estes resultados em um único rótulo por meio de funções de escolha social que trabalham com agregação de rankings sobre as instâncias. Especificamente, a solução trabalha com casos de classificação binária. Os resultados mostram que trabalhar com o ranking completo, ou seja, rotular todas as instâncias não rotuladas, é capaz de reduzir o erro de classificação para alguns conjuntos de dados da base da UCI utilizados. / Given the huge quantity of data currently being generated, just a small portion of it can be manually labeled by human experts. This is a challenge for machine learning applications. Semi-supervised learning addresses this problem by handling unlabeled data alongside labeled ones. However, if only a limited quantity of labeled examples is available, the performance of the machine learning task (e.g., classification) can be very unsatisfactory. Many solutions address this issue by using a classifier ensemble because this increases diversity. Algorithms such as co-training and tri-training use multiple views or multiple learning algorithms in order to improve the classification of unlabeled instances through simple majority agreement. Also, there are approaches that extend this idea and adopt less trivial voting processes to define the labels, like weighted majority voting. Nevertheless, these solutions require some confidence level on the label in order to use it for training. Hence, not all information is used, i.e., information associated with low confidence level is disregarded completely. An approach called social-training is proposed, which uses all information available in the semi-supervised learning task. For this, multiple heterogeneous classifiers are trained with the labeled data and generate diverse classifications for the same unlabeled instances. Social-training then aggregates these results into a single label by means of social choice functions that work with rank aggregation over the instances. The solution addresses binary classification cases. The results show that working with the full ranking, i.e., labeling all unlabeled instances, is able to reduce the classification error for some UCI data sets used.

Des réseaux aléatoires de nanoélectrodes ont été utilisés comme plateformes analytiques pour développer de nouveaux biocapteurs enzymatiques ou d’affinité. Dans ce travail de thèse, il s’est agi de préparer un biocapteur à glucose miniaturisé et des immunocapteurs électrochimiques et électrochimiluminescents (ECL) pour le diagnostic de la maladie de coeliaque. Dans un premier temps, un biocapteur enzymatique de seconde génération a été développé en exploitant les propriétés de réseaux aléatoires de nanoélectrodes. Ces réseaux ont été préparés par dépôt d’or au niveau de membranes "track-etched" de polycarbonate. Le capteur à glucose a été obtenu en immobilisant la glucose oxydase sur la surface de polycarbonate non-conductrice alors que les nanoélectrodes d’or sont exploitées comme transducteur. Le cation (ferrocènylmethyl)triméthylammonium a servi comme médiateur redox dans cette configuration expérimentale qui a conduit à une limite de détection de 36 μM pour le glucose.Dans un second temps, ce travail a porté sur l’élaboration d’outils de diagnostic pour la maladie de coeliaque. C’est une maladie auto-immune qui induit une concentration anormalement élevée de l’anticorps anti-transglutaminase (anti-tTg) dans le sang. Cette molécule anti-tTG est un biomarqueur adapté pour le diagnostic de cette pathologie. Les techniques de diagnostic actuelles souffrent d’une spécificité et d’une sensibilité insuffisantes. Pour améliorer ces aspects analytiques, deux types d’immunocapteurs ont été développés. Ils différent par la nature du signal, soit électrochimique soit ECL. La première étape commune est l’immobilisation, à la surface du polycarbonate entourant les nanoélectrodes, de la protéine tTG qui permet de capturer l’anticorps anti-tTg. Pour la détection électrochimique, un anticorps secondaire marqué par la peroxydase du raifort peut réagir avec un méditeur redox tel que l’hydroquinone et ainsi induire un signal électrochimique au niveau des nanoélectrodes. Pour le capteur ECL, la capture de l’anticorps cible anti-tTG permet de fixer ensuite un anticorps secondaire biotinylé qui se lie avec le luminophore, Ru(bpy)3+2, modifié par une streptavidine. L’imposition d’un potentiel suffisamment anodique au niveau des nanoélectrodes oxyde le co-réactif, la tri-n-propylamine, et génère ainsi des flux importants de radicaux qui diffusent et induisent l’émission ECL en réagissant avec le luminophore immobilisé. Cela conduit à une limite de détection de 0,5 ng.mL-1 qui est inférieure à celle obtenue par la voie électrochimique. Les 2 immunocapteurs ont été appliqués à l’analyse d’échantillons de sérum sanguins de patients et cela a permis de discriminer les échantillons des patients sains de ceux atteints de cette pathologie. / Nanoelectrode ensembles (NEES) are prepared, functionalized and tested to prepare enzymatic and affinity sensors suitable for advanced molecular diagnostics purposes, namely the development of a miniaturized glucose biosensor and the preparation of novel electrochemical and electrochemiluminescence immunosensors for celiac disease diagnostics.For the first goal, a second generation enzymatic microbiosensor was developed exploiting the properties of NEEs prepared by electroless gold deposition in track-etched polycarbonate (PC) membrane. The micro-NEE glucose biosensor (overall radius of 400 μm) was obtained by immobilizing glucose oxidase (GOx) on the nonconductive PC component of the NEE, while the Au nanoelectrodes were used exclusively as transducers. The (Ferrocenylmethyl)trimethylammonium cation (FA+) was used as the redox mediator. The proposed biosensor showed outstanding analytical performances with a detection limit of 36 μM for glucose.The second goal concerns celiac disease (CD) diagnostics. CD is an auto-immune disorder which reflects in abnormally high blood levels of the anti-tissue transglutaminase (anti-tTG) antibody, suitable as biomarker for CD diagnosis. Existing diagnostic techniques lack the desired level of sensitivity and specificity so that a confirmatory biopsy test is required. To overcome this limit, in this work electrochemical (EC) and electrogenerated chemiluminescence (ECL) immunosensors are proposed and studied. The two kinds of sensor employ the same biorecognition platform, based on tTG as biorecognition layer and NEEs as electrochemical transducers. EC and ECL sensors differ by the label used to develop the detection signal. By exploiting the high affinity of PC for proteins, the capture agent tTG is at first immobilized on the PC of the NEEs obtaining a tTG-NEEs which captures anti-tTG. For EC detection, the label is a secondary Ab labeled with horseradish peroxidase, using hydroquinone as redox mediator to generate the detection signal. For ECL, the sensor, after capturing anti-tTG, is reacted with a biotinylated secondary antibody to bind streptavidinatede Ru(bpy)3+2 luminophore. Application of an oxidizing potential in tripropylamine (TPrA) solution generates an intense ECL suitable for the sensitive ECL detection of anti-TG. Note that TPrA acts as redox mediator and ECL co-reactant. Both EC and ECL sensors are applied to human serum samples, showing to be suitable to discriminate between healthy and celiac patients. A comparison between the two approaches indicates that the lowest detection limit, namely 0.5 ng mL-1 of anti-TG, is achieved with the ECL immunosensor.

Neste trabalho estudamos alguns modelos com conservacao de particulas, que apresentam uma transicao de fase entre um estado estacionario ativo e infinitos estados absorventes. Os estados ativos de cada modelo sao compostos por configuracoes equiprovaveis, correspondendo, de acordo com a formulacao gibbsiana da Mecanica Estatistica, a um ensemble microcanonico. Efetuando uma mudanca de ensemble, podemos calcular as grandezas fisicas para cada um destes modelos utilizando a tecnica de matrizes de transferencia, explicada neste trabalho. Realizamos simulacoes destes modelos e confirmamos as hipoteses que sustentam o uso desta tecnica. Por fim, analisamos dois modelos derivados dos anteriores que nao podem ser estudados com base nesta tecnica. / In this work we studied some models with particle conservation which present a phase transition between an active stationary state and infinitely many absorbing states. The active states of each model consist of equiprobable configurations, corresponding, according to Gibbs's formulation of Statistical Mechanics, to a microcanonical ensemble. Carrying out an ensemble change, we can calculate the physical quantities for each one of these models using the transfer matrix technique, explained in this work. We performed simulations of these models and confirmed the hypothesis that sustain the use of this technique. Finally, we analysed two models derived from the previous ones for which this technique cannot be applied.

Classification is a data mining problem that arises in many real-world applications. A popular approach to tackle these classification problems is using an ensemble of classifiers that combines the collective knowledge of several classifiers. Most popular methods create a static ensemble, in which a single ensemble is constructed or chosen from a pool of classifiers and used for all new data instances. Two factors that have been frequently used to construct a static ensemble are the accuracy of and diversity among the individual classifiers. There have been many studies investigating how these factors should be combined and how much diversity is required to increase the ensemble's performance. These results have concluded that it is not trivial to build a static ensemble that generalizes well. Recently, a different approach has been undertaken: dynamic ensemble construction. Using a different set of classifiers for each new data instance rather than a single static ensemble of classifiers may increase performance since the dynamic ensemble is not required to generalize across the feature space. Most studies on dynamic ensembles focus on classifiers' competency in the local region in which a new data instance resides or agreement among the classifiers. In this thesis, we propose several other approaches for dynamic class prediction. Existing methods focus on assigned labels or their correctness. We hypothesize that using the class probability estimates returned by the classifiers can enhance our estimate of the competency of classifiers on the prediction. We focus on how to use class prediction probabilities (confidence) along with accuracy and diversity to create dynamic ensembles and analyze the contribution of confidence to the system. Our results show that confidence is a significant factor in the dynamic setting. However, it is still unclear how accurate, diverse, and confident ensemble can best be formed to increase the prediction capability of the system. Second, we propose a system for dynamic ensemble classification based on a new distance measure to evaluate the distance between data instances. We first map data instances into a space defined by the class probability estimates from a pool of two-class classifiers. We dynamically select classifiers (features) and the k-nearest neighbors of a new instance by minimizing the distance between the neighbors and the new instance in a two-step framework. Results of our experiments show that our measure is effective for finding similar instances and our framework helps making more accurate predictions. Classifiers' agreement in the region where a new data instance resides has been considered a major factor in dynamic ensembles. We postulate that the classifiers chosen for a dynamic ensemble should behave similarly in the region in which the new instance resides, but differently outside of this area. In other words, we hypothesize that high local accuracy, combined with high diversity in other regions, is desirable. To verify the validity of this hypothesis we propose two approaches. The first approach focuses on finding the k-nearest data instances to the new instance, which then defines a neighborhood, and maximizes simultaneously local accuracy and distant diversity, based on data instances outside of the neighborhood. The second method considers all data instances to be in the neighborhood, and assigns them weights depending on the distance to the new instance. We demonstrate through several experiments that weighted distant diversity and weighted local accuracy outperform all benchmark methods.

A great deal of theoretical activity has resulted from blending the fields of computer science and quantum mechanics. Out of this work has come the concept of a quantum computer, which promises to solve problems currently intractable for classical computers. This promise has, in turn, generated a large amount of effort directed toward investigating quantum computing experimentally. ¶ Quantum computing is difficult because fragile quantum superposition states of the computer’s register must be protected from the environment. This is made more difficult by the need to manipulate and measure these states. ¶ This thesis describes work that was carried out both to investigate and to demonstrate the utility of rare earth ion dopants for quantum computation. Dopants in solids are seen by many as a potential means of achieving scalable quantum computing. Rare earth ion dopants are an obvious choice for investigating such quantum computation. Long coherence times for both optical and nuclear spin transitions have been observed as well as optical manipulation of the spin states. The advantage that the scheme developed here has over nearly all of its competitors is that no complex nanofabrication is required. The advantages of avoiding nano-fabrication are two fold. Firstly, coherence times are likely to be adversely effected by the “damage” to the crystal structure that this manufacture represents. Secondly, the nano-fabrication presents a very serious difficulty in itself. ¶ Because of these advantages it was possible to perform two-qubit operations between independent qubits. This is the first time that such operations have been performed and presents a milestone in quantum computation using dopants in solids. It is only the second time two-qubit operations have been demonstrated in a solid. ¶ The experiments performed in this thesis were in two main areas: The first was the characterisation of hyperfine interactions in rare earth ion dopants; the second, simple demonstrations directly related to quantum computation. ¶ The first experiments that were carried out were to characterise the hyperfine interactions in Pr[superscript 3]+:Y[subscript 2]SiO[subscript 5]. The characterisation was the first carried out for the dopants in a site of such low symmetry. The resulting information about oscillator strengths and transition frequencies should prove indispensable when using such a system for quantum computation. It has already enabled an increase in the coherence times of nuclear spin transitions by two orders of magnitudes. ¶ The experiments directly related to the demonstration of quantum computation were all carried out using ensembles. The presence of a significant distribution of resonant frequencies, or inhomogeneous broadening, meant that many different sub-ensembles could be addressed, based on their resonant frequencies. Furthermore, the properties of the sub-ensembles could be engineered by optically pumping unwanted members to different hyperfine states away from resonance with the laser. ¶ A previously demonstrated technique for realising ensembles that could be used as single qubits was investigated and improved. Also, experiments were carried out to demonstrate the resulting ensembles’ utility as qubits. Further to this, ions from one of the ensembles were selected out, based on their interaction with the ions of another. Elementary two qubit operations were then demonstrated using these ensembles.