51 research outputs found

    Hyperscore : a new approach to interactive, computer-generated music

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    Thesis (S.M.)--Massachusetts Institute of Technology, School of Architecture and Planning, Program in Media Arts and Sciences, 2001.Includes bibliographical references (leaves 78-81).This thesis discusses the design and implementation of Hyperscore, a computer-assisted composition system intended for users of all musical backgrounds. Hyperscore presents a unique graphical interface which takes input in the form of freehand drawing. The strokes in the drawing are mapped to structural and gestural elements in the music, allowing the user to describe the large scale-structure of a piece visually. Hyperscore's graphical notation also enables the depiction of musical ideas on a detailed level. Additional annotations around a main curve indicate the placement and emphasis of selected motives. These motives are short melodic fragments that are either composed by the user or selected from a set of pre-composed material. Changing qualitative aspects of the annotations such as texture and shape let the user alter different musical parameters. The ultimate goal of Hyperscore is to provide an intuitive, interactive graphical environment for creating and editing compositions.by Mary Farbood.S.M

    Processing, Characterization And Performance Of Carbon Nanopaper Based Multifunctional Nanocomposites

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    Carbon nanofibers (CNFs) used as nano-scale reinforcement have been extensively studied since they are capable of improving the physical and mechanical properties of conventional fiber reinforced polymer composites. However, the properties of CNFs are far away from being fully utilized in the composites due to processing challenges including the dispersion of CNFs and the viscosity increase of polymer matrix. To overcome these issues, a unique approach was developed by making carbon nanopaper sheet through the filtration of well-dispersed carbon nanofibers under controlled processing conditions, and integrating carbon nanopaper sheets into composite laminates using autoclave process and resin transfer molding (RTM). This research aims to fundamentally study the processing-structure-property-performance relationship of carbon nanopaper-based nanocomposites multifunctional applications: a) Vibrational damping. Carbon nanofibers with extremely high aspect ratios and low density present an ideal candidate as vibrational damping material; specifically, the large specific area and aspect ratio of carbon nanofibers promote significant interfacial friction between carbon nanofiber and polymer matrix, causing higher energy dissipation in the matrix. Polymer composites with the reinforcement of carbon nanofibers in the form of a paper sheet have shown significant vibration damping improvement with a damping ratio increase of 300% in the nanocomposites. b) Wear resistance. In response to the iv observed increase in toughness of the nanocomposites, tribological properties of the nanocomposite coated with carbon nanofiber/ceramic particles hybrid paper have been studied. Due to high strength and toughness, carbon nanofibers can act as microcrack reducer; additionally, the composites coated with such hybrid nanopaper of carbon nanofiber and ceramic particles shown an improvement of reducing coefficient of friction (COF) and wear rate. c) High electrical conductivity. A highly conductive coating material was developed and applied on the surface of the composites for the electromagnetic interference shielding and lightning strike protection. To increase the conductivity of the carbon nanofiber paper, carbon nanofibers were modified with nickel nanostrands. d) Electrical actuation of SMP composites. Compared with other methods of SMP actuation, the use of electricity to induce the shape-memory effect of SMP is desirable due to the controllability and effectiveness. The electrical conductivity of carbon fiber reinforced SMP composites can be significantly improved by incorporating CNFs and CNF paper into them. A vision-based system was designed to control the deflection angle of SMP composites to desired values. The funding support from National Science Foundation and FAA Center of Excellence for Commercial Space Transportation (FAA COE CST) is acknowledged

    A Knowledge-based Approach for Creating Detailed Landscape Representations by Fusing GIS Data Collections with Associated Uncertainty

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    Geographic Information Systems (GIS) data for a region is of different types and collected from different sources, such as aerial digitized color imagery, elevation data consisting of terrain height at different points in that region, and feature data consisting of geometric information and properties about entities above/below the ground in that region. Merging GIS data and understanding the real world information present explicitly or implicitly in that data is a challenging task. This is often done manually by domain experts because of their superior capability to efficiently recognize patterns, combine, reason, and relate information. When a detailed digital representation of the region is to be created, domain experts are required to make best-guess decisions about each object. For example, a human would create representations of entities by collectively looking at the data layers, noting even elements that are not visible, like a covered overpass or underwater tunnel of a certain width and length. Such detailed representations are needed for use by processes like visualization or 3D modeling in applications used by military, simulation, earth sciences and gaming communities. Many of these applications are increasingly using digitally synthesized visuals and require detailed digital 3D representations to be generated quickly after acquiring the necessary initial data. Our main thesis, and a significant research contribution of this work, is that this task of creating detailed representations can be automated to a very large extent using a methodology which first fuses all Geographic Information System (GIS) data sources available into knowledge base (KB) assertions (instances) representing real world objects using a subprocess called GIS2KB. Then using reasoning, implicit information is inferred to define detailed 3D entity representations using a geometry definition engine called KB2Scene. Semantic Web is used as the semantic inferencing system and is extended with a data extraction framework. This framework enables the extraction of implicit property information using data and image analysis techniques. The data extraction framework supports extraction of spatial relationship values and attribution of uncertainties to inferred details. Uncertainty is recorded per property and used under Zadeh fuzzy semantics to compute a resulting uncertainty for inferred assertional axioms. This is achieved by another major contribution of our research, a unique extension of the KB ABox Realization service using KB explanation services. Previous semantics based research in this domain has concentrated more on improving represented details through the addition of artifacts like lights, signage, crosswalks, etc. Previous attempts regarding uncertainty in assertions use a modified reasoner expressivity and calculus. Our work differs in that separating formal knowledge from data processing allows fusion of different heterogeneous data sources which share the same context. Imprecision is modeled through uncertainty on assertions without defining a new expressivity as long as KB explanation services are available for the used expressivity. We also believe that in our use case, this simplifies uncertainty calculations. The uncertainties are then available for user-decision at output. We show that the process of creating 3D visuals from GIS data sources can be more automated, modular, verifiable, and the knowledge base instances available for other applications to use as part of a common knowledge base. We define our method’s components, discuss advantages and limitations, and show sample results for the transportation domain

    Carbon Nano Tubes (CNTS) for the development of high-performance and smart composites.

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    Los nanotubos de carbono han atraído una enorme atención en los últimos años debido a sus propiedades multifuncionales sobresalientes. Un número cada vez mayor de trabajos de investigación de primera línea centran su interés en la búsqueda de aplicaciones prácticas que den uso de las notables propiedades de los nanotubos de carbono, incluyendo una elevada resistencia mecánica, propiedades piezorestivas, alta conductividad eléctrica, ligereza, excelente estabilidad química y térmica. En concreto, los estudios más recientes plantean dos grandes ramas de aplicación: fabricación de estructuras aligeradas de alta resistencia, y desarrollo de estructuras inteligentes. Con respecto a la primera línea de aplicación, el desarrollo de materiales compuestos ligeros de alta resistencia conecta con la creciente tendencia de la ingeniería estructural a incorporar materiales compuestos innovadores. Ejemplos recientes como el avión comercial Boeing 787, en el que la mitad del peso fue diseñado con materiales compuestos, predicen un futuro auspicioso para los nanotubos de carbono en la ingeniería aeronáutica. Sin embargo, aún resulta más interesante el comportamiento piezorresistivo de los compuestos reforzados con nanotubos de carbono, ya que posibilita la creación de estructuras que no sólo presentan altas capacidades portantes y reducido peso específico, sino que también ofrecen capacidades de auto-detección de deformaciones. Cuando el material se ve sometido a una deformación externa, en virtud de dicha propiedad piezoresistiva, la conductividad eléctrica varía de modo que es posible correlacionar su respuesta eléctrica con el campo deformacional aplicado. Estas propiedades multifuncionales entroncan con el nuevo paradigma de la Vigilancia de la Salud Estructural el cual aboga por el uso de materiales/estructuras inteligentes para resolver el problema de escalabilidad. En este contexto, la estructura o parte de ella presenta capacidades de auto-detección de tal manera que el mantenimiento basado en la condición puede llevarse a cabo sin necesidad de incluir sensores externos. En ambas líneas, la mayoría de las investigaciones han centrado el estudio en la experimentación, siendo mucho menor el número de trabajos que plantean modelos teóricos capaces de simular las propiedades mecánicas, eléctricas y electromecánicas de estos compuestos. Desde un punto de vista mecánico, existen estudios experimentales que informan acerca de los efectos perjudiciales sobre la respuesta macroscópica de aspectos micromecánicos tales como la tendencia a formar aglomerados, así como la curvatura de los nanotubos de carbono. Es por ello esencial desarrollar modelos teóricos que incorporen estos efectos y asistan al diseño de elementos estructurales reforzados con nanotubos de carbono. Respecto al estudio de las propiedades de conductividad y piezoresistividad, es esencial desarrollar formulaciones teóricas capaces de abordar la optimización de las propiedades de autodetección de deformaciones. Asimismo, es crucial comprender los diferentes mecanismos físicos que rigen la conductividad eléctrica de estos compuestos, de modo que sea posible incorporar su efecto diferencial dentro de un marco teórico. Por último, también es fundamental avanzar hacia el dominio del tiempo con el fin de desarrollar aplicaciones de vigilancia de la salud estructural basada en vibraciones. Con todo ello, los esfuerzos de esta tesis se han centrado en el modelado de las propiedades mecánicas, conductivas y electromecánicas de los compuestos reforzados con nanotubos de carbono para el desarrollo de estructuras inteligentes y de alta resistencia. Estas dos aplicaciones, a saber, compuestos de alta resistencia e inteligentes, han sido enmarcadas en el ámbito de los materiales poliméricos y de cemento, respectivamente. La razón de esta distinción se debe a la presunción de que los compuestos poliméricos pueden encontrar aplicaciones directas como paneles de fuselaje para estructuras de aeronaves, así como refuerzos mecánicos sobre estructuras pre-existentes. En cuanto al uso de nanotubos de carbono como inclusiones multifuncionales para compuestos inteligentes, tanto los materiales poliméricos como los de base cemento ofrecen una amplia gama de aplicaciones potenciales. Sin embargo, la similitud entre los compuestos de base cemento y el hormigón estructural convencional sugiere la idea de desarrollar sensores embebidos que ofrezcan una monitorización continua integrada sin comprometer a priori la durabilidad de la estructura huésped. Tanto las propiedades mecánicas como las conductivas han sido estudiadas mediante métodos de homogeneización de campo medio. Aspectos micromecánicos tales como la relación de aspecto, el contenido, la distribución de la orientación, la ondulación o la aglomeración de los nanotubos se han estudiado en detalle e incorporado al análisis de diferentes elementos estructurales. De manera similar, se han estudiado las propiedades de conductividad eléctrica y auto-detección de deformaciones bajo cargas cuasi-estáticas mediante modelos mixtos de homogenización micromecánica de Mori-Tanaka. Los principales mecanismos que gobiernan las propiedades de transporte eléctrico de estos compuestos, a saber, los efectos de túnel cuántico y la formación de canales conductores, se han incorporado por separado en las simulaciones a través de la teoría de percolación de fibras conductoras. Los resultados teóricos han sido validados con éxito mediante experimentos en condiciones de laboratorio. Finalmente, se ha desarrollado un nuevo circuito equivalente piezorresistivo/piezoeléctrico para el modelado electromecánico de materiales de base cemento reforzado con nanotubos de carbono en el dominio del tiempo. Con los experimentos como base de validación, se ha demostrado que el enfoque propuesto proporciona resultados precisos y ofrece un marco teórico apto para aplicaciones de procesamiento de señales y monitorización de la salud estructural. Se espera que el trabajo desarrollado en esta tesis pueda proporcionar herramientas valiosas que permitan profundizar en la comprensión de los principales aspectos físicos que controlan las propiedades mecánicas, eléctricas y electromecánicas de los compuestos reforzados con nanotubos de carbono. Además, se espera que los resultados presentados en esta tesis impulsen el desarrollo de materiales compuestos auto-sensibles embebidos para aplicaciones de vigilancia de la salud estructural.Carbon nanotubes have drawn enormous attention in recent years due to their outstanding multifunctional properties. A constantly growing number of works at the front line of research pursue potential applications of their remarkable physical properties, including elevated load-bearing capacity, piezoresistive properties, high electrical conductivity, lightness, and excellent chemical and thermal stability. In particular, most recent works contemplate two different application branches: manufacture of light-weight high-strength structures, and development of smart structures. With regard to the first line of application, the development of high-strength lightweight composites connects with the growing tendency of structural engineering to incorporate advanced composite materials. Recent noticeable examples such as the commercial aircraft Boeing 787, in which half of the total weight was designed with composite materials, predict an auspicious future for carbon nanotubes in aircraft structures. Nonetheless, what is even more interesting is the piezoresistive behavior of carbon nanotube-reinforced composites, which allows us to create structures that are not only high-strength and lightweight but also strain-sensitive. When the composites are subjected to external strain fields, in virtue of such piezoresistive properties, the overall electrical conductivity varies in such a way that it is possible to correlate the electrical response with the deformational state of the material. These multifunctional properties are in line with the new paradigm of Structural Health Monitoring which advocates the use of smart materials/structures to solve the scalability issue. In this context, the structure or part of it presents self-sensing capabilities in such a way that the condition-based maintenance can be conducted without necessitating external off-the-shelf sensors. In both lines, most investigations have focused on experimentation. Conversely, the number of theoretical models capable of simulating the mechanical, electrical, and electromechanical properties of these composites is still scarce. From a mechanical point of view, experiments have reported about the detrimental effects of micromechanical aspects such as agglomeration of fillers and curviness on the macroscopic properties. Hence, it is essential to develop theoretical models that allow us to include these effects and assist the design of composite structural elements. With regard to the study of the conductivity and piezoresistivity of carbon nanotube-reinforced composites, it is essential to develop theoretical formulations capable of tackling the optimization of their strain sensitivity. In addition, it is crucial to understand the different physical mechanisms that govern the electrical conductivity of these composites and include them separately in the theoretical framework. Finally, it is also fundamental to move towards the time domain in order to develop applications for vibration-based structural health monitoring. Overall, all the efforts of this thesis have been put into the modeling of the mechanical, conductive and electromechanical properties of carbon nanotube-reinforced composites for the development of high-strength and smart structures. These two applications, namely high-strength and smart composites, have been framed in the realm of polymeric and cement-based materials, respectively. The reason for this distinction is the idea that polymer composites with high load-bearing capacity can find direct applications as fuselage panels for aircraft structures, as well as mechanical reinforcements attached to pre-existing structures. With regard to the use of carbon nanotubes as fillers for smart composites, both polymer and cement-based materials offer an enormous range of potential applications. Nonetheless, the similarity between cement-based composites and regular structural concrete suggests the idea of developing continuous embedded monitoring systems without compromising the durability of the hosting structure a priori. Both mechanical and conductive properties have been studied by means of mean-field homogenization methods. Micromechanical aspects such as filler aspect ratio, content, orientation distribution, waviness or agglomeration have been studied in detail and incorporated to the analysis of different structural elements. Similarly, the electrical conductivity and strain-sensing properties of these composites under quasi-static loadings have been studied by means of mixed Mori-Tanaka micromechanics models. The main mechanisms that underlie the electrical conduction of these composites, namely quantum tunneling effects and conductive networks, have been distinguished by a percolative-type behavior. The theoretical results have been successfully validated by means of experiments under laboratory conditions. Finally, a novel piezoresistive/piezoelectric equivalent lumped circuit has been developed for the electromechanical modeling of carbon nanotube-reinforced cement-based materials in the time domain. With experiments as validating basis, the proposed approach has been shown to provide accurate results and offers a theoretical framework readily applicable to signal processing applications and structural health monitoring. The work developed in this thesis is envisaged to provide valuable tools to further the understanding of the main physical aspects that control the mechanical, electrical and electromechanical properties of composites doped with carbon nanotubes. Furthermore, it is expected to boost the development of embedded self-sensing carbon nanotube-reinforced composites for structural health monitoring applications.Premio Extraordinario de Doctorado U

    Electrochemical Metal Nanowire Growth From Solution

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    The aim of this work is to make electrochemical metal nanowire growth a competitive method, being up to par with more standardized procedures, like e.g. lithography. This includes on the one hand the production of nanowires as reliable and reproducible parts, potentially suited for nanoelectronic circuit design. Therefore, this work presents a systematic investigation of the causes of nanowire branching, the necessary conditions to achieve straight growth and the parameters affecting the diameter of the wires. The growth of ultrathin (down to 15 nm), straight and unbranched platinum nanowires assembly is demonstrated. On the other hand, it is the objective to go beyond purely electronic applications. An examination of the crystallography of the wires reveals nanoclusters inside the wire with a common crystallographic orientation. The versatility of the wires is illustrated by implementing them into an impedimetric sensor capable of the detection of single nanoscaled objects, such as bacteria.Die Zielstellung der vorliegenden Arbeit ist es, die elektrochemische Herstellung von metallischen Nanodrähten zu einer wettbewerbsfähigen Methode zu machen, die sich mit standardisierten Prozessen, wie z. B. der Lithographie messen kann. Dies beinhält auf der einen Seite die Produktion der Nanodrähte als zuverlässige und reproduzierbare Bauteile, die im nanoelektrischen Schaltungsdesign Verwendung finden können. Daher befasst sich diese Arbeit mit einer systematischen Untersuchung der Ursachen für die Verzweigung von Nanodrähten, den notwendigen Bedingungen um gerades Wachstum zu erlangen und mit den Parametern, die Einfluss auf den Durchmesser der Drähte haben. Der Wuchs von sehr dünnen (bis zu 15 nm), geraden und unverzweigten Nanodrähten aus Platin wird gezeigt. Auf der anderen Seite ist es erklärtes Ziel, über rein elektronische Anwendungen hinaus zu gehen. Eine Untersuchung der Kristallographie der Nanodrähte zeigt, dass die Drähte aus Nanopartikeln bestehen, die eine gemeinsame kristallographische Orientierung aufweisen. Die Vielseitigkeit der Drähte wird anhand einer Sensoranwendung gezeigt, mit der es möglich ist, einzelne nanoskalige Objekte (wie z. B. Bakterien) zu detektieren

    LABORATORY DEVELOPMENT AND MOLECULAR-SCALE SIMULATION OF SENSOR-ENABLED GEOGRIDS

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    Structural health monitoring (SHM) and performance assessment are increasingly integrated to modern civil engineering projects in order to prevent and mitigate their catastrophic premature failures. Significant advancements in sensor and communication technologies during the last decades have boosted research on SHM and revolutionized its traditional and low-tech techniques. Inherent variability and uncertainties in soils arising from different sources (e.g. data insufficiency) pose significant challenges to the design of geosystems (e.g. geosynthetically-modified structures), but the increasing trend in using SHM and performance evaluation techniques could offer substantial help in counterbalancing the design uncertainties and to identify the impending failure of high-risk geosystems. Strain gauges, optical fibers and extensometers are current technologies to measure strains in geosynthetics where the sensing is achieved by attaching these devices to a geosynthetic layer in desirable positions. However, these devices require complex and expensive data acquisition systems to collect information. Also, strain gauges attached to a reinforcement material need to be calibrated against global strains from crosshead displacements in in-isolation tensile tests. However, the resulting calibration factors are typically not accurate when the reinforcement layer is embedded in soil due to the local stiffening effect of the bonding assembly, difference in the in-soil mechanical properties and other complications such as soil arching. During the last few years, a novel technique has been under development at the University of Oklahoma based on the strain sensitivity of polymer nanocomposites to measure the tensile strain in modified geosynthetics without the need for conventional instrumentation. In this technique, electrically-conductive fillers are used to induce conductivity in geosynthetics in order to produce sensor-enabled geosynthetics (SEG). The electrical conductivity of a SEG product with a prescribed concentration of a conductive filler is highly sensitive to the applied strain, affording the product the self-sensing function. As part of this long-term study to develop SEG materials, an interdisciplinary study was carried out as described in this dissertation to develop sensor-enabled geogrids (SEGG) through laboratory experiments and molecular-scale simulations. The study yielded several formulations and production processes in the laboratory to fabricate nanocomposites that would exhibit adequate mechanical and strain-sensitive electrical properties for SEGG applications. Molecular dynamics and Monte Carlo simulations were used to gain insight into the laboratory results on a more fundamental level. The molecular dynamics simulations were carried out to study the mechanical properties of the composites whereas Monte Carlo simulations were used to examine their electrical conductivity (i.e. percolation) behavior. Results showed that, contingent upon further development and addressing practical issues such as durability and protective measures for field installation, the SEGG technology holds promise to offer a practical and cost-effective alternative to the existing technologies for performance-monitoring of a wide range of geotechnical structures during and after construction

    Doctor of Philosophy

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    dissertationDigital image processing has wide ranging applications in combustion research. The analysis of digital images is used in practically every scale of studying combustion phenomena from the scale of individual atoms to diagnosing and controlling large-scale combustors. Digital image processing is one of the fastest-growing scientific areas in the world today. From being able to reconstruct low-resolution grayscale images from transmitted signals, the capabilities have grown to enabling machines carrying out tasks that would normally require human vision, perception, and reasoning. Certain applications in combustion science benefit greatly from recent advances in image processing. Unfortunately, since the two fields - combustion and image processing research - stand relatively far from each other, the most recent results are often not known well enough in the areas where they may be applied with great benefits. This work aims to improve the accuracy and reliability of certain measurements in combustion science by selecting, adapting, and implementing the appropriate techniques originally developed in the image processing area. A number of specific applications were chosen that cover a wide range of physical scales of combustion phenomena, and specific image processing methodologies were proposed to improve or enable measurements in studying such phenomena. The selected applications include the description and quantification of combustion-derived carbon nanostructure, the three-dimensional optical diagnostics of combusting pulverized-coal particles and the optical flow velocimetry and quantitative radiation imaging of a pilot-scale oxy-coal flame. In the field of the structural analysis of soot, new structural parameters were derived and the extraction and fidelity of existing ones were improved. In the field of pulverized-coal combustion, the developed methodologies allow for studying the detailed mechanisms of particle combustion in three dimensions. At larger scales, the simultaneous measurement of flame velocity, spectral radiation, and pyrometric properties were realized

    Tactile Perception And Visuotactile Integration For Robotic Exploration

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    As the close perceptual sibling of vision, the sense of touch has historically received less than deserved attention in both human psychology and robotics. In robotics, this may be attributed to at least two reasons. First, it suffers from the vicious cycle of immature sensor technology, which causes industry demand to be low, and then there is even less incentive to make existing sensors in research labs easy to manufacture and marketable. Second, the situation stems from a fear of making contact with the environment, avoided in every way so that visually perceived states do not change before a carefully estimated and ballistically executed physical interaction. Fortunately, the latter viewpoint is starting to change. Work in interactive perception and contact-rich manipulation are on the rise. Good reasons are steering the manipulation and locomotion communities’ attention towards deliberate physical interaction with the environment prior to, during, and after a task. We approach the problem of perception prior to manipulation, using the sense of touch, for the purpose of understanding the surroundings of an autonomous robot. The overwhelming majority of work in perception for manipulation is based on vision. While vision is a fast and global modality, it is insufficient as the sole modality, especially in environments where the ambient light or the objects therein do not lend themselves to vision, such as in darkness, smoky or dusty rooms in search and rescue, underwater, transparent and reflective objects, and retrieving items inside a bag. Even in normal lighting conditions, during a manipulation task, the target object and fingers are usually occluded from view by the gripper. Moreover, vision-based grasp planners, typically trained in simulation, often make errors that cannot be foreseen until contact. As a step towards addressing these problems, we present first a global shape-based feature descriptor for object recognition using non-prehensile tactile probing alone. Then, we investigate in making the tactile modality, local and slow by nature, more efficient for the task by predicting the most cost-effective moves using active exploration. To combine the local and physical advantages of touch and the fast and global advantages of vision, we propose and evaluate a learning-based method for visuotactile integration for grasping

    A robust audio-based symbol recognition system using machine learning techniques

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    Masters of ScienceThis research investigates the creation of an audio-shape recognition system that is able to interpret a user’s drawn audio shapes—fundamental shapes, digits and/or letters— on a given surface such as a table-top using a generic stylus such as the back of a pen. The system aims to make use of one, two or three Piezo microphones, as required, to capture the sound of the audio gestures, and a combination of the Mel-Frequency Cepstral Coefficients (MFCC) feature descriptor and Support Vector Machines (SVMs) to recognise audio shapes. The novelty of the system is in the use of piezo microphones which are low cost, light-weight and portable, and the main investigation is around determining whether these microphones are able to provide sufficiently rich information to recognise the audio shapes mentioned in such a framework
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