A method for Kinematic Calibration of a Parallel Robot by using one camera in hand and a spherical object

The main purpose of robot calibration is the correction of the possible errors in the robot parameters. This paper presents a method for a kinematic calibration of a parallel robot that is equipped with one camera in hand. In order to preserve the mechanical configuration of the robot, the camera is utilized to acquire incremental positions of the end effector from a spherical object that is fixed in the word reference frame. The positions of the end effector are related to incremental positions of resolvers of the motors of the robot, and a kinematic model of the robot is used to find a new group of parameters which minimizes errors in the kinematic equations. Additionally, properties of the spherical object and intrinsic camera parameters are utilized to model the projection of the object in the image and improving spatial measurements. Finally, the robotic system is designed to carry out tracking tasks and the calibration of the robot is validated by means of integrating the errors of the visual controller.

An Efficient Multiple Object Detection and Tracking Framework for Automatic Counting and Video Surveillance Applications

Automatic visual object counting and video surveillance have important applications for home and business environments, such as security and management of access points. However, in order to obtain a satisfactory performance these technologies need professional and expensive hardware, complex installations and setups, and the supervision of qualified workers. In this paper, an efficient visual detection and tracking framework is proposed for the tasks of object counting and surveillance, which meets the requirements of the consumer electronics: off-the-shelf equipment, easy installation and configuration, and unsupervised working conditions. This is accomplished by a novel Bayesian tracking model that can manage multimodal distributions without explicitly computing the association between tracked objects and detections. In addition, it is robust to erroneous, distorted and missing detections. The proposed algorithm is compared with a recent work, also focused on consumer electronics, proving its superior performance.

Adaptive Multi-Camera System for Real Time Object Detection

In this paper we present an adaptive multi-camera system for real time object detection able to efficiently adjust the computational requirements of video processing blocks to the available processing power and the activity of the scene. The system is based on a two level adaptation strategy that works at local and at global level. Object detection is based on a Gaussian mixtures model background subtraction algorithm. Results show that the system can efficiently adapt the algorithm parameters without a significant loss in the detection accuracy.

Automatic Bandwidth Estimation Strategy for High-Quality Non-Parametric Modeling based Moving Object Detection

Here, a novel and efficient moving object detection strategy by non-parametric modeling is presented. Whereas the foreground is modeled by combining color and spatial information, the background model is constructed exclusively with color information, thus resulting in a great reduction of the computational and memory requirements. The estimation of the background and foreground covariance matrices, allows us to obtain compact moving regions while the number of false detections is reduced. Additionally, the application of a tracking strategy provides a priori knowledge about the spatial position of the moving objects, which improves the performance of the Bayesian classifier

OntoTag - A Linguistic and Ontological Annotation Model Suitable for the Semantic Web

OntoTag - A Linguistic and Ontological Annotation Model Suitable for the Semantic Web 1. INTRODUCTION. LINGUISTIC TOOLS AND ANNOTATIONS: THEIR LIGHTS AND SHADOWS Computational Linguistics is already a consolidated research area. It builds upon the results of other two major ones, namely Linguistics and Computer Science and Engineering, and it aims at developing computational models of human language (or natural language, as it is termed in this area). Possibly, its most well-known applications are the different tools developed so far for processing human language, such as machine translation systems and speech recognizers or dictation programs. These tools for processing human language are commonly referred to as linguistic tools. Apart from the examples mentioned above, there are also other types of linguistic tools that perhaps are not so well-known, but on which most of the other applications of Computational Linguistics are built. These other types of linguistic tools comprise POS taggers, natural language parsers and semantic taggers, amongst others. All of them can be termed linguistic annotation tools. Linguistic annotation tools are important assets. In fact, POS and semantic taggers (and, to a lesser extent, also natural language parsers) have become critical resources for the computer applications that process natural language. Hence, any computer application that has to analyse a text automatically and ‘intelligently’ will include at least a module for POS tagging. The more an application needs to ‘understand’ the meaning of the text it processes, the more linguistic tools and/or modules it will incorporate and integrate. However, linguistic annotation tools have still some limitations, which can be summarised as follows: 1. Normally, they perform annotations only at a certain linguistic level (that is, Morphology, Syntax, Semantics, etc.). 2. They usually introduce a certain rate of errors and ambiguities when tagging. This error rate ranges from 10 percent up to 50 percent of the units annotated for unrestricted, general texts. 3. Their annotations are most frequently formulated in terms of an annotation schema designed and implemented ad hoc. A priori, it seems that the interoperation and the integration of several linguistic tools into an appropriate software architecture could most likely solve the limitations stated in (1). Besides, integrating several linguistic annotation tools and making them interoperate could also minimise the limitation stated in (2). Nevertheless, in the latter case, all these tools should produce annotations for a common level, which would have to be combined in order to correct their corresponding errors and inaccuracies. Yet, the limitation stated in (3) prevents both types of integration and interoperation from being easily achieved. In addition, most high-level annotation tools rely on other lower-level annotation tools and their outputs to generate their own ones. For example, sense-tagging tools (operating at the semantic level) often use POS taggers (operating at a lower level, i.e., the morphosyntactic) to identify the grammatical category of the word or lexical unit they are annotating. Accordingly, if a faulty or inaccurate low-level annotation tool is to be used by other higher-level one in its process, the errors and inaccuracies of the former should be minimised in advance. Otherwise, these errors and inaccuracies would be transferred to (and even magnified in) the annotations of the high-level annotation tool. Therefore, it would be quite useful to find a way to (i) correct or, at least, reduce the errors and the inaccuracies of lower-level linguistic tools; (ii) unify the annotation schemas of different linguistic annotation tools or, more generally speaking, make these tools (as well as their annotations) interoperate. Clearly, solving (i) and (ii) should ease the automatic annotation of web pages by means of linguistic tools, and their transformation into Semantic Web pages (Berners-Lee, Hendler and Lassila, 2001). Yet, as stated above, (ii) is a type of interoperability problem. There again, ontologies (Gruber, 1993; Borst, 1997) have been successfully applied thus far to solve several interoperability problems. Hence, ontologies should help solve also the problems and limitations of linguistic annotation tools aforementioned. Thus, to summarise, the main aim of the present work was to combine somehow these separated approaches, mechanisms and tools for annotation from Linguistics and Ontological Engineering (and the Semantic Web) in a sort of hybrid (linguistic and ontological) annotation model, suitable for both areas. This hybrid (semantic) annotation model should (a) benefit from the advances, models, techniques, mechanisms and tools of these two areas; (b) minimise (and even solve, when possible) some of the problems found in each of them; and (c) be suitable for the Semantic Web. The concrete goals that helped attain this aim are presented in the following section. 2. GOALS OF THE PRESENT WORK As mentioned above, the main goal of this work was to specify a hybrid (that is, linguistically-motivated and ontology-based) model of annotation suitable for the Semantic Web (i.e. it had to produce a semantic annotation of web page contents). This entailed that the tags included in the annotations of the model had to (1) represent linguistic concepts (or linguistic categories, as they are termed in ISO/DCR (2008)), in order for this model to be linguistically-motivated; (2) be ontological terms (i.e., use an ontological vocabulary), in order for the model to be ontology-based; and (3) be structured (linked) as a collection of ontology-based Object> triples, as in the usual Semantic Web languages (namely RDF(S) and OWL), in order for the model to be considered suitable for the Semantic Web. Besides, to be useful for the Semantic Web, this model should provide a way to automate the annotation of web pages. As for the present work, this requirement involved reusing the linguistic annotation tools purchased by the OEG research group (http://www.oeg-upm.net), but solving beforehand (or, at least, minimising) some of their limitations. Therefore, this model had to minimise these limitations by means of the integration of several linguistic annotation tools into a common architecture. Since this integration required the interoperation of tools and their annotations, ontologies were proposed as the main technological component to make them effectively interoperate. From the very beginning, it seemed that the formalisation of the elements and the knowledge underlying linguistic annotations within an appropriate set of ontologies would be a great step forward towards the formulation of such a model (henceforth referred to as OntoTag). Obviously, first, to combine the results of the linguistic annotation tools that operated at the same level, their annotation schemas had to be unified (or, preferably, standardised) in advance. This entailed the unification (id. standardisation) of their tags (both their representation and their meaning), and their format or syntax. Second, to merge the results of the linguistic annotation tools operating at different levels, their respective annotation schemas had to be (a) made interoperable and (b) integrated. And third, in order for the resulting annotations to suit the Semantic Web, they had to be specified by means of an ontology-based vocabulary, and structured by means of ontology-based Object> triples, as hinted above. Therefore, a new annotation scheme had to be devised, based both on ontologies and on this type of triples, which allowed for the combination and the integration of the annotations of any set of linguistic annotation tools. This annotation scheme was considered a fundamental part of the model proposed here, and its development was, accordingly, another major objective of the present work. All these goals, aims and objectives could be re-stated more clearly as follows: Goal 1: Development of a set of ontologies for the formalisation of the linguistic knowledge relating linguistic annotation. Sub-goal 1.1: Ontological formalisation of the EAGLES (1996a; 1996b) de facto standards for morphosyntactic and syntactic annotation, in a way that helps respect the triple structure recommended for annotations in these works (which is isomorphic to the Object> triple structures used in the context of the Semantic Web). Sub-goal 1.2: Incorporation into this preliminary ontological formalisation of other existing standards and standard proposals relating the levels mentioned above, such as those currently under development within ISO/TC 37 (the ISO Technical Committee dealing with Terminology, which deals also with linguistic resources and annotations). Sub-goal 1.3: Generalisation and extension of the recommendations in EAGLES (1996a; 1996b) and ISO/TC 37 to the semantic level, for which no ISO/TC 37 standards have been developed yet. Sub-goal 1.4: Ontological formalisation of the generalisations and/or extensions obtained in the previous sub-goal as generalisations and/or extensions of the corresponding ontology (or ontologies). Sub-goal 1.5: Ontological formalisation of the knowledge required to link, combine and unite the knowledge represented in the previously developed ontology (or ontologies). Goal 2: Development of OntoTag’s annotation scheme, a standard-based abstract scheme for the hybrid (linguistically-motivated and ontological-based) annotation of texts. Sub-goal 2.1: Development of the standard-based morphosyntactic annotation level of OntoTag’s scheme. This level should include, and possibly extend, the recommendations of EAGLES (1996a) and also the recommendations included in the ISO/MAF (2008) standard draft. Sub-goal 2.2: Development of the standard-based syntactic annotation level of the hybrid abstract scheme. This level should include, and possibly extend, the recommendations of EAGLES (1996b) and the ISO/SynAF (2010) standard draft. Sub-goal 2.3: Development of the standard-based semantic annotation level of OntoTag’s (abstract) scheme. Sub-goal 2.4: Development of the mechanisms for a convenient integration of the three annotation levels already mentioned. These mechanisms should take into account the recommendations included in the ISO/LAF (2009) standard draft. Goal 3: Design of OntoTag’s (abstract) annotation architecture, an abstract architecture for the hybrid (semantic) annotation of texts (i) that facilitates the integration and interoperation of different linguistic annotation tools, and (ii) whose results comply with OntoTag’s annotation scheme. Sub-goal 3.1: Specification of the decanting processes that allow for the classification and separation, according to their corresponding levels, of the results of the linguistic tools annotating at several different levels. Sub-goal 3.2: Specification of the standardisation processes that allow (a) complying with the standardisation requirements of OntoTag’s annotation scheme, as well as (b) combining the results of those linguistic tools that share some level of annotation. Sub-goal 3.3: Specification of the merging processes that allow for the combination of the output annotations and the interoperation of those linguistic tools that share some level of annotation. Sub-goal 3.4: Specification of the merge processes that allow for the integration of the results and the interoperation of those tools performing their annotations at different levels. Goal 4: Generation of OntoTagger’s schema, a concrete instance of OntoTag’s abstract scheme for a concrete set of linguistic annotations. These linguistic annotations result from the tools and the resources available in the research group, namely • Bitext’s DataLexica (http://www.bitext.com/EN/datalexica.asp), • LACELL’s (POS) tagger (http://www.um.es/grupos/grupo-lacell/quees.php), • Connexor’s FDG (http://www.connexor.eu/technology/machinese/glossary/fdg/), and • EuroWordNet (Vossen et al., 1998). This schema should help evaluate OntoTag’s underlying hypotheses, stated below. Consequently, it should implement, at least, those levels of the abstract scheme dealing with the annotations of the set of tools considered in this implementation. This includes the morphosyntactic, the syntactic and the semantic levels. Goal 5: Implementation of OntoTagger’s configuration, a concrete instance of OntoTag’s abstract architecture for this set of linguistic tools and annotations. This configuration (1) had to use the schema generated in the previous goal; and (2) should help support or refute the hypotheses of this work as well (see the next section). Sub-goal 5.1: Implementation of the decanting processes that facilitate the classification and separation of the results of those linguistic resources that provide annotations at several different levels (on the one hand, LACELL’s tagger operates at the morphosyntactic level and, minimally, also at the semantic level; on the other hand, FDG operates at the morphosyntactic and the syntactic levels and, minimally, at the semantic level as well). Sub-goal 5.2: Implementation of the standardisation processes that allow (i) specifying the results of those linguistic tools that share some level of annotation according to the requirements of OntoTagger’s schema, as well as (ii) combining these shared level results. In particular, all the tools selected perform morphosyntactic annotations and they had to be conveniently combined by means of these processes. Sub-goal 5.3: Implementation of the merging processes that allow for the combination (and possibly the improvement) of the annotations and the interoperation of the tools that share some level of annotation (in particular, those relating the morphosyntactic level, as in the previous sub-goal). Sub-goal 5.4: Implementation of the merging processes that allow for the integration of the different standardised and combined annotations aforementioned, relating all the levels considered. Sub-goal 5.5: Improvement of the semantic level of this configuration by adding a named entity recognition, (sub-)classification and annotation subsystem, which also uses the named entities annotated to populate a domain ontology, in order to provide a concrete application of the present work in the two areas involved (the Semantic Web and Corpus Linguistics). 3. MAIN RESULTS: ASSESSMENT OF ONTOTAG’S UNDERLYING HYPOTHESES The model developed in the present thesis tries to shed some light on (i) whether linguistic annotation tools can effectively interoperate; (ii) whether their results can be combined and integrated; and, if they can, (iii) how they can, respectively, interoperate and be combined and integrated. Accordingly, several hypotheses had to be supported (or rejected) by the development of the OntoTag model and OntoTagger (its implementation). The hypotheses underlying OntoTag are surveyed below. Only one of the hypotheses (H.6) was rejected; the other five could be confirmed. H.1 The annotations of different levels (or layers) can be integrated into a sort of overall, comprehensive, multilayer and multilevel annotation, so that their elements can complement and refer to each other. • CONFIRMED by the development of: o OntoTag’s annotation scheme, o OntoTag’s annotation architecture, o OntoTagger’s (XML, RDF, OWL) annotation schemas, o OntoTagger’s configuration. H.2 Tool-dependent annotations can be mapped onto a sort of tool-independent annotations and, thus, can be standardised. • CONFIRMED by means of the standardisation phase incorporated into OntoTag and OntoTagger for the annotations yielded by the tools. H.3 Standardisation should ease: H.3.1: The interoperation of linguistic tools. H.3.2: The comparison, combination (at the same level and layer) and integration (at different levels or layers) of annotations. • H.3 was CONFIRMED by means of the development of OntoTagger’s ontology-based configuration: o Interoperation, comparison, combination and integration of the annotations of three different linguistic tools (Connexor’s FDG, Bitext’s DataLexica and LACELL’s tagger); o Integration of EuroWordNet-based, domain-ontology-based and named entity annotations at the semantic level. o Integration of morphosyntactic, syntactic and semantic annotations. H.4 Ontologies and Semantic Web technologies (can) play a crucial role in the standardisation of linguistic annotations, by providing consensual vocabularies and standardised formats for annotation (e.g., RDF triples). • CONFIRMED by means of the development of OntoTagger’s RDF-triple-based annotation schemas. H.5 The rate of errors introduced by a linguistic tool at a given level, when annotating, can be reduced automatically by contrasting and combining its results with the ones coming from other tools, operating at the same level. However, these other tools might be built following a different technological (stochastic vs. rule-based, for example) or theoretical (dependency vs. HPS-grammar-based, for instance) approach. • CONFIRMED by the results yielded by the evaluation of OntoTagger. H.6 Each linguistic level can be managed and annotated independently. • REJECTED: OntoTagger’s experiments and the dependencies observed among the morphosyntactic annotations, and between them and the syntactic annotations. In fact, Hypothesis H.6 was already rejected when OntoTag’s ontologies were developed. We observed then that several linguistic units stand on an interface between levels, belonging thereby to both of them (such as morphosyntactic units, which belong to both the morphological level and the syntactic level). Therefore, the annotations of these levels overlap and cannot be handled independently when merged into a unique multileveled annotation. 4. OTHER MAIN RESULTS AND CONTRIBUTIONS First, interoperability is a hot topic for both the linguistic annotation community and the whole Computer Science field. The specification (and implementation) of OntoTag’s architecture for the combination and integration of linguistic (annotation) tools and annotations by means of ontologies shows a way to make these different linguistic annotation tools and annotations interoperate in practice. Second, as mentioned above, the elements involved in linguistic annotation were formalised in a set (or network) of ontologies (OntoTag’s linguistic ontologies). • On the one hand, OntoTag’s network of ontologies consists of − The Linguistic Unit Ontology (LUO), which includes a mostly hierarchical formalisation of the different types of linguistic elements (i.e., units) identifiable in a written text; − The Linguistic Attribute Ontology (LAO), which includes also a mostly hierarchical formalisation of the different types of features that characterise the linguistic units included in the LUO; − The Linguistic Value Ontology (LVO), which includes the corresponding formalisation of the different values that the attributes in the LAO can take; − The OIO (OntoTag’s Integration Ontology), which  Includes the knowledge required to link, combine and unite the knowledge represented in the LUO, the LAO and the LVO;  Can be viewed as a knowledge representation ontology that describes the most elementary vocabulary used in the area of annotation. • On the other hand, OntoTag’s ontologies incorporate the knowledge included in the different standards and recommendations for linguistic annotation released so far, such as those developed within the EAGLES and the SIMPLE European projects or by the ISO/TC 37 committee: − As far as morphosyntactic annotations are concerned, OntoTag’s ontologies formalise the terms in the EAGLES (1996a) recommendations and their corresponding terms within the ISO Morphosyntactic Annotation Framework (ISO/MAF, 2008) standard; − As for syntactic annotations, OntoTag’s ontologies incorporate the terms in the EAGLES (1996b) recommendations and their corresponding terms within the ISO Syntactic Annotation Framework (ISO/SynAF, 2010) standard draft; − Regarding semantic annotations, OntoTag’s ontologies generalise and extend the recommendations in EAGLES (1996a; 1996b) and, since no stable standards or standard drafts have been released for semantic annotation by ISO/TC 37 yet, they incorporate the terms in SIMPLE (2000) instead; − The terms coming from all these recommendations and standards were supplemented by those within the ISO Data Category Registry (ISO/DCR, 2008) and also of the ISO Linguistic Annotation Framework (ISO/LAF, 2009) standard draft when developing OntoTag’s ontologies. Third, we showed that the combination of the results of tools annotating at the same level can yield better results (both in precision and in recall) than each tool separately. In particular, 1. OntoTagger clearly outperformed two of the tools integrated into its configuration, namely DataLexica and FDG in all the combination sub-phases in which they overlapped (i.e. POS tagging, lemma annotation and morphological feature annotation). As far as the remaining tool is concerned, i.e. LACELL’s tagger, it was also outperformed by OntoTagger in POS tagging and lemma annotation, and it did not behave better than OntoTagger in the morphological feature annotation layer. 2. As an immediate result, this implies that a) This type of combination architecture configurations can be applied in order to improve significantly the accuracy of linguistic annotations; and b) Concerning the morphosyntactic level, this could be regarded as a way of constructing more robust and more accurate POS tagging systems. Fourth, Semantic Web annotations are usually performed by humans or else by machine learning systems. Both of them leave much to be desired: the former, with respect to their annotation rate; the latter, with respect to their (average) precision and recall. In this work, we showed how linguistic tools can be wrapped in order to annotate automatically Semantic Web pages using ontologies. This entails their fast, robust and accurate semantic annotation. As a way of example, as mentioned in Sub-goal 5.5, we developed a particular OntoTagger module for the recognition, classification and labelling of named entities, according to the MUC and ACE tagsets (Chinchor, 1997; Doddington et al., 2004). These tagsets were further specified by means of a domain ontology, namely the Cinema Named Entities Ontology (CNEO). This module was applied to the automatic annotation of ten different web pages containing cinema reviews (that is, around 5000 words). In addition, the named entities annotated with this module were also labelled as instances (or individuals) of the classes included in the CNEO and, then, were used to populate this domain ontology. • The statistical results obtained from the evaluation of this particular module of OntoTagger can be summarised as follows. On the one hand, as far as recall (R) is concerned, (R.1) the lowest value was 76,40% (for file 7); (R.2) the highest value was 97, 50% (for file 3); and (R.3) the average value was 88,73%. On the other hand, as far as the precision rate (P) is concerned, (P.1) its minimum was 93,75% (for file 4); (R.2) its maximum was 100% (for files 1, 5, 7, 8, 9, and 10); and (R.3) its average value was 98,99%. • These results, which apply to the tasks of named entity annotation and ontology population, are extraordinary good for both of them. They can be explained on the basis of the high accuracy of the annotations provided by OntoTagger at the lower levels (mainly at the morphosyntactic level). However, they should be conveniently qualified, since they might be too domain- and/or language-dependent. It should be further experimented how our approach works in a different domain or a different language, such as French, English, or German. • In any case, the results of this application of Human Language Technologies to Ontology Population (and, accordingly, to Ontological Engineering) seem very promising and encouraging in order for these two areas to collaborate and complement each other in the area of semantic annotation. Fifth, as shown in the State of the Art of this work, there are different approaches and models for the semantic annotation of texts, but all of them focus on a particular view of the semantic level. Clearly, all these approaches and models should be integrated in order to bear a coherent and joint semantic annotation level. OntoTag shows how (i) these semantic annotation layers could be integrated together; and (ii) they could be integrated with the annotations associated to other annotation levels. Sixth, we identified some recommendations, best practices and lessons learned for annotation standardisation, interoperation and merge. They show how standardisation (via ontologies, in this case) enables the combination, integration and interoperation of different linguistic tools and their annotations into a multilayered (or multileveled) linguistic annotation, which is one of the hot topics in the area of Linguistic Annotation. And last but not least, OntoTag’s annotation scheme and OntoTagger’s annotation schemas show a way to formalise and annotate coherently and uniformly the different units and features associated to the different levels and layers of linguistic annotation. This is a great scientific step ahead towards the global standardisation of this area, which is the aim of ISO/TC 37 (in particular, Subcommittee 4, dealing with the standardisation of linguistic annotations and resources).

Technoblast Australia Pty.Ltd: Iniciativa Emprendedora

El proyecto desarrolla el modelo de negocio para la creación de una empresa de voladuras asentada en Australia. Se han analizado todos los puntos considerados estratégicos para poder llevar a cabo esta labor, esto es un estudio tecnológico, comercial y financiero que constituyen este plan de negocios. ABSTRACT The main object of this project is to develop a document which contemplates a model for the establishment of a blasting company in Australia. To do so in this text we have analyzed the considered strategic points that will help us in this hard effort. All this is included in a technological, commercial and financial study that makes up a business plan

Modelado en alto nivel con SystemC

El presente proyecto final de carrera titulado “Modelado de alto nivel con SystemC” tiene como objetivo principal el modelado de algunos módulos de un codificador de vídeo MPEG-2 utilizando el lenguaje de descripción de sistemas igitales SystemC con un nivel de abstracción TLM o Transaction Level Modeling. SystemC es un lenguaje de descripción de sistemas digitales basado en C++. En él hay un conjunto de rutinas y librerías que implementan tipos de datos, estructuras y procesos especiales para el modelado de sistemas digitales. Su descripción se puede consultar en [GLMS02] El nivel de abstracción TLM se caracteriza por separar la comunicación entre los módulos de su funcionalidad. Este nivel de abstracción hace un mayor énfasis en la funcionalidad de la comunicación entre los módulos (de donde a donde van datos) que la implementación exacta de la misma. En los documentos [RSPF] y [HG] se describen el TLM y un ejemplo de implementación. La arquitectura del modelo se basa en el codificador MVIP-2 descrito en [Gar04], de dicho modelo, los módulos implementados son: · IVIDEOH: módulo que realiza un filtrado del vídeo de entrada en la dimensión horizontal y guarda en memoria el video filtrado. · IVIDEOV: módulo que lee de la memoria el vídeo filtrado por IVIDEOH, realiza el filtrado en la dimensión horizontal y escribe el video filtrado en memoria. · DCT: módulo que lee el video filtrado por IVIDEOV, hace la transformada discreta del coseno y guarda el vídeo transformado en la memoria. · QUANT: módulo que lee el video transformado por DCT, lo cuantifica y guarda el resultado en la memoria. · IQUANT: módulo que lee el video cuantificado por QUANT, realiza la cuantificación inversa y guarda el resultado en memoria. · IDCT: módulo que lee el video procesado por IQUANT, realiza la transformada inversa del coseno y guarda el resultado en memoria. · IMEM: módulo que hace de interfaz entre los módulos anteriores y la memoria. Gestiona las peticiones simultáneas de acceso a la memoria y asegura el acceso exclusivo a la memoria en cada instante de tiempo. Todos estos módulos aparecen en gris en la siguiente figura en la que se muestra la arquitectura del modelo: Figura 1. Arquitectura del modelo (VER PDF DEL PFC) En figura también aparecen unos módulos en blanco, dichos módulos son de pruebas y se han añadido para realizar simulaciones y probar los módulos del modelo: · CAMARA: módulo que simula una cámara en blanco y negro, lee la luminancia de un fichero de vídeo y lo envía al modelo a través de una FIFO. · FIFO: hace de interfaz entre la cámara y el modelo, guarda los datos que envía la cámara hasta que IVIDEOH los lee. · CONTROL: módulo que se encarga de controlar los módulos que procesan el vídeo, estos le indican cuando terminan de procesar un frame de vídeo y este módulo se encarga de iniciar los módulos que sean necesarios para seguir con la codificación. Este módulo se encarga del correcto secuenciamiento de los módulos procesadores de vídeo. · RAM: módulo que simula una memoria RAM, incluye un retardo programable en el acceso. Para las pruebas también se han generado ficheros de vídeo con el resultado de cada módulo procesador de vídeo, ficheros con mensajes y un fichero de trazas en el que se muestra el secuenciamiento de los procesadores. Como resultado del trabajo en el presente PFC se puede concluir que SystemC permite el modelado de sistemas digitales con bastante sencillez (hace falta conocimientos previos de C++ y programación orientada objetos) y permite la realización de modelos con un nivel de abstracción mayor a RTL, el habitual en Verilog y VHDL, en el caso del presente PFC, el TLM. ABSTRACT This final career project titled “High level modeling with SystemC” have as main objective the modeling of some of the modules of an MPEG-2 video coder using the SystemC digital systems description language at the TLM or Transaction Level Modeling abstraction level. SystemC is a digital systems description language based in C++. It contains routines and libraries that define special data types, structures and process to model digital systems. There is a complete description of the SystemC language in the document [GLMS02]. The main characteristic of TLM abstraction level is that it separates the communication among modules of their functionality. This abstraction level puts a higher emphasis in the functionality of the communication (from where to where the data go) than the exact implementation of it. The TLM and an example are described in the documents [RSPF] and [HG]. The architecture of the model is based in the MVIP-2 video coder (described in the document [Gar04]) The modeled modules are: · IVIDEOH: module that filter the video input in the horizontal dimension. It saves the filtered video in the memory. · IVIDEOV: module that read the IVIDEOH filtered video, filter it in the vertical dimension and save the filtered video in the memory. · DCT: module that read the IVIDEOV filtered video, do the discrete cosine transform and save the transformed video in the memory. · QUANT: module that read the DCT transformed video, quantify it and save the quantified video in the memory. · IQUANT: module that read the QUANT processed video, do the inverse quantification and save the result in the memory. · IDCT: module that read the IQUANT processed video, do the inverse cosine transform and save the result in the memory. · IMEM: this module is the interface between the modules described previously and the memory. It manage the simultaneous accesses to the memory and ensure an unique access at each instant of time All this modules are included in grey in the following figure (SEE PDF OF PFC). This figure shows the architecture of the model: Figure 1. Architecture of the model This figure also includes other modules in white, these modules have been added to the model in order to simulate and prove the modules of the model: · CAMARA: simulates a black and white video camera, it reads the luminance of a video file and sends it to the model through a FIFO. · FIFO: is the interface between the camera and the model, it saves the video data sent by the camera until the IVIDEOH module reads it. · CONTROL: controls the modules that process the video. These modules indicate the CONTROL module when they have finished the processing of a video frame. The CONTROL module, then, init the necessary modules to continue with the video coding. This module is responsible of the right sequence of the video processing modules. · RAM: it simulates a RAM memory; it also simulates a programmable delay in the access to the memory. It has been generated video files, text files and a trace file to check the correct function of the model. The trace file shows the sequence of the video processing modules. As a result of the present final career project, it can be deduced that it is quite easy to model digital systems with SystemC (it is only needed previous knowledge of C++ and object oriented programming) and it also allow the modeling with a level of abstraction higher than the RTL used in Verilog and VHDL, in the case of the present final career project, the TLM.

An approach to a model of the visual cortex: disorders of vision

One of the most challenging problems that must be solved by any theoretical model purporting to explain the competence of the human brain for relational tasks is the one related with the analysis and representation of the internal structure in an extended spatial layout of múltiple objects. In this way, some of the problems are related with specific aims as how can we extract and represent spatial relationships among objects, how can we represent the movement of a selected object and so on. The main objective of this paper is the study of some plausible brain structures that can provide answers in these problems. Moreover, in order to achieve a more concrete knowledge, our study will be focused on the response of the retinal layers for optical information processing and how this information can be processed in the first cortex layers. The model to be reported is just a first trial and some major additions are needed to complete the whole vision process.

An architecture for integrating learning object repository resources into web videoconference services

Reusing Learning Objects saves time and reduce development costs. Hence, achieving their interoperability in multiple contexts is essential when creating a Learning Object Repository. On the other hand, novel web videoconference services are available due to technological advancements. Several benefits can be gained by integrating Learning Objects into these services. For instance, they can allow sharing, co-viewing and synchronized co-browsing of these resources at the same time that provide real time communication. However, several efforts need to be undertaken to achieve the interoperability with these systems. In this paper, we propose a model to integrate the resources of the Learning Object Repositories into web videoconference services. The experience of applying this model in a real e-Learning scenario achieving interoperability with two different web videoconference services is also described.

Using Electronic Documents for Knowledge Acquisition and Model Maintenance

Knowledge acquisition and model maintenance are key problems in knowledge engineering to improve the productivity in the development of intelligent systems. Although historically a number of technical solutions have been proposed in this area, the recent experience shows that there is still an important gap between the way end-users describe their expertise and the way intelligent systems represent knowledge. In this paper we propose an original way to cope with this problem based on electronic documents. We propose the concept of intelligent document processor as a tool that allows the end-user to read/write a document explaining how an intelligent system operates in such a way that, if the user changes the content of the document, the intelligent system will react to these changes. The paper presents the structure of such a document based on knowledge categories derived from the modern knowledge modeling methodologies together with a number of requirements to be understandable by end-users and problem solvers.

KATS: A Knowledge Acquisition Tool Based on Electronic Document Processing

This paper describes a particular knowledge acquisition tool for the construction and maintenance of the knowledge model of an intelligent system for emergency management in the field of hydrology. This tool has been developed following an innovative approach directed to end-users non familiarized in computer oriented terminology. According to this approach, the tool is conceived as a document processor specialized in a particular domain (hydrology) in such a way that the whole knowledge model is viewed by the user as an electronic document. The paper first describes the characteristics of the knowledge model of the intelligent system and summarizes the problems that we found during the development and maintenance of such type of model. Then, the paper describes the KATS tool, a software application that we have designed to help in this task to be used by users who are not experts in computer programming. Finally, the paper shows a comparison between KATS and other approaches for knowledge acquisition.

High Fidelity Models for Near-Earth Object Dynamics

Motivado por los últimos hallazgos realizados gracias a los recientes avances tecnológicos y misiones espaciales, el estudio de los asteroides ha despertado el interés de la comunidad científica. Tal es así que las misiones a asteroides han proliferado en los últimos años (Hayabusa, Dawn, OSIRIX-REx, ARM, AIMS-DART, ...) incentivadas por su enorme interés científico. Los asteroides son constituyentes fundamentales en la evolución del Sistema Solar, son además grandes concentraciones de valiosos recursos naturales, y también pueden considerarse como objectivos estratégicos para la futura exploración espacial. Desde hace tiempo se viene especulando con la posibilidad de capturar objetos próximos a la Tierra (NEOs en su acrónimo anglosajón) y acercarlos a nuestro planeta, permitiendo así un acceso asequible a los mismos para estudiarlos in-situ, explotar sus recursos u otras finalidades. Por otro lado, las asteroides se consideran con frecuencia como posibles peligros de magnitud planetaria, ya que impactos de estos objetos con la Tierra suceden constantemente, y un asteroide suficientemente grande podría desencadenar eventos catastróficos. Pese a la gravedad de tales acontecimientos, lo cierto es que son ciertamente difíciles de predecir. De hecho, los ricos aspectos dinámicos de los asteroides, su modelado complejo y las incertidumbres observaciones hacen que predecir su posición futura con la precisión necesaria sea todo un reto. Este hecho se hace más relevante cuando los asteroides sufren encuentros próximos con la Tierra, y más aún cuando estos son recurrentes. En tales situaciones en las cuales fuera necesario tomar medidas para mitigar este tipo de riesgos, saber estimar con precisión sus trayectorias y probabilidades de colisión es de una importancia vital. Por ello, se necesitan herramientas avanzadas para modelar su dinámica y predecir sus órbitas con precisión, y son también necesarios nuevos conceptos tecnológicos para manipular sus órbitas llegado el caso. El objetivo de esta Tesis es proporcionar nuevos métodos, técnicas y soluciones para abordar estos retos. Las contribuciones de esta Tesis se engloban en dos áreas: una dedicada a la propagación numérica de asteroides, y otra a conceptos de deflexión y captura de asteroides. Por lo tanto, la primera parte de este documento presenta novedosos avances de apliación a la propagación dinámica de alta precisión de NEOs empleando métodos de regularización y perturbaciones, con especial énfasis en el método DROMO, mientras que la segunda parte expone ideas innovadoras para la captura de asteroides y comenta el uso del “ion beam shepherd” (IBS) como tecnología para deflectarlos. Abstract Driven by the latest discoveries enabled by recent technological advances and space missions, the study of asteroids has awakened the interest of the scientific community. In fact, asteroid missions have become very popular in the recent years (Hayabusa, Dawn, OSIRIX-REx, ARM, AIMS-DART, ...) motivated by their outstanding scientific interest. Asteroids are fundamental constituents in the evolution of the Solar System, can be seen as vast concentrations of valuable natural resources, and are also considered as strategic targets for the future of space exploration. For long it has been hypothesized with the possibility of capturing small near-Earth asteroids and delivering them to the vicinity of the Earth in order to allow an affordable access to them for in-situ science, resource utilization and other purposes. On the other side of the balance, asteroids are often seen as potential planetary hazards, since impacts with the Earth happen all the time, and eventually an asteroid large enough could trigger catastrophic events. In spite of the severity of such occurrences, they are also utterly hard to predict. In fact, the rich dynamical aspects of asteroids, their complex modeling and observational uncertainties make exceptionally challenging to predict their future position accurately enough. This becomes particularly relevant when asteroids exhibit close encounters with the Earth, and more so when these happen recurrently. In such situations, where mitigation measures may need to be taken, it is of paramount importance to be able to accurately estimate their trajectories and collision probabilities. As a consequence, advanced tools are needed to model their dynamics and accurately predict their orbits, as well as new technological concepts to manipulate their orbits if necessary. The goal of this Thesis is to provide new methods, techniques and solutions to address these challenges. The contributions of this Thesis fall into two areas: one devoted to the numerical propagation of asteroids, and another to asteroid deflection and capture concepts. Hence, the first part of the dissertation presents novel advances applicable to the high accuracy dynamical propagation of near-Earth asteroids using regularization and perturbations techniques, with a special emphasis in the DROMO method, whereas the second part exposes pioneering ideas for asteroid retrieval missions and discusses the use of an “ion beam shepherd” (IBS) for asteroid deflection purposes.

A computational model of the anterior intraparietal area

The monkey anterior intraparietal area (AIP) encodes visual information about three-dimensional object shape that is used to shape the hand for grasping. In robotics a similar role has been played by modules that fit point cloud data to the superquadric family of shapes and its various extensions. We developed a model of shape tuning in AIP based on cosine tuning to superquadric parameters. However, the model did not fit the data well, and we also found that it was difficult to accurately reproduce these parameters using neural networks with the appropriate inputs (modelled on the caudal intraparietal area, CIP). The latter difficulty was related to the fact that there are large discontinuities in the superquadric parameters between very similar shapes. To address these limitations we adopted an alternative shape parameterization based on an Isomap nonlinear dimension reduction. The Isomap was built using gradients and curvatures of object surface depth. This alternative parameterization was low-dimensional (like superquadrics), but data-driven (similar to an alternative clustering approach that is also sometimes used in robotics) and lacked large discontinuities. Isomaps with 16 or more dimensions reproduced the AIP data fairly well. Moreover, we found that the Isomap parameters could be approximated from CIP-like input much more accurately than the superquadric parameters. We conclude that Isomaps, or perhaps alternative dimension reductions of CIP signals, provide a promising model of AIP tuning. We have now started to integrate our model with a robot hand, to explore the efficacy of Isomap shape reductions in grasp planning. Future work will consider dynamics of spike responses and integration with related visual and motor area models.

A flexible open source web platform to facilitate Learning Object evaluation

Systematic evaluation of Learning Objects is essential to make high quality Web-based education possible. For this reason, several educational repositories and e-Learning systems have developed their own evaluation models and tools. However, the differences of the context in which Learning Objects are produced and consumed suggest that no single evaluation model is sufficient for all scenarios. Besides, no much effort has been put in developing open tools to facilitate Learning Object evaluation and use the quality information for the benefit of end users. This paper presents LOEP, an open source web platform that aims to facilitate Learning Object evaluation in different scenarios and educational settings by supporting and integrating several evaluation models and quality metrics. The work exposed in this paper shows that LOEP is capable of providing Learning Object evaluation to e-Learning systems in an open, low cost, reliable and effective way. Possible scenarios where LOEP could be used to implement quality control policies and to enhance search engines are also described. Finally, we report the results of a survey conducted among reviewers that used LOEP, showing that they perceived LOEP as a powerful and easy to use tool for evaluating Learning Objects.

First 20 years of DNDC: Model evolution and GRAMP.

The DNDC (DeNitrification and DeComposition) model was first developed by Li et al. (1992) as a rain event-driven process-orientated simulation model for nitrous oxide, carbon dioxide and nitrogen gas emissions from the agricultural soils in the U.S. Over the last 20 years, the model has been modified and adapted by various research groups around the world to suit specific purposes and circumstances. The Global Research Alliance Modelling Platform (GRAMP) is a UK-led initiative for the establishment of a purposeful and credible web-based platform initially aimed at users of the DNDC model. With the aim of improving the predictions of soil C and N cycling in the context of climate change the objectives of GRAMP are to: 1) to document the existing versions of the DNDC model; 2) to create a family tree of the individual DNDC versions; 3) to provide information on model use and development; and 4) to identify strengths, weaknesses and potential improvements for the model.