Remote Delivery of Video Services over video links

The spreading of new systems of broadcasting and distribution of multimedia content has had as a consequence a larger need for aggregation of data and metadata to traditionally based contents of video and audio supply. Broadcasting chains of this type of channels have become overwhelmed by the quantity of resources, infrastructures and development needed for these channels to provide information. In order to avoid this kind of shortcomings, several recommendations and standards have been created to exchange metadata between production and distribution of taped programs. The problem lies in live programs, producers sometimes offer data to channels but most often, channels are not able to face required developments. The key to this problem is cost reduction. In this work, a study is conducted on added services which producers may provide to the media about content; a system is found by which additional communication expenses are not made and a model of information transfer is offered which allows low cost developments to supply new media platforms.

Short paper: From streaming data to linked data. A case study with bike sharing systems

Current methods and tools that support Linked Data publication have mainly focused so far on static data, without considering the growing amount of streaming data available on the Web. In this paper we describe a case study that involves the publication of static and streaming Linked Data for bike sharing systems and related entities. We describe some of the challenges that we have faced, the solutions that we have explored, the lessons that we have learned, and the opportunities that lie in the future for exploiting Linked Stream Data.

SRBench: A streaming RDF/SPARQL benchmark

We introduce SRBench, a general-purpose benchmark primarily designed for streaming RDF/SPARQL engines, completely based on real-world data sets from the Linked Open Data cloud. With the increasing problem of too much streaming data but not enough tools to gain knowledge from them, researchers have set out for solutions in which Semantic Web technologies are adapted and extended for publishing, sharing, analysing and understanding streaming data. To help researchers and users comparing streaming RDF/SPARQL (strRS) engines in a standardised application scenario, we have designed SRBench, with which one can assess the abilities of a strRS engine to cope with a broad range of use cases typically encountered in real-world scenarios. The data sets used in the benchmark have been carefully chosen, such that they represent a realistic and relevant usage of streaming data. The benchmark defines a concise, yet omprehensive set of queries that cover the major aspects of strRS processing. Finally, our work is complemented with a functional evaluation on three representative strRS engines: SPARQLStream, C-SPARQL and CQELS. The presented results are meant to give a first baseline and illustrate the state-of-the-art.

Experimental Prototype Merging Stereo Panoramic Video and Interactive 3D Content in a 5-sided CAVETM

Immersion and interaction have been identified as key factors influencing the quality of experience in stereoscopic video systems. An experimental prototype designed to explore the influence of these factors in 3D video applications is described here1. The focus is on the real-time insertion algorithm of new 3D models into the original video streams. Using this algorithm, our prototype is aimed to explore a new interaction paradigm ? similar to the augmented reality approach ? with 3D video applications.

Adaptación de un reproductor de contenidos multimedia para la recepción de vídeo escalable sobre un protocolo de tiempo real

En los últimos años, debido al notable desarrollo de los terminales portátiles, que han pasado de ser “simples” teléfonos o reproductores a puros ordenadores, ha crecido el número de servicios que ofrecen cada vez mayor cantidad de contenido multimedia a través de internet. Además, la distinta evolución de estos terminales hace que nos encontremos en el mercado con una amplísima gama de productos de diferentes tamaños y capacidades de procesamiento, lo que hace necesario encontrar una fórmula que permita satisfacer la demanda de dichos servicios sea cual sea la naturaleza de nuestro dispositivo. Para poder ofrecer una solución adecuada se ha optado por la integración de un protocolo como RTP y un estándar de video como SVC. RTP (Real-time Transport Protocol), en contraposición a los protocolos de propósito general fue diseñado para aplicaciones de tiempo real por lo que es ideal para el streaming de contenido multimedia. Por su parte, SVC es un estándar de video escalable que permite transmitir en un mismo stream una capa base y múltiples capas de mejora, por lo que podremos adaptar la calidad y tamaño del contenido a la capacidad y tamaño de nuestro dispositivo. El objetivo de este proyecto consiste en integrar y modificar tanto el reproductor MPlayer como la librería RTP live555 de tal forma que sean capaces de soportar el formato SVC sobre el protocolo RTP y montar un sistema servidorcliente para comprobar su funcionamiento. Aunque este proceso esté orientado a llevarse a cabo en un dispositivo móvil, para este proyecto se ha optado por realizarlo en el escenario más sencillo posible, para lo cual, se emitirán secuencias a una máquina virtual alojada en el mismo ordenador que el servidor. ABSTRACT In recent years, due to the remarkable development of mobile devices, which have evolved from "simple" phones or players to computers, the amount of services that offer multimedia content over the internet have shot up. Furthermore, the different evolution of these terminals causes that we can find in the market a wide range of different sizes and processing capabilities, making necessary to find a formula that will satisfy the demand for such services regardless of the nature of our device. In order to provide a suitable solution we have chosen to integrate a protocol as RTP and a video standard as SVC. RTP (Real-time Transport Protocol), in opposition to general purpose protocols was designed for real-time applications making it ideal for media streaming. Meanwhile, SVC is a scalable video standard which can transmit a single stream in a base layer and multiple enhancement layers, so that we can adapt the quality and size of the content to the capacity and size of our device. The objective of this project is to integrate and modify both MPlayer and RTP library live555 so that they support the SVC format over RTP protocol and set up a client-server system to check its behavior. Although this process has been designed to be done on a mobile device, for this project we have chosen to do it in the simplest possible scenario so we will stream to a virtual machine hosted on the same computer where we have the server.

Methodologies for implement video decoders over multiprocessor platforms

The latest video coding standards developed, like HEVC (High Efficiency Video Coding, approved in January 2013), require for their implementation the use of devices able to support a high computational load. Considering that currently it is not enough the usage of one unique Digital Signal Processor (DSP), multicore devices have appeared recently in the market. However, due to its novelty, the working methodology that allows produce solutions for these configurations is in a very initial state, since currently the most part of the work needs to be performed manually. In consequence, the objective set consists on finding methodologies that ease this process. The study has been focused on extend a methodology, under development, for the generation of solutions for PCs and embedded systems. During this study, the standards RVC (Reconfigurable Video Coding) and HEVC have been employed, as well as DSPs of the Texas Instruments company. In its development, it has been tried to address all the factors that influence both the development and deployment of these new implementations of video decoders, ranging from tools up to aspects of the partitioning of algorithms, without this can cause a drop in application performance. The results of this study are the description of the employed methodology, the characterization of the software migration process and performance measurements for the HEVC standard in an RVC-based implementation. RESUMEN Los estándares de codificación de vídeo desarrollados más recientemente, como HEVC (High Efficiency Video Coding, aprobado en enero de 2013), requieren para su implementación el uso de dispositivos capaces de soportar una elevada carga computacional. Teniendo en cuenta que actualmente no es suficiente con utilizar un único Procesador Digital de Señal (DSP), han aparecido recientemente dispositivos multinúcleo en el mercado. Sin embargo, debido a su novedad, la metodología de trabajo que permite elaborar soluciones para tales configuraciones se encuentra en un estado muy inicial, ya que actualmente la mayor parte del trabajo debe realizarse manualmente. En consecuencia, el objetivo marcado consiste en encontrar metodologías que faciliten este proceso. El estudio se ha centrado en extender una metodología, en desarrollo, para la generación de soluciones para PC y sistemas empotrados. Durante dicho estudio se han empleado los estándares RVC (Reconfigurable Video Coding) y HEVC, así como DSPs de la compañía Texas Instruments. En su desarrollo se ha tratado de atender a todos los factores que influyen tanto en el desarrollo como en la puesta en marcha de estas nuevas implementaciones de descodificadores de vídeo; abarcando desde las herramientas a utilizar hasta aspectos del particionado de los algoritmos, sin que por ello se produzca una reducción en el rendimiento de las aplicaciones. Los resultados de este estudio son una descripción de la metodología empleada, la caracterización del proceso de migración de software, y medidas de rendimiento para el estándar HEVC en una implementación basada en RVC.

Multi-dimensional classification using Bayesian networks for stationary and evolving streaming data

Hoy en día, con la evolución continua y rápida de las tecnologías de la información y los dispositivos de computación, se recogen y almacenan continuamente grandes volúmenes de datos en distintos dominios y a través de diversas aplicaciones del mundo real. La extracción de conocimiento útil de una cantidad tan enorme de datos no se puede realizar habitualmente de forma manual, y requiere el uso de técnicas adecuadas de aprendizaje automático y de minería de datos. La clasificación es una de las técnicas más importantes que ha sido aplicada con éxito a varias áreas. En general, la clasificación se compone de dos pasos principales: en primer lugar, aprender un modelo de clasificación o clasificador a partir de un conjunto de datos de entrenamiento, y en segundo lugar, clasificar las nuevas instancias de datos utilizando el clasificador aprendido. La clasificación es supervisada cuando todas las etiquetas están presentes en los datos de entrenamiento (es decir, datos completamente etiquetados), semi-supervisada cuando sólo algunas etiquetas son conocidas (es decir, datos parcialmente etiquetados), y no supervisada cuando todas las etiquetas están ausentes en los datos de entrenamiento (es decir, datos no etiquetados). Además, aparte de esta taxonomía, el problema de clasificación se puede categorizar en unidimensional o multidimensional en función del número de variables clase, una o más, respectivamente; o también puede ser categorizado en estacionario o cambiante con el tiempo en función de las características de los datos y de la tasa de cambio subyacente. A lo largo de esta tesis, tratamos el problema de clasificación desde tres perspectivas diferentes, a saber, clasificación supervisada multidimensional estacionaria, clasificación semisupervisada unidimensional cambiante con el tiempo, y clasificación supervisada multidimensional cambiante con el tiempo. Para llevar a cabo esta tarea, hemos usado básicamente los clasificadores Bayesianos como modelos. La primera contribución, dirigiéndose al problema de clasificación supervisada multidimensional estacionaria, se compone de dos nuevos métodos de aprendizaje de clasificadores Bayesianos multidimensionales a partir de datos estacionarios. Los métodos se proponen desde dos puntos de vista diferentes. El primer método, denominado CB-MBC, se basa en una estrategia de envoltura de selección de variables que es voraz y hacia delante, mientras que el segundo, denominado MB-MBC, es una estrategia de filtrado de variables con una aproximación basada en restricciones y en el manto de Markov. Ambos métodos han sido aplicados a dos problemas reales importantes, a saber, la predicción de los inhibidores de la transcriptasa inversa y de la proteasa para el problema de infección por el virus de la inmunodeficiencia humana tipo 1 (HIV-1), y la predicción del European Quality of Life-5 Dimensions (EQ-5D) a partir de los cuestionarios de la enfermedad de Parkinson con 39 ítems (PDQ-39). El estudio experimental incluye comparaciones de CB-MBC y MB-MBC con los métodos del estado del arte de la clasificación multidimensional, así como con métodos comúnmente utilizados para resolver el problema de predicción de la enfermedad de Parkinson, a saber, la regresión logística multinomial, mínimos cuadrados ordinarios, y mínimas desviaciones absolutas censuradas. En ambas aplicaciones, los resultados han sido prometedores con respecto a la precisión de la clasificación, así como en relación al análisis de las estructuras gráficas que identifican interacciones conocidas y novedosas entre las variables. La segunda contribución, referida al problema de clasificación semi-supervisada unidimensional cambiante con el tiempo, consiste en un método nuevo (CPL-DS) para clasificar flujos de datos parcialmente etiquetados. Los flujos de datos difieren de los conjuntos de datos estacionarios en su proceso de generación muy rápido y en su aspecto de cambio de concepto. Es decir, los conceptos aprendidos y/o la distribución subyacente están probablemente cambiando y evolucionando en el tiempo, lo que hace que el modelo de clasificación actual sea obsoleto y deba ser actualizado. CPL-DS utiliza la divergencia de Kullback-Leibler y el método de bootstrapping para cuantificar y detectar tres tipos posibles de cambio: en las predictoras, en la a posteriori de la clase o en ambas. Después, si se detecta cualquier cambio, un nuevo modelo de clasificación se aprende usando el algoritmo EM; si no, el modelo de clasificación actual se mantiene sin modificaciones. CPL-DS es general, ya que puede ser aplicado a varios modelos de clasificación. Usando dos modelos diferentes, el clasificador naive Bayes y la regresión logística, CPL-DS se ha probado con flujos de datos sintéticos y también se ha aplicado al problema real de la detección de código malware, en el cual los nuevos ficheros recibidos deben ser continuamente clasificados en malware o goodware. Los resultados experimentales muestran que nuestro método es efectivo para la detección de diferentes tipos de cambio a partir de los flujos de datos parcialmente etiquetados y también tiene una buena precisión de la clasificación. Finalmente, la tercera contribución, sobre el problema de clasificación supervisada multidimensional cambiante con el tiempo, consiste en dos métodos adaptativos, a saber, Locally Adpative-MB-MBC (LA-MB-MBC) y Globally Adpative-MB-MBC (GA-MB-MBC). Ambos métodos monitorizan el cambio de concepto a lo largo del tiempo utilizando la log-verosimilitud media como métrica y el test de Page-Hinkley. Luego, si se detecta un cambio de concepto, LA-MB-MBC adapta el actual clasificador Bayesiano multidimensional localmente alrededor de cada nodo cambiado, mientras que GA-MB-MBC aprende un nuevo clasificador Bayesiano multidimensional. El estudio experimental realizado usando flujos de datos sintéticos multidimensionales indica los méritos de los métodos adaptativos propuestos. ABSTRACT Nowadays, with the ongoing and rapid evolution of information technology and computing devices, large volumes of data are continuously collected and stored in different domains and through various real-world applications. Extracting useful knowledge from such a huge amount of data usually cannot be performed manually, and requires the use of adequate machine learning and data mining techniques. Classification is one of the most important techniques that has been successfully applied to several areas. Roughly speaking, classification consists of two main steps: first, learn a classification model or classifier from an available training data, and secondly, classify the new incoming unseen data instances using the learned classifier. Classification is supervised when the whole class values are present in the training data (i.e., fully labeled data), semi-supervised when only some class values are known (i.e., partially labeled data), and unsupervised when the whole class values are missing in the training data (i.e., unlabeled data). In addition, besides this taxonomy, the classification problem can be categorized into uni-dimensional or multi-dimensional depending on the number of class variables, one or more, respectively; or can be also categorized into stationary or streaming depending on the characteristics of the data and the rate of change underlying it. Through this thesis, we deal with the classification problem under three different settings, namely, supervised multi-dimensional stationary classification, semi-supervised unidimensional streaming classification, and supervised multi-dimensional streaming classification. To accomplish this task, we basically used Bayesian network classifiers as models. The first contribution, addressing the supervised multi-dimensional stationary classification problem, consists of two new methods for learning multi-dimensional Bayesian network classifiers from stationary data. They are proposed from two different points of view. The first method, named CB-MBC, is based on a wrapper greedy forward selection approach, while the second one, named MB-MBC, is a filter constraint-based approach based on Markov blankets. Both methods are applied to two important real-world problems, namely, the prediction of the human immunodeficiency virus type 1 (HIV-1) reverse transcriptase and protease inhibitors, and the prediction of the European Quality of Life-5 Dimensions (EQ-5D) from 39-item Parkinson’s Disease Questionnaire (PDQ-39). The experimental study includes comparisons of CB-MBC and MB-MBC against state-of-the-art multi-dimensional classification methods, as well as against commonly used methods for solving the Parkinson’s disease prediction problem, namely, multinomial logistic regression, ordinary least squares, and censored least absolute deviations. For both considered case studies, results are promising in terms of classification accuracy as well as regarding the analysis of the learned MBC graphical structures identifying known and novel interactions among variables. The second contribution, addressing the semi-supervised uni-dimensional streaming classification problem, consists of a novel method (CPL-DS) for classifying partially labeled data streams. Data streams differ from the stationary data sets by their highly rapid generation process and their concept-drifting aspect. That is, the learned concepts and/or the underlying distribution are likely changing and evolving over time, which makes the current classification model out-of-date requiring to be updated. CPL-DS uses the Kullback-Leibler divergence and bootstrapping method to quantify and detect three possible kinds of drift: feature, conditional or dual. Then, if any occurs, a new classification model is learned using the expectation-maximization algorithm; otherwise, the current classification model is kept unchanged. CPL-DS is general as it can be applied to several classification models. Using two different models, namely, naive Bayes classifier and logistic regression, CPL-DS is tested with synthetic data streams and applied to the real-world problem of malware detection, where the new received files should be continuously classified into malware or goodware. Experimental results show that our approach is effective for detecting different kinds of drift from partially labeled data streams, as well as having a good classification performance. Finally, the third contribution, addressing the supervised multi-dimensional streaming classification problem, consists of two adaptive methods, namely, Locally Adaptive-MB-MBC (LA-MB-MBC) and Globally Adaptive-MB-MBC (GA-MB-MBC). Both methods monitor the concept drift over time using the average log-likelihood score and the Page-Hinkley test. Then, if a drift is detected, LA-MB-MBC adapts the current multi-dimensional Bayesian network classifier locally around each changed node, whereas GA-MB-MBC learns a new multi-dimensional Bayesian network classifier from scratch. Experimental study carried out using synthetic multi-dimensional data streams shows the merits of both proposed adaptive methods.

Business model with discount incentive in a P2P-cloud multimedia streaming system

Today P2P faces two important challenges: design of mechanisms to encourage users' collaboration in multimedia live streaming services; design of reliable algorithms with QoS provision, to encourage the multimedia providers employ the P2P topology in commercial live streaming systems. We believe that these two challenges are tightly-related and there is much to be done with respect. This paper analyzes the effect of user behavior in a multi-tree P2P overlay and describes a business model based on monetary discount as incentive in a P2P-Cloud multimedia streaming system. We believe a discount model can boost up users' cooperation and loyalty and enhance the overall system integrity and performance. Moreover the model bounds the constraints for a provider's revenue and cost if the P2P system is leveraged on a cloud infrastructure. Our case study shows that a streaming system provider can establish or adapt his business model by applying the described bounds to achieve a good discount-revenue trade-off and promote the system to the users.

Aerial video geo-registration using terrain models from dense and coherent stereo matching

In the context of aerial imagery, one of the first steps toward a coherent processing of the information contained in multiple images is geo-registration, which consists in assigning geographic 3D coordinates to the pixels of the image. This enables accurate alignment and geo-positioning of multiple images, detection of moving objects and fusion of data acquired from multiple sensors. To solve this problem there are different approaches that require, in addition to a precise characterization of the camera sensor, high resolution referenced images or terrain elevation models, which are usually not publicly available or out of date. Building upon the idea of developing technology that does not need a reference terrain elevation model, we propose a geo-registration technique that applies variational methods to obtain a dense and coherent surface elevation model that is used to replace the reference model. The surface elevation model is built by interpolation of scattered 3D points, which are obtained in a two-step process following a classical stereo pipeline: first, coherent disparity maps between image pairs of a video sequence are estimated and then image point correspondences are back-projected. The proposed variational method enforces continuity of the disparity map not only along epipolar lines (as done by previous geo-registration techniques) but also across them, in the full 2D image domain. In the experiments, aerial images from synthetic video sequences have been used to validate the proposed technique.

Aphasic seizures in patients with temporopolar and anterior temporobasal lesions: a video-EEG study

Studies of patients with temporal lobe epilepsy provide few descriptions of seizures that arise in the temporopolar and the anterior temporobasal brain region. Based on connectivity, it might be assumed that the semiology of these seizures is similar to that of medial temporal lobe epilepsy. However, accumulating evidence suggests that the anterior temporobasal cortex may play an important role in the language system, which could account for particular features of seizures arising here. We studied the electroclinical features of seizures in patients with circumscribed temporopolar and temporobasal lesions in order to identify specific features that might differentiate them from seizures that originate in other temporal areas. Among 172 patients with temporal lobe seizures registered in our epilepsy unit in the last 15 years, 15 (8.7%) patients had seizures caused by temporopolar or anterior temporobasal lesions (11 left-sided lesions). The main finding in our study is that patients with left-sided lesions had aphasia during their seizures as the most prominent feature. In addition, while all patients showed normal to high intellectual functioning in standard neuropsychological testing, semantic impairment was found in a subset of 9 patients with left-sided lesions. This case series demonstrates that aphasic seizures without impairment of consciousness can result from small, circumscribed left anterior temporobasal and temporopolar lesions. Thus, the presence of speech manifestation during seizures should prompt detailed assessment of the structural integrity of the basal surface of the temporal lobe in addition to the evaluation of primary language areas.

Cognitive neurorehabilitation based on interactive video technology

Cognitive impairment is the main cause of disability in developed societies. New interactive technologies help therapists in neurorehabilitation in order to increase patients’ autonomy and quality of life. This work proposes Interactive Video (IV) as a technology to develop cognitive rehabilitation tasks based on Activities of Daily Living (ADL). ADL cognitive task has been developed and integrated with eye-tracking technology for task interaction and patients’ performance monitoring.

Video-based tasks for emotional processing rehabilitation in schizophrenia

Schizophrenia is a mental disorder characterized by a breakdown of cognitive processes and by a deficit of typi-cal emotional responses. Effectiveness of computerized task has been demonstrated in the field of cognitive rehabilitation. However, current rehabilitation programs based on virtual environments normally focus on higher cognitive functions, not covering social cognition training. This paper presents a set of video-based tasks specifically designed for the rehabilita-tion of emotional processing deficits in patients in early stages of schizophrenia or schizoaffective disorders. These tasks are part of the Mental Health program of Guttmann NeuroPer-sonalTrainer® cognitive tele-rehabilitation platform, and entail innovation both from a clinical and technological per-spective in relation with former traditional therapeutic con-tents.

Multiple Hashing Integration for Real-Time Large Scale Part-to-Part Video Matching

A real-time large scale part-to-part video matching algorithm, based on the cross correlation of the intensity of motion curves, is proposed with a view to originality recognition, video database cleansing, copyright enforcement, video tagging or video result re-ranking. Moreover, it is suggested how the most representative hashes and distance functions - strada, discrete cosine transformation, Marr-Hildreth and radial - should be integrated in order for the matching algorithm to be invariant against blur, compression and rotation distortions: (R; _) 2 [1; 20]_[1; 8], from 512_512 to 32_32pixels2 and from 10 to 180_. The DCT hash is invariant against blur and compression up to 64x64 pixels2. Nevertheless, although its performance against rotation is the best, with a success up to 70%, it should be combined with the Marr-Hildreth distance function. With the latter, the image selected by the DCT hash should be at a distance lower than 1.15 times the Marr-Hildreth minimum distance.

A graphical tool for designing interactive video cognitive rehabilitation therapies

Acquired Brain Injury (ABI) has become one of the most common causes of neurological disability in developed countries. Cognitive disorders result in a loss of independence and therefore patients? quality of life. Cognitive rehabilitation aims to promote patients? skills to achieve their highest degree of personal autonomy. New technologies such as interactive video, whereby real situations of daily living are reproduced within a controlled virtual environment, enable the design of personalized therapies with a high level of generalization and a great ecological validity. This paper presents a graphical tool that allows neuropsychologists to design, modify, and configure interactive video therapeutic activities, through the combination of graphic and natural language. The tool has been validated creating several Activities of Daily Living and a preliminary usability evaluation has been performed showing a good clinical acceptance in the definition of complex interactive video therapies for cognitive rehabilitation.

Monitoring visual attention on a neurorehabilitation environment based on interactive video

The use of new technologies in neurorehabilitation has led to higher intensity rehabilitation processes, extending therapies in an economically sustainable way. Interactive Video (IV) technology allows therapists to work with virtual environments that reproduce real situations. In this way, patients deal with Activities of the Daily Living (ADL) immersed within enhanced environments [1]. These rehabilitation exercises, which focus in re-learning lost functions, will try to modulate the neural plasticity processes [2]. This research presents a system where a neurorehabilitation IV-based environment has been integrated with an eye-tracker device in order to monitor and to interact using visual attention. While patients are interacting with the neurorehabilitation environment, their visual behavior is closely related with their cognitive state, which in turn mirrors the brain damage condition suffered by them [3] [4]. Patients’ gaze data can provide knowledge on their attention focus and their cognitive state, as well as on the validity of the rehabilitation tasks proposed [5].