Online Learning-based Robust Visual Tracking for Autonomous Landing of Unmanned Aerial Vehicles

Autonomous landing is a challenging and important technology for both military and civilian applications of Unmanned Aerial Vehicles (UAVs). In this paper, we present a novel online adaptive visual tracking algorithm for UAVs to land on an arbitrary field (that can be used as the helipad) autonomously at real-time frame rates of more than twenty frames per second. The integration of low-dimensional subspace representation method, online incremental learning approach and hierarchical tracking strategy allows the autolanding task to overcome the problems generated by the challenging situations such as significant appearance change, variant surrounding illumination, partial helipad occlusion, rapid pose variation, onboard mechanical vibration (no video stabilization), low computational capacity and delayed information communication between UAV and Ground Control Station (GCS). The tracking performance of this presented algorithm is evaluated with aerial images from real autolanding flights using manually- labelled ground truth database. The evaluation results show that this new algorithm is highly robust to track the helipad and accurate enough for closing the vision-based control loop.

Real-Time Adaptive Multi-Classifier Multi-Resolution Visual Tracking Framework for Unmanned Aerial Vehicles

This paper presents a novel robust visual tracking framework, based on discriminative method, for Unmanned Aerial Vehicles (UAVs) to track an arbitrary 2D/3D target at real-time frame rates, that is called the Adaptive Multi-Classifier Multi-Resolution (AMCMR) framework. In this framework, adaptive Multiple Classifiers (MC) are updated in the (k-1)th frame-based Multiple Resolutions (MR) structure with compressed positive and negative samples, and then applied them in the kth frame-based Multiple Resolutions (MR) structure to detect the current target. The sample importance has been integrated into this framework to improve the tracking stability and accuracy. The performance of this framework was evaluated with the Ground Truth (GT) in different types of public image databases and real flight-based aerial image datasets firstly, then the framework has been applied in the UAV to inspect the Offshore Floating Platform (OFP). The evaluation and application results show that this framework is more robust, efficient and accurate against the existing state-of-art trackers, overcoming the problems generated by the challenging situations such as obvious appearance change, variant illumination, partial/full target occlusion, blur motion, rapid pose variation and onboard mechanical vibration, among others. To our best knowledge, this is the first work to present this framework for solving the online learning and tracking freewill 2D/3D target problems, and applied it in the UAVs.

Mobile assisted language learning : prototipo de la aplicación UP2B2

La forma de consumir contenidos en Internet ha cambiado durante los últimos años. Inicialmente se empleaban webs estáticas y con contenidos pobres visualmente. Con la evolución de las redes de comunicación, esta tendencia ha variado. A día de hoy, deseamos páginas agradables, accesibles y que nos presenten temas variados. Todo esto ha cambiado la forma de crear páginas web y en todos los casos se persigue el objetivo de atraer a los usuarios. El gran auge de los smartphones y las aplicaciones móviles que invaden el mercado actual han revolucionado el mundo del estudio de los idiomas permitiendo compatibilizar los recursos punteros con el aprendizaje tradicional. La popularidad de los dispositivos móviles y de las aplicaciones ha sido el principal motivo de la realización de este proyecto. En él se realizará un análisis de las diferentes tecnologías existentes y se elegirá la mejor opción que se ajuste a nuestras necesidades para poder desarrollar un sistema que implemente el enfoque llamado Mobile Assisted Language Learning (MALL) que supone una aproximación innovadora al aprendizaje de idiomas con la ayuda de un dispositivo móvil. En este documento se va a ofrecer una panorámica general acerca del desarrollo de aplicaciones para dispositivos móviles en el entorno del e-learning. Se estudiarán características técnicas de diferentes plataformas seleccionando la mejor opción para la implementación de un sistema que proporcione los contenidos básicos para el aprendizaje de un idioma, en este caso del inglés, de forma intuitiva y divertida. Dicho sistema permitirá al usuario mejorar su nivel de inglés mediante una interfaz web de forma dinámica y cercana empleando los recursos que ofrecen los dispositivos móviles y haciendo uso del diseño adaptativo. Este proyecto está pensado para los usuarios que dispongan de poco tiempo libre para realizar un curso de forma presencial o, mejor aún, para reforzar o repasar contenidos ya aprendidos por otros medios más tradicionales o no. La aplicación ofrece la posibilidad de que se haga uso del sistema de forma fácil y sencilla desde cualquier dispositivo móvil del que se disponga como es un smartphone, tablet o un ordenador personal, compitiendo con otros usuarios o contra uno mismo y mejorando así el nivel de partida a través de las actividades propuestas. Durante el proyecto se han comparado diversas soluciones, la mayoría de código abierto y de libre distribución que permiten desplegar servicios de almacenamiento accesibles mediante Internet. Se concluirá con un caso práctico analizando los requisitos técnicos y llevando a cabo las fases de análisis, diseño, creación de la base de datos, implementación y pruebas dentro del ciclo de vida del software. Finalmente, se migrará la aplicación con toda la información a un servidor en la nube. ABSTRACT. The way of consuming content on the Internet has changed over the past years. Initially, static websites were used with poor visual contents. Nevertheless, with the evolution of communication networks this trend has changed. Nowadays, we expect pleasant, accessible and varied topic pages and such expectations have changed the way to create web pages generally aiming at appealing and therefore, attracting users. The great boom of smartphones and mobile applications in the current market, have revolutionized the world of language learning as they make it possible to combine computing with traditional learning resources. The popularity of mobile devices and applications has been the main reason for the development of this project. Here, the different existing technologies will be examined and we will try to select the best option that adapts to our needs in order to develop a system that implements Mobile Assisted Language Learning (MALL) that in broad terms implies an approach to language learning with the help of a mobile device. This report provides an overview of the development of applications for mobile devices in the e-learning environment. We will study the technical characteristics of different platforms and we will select the best option for the implementation of a system that provide the basic content for learning a language, in this case English, by means of an intuitive and fun method. This system will allow the user to improve their level of English with a web interface in a dynamic and close way employing the resources offered by mobile devices using the adaptive design. This project is intended for users who do not have enough free time to make a classroom course or to review contents from more traditional courses as it offers the possibility to make use of the system quickly and easily from any mobile device available such as a smartphone, a tablet or a personal computer, competing with other users or against oneself and thus improving their departing level through different activities. During the project, different solutions have been compared. Most of them, open source and free distribution that allow to deploy storage services accessible via the Internet. It will conclude with a case study analyzing the technical requirements and conducting phases of analysis, design and creation of a database, implementation and testing in the software lifecycle. Finally, the application will be migrated with all the information to a server in the cloud.

Self-Learning Embedded System for Object Identification in Intelligent Infrastructure Sensors

The emergence of new horizons in the field of travel assistant management leads to the development of cutting-edge systems focused on improving the existing ones. Moreover, new opportunities are being also presented since systems trend to be more reliable and autonomous. In this paper, a self-learning embedded system for object identification based on adaptive-cooperative dynamic approaches is presented for intelligent sensor’s infrastructures. The proposed system is able to detect and identify moving objects using a dynamic decision tree. Consequently, it combines machine learning algorithms and cooperative strategies in order to make the system more adaptive to changing environments. Therefore, the proposed system may be very useful for many applications like shadow tolls since several types of vehicles may be distinguished, parking optimization systems, improved traffic conditions systems, etc.

Nitric oxide plays a key role in adaptive control of locomotion in cat

Diverse roles in cellular functions have been ascribed to nitric oxide (NO), and its involvement in induction of long-term depression in cerebellar Purkinje cells has been demonstrated. Manipulations of NO concentration or its synthesis in cerebellar tissues therefore provide a means for investigating roles of NO in cerebellar functions at both cellular and behavioral levels. We tested adaptive control of locomotion to perturbation in cats, and found that this form of motor learning was abolished by application of either an inhibitor of NO synthase or a scavenger of NO to the cerebellar cortical locomotion area. This finding supports the view that NO in the cerebellum plays a key role in motor learning.

Diffusion of nitric oxide can facilitate cerebellar learning: A simulation study

The gaseous second messenger nitric oxide (NO), which readily diffuses in brain tissue, has been implicated in cerebellar long-term depression (LTD), a form of synaptic plasticity thought to be involved in cerebellar learning. Can NO diffusion facilitate cerebellar learning? The inferior olive (IO) cells, which provide the error signals necessary for modifying the granule cell–Purkinje cell (PC) synapses by LTD, fire at ultra-low firing rates in vivo, rarely more than 2–4 spikes within a second. In this paper, we show that NO diffusion can improve the transmission of sporadic IO error signals to PCs within cerebellar cortical functional units, or microzones. To relate NO diffusion to adaptive behavior, we add NO diffusion and a “volumic” LTD learning rule, i.e., a learning rule that depends both on the synaptic activity and on the NO concentration at the synapse, to a cerebellar model for arm movement control. Our results show that biologically plausible diffusion leads to an increase in information transfer of the error signals to the PCs when the IO firing rate is ultra-low. This, in turn, enhances cerebellar learning as shown by improved performance in an arm-reaching task.

Traces of learning in the auditory localization pathway

One of the fascinating properties of the central nervous system is its ability to learn: the ability to alter its functional properties adaptively as a consequence of the interactions of an animal with the environment. The auditory localization pathway provides an opportunity to observe such adaptive changes and to study the cellular mechanisms that underlie them. The midbrain localization pathway creates a multimodal map of space that represents the nervous system's associations of auditory cues with locations in visual space. Various manipulations of auditory or visual experience, especially during early life, that change the relationship between auditory cues and locations in space lead to adaptive changes in auditory localization behavior and to corresponding changes in the functional and anatomical properties of this pathway. Traces of this early learning persist into adulthood, enabling adults to reacquire patterns of connectivity that were learned initially during the juvenile period.

O sistema PBL, problem-based learning, no ensino de medicina no Brasil : análise bibliográfica sobre a sua execução

Introdução: Entre as estratégias de ensino e aprendizagem utilizadas nas práticas pedagógicas, a Problem Based Learning (PBL) (Aprendizagem Baseada em Problemas) é utilizada desde 1960, em especial nos cursos de Medicina. Mesmo sendo uma estratégia valiosa, um dos seus obstáculos é a pouca prática dos alunos em atividades autodirigidas, pesquisa e construção coletiva do conhecimento. Objetivo: Rastrear elementos constitutivos da PBL através de dados colhidos em artigos pesquisados em sítios de divulgação científica; Avaliar, nos estudos selecionados, os aspectos positivos e negativos que estejam relacionados com a metodologia do Sistema PBL aplicada ao ensino médico no Brasil. Metodologia: Estudo bibliográfico de 13 textos utilizando um modelo de desconstrução, denominada Análise Textual Discursiva (ATD) que consiste em: transformação dos artigos em pedaços menores; análise textual; identificação de padrões convergentes e divergentes em relação a PBL; organização e síntese dos dados, culminando com a elaboração de estratégia adaptativa da PBL para o curso de Medicina. Resultados: Foram encontradas 116 citações que convergiam para referências positivos acerca da metodologia PBL e 40 citações que divergiam acerca dos pontos positivos. Os aspectos positivos como o desenvolvimento de atitudes e habilidades; desenvolvimento de competências anteriores ao curso; efeitos positivos depois de terminada a graduação, como autonomia de estudo e a articulação entre currículo e realidade profissional, representam pontos a serem reforçados na aula. Em contraponto, foi observado que dentre os negativos a não compreensão do papel do professor como tutor; necessidade de conteúdo formal tradicional pelos alunos e a expectativa que o professor retire as suas dúvidas são pontos a serem evitados. Conclusões: A metodologia PBL deverá servir como metodologia ativa para aproveitar ao máximo as habilidades que os alunos já apresentam, potencializando o aprendizado na educação médica em sala de aula. Palavras-Chave: PBL; curso de medicina; metodologia ativa; educação médica.

Real leadership and the U.S. Army : overcoming a failure of imagination to conduct adaptive work /

This monograph begins with a case study that provides a means for analyzing the complexity of organizational leadership in the contemporary security environment. As such, it presents a high stakes problem-set that required an operational adaptation by a cavalry squadron conducting combat operations in Baghdad. This problematic reality triggered the struggle to find a creative response to a very deadly problem, while cultural norms served as barriers that prevented the rejection of previously accepted solutions that had proven successful in the past, even though those successful solutions no longer fit in the context of the reality of the present. The case study highlights leaders who were constrained by deeply-held assumptions that inhibited their ability to adapt quickly to a changed environment. The case study then moves on to provide an example of a successful application of adaptive leadership and adaptive work that was performed by the organization after a period of reflection and the willingness to experiment and assume risk. The case study serves as a microcosm of the challenges facing the U.S. Army, and the corresponding leadership framework presented in this monograph can be used as a model for the Army as it attempts to move forward in its effort to make adaptation an institutional imperative. The paper presents a more holistic approach to leadership where the leader transcends that of simply being an authority figure and becomes a real leader who provides a safe and creative learning environment where the organization can tackle and solve adaptive challenges. The paper concludes by recommending that U.S. Army leaders apply Harvard Professor Dean Williams's theory to the challenges confronting the Army's leader development process thereby fostering a culture of adaptive leaders.

RESEARCHING SITUATED LEARNING AS THE DEVELOPMENT OF PRACTICE FOR COLLEGE ACCESS PRACTITIONERS: A CASE STUDY OF THE COLLEGE ASSISTANCE MIGRANT PROGRAM

Thesis (Ph.D.)--University of Washington, 2016-03

Integrating science and management through collaborative learning and better information management

Achieving more sustainable land and water use depends on high-quality information and its improved use. In other words, better linkages are needed between science and management. Since many stakeholders with different relationships to the natural resources are inevitably involved, we suggest that collaborative learning environments and improved information management are prerequisites for integrating science and management. Case studies that deal with resource management issues are presented that illustrate the creation of collaborative learning environments through systems analyses with communities, and an integration of scientific and experiential knowledge of components of the system. This new knowledge needs to be captured and made accessible through innovative information management systems designed collaboratively with users, in forms which fit the users' 'mental models' of how their systems work. A model for linking science and resource management more effectively is suggested. This model entails systems thinking in a collaborative learning environment, and processes to help convergence of views and value systems, and make scientists and different kinds of managers aware of their interdependence. Adaptive management provides a mechanism for applying and refining scientists' and managers' knowledge. Copyright (C) 2003 John Wiley Sons, Ltd.

Intelligent agent supported personalization for virtual learning environments

Virtual learning environments (VLEs) are computer-based online learning environments, which provide opportunities for online learners to learn at the time and location of their choosing, whilst allowing interactions and encounters with other online learners, as well as affording access to a wide range of resources. They have the capability of reaching learners in remote areas around the country or across country boundaries at very low cost. Personalized VLEs are those VLEs that provide a set of personalization functionalities, such as personalizing learning plans, learning materials, tests, and are capable of initializing the interaction with learners by providing advice, necessary instant messages, etc., to online learners. One of the major challenges involved in developing personalized VLEs is to achieve effective personalization functionalities, such as personalized content management, learner model, learner plan and adaptive instant interaction. Autonomous intelligent agents provide an important technology for accomplishing personalization in VLEs. A number of agents work collaboratively to enable personalization by recognizing an individual's eLeaming pace and reacting correspondingly. In this research, a personalization model has been developed that demonstrates dynamic eLearning processes; secondly, this study proposes an architecture for PVLE by using intelligent decision-making agents' autonomous, pre-active and proactive behaviors. A prototype system has been developed to demonstrate the implementation of this architecture. Furthemore, a field experiment has been conducted to investigate the performance of the prototype by comparing PVLE eLearning effectiveness with a non-personalized VLE. Data regarding participants' final exam scores were collected and analyzed. The results indicate that intelligent agent technology can be employed to achieve personalization in VLEs, and as a consequence to improve eLeaming effectiveness dramatically.

An examination of learning processes during critical incident training: Implications for the development of adaptable trainees

Three experiments are reported that examined the process by which trainees learn decision-making skills during a critical incident training program. Formal theories of category learning were used to identify two processes that may be responsible for the acquisition of decision-making skills: rule learning and exemplar learning. Experiments I and 2 used the process dissociation procedure (L. L. Jacoby, 1998) to evaluate the contribution of these processes to performance. The results suggest that trainees used a mixture of rule and exemplar learning. Furthermore, these learning processes were influenced by different aspects of training structure and design. The goal of Experiment 3 was to develop training techniques that enable trainees to use a rule adaptively. Trainees were tested on cases that represented exceptions to the rule. Unexpectedly, the results suggest that providing general instruction regarding the kinds of conditions in which a decision rule does not apply caused them to fixate on the specific conditions mentioned and impaired their ability to identify other conditions in which the rule might not apply. The theoretical, methodological, and practical implications of the results are discussed.

Playing in continuous spaces: Some analysis and extension of population-based incremental learning

As an alternative to traditional evolutionary algorithms (EAs), population-based incremental learning (PBIL) maintains a probabilistic model of the best individual(s). Originally, PBIL was applied in binary search spaces. Recently, some work has been done to extend it to continuous spaces. In this paper, we review two such extensions of PBIL. An improved version of the PBIL based on Gaussian model is proposed that combines two main features: a new updating rule that takes into account all the individuals and their fitness values and a self-adaptive learning rate parameter. Furthermore, a new continuous PBIL employing a histogram probabilistic model is proposed. Some experiments results are presented that highlight the features of the new algorithms.

User-centred ontology learning for knowledge management

Automatic ontology building is a vital issue in many fields where they are currently built manually. This paper presents a user-centred methodology for ontology construction based on the use of Machine Learning and Natural Language Processing. In our approach, the user selects a corpus of texts and sketches a preliminary ontology (or selects an existing one) for a domain with a preliminary vocabulary associated to the elements in the ontology (lexicalisations). Examples of sentences involving such lexicalisation (e.g. ISA relation) in the corpus are automatically retrieved by the system. Retrieved examples are validated by the user and used by an adaptive Information Extraction system to generate patterns that discover other lexicalisations of the same objects in the ontology, possibly identifying new concepts or relations. New instances are added to the existing ontology or used to tune it. This process is repeated until a satisfactory ontology is obtained. The methodology largely automates the ontology construction process and the output is an ontology with an associated trained leaner to be used for further ontology modifications.