Warped K-Means: An algorithm to cluster sequentially-distributed data

[EN] Many devices generate large amounts of data that follow some sort of sequentiality, e.g., motion sensors, e-pens, eye trackers, etc. and often these data need to be compressed for classification, storage, and/or retrieval tasks. Traditional clustering algorithms can be used for this purpose, but unfortunately they do not cope with the sequential information implicitly embedded in such data. Thus, we revisit the well-known K-means algorithm and provide a general method to properly cluster sequentially-distributed data. We present Warped K-Means (WKM), a multi-purpose partitional clustering procedure that minimizes the sum of squared error criterion, while imposing a hard sequentiality constraint in the classification step. We illustrate the properties of WKM in three applications, one being the segmentation and classification of human activity. WKM outperformed five state-of- the-art clustering techniques to simplify data trajectories, achieving a recognition accuracy of near 97%, which is an improvement of around 66% over their peers. Moreover, such an improvement came with a reduction in the computational cost of more than one order of magnitude.This work has been partially supported by Casmacat (FP7-ICT-2011-7, Project 287576), tranScriptorium (FP7-ICT-2011-9, Project 600707), STraDA (MINECO, TIN2012-37475-0O2-01), and ALMPR (GVA, Prometeo/20091014) projects.Leiva Torres, LA.; Vidal, E. (2013). Warped K-Means: An algorithm to cluster sequentially-distributed data. Information Sciences. 237:196-210. https://doi.org/10.1016/j.ins.2013.02.042S19621023

DROUGHT TOLERANT INDICES OF LOWLAND TOMATO CULTIVARS

The released lowland tomato cultivars are known for their resistance to plant diseases and high temperatures. The study aimed to identify the drought tolerance of lowland tomato cultivars based on the drought tolerant indices. The study was arranged in a split plot design, using seven lowland tomato cultivars (Zamrud, Permata F1, Ratna, Mirah, Tombatu F1, Tyrana F1, and Tymoti F1) as the main plot and watering (standard conditions and once every eight days as the drought conditions) as the subplot. Parameters observed were morpho physiological characters (plant height, leaf area, biomass, root length, root surface area, shoot root ratio, relative moisture content, membrane stability index, chlorophyll levels, and proline levels). The parameters observed in each character included the sensitivity stress index (SSI), stress tolerance index (STI), and yield stability index (YSI). Results showed that four cultivars (Tyrana F1, Tymoty F1, Mirah, and Tombatu F1) were drought tolerance, and three cultivars (Ratna, Permata F1, and Zamrud F1) were susceptible. The water stress decreased agronomic and physiological traits performance, but the drought-tolerant cultivars were less affected to the stress and produced higher fruit weight. The study implies that the drought-tolerant cultivars could be used as a promising source for drought tolerant genotypes

Two improved methods for mobile robot localization

Mobile robot localization is the problem of determining the robot\u27s pose given the map of its environment, based on the sensor reading and its movement. It is a fundamental and very important problem in the research of mobile robotics. Grid localization and Monte Carlo localization (MCL) are two of the most widely used approaches for localization, especially the MCL. However each of these two popular methods has its own problems. How to reduce the computation cost and better the accuracy is our main concern. In order to improve the performance of localization, we propose two improved localization algorithms. The first algorithm is called moving grid cell based MCL, which takes advantages of both grid localization and MCL and overcomes their respective shortcomings. The second algorithm is dynamic MCL based on clustering, which uses a cluster analysis component to reduce the computation cost

Bring Consciousness to Mobile Robot Being Localized

Mobile robot localization is one of the most important problems in robotics research. A number of successful localization solutions have been proposed, among them, the well-known and popular Monte Carlo Localization (MCL) method. However, in all these methods, the robot itself does not carry a notion whether it has or has not been localized, and the success or failure of localization is judged by normally a human operator of the robot. In this paper, we put forth a novel method to bring consciousness to a mobile robot so that the robot can judge by itself whether it has been localized or not without any intervention from human operator. In addition, the robot is capable to notice the change between global localization and position tracking, hence, adjusting itself based on the status of localization. A mobile robot with consciousness being localized is obviously more autonomous and intelligent than one without

Diverse Contributions to Implicit Human-Computer Interaction

Cuando las personas interactúan con los ordenadores, hay mucha información que no se proporciona a propósito. Mediante el estudio de estas interacciones implícitas es posible entender qué características de la interfaz de usuario son beneficiosas (o no), derivando así en implicaciones para el diseño de futuros sistemas interactivos. La principal ventaja de aprovechar datos implícitos del usuario en aplicaciones informáticas es que cualquier interacción con el sistema puede contribuir a mejorar su utilidad. Además, dichos datos eliminan el coste de tener que interrumpir al usuario para que envíe información explícitamente sobre un tema que en principio no tiene por qué guardar relación con la intención de utilizar el sistema. Por el contrario, en ocasiones las interacciones implícitas no proporcionan datos claros y concretos. Por ello, hay que prestar especial atención a la manera de gestionar esta fuente de información. El propósito de esta investigación es doble: 1) aplicar una nueva visión tanto al diseño como al desarrollo de aplicaciones que puedan reaccionar consecuentemente a las interacciones implícitas del usuario, y 2) proporcionar una serie de metodologías para la evaluación de dichos sistemas interactivos. Cinco escenarios sirven para ilustrar la viabilidad y la adecuación del marco de trabajo de la tesis. Resultados empíricos con usuarios reales demuestran que aprovechar la interacción implícita es un medio tanto adecuado como conveniente para mejorar de múltiples maneras los sistemas interactivos.Leiva Torres, LA. (2012). Diverse Contributions to Implicit Human-Computer Interaction [Tesis doctoral no publicada]. Universitat Politècnica de València. https://doi.org/10.4995/Thesis/10251/17803Palanci

An Improved Clustering based Monte Carlo Localization approach for Cooperative Multi-robot Localization

This thesis describes an approach for cooperative multi-robot localization based on probabilistic method (Monte Carlo Localization) used in assistant robots which are capable of sensing and communicating one with another. In our approach, each of the robots maintains its own clustering based MCL algorithm, and communicates with each other whenever it detects another robot. We develop a new information exchange mechanism, which makes use of the information extracted from the clustering component, to synchronize the beliefs of detected robots. By avoiding unnecessary information exchange whenever detection occurs through a belief comparison, our approach can solve the delayed integration problem to improve the effectiveness and efficiency of multi-robot localization. This approach has been tested in both real and simulated environments. Compared with single robot localization, the experimental results demonstrate that our approach can notably improve the performance, especially when the environments are highly symmetric