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  • 51.
    Loutfi, Amy
    et al.
    Örebro University, Department of Technology.
    Coradeschi, Silvia
    Örebro University, Department of Technology.
    Karlsson, Lars
    Örebro University, Department of Technology.
    Broxvall, Mathias
    Örebro University, Department of Technology.
    Putting olfaction into action: using an electronic nose on an multi-sensing mobile robot2004In: Proceedings of the 2004 IEEE/RSJ international conference on intelligent robots and systems (IROS 2004), 2004, p. 337-342Conference paper (Refereed)
    Abstract [en]

    Olfaction is a challenging new sensing modality for intelligent systems. With the emergence of electronic noses it is now possible to detect and recognise a range of different odours for a variety of applications. An existing application is to use electronic olfaction on mobile robots for the purpose of odour based navigation. In this work, we introduce a new application where electronic olfaction is used in cooperation with other types of sensors on a mobile robot in order to acquire the odour

  • 52.
    Loutfi, Amy
    et al.
    Örebro University, Department of Technology.
    Coradeschi, Silvia
    Örebro University, Department of Technology.
    Saffiotti, Alessandro
    Örebro University, Department of Technology.
    Maintaining coherent perceptual information using anchoring2005In: Proceedings of the 19th international joint conference on Artificial intelligence, 2005, p. 1477-1482Conference paper (Refereed)
    Abstract [en]

    The purpose of this paper is to address the problem of maintaining coherent perceptual information in a mobile robotic system working over extended periods of time, interacting with a user and using multiple sensing modalities to gather information about the environment and specific objects. We present a system which is able to use spatial and olfactory sensors to patrol a corridor and execute user requested tasks. To cope with perceptual maintenance we present an extension of the anchoring framework capable of maintaining the correspondence between sensor data and the symbolic descriptions referring to objects. It is also capable of tracking and acquiring information from observations derived from sensor-data as well as information from a priori symbolic concepts. The general system is described and an experimental validation on a mobile robot is presented.

  • 53.
    Längkvist, Martin
    et al.
    Örebro University, School of Science and Technology.
    Coradeschi, Silvia
    Örebro University, School of Science and Technology.
    Loutfi, Amy
    Örebro University, School of Science and Technology.
    Rayappan, John Bosco Balaguru
    SASTRA University, Thanjavur, India.
    Fast Classification of Meat Spoilage Markers Using Nanostructured ZnO Thin Films and Unsupervised Feature Learning2013In: Sensors, ISSN 1424-8220, E-ISSN 1424-8220, Vol. 13, no 2, p. 1578-1592Article in journal (Refereed)
    Abstract [en]

    This paper investigates a rapid and accurate detection system for spoilage in meat. We use unsupervised feature learning techniques (stacked restricted Boltzmann machines and auto-encoders) that consider only the transient response from undoped zinc oxide, manganese-doped zinc oxide, and fluorine-doped zinc oxide in order to classify three categories: the type of thin film that is used, the type of gas, and the approximate ppm-level of the gas. These models mainly offer the advantage that features are learned from data instead of being hand-designed. We compare our results to a feature-based approach using samples with various ppm level of ethanol and trimethylamine (TMA) that are good markers for meat spoilage. The result is that deep networks give a better and faster classification than the feature-based approach, and we thus conclude that the fine-tuning of our deep models are more efficient for this kind of multi-label classification task.

  • 54.
    Melchert, Jonas
    et al.
    Örebro University, Department of Technology.
    Coradeschi, Silvia
    Örebro University, Department of Technology.
    Loutfi, Amy
    Örebro University, Department of Technology.
    Knowledge representation and reasoning for perceptual anchoring2007In: 19th IEEE international conference on tools with artificial intelligence, ICTAI 2007, New York: IEEE , 2007, p. 129-136Conference paper (Refereed)
    Abstract [en]

    In this work we report results on the use of symbolic knowledge representation and reasoning (KRR) for perceptual anchoring. Anchoring is the creation and maintenance of a connection between the symbolic and perceptual description that refer to the same physical object in the environment. We extend the anchoring framework to manage the symbolic information in a KRR system, and to exploit this knowledge and the inference mechanism to recover from failures in the anchoring of symbols. We show a simulated scenario where the system communicates with a user to interactively resolve an ambiguous description using the knowledge base, and in particular spatial relations. This is a first step towards a KRR-supported anchoring framework that we will use for human-robot communication.

  • 55.
    Melchert, Jonas
    et al.
    Örebro University, Department of Technology.
    Coradeschi, Silvia
    Örebro University, Department of Technology.
    Loutfi, Amy
    Örebro University, Department of Technology.
    Spatial Relations for Perceptual Anchoring2007In: Proceedings of AISB'07, 2007, p. 459-463Conference paper (Other academic)
    Abstract [en]

    In this work we show how a mobile robot can use spatial information of objects to improve communication with humans and other devices located in an intelligent environment. In particular, this work focuses on using spatial relations to facilitate the creation of a connection between symbolic and perceptual representation that refer to the same physical object (anchoring). We extend an anchoring framework to include a set of binary spatial relations which can then be used to exchange information about objects with a human user. To illustrate the performance of the framework, a number of scenarios are presented using a mobile robot. These scenarios are a first step towards the goal of having mobile robots integrated in an intelligent environment and communicating with human users.

  • 56.
    Palumbo, Filippo
    et al.
    Istituto di Scienza e Tecnologie dell’Informazione (ISTI), Natl Res Council, Pisa, Italy; Dept Comp Sci, Univ Pisa, Pisa, Italy.
    Ullberg, Jonas
    Örebro University, School of Science and Technology.
    Stimec, Ales
    XLAB Doo, XLAB Res, Ljubljana, Slovenia.
    Furfari, Francesco
    Istituto di Scienza e Tecnologie dell’Informazione (ISTI), Natl Res Council, Pisa, Italy.
    Karlsson, Lars
    Örebro University, School of Science and Technology.
    Coradeschi, Silvia
    Örebro University, School of Science and Technology.
    Sensor Network Infrastructure for a Home Care Monitoring System2014In: Sensors, ISSN 1424-8220, E-ISSN 1424-8220, Vol. 14, no 3, p. 3833-3860Article in journal (Refereed)
    Abstract [en]

    This paper presents the sensor network infrastructure for a home care system that allows long-term monitoring of physiological data and everyday activities. The aim of the proposed system is to allow the elderly to live longer in their home without compromising safety and ensuring the detection of health problems. The system offers the possibility of a virtual visit via a teleoperated robot. During the visit, physiological data and activities occurring during a period of time can be discussed. These data are collected from physiological sensors (e.g., temperature, blood pressure, glucose) and environmental sensors (e.g., motion, bed/chair occupancy, electrical usage). The system can also give alarms if sudden problems occur, like a fall, and warnings based on more long-term trends, such as the deterioration of health being detected. It has been implemented and tested in a test environment and has been deployed in six real homes for a year-long evaluation. The key contribution of the paper is the presentation of an implemented system for ambient assisted living (AAL) tested in a real environment, combining the acquisition of sensor data, a flexible and adaptable middleware compliant with the OSGistandard and a context recognition application. The system has been developed in a European project called GiraffPlus.

  • 57.
    Persson, Andreas
    et al.
    Örebro University, School of Science and Technology.
    Coradeschi, Silvia
    Örebro University, School of Science and Technology.
    Rajasekaran, Balasubramanian
    Dept Sci & Technol, Ctr Appl Autonomous Sensor Syst AASS, Univ Örebro, Örebro, Sweden.
    Krishna, Vamsi
    Dept Science & Technology, Center for Applied Autonomous Sensor Syst (AASS), Örebro University, Örebro, Sweden.
    Loutfi, Amy
    Örebro University, School of Science and Technology.
    Alirezaie, Marjan
    Örebro University, School of Science and Technology.
    I would like some food: anchoring objects to semantic web informationin human-robot dialogue interactions2013In: Social Robotics: Proceedings of 5th International Conference, ICSR 2013, Bristol, UK, October 27-29, 2013. / [ed] Guido Herrmann, Martin J. Pearson, Alexander Lenz, Paul Bremner, Adam Spiers, Ute Leonards, Springer, 2013, p. 361-370Conference paper (Refereed)
    Abstract [en]

    Ubiquitous robotic systems present a number of interesting application areas for socially assistive robots that aim to improve quality of life. In particular the combination of smart home environments and relatively inexpensive robots can be a viable technological solutions for assisting elderly and persons with disability in their own home. Such services require an easy interface like spoken dialogue and the ability to refer to physical objects using semantic terms. This paper presents an implemented system combining a robot and a sensor network deployed in a test apartment in an elderly residence area. The paper focuses on the creation and maintenance (anchoring) of the connection between the semantic information present in the dialogue with perceived physical objects in the home. Semantic knowledge about concepts and their correlations are retrieved from on-line resources and ontologies, e.g. WordNet, and sensor information is provided by cameras distributed in the apartment.

  • 58.
    Trincavelli, Marco
    et al.
    Örebro University, School of Science and Technology.
    Coradeschi, Silvia
    Örebro University, School of Science and Technology.
    Loutfi, Amy
    Örebro University, School of Science and Technology.
    Classification of odours for mobile robots using an ensemble of linear classifiers2009In: Olfaction and electronic nose: proceedings of the 13th international symposium on olfaction and electronic nose / [ed] Matteo Pardo, Giorgio Sberveglieri, American Institute of Physics (AIP), 2009, p. 475-478Conference paper (Refereed)
    Abstract [en]

    This paper investigates the classification of odours using an electronic nose mounted on a mobile robot. The samples are collected as the robot explores the environment. Under such conditions, the sensor response differs from typical three phase sampling processes. In this paper, we focus particularly on the classification problem and how it is influenced by the movement of the robot. To cope with these influences, an algorithm consisting of an ensemble of classifiers is resented. Experimental results show that this algorithm increases classification performance compared to other traditional classification methods.

  • 59.
    Trincavelli, Marco
    et al.
    Örebro University, School of Science and Technology.
    Coradeschi, Silvia
    Örebro University, Department of Technology.
    Loutfi, Amy
    Örebro University, Department of Technology.
    Classification of odours with mobile robots based on transient response2008In: IEEE/RSJ international conference on intelligent robots and systems, IROS 2008, New York: IEEE , 2008, p. 4110-4115Conference paper (Refereed)
    Abstract [en]

    Classification of odours with an array of gas sensors mounted on a mobile robot is a challenging and still relatively unexplored topic. Mobile robots able to classify an odour could navigate to a specific source or isolate high concentration areas in applications such as environmental monitoring. A key aspect to classification is to be able to process the data collected while moving the robot and using a simple and compact sensor system. In order to achieve this, we present a classification algorithm that is based in the transient response from the sensors. An analysis of how classification results vary with regards to the movement of the robot is provided and subsequently the experimental validations show that the classification performance depends more on how

    the robot traverses the odour plume and the quality of the transient than on the distance from the source location. The experimental validation has been done in a large unmodified indoor environment.

  • 60.
    Trincavelli, Marco
    et al.
    Örebro University, School of Science and Technology.
    Coradeschi, Silvia
    Örebro University, School of Science and Technology.
    Loutfi, Amy
    Örebro University, School of Science and Technology.
    Odour classification system for continuous monitoring applications2009In: Sensors and actuators. B, Chemical, ISSN 0925-4005, E-ISSN 1873-3077, Vol. 58, no 2, p. 265-273Article in journal (Refereed)
    Abstract [en]

    In this paper, we investigate the classification performance of an electronic nose system, based on tin dioxide gas sensors. In contrast to previous studies, the electronic nose is mounted on a mobile platform and samples are analyzed using only transient information in the signals. The motivation behind this work is to explore the feasibility of using electronic nose devices for odour classification in a number of future application domains which require fast and possibly real-time odour identification. To perform transient based analysis of the signals, a comparative study of different methods for feature extraction was performed. Additionally, the application of a relevance vector machine classifier is explored to further analyze the classification performance based on quality of the obtained samples. The results presented in this study can be used for the development of electronic nose devices particularly suitable for environmental monitoring applications.

  • 61.
    Trincavelli, Marco
    et al.
    Örebro University, School of Science and Technology.
    Coradeschi, Silvia
    Örebro University, School of Science and Technology.
    Loutfi, Amy
    Örebro University, School of Science and Technology.
    Online classification of gases for environmental exploration2009In: IEEE/RSJ international conference on intelligent robots and systems, IROS 2009, New York: IEEE, 2009, p. 3311-3316Conference paper (Refereed)
    Abstract [en]

    In this paper we investigate how a mobile robot equipped with tin dioxide gas sensors and an anemometer can use an online classification algorithm in order to improve the exploration strategy. The purpose of the platform is to establish the character of a gas source with accuracy while minimizing the time required for exploration. For this to be possible, the output of the classification algorithm is probabilistic, feeding in a sequence of posterior probabilities to a path planner. To further assist path planning, a 3d-ultrasonic anemometer is available which give indication on the average wind speed and direction. In addition to evaluating different olfaction driven path planning strategies, experimental validations also evaluate the classification algorithms and its application to different environments with varying characteristics.

  • 62.
    Trincavelli, Marco
    et al.
    Örebro University, School of Science and Technology.
    Coradeschi, Silvia
    Örebro University, School of Science and Technology.
    Loutfi, Amy
    Örebro University, School of Science and Technology.
    Söderquist, Bo
    Örebro University Hospital, Örebro, Sweden .
    Thunberg, Per
    Örebro University Hospital, Örebro, Sweden .
    Direct identification of bacteria in blood culture samples using an electronic nose2010In: IEEE Transactions on Biomedical Engineering, ISSN 0018-9294, E-ISSN 1558-2531, Vol. 57, no 12, p. 2884-2890Article in journal (Refereed)
    Abstract [en]

    In this paper, we introduce a method for identification of bacteria in human blood culture samples using an electronic nose. The method uses features, which capture the static (steady state) and dynamic (transient) properties of the signal from the gas sensor array and proposes a means to ensemble results from consecutive samples. The underlying mechanism for ensembling is based on an estimation of posterior probability, which is extracted from a support vector machine classifier. A large dataset representing ten different bacteria cultures has been used to validate the presented methods. The results detail the performance of the proposed algorithm and show that through ensembling decisions on consecutive samples, significant reliability in classification accuracy can be achieved.

  • 63.
    Trincavelli, Marco
    et al.
    Örebro University, Department of Technology.
    Reggente, Matteo
    Örebro University, Department of Technology.
    Coradeschi, Silvia
    Örebro University, Department of Technology.
    Loutfi, Amy
    Örebro University, Department of Technology.
    Ishida, Hiroshi
    Department of Mechanical Systems Engineering, Tokyo University of Agriculture and Technology, Tokyo, Japan.
    Lilienthal, Achim J.
    Örebro University, Department of Technology.
    Towards environmental monitoring with mobile robots2008In: 2008 IEEE/RSJ International Conference on Intelligent Robots and Systems, New York, NY, USA: IEEE, 2008, p. 2210-2215, article id 4650755Conference paper (Refereed)
    Abstract [en]

    In this paper we present initial experiments towards environmental monitoring with a mobile platform. A prototype of a pollution monitoring robot was set up which measures the gas distribution using an “electronic nose” and provides three dimensional wind measurements using an ultrasonic anemometer. We describe the design of the robot and the experimental setup used to run trials under varying environmental conditions. We then present the results of the gas distribution mapping. The trials which were carried out in three uncontrolled environments with very different properties:

    an enclosed indoor area, a part of a long corridor with open ends and a high ceiling, and an outdoor scenario are presented and discussed.

  • 64.
    Ullberg, Jonas
    et al.
    Örebro University, School of Science and Technology.
    Coradeschi, Silvia
    Örebro University, School of Science and Technology.
    Pecora, Federico
    Örebro University, School of Science and Technology.
    On-line ADL recognition with prior knowledge2010In: STAIRS 2010: proceedings of the fifth Starting AI Researchers' Symposium / [ed] Thomas Ågotnes, Amsterdam: IOS Press, 2010, p. 354-366Conference paper (Refereed)
    Abstract [en]

    This paper addresses the problem of recognizing activities of daily living. The novelty lies in the use of an existing knowledge base (ConceptNet) to introduce prior knowledge into the system in order to reduce the amount of learning required to deploy the system in a real environment. The use of household objects is central in the recognition of activities that are being performed, and we attach semantic meaning to both the objects and activities that are being recognized. The paper describes a framework which is specifically geared towards realizing activity recognition systems which leverage prior knowledge. A preliminary implementation of a neural network based recognition system built on this framework is shown, and the added value of prior knowledge is evaluated through the use of various data sets.

12 51 - 64 of 64
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