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  • 51.
    Almeida, Tiago
    et al.
    Örebro University, School of Science and Technology. IEETA, DEM, University of Aveiro, Aveiro, Portugal.
    Santos, Vitor
    IEETA, DEM, University of Aveiro, Aveiro, Portugal.
    Martinez Mozos, Oscar
    Örebro University, School of Science and Technology.
    Lourenco, Bernardo
    IEETA, DEM, University of Aveiro, Aveiro, Portugal.
    Comparative Analysis of Deep Neural Networks for the Detection and Decoding of Data Matrix Landmarks in Cluttered Indoor Environments2021In: Journal of Intelligent and Robotic Systems, ISSN 0921-0296, E-ISSN 1573-0409, Vol. 103, no 1, article id 13Article in journal (Refereed)
    Abstract [en]

    Data Matrix patterns imprinted as passive visual landmarks have shown to be a valid solution for the self-localization of Automated Guided Vehicles (AGVs) in shop floors. However, existing Data Matrix decoding applications take a long time to detect and segment the markers in the input image. Therefore, this paper proposes a pipeline where the detector is based on a real-time Deep Learning network and the decoder is a conventional method, i.e. the implementation in libdmtx. To do so, several types of Deep Neural Networks (DNNs) for object detection were studied, trained, compared, and assessed. The architectures range from region proposals (Faster R-CNN) to single-shot methods (SSD and YOLO). This study focused on performance and processing time to select the best Deep Learning (DL) model to carry out the detection of the visual markers. Additionally, a specific data set was created to evaluate those networks. This test set includes demanding situations, such as high illumination gradients in the same scene and Data Matrix markers positioned in skewed planes. The proposed approach outperformed the best known and most used Data Matrix decoder available in libraries like libdmtx.

  • 52.
    Almousa, Sami
    et al.
    Örebro University, School of Science and Technology.
    Morad, Gorgis
    Örebro University, School of Science and Technology.
    Detecting Successful Throws2023Independent thesis Basic level (professional degree), 10 credits / 15 HE creditsStudent thesis
    Abstract [en]

    This project aims to create a robot system that can accurately figure out if the throws are successful. This can help make various industrial tasks more efficient. The system uses implemented methods to process data from fisheye camera data and depth sensor data, to check the quality of the throws. The main goal is to find out if the thrown object reaches its target or not, with more advanced tasks including predicting its path when frames are lost or not tracked properly.To put the system together the Robot Operating System (ROS) was used for handling data and processing, as well as different tools and techniques, like bag files and OpenCV. A variety of methods and algorithms were used to apply background subtraction, clustering, curve fitting, marking objects and drawing the path they take in the air. The depth sensor data processing is included to make up for the limitations of 2D camera data, providing more accurate and reliable tracking of thrown objects. 

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  • 53.
    Almqvist, Håkan
    et al.
    Örebro University, School of Science and Technology.
    Magnusson, Martin
    Örebro University, School of Science and Technology.
    Kucner, Tomasz Piotr
    Örebro University, School of Science and Technology.
    Lilienthal, Achim
    Örebro University, School of Science and Technology.
    Learning to detect misaligned point clouds2018In: Journal of Field Robotics, ISSN 1556-4959, E-ISSN 1556-4967, Vol. 35, no 5, p. 662-677Article in journal (Refereed)
    Abstract [en]

    Matching and merging overlapping point clouds is a common procedure in many applications, including mobile robotics, three-dimensional mapping, and object visualization. However, fully automatic point-cloud matching, without manual verification, is still not possible because no matching algorithms exist today that can provide any certain methods for detecting misaligned point clouds. In this article, we make a comparative evaluation of geometric consistency methods for classifying aligned and nonaligned point-cloud pairs. We also propose a method that combines the results of the evaluated methods to further improve the classification of the point clouds. We compare a range of methods on two data sets from different environments related to mobile robotics and mapping. The results show that methods based on a Normal Distributions Transform representation of the point clouds perform best under the circumstances presented herein.

  • 54.
    Almqvist, Håkan
    et al.
    Örebro University, School of Science and Technology.
    Magnusson, Martin
    Örebro University, School of Science and Technology.
    Lilienthal, Achim J.
    Örebro University, School of Science and Technology.
    Improving Point Cloud Accuracy Obtained from a Moving Platform for Consistent Pile Attack Pose Estimation2014In: Journal of Intelligent and Robotic Systems, ISSN 0921-0296, E-ISSN 1573-0409, Vol. 75, no 1, p. 101-128Article in journal (Refereed)
    Abstract [en]

    We present a perception system for enabling automated loading with waist-articulated wheel loaders. To enable autonomous loading of piled materials, using either above-ground wheel loaders or underground load-haul-dump vehicles, 3D data of the pile shape is needed. However, using common 3D scanners, the scan data is distorted while the wheel loader is moving towards the pile. Existing methods that make use of 3D scan data (for autonomous loading as well as tasks such as mapping, localisation, and object detection) typically assume that each 3D scan is accurate. For autonomous robots moving over rough terrain, it is often the case that the vehicle moves a substantial amount during the acquisition of one 3D scan, in which case the scan data will be distorted. We present a study of auto-loading methods, and how to locate piles in real-world scenarios with nontrivial ground geometry. We have compared how consistently each method performs for live scans acquired in motion, and also how the methods perform with different view points and scan configurations. The system described in this paper uses a novel method for improving the quality of distorted 3D scans made from a vehicle moving over uneven terrain. The proposed method for improving scan quality is capable of increasing the accuracy of point clouds without assuming any specific features of the environment (such as planar walls), without resorting to a “stop-scan-go” approach, and without relying on specialised and expensive hardware. Each new 3D scan is registered to the preceding using the normal-distributions transform (NDT). After each registration, a mini-loop closure is performed with a local, per-scan, graph-based SLAM method. To verify the impact of the quality improvement, we present data that shows how auto-loading methods benefit from the corrected scans. The presented methods are validated on data from an autonomous wheel loader, as well as with simulated data. The proposed scan-correction method increases the accuracy of both the vehicle trajectory and the point cloud. We also show that it increases the reliability of pile-shape measures used to plan an efficient attack pose when performing autonomous loading.

  • 55.
    Almqvist, Håkan
    et al.
    Örebro University, School of Science and Technology.
    Magnusson, Martin
    Örebro University, School of Science and Technology.
    Stoyanov, Todor
    Örebro University, School of Science and Technology.
    Lilienthal, Achim J.
    Örebro University, School of Science and Technology.
    Improving Point-Cloud Accuracy from a Moving Platform in Field Operations2013In: 2013 IEEE International Conference on Robotics and Automation (ICRA), IEEE conference proceedings, 2013, p. 733-738Conference paper (Refereed)
    Abstract [en]

    This paper presents a method for improving the quality of distorted 3D point clouds made from a vehicle equipped with a laser scanner moving over uneven terrain. Existing methods that use 3D point-cloud data (for tasks such as mapping, localisation, and object detection) typically assume that each point cloud is accurate. For autonomous robots moving in rough terrain, it is often the case that the vehicle moves a substantial amount during the acquisition of one point cloud, in which case the data will be distorted. The method proposed in this paper is capable of increasing the accuracy of 3D point clouds, without assuming any specific features of the environment (such as planar walls), without resorting to a "stop-scan-go" approach, and without relying on specialised and expensive hardware. Each new point cloud is matched to the previous using normal-distribution-transform (NDT) registration, after which a mini-loop closure is performed with a local, per-scan, graph-based SLAM method. The proposed method increases the accuracy of both the measured platform trajectory and the point cloud. The method is validated on both real-world and simulated data.

  • 56.
    Alzghoul, Ahmad
    et al.
    Department of Information Technology, Division of Computing Science, Uppsala, Sweden.
    Backe, Björn
    Division of Computer Aided Design, Luleå University of Technology, Luleå, Sweden.
    Löfstrand, Magnus
    Department of Information Technology, Division of Computing Science, Uppsala, Sweden.
    Byström, Arne
    Bosch Rexroth Mellansel AB, Mellansel, Sweden.
    Liljedahl, Bengt
    Bosch Rexroth Mellansel AB, Mellansel, Sweden.
    Comparing a knowledge-based and a data-driven method in querying data streams for system fault detection: A hydraulic drive system application2014In: Computers in industry (Print), ISSN 0166-3615, E-ISSN 1872-6194, Vol. 65, no 8, p. 1126-1135Article in journal (Refereed)
    Abstract [en]

    The field of fault detection and diagnosis has been the subject of considerable interest in industry. Fault detection may increase the availability of products, thereby improving their quality. Fault detection and diagnosis methods can be classified in three categories: data-driven, analytically based, and knowledge-based methods.

    In this work, we investigated the ability and the performance of applying two fault detection methods to query data streams produced from hydraulic drive systems. A knowledge-based method was compared to a data-driven method. A fault detection system based on a data stream management system (DSMS) was developed in order to test and compare the two methods using data from real hydraulic drive systems.

    The knowledge-based method was based on causal models (fault trees), and principal component analysis (PCA) was used to build the data-driven model. The performance of the methods in terms of accuracy and speed, was examined using normal and physically simulated fault data. The results show that both methods generate queries fast enough to query the data streams online, with a similar level of fault detection accuracy. The industrial applications of both methods include monitoring of individual industrial mechanical systems as well as fleets of such systems. One can conclude that both methods may be used to increase industrial system availability.

  • 57.
    Alzghoul, Ahmad
    et al.
    Department of Information Technology, Division of Computing Science, Uppsala, Sweden.
    Löfstrand, Magnus
    Department of Information Technology, Division of Computing Science, Uppsala, Sweden.
    Addressing concept drift to improve system availability by updating one-class data-driven models2015In: Evolving Systems, ISSN 1868-6478, E-ISSN 1868-6486, Vol. 6, no 3, p. 187-198Article in journal (Refereed)
    Abstract [en]

    Data-driven models have been used to detect system faults, thereby increasing industrial system availability. The ability to search data streams while dealing with concept drift are challenges for data-driven models. The objective of this work is to demonstrate a general method to manage concept drift when using one-class data-driven models. The method has been used to develop an automatically retrained and updated polygon-based model. In this paper, the available industrial data allowed for use of one-class data-driven models, and the polygon-based model was selected because it has previously been successful. Possible scenarios that allow one-class data-driven models to be retrained or updated were identified. Based on the identified scenarios, a method to automatically update a polygon-based model online is proposed. The method has been tested and verified using data collected from a Bosch Rexroth Mellansel AB hydraulic drive system. Data representing relevant faults was inserted into the data set in close collaboration with engineers from the company. The results show that the developed polygon-based model method was able to address the concept drift issue and was able to significantly improve the classification accuracy compared to the static polygon-based model. Thereby, the model could significantly improve industrial system availability when applied in the relevant production process. This paper shows that the developed polygon-based model requires small memory space while its updating procedure is simple and fast. Finally, the identified scenarios may be helpful as input for supporting other one-class data-driven models to cope with concept drift, thus increasing the generalizability of the results.

  • 58.
    Alzghoul, Ahmad
    et al.
    Division of Computer Aided Design, Luleå University of Technology, Luleå, Sweden.
    Löfstrand, Magnus
    Division of Computer Aided Design, Luleå University of Technology, Luleå, Sweden.
    Increasing availability of industrial systems through data stream mining2011In: Computers & industrial engineering, ISSN 0360-8352, E-ISSN 1879-0550, Vol. 60, no 2, p. 195-205Article in journal (Refereed)
    Abstract [en]

    Improving industrial product reliability, maintainability and thus availability is a challenging task for many industrial companies. In industry, there is a growing need to process data in real time, since the generated data volume exceeds the available storage capacity. This paper consists of a review of data stream mining and data stream management systems aimed at improving product availability. Further, a newly developed and validated grid-based classifier method is presented and compared to one-class support vector machine (OCSVM) and a polygon-based classifier.

    The results showed that, using 10% of the total data set to train the algorithm, all three methods achieved good (>95% correct) overall classification accuracy. In addition, all three methods can be applied on both offline and online data.

    The speed of the resultant function from the OCSVM method was, not surprisingly, higher than the other two methods, but in industrial applications the OCSVMs' comparatively long time needed for training is a possible challenge. The main advantage of the grid-based classification method is that it allows for calculation of the probability (%) that a data point belongs to a specific class, and the method can be easily modified to be incremental.

    The high classification accuracy can be utilized to detect the failures at an early stage, thereby increasing the reliability and thus the availability of the product (since availability is a function of maintainability and reliability). In addition, the consequences of equipment failures in terms of time and cost can be mitigated.

  • 59.
    Alzghoul, Ahmad
    et al.
    Division of Computer Aided Design, Luleå University of Technology, Luleå, Sweden.
    Löfstrand, Magnus
    Division of Computer Aided Design, Luleå University of Technology, Luleå, Sweden.
    Backe, Björn
    Division of Computer Aided Design, Luleå University of Technology, Luleå, Sweden.
    Data stream forecasting for system fault prediction2012In: Computers & industrial engineering, ISSN 0360-8352, E-ISSN 1879-0550, Vol. 62, no 4, p. 972-978Article in journal (Refereed)
    Abstract [en]

    Competition among today’s industrial companies is very high. Therefore, system availability plays an important role and is a critical point for most companies. Detecting failures at an early stage or foreseeing them before they occur is crucial for machinery availability. Data analysis is the most common method for machine health condition monitoring. In this paper we propose a fault-detection system based on data stream prediction, data stream mining, and data stream management system (DSMS). Companies that are able to predict and avoid the occurrence of failures have an advantage over their competitors. The literature has shown that data prediction can also reduce the consumption of communication resources in distributed data stream processing. In this paper different data-stream-based linear regression prediction methods have been tested and compared within a newly developed fault detection system. Based on the fault detection system, three DSM algorithms outputs are compared to each other and to real data. The three applied and evaluated data stream mining algorithms were: Grid-based classifier, polygon-based method, and one-class support vector machines (OCSVM). The results showed that the linear regression method generally achieved good performance in predicting short-term data. (The best achieved performance was with a Mean Absolute Error (MAE) around 0.4, representing prediction accuracy of 87.5%). Not surprisingly, results showed that the classification accuracy was reduced when using the predicted data. However, the fault-detection system was able to attain an acceptable performance of around 89% classification accuracy when using predicted data.

  • 60.
    Alzghoul, Ahmad
    et al.
    Division of Computer Aided Design, Luleå University of Technology, Luleå, Sweden.
    Löfstrand, Magnus
    Division of Computer Aided Design, Luleå University of Technology, Luleå, Sweden.
    Karlsson, Lennart
    Division of Computer Aided Design, Luleå University of Technology, Luleå, Sweden.
    Karlberg, Magnus
    Division of Computer Aided Design, Luleå University of Technology, Luleå, Sweden.
    Data stream mining for increased functional product availability awareness2011In: Functional Thinking for Value Creation: Proceedings of the 3rd CIRP International Conference on Industrial Product Service Systems, Technische Universität Braunschweig, Braunschweig, Germany, May 5th - 6th, 2011 / [ed] Hesselbach, J. & Herrmann, C., Springer Berlin/Heidelberg, 2011, p. 237-241Conference paper (Refereed)
    Abstract [en]

    Functional Products (FP) and Product Service Systems (PSS) may be seen as integrated systems comprising hardware and support services. For such offerings, availability is key. Little research has been done on integrating Data Stream Management Systems (DSMS) for monitoring (parts of) a FP to improve system availability. This paper introduces an approach for how data stream mining may be applied to monitor hardware being part of a Functional Product. The result shows that DSMS have the potential to significantly support continuous availability awareness of industrial systems, especially important when the supplier is to supply a function with certain availability.

  • 61.
    Amato, G.
    et al.
    Consiglio Nazionale delle Ricerche-Istituto di Scienza e Tecnologie dell'Informazione (CNR-ISTI), Pisa, Italy.
    Bacciu, D.
    Università di Pisa, Pisa, Italy.
    Broxvall, Mathias
    Örebro Universitet, Örebro, Sweden.
    Chessa, S.
    Università di Pisa, Pisa, Italy.
    Coleman, S.
    University of Ulster, Ulster, UK.
    Di Rocco, Maurizio
    Örebro Universitet, Örebro, Sweden.
    Dragone, M.
    Trinity College Dublin, Dublin, Ireland.
    Gallicchio, C.
    Università di Pisa, Pisa, Italy.
    Gennaro, C.
    Consiglio Nazionale delle Ricerche-Istituto di Scienza e Tecnologie dell'Informazione (CNR-ISTI), Pisa, Italy.
    Lozano, H.
    Tecnalia, Madrid, Spain.
    McGinnity, T. M.
    University of Ulster, Ulster, UK.
    Micheli, A.
    Università di Pisa, Pisa, Italy.
    Ray, A. K.
    University of Ulster, Ulster, UK.
    Renteria, A.
    Tecnalia, Madrid, Spain.
    Saffiotti, Alessandro
    Örebro University, School of Science and Technology.
    Swords, D.
    University College Dublin, Dublin, Ireland.
    Vairo, C.
    Consiglio Nazionale delle Ricerche (CNR)-Istituto di Scienza e Tecnologie dell'Informazione (ISTI), Pisa, Italy.
    Vance, P.
    University of Ulster, Ulster, UK.
    Robotic Ubiquitous Cognitive Ecology for Smart Homes2015In: Journal of Intelligent and Robotic Systems, ISSN 0921-0296, E-ISSN 1573-0409, Vol. 80, p. S57-S81Article in journal (Refereed)
    Abstract [en]

    Robotic ecologies are networks of heterogeneous robotic devices pervasively embedded in everyday environments, where they cooperate to perform complex tasks. While their potential makes them increasingly popular, one fundamental problem is how to make them both autonomous and adaptive, so as to reduce the amount of preparation, pre-programming and human supervision that they require in real world applications. The project RUBICON develops learning solutions which yield cheaper, adaptive and efficient coordination of robotic ecologies. The approach we pursue builds upon a unique combination of methods from cognitive robotics, machine learning, planning and agent-based control, and wireless sensor networks. This paper illustrates the innovations advanced by RUBICON in each of these fronts before describing how the resulting techniques have been integrated and applied to a proof of concept smart home scenario. The resulting system is able to provide useful services and pro-actively assist the users in their activities. RUBICON learns through an incremental and progressive approach driven by the feedback received from its own activities and from the user, while also self-organizing the manner in which it uses available sensors, actuators and other functional components in the process. This paper summarises some of the lessons learned by adopting such an approach and outlines promising directions for future work.

  • 62.
    Amnå, Erik
    et al.
    Örebro University, Department of Social and Political Sciences.
    Halleröd, Björn
    Umeå universitet.
    Hallqvist, Johan
    Karolinska Institutet.
    Lundberg, Ingvar
    Uppsala universitet.
    Sundqvist, Jan
    Karolinska Institutet.
    Theorell, Töres
    Karolinska Institutet.
    Thorslund, Mats
    Karolinska Institutet.
    Vingård, Eva
    Uppsala universitet.
    Wall, Stig
    Umeå universitet.
    Åkerstedt, Torbjörn
    Karolinska Institutet.
    Östergren, Per Olof
    Lunds universitet.
    En halv miljard av statens pengar riskerar att slösas bort2007In: Göteborgs-Posten, Vol. 2007-09-13, p. 47-47Article in journal (Other (popular science, discussion, etc.))
    Abstract [sv]

    Minskade anslag gör att den årliga undersökningen om våra levnadsförhållanden hotas att halveras. Det kan drabba redan svaga grupper som äldre, invandrare och ensamstående föräldrar.

  • 63.
    Amouri, Humam
    Örebro University, School of Science and Technology.
    Semi-Supervised Adaptive Object Detection for Efficient PrecisionAgriculture2021Independent thesis Advanced level (professional degree), 20 credits / 30 HE creditsStudent thesis
    Abstract [en]

    Existing supervised learning-based detectors for precision agriculturehave previously achieved high accuracy in challenging classificationtasks. However, their performance deteriorate when presented with new environmentsdue to variations in observed objects and surrounding environment.Accordingly, it is desired to accelerate a detector’s adaptability when operatingon new environments. Therefore, this thesis proposes an effective methodfor semi-supervised object detection that can adapt detectors to new environmentswith minimal manual labeling effort. Experimental results show thatthe proposed method reduces annotation efforts by more than 400x while attainingsimilar accuracy to supervised learning alternatives.

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  • 64.
    Amouzgar, Kaveh
    et al.
    Product Development Department, School of Engineering, Jönköping University, Jönköping, Sweden; School of Engineering Science, University of Skövde, Skövde, Sweden.
    Strömberg, Niclas
    Örebro University, School of Science and Technology. Department of Mechanical Engineering.
    Radial Basis Functions as Surrogate Models with A Priori Bias in Comparison with a Posteriori Bias2017In: Structural and multidisciplinary optimization (Print), ISSN 1615-147X, E-ISSN 1615-1488, Vol. 55, no 4, p. 1453-1469Article in journal (Refereed)
    Abstract [en]

    In order to obtain a robust performance, the established approach when using radial basis function networks (RBF) as metamodels is to add a posteriori bias which is defined by extra orthogonality constraints. We mean that this is not needed, instead the bias can simply be set a priori by using the normal equation, i.e. the bias becomes the corresponding regression model. In this paper we demonstrate that the performance of our suggested approach with a priori bias is in general as good as, or even for many test examples better than, the performance of RBF with a posteriori bias. Using our approach, it is clear that the global response is modelled with the bias and that the details are captured with radial basis functions. The accuracy of the two approaches are investigated by using multiple test functions with different degrees of dimensionality. Furthermore, several modeling criteria, such as the type of radial basis functions used in the RBFs, dimension of the test functions, sampling techniques and size of samples, are considered to study their affect on the performance of the approaches. The power of RBF with a priori bias for surrogate based design optimization is also demonstrated by solving an established engineering benchmark of a welded beam and another benchmark for different sampling sets generated by successive screening, random, Latin hypercube and Hammersley sampling, respectively. The results obtained by evaluation of the performance metrics, the modeling criteria and the presented optimal solutions, demonstrate promising potentials of our RBF with a priori bias, in addition to the simplicity and straight-forward use of the approach.

  • 65.
    Andersson, Robin
    et al.
    Örebro University, School of Science and Technology.
    Franzén, Simon
    Örebro University, School of Science and Technology.
    Realtidsuppdaterad dashboard2017Independent thesis Basic level (professional degree), 10 credits / 15 HE creditsStudent thesis
    Abstract [en]

    This report will be covering the creation of the Realtime-Updated Dashboard, made for Flex Applications. The Dashboard, which could be seen as an interactive pinboard, is a new product which will be implemented in Flex Applications existing system for employee administration.

    A deep-dive into the subject of information overload was also made during thedevelopment of the application. This was later used to question the design choices made. The results of this showed that there is no one correct way of designing an interface, but rather guidelines to help in certain situations.

    The application was written in the TypeScript language together with the framework Angular 2. The application was at first developed as a stand-alone project as there was no need for it to be integrated into the existing system from the start. This also gave a more relaxed environment while learning TypeScript and Angular2.

    The application was later integrated with the existing system. This integration was seen as a success as the handling of the data from the database worked as expected.

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  • 66.
    Andreasson, Henrik
    Örebro University, School of Science and Technology.
    Camera based navigation by mobile robots: local visual feature based localisation and mapping2009Book (Other academic)
    Abstract [en]

    The most important property of a mobile robot is the fact that it is mobile. How to give a robot the skills required to navigate around its environment is therefore an important topic in mobile robotics. Navigation, both for robots and humans, typically involves a map. The map can be used, for example, to estimate a pose based on observations (localisation) or determine a suitable path between to locations. Maps are available nowadays for us humans with few exceptions, however, maps suitable for mobile robots rarely exists. In addition, to relate sensor readings to a map requires that the map content and the observation is compatible, i.e. different robots may require different maps for the same area. This book addresses some of the fundamental problems related to mobile robot navigation (registration, localisation and mapping) using cameras as the primary sensor input. Small salient regions (local visual features) are extracted from each camera image, where each region can be seen as a fingerprint. Many fingerprint matches implicates a high likelihood that they corresponding images originate from a similar location, which is a central property utilised in this work.

  • 67.
    Andreasson, Henrik
    Örebro University, Department of Technology.
    Local visual feature based localisation and mapping by mobile robots2008Doctoral thesis, monograph (Other academic)
    Abstract [en]

    This thesis addresses the problems of registration, localisation and simultaneous localisation and mapping (SLAM), relying particularly on local visual features extracted from camera images. These fundamental problems in mobile robot navigation are tightly coupled. Localisation requires a representation of the environment (a map) and registration methods to estimate the pose of the robot relative to the map given the robot’s sensory readings. To create a map, sensor data must be accumulated into a consistent representation and therefore the pose of the robot needs to be estimated, which is again the problem of localisation.

    The major contributions of this thesis are new methods proposed to address the registration, localisation and SLAM problems, considering two different sensor configurations. The first part of the thesis concerns a sensor configuration consisting of an omni-directional camera and odometry, while the second part assumes a standard camera together with a 3D laser range scanner. The main difference is that the former configuration allows for a very inexpensive set-up and (considering the possibility to include visual odometry) the realisation of purely visual navigation approaches. By contrast, the second configuration was chosen to study the usefulness of colour or intensity information in connection with 3D point clouds (“coloured point clouds”), both for improved 3D resolution (“super resolution”) and approaches to the fundamental problems of navigation that exploit the complementary strengths of visual and range information.

    Considering the omni-directional camera/odometry setup, the first part introduces a new registration method based on a measure of image similarity. This registration method is then used to develop a localisation method, which is robust to the changes in dynamic environments, and a visual approach to metric SLAM, which does not require position estimation of local image features and thus provides a very efficient approach.

    The second part, which considers a standard camera together with a 3D laser range scanner, starts with the proposal and evaluation of non-iterative interpolation methods. These methods use colour information from the camera to obtain range information at the resolution of the camera image, or even with sub-pixel accuracy, from the low resolution range information provided by the range scanner. Based on the ability to determine depth values for local visual features, a new registration method is then introduced, which combines the depth of local image features and variance estimates obtained from the 3D laser range scanner to realise a vision-aided 6D registration method, which does not require an initial pose estimate. This is possible because of the discriminative power of the local image features used to determine point correspondences (data association). The vision-aided registration method is further developed into a 6D SLAM approach where the optimisation constraint is based on distances of paired local visual features. Finally, the methods introduced in the second part are combined with a novel adaptive normal distribution transform (NDT) representation of coloured 3D point clouds into a robotic difference detection system.

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  • 68.
    Andreasson, Henrik
    et al.
    Örebro University, School of Science and Technology.
    Bouguerra, Abdelbaki
    Örebro University, School of Science and Technology.
    Åstrand, Björn
    CAISR Centrum för tillämpade intelligenta system (IS-lab), Högskolan i Halmstad, Halmstad, Sweden.
    Rögnvaldsson, Thorsteinn
    CAISR Centrum för tillämpade intelligenta system (IS-lab), Högskolan i Halmstad, Halmstad, Sweden.
    Gold-Fish SLAM: An Application of SLAM to Localize AGVs2014In: Field and Service Robotics: Results of the 8th International Conference / [ed] Yoshida, Kazuya; Tadokoro, Satoshi, Heidelberg, Germany: Springer Berlin/Heidelberg, 2014, p. 585-598Chapter in book (Refereed)
    Abstract [en]

    The main focus of this paper is to present a case study of a SLAM solution for Automated Guided Vehicles (AGVs) operating in real-world industrial environments. The studied solution, called Gold-fish SLAM, was implemented to provide localization estimates in dynamic industrial environments, where there are static landmarks that are only rarely perceived by the AGVs. The main idea of Gold-fish SLAM is to consider the goods that enter and leave the environment as temporary landmarks that can be used in combination with the rarely seen static landmarks to compute online estimates of AGV poses. The solution is tested and verified in a factory of paper using an eight ton diesel-truck retrofitted with an AGV control system running at speeds up to 3 m/s. The paper includes also a general discussion on how SLAM can be used in industrial applications with AGVs

  • 69.
    Andreasson, Henrik
    et al.
    Örebro University, School of Science and Technology.
    Bouguerra, Abdelbaki
    Örebro University, School of Science and Technology.
    Åstrand, Björn
    Rögnvaldsson, Thorsteinn
    Örebro University, School of Science and Technology.
    Gold-fish SLAM: an application of SLAM to localize AGVs2012In: Proceedings of the International Conference on Field and Service Robotics (FSR), July 2012., 2012Conference paper (Other academic)
    Abstract [en]

    The main focus of this paper is to present a case study of a SLAM solution for Automated Guided Vehicles (AGVs) operating in real-world industrial environ- ments. The studied solution, called Gold-fish SLAM, was implemented to provide localization estimates in dynamic industrial environments, where there are static landmarks that are only rarely perceived by the AGVs. The main idea of Gold-fish SLAM is to consider the goods that enter and leave the environment as temporary landmarks that can be used in combination with the rarely seen static landmarks to compute online estimates of AGV poses. The solution is tested and verified in a factory of paper using an eight ton diesel-truck retrofitted with an AGV control sys- tem running at speeds up to 3 meters per second. The paper includes also a general discussion on how SLAM can be used in industrial applications with AGVs.

  • 70.
    Andreasson, Henrik
    et al.
    Örebro University, Department of Technology.
    Duckett, Tom
    University of Lincoln, University of Lincoln, UK.
    Lilienthal, Achim J.
    A Minimalistic Approach to Appearance-Based Visual SLAM2008In: IEEE Transactions on Robotics, ISSN 1552-3098, Vol. 24, no 5, p. 991-1001Article in journal (Refereed)
    Abstract [en]

    This paper presents a vision-based approach to SLAM in indoor / outdoor environments with minimalistic sensing and computational requirements. The approach is based on a graph representation of robot poses, using a relaxation algorithm to obtain a globally consistent map. Each link corresponds to a relative measurement of the spatial relation between the two nodes it connects. The links describe the likelihood distribution of the relative pose as a Gaussian distribution. To estimate the covariance matrix for links obtained from an omni-directional vision sensor, a novel method is introduced based on the relative similarity of neighbouring images. This new method does not require determining distances to image features using multiple view geometry, for example. Combined indoor and outdoor experiments demonstrate that the approach can handle qualitatively different environments (without modification of the parameters), that it can cope with violations of the “flat floor assumption” to some degree, and that it scales well with increasing size of the environment, producing topologically correct and geometrically accurate maps at low computational cost. Further experiments demonstrate that the approach is also suitable for combining multiple overlapping maps, e.g. for solving the multi-robot SLAM problem with unknown initial poses.

    Download full text (pdf)
    A Minimalistic Approach to Appearance based Visual SLAM
  • 71.
    Andreasson, Henrik
    et al.
    Örebro University, School of Science and Technology.
    Saarinen, Jari
    Örebro University, School of Science and Technology.
    Cirillo, Marcello
    Örebro University, School of Science and Technology.
    Stoyanov, Todor
    Örebro University, School of Science and Technology.
    Lilienthal, Achim
    Örebro University, School of Science and Technology.
    Drive the Drive: From Discrete Motion Plans to Smooth Drivable Trajectories2014In: Robotics, E-ISSN 2218-6581, Vol. 3, no 4, p. 400-416Article in journal (Refereed)
    Abstract [en]

    Autonomous navigation in real-world industrial environments is a challenging task in many respects. One of the key open challenges is fast planning and execution of trajectories to reach arbitrary target positions and orientations with high accuracy and precision, while taking into account non-holonomic vehicle constraints. In recent years, lattice-based motion planners have been successfully used to generate kinematically and kinodynamically feasible motions for non-holonomic vehicles. However, the discretized nature of these algorithms induces discontinuities in both state and control space of the obtained trajectories, resulting in a mismatch between the achieved and the target end pose of the vehicle. As endpose accuracy is critical for the successful loading and unloading of cargo in typical industrial applications, automatically planned paths have not been widely adopted in commercial AGV systems. The main contribution of this paper is a path smoothing approach, which builds on the output of a lattice-based motion planner to generate smooth drivable trajectories for non-holonomic industrial vehicles. The proposed approach is evaluated in several industrially relevant scenarios and found to be both fast (less than 2 s per vehicle trajectory) and accurate (end-point pose errors below 0.01 m in translation and 0.005 radians in orientation).

    Download full text (pdf)
    fulltext
  • 72.
    Andreasson, Henrik
    et al.
    Örebro University, School of Science and Technology.
    Saarinen, Jari
    Örebro University, School of Science and Technology.
    Cirillo, Marcello
    Örebro University, School of Science and Technology. SCANIA AB, Södertälje, Sweden.
    Stoyanov, Todor
    Örebro University, School of Science and Technology.
    Lilienthal, Achim
    Örebro University, School of Science and Technology.
    Fast, continuous state path smoothing to improve navigation accuracy2015In: IEEE International Conference on Robotics and Automation (ICRA), 2015, IEEE Computer Society, 2015, p. 662-669Conference paper (Refereed)
    Abstract [en]

    Autonomous navigation in real-world industrial environments is a challenging task in many respects. One of the key open challenges is fast planning and execution of trajectories to reach arbitrary target positions and orientations with high accuracy and precision, while taking into account non-holonomic vehicle constraints. In recent years, lattice-based motion planners have been successfully used to generate kinematically and kinodynamically feasible motions for non-holonomic vehicles. However, the discretized nature of these algorithms induces discontinuities in both state and control space of the obtained trajectories, resulting in a mismatch between the achieved and the target end pose of the vehicle. As endpose accuracy is critical for the successful loading and unloading of cargo in typical industrial applications, automatically planned paths have not be widely adopted in commercial AGV systems. The main contribution of this paper addresses this shortcoming by introducing a path smoothing approach, which builds on the output of a lattice-based motion planner to generate smooth drivable trajectories for non-holonomic industrial vehicles. In real world tests presented in this paper we demonstrate that the proposed approach is fast enough for online use (it computes trajectories faster than they can be driven) and highly accurate. In 100 repetitions we achieve mean end-point pose errors below 0.01 meters in translation and 0.002 radians in orientation. Even the maximum errors are very small: only 0.02 meters in translation and 0.008 radians in orientation.

  • 73.
    Andreasson, Henrik
    et al.
    Örebro University, School of Science and Technology.
    Stoyanov, Todor
    Örebro University, School of Science and Technology.
    Real time registration of RGB-D data using local visual features and 3D-NDT registration2012In: Proc. of International Conference on Robotics and Automation (ICRA) Workshop on Semantic Perception, Mapping and Exploration (SPME), IEEE, 2012Conference paper (Refereed)
    Abstract [en]

    Recent increased popularity of RGB-D capable sensors in robotics has resulted in a surge of related RGBD registration methods. This paper presents several RGB-D registration algorithms based on combinations between local visual feature and geometric registration. Fast and accurate transformation refinement is obtained by using a recently proposed geometric registration algorithm, based on the Three-Dimensional Normal Distributions Transform (3D-NDT). Results obtained on standard data sets have demonstrated mean translational errors on the order of 1 cm and rotational errors bellow 1 degree, at frame processing rates of about 15 Hz.

  • 74.
    Andreasson, Henrik
    et al.
    Örebro University, Department of Technology.
    Treptow, André
    University of Tübingen.
    Duckett, Tom
    Örebro University, Department of Technology.
    Localization for mobile robots using panoramic vision, local features and particle filter2005In: Proceedings of the 2005 IEEE International Converence on Robotics and Automation: ICRA - 2005, 2005, p. 3348-3353Conference paper (Refereed)
    Abstract [en]

    In this paper we present a vision-based approach to self-localization that uses a novel scheme to integrate featurebased matching of panoramic images with Monte Carlo localization. A specially modified version of Lowe’s SIFT algorithm is used to match features extracted from local interest points in the image, rather than using global features calculated from the whole image. Experiments conducted in a large, populated indoor environment (up to 5 persons visible) over a period of several months demonstrate the robustness of the approach, including kidnapping and occlusion of up to 90% of the robot’s field of view.

  • 75.
    Andreasson, Henrik
    et al.
    Örebro University, Department of Technology.
    Triebel, Rudolph
    University of Friburg.
    Burgard, Wolfram
    University of Friburg.
    Improving plane extraction from 3D data by fusing laser data and vision2005In: 2005 IEEE/RSJ International Conference on Intelligent Robots and Systems, 2005. (IROS 2005): IROS 2005 IEEE/RSJ, 2005, p. 2656-2661Conference paper (Refereed)
    Abstract [en]

    The problem of extracting three-dimensional structures from data acquired with mobile robots has received considerable attention over the past years. Robots that are able to perceive their three-dimensional environment are envisioned to more robustly perform tasks like navigation, rescue, and manipulation. In this paper we present an approach that simultaneously uses color and range information to cluster 3d points into planar structures. Our current system also is able to calibrate the camera and the laser based on the remission values provided by the range scanner and the brightness of the pixels in the image. It has been implemented on a mobile robot equipped with a manipulator that carries a range scanner and a camera for acquiring colored range scans. Several experiments carried out on real data and in simulations demonstrate that our approach yields highly accurate results also in comparison with previous approaches

  • 76.
    Anoop, K.
    et al.
    Department of Computer Science, University of Calicut, Malappuram, Kerala, India.
    Deepak, P.
    School of Electronics, Electrical Engineering and Computer Science, Queen’s University Belfast, Belfast, Northern Ireland, UK.
    Sam Abraham, Savitha
    Örebro University, School of Science and Technology.
    Lajish, V. L.
    Department of Computer Science, University of Calicut, Malappuram, Kerala, India.
    Gangan, Manjary P.
    Department of Computer Science, University of Calicut, Malappuram, Kerala, India.
    Readers' affect: predicting and understanding readers' emotions with deep learning2022In: Journal of Big Data, E-ISSN 2196-1115, Vol. 9, no 1, article id 82Article in journal (Refereed)
    Abstract [en]

    Emotions are highly useful to model human behavior being at the core of what makes us human. Today, people abundantly express and share emotions through social media. Technological advancements in such platforms enable sharing opinions or expressing any specific emotions towards what others have shared, mainly in the form of textual data. This entails an interesting arena for analysis; as to whether there is a disconnect between the writer's intended emotion and the reader's perception of textual content. In this paper, we present experiments for Readers' Emotion Detection through multi-target regression settings by exploring a Bi-LSTM-based Attention model, where our major intention is to analyze the interpretability and effectiveness of the deep learning model for the task. To conduct experiments, we procure two extensive datasets REN-10k and RENh-4k, apart from using a popular benchmark dataset from SemEval-2007. We perform a two-phase experimental evaluation, first being various coarse-grained and fine-grained evaluations of our model performance in comparison with several baselines belonging to different categories of emotion detection, viz., deep learning, lexicon based, and classical machine learning. Secondly, we evaluate model behavior towards readers' emotion detection assessing attention maps generated by the model through devising a novel set of qualitative and quantitative metrics. The first phase of experiments shows that our Bi-LSTM + Attention model significantly outperforms all baselines. The second analysis reveals that emotions may be correlated to specific words as well as named entities.

  • 77.
    Antanas, Laura
    et al.
    Department of Computer Science, Katholieke Universiteit Leuven, Heverlee, Belgium.
    Van Otterlo, Martijn
    Cognitive Artificial Intelligence, Radboud University Nijmegen, Nijmegen, The Netherlands.
    Oramas Mogrovejo, José
    Department of Electrical Engineering, Katholieke Universiteit Leuven, Leuven, Belgium.
    Tuytelaars, Tinne
    Department of Electrical Engineering, Katholieke Universiteit Leuven, Leuven, Belgium.
    De Raedt, Luc
    Department of Computer Science, Katholieke Universiteit Leuven, Heverlee, Belgium.
    There are plenty of places like home: Using relational representations in hierarchies for distance-based image understanding2014In: Neurocomputing, ISSN 0925-2312, E-ISSN 1872-8286, Vol. 123, p. 75-85Article in journal (Refereed)
    Abstract [en]

    Understanding images in terms of logical and hierarchical structures is crucial for many semantic tasks, including image retrieval, scene understanding and robotic vision. This paper combines robust feature extraction, qualitative spatial relations, relational instance-based learning and compositional hierarchies in one framework. For each layer in the hierarchy, qualitative spatial structures in images are detected, classified and then employed one layer up the hierarchy to obtain higher-level semantic structures. We apply a four-layer hierarchy to street view images and subsequently detect corners, windows, doors, and individual houses.

  • 78.
    Anton, Frans
    Örebro University, School of Science and Technology.
    Mobile Robot Reflectance Acquisition to Detect Plastic Wrapping on Pallets2018Independent thesis Basic level (degree of Bachelor), 10 credits / 15 HE creditsStudent thesis
    Abstract [en]

    The classification of the plastic wrapped pallet was done by using a SVM. The road for the classification was long, but began by studying the point clouds for plastic pallets and non-plastic pallets. After studying the point cloud a graph could be drawn using the angle and intensity for each point to see if there was a difference between plastic pallet and non-plastic pallet. The experiments that were the heart of my project were to study how good the 28x28 feature which the 28x28 is the dimensions of the histogram that were built using the intensity and angle of each point and the 14x14x14 feature,14x14x14 which are the dimensions of the histogram that were built using the intensity, angle and the distance for each point performs classifications wise on new pallets that the SVM was not trained on. The result was that the

    14x14x14 histogram generalized other pallets that were not included in the training data performed better than the 28x28 histogram.

     

  • 79.
    Antonova, Rika
    et al.
    Robotics, Perception and Learning, CSC, Royal Institute of Technology, Stockholm, Sweden.
    Kokic, Mia
    Robotics, Perception and Learning, CSC, Royal Institute of Technology, Stockholm, Sweden.
    Stork, Johannes Andreas
    Robotics, Perception and Learning, CSC, Royal Institute of Technology, Stockholm, Sweden.
    Kragic, Danica
    Robotics, Perception and Learning, CSC, Royal Institute of Technology, Stockholm, Sweden.
    Global Search with Bernoulli Alternation Kernel for Task-oriented Grasping Informed by Simulation2018In: Proceedings of Machine Learning Research: Conference on Robot Learning 2018, PMLR , 2018, Vol. 87, p. 641-650Conference paper (Refereed)
    Abstract [en]

    We develop an approach that benefits from large simulated datasets and takes full advantage of the limited online data that is most relevant. We propose a variant of Bayesian optimization that alternates between using informed and uninformed kernels. With this Bernoulli Alternation Kernel we ensure that discrepancies between simulation and reality do not hinder adapting robot control policies online. The proposed approach is applied to a challenging real-world problem of task-oriented grasping with novel objects. Our further contribution is a neural network architecture and training pipeline that use experience from grasping objects in simulation to learn grasp stability scores. We learn task scores from a labeled dataset with a convolutional network, which is used to construct an informed kernel for our variant of Bayesian optimization. Experiments on an ABB Yumi robot with real sensor data demonstrate success of our approach, despite the challenge of fulfilling task requirements and high uncertainty over physical properties of objects.

  • 80.
    Arafat, Yeasin
    et al.
    Office of the President of the General Assembly United Nations, New York, USA.
    Hellström, Thomas
    Umeå University, Umeå, Sweden.
    Rashid, Jayedur
    Örebro University, School of Science and Technology.
    Parameterized sensor model and an approach for measuring goodness of robotic maps2010Conference paper (Refereed)
    Abstract [en]

    Map building is a classical problem in mobile and au tonomous robotics, and sensor models is a way to interpret raw sensory information, especially for building maps. In this paper we propose a parameterized sensor model, and optimize map goodness with respect to these parameters. A new approach, measuring the goodness of maps without a handcrafted map of the actual environment is introduced and evaluated. Three different techniques; statistical anal ysis, derivative of images, and comparison of binary maps have been used as estimates of map goodness. The results show that the proposed sensor model generates better maps than a standard sensor model. However, the proposed ap proach of measuring goodness of maps does not improve the results as much as expected.

  • 81.
    Arain, Muhammad Asif
    Örebro University, School of Science and Technology.
    Efficient Remote Gas Inspection with an Autonomous Mobile Robot2020Doctoral thesis, comprehensive summary (Other academic)
    Abstract [en]

    Human-caused greenhouse gas emissions are one of the major sources of global warming, which is threatening to reach a tipping point. Inspection systems that can provide direct information about critical factors causing global warming, such as systems for gas detection and location of gas sources, are urgently needed to analyze the fugitive emissions and take necessary actions.

    This thesis presents an autonomous robotic system capable of performing efficient exploration by selecting informative sampling positions for gas detection and gas distribution mapping – the Autonomous Remote Methane Explorer (ARMEx). In the design choice of ARMEx, a ground robot carries a spectroscopybased remote gas sensor, such as a Remote Methane Leak Detector (RMLD), that collects integral gas measurements along up to 30 m long optical-beams. The sensor is actuated to sample a large area inside an adjustable field of view, and with the mobility of the robot, adaptive sampling for high spatial resolution in the areas of interest is made possible to inspect large environments.

    In a typical gas sampling mission, the robot needs to localize itself and plan a traveling path to visit different locations in the area, which is a largely solved problem. However, the state-of-the-art prior to this thesis fell short of providing the capability to select informative sampling positions autonomously. This thesis introduces efficient measurement strategies to bring autonomy to mobile remote gas sensing. The strategies are based on sensor planning algorithms that minimize the number of measurements and distance traveled while optimizing the inspection criteria: full sensing coverage of the area for gas detection, and suitably overlapping sensing coverage of different viewpoints around areas of interest for gas distribution mapping.

    A prototype implementation of ARMEx was deployed in a large, real-world environment where inspection missions performed by the autonomous system were compared with runs teleoperated by human experts. In six experimental trials, the autonomous system created better gas maps, located more gas sources correctly, and provided better sensing coverage with fewer sensing positions than human experts.

    List of papers
    1. Global coverage measurement planning strategies for mobile robots equipped with a remote gas sensor
    Open this publication in new window or tab >>Global coverage measurement planning strategies for mobile robots equipped with a remote gas sensor
    Show others...
    2015 (English)In: Sensors, E-ISSN 1424-8220, Vol. 15, no 3, p. 6845-6871Article in journal (Refereed) Published
    Abstract [en]

    The problem of gas detection is relevant to many real-world applications, such as leak detection in industrial settings and landfill monitoring. In this paper, we address the problem of gas detection in large areas with a mobile robotic platform equipped with a remote gas sensor. We propose an algorithm that leverages a novel method based on convex relaxation for quickly solving sensor placement problems, and for generating an efficient exploration plan for the robot. To demonstrate the applicability of our method to real-world environments, we performed a large number of experimental trials, both on randomly generated maps and on the map of a real environment. Our approach proves to be highly efficient in terms of computational requirements and to provide nearly-optimal solutions.

    Place, publisher, year, edition, pages
    Basel, Switzerland: MDPI, 2015
    Keywords
    Coverage planning, Mobile robot olfaction, Remote gas detection, Sensor planning, Surveillance robots
    National Category
    Computer Sciences
    Research subject
    Computer Science
    Identifiers
    urn:nbn:se:oru:diva-44407 (URN)10.3390/s150306845 (DOI)000354160900112 ()25803707 (PubMedID)2-s2.0-84928681961 (Scopus ID)
    Available from: 2015-04-22 Created: 2015-04-22 Last updated: 2024-01-03Bibliographically approved
    2. Efficient Measurement Planning for Remote Gas Sensing with Mobile Robots
    Open this publication in new window or tab >>Efficient Measurement Planning for Remote Gas Sensing with Mobile Robots
    Show others...
    2015 (English)In: 2015 IEEE International Conference on Robotics and Automation (ICRA), Washington, USA: IEEE, 2015, p. 3428-3434Conference paper, Published paper (Refereed)
    Abstract [en]

    The problem of gas detection is relevant to manyreal-world applications, such as leak detection in industrialsettings and surveillance. In this paper we address the problemof gas detection in large areas with a mobile robotic platformequipped with a remote gas sensor. We propose a novelmethod based on convex relaxation for quickly finding anexploration plan that guarantees a complete coverage of theenvironment. Our method proves to be highly efficient in termsof computational requirements and to provide nearly-optimalsolutions. We validate our approach both in simulation andin real environments, thus demonstrating its applicability toreal-world problems.

    Place, publisher, year, edition, pages
    Washington, USA: IEEE, 2015
    Keywords
    Sensor planning, mobile robot olfaction, remote gas sensing
    National Category
    Computer Sciences
    Research subject
    Computer Science
    Identifiers
    urn:nbn:se:oru:diva-46796 (URN)10.1109/ICRA.2015.7139673 (DOI)000370974903063 ()978-1-4799-6923-4 (ISBN)
    Conference
    2015 IEEE International Conference on Robotics and Automation (ICRA), Seattle, Washington, USA, May 26-30, 2015
    Available from: 2015-11-25 Created: 2015-11-25 Last updated: 2024-01-03Bibliographically approved
    3. The Right Direction to Smell: Efficient Sensor Planning Strategies for Robot Assisted Gas Tomography
    Open this publication in new window or tab >>The Right Direction to Smell: Efficient Sensor Planning Strategies for Robot Assisted Gas Tomography
    2016 (English)In: 2016 IEEE International Conference on Robotics and Automation (ICRA), New York, USA: IEEE Robotics and Automation Society, 2016, p. 4275-4281Conference paper, Published paper (Refereed)
    Abstract [en]

    Creating an accurate model of gas emissions is an important task in monitoring and surveillance applications. A promising solution for a range of real-world applications are gas-sensitive mobile robots with spectroscopy-based remote sensors that are used to create a tomographic reconstruction of the gas distribution. The quality of these reconstructions depends crucially on the chosen sensing geometry. In this paper we address the problem of sensor planning by investigating sensing geometries that minimize reconstruction errors, and then formulate an optimization algorithm that chooses sensing configurations accordingly. The algorithm decouples sensor planning for single high concentration regions (hotspots) and subsequently fuses the individual solutions to a global solution consisting of sensing poses and the shortest path between them. The proposed algorithm compares favorably to a template matching technique in a simple simulation and in a real-world experiment. In the latter, we also compare the proposed sensor planning strategy to the sensing strategy of a human expert and find indications that the quality of the reconstructed map is higher with the proposed algorithm.

    Place, publisher, year, edition, pages
    New York, USA: IEEE Robotics and Automation Society, 2016
    Keywords
    Sensor planning, robot exploration, sensing geometry, robot assisted gas tomography, mobile robot olfaction, coverage planning, surveillance robots
    National Category
    Robotics Computer Systems
    Research subject
    Computer Science
    Identifiers
    urn:nbn:se:oru:diva-50886 (URN)10.1109/ICRA.2016.7487624 (DOI)000389516203101 ()2-s2.0-84977543569 (Scopus ID)
    Conference
    IEEE International Conference on Robotics and Automation (ICRA), Stockholm, Sweden, May 16-21, 2016
    Available from: 2016-06-16 Created: 2016-06-16 Last updated: 2022-08-09Bibliographically approved
    4. Improving Gas Tomography With Mobile Robots: An Evaluation of Sensing Geometries in Complex Environments
    Open this publication in new window or tab >>Improving Gas Tomography With Mobile Robots: An Evaluation of Sensing Geometries in Complex Environments
    Show others...
    2017 (English)In: 2017 ISOCS/IEEE International Symposium on Olfaction andElectronic Nose (ISOEN 2017) Proceedings, IEEE, 2017, article id 7968895Conference paper, Published paper (Refereed)
    Abstract [en]

    An accurate model of gas emissions is of high importance in several real-world applications related to monitoring and surveillance. Gas tomography is a non-intrusive optical method to estimate the spatial distribution of gas concentrations using remote sensors. The choice of sensing geometry, which is the arrangement of sensing positions to perform gas tomography, directly affects the reconstruction quality of the obtained gas distribution maps. In this paper, we present an investigation of criteria that allow to determine suitable sensing geometries for gas tomography. We consider an actuated remote gas sensor installed on a mobile robot, and evaluated a large number of sensing configurations. Experiments in complex settings were conducted using a state-of-the-art CFD-based filament gas dispersal simulator. Our quantitative comparison yields preferred sensing geometries for sensor planning, which allows to better reconstruct gas distributions.

    Place, publisher, year, edition, pages
    IEEE, 2017
    National Category
    Computer Sciences Robotics
    Identifiers
    urn:nbn:se:oru:diva-60646 (URN)10.1109/ISOEN.2017.7968895 (DOI)978-1-5090-2392-9 (ISBN)978-1-5090-2393-6 (ISBN)
    Conference
    2017 ISOCS/IEEE International Symposium on Olfaction and Electronic Nose (ISOEN) 28-31 May 2017 Montreal QC, Canada
    Available from: 2017-09-06 Created: 2017-09-06 Last updated: 2024-01-03Bibliographically approved
    5. Improving Gas Dispersal Simulation For Mobile Robot Olfaction: Using Robot-Created Occupancy Maps And Remote Gas Sensors In The Simulation Loop
    Open this publication in new window or tab >>Improving Gas Dispersal Simulation For Mobile Robot Olfaction: Using Robot-Created Occupancy Maps And Remote Gas Sensors In The Simulation Loop
    Show others...
    2017 (English)In: 2017 ISOCS/IEEE International Symposium on Olfaction andElectronic Nose (ISOEN 2017) Proceedings, IEEE conference proceedings, 2017, article id 17013581Conference paper, Published paper (Refereed)
    Abstract [en]

    Mobile robot platforms equipped with olfaction systems have been used in many gas sensing applications. However, in-field validation of mobile robot olfaction systems is time consuming, expensive, cumbersome and lacks repeatability. In order to address these issues, simulation tools are used. However, the available mobile robot olfaction simulations lack models for remote gas sensors, and the possibility to import geometrical representations of actual real-world environments in a convenient way. In this paper, we describe extensions to an open-source CFD-based filament gas dispersal simulator. These improvements arrow to use robot-created occupancy maps and offer remote sensing capabilities in the simulation loop. We demonstrate the novel features in an example application: we created a 3D map a complex indoor environment, and performed a gas emission monitoring task with a Tunable Diode Laser Absorption Spectroscopy based remote gas sensor in a simulated version of the environment.

    Place, publisher, year, edition, pages
    IEEE conference proceedings, 2017
    National Category
    Computer Sciences Robotics
    Identifiers
    urn:nbn:se:oru:diva-60633 (URN)10.1109/ISOEN.2017.7968874 (DOI)978-1-5090-2392-9 (ISBN)978-1-5090-2393-6 (ISBN)
    Conference
    2017 ISOCS/IEEE International Symposium on Olfaction and Electronic Nose (ISOEN) 28-31 May 2017 Montreal, QC, Canada
    Available from: 2017-09-06 Created: 2017-09-06 Last updated: 2024-01-03Bibliographically approved
    6. Sniffing out fugitive methane emissions: autonomous remote gas inspection with a mobile robot
    Open this publication in new window or tab >>Sniffing out fugitive methane emissions: autonomous remote gas inspection with a mobile robot
    2021 (English)In: The international journal of robotics research, ISSN 0278-3649, E-ISSN 1741-3176, Vol. 40, no 4-5, p. 782-814Article in journal (Refereed) Published
    Abstract [en]

    Air pollution causes millions of premature deaths every year, and fugitive emissions of, e.g., methane are major causes of global warming. Correspondingly, air pollution monitoring systems are urgently needed. Mobile, autonomous monitoring can provide adaptive and higher spatial resolution compared with traditional monitoring stations and allows fast deployment and operation in adverse environments. We present a mobile robot solution for autonomous gas detection and gas distribution mapping using remote gas sensing. Our ‘‘Autonomous Remote Methane Explorer’’ (ARMEx) is equipped with an actuated spectroscopy-based remote gas sensor, which collects integral gas measurements along up to 30 m long optical beams. State-of-the-art 3D mapping and robot localization allow the precise location of the optical beams to be determined, which then facilitates gas tomography (tomographic reconstruction of local gas distributions from sets of integral gas measurements). To autonomously obtain informative sampling strategies for gas tomography, we reduce the search space for gas inspection missions by defining a sweep of the remote gas sensor over a selectable field of view as a sensing configuration. We describe two different ways to find sequences of sensing configurations that optimize the criteria for gas detection and gas distribution mapping while minimizing the number of measurements and distance traveled. We evaluated anARMExprototype deployed in a large, challenging indoor environment with eight gas sources. In comparison with human experts teleoperating the platform from a distant building, the autonomous strategy produced better gas maps with a lower number of sensing configurations and a slightly longer route.

    Place, publisher, year, edition, pages
    Sage Publications, 2021
    Keywords
    Environmental monitoring, autonomous exploration, remote gas inspection, mobile robot olfaction, fugitivemethane emissions
    National Category
    Computer Sciences
    Identifiers
    urn:nbn:se:oru:diva-87622 (URN)10.1177/0278364920954907 (DOI)000648404100007 ()2-s2.0-85096537707 (Scopus ID)
    Funder
    EU, Horizon 2020, ICT-23-2014 645101
    Note

    Funding Agencies:

    European Commission ICT-23-2014 645101

    SURVEYOR (Vinnova) 2017-05468

    project RAISE 20130196

    Available from: 2020-11-26 Created: 2020-11-26 Last updated: 2024-01-03Bibliographically approved
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    Efficient Remote Gas Inspection with an Autonomous Mobile Robot
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  • 82.
    Arain, Muhammad Asif
    et al.
    Örebro University, School of Science and Technology.
    Cirillo, Marcello
    Örebro University, School of Science and Technology. Scania AB, Södertälje, Sweden.
    Hernandez Bennetts, Victor
    Örebro University, School of Science and Technology.
    Schaffernicht, Erik
    Örebro University, School of Science and Technology.
    Trincavelli, Marco
    Örebro University, School of Science and Technology.
    Lilienthal, Achim J.
    Örebro University, School of Science and Technology.
    Efficient Measurement Planning for Remote Gas Sensing with Mobile Robots2015In: 2015 IEEE International Conference on Robotics and Automation (ICRA), Washington, USA: IEEE, 2015, p. 3428-3434Conference paper (Refereed)
    Abstract [en]

    The problem of gas detection is relevant to manyreal-world applications, such as leak detection in industrialsettings and surveillance. In this paper we address the problemof gas detection in large areas with a mobile robotic platformequipped with a remote gas sensor. We propose a novelmethod based on convex relaxation for quickly finding anexploration plan that guarantees a complete coverage of theenvironment. Our method proves to be highly efficient in termsof computational requirements and to provide nearly-optimalsolutions. We validate our approach both in simulation andin real environments, thus demonstrating its applicability toreal-world problems.

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  • 83.
    Arain, Muhammad Asif
    et al.
    Örebro University, School of Science and Technology.
    Fan, Han
    Örebro University, School of Science and Technology.
    Hernandez Bennetts, Victor
    Örebro University, School of Science and Technology.
    Schaffernicht, Erik
    Örebro University, School of Science and Technology.
    Lilienthal, Achim J.
    Örebro University, School of Science and Technology.
    Improving Gas Tomography With Mobile Robots: An Evaluation of Sensing Geometries in Complex Environments2017In: 2017 ISOCS/IEEE International Symposium on Olfaction andElectronic Nose (ISOEN 2017) Proceedings, IEEE, 2017, article id 7968895Conference paper (Refereed)
    Abstract [en]

    An accurate model of gas emissions is of high importance in several real-world applications related to monitoring and surveillance. Gas tomography is a non-intrusive optical method to estimate the spatial distribution of gas concentrations using remote sensors. The choice of sensing geometry, which is the arrangement of sensing positions to perform gas tomography, directly affects the reconstruction quality of the obtained gas distribution maps. In this paper, we present an investigation of criteria that allow to determine suitable sensing geometries for gas tomography. We consider an actuated remote gas sensor installed on a mobile robot, and evaluated a large number of sensing configurations. Experiments in complex settings were conducted using a state-of-the-art CFD-based filament gas dispersal simulator. Our quantitative comparison yields preferred sensing geometries for sensor planning, which allows to better reconstruct gas distributions.

  • 84.
    Arain, Muhammad Asif
    et al.
    Örebro University, School of Science and Technology.
    Hernandez Bennetts, Victor
    Mobile Robotics and Olfaction (MRO) Lab, Center for Applied Autonomous Sensor Systems (AASS), School of Science and Technology, Örebro University, Örebro, Sweden.
    Schaffernicht, Erik
    Örebro University, School of Science and Technology.
    Lilienthal, Achim
    Örebro University, School of Science and Technology.
    Sniffing out fugitive methane emissions: autonomous remote gas inspection with a mobile robot2021In: The international journal of robotics research, ISSN 0278-3649, E-ISSN 1741-3176, Vol. 40, no 4-5, p. 782-814Article in journal (Refereed)
    Abstract [en]

    Air pollution causes millions of premature deaths every year, and fugitive emissions of, e.g., methane are major causes of global warming. Correspondingly, air pollution monitoring systems are urgently needed. Mobile, autonomous monitoring can provide adaptive and higher spatial resolution compared with traditional monitoring stations and allows fast deployment and operation in adverse environments. We present a mobile robot solution for autonomous gas detection and gas distribution mapping using remote gas sensing. Our ‘‘Autonomous Remote Methane Explorer’’ (ARMEx) is equipped with an actuated spectroscopy-based remote gas sensor, which collects integral gas measurements along up to 30 m long optical beams. State-of-the-art 3D mapping and robot localization allow the precise location of the optical beams to be determined, which then facilitates gas tomography (tomographic reconstruction of local gas distributions from sets of integral gas measurements). To autonomously obtain informative sampling strategies for gas tomography, we reduce the search space for gas inspection missions by defining a sweep of the remote gas sensor over a selectable field of view as a sensing configuration. We describe two different ways to find sequences of sensing configurations that optimize the criteria for gas detection and gas distribution mapping while minimizing the number of measurements and distance traveled. We evaluated anARMExprototype deployed in a large, challenging indoor environment with eight gas sources. In comparison with human experts teleoperating the platform from a distant building, the autonomous strategy produced better gas maps with a lower number of sensing configurations and a slightly longer route.

  • 85.
    Arain, Muhammad Asif
    et al.
    Örebro University, School of Science and Technology.
    Trincavelli, Marco
    Örebro University, School of Science and Technology.
    Cirillo, Marcello
    Örebro University, School of Science and Technology.
    Schaffernicht, Erik
    Örebro University, School of Science and Technology.
    Lilienthal, Achim J.
    Örebro University, School of Science and Technology.
    Global coverage measurement planning strategies for mobile robots equipped with a remote gas sensor2015In: Sensors, E-ISSN 1424-8220, Vol. 15, no 3, p. 6845-6871Article in journal (Refereed)
    Abstract [en]

    The problem of gas detection is relevant to many real-world applications, such as leak detection in industrial settings and landfill monitoring. In this paper, we address the problem of gas detection in large areas with a mobile robotic platform equipped with a remote gas sensor. We propose an algorithm that leverages a novel method based on convex relaxation for quickly solving sensor placement problems, and for generating an efficient exploration plan for the robot. To demonstrate the applicability of our method to real-world environments, we performed a large number of experimental trials, both on randomly generated maps and on the map of a real environment. Our approach proves to be highly efficient in terms of computational requirements and to provide nearly-optimal solutions.

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  • 86.
    Argyriou, Marios
    et al.
    DTU Compute, Technical University of Denmark, Lyngby, Denmark.
    Dragoni, Nicola
    Örebro University, School of Science and Technology. DTU Compute, Technical University of Denmark, Lyngby, Denmark.
    Spognardi, Angelo
    Dipartimento Informatica, Sapienza Università di Roma, Rome, Italy.
    Analysis and Evaluation of SafeDroid v2.0, a Framework for Detecting Malicious Android Applications2018In: Security and Communication Networks, ISSN 1939-0114, E-ISSN 1939-0122, article id UNSP 4672072Article in journal (Refereed)
    Abstract [en]

    Android smartphones have become a vital component of the daily routine of millions of people, running a plethora of applications available in the official and alternative marketplaces. Although there are many security mechanisms to scan and filter malicious applications, malware is still able to reach the devices of many end-users. In this paper, we introduce the SafeDroid v2.0 framework, that is a flexible, robust, and versatile open-source solution for statically analysing Android applications, based on machine learning techniques. The main goal of our work, besides the automated production of fully sufficient prediction and classification models in terms of maximum accuracy scores and minimum negative errors, is to offer an out-of-the-box framework that can be employed by the Android security researchers to efficiently experiment to find effective solutions: the SafeDroid v2.0 framework makes it possible to test many different combinations of machine learning classifiers, with a high degree of freedom and flexibility in the choice of features to consider, such as dataset balance and dataset selection. The framework also provides a server, for generating experiment reports, and an Android application, for the verification of the produced models in real-life scenarios. An extensive campaign of experiments is also presented to show how it is possible to efficiently find competitive solutions: the results of our experiments confirm that SafeDroid v2.0 can reach very good performances, even with highly unbalanced dataset inputs and always with a very limited overhead.

  • 87.
    Argyriou, Marios
    et al.
    DTU Compute, Technical University of Denmark, Lyngby, Denmark.
    Dragoni, Nicola
    Örebro University, School of Science and Technology. DTU Compute, Technical University of Denmark, Lyngby, Denmark.
    Spognardi, Angelo
    DTU Compute, Technical University of Denmark, Lyngby, Denmark; Dipartimento Informatica, Sapienza Università di Roma, Rome, Italy.
    Security Flows in OAuth 2.0 Framework: A Case Study2017In: Computer safety, reliability, and security: SAFECOMP 2017 Workshops, ASSURE, DECSoS, SASSUR, TELERISE, and TIPS, Trento, Italy, September 12, 2017, Proceedings / [ed] Tonetta S., Schoitsch E., Bitsch F., Springer, 2017, Vol. 10489, p. 396-406Conference paper (Refereed)
    Abstract [en]

    The burst in smartphone use, handy design in laptops and tablets as well as other smart products, like cars with the ability to drive you around, manifests the exponential growth of network usage and the demand of accessing remote data on a large variety of services. However, users notoriously struggle to maintain distinct accounts for every single service that they use. The solution to this problem is the use of a Single Sign On (SSO) framework, with a unified single account to authenticate user’s identity throughout the different services. In April 2007, AOL introduced OpenAuth framework. After several revisions and despite its wide adoption, OpenAuth 2.0 has still several flaws that need to be fixed in several implementations. In this paper, we present a thorough review about both benefits of this single token authentication mechanism and its open flaws.

  • 88.
    Arnekvist, Isac
    et al.
    Robotics, Perception, and Learning lab, Royal Institute of Technology, Stockholm, Sweden.
    Kragic, Danica
    Robotics, Perception, and Learning lab, Royal Institute of Technology, Stockholm, Sweden.
    Stork, Johannes Andreas
    Robotics, Perception, and Learning lab, Royal Institute of Technology, Stockholm, Sweden.
    VPE: Variational Policy Embedding for Transfer Reinforcement Learning2018Manuscript (preprint) (Other academic)
  • 89.
    Arunachalam, Ajay
    Department of Computer Science, School of Applied Statistics, National Institute of Development Administration (NIDA), Bangkok, Thailand.
    Rock, Paper, Scissors Game Based Model for Content Discovery in P2P MANETs2020In: Wireless personal communications, ISSN 0929-6212, E-ISSN 1572-834XArticle in journal (Refereed)
    Abstract [en]

    Resource discovery is a key challenge in dynamic environment such as Peer-to-Peer (P2P) MANETs. To leverage the lookup costs and efficiently discover the resources, the peers in a P2P network communicate with each other forming one-or-more overlay structure. And, thus the peer’s connections in such overlay networks plays a crucial role. To harness this, we propose a model that focuses on the underlying network topology as each virtual link of the overlay network is supported by a path in the underlying physical network. Also, we design a new resource discovery algorithm that uses the famous Rock-Paper-Scis-sors (RPS)  game ideology. In this work, we present a Rock-Paper-Scissors-Game-Based (RPSGB) algorithm for content discovery in mobile peer-to-peer network. The proposed work focuses on providing efficient resource discovery scheme in such a dynamic network, using the game theory concepts. Our scheme is a light-weight model aimed to suit the unstructured architecture better. The major goal of this work, is to reduce the consumption of power, minimize the lookup cost, lower the bandwidth consumption, and increase the hit ratio. We compare our scheme with the traditional well-known benchmarked schemes. After evaluation, the  simulation results justify the effectiveness of the proposed protocol. The obtained results show that the proposed scheme substantially decreases the network traffic, lowers the battery power and bandwidth consumption, while having good search efficiency. Also, the search latency is minimized. The result justifies that RPSGB algo-rithm proposes to make resource searching much more efficient, and improves the statistics against the posed challenges.

  • 90.
    Arunachalam, Ajay
    et al.
    Örebro University, School of Science and Technology.
    Andreasson, Henrik
    Örebro University, School of Science and Technology.
    MSI-RPi: Affordable, Portable, and Modular Multispectral Imaging Prototype Suited to Operate in UV, Visible and Mid-Infrared Regions2022In: Journal of Mobile Multimedia, ISSN 1550-4646, E-ISSN 1550-4654, Vol. 18, no 3, p. 723-742Article in journal (Refereed)
    Abstract [en]

    Digital plant inventory provides critical growth insights, given the associated data quality is good. Stable & high-quality image acquisition is critical for further examination. In this work, we showcase an affordable, portable, and modular spectral camera prototype, designed with open hardware’s and open-source software’s. The image sensors used were color, and infrared Pi micro-camera. The designed prototype presents the advantage as being low-cost and modular with respect to other general commercial market available spectral devices. The micro-size connected sensors make it a compact instrument that can be used for any general spectral acquisition purposes, along with the provision of custom selection of the bands, making the presented prototype design a Plug-nd-Play (PnP) setup that can be used in different wide application areas. The images acquired from our custom-built prototype were back-tested by performing image analysis and qualitative assessments. The image acquisition software, and processing algorithm has been programmed, which is bundled with our developed system. Further, an end-to-end automation script is integrated for the users to readily leverage the services on-demand. The design files, schematics, and all the related materials of the spectral block design is open-sourced with open-hardware license & is made available at https://github.com/ajayarunachalam/Multi-Spectral-Imaging-RaspberryPi-Design. The automated data acquisition scripts & the spectral image analysis done is made available at https://github.com/ajayarunachalam/SI-RPi.

  • 91.
    Arunachalam, Ajay
    et al.
    Örebro University, School of Science and Technology.
    Andreasson, Henrik
    Örebro University, School of Science and Technology.
    RaspberryPi‐Arduino (RPA) powered smart mirrored and reconfigurable IoT facility for plant science research2022In: Internet Technology Letters, E-ISSN 2476-1508, Vol. 5, no 1, article id e272Article in journal (Refereed)
    Abstract [en]

    Continuous monitoring of crops is critical for the sustainability of agriculture. The effects of changes in temperature, light intensity, humidity, pH, soil moisture, gas intensities, etc. have an overall impact on the plant growth. Growth chambers are environmental controlled facilities which needs to be monitored round-the-clock. To improve both the reproducibility, and maintenance of such facilities, remote monitoring plays a very pivotal role. An automated re-configurable & persistent mirrored storage-based remote monitoring system is developed with low-cost open source hardwares and softwares. The system automates sensors deployment, storage (database, logs), and provides an elegant dashboard to visualize the real-time continuous data stream. We propose a new smart AGRO IoT system with robust data acquisition mechanism, and also propose two software component nodes, (i.e., Mirroring and Reconfiguration) running as an instance of the whole IoT facility. The former one is aimed to minimize/avoid the downtime, while the latter one is aimed to leverage the available cores, and better utilization of the computational resources. Our system can be easily deployed in growth chambers, greenhouses, CNC farming test-bed setup, cultivation plots, and further can be also extended to support large-farms with either using multiple individual standalone setup as heterogeneous instances of this facility, or by extending it as master-slave cluster configuration for communication as a single homogeneous instance. Our RaspberryPi-Arduino (RPA) powered solution is scalable, and provides stability for monitoring any environment continuously at ease.

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    RaspberryPi-Arduino (RPA) powered smart mirrored and reconfigurable IoT facility for plant science research
  • 92.
    Arunachalam, Ajay
    et al.
    Örebro University, School of Science and Technology.
    Ravi, Vinayakumar
    Center for Artificial Intelligence, Prince Mohammad Bin Fahd University, Khobar, Saudi Arabia .
    Acharya, Vasundhara
    Manipal Institute of Technology (MIT), Manipal Academy of Higher Education (MAHE), Manipal, India.
    Pham, Tuan D.
    Center for Artificial Intelligence, Prince Mohammad Bin Fahd University, Khobar, Saudi Arabia .
    Toward Data-Model-Agnostic Autonomous Machine-Generated Data Labeling and Annotation Platform: COVID-19 Autoannotation Use Case2023In: IEEE transactions on engineering management, ISSN 0018-9391, E-ISSN 1558-0040, Vol. 70, no 8, p. 2695-2706Article in journal (Refereed)
    Abstract [en]

    Quick, early, and precise detection is important for diagnosis to control the spread of COVID-19 infection. Artificial Intelligence (AI) technology could certainly be used as a modulating tool to ease the detection, and help with the preventive steps further. Convolutional neural networks (CNNs) have achieved state-of-the-art performance in many visual recognition tasks. Nevertheless, most of these state-of-the-art networks highly rely on the availability of a high amount of labeled data, being an essential step in supervised machine learning tasks. Conventionally, this manual, mundane, and time-consuming process of annotating images is done by humans. Learning to localize or detect COVID-19 infection masks in our specific case study typically requires the collection of CT scan data that has been labeled with bounding boxes or similar annotations, which generally is limited. A technique that could perform such learning with much less annotations, and transfer the learned proposals that are algorithm-driven to generate more synthetic annotated samples would be helpful & quite valuable. We present such a technique inspired by weakly trained mask region based convolutional neural networks (R-CNN) architecture for localization, in which the number of images with their pixel-level masks can be a small proportion of the total dataset, and then further improvise CNNs by inversely generating dense annotations on-the-go using an algorithmic-based computational approach. We focus on alleviating the bottleneck associated with deep learning models needing annotated data for training in an intuitive reverse engineering fashion through this work. Our proposed solution can certainly provide the prospect of automated labeling on-the-fly, thereby reducing much of the manual work. As a result, one can quickly train a precise COVID-19 infection detector with the leverage of autonomous frame-by-frame machine generated annotations. The model achieved mean precision accuracy (%) of 0.99, 0.931, and 0.8 for train, validation, and test set, respectively. The results demonstrate that the proposed method can be adopted in a clinical setting for assisting radiologists, and also our fully autonomous approach can be generalized to any detection/recognition tasks at ease.

  • 93.
    Arunachalam, Ajay
    et al.
    Örebro University, School of Science and Technology.
    Ravi, Vinayakumar
    Center for Artificial Intelligence, Prince Mohammad Bin Fahd University, Khobar, Saudi Arabia.
    Krichen, Moez
    Faculty of CSIT, Al-Baha University, Saudi Arabia ReDCAD Laboratory, University of Sfax, Tunisia.
    Alroobaea, Roobaea
    Department of Computer Science, College of Computers and Information Technology, Taif University, Taif, Saudi Arabia.
    Alqurni, Jehad Saad
    Department of Education Technologies, College of Education, Imam Abdulrahman Bin Faisal University, Saudi Arabia .
    Analytical Comparison of Resource Search Algorithms in Non-DHT Mobile Peer-to-Peer Networks2021In: Computers, Materials and Continua, ISSN 1546-2218, E-ISSN 1546-2226, Vol. 68, no 1, p. 983-1001Article in journal (Refereed)
    Abstract [en]

    One of the key challenges in ad-hoc networks is the resource discovery problem. How efficiently & quickly the queried resource/object can be resolved in such a highly dynamic self-evolving network is the underlying question? Broadcasting is a basic technique in the Mobile Ad-hoc Networks (MANETs), and it refers to sending a packet from one node to every other node within the transmission range. Flooding is a type of broadcast where the received packet is retransmitted once by every node. The naive flooding technique floods the network with query messages, while the random walk scheme operates by contacting subsets of each node's neighbors at every step, thereby restricting the search space. Many earlier works have mainly focused on the simulation-based analysis of flooding technique, and its variants, in a wired network scenario. Although, there have been some empirical studies in peer-to-peer (P2P) networks, the analytical results are still lacking, especially in the context of mobile P2P networks. In this article, we mathematically model different widely used existing search techniques, and compare with the proposed improved random walk method, a simple lightweight approach suitable for the non-DHT architecture. We provide analytical expressions to measure the performance of the different flooding-based search techniques, and our proposed technique. We analytically derive 3 relevant key performance measures, i.e., the avg. number of steps needed to find a resource, the probability of locating a resource, and the avg. number of messages generated during the entire search process.

  • 94.
    Arunachalam, Ajay
    et al.
    Örebro University, School of Science and Technology.
    Ravi, Vinayakumar
    Center for Artificial Intelligence, Prince Mohammad Bin Fahd University, Khobar, Saudi Arabia.
    Krichen, Moez
    Faculty of CSIT, Al-Baha University, Saudi Arabia ReDCAD Laboratory, University of Sfax, Tunisia.
    Alroobaea, Roobaea
    Department of Computer Science, College of Computers and Information Technology, Taif University, Taif, Saudi Arabia.
    Rubaiee, Saeed
    Department of Industrial and Systems Engineering, College of Engineering, University of Jeddah, Jeddah, Saudi Arabia.
    Mathematical Model Validation of Search Protocols in MP2P Networks2021In: Computers, Materials and Continua, ISSN 1546-2218, E-ISSN 1546-2226, Vol. 68, no 2, p. 1807-1829Article in journal (Refereed)
    Abstract [en]

    Broadcasting is a basic technique in Mobile ad-hoc network (MANET), and it refers to sending a packet from one node to every other node within the transmission range. Flooding is a type of broadcast where the received packet is retransmitted once by every node. The naive flooding technique, floods the network with query messages, while the random walk technique operates by contacting the subsets of every node's neighbors at each step, thereby restricting the search space. One of the key challenges in an ad-hoc network is the resource or content discovery problem which is about locating the queried resource. Many earlier works have mainly focused on the simulation-based analysis of flooding, and its variants under a wired network. Although, there have been some empirical studies in peer-to-peer (P2P) networks, the analytical results are still lacking, especially in the context of P2P systems running over MANET. In this paper, we describe how P2P resource discovery protocols perform badly over MANETs. To address the limitations, we propose a new protocol named ABRW (Address Broadcast Random Walk), which is a lightweight search approach, designed considering the underlay topology aimed to better suit the unstructured architecture. We provide the mathematical model, measuring the performance of our proposed search scheme with different widely popular benchmarked search techniques. Further, we also derive three relevant search performance metrics, i.e., mean no. of steps needed to find a resource, the probability of finding a resource, and the mean no. of message overhead. We validated the analytical expressions through simulations. The simulation results closely matched with our analyticalmodel, justifying our findings. Our proposed search algorithm under such highly dynamic self-evolving networks performed better, as it reduced the search latency, decreased the overall message overhead, and still equally had a good success rate. 

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    Mathematical Model Validation of Search Protocols in MP2P Networks
  • 95.
    Arunachalam, Ajay
    et al.
    Department of Computer Science, National Institute of Development Administration (NIDA), Thailand.
    Sornil, Ohm
    Department of Computer Science, National Institute of Development Administration (NIDA), Thailand.
    An Analysis of the Overhead and Energy Consumption in Flooding, Random Walk and Gossip Based Resource Discovery Protocols in MP2P Networks2015In: 2015 Fifth International Conference on Advanced Computing & Communication Technologies, ISSN 2327-0632, p. 292-297Article in journal (Refereed)
    Abstract [en]

    All major mobile communication architectures are mainly centralized. When the mobile devices are switched on it will search for nearby base station or access point. The content being searched is mostly stored in a centralized directory manner. Peer-to-Peer platform can be one of the best possibilities to overcome the restrictions and resolve issues incurred due to centralization. Mobile environment poses additional challenges on such P2P networks - due to limited resources, dynamic and wireless network characteristics, heterogeneity of nodes, limitations on processing power and wireless bandwidth. Hence resource discovery becomes further challenging. Even today mostly the traditional methods like flooding, random walk or gossip based forwarding methods have to be considered along with major limitations and drawbacks. Further in Mobile Peer-to-Peer (MP2P) system the energy aspect is very crucial with regards to the participation of nodes in the system. The search failure rate may increase if a mobile device uses all its energy and hence not participate in the resource discovery process. In this paper, we simulate the existing standard flooding, random walk and gossip based resource discovery algorithms on a P2P Mobile Adhoc Network (MANET) and studied their performance under such highly dynamic mobile network scenario. The efficiency of the resource discovery protocols are validated through extensiveNS-2 simulations.

  • 96.
    Arunachalam, Ajay
    et al.
    Department of Computer Science, National Institute of Development Administration (NIDA), Bangkok, Thailand.
    Sornil, Ohm
    Department of Computer Science, National Institute of Development Administration (NIDA), Bangkok, Thailand.
    Issues of Implementing Random Walk and Gossip Based Resource Discovery Protocols in P2P MANETs & Suggestions for Improvement2015In: Procedia Computer Science, E-ISSN 1877-0509, Vol. 57, p. 509-518Article in journal (Refereed)
    Abstract [en]

    Wireless multi-hop networks attracted much attention in recent years. Mobile Ad-hoc Network (MANET) being one of such networks has its own limitations in terms of resource discovery with unstable topology and paths through the networks. So eventually traditional searching techniques are still widely used. Peer-to-Peer (P2P) model is the major candidate for the internet traffic mainly due to its decentralized nature. This article evaluates classic flooding, random walk and gossip based resource discovery algorithms under mobile peer-to-peer (MP2P) networks and studied their performance. Further we suggest way to improve these algorithms to suit and work better under MANET. We compare the performance in terms of success rate, query response time, network overhead, battery power consumed, overall dropped packets, MAC load, network bandwidth, packet delivery ratio, network routing load and end to end delay. The experiments are validated through NS-2 simulations.

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    Issues of Implementing Random Walk and Gossip Based Resource Discovery Protocols in P2P MANETs & Suggestions for Improvement
  • 97.
    Asadi, Sahar
    Örebro University, School of Science and Technology.
    Towards Dense Air Quality Monitoring: Time-Dependent Statistical Gas Distribution Modelling and Sensor Planning2017Doctoral thesis, monograph (Other academic)
    Abstract [en]

    This thesis addresses the problem of gas distribution modelling for gas monitoring and gas detection. The presented research is particularly focused on the methods that are suitable for uncontrolled environments. In such environments, gas source locations and the physical properties of the environment, such as humidity and temperature may be unknown or only sparse noisy local measurements are available. Example applications include air pollution monitoring, leakage detection, and search and rescue operations.

    This thesis addresses how to efficiently obtain and compute predictive models that accurately represent spatio-temporal gas distribution.

    Most statistical gas distribution modelling methods assume that gas dispersion can be modelled as a time-constant random process. While this assumption may hold in some situations, it is necessary to model variations over time in order to enable applications of gas distribution modelling for a wider range of realistic scenarios.

    This thesis proposes two time-dependent gas distribution modelling methods. In the first method, a temporal (sub-)sampling strategy is introduced. In the second method, a time-dependent gas distribution modelling approach is presented, which introduces a recency weight that relates measurement to prediction time. These contributions are presented and evaluated as an extension of a previously proposed method called Kernel DM+V using several simulation and real-world experiments. The results of comparing the proposed time-dependent gas distribution modelling approaches to the time-independent version Kernel DM+V indicate a consistent improvement in the prediction of unseen measurements, particularly in dynamic scenarios under the condition that there is a sufficient spatial coverage. Dynamic scenarios are often defined as environments where strong fluctuations and gas plume development are present.

    For mobile robot olfaction, we are interested in sampling strategies that provide accurate gas distribution models given a small number of samples in a limited time span. Correspondingly, this thesis addresses the problem of selecting the most informative locations to acquire the next samples.

    As a further contribution, this thesis proposes a novel adaptive sensor planning method. This method is based on a modified artificial potential field, which selects the next sampling location based on the currently predicted gas distribution and the spatial distribution of previously collected samples. In particular, three objectives are used that direct the sensor towards areas of (1) high predictive mean and (2) high predictive variance, while (3) maximising the coverage area. The relative weight of these objectives corresponds to a trade-off between exploration and exploitation in the sampling strategy. This thesis discusses the weights or importance factors and evaluates the performance of the proposed sampling strategy. The results of the simulation experiments indicate an improved quality of the gas distribution models when using the proposed sensor planning method compared to commonly used methods, such as random sampling and sampling along a predefined sweeping trajectory. In this thesis, we show that applying a locality constraint on the proposed sampling method decreases the travelling distance, which makes the proposed sensor planning approach suitable for real-world applications where limited resources and time are available. As a real-world use-case, we applied the proposed sensor planning approach on a micro-drone in outdoor experiments.

    Finally, this thesis discusses the potential of using gas distribution modelling and sensor planning in large-scale outdoor real-world applications. We integrated the proposed methods in a framework for decision-making in hazardous inncidents where gas leakage is involved and applied the gas distribution modelling in two real-world use-cases. Our investigation indicates that the proposed sensor planning and gas distribution modelling approaches can be used to inform experts both about the gas plume and the distribution of gas in order to improve the assessment of an incident.

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    Towards Dense Air Quality Monitoring: Time-Dependent Statistical Gas Distribution Modelling and Sensor Planning
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  • 98.
    Asadi, Sahar
    et al.
    Örebro University, School of Science and Technology.
    Badica, Costin
    University of Craiova, Craiova, Romania.
    Comes, Tina
    Karslruhe Institute of Technology, Karslruhe, Germany.
    Conrado, Claudine
    Thales Research and Technology, Delft, The Netherlands.
    Evers, Vanessa
    University of Amsterdam, Amsterdam, The Netherlands.
    Groen, Frans
    University of Amsterdam, Amsterdam, The Netherlands.
    Illie, Sorin
    University of Craiova, Craiova, Romania.
    Steen Jensen, Jan
    Danish Emergency Management Agency (DEMA), Birkerød, Denmark.
    Lilienthal, Achim J.
    Örebro University, School of Science and Technology.
    Milan, Bianca
    DCMR, Delft, The Netherlands.
    Neidhart, Thomas
    Space Applications Services, Zaventem, Belgium.
    Nieuwenhuis, Kees
    Thales Research and Technology, Delft, The Netherlands.
    Pashami, Sepideh
    Örebro University, School of Science and Technology.
    Pavlin, Gregor
    Thales Research and Technology, Delft, The Netherlands.
    Pehrsson, Jan
    Prolog Development Center, Brøndby Copenhagen, Denmark.
    Pinchuk, Rani
    Space Applications and Services, Zaventem, Belgium.
    Scafes, Mihnea
    University of Craiova, Craiova, Romania.
    Schou-Jensen, Leo
    DCMR, Brøndby Copenhagen, Denmark.
    Schultmann, Frank
    Karslruhe Institute of Technology, Karlsruhe, Germany.
    Wijngaards, Niek
    Thales Research and Technology, Delft, the Netherlands.
    ICT solutions supporting collaborative information acquisition, situation assessment and decision making in contemporary environmental management problems: the DIADEM approach2011In: Proceedings of the 25th EnviroInfo Conference "Environmental Informatics", Herzogenrath: Shaker Verlag, 2011, p. 920-931Conference paper (Refereed)
    Abstract [en]

    This paper presents a framework of ICT solutions developed in the EU research project DIADEM that supports environmental management with an enhanced capacity to assess population exposure and health risks, to alert relevant groups and to organize efficient response. The emphasis is on advanced solutions which are economically feasible and maximally exploit the existing communication, computing and sensing resources. This approach enables efficient situation assessment in complex environmental management problems by exploiting relevant information obtained from citizens via the standard communication infrastructure as well as heterogeneous data acquired through dedicated sensing systems. This is achieved through a combination of (i) advanced approaches to gas detection and gas distribution modelling, (ii) a novel service-oriented approach supporting seamless integration of human-based and automated reasoning processes in large-scale collaborative sense making processes and (iii) solutions combining Multi-Criteria Decision Analysis, Scenario-Based Reasoning and advanced human-machine interfaces. This paper presents the basic principles of the DIADEM solutions, explains how different techniques are combined to a coherent decision support system and briefly discusses evaluation principles and activities in the DIADEM project.

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  • 99.
    Asadi, Sahar
    et al.
    Örebro University, School of Science and Technology.
    Lilienthal, Achim
    Örebro University, School of Science and Technology.
    Approaches to Time-Dependent Gas Distribution Modelling2015In: 2015 European Conference on Mobile Robots (ECMR), New York: IEEE conference proceedings , 2015, article id 7324215Conference paper (Refereed)
    Abstract [en]

    Mobile robot olfaction solutions for gas distribution modelling offer a number of advantages, among them autonomous monitoring in different environments, mobility to select sampling locations, and ability to cooperate with other systems. However, most data-driven, statistical gas distribution modelling approaches assume that the gas distribution is generated by a time-invariant random process. Such time-invariant approaches cannot model well developing plumes or fundamental changes in the gas distribution. In this paper, we discuss approaches that explicitly consider the measurement time, either by sub-sampling according to a given time-scale or by introducing a recency weight that relates measurement and prediction time. We evaluate the performance of these time-dependent approaches in simulation and in real-world experiments using mobile robots. The results demonstrate that in dynamic scenarios improved gas distribution models can be obtained with time-dependent approaches.

  • 100.
    Asadi, Sahar
    et al.
    Örebro University, School of Science and Technology.
    Reggente, Matteo
    Örebro University, School of Science and Technology.
    Stachniss, Cyrill
    University of Freiburg, Freiburg, Germany.
    Plagemann, Christian
    Stanford University, Stanford CA, USA.
    Lilienthal, Achim J.
    Örebro University, School of Science and Technology.
    Statistical gas distribution modeling using kernel methods2011In: Intelligent systems for machine olfaction: tools and methodologies / [ed] E. L. Hines and M. S. Leeson, IGI Global, 2011, 1, p. 153-179Chapter in book (Refereed)
    Abstract [en]

    Gas distribution models can provide comprehensive information about a large number of gas concentration measurements, highlighting, for example, areas of unusual gas accumulation. They can also help to locate gas sources and to plan where future measurements should be carried out. Current physical modeling methods, however, are computationally expensive and not applicable for real world scenarios with real-time and high resolution demands. This chapter reviews kernel methodsthat statistically model gas distribution. Gas measurements are treated as randomvariables, and the gas distribution is predicted at unseen locations either using akernel density estimation or a kernel regression approach. The resulting statistical 

    apmodelsdo not make strong assumptions about the functional form of the gas distribution,such as the number or locations of gas sources, for example. The majorfocus of this chapter is on two-dimensional models that provide estimates for themeans and predictive variances of the distribution. Furthermore, three extensionsto the presented kernel density estimation algorithm are described, which allow toinclude wind information, to extend the model to three dimensions, and to reflecttime-dependent changes of the random process that generates the gas distributionmeasurements. All methods are discussed based on experimental validation usingreal sensor data.

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