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  • 51.
    Akos, Dennis
    et al.
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Embedded Internet Systems Lab.
    Stockmaster, Michael
    Rockwell Collins, Cedar Wells.
    Tsui, James B.Y.
    Wright-Patterson Air Force Base, Dayton.
    Caschera, Joe
    Wright-Patterson Air Force Base, Dayton.
    Direct bandpass sampling of multiple distinct RF signals1999In: IEEE Transactions on Communications, ISSN 0090-6778, E-ISSN 1558-0857, Vol. 47, no 7, p. 983-988Article in journal (Refereed)
    Abstract [en]

    A goal in the software radio design philosophy is to place the analog-to-digital converter as near the antenna as possible. This objective has been demonstrated for the case of a single input signal. Bandpass sampling has been applied to downconvert, or intentionally alias, the information bandwidth of a radio frequency (RF) signal to a desired intermediate frequency. The design of the software radio becomes more interesting when two or more distinct signals are received. The traditional approach for multiple signals would be to bandpass sample a continuous span of spectrum containing all the desired signals. The disadvantage with this approach is that the sampling rate and associated discrete processing rate are based on the span of spectrum as opposed to the information bandwidths of the signals of interest. Proposed here is a technique to determine the absolute minimum sampling frequency for direct digitization of multiple, nonadjacent, frequency bands. The entire process is based on the calculation of a single parameter-the sampling frequency. The result is a simple, yet elegant, front-end design for the reception and bandpass sampling of multiple RF signals. Experimental results using RF transmissions from the US Global Positioning System-Standard Position Service (GPS-SPS) and the Russian Global Navigation Satellite System (GLONASS) are used to illustrate and verify the theory

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  • 52.
    Al-Azzawi, Sana
    et al.
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Embedded Internet Systems Lab.
    Kovács, György
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Embedded Internet Systems Lab.
    Nilsson, Filip
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Embedded Internet Systems Lab.
    Adewumi, Tosin
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Embedded Internet Systems Lab.
    Liwicki, Marcus
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Embedded Internet Systems Lab.
    NLP-LTU at SemEval-2023 Task 10: The Impact of Data Augmentation and Semi-Supervised Learning Techniques on Text Classification Performance on an Imbalanced Dataset2023In: 17th International Workshop on Semantic Evaluation, SemEval 2023: Proceedings of the Workshop, Association for Computational Linguistics, 2023, p. 1421-1427Conference paper (Refereed)
  • 53.
    Al-Azzawi, Sana Sabah
    et al.
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Embedded Internet Systems Lab. College of Engineering, University of Information Technology and Communications, Baghdad 10013, Iraq.
    Khaksar, Siavash
    School of Electrical Engineering, Computing and Mathematical Sciences, Curtin University, Bentley, WA 6102, Australia.
    Hadi, Emad Khdhair
    Rehabilitation Medical Center and Joint Diseases, Baghdad 10001, Iraq.
    Agrawal, Himanshu
    School of Electrical Engineering, Computing and Mathematical Sciences, Curtin University, Bentley, WA 6102, Australia.
    Murray, Iain
    School of Electrical Engineering, Computing and Mathematical Sciences, Curtin University, Bentley, WA 6102, Australia.
    HeadUp: A Low-Cost Solution for Tracking Head Movement of Children with Cerebral Palsy Using IMU2021In: Sensors, E-ISSN 1424-8220, Vol. 21, no 23, article id 8148Article in journal (Refereed)
    Abstract [en]

    Cerebral palsy (CP) is a common reason for human motor ability limitations caused before birth, through infancy or early childhood. Poor head control is one of the most important problems in children with level IV CP and level V CP, which can affect many aspects of children's lives. The current visual assessment method for measuring head control ability and cervical range of motion (CROM) lacks accuracy and reliability. In this paper, a HeadUp system that is based on a low-cost, 9-axis, inertial measurement unit (IMU) is proposed to capture and evaluate the head control ability for children with CP. The proposed system wirelessly measures CROM in frontal, sagittal, and transverse planes during ordinary life activities. The system is designed to provide real-time, bidirectional communication with an Euler-based, sensor fusion algorithm (SFA) to estimate the head orientation and its control ability tracking. The experimental results for the proposed SFA show high accuracy in noise reduction with faster system response. The system is clinically tested on five typically developing children and five children with CP (age range: 2-5 years). The proposed HeadUp system can be implemented as a head control trainer in an entertaining way to motivate the child with CP to keep their head up.

  • 54.
    Al-Azzawi, Sana Sabah Sabry
    et al.
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Embedded Internet Systems Lab.
    Kovács, György
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Embedded Internet Systems Lab.
    Mokayed, Hamam
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Embedded Internet Systems Lab.
    Chronéer, Diana
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Digital Services and Systems.
    Liwicki, Foteini
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Embedded Internet Systems Lab.
    Liwicki, Marcus
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Embedded Internet Systems Lab.
    Innovative Education Approach Toward Active Distance Education: a Case Study in the Introduction to AI course2022In: Conference Proceedings. The Future of Education 2022, 2022Conference paper (Refereed)
    Abstract [en]

    In this paper, we first describe various synchronous and asynchronous methods for enhancing student engagement in big online courses. We showcase the implementation of these methods in the “Introduction to Artificial Intelligence (AI)” course at Luleå University of Technology, which has attracted around 500 students in each of its iterations (twice yearly, since 2019). We also show that these methods can be applied efficiently, in terms of the teaching hours required. With the increase in digitization and student mobility, the demand for improved and personalized content delivery for distance education has also increased. This applies not only in the context of traditional undergraduate education, but also in the context of adult education and lifelong learning. This higher level of demand, however, introduces a challenge, especially as it is typically combined with a shortage of staff and needs for efficient education. This challenge is further amplified by the current pandemic situation, which led to an even bigger risk of student-dropout. To mitigate this risk, as well as to meet the increased demand, we applied various methods for creating engaging interaction in our pedagogy based on Moor’s framework: learner-to-learner, learner-to-instructor, and learner-to-content engagement strategies. The main methods of this pedagogy are as follows: short, and interactive videos, active discussions in topic-based forums, regular live sessions with group discussions, and the introduction of optional content at many points in the course, to address different target groups. In this paper, we show how we originally designed and continuously improved the course, without requiring more than 500 teaching hours per iteration (one hour per enrolled student), while we also managed to increase the successful completion rate of the participants by 10%, and improved student engagement and feedback for the course by 50%. We intend to share a set of best-practices applicable to many other e-learning courses in ICT.

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  • 55.
    Albano, Michele
    et al.
    CISTER, ISEP/INESC-TEC Polytechnic Institute of Porto.
    Barbosa, Paulo Miguel
    CISTER, ISEP/INESC-TEC Polytechnic Institute of Porto.
    Silva, Jose
    CISTER, ISEP/INESC-TEC Polytechnic Institute of Porto.
    Duarte, Roberto
    CISTER, ISEP/INESC-TEC Polytechnic Institute of Porto.
    Ferreira, Luis Lino
    CISTER, ISEP/INESC-TEC Polytechnic Institute of Porto.
    Delsing, Jerker
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Embedded Internet Systems Lab.
    Quality of Service on the Arrowhead Framework2017In: 2017 IEEE 13th International Workshop on Factory Communication Systems (WFCS), Piscataway, NJ: Institute of Electrical and Electronics Engineers (IEEE), 2017, article id 7991959Conference paper (Refereed)
    Abstract [en]

    Quality of Service (QoS) is an important enabler for communication in industrial environments. The Arrowhead Framework was created to support local cloud functionalities for automation applications by means of a Service Oriented Architecture. To this aim, the framework offers a number of services that ease application development, among them the QoSSetup and the Monitor services, the first used to verify and configure QoS in the local cloud, and the second for online monitoring of QoS. This paper describes how the QoSSetup and Monitor services are provided in a Arrowhead-compliant System of Systems, detailing both the principles and algorithms employed, and how the services are implemented. Experimental results are provided, from a demonstrator built over a real-time Ethernet network.

  • 56.
    Alberti, M.
    et al.
    Document Image and Voice Analysis Group (DIVA), University of Fribourg, Switzerland.
    Pondenkandath, V.
    Document Image and Voice Analysis Group (DIVA), University of Fribourg, Switzerland.
    Wursch, M.
    Document Image and Voice Analysis Group (DIVA), University of Fribourg, Switzerland.
    Ingold, R.
    Document Image and Voice Analysis Group (DIVA), University of Fribourg, Switzerland.
    Liwicki, Marcus
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Embedded Internet Systems Lab. Document Image and Voice Analysis Group (DIVA), University of Fribourg, Switzerland.
    DeepDIVA: A Highly-Functional Python Framework for Reproducible Experiments2018In: Proceedings of International Conference on Frontiers in Handwriting Recognition, ICFHR 2018, IEEE, 2018, p. 423-428, article id 8583798Conference paper (Refereed)
    Abstract [en]

    We introduce DeepDIVA: an infrastructure designed to enable quick and intuitive setup of reproducible experiments with a large range of useful analysis functionality. Reproducing scientific results can be a frustrating experience, not only in document image analysis but in machine learning in general. Using DeepDIVA a researcher can either reproduce a given experiment or share their own experiments with others. Moreover, the framework offers a large range of functions, such as boilerplate code, keeping track of experiments, hyper-parameter optimization, and visualization of data and results. To demonstrate the effectiveness of this framework, this paper presents case studies in the area of handwritten document analysis where researchers benefit from the integrated functionality. DeepDIVA is implemented in Python and uses the deep learning framework PyTorch. It is completely open source(1), and accessible as Web Service through DIVAServices(2).

  • 57.
    Alberti, Michele
    et al.
    Document Image and Voice Analysis Group (DIVA), University of Fribourg, Switzerland; V7 Ltd, London, United Kingdom.
    Botros, Angela
    ARTORG Center for Biomedical Engineering Research, University of Bern, Switzerland.
    Schütz, Narayan
    ARTORG Center for Biomedical Engineering Research, University of Bern, Switzerland.
    Ingold, Rolf
    Document Image and Voice Analysis Group (DIVA), University of Fribourg, Switzerland.
    Liwicki, Marcus
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Embedded Internet Systems Lab.
    Seuret, Mathias
    Pattern Recognition Lab, Friedrich-Alexander-Universität Erlangen-Nürnberg, Germany.
    Trainable Spectrally Initializable Matrix Transformations in Convolutional Neural Networks2021In: Proceedings of ICPR 2020: 25th International Conference on Pattern Recognition, IEEE, 2021, p. 8204-8211Conference paper (Refereed)
    Abstract [en]

    In this work, we introduce a new architectural component to Neural Network (NN), i.e., trainable and spectrally initializable matrix transformations on feature maps. While previous literature has already demonstrated the possibility of adding static spectral transformations as feature processors, our focus is on more general trainable transforms. We study the transforms in various architectural configurations on four datasets of different nature: from medical (ColorectalHist, HAM10000) and natural (Flowers) images to historical documents (CB55). With rigorous experiments that control for the number of parameters and randomness, we show that networks utilizing the introduced matrix transformations outperform vanilla neural networks. The observed accuracy increases appreciably across all datasets. In addition, we show that the benefit of spectral initialization leads to significantly faster convergence, as opposed to randomly initialized matrix transformations. The transformations are implemented as auto-differentiable PyTorch modules that can be incorporated into any neural network architecture. The entire code base is open-source.

  • 58.
    Alberti, Michele
    et al.
    Document Image and Voice Analysis Group (DIVA), University of Fribourg, Switzerland.
    Pondenkandath, Vinaychandran
    Document Image and Voice Analysis Group (DIVA), University of Fribourg, Switzerland.
    Vögtlin, Lars
    Document Image and Voice Analysis Group (DIVA), University of Fribourg, Switzerland.
    Würsch, Marcel
    Document Image and Voice Analysis Group (DIVA), University of Fribourg, Switzerland; Institute for Interactive Technologies (IIT), FHNW University of Applied Sciences and Arts Northwestern Switzerland, Switzerland.
    Ingold, Rolf
    Document Image and Voice Analysis Group (DIVA), University of Fribourg, Switzerland.
    Liwicki, Marcus
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Embedded Internet Systems Lab. Document Image and Voice Analysis Group (DIVA), University of Fribourg, Switzerland.
    Improving Reproducible Deep Learning Workflows with DeepDIVA2019In: Proceedings 6th Swiss Conference on Data Science: SDS2019, IEEE, 2019, p. 13-18Conference paper (Refereed)
    Abstract [en]

    The field of deep learning is experiencing a trend towards producing reproducible research. Nevertheless, it is still often a frustrating experience to reproduce scientific results. This is especially true in the machine learning community, where it is considered acceptable to have black boxes in your experiments. We present DeepDIVA, a framework designed to facilitate easy experimentation and their reproduction. This framework allows researchers to share their experiments with others, while providing functionality that allows for easy experimentation, such as: boilerplate code, experiment management, hyper-parameter optimization, verification of data integrity and visualization of data and results. Additionally, the code of DeepDIVA is well-documented and supported by several tutorials that allow a new user to quickly familiarize themselves with the framework.

  • 59.
    Alberti, Michele
    et al.
    Document Image and Voice Analysis Group (DIVA), University of Fribourg, Fribourg, Switzerland.
    Pondenkandath, Vinaychandran
    Document Image and Voice Analysis Group (DIVA), University of Fribourg, Fribourg, Switzerland.
    Würsch, Marcel
    Document Image and Voice Analysis Group (DIVA), University of Fribourg, Fribourg, Switzerland.
    Bouillon, Manuel
    Document Image and Voice Analysis Group (DIVA), University of Fribourg, Fribourg, Switzerland.
    Seuret, Mathias
    Document Image and Voice Analysis Group (DIVA), University of Fribourg, Fribourg, Switzerland.
    Ingold, Rolf
    Document Image and Voice Analysis Group (DIVA), University of Fribourg, Fribourg, Switzerland.
    Liwicki, Marcus
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Embedded Internet Systems Lab. Document Image and Voice Analysis Group (DIVA), University of Fribourg, Fribourg, Switzerland.
    Are You Tampering with My Data?2019In: Computer Vision – ECCV 2018 Workshops: Proceedings, Part II / [ed] Laura Leal-Taixé & Stefan Roth, Springer, 2019, p. 296-312Conference paper (Refereed)
    Abstract [en]

    We propose a novel approach towards adversarial attacks on neural networks (NN), focusing on tampering the data used for training instead of generating attacks on trained models. Our network-agnostic method creates a backdoor during training which can be exploited at test time to force a neural network to exhibit abnormal behaviour. We demonstrate on two widely used datasets (CIFAR-10 and SVHN) that a universal modification of just one pixel per image for all the images of a class in the training set is enough to corrupt the training procedure of several state-of-the-art deep neural networks, causing the networks to misclassify any images to which the modification is applied. Our aim is to bring to the attention of the machine learning community, the possibility that even learning-based methods that are personally trained on public datasets can be subject to attacks by a skillful adversary.

  • 60.
    Alberti, Michele
    et al.
    Document Image and Voice Analysis Group (DIVA), University of Fribourg, Switzerland.
    Vögtlin, Lars
    Document Image and Voice Analysis Group (DIVA), University of Fribourg, Switzerland.
    Pondenkandath, Vinaychandran
    Document Image and Voice Analysis Group (DIVA), University of Fribourg, Switzerland.
    Seuret, Mathias
    Document Image and Voice Analysis Group (DIVA), University of Fribourg, Switzerland. Pattern Recognition Lab, Friedrich-Alexander-Universität Erlangen-Nürnberg, Erlangen, Germany.
    Ingold, Rolf
    Document Image and Voice Analysis Group (DIVA), University of Fribourg, Switzerland.
    Liwicki, Marcus
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Embedded Internet Systems Lab. Document Image and Voice Analysis Group (DIVA), University of Fribourg, Switzerland.
    Labeling, Cutting, Grouping: An Efficient Text Line Segmentation Method for Medieval Manuscripts2019In: The 15th IAPR International Conference on Document Analysis and Recognition: ICDAR 2019, IEEE, 2019, p. 1200-1206Conference paper (Other academic)
    Abstract [en]

    This paper introduces a new way for text-line extraction by integrating deep-learning based pre-classification and state-of-the-art segmentation methods. Text-line extraction in complex handwritten documents poses a significant challenge, even to the most modern computer vision algorithms. Historical manuscripts are a particularly hard class of documents as they present several forms of noise, such as degradation, bleed-through, interlinear glosses, and elaborated scripts. In this work, we propose a novel method which uses semantic segmentation at pixel level as intermediate task, followed by a text-line extraction step. We measured the performance of our method on a recent dataset of challenging medieval manuscripts and surpassed state-of-the-art results by reducing the error by 80.7%. Furthermore, we demonstrate the effectiveness of our approach on various other datasets written in different scripts. Hence, our contribution is two-fold. First, we demonstrate that semantic pixel segmentation can be used as strong denoising pre-processing step before performing text line extraction. Second, we introduce a novel, simple and robust algorithm that leverages the high-quality semantic segmentation to achieve a text-line extraction performance of 99.42% line IU on a challenging dataset.

  • 61.
    Albertsson, Kim
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Embedded Internet Systems Lab.
    Machine Learning in High-Energy Physics: Displaced Event Detection and Developments in ROOT/TMVA2021Doctoral thesis, comprehensive summary (Other academic)
    Abstract [en]

    Many proposed extensions to the Standard Model of particle physics predict long-lived particles, which can decay at a significant distance from the primary interaction point. Such events produce displaced vertices with distinct detector signatures when compared to standard model processes. The Large Hadron Collider (LHC) operates at a collision rate where it is not feasible to record all generated data—a problem that will be exac-erbated in the coming high-luminosity upgrade—necessitating an online trigger system to decide which events to keep based on partial information. However, the trigger is not directly sensitive to signatures with displaced vertices from Long-lived particles (LLPs). Current LLP detection approaches require a computationally expensive reconstruction step, or rely on auxiliary signatures such as energetic particles or missing energy. An improved trigger sensitivity increases the reach of searches for extensions to the standard model.This thesis explores the possibility to apply machine learning methods directly on low-level tracking features, such as detector hits and hit-pairs to identify displaced high-mass decays while avoiding a full vertex and track reconstruction step.A dataset is developed where modelled displaced signatures from novel and known physics processes are mixed in a custom simulation environment, which models the in-ner detector of a general purpose particle detector. Two machine learning models are evaluated using the dataset: a multi-layer dense Artificial Neural Network (ANN), and a Graph Neural Network (GNN). Two case studies suggest that dense ANNs have difficulty capturing relational information in low-level data, while GNNs can feasibily discriminate heavy displaced decay signatures from a Standard Model background. Furthermore it was found that GNNs can perform at a background rejection factor of 103 and a signal efficiency of 20% in collision environments with moderate levels of pile-up interactions, i.e. low-energy particle collisions simultaneous with the primary hard scatter. Further work is required to integrate the approach into a trigger environment. In particular, detector material and measurement resolution effects should be included in the simulation, which should be scaled to model the High-Luminosity Large Hadron Collider (HL-LHC) with its more complicated geometry and its high levels of pile-up.In parallel, the machine learning landscape is quickly evolving and concentrating into large software frameworks with expanding scope, while the High-Energy Physics (HEP) community maintains its own set of tools and frameworks, one example being the Toolkit for Multivariate Analysis (TMVA) which is part of the ROOT framework. This thesis discusses the long- and short-term evolution of these tools, both current trends and some relations to parallel developments in Industry 4.0.

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  • 62.
    Albertsson, Kim
    et al.
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Embedded Internet Systems Lab. CERN.
    An, Sitong
    CERN; Carnegie Mellon University .
    Gleyzer, Sergei
    University of Alabama .
    Moneta, Lorenzo
    CERN.
    Niermann, Joana
    CERN.
    Wunsch, Stefan
    CERN; Karlsruhe Institute of Technology .
    Zampieri, Luca
    CERN.
    Mesa, Omar Andres Zapata
    CERN.
    Machine Learning with ROOT/TMVA2020In: 24th international conference on computing in high energy and nuclear physics (CHEP 2019), EDP Sciences, 2020, Vol. 245Conference paper (Refereed)
    Abstract [en]

    ROOT provides, through TMVA, machine learning tools for data analysis at HEP experiments and beyond. We present recently included features in TMVA and the strategy for future developments in the diversified machine learning landscape. Focus is put on fast machine learning inference, which enables analysts to deploy their machine learning models rapidly on large scale datasets. The new developments are paired with newly designed C++ and Python interfaces supporting modern C++ paradigms and full interoperability in the Python ecosystem. We present as well a new deep learning implementation for convolutional neural network using the cuDNN library for GPU. We show benchmarking results in term of training time and inference time, when comparing with other machine learning libraries such as Keras/Tensorflow.

  • 63.
    Albertsson, Kim
    et al.
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Embedded Internet Systems Lab. CERN.
    An, Sitong
    CERN; Carnegie Mellon University.
    Moneta, Lorenzo
    CERN.
    Wunsch, Stefan
    CERN; Karlsruhe Institute of Technology .
    Zampieri, Luca
    École polytechnique fédérale de Lausanne.
    Fast Inference for Machine Learning in ROOT/TMVA2020In: 24th international conference on computing in high energy and nuclear physics (CHEP 2019), EDP Sciences, 2020, Vol. 245Conference paper (Refereed)
    Abstract [en]

    ROOT provides, through TMVA, machine learning tools for data analysis at HEP experiments and beyond. However, with the rapidly evolving ecosystem for machine learning, the focus of TMVA is shifting. We present the new developments and strategy of TMVA, which will allow the analyst to integrate seamlessly, and effectively, different workflows in the diversified machine-learning landscape. Focus is put on a fast machine learning inference system, which will enable analysts to deploy their machine learning models rapidly on large scale datasets. We present the technical details of a fast inference system for decision tree algorithms, included in the next ROOT release (6.20). We further present development status and proposal for a fast inference interface and code generator for ONNX-based Deep Learning models.

  • 64.
    Albertsson, Kim
    et al.
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Embedded Internet Systems Lab. CERN.
    Gleyze, Sergei
    University of Florida.
    Huwiler, Marc
    EPFL.
    Ilievski, Vladimir
    EPFL.
    Moneta, Lorenzo
    CERN.
    Shekar, Saurav
    ETH Zurich.
    Estrade, Victor
    CERN.
    Vashistha, Akshay
    CERN. Karlsruhe Institute of Technology.
    Wunsch, Stefan
    CERN. Karlsruhe Institute of Technology.
    Mesa, Omar Andres Zapata
    University of Antioquia. Metropolitan Institute of Technology.
    New Machine Learning Developments in ROOT/TMVA2019In: 23rd International Conference on Computing in High Energy and Nuclear Physics (CHEP 2018), EDP Sciences, 2019, Vol. 214, article id 06014Conference paper (Refereed)
    Abstract [en]

    The Toolkit for Multivariate Analysis, TMVA, the machine learning package integrated into the ROOT data analysis framework, has recently seen improvements to its deep learning module, parallelisation of multivariate methods and cross validation. Performance benchmarks on datasets from high-energy physics are presented with a particular focus on the new deep learning module which contains robust fully-connected, convolutional and recurrent deep neural networks implemented on CPU and GPU architectures. Both dense and convo-lutional layers are shown to be competitive on small-scale networks suitable for high-level physics analyses in both training and in single-event evaluation. Par-allelisation efforts show an asymptotical 3-fold reduction in boosted decision tree training time while the cross validation implementation shows significant speed up with parallel fold evaluation.

  • 65.
    Albertsson, Kim
    et al.
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Embedded Internet Systems Lab.
    Gleyzer, Sergei
    University of Florida.
    Zapata, Omar
    OProject and University of Antioquia.
    Machine Learning in High Energy Physics Community White Paper2018In: Journal of Physics, Conference Series, ISSN 1742-6588, E-ISSN 1742-6596, Vol. 1085, article id 022008Article in journal (Refereed)
    Abstract [en]

    Machine learning is an important applied research area in particle physics, beginning with applications to high-level physics analysis in the 1990s and 2000s, followed by an explosion of applications in particle and event identification and reconstruction in the 2010s. In this document we discuss promising future research and development areas in machine learning in particle physics with a roadmap for their implementation, software and hardware resource requirements, collaborative initiatives with the data science community, academia and industry, and training the particle physics community in data science. The main objective of the document is to connect and motivate these areas of research and development with the physics drivers of the High-Luminosity Large Hadron Collider and future neutrino experiments and identify the resource needs for their implementation. Additionally we identify areas where collaboration with external communities will be of great benefit.

  • 66.
    Albertsson, Kim
    et al.
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Embedded Internet Systems Lab. CERN.
    Meloni, Federico
    DESY.
    Displaced Event Classification Using Graph Networks2020In: Proceedings of the 2020 Connecting The Dots Workshop / [ed] David Lange, CTD/WIT IAC committee , 2020, article id PROC-CTD2020-25Conference paper (Refereed)
    Abstract [en]

    Long-lived massive particles, predicted in numerous Standard Model extensions, are a particularly difficult target for online event reconstruction. This work presents a study of detecting the presence of displaced vertices in a collider experiment in several environmental conditions. In particular Graph Neural Networks performing classification on input hit-level data are shown to perform well in the task of separating prompt against displaced events with results translating, with some degradation, into more busy environments. Furthermore, promising results are shown for identifying events from a benchmark supersymmetric process with future work investigating higher pileup environments.

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  • 67.
    Albertsson, Kim
    et al.
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Embedded Internet Systems Lab. CERN.
    Meloni, Federico
    DESY.
    Towards Fast Displaced Vertex Finding2019In: CTDWIT 19 proceedings, CERN , 2019, article id PROC-CTD19-014Conference paper (Refereed)
    Abstract [en]

    Many Standard Model extensions predict metastable massive particles that can be detected by looking for displaced decay vertices in the inner detector volume. Current approaches to search for these events in high-energy particle collisions rely on the presence of additional energetic signatures to make an online selection during data-taking, as the reconstruction of displaced vertices is computationally intensive. Enabling trigger-level reconstruction of displaced vertices could significantly enhance the reach of such searches.

    This work is a first step approximating the location of the primary vertex in an idealised detector geometry using a 4-layer dense neural networks for regression of the vertex location yielding a precision of O(1 mm) [O(20 mm)] RMS in a low [high] track multiplicity environment.

  • 68.
    Alizadeh, Morteza
    et al.
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Computer Science.
    Andersson, Karl
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Computer Science.
    Schelén, Olov
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Embedded Internet Systems Lab.
    Comparative Analysis of Decentralized Identity Approaches2022In: IEEE Access, E-ISSN 2169-3536, Vol. 10, p. 92273-92283Article in journal (Refereed)
    Abstract [en]

    Decentralization is essential when trust and performance must not depend on a single organization. Distributed Ledger Technologies (DLTs) and Decentralized Hash Tables (DHTs) are examples where the DLT is useful for transactional events, and the DHT is useful for large-scale data storage. The combination of these two technologies can meet many challenges. The blockchain is a DLT with immutable history protected by cryptographic signatures in data blocks. Identification is an essential issue traditionally provided by centralized trust anchors. Self-sovereign identities (SSIs) are proposed decentralized models where users can control and manage their identities with the help of DHT. However, slowness is a challenge among decentralized identification systems because of many connections and requests among participants. In this article, we focus on decentralized identification by DLT and DHT, where users can control their information and store biometrics. We survey some existing alternatives and address the performance challenge by comparing different decentralized identification technologies based on execution time and throughput. We show that the DHT and machine learning model (BioIPFS) performs better than other solutions such as uPort, ShoCard, and BBID.

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  • 69.
    Alizadeh, Morteza
    et al.
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Computer Science.
    Andersson, Karl
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Computer Science.
    Schelén, Olov
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Embedded Internet Systems Lab.
    DHT- and Blockchain-based Smart Identification for Video Conferencing2022In: Blockchain: Research and Applications, ISSN 2096-7209, Vol. 3, no 2, article id 100066Article in journal (Refereed)
    Abstract [en]

    Video conferencing applications help people communicate via the Internet and provide a significant and consistent basis for virtual meetings. However, integrity, security, identification, and authentication problems are still universal. Current video conference technologies typically rely on cloud systems to provide a stable and secure basis for executing tasks and processes. At the same time, video conferencing applications are being migrated from centralized to decentralized solutions for better performance without the need for third-party interactions. This article demonstrates a decentralized smart identification scheme for video conferencing applications based on biometric technology, machine learning, and a decentralized hash table combined with blockchain technology. We store users' information on a distributed hash table and transactional events on the distributed ledger after identifying users by implementing machine learning functions. Furthermore, we leverage distributed ledger technology's immutability and traceability properties and distributed hash table unlimited storage feature to improve the system's storage capacity and immutability by evaluating three possible architectures. The experimental results show that an architecture based on blockchain and distributed hash table has better efficiency but needs a longer time to execute than the two other architectures using a centralized database.

  • 70.
    Alizadeh, Morteza
    et al.
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Computer Science.
    Andersson, Karl
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Computer Science.
    Schelén, Olov
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Embedded Internet Systems Lab.
    Efficient Decentralized Data Storage Based on Public Blockchain and IPFS2020In: 2020 IEEE Asia-Pacific Conference on Computer Science and Data Engineering (CSDE), IEEE, 2020Conference paper (Refereed)
    Abstract [en]

    Blockchain technology has enabled the keeping of a decentralized, tamper-proof, immutable, and ordered ledger of transactional events. Efforts to leverage such a ledger may be challenging when data storage requirements exceed most blockchain protocols’ current capacities. Storing large amounts of decentralized data while maintaining system efficiency is the challenge that we target. This paper proposes using the IPFS distributed hash table (DHT) technology to store information immutably and in a decentralized manner to mitigate the high cost of storage. A storage system involving blockchain and other storage systems in concert should be based on immutable data and allow removal of data from malicious users in the DHT. Efficiency is improved by decreasing the overall processing time in the blockchain with the help of DHT technology and introducing an agreement service that communicate with the blockchain via a RESTful API. We demonstrate the applicability of the proposed method and conclude that the combination of IPFS and blockchain provides efficient cryptographic storage, immutable history and overall better efficiency in a decentralized manner.

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  • 71.
    Alkhaled, Lama
    et al.
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Embedded Internet Systems Lab.
    Adewumi, Oluwatosin
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Embedded Internet Systems Lab.
    Sabry, Sana Sabah
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Embedded Internet Systems Lab.
    Bipol: A novel multi-axes bias evaluation metric with explainability for NLP2023In: Natural Language Processing Journal, ISSN 2949-7191, Vol. 4, article id 100030Article in journal (Refereed)
    Abstract [en]

    We introduce bipol, a new metric with explainability, for estimating social bias in text data. Harmful bias is prevalent in many online sources of data that are used for training machine learning (ML) models. In a step to address this challenge we create a novel metric that involves a two-step process: corpus-level evaluation based on model classification and sentence-level evaluation based on (sensitive) term frequency (TF). After creating new models to classify bias using SotA architectures, we evaluate two popular NLP datasets (COPA and SQuADv2) and the WinoBias dataset. As additional contribution, we created a large English dataset (with almost 2 million labeled samples) for training models in bias classification and make it publicly available. We also make public our codes.

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  • 72.
    Al-Maqdasi, Zainab
    et al.
    Luleå University of Technology, Department of Engineering Sciences and Mathematics, Material Science.
    Hajlane, Abdelghani
    Luleå University of Technology, Department of Engineering Sciences and Mathematics, Material Science. Materials Science and Nano-engineering, Mohammed VI Polytechnic University, Benguerir, Morocco.
    Renbi, Abdelghani
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Embedded Internet Systems Lab.
    Ouarga, Ayoub
    Materials Science and Nano-engineering, Mohammed VI Polytechnic University, Benguerir, Morocco.
    Chouhan, Shailesh Singh
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Embedded Internet Systems Lab.
    Joffe, Roberts
    Luleå University of Technology, Department of Engineering Sciences and Mathematics, Material Science.
    Conductive Regenerated Cellulose Fibers by Electroless Plating2019In: Fibers, ISSN 2079-6439, Vol. 7, no 5, article id 38Article in journal (Refereed)
    Abstract [en]

    Continuous metallized regenerated cellulose fibers for advanced applications (e.g. multi-functional composites) are produced by electroless copper plating. Copper is successfully deposited on the surface of cellulose fibers using commercial cyanide-free electroless copper plating package commonly available for manufacturing of printed wiring boards. The deposited copper is found to enhance the thermal stability, electrical conductivity and resistance to moisture uptake of the fibers. On the other hand, involved chemistry results in altering the molecular structure of the fibers as is indicated by the degradation of their mechanical performance (tensile strength and modulus).

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  • 73.
    Al-Maqdasi, Zainab
    et al.
    Luleå University of Technology, Department of Engineering Sciences and Mathematics, Material Science.
    Joffe, Roberts
    Luleå University of Technology, Department of Engineering Sciences and Mathematics, Material Science.
    Ouarga, Ayoub
    High Throughput Multidisciplinary Research Laboratory, Mohammed VI Polytechnic University (UM6P), Lot 660—Hay Moulay Rachid, 43150 Benguerir, Morocco.
    Emami, Nazanin
    Luleå University of Technology, Department of Engineering Sciences and Mathematics, Machine Elements.
    Chouhan, Shailesh Singh
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Embedded Internet Systems Lab.
    Landström, Anton
    Luleå University of Technology, Department of Engineering Sciences and Mathematics, Material Science.
    Hajlane, Abdelghani
    Laboratory of Crystallography and Materials Sciences, National Graduate School of Engineering of Caen, 6 Boulevard Maréchal Juin, 14000 Caen, France.
    Conductive Regenerated Cellulose Fibers for Multi-Functional Composites: Mechanical and Structural Investigation2021In: Materials, ISSN 1996-1944, E-ISSN 1996-1944, Vol. 14, no 7, article id 1746Article in journal (Refereed)
    Abstract [en]

    Regenerated cellulose fibers coated with copper via electroless plating process are investigated for their mechanical properties, molecular structure changes, and suitability for use in sensing applications. Mechanical properties are evaluated in terms of tensile stiffness and strength of fiber tows before, during and after the plating process. The effect of the treatment on the molecular structure of fibers is investigated by measuring their thermal stability with differential scanning calorimetry and obtaining Raman spectra of fibers at different stages of the treatment. Results show that the last stage in the electroless process (the plating step) is the most detrimental, causing changes in fibers’ properties. Fibers seem to lose their structural integrity and develop surface defects that result in a substantial loss in their mechanical strength. However, repeating the process more than once or elongating the residence time in the plating bath does not show a further negative effect on the strength but contributes to the increase in the copper coating thickness, and, subsequently, the final stiffness of the tows. Monitoring the changes in resistance values with applied strain on a model composite made of these conductive tows show an excellent correlation between the increase in strain and increase in electrical resistance. These results indicate that these fibers show potential when combined with conventional composites of glass or carbon fibers as structure monitoring devices without largely affecting their mechanical performance.

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  • 74.
    Alonso, Pedro
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Embedded Internet Systems Lab.
    Faster and More Resource-Efficient Intent Classification2020Licentiate thesis, comprehensive summary (Other academic)
    Abstract [en]

    Intent classification is known to be a complex problem in Natural Language Processing (NLP) research. This problem represents one of the stepping stones to obtain machines that can understand our language. Several different models recently appeared to tackle the problem. The solution has become reachable with deep learning models. However, they have not achieved the goal yet.Nevertheless, the energy and computational resources of these modern models (especially deep learning ones) are very high. The utilization of energy and computational resources should be kept at a minimum to deploy them on resource-constrained devices efficiently.Furthermore, these resource savings will help to minimize the environmental impact of NLP.

    This thesis considers two main questions.First, which deep learning model is optimal for intent classification?Which model can more accurately infer a written piece of text (here inference equals to hate-speech) in a short text environment. Second, can we make intent classification models to be simpler and more resource-efficient than deep learning?.

    Concerning the first question, the work here shows that intent classification in written language is still a complex problem for modern models.However, deep learning has shown successful results in every area it has been applied.The work here shows the optimal model that was used in short texts.The second question shows that we can achieve results similar to the deep learning models by more straightforward solutions.To show that, when combining classical machine learning models, pre-processing techniques, and a hyperdimensional computing approach.

    This thesis presents a research done for a more resource-efficient machine learning approach to intent classification. It does this by first showing a high baseline using tweets filled with hate-speech and one of the best deep learning models available now (RoBERTa, as an example). Next, by showing the steps taken to arrive at the final model with hyperdimensional computing, which minimizes the required resources.This model can help make intent classification faster and more resource-efficient by trading a few performance points to achieve such resource-saving.Here, a hyperdimensional computing model is proposed. The model is inspired by hyperdimensional computing and its called ``hyperembed,'' which shows the capabilities of the hyperdimensional computing paradigm.When considering resource-efficiency, the models proposed were tested on intent classification on short texts, tweets (for hate-speech where intents are to offend or not to), and questions posed to Chatbots.

    In summary, the work proposed here covers two aspects. First, the deep learning models have an advantage in performance when there are sufficient data. They, however, tend to fail when the amount of available data is not sufficient. In contrast to the deep learning models, the proposed models work well even on small datasets.Second, the deep learning models require substantial resources to train and run them while the models proposed here aim at trading off the computational resources spend to obtaining and running the model against the classification performance of the model.

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  • 75.
    Alonso, Pedro
    et al.
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Embedded Internet Systems Lab.
    Saini, Rajkumar
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Embedded Internet Systems Lab.
    Kovács, György
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Embedded Internet Systems Lab. MTA-SZTE Research Group on Artificial Intelligence, Szeged, Hungary.
    Hate Speech Detection using Transformer Ensembles on the HASOC Dataset2020In: Speech and Computer: 22nd International Conference, SPECOM 2020, St. Petersburg, Russia, October 7–9, 2020, Proceedings / [ed] Alexey Karpov, Rodmonga Potapova, Springer, 2020, p. 13-21Conference paper (Refereed)
    Abstract [en]

    With the ubiquity and anonymity of the Internet, the spread of hate speech has been a growing concern for many years now. The language used for the purpose of dehumanizing, defaming or threatening individuals and marginalized groups not only threatens the mental health of its targets, as well as their democratic access to the Internet, but also the fabric of our society. Because of this, much effort has been devoted to manual moderation. The amount of data generated each day, particularly on social media platforms such as Facebook and twitter, however makes this a Sisyphean task. This has led to an increased demand for automatic methods of hate speech detection.

    Here, to contribute towards solving the task of hate speech detection, we worked with a simple ensemble of transformer models on a twitter-based hate speech benchmark. Using this method, we attained a weighted F1-score of 0.8426, which we managed to further improve by leveraging more training data, achieving a weighted F1-score of 0.8504. Thus markedly outperforming the best performing system in the literature.

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  • 76.
    Alonso, Pedro
    et al.
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Embedded Internet Systems Lab.
    Saini, Rajkumar
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Embedded Internet Systems Lab.
    Kovács, György
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Embedded Internet Systems Lab.
    TheNorth at HASOC 2019: Hate Speech Detection in Social Media Data2019In: Working Notes of FIRE 2019 - Forum for Information Retrieval Evaluation / [ed] Parth Mehta, Paolo Rosso, Prasenjit Majumder, Mandar Mitra,, RWTH Aachen University , 2019, p. 293-299Conference paper (Refereed)
    Abstract [en]

    The detection of hate speech in social media is a crucial task. The uncontrolled spread of hate speech can be detrimental to maintaining the peace and harmony in society. Particularly when hate speech is spread with the intention to defame people, or spoil the image of a person, a community, or a nation. A major ground for spreading hate speech is that of social media. This significantly contributes to the difficultyof the task, as social media posts not only include paralinguistic tools (e.g. emoticons, and hashtags), their linguistic content contains plenty of poorly written text that does not adhere to grammar rules. With the recent development in Natural Language Processing (NLP), particularly with deep architecture, it is now possible to anlayze unstructured composite natural language text. For this reason, we propose a deep NLP model for the detection of automatic hate speech in social media data. We have applied our model on the HASOC2019 hate speech corpus, and attained a macro F1 score of 0.63 in the detection of hate speech.

  • 77.
    Alonso, Pedro
    et al.
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Embedded Internet Systems Lab.
    Saini, Rajkumar
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Embedded Internet Systems Lab.
    Kovács, György
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Embedded Internet Systems Lab.
    TheNorth at SemEval-2020 Task 12: Hate Speech Detection using RoBERTa2020In: The International Workshop on Semantic Evaluation: Proceedings of the Fourteenth Workshop, International Committee for Computational Linguistics , 2020, p. 2197-2202Conference paper (Refereed)
    Abstract [en]

    Hate speech detection on social media platforms is crucial as it helps to avoid severe harm to marginalized people and groups. The application of Natural Language Processing (NLP) and Deep Learning has garnered encouraging results in the task of hate speech detection. The expressionof hate, however, is varied and ever-evolving. Thus better detection systems need to adapt to this variance. Because of this, researchers keep on collecting data and regularly come up with hate speech detection competitions. In this paper, we discuss our entry to one such competition,namely the English version of sub-task A for the OffensEval competition. Our contribution can be perceived through our results, that was first an F1-score of 0.9087, and with further refinementsdescribed here climb up to 0.9166. It serves to give more support to our hypothesis that one ofthe variants of BERT, namely RoBERTa can successfully differentiate between offensive and non-offensive tweets, given the proper preprocessing steps

  • 78.
    Alonso, Pedro
    et al.
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Embedded Internet Systems Lab.
    Shridhar, Kumar
    Department of Computer Science, ETH Zürich, Zürich, Switzerland.
    Kleyko, Denis
    UC Berkeley, Berkeley, USA; Research Institutes of Sweden, Kista, Sweden.
    Osipov, Evgeny
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Computer Science.
    Liwicki, Marcus
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Embedded Internet Systems Lab.
    HyperEmbed: Tradeoffs Between Resources and Performance in NLP Tasks with Hyperdimensional Computing Enabled Embedding of n-gram Statistics2021In: 2021 International Joint Conference on Neural Networks (IJCNN) Proceedings, IEEE, 2021Conference paper (Refereed)
    Abstract [en]

    Recent advances in Deep Learning have led to a significant performance increase on several NLP tasks, however, the models become more and more computationally demanding. Therefore, this paper tackles the domain of computationally efficient algorithms for NLP tasks. In particular, it investigates distributed representations of n -gram statistics of texts. The representations are formed using hyperdimensional computing enabled embedding. These representations then serve as features, which are used as input to standard classifiers. We investigate the applicability of the embedding on one large and three small standard datasets for classification tasks using nine classifiers. The embedding achieved on par F1 scores while decreasing the time and memory requirements by several times compared to the conventional n -gram statistics, e.g., for one of the classifiers on a small dataset, the memory reduction was 6.18 times; while train and test speed-ups were 4.62 and 3.84 times, respectively. For many classifiers on the large dataset, memory reduction was ca. 100 times and train and test speed-ups were over 100 times. Importantly, the usage of distributed representations formed via hyperdimensional computing allows dissecting strict dependency between the dimensionality of the representation and n-gram size, thus, opening a room for tradeoffs.

  • 79.
    Alrifaiy, Ahmed
    et al.
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Signals and Systems. Institute of Neuroscience and Physiology, Section of physiology, Gothenburg University - Sahlgrenska Academy, Göteborg, 405 30, Sweden; CMTF, Centre for Biomedical Engineering and Physics, Luleå and Umeå, Sweden.
    Borg, Johan
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Embedded Internet Systems Lab.
    Lindahl, Olof A.
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Signals and Systems. Luleå University of Technology, Department of Engineering Sciences and Mathematics, Fluid and Experimental Mechanics. CMTF, Centre for Biomedical Engineering and Physics, Luleå and Umeå, Sweden; Department of Radiation Sciences, Biomedical Engineering, Umeå, 901 87, Sweden.
    Ramser, Kerstin
    Luleå University of Technology, Department of Engineering Sciences and Mathematics, Fluid and Experimental Mechanics. Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Signals and Systems. CMTF, Centre for Biomedical Engineering and Physics, Luleå and Umeå, Sweden.
    A lab-on-a-chip for hypoxic patch clamp measurements combined with optical tweezers and spectroscopy: first investigations of single biological cells2015In: Biomedical engineering online, E-ISSN 1475-925X, Vol. 14, article id 36Article in journal (Refereed)
    Abstract [en]

    The response and the reaction of the brain system to hypoxia is a vital research subject that requires special instrumentation. With this research subject in focus, a new multifunctional lab-on-a-chip (LOC) system with control over the oxygen content for studies on biological cells was developed. The chip was designed to incorporate the patch clamp technique, optical tweezers and absorption spectroscopy. The performance of the LOC was tested by a series of experiments. The oxygen content within the channels of the LOC was monitored by an oxygen sensor and verified by simultaneously studying the oxygenation state of chicken red blood cells (RBCs) with absorption spectra. The chicken RBCs were manipulated optically and steered in three dimensions towards a patch-clamp micropipette in a closed microfluidic channel. The oxygen level within the channels could be changed from a normoxic value of 18% O 2 to an anoxic value of 0.0-0.5% O 2. A time series of 3 experiments were performed, showing that the spectral transfer from the oxygenated to the deoxygenated state occurred after about 227 ± 1 s and a fully developed deoxygenated spectrum was observed after 298 ± 1 s, a mean value of 3 experiments. The tightness of the chamber to oxygen diffusion was verified by stopping the flow into the channel system while continuously recording absorption spectra showing an unchanged deoxygenated state during 5400 ± 2 s. A transfer of the oxygenated absorption spectra was achieved after 426 ± 1 s when exposing the cell to normoxic buffer. This showed the long time viability of the investigated cells. Successful patching and sealing were established on a trapped RBC and the whole-cell access (Ra) and membrane (Rm) resistances were measured to be 5.033 ± 0.412 M Ω and 889.7 ± 1.74 M Ω respectively.

  • 80.
    Altmann, Peter
    et al.
    Department of Industrial Systems, RISE Research Institutes of Sweden AB, SE-16440 Kista, Sweden.
    Abbasi, Abdul Ghafoor
    Department of Industrial Systems, RISE Research Institutes of Sweden AB, SE-16440 Kista, Sweden.
    Schelén, Olov
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Embedded Internet Systems Lab.
    Andersson, Karl
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Computer Science.
    Alizadeh, Morteza
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Computer Science.
    Creating a Traceable Product Story in Manufacturing Supply Chains Using IPFS2020In: 2020 IEEE 19th International Symposium on Network Computing and Applications (NCA) / [ed] Aris Gkoulalas-Divanis, Mirco Marchetti, Dimiter R. Avresky, Boston/New York: IEEE, 2020, p. 11-18Conference paper (Refereed)
    Abstract [en]

    Evolving traceability requirements increasingly challenge manufacturing supply chain actors to collect tamperproof and auditable evidence about what inputs they process, in what way these inputs are used, and what the resulting process outputs are. Traceability solutions based on blockchain technology have shown ways to satisfy the requirements of creating a tamper-proof and auditable trail of traceability data. However, the existing solutions struggle to meet the increasing storage requirements necessary to create an evidence trail using manufacturing data. In this paper, we show a way to create a tamper-proof and auditable evolving product story that uses a decentralized file system called the InterPlanetary File System (IPFS). We also show how using linked data can help auditors derive a traceable product story from such an accumulating evidence trail. The solution proposed herein can supplement existing blockchain-based traceability solutions and enable traceability in global manufacturing supply chains where forming a consortium incurs prohibitive costs and where storage requirements are high.

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  • 81.
    Aminu Sanda, Mohammed
    et al.
    Luleå University of Technology, Department of Business Administration, Technology and Social Sciences.
    Abrahamsson, Lena
    Luleå University of Technology, Department of Business Administration, Technology and Social Sciences, Human Work Science.
    Galar, Diego
    Luleå University of Technology, Department of Civil, Environmental and Natural Resources Engineering, Operation, Maintenance and Acoustics.
    Kumar, Uday
    Luleå University of Technology, Department of Civil, Environmental and Natural Resources Engineering, Operation, Maintenance and Acoustics.
    Sandin, Fredrik
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Embedded Internet Systems Lab.
    Delsing, Jerker
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Embedded Internet Systems Lab.
    Lean instrumentation framework for sensor pruning and optimization in condition monitoring2011In: The Eighth International Conference on Condition Monitoring and Machinery Failure Prevention Technologies: St. David's Hotel, Cardiff, Wales, 20 - 22 June 2011 ; CM2011/MFPT2011, Longborough, Glos: Coxmoor Publishing Co. , 2011, Vol. 1, p. 202-215Conference paper (Refereed)
    Abstract [en]

    This paper discusses a lean instrumentation framework for guiding the introduction of the lean concept in condition monitoring in order to enhance the organizational capability (i.e. human, technical and management trichotomy) and reduce the complexity in the maintenance management systems of industrial companies. Additionally, decision-making, based on severity diagnosis and prognosis in condition monitoring, is a complex maintenance function which is based on large data-set of sensors measurements. Yet, the entirety of such decision-making is not dependent on only the sensors measurements, but also on other important indices, such as the human factors, organizational aspects and knowledge management. This is because, the ability to identify significant features from large amount of measured data is a major challenge for automated defect diagnosis, a situation that necessitate the need to identify signal transformations and features in new domains. The need for the lean instrumentation framework is justified by the desire to have a modern condition monitoring system with the capability of pruning to the optimal level the number of sensors required for efficient and effective serviceability of the maintenance process. It is concluded that there are methodologies that can be developed to enable more efficient condition monitoring systems, with benefits for many processes along the value chain.

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  • 82.
    Andersson, Mathias
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Embedded Internet Systems Lab.
    EMC robot1997Report (Other academic)
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  • 83.
    Andersson, Tobias
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Embedded Internet Systems Lab.
    Analysis of 3D surface data for on-line determination of the size distribution of iron ore pellet piles on conveyor belt2007Licentiate thesis, comprehensive summary (Other academic)
    Abstract [en]

    Size measurement of iron ore pellets in industry is usually performed by manual sampling and sieving techniques. The manual sampling is performed infrequently and is inconsistent, invasive and time-consuming. Iron ore pellet's sizes are critical to the efficiency of the blast furnace process in the production of steel. Overly coarse pellets affect the blast furnace process negatively, however this affect can be minimized by operating the furnace with different parameters. An on-line system for measurement of pellet sizes would improve productivity through fast feedback and efficient control of the blast furnace. Also, fast feedback of pellet sizes would improve pellet quality in pellet production. Image analysis techniques promise a quick, inexpensive, consistent and non-contact solution to determining the size distribution of a pellet pile. Such techniques capture information of the surface of the pellet pile which is then used to infer the pile size distribution. However, there are a number of sources of error relevant to surface analysis techniques. The objective of this thesis is to address and overcome aspects of these sources of error relevant to surface analysis techniques. The research problem is stated as: How can the pellet pile size distribution be estimated with surface analysis techniques using image analysis? This problem is addressed by dividing the problem into sub-problems. The focus of the presented work is to develop techniques to overcome, or minimize, two of these sources of error; overlapped particle error and profile error. Overlapped particle error describes the fact that many pellets on the surface of a pile are only partially visible and a large bias results if they are sized as if they were smaller entirely visible pellets. No other researchers make this determination. Profile error describes the fact that only one side of an entirely visible pellet can be seen making it difficult to estimate pellets size. Statistical classification methods are used to overcome these sources of error. The thesis is divided into two parts. The first part contains an introduction to the research area together with a summary of the contributions, and the second part is a collection of four papers describing the research.

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  • 84.
    Andersson, Ulf
    et al.
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Signals and Systems.
    Bortolin, Gianantonio
    Volvo CE.
    Backén, Staffan
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Embedded Internet Systems Lab.
    Gustafsson, Thomas
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Signals and Systems.
    Estimation of side-slip angles of a Volvo A25E articulated all-wheel drive hauler based on GPS/INS measurements2011In: Proceedings of SAE 2011 Commercial Vehicle Engineering Congress and Exhibition, Society of Automotive Engineers, Incorporated , 2011Conference paper (Refereed)
    Abstract [en]

    Traction control for off-road vehicles such as articulated all-wheel drive haulers is of great importance to improve the vehicle performance. A well-known method to reduce the slip and thereby improve the traction is to engage differential locks in the driveline of the vehicle. The drawbacks of differential locks engaged are for instance increased wear, increased fuel consumption but also reduced turnability of the vehicle. Therefore, the differentials should be locked only when necessary, ideally only when slip occurs or is about to occur. A number of methods to detect slip has been reported in the literature. Some of them utilize dynamical models of the vehicle where side-slip angles are important inputs. This paper describes an off-line estimator for the side-slip angles of an articulated vehicle based on measurements from Global Positioning System (GPS) and Inertial Navigation System (INS). The current implementation is a proof of concept and the intention is to develop a system that can be used as a reference for on-line estimators. By comparing measurements from two GPS/INS units, mounted on the front and rear part of the vehicle, it is possible to estimate the side-slip angles of both the front and rear part. The method has been tested on a Volvo A25E articulated all-wheel drive hauler equipped with two high precision GPS/INS units (NovAtel's SPAN-CPT). Tests have been performed when driving on asphalt, gravel and snow. The results from the tests are discussed.

  • 85.
    Andersson, Ulf
    et al.
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Signals and Systems.
    Mrozek, Kent
    Åström, Kalle
    Hyyppä, Kalevi
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Embedded Internet Systems Lab.
    Path design and control algorithms for articulated mobile robots1997In: Proceedings of the International Conference on Field and Service Robotics / [ed] Alexander Zelinsky, 1997, p. 405-411Conference paper (Refereed)
  • 86. Antonini, G.
    et al.
    Ekman, Jonas
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Embedded Internet Systems Lab.
    Ruehli, A.E.
    Accuracy and stability enhancement of PEEC models for the time and frequency domain2006In: EMC Europe 2006 Barcelona: [International Symposium on Electromagnetic Compatibility ; September 4 - 8, 2006, Barcelona, Spain] / [ed] Ferran Silva, Barcelona: Universidad Politécnica de Cataluña , 2006Conference paper (Refereed)
  • 87.
    Antonini, G.
    et al.
    Università Degli Studi di l'Aquila.
    Miscione, G.
    Università Degli Studi di l'Aquila.
    Ekman, Jonas
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Embedded Internet Systems Lab.
    PEEC modeling of automotive electromagnetic problems2008In: Applied Computational Electromagnetics Society Newsletter, ISSN 1056-9170, Vol. 23, no 1, p. 39-50Article in journal (Refereed)
    Abstract [en]

    This paper presents the combination of the nonorthogonal Partial Element Equivalent Circuit (PEEC) models and interconnect structures through a macromodel approach for the analysis of automotive electromagnetic problems. The applications are within automotive computational electromagnetics due to the typical combination of cable harnesses and chassis structures. It is shown that PEEC-based solvers are capable of handling electrically large problems with high geometrical complexity for detailed analysis in both the time- and frequency- domain with attached multi-conductor transmission lines.

  • 88. Antonini, G.
    et al.
    Miscione, G.
    Ekman, Jonas
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Embedded Internet Systems Lab.
    Anttu, Peter
    PEEC modeling of automotive electromagnetic problems2006In: Proceedings of the International Conference of Numerical Analysis and Applied Mathematics: official conference European Society of Computational Methods in Sciences and Engineering (ESCMSE) / [ed] T. E. Simos, John Wiley & Sons, 2006Conference paper (Refereed)
  • 89.
    Antonini, Giulio
    et al.
    University of L'Aquila (Italy).
    De Lauretis, Maria
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Embedded Internet Systems Lab.
    Ekman, Jonas
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Embedded Internet Systems Lab.
    Miroshnikova, Elena
    Luleå University of Technology, Department of Engineering Sciences and Mathematics, Mathematical Science.
    On the passivity of the Delay-Rational Green’s-function-based model for Transmission Lines2019In: Analysis, Probability, Applications, and Computation: Proceedings of the 11th ISAAC Congress / [ed] Karl‐Olof Lindahl,Torsten Lindström, Luigi G. Rodino, Joachim Toft, Patrik Wahlberg, 2019, p. 71-81Conference paper (Refereed)
    Abstract [en]

    In this paper, we study the delay-rational Green’s-function-based (DeRaG) model for transmission lines. This model is described in terms of impedance representation and it contains a rational and a hyperbolic part. The crucial property of transmission lines models is to be passive. The passivity of the rational part has been studied by the authors in a previous work. Here, we extend the results to the rational part of the DeRaG model. Moreover, we prove the passivity of the hyperbolic part. 

  • 90.
    Antonini, Giulio
    et al.
    Department of Electrical Engineering, University of L’Aquila.
    Delsing, Jerker
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Embedded Internet Systems Lab.
    Ekman, Jonas
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Embedded Internet Systems Lab.
    Orlandi, Antonio
    Department of Electrical Engineering, University of L’Aquila.
    Ruehli, Albert
    IBM T.J. Watson Research Center, Yorktown Heights.
    PEEC development road map 20072007Report (Other academic)
    Abstract [en]

    A road map for the long term development of the partial element equivalent circuit (PEEC) method is presented. Emerging areas are pointed out together with a solution strategy. Special attention is given to speed up approaches, mesh generation, and time domain stability. The purpose with the road map is to facilitate a unified development of the method into an electromagnetic modeling method suitable for incorporation in integrated analysis tools for engineers for electromagnetic compatibility and electromagnetic interference purpose.

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  • 91.
    Antonini, Giulio
    et al.
    Department of Electrical Engineering, University of L’Aquila.
    Ekman, Jonas
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Embedded Internet Systems Lab.
    Ruehli, Albert
    IBM T.J. Watson Research Center, Yorktown Heights.
    Parallel waveform relaxation and matrix solution for large PEEC model problems2007In: 2007 IEEE Electrical Performance of Electronic Packaging, IEEE Communications Society, 2007, p. 241-244Conference paper (Refereed)
    Abstract [en]

    Excessive compute time is becoming a key problem for high performance system modeling as the complexity of the electromagnetic and circuit models are increasing. At the same time the PEEC models are locally becoming more complex with the increased importance of dielectric and skin-effect losses. Fortunately, parallel processing removes the restriction on the availability of compute resources. In this paper,we consider a combined approach where WR is used for the predominant weak coupling while a Gaussian matrix solver is used for the parallelization of the strongly coupled parts of the overall system.

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  • 92.
    Antonini, Giulio
    et al.
    Department of Electrical Engineering, University of L’Aquila.
    Ekman, Jonas
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Embedded Internet Systems Lab.
    Ruehli, Albert E.
    IBM T.J. Watson Research Center, Yorktown Heights.
    Waveform relaxation for the parallel solution of large PEEC model problems2007In: 2007 IEEE International Symposium on Electromagnetic Compatibility: workshop and tutorial notes : July 8-13, 2007 Honolulu, Hawaii., Piscataway, NJ: IEEE Communications Society, 2007Conference paper (Refereed)
    Abstract [en]

    The solution of large 3D electromagnetic models is important for the modeling of a multitude of EMC, PI and SI problems. In this paper, we explore new algorithms for the parallel solution of large time domain 3D electromagnetic problems. Our approach is to use a volume Partial Element Equivalent Circuit (PEEC) electromagnetic formulation in combination with a Waveform Relaxation (WR) algorithm. In WR, we split the system into smaller subsystems and we break weak couplings so that the problem can be solved iteratively. WR has been used to solve a multitude of different problems. It is especially suited for parallel processing due to its favorable compute time to communication ratio. We consider a specific example for the application of WR to PEEC models.

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  • 93.
    Antonini, Giulio
    et al.
    University of L’Aquila.
    Ekman, Jonas
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Embedded Internet Systems Lab.
    Scogna, Antonio Ciccomancini
    University of L’Aquila.
    Ruehli, Albert E.
    IBM Research Division, Yorktown Heights, NY.
    A comparative study of PEEC circuit elements computation2003In: IEEE International Symposium on Electromagnetic Compatibility: symposium record : Boston, August 18-22, 2003, Piscataway, NJ: IEEE Communications Society, 2003, p. 810-813Conference paper (Refereed)
    Abstract [en]

    A key use of the PEEC method is the solution of combined electromagnetic and circuit problems as they occur in many situations in todays very large scale integrated circuits (VLSI) and systems. An important aspect of this approach is the fast and accurate computation of PEEC circuit matrix elements, the partial inductances and normalized coefficients of potential. Recently, fast multipole methods (FMM) have been applied to the PEEC method in the frequency domain as a way to speed up the solution. In this paper, we consider the fast evaluation of the PEEC circuit matrix elements by two different methods, a matrix version of the (FMM) PEEC method and a method, which we call the fast multi-function (FMF) PEEC approach. In this technique, the matrix coefficients are evaluated using analytical functions approximation of the coefficients in combination with a proper choice of numerical quadrature formulas.

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  • 94.
    Aparicio Rivera, Jorge
    et al.
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Computer Science.
    Lindner, Marcus
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Embedded Internet Systems Lab. Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Computer Science.
    Lindgren, Per
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Embedded Internet Systems Lab. Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Computer Science.
    Heapless: Dynamic Data Structures without Dynamic Heap Allocator for Rust2018In: 2018 IEEE 16TH INTERNATIONAL CONFERENCE ON INDUSTRIAL INFORMATICS (INDIN), Piscataway, NJ: IEEE, 2018, p. 87-94, article id 8472097Conference paper (Refereed)
    Abstract [en]

    Dynamic memory management is typically implemented using a global memory allocator, which may negatively impact the performance, reliability, and predictability of a program; in effect standards around safety-critical applications often discourage or even disallow dynamic memory management. This paper presents heapless, a collection of dynamic data structures (for vectors, strings, and circular buffers) that can be either stack or statically allocated, thus free of global allocator dependencies. The proposed data structures for vectors and strings closely mimic the Rust standard library implementations while adding support to gracefully handling cases of capacity exceedance. Our circular buffers act as queues and allowing channel like usage (by splitting). The Rust memory model together with the ability of local reasoning on memory requirements (brought by heapless) facilitates establishing robustness/safety guarantees and minimize attack surfaces of (industrial) IoT systems. We show that the heapless data structures are highly efficient and have predictable performance, thus suitable for hard real-time applications. Moreover, in our implementation heapless data structures are non-relocatable allowing mapping to hardware, useful, e.g., to DMA transfers. The feasibility, performance, and advantages of heapless are demonstrated by implementing a JSON serialization and de-serialization library for an ARM Cortex-M based IoT platform.

  • 95.
    Axell, Erik
    et al.
    Swedish Defence Research Agency, Dept. of Robust Telecommunications, Sweden.
    Eklöf, Fredrik M.
    Swedish Defence Research Agency, Dept. of Robust Telecommunications, Sweden.
    Alexandersson, Mikael
    Swedish Defence Research Agency, Dept. of Robust Telecommunications, Sweden.
    Johansson, Peter
    Swedish Defence Research Agency, Dept. of Robust Telecommunications, Sweden.
    Akos, Dennis
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Embedded Internet Systems Lab.
    Jamming detection in GNSS receivers: Performance evaluation of field trials2013In: Proceedings of the 26th international technical meeting of the Satellite Division of The Institute of Navigation, ION GNSS+ 2013, Sept. 16 - 20, 2013, Nashville Convention Cener, Nashville, Tennessee, Manassas, VA: Institute of Navigation, The , 2013, Vol. 3, p. 2542-2551Conference paper (Refereed)
    Abstract [en]

    In this work, we evaluate the detection performance of a number of commercial interference detectors and, in addition, of a detector that uses the automatic gain control (AGC) levels as test statistic. The AGC detector has been implemented on a Novatel GPS receiver and on a Universal Software Radio Peripheral (USRP). The evaluations are based on actual measurements of GPS signals and different types of jamming signals, which have been performed at the Vidsel test range in northern Sweden. The AGC detector was shown to work well for all types of jamming signals, in particular the one implemented on the USRP. The Chronos CTL-3500 was also shown to perform quite well for all kinds of signals, although not as good as the USRP with an AGC detector. Quite surprisingly, the J-alert was only able to detect the wideband (20 MHz) signal but not the narrow band (<2MHz) signals. By contrast, the jamming indicator on the Ublox 6 receiver was only able to detect a slowly varying modulated CW (MCW) signal, but not the signals with larger bandwidth (2 and 20 MHz). We confirmed that C/N0-based detectors could work well in a static scenario, but are not suitable in a dynamic scenario, since they cannot distinguish between decreased GPS signal strength (e.g. indoors) and an increased interference level.

  • 96.
    Axell, Erik
    et al.
    Dept. of Robust Telecommunications, Swedish Defence Research Agency, Linköping, Sweden.
    Eklöf, Fredrik M.
    Dept. of Robust Telecommunications, Swedish Defence Research Agency, Linköping, Sweden.
    Johansson, Peter
    Dept. of Robust Telecommunications, Swedish Defence Research Agency, Linköping, Sweden.
    Alexandersson, Mikael
    Dept. of Robust Telecommunications, Swedish Defence Research Agency, Linköping, Sweden.
    Akos, Dennis M.
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Embedded Internet Systems Lab.
    Jamming Detection in GNSS Receivers: Performance Evaluation of Field Trials2015In: Navigation, ISSN 0028-1522, E-ISSN 2161-4296, Vol. 62, no 1, p. 73-82Article in journal (Refereed)
    Abstract [en]

    We evaluate the detection performance of several commercial interference detectors and of a detector that uses the automatic gain control (AGC) level as a test statistic. The evaluations are based on actual measurements of GPS signals and different types of jamming signals, and were performed at the Vidsel test range in northern Sweden.The AGC detector and the Chronos CTL-3500 were shown to work well for all types of jamming signals. The J-alert was able to detect a wideband (20 MHz) signal but not the narrow band (<2 MHz) signals. By contrast, the jamming indicator on a Ublox 6H receiver was only able to detect a slowly varying modulated CW signal, but not signals with larger bandwidth (>2 MHz). We confirmed that C/N0-based Android application detectors could work well in static scenarios but are not suitable in dynamic scenarios, since they cannot distinguish between decreased GPS signal strength and increased interference

  • 97.
    Azime, Israel Abebe
    et al.
    Saarland University, Germany.
    Al-Azzawi, Sana Sabah
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Embedded Internet Systems Lab.
    Tonja, Atnafu Lambebo
    Instituto Politécnico Nacional, Mexico.
    Shode, Iyanuoluwa
    Montclair State University, USA.
    Alabi, Jesujoba
    Saarland University, Germany.
    Awokoya, Ayodele
    University of Ibadan, Nigeria.
    Oduwole, Mardiyyah
    Adewumi, Tosin
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Embedded Internet Systems Lab.
    Fanijo, Samuel
    Iowa State University, USA.
    Oyinkansola, Awosan
    Yousuf, Oreen
    Masakhane-Afrisenti at SemEval-2023 Task 12: Sentiment Analysis using Afro-centric Language Models and Adapters for Low-resource African Languages2023In: The 17th International Workshop on Semantic Evaluation (SemEval-2023): Proceedings of the Workshop / [ed] Atul Kr. Ojha; A. Seza Dogruoz; Giovanni Da San Martino; Harish Tayyar Madabushi; Ritesh Kumar; Elisa Sartori, Association for Computational Linguistics , 2023, p. 1311-1316Conference paper (Refereed)
  • 98.
    Aziz, Abdullah
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Embedded Internet Systems Lab.
    Industrial IoT, Cyber-Physical Systems, and Digital Twins2021Licentiate thesis, comprehensive summary (Other academic)
    Abstract [en]

    Industrial digitalization (Industry 4.0) is the digital transformation of industries in a connected environment of people, processes, services, systems, data, and industrial assets to realize smart industry and ecosystem of industrial innovation and collaboration. Three promising aspects of Industry 4.0 are Industrial IoT (IIoT), Cyber-Physical Systems (CPS), and Digital Twin (DT).There are challenges with these aspects. First, there are many generic IIoT stan-dards & frameworks that are alone insufficient in various industrial use-cases. Therefore, engineers must use many standards guidelines to design IIoT architectures from case to case. Second, systems deployed in industries based on different standards and tech-nologies that cannot inter-work with other systems, hence operating in isolation, so that gathering data for analysis, reporting, and decision-making is challenging. Third, in an Industrial-CPS (ICPS) environment, most of the embedded systems are resource con-straint battery-driven devices that are facing challenges such as a short life span due to high energy consumption, lower availability of the services, and low-security capabilities.The scope of this thesis is to research industrial digitalization on the above-mentioned aspects and challenges. First, we study general IIoT standards & frameworks and use them to synthesize a high-level IIoT architecture. As a use-case for verification and validation of the architecture, we specifically study the mining industry, since it is chal-lenging in terms of geographical distribution, infrastructural limitations in communi-cation, data management across different silos, storage, and exchange of information. Second, we study the ad hoc implementation of common models for integrating data between different isolated industrial systems. Most often companies build their ad hoc data integration models based on two architectural designs of Event-Driven Architecture (EDA) and Service-Oriented Architecture (SOA). We conceptually compare and analyze the implementation of both models based on selected criteria essential in an industrial environment with the aim to provide guidelines to achieve data integration between het-erogeneous industrial systems according to their requirements. Third, we define critical properties for ICPS services and we define a digital twin as a proxy (DTaaP) architecture that can meet these properties.The contribution of this thesis includes the proposal of the high-level IIoT architecture suitable for the mining industry use-case, the conceptual analysis of the data integration models, and the concept of DTaaP for ICPS. DTaaP is a four-layer architectural model that provides valuable properties such as energy efficiency, high availability & state per-sistence, remote & contention control, and security. We present a generic proof of concept DTaaP implementation by using open source technologies.

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  • 99.
    Aziz, Abdullah
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Embedded Internet Systems Lab.
    Virtualizing Operational Technology by Distributed Digital Twins2023Doctoral thesis, comprehensive summary (Other academic)
    Abstract [en]

    Industrial digitalization, stemming from the convergence of Information Technology (IT) and Operational Technology (OT), is a transformative force in modern industries. The Industrial Internet of Things (IIoT) and Industrial Cyber-Physical Systems (ICPS) empower industries with enhanced production processes, data-driven insights, and advanced automation. Accompanying these trends, digital twins bridge the gap between the physical and digital realms, promising dynamic representations of entities. This convergence holds great promise for fostering efficient and agile industrial ecosystems.

    However, amidst the promise, a series of challenges loom large across a multitude of domains. These challenges span a multitude of domains. The convergence of IT and OT engenders a spectrum of complexities, including interoperability issues, data integration dilemmas, and the imperative need to tackle the historical hardware-centricity of industrial systems. This includes enhancing energy efficiency and security in the digital realm and addressing fundamental issues within the fabric of modern industries.

    The scope of our research addresses these multifaceted challenges by encompassing three overarching research questions. The first explores the integration of IIoT architectures and data integration models, striving to augment interoperability and data exchange for industries, offering practical benefits. The second delves into the realm of ICPS and industrial automation, investigating how Digital Twins can optimize energy efficiency, security, and service availability. The third widens the horizon by examining the potential of distributed digital twins as proxies to foster composability and adaptability, bridge the physical-virtual gap, and meet the evolving needs of industrial IoT and cyber-physical systems.

    Our thesis unfolds with five key contributions, each addressing fundamental challenges in industrial digitalization. First, we present a synthesized IIoT architecture tailored for the mining industry, aligning seamlessly with IoT and Industry 4.0 standards and frameworks. Second, we explore data integration through service-based and event-driven communication models across industries. We provide a qualitative analysis of these models to present guidelines for designing data integration solutions according to needs.

    In the third contribution, we focus on digital twins for Industrial Cyber-Physical Systems (ICPS) and introduce the concept of a digital twin as proxy. This concept enables the virtualization of tangible devices and assets from the OT domain to the IT domain. This contribution addresses energy efficiency, security, and service availability challenges. Building on this, our fourth contribution implements and integrates the concept of the digital twin as a proxy with the Eclipse Arrowhead Framework, extending its applicability to industrial automation and reinforcing our response to the second research question.

    Our fifth contribution further envisions the virtualization of the OT within IT. Grounded in service-oriented and microservice architectural principles, we propose the concept of purpose-oriented composable digital twins by utilizing distributed digital twins as proxies. This concept offers a forward-looking solution to address evolving needs. Together with the third and fourth contributions, our work ensures a comprehensive and forward-looking impact on the discourse of industrial digitalization.

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  • 100.
    Aziz, Abdullah
    et al.
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Embedded Internet Systems Lab.
    Chouhan, Shailesh Singh
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Embedded Internet Systems Lab.
    Schelén, Olov
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Embedded Internet Systems Lab.
    Bodin, Ulf
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Embedded Internet Systems Lab.
    Distributed Digital Twins as Proxies-Unlocking Composability & Flexibility for Purpose-Oriented Digital Twins2023In: IEEE Access, E-ISSN 2169-3536, Vol. 11, p. 137577-137593Article in journal (Refereed)
    Abstract [en]

    In the realm of Industrial Internet of Things (IoT) and Industrial Cyber-Physical Systems (ICPS), Digital Twins (DTs) have revolutionized the management of physical entities. However, existing implementations often face constraints due to hardware-centric approaches and limited flexibility. This article introduces a transformative paradigm that harnesses the potential of distributed Digital Twins as proxies, enabling software-centricity and unlocking composability and flexibility for purpose-oriented digital twin development and deployment. The proposed microservices-based architecture, rooted in service-oriented architecture (SOA) and microservices principles, emphasizes reusability, modularity, and scalability. Leveraging the Lean Digital Twin Methodology and packaged business capabilities expedites digital twin creation and deployment, facilitating dynamic responses to evolving industrial demands. This architecture segments the industrial realm into physical and virtual spaces, where core components are responsible for digital twin management, deployment, and secure interactions. By abstracting and virtualizing physical entities into individual digital twins, this approach establishes the groundwork for purpose-oriented composite digital twin creation. Our key contributions involve a comprehensive exposition of the architecture, a practical proof-of-concept (PoC) implementation, and the application of the architecture in a use-case scenario. Additionally, we provide an analysis, including a quantitative evaluation of the proxy aspect and a qualitative comparison with traditional approaches. This assessment emphasizes key properties such as reusability, modularity, abstraction, discoverability, and security, transcending the limitations of contemporary industrial systems and enabling agile, adaptable digital proxies to meet modern industrial demands.

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