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  • 201.
    Muhammad, Naveed
    et al.
    Högskolan i Halmstad, Akademin för informationsteknologi, Halmstad Embedded and Intelligent Systems Research (EIS), CAISR Centrum för tillämpade intelligenta system (IS-lab).
    Åstrand, Björn
    Högskolan i Halmstad, Akademin för informationsteknologi, Halmstad Embedded and Intelligent Systems Research (EIS), CAISR Centrum för tillämpade intelligenta system (IS-lab).
    Intention Estimation Using Set of Reference Trajectories as Behaviour Model2018Ingår i: Sensors, E-ISSN 1424-8220, Vol. 18, nr 12, artikel-id 4423Artikel i tidskrift (Refereegranskat)
    Abstract [en]

    Autonomous robotic systems operating in the vicinity of other agents, such as humans, manually driven vehicles and other robots, can model the behaviour and estimate intentions of the other agents to enhance efficiency of their operation, while preserving safety. We propose a data-driven approach to model the behaviour of other agents, which is based on a set of trajectories navigated by other agents. Then, to evaluate the proposed behaviour modelling approach, we propose and compare two methods for agent intention estimation based on: (i) particle filtering; and (ii) decision trees. The proposed methods were validated using three datasets that consist of real-world bicycle and car trajectories in two different scenarios, at a roundabout and at a t-junction with a pedestrian crossing. The results validate the utility of the data-driven behaviour model, and show that decision-tree based intention estimation works better on a binary-class problem, whereas the particle-filter based technique performs better on a multi-class problem, such as the roundabout, where the method yielded an average gain of 14.88 m for correct intention estimation locations compared to the decision-tree based method. © 2018 by the authors

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  • 202.
    Muhammad, Naveed
    et al.
    Högskolan i Halmstad, Akademin för informationsteknologi, Halmstad Embedded and Intelligent Systems Research (EIS), CAISR Centrum för tillämpade intelligenta system (IS-lab).
    Åstrand, Björn
    Högskolan i Halmstad, Akademin för informationsteknologi, Halmstad Embedded and Intelligent Systems Research (EIS), CAISR Centrum för tillämpade intelligenta system (IS-lab).
    Predicting Agent Behaviour and State for Applications in a Roundabout-Scenario Autonomous Driving2019Ingår i: Sensors, E-ISSN 1424-8220, Vol. 19, nr 19, artikel-id 4279Artikel i tidskrift (Refereegranskat)
    Abstract [en]

    As human drivers, we instinctively employ our understanding of other road users' behaviour for enhanced efficiency of our drive and safety of the traffic. In recent years, different aspects of assisted and autonomous driving have gotten a lot of attention from the research and industrial community, including the aspects of behaviour modelling and prediction of future state. In this paper, we address the problem of modelling and predicting agent behaviour and state in a roundabout traffic scenario. We present three ways of modelling traffic in a roundabout based on: (i) the roundabout geometry; (ii) mean path taken by vehicles inside the roundabout; and (iii) a set of reference trajectories traversed by vehicles inside the roundabout. The roundabout models are compared in terms of exit-direction classification and state (i.e., position inside the roundabout) prediction of query vehicles inside the roundabout. The exit-direction classification and state prediction are based on a particle-filter classifier algorithm. The results show that the roundabout model based on set of reference trajectories is better suited for both the exit-direction and state prediction.

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  • 203.
    Mühlfellner, Peter
    Högskolan i Halmstad, Akademin för informationsteknologi, Halmstad Embedded and Intelligent Systems Research (EIS), Intelligenta system (IS-lab).
    Selection, Analysis and Implementationof Image-based Feature Extraction Approaches for a Heterogenous, Modular and FPGA-based Architecture for Camera-based Driver Assistance Systems2011Självständigt arbete på avancerad nivå (masterexamen), 30 poäng / 45 hpStudentuppsats (Examensarbete)
    Abstract [en]

    We propose a scalable and fexible hardware architecture for the extraction of image features, used in conjunction with an attentional cascade classifier for appearance-based object detection. Individual feature processors calculate feature-values in parallel, using parameter-sets and image data that is distributed via BRAM buffers. This approach can provide high utilization- and throughput-rates for a cascade classifier. Unlike previous hardware implementations, we are able to flexibly assign feature processors to either work on a single- or multiple image windows in parallel, depending on the complexity of the current cascade stage.

    The core of the architecture was implemented in the form of a streaming based FPGA design, and validated in simulation, synthesis, as well as via the use of a Logic Analyser for the verification of the on-chip functionality. For the given implementation, we focused on the design of Haar-like feature processors, but feature processors for a variety of heterogenous feature types, such as Gabor-like features, can also be accomodated by the proposed hardware architecture.

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    muehlfellner-thesis-2011
  • 204.
    Nelson, Christian
    et al.
    Lund University, Lund, Sweden.
    Lyamin, Nikita
    Högskolan i Halmstad, Akademin för informationsteknologi, Halmstad Embedded and Intelligent Systems Research (EIS), Centrum för forskning om inbyggda system (CERES).
    Vinel, Alexey
    Högskolan i Halmstad, Akademin för informationsteknologi, Halmstad Embedded and Intelligent Systems Research (EIS), Centrum för forskning om inbyggda system (CERES).
    Gustafson, Carl
    Lund University, Lund, Sweden.
    Tufvesson, Fredrik
    Lund University, Lund, Sweden.
    Geometry Based Channel Models with Cross- and Autocorrelation for Vehicular Network Simulations2018Ingår i: 2018 IEEE 87th Vehicular Technology Conference (VTC Spring), Piscataway, NJ: IEEE, 2018Konferensbidrag (Refereegranskat)
    Abstract [en]

    Realistic network simulations are necessary to assess the performance of any communication system. In this paper, we describe an implementation of a channel model for vehicle-to-vehicle (V2V) communication in the OMNeT++/Plexe simulation environment. The model is based on previous extensive measurements in a V2V multilink highway scenario and cover line-of-sight (LOS) as well as obstructed LOS (OLOS) scenarios, which occurs when one or more vehicles obstruct the LOS component. The implementation captures both the temporal autocorrelation and the joint multilink cross-correlation processes to achieve a realistic behavior. Preliminary results show that the implementation now generates stochastic large-scale fading with an autocorrelation function that agrees well with measured data. A representation of the cross-correlation process is now implemented through proper channel model selection since the geometry and location of objects are known in Plexe. We also show the impact of the suggested V2V physical layer (PHY) on the performance evaluation results observed at the facilities layer. As a metric, we use the data age, which is a measure how old the information about a vehicle is. When considering the autocorrelation in simulations, the experienced data-age increases. Examples show an increase of the 10% percentile data-age from 0.1s to 1.5s, which may affect the application performance significantly in critical situations. © 2018 IEEE.

  • 205.
    Nemati, Hassan
    et al.
    Högskolan i Halmstad, Akademin för informationsteknologi, Halmstad Embedded and Intelligent Systems Research (EIS), CAISR Centrum för tillämpade intelligenta system (IS-lab).
    Åstrand, Björn
    Högskolan i Halmstad, Akademin för informationsteknologi, Halmstad Embedded and Intelligent Systems Research (EIS), CAISR Centrum för tillämpade intelligenta system (IS-lab).
    Tracking of People in Paper Mill Warehouse Using Laser Range Sensor2014Ingår i: UKSim-AMSS Eighth European Modelling Symposium on Computer Modelling and Simulation, EMS 2014 / [ed] David Al-Dabass, Valentina Colla, Marco Vannucci & Athanasios Pantelous, Los Alamitos, CA: IEEE Computer Society, 2014, s. 52-57, artikel-id 7153974Konferensbidrag (Refereegranskat)
    Abstract [en]

    In this paper a laser scanner based approach for simultaneous detection and tracking of people in an indoor environment is presented. The operation of an autonomous truck, for transporting paper reels in a dynamic environment shared with humans, is considered as the application setting for this work. Here, a human leg detection procedure and an Extended Kalman Filter (EKF) based tracking method are employed for real-time performance. Several experiments with different data sets collected from an autonomous forklift truck in a paper mill warehouse have been performed in an offline situation. The results show how the system is able to detect and track multiple moving people. ©2014 IEEE.

  • 206.
    Nguyen, Kien
    et al.
    Queensland University of Technology, Brisbane, Australia.
    Proença, Hugo
    University of Beira Interior, Covilhã, Portugal.
    Alonso-Fernandez, Fernando
    Högskolan i Halmstad, Akademin för informationsteknologi, Centrum för forskning om tillämpade intelligenta system (CAISR).
    Deep Learning for Iris Recognition: A Survey2024Ingår i: ACM Computing Surveys, ISSN 0360-0300, E-ISSN 1557-7341, Vol. 56, nr 9, artikel-id 223Artikel i tidskrift (Refereegranskat)
    Abstract [en]

    In this survey, we provide a comprehensive review of more than 200 articles, technical reports, and GitHub repositories published over the last 10 years on the recent developments of deep learning techniques for iris recognition, covering broad topics on algorithm designs, open-source tools, open challenges, and emerging research. First, we conduct a comprehensive analysis of deep learning techniques developed for two main sub-tasks in iris biometrics: segmentation and recognition. Second, we focus on deep learning techniques for the robustness of iris recognition systems against presentation attacks and via human-machine pairing. Third, we delve deep into deep learning techniques for forensic application, especially in post-mortem iris recognition. Fourth, we review open-source resources and tools in deep learning techniques for iris recognition. Finally, we highlight the technical challenges, emerging research trends, and outlook for the future of deep learning in iris recognition. © 2024 Copyright held by the owner/author(s).

  • 207.
    Nilsson, Emil
    et al.
    Högskolan i Halmstad, Akademin för informationsteknologi, Halmstad Embedded and Intelligent Systems Research (EIS), Tillämpad matematik och fysik (MPE-lab).
    Svensson, Christer
    Linkoping Univ, Dept Elect Engn ISY, SE-58183 Linkoping, Sweden..
    Ultra Low Power Wake-Up Radio Using Envelope Detector and Transmission Line Voltage Transformer2013Ingår i: IEEE Journal on Emerging and Selected Topics in Circuits and Systems, ISSN 2156-3357, E-ISSN 2156-3365, Vol. 3, nr 1, s. 5-12Artikel i tidskrift (Refereegranskat)
    Abstract [en]

    An ultra-low power wake-up radio receiver using no oscillators is described. The radio utilizes an envelope detector followed by a baseband amplifier and is fabricated in a 130-nm complementary metal-oxide-semiconductor process. The receiver is preceded by a passive radio-frequency voltage transformer, also providing 50 Omega antenna matching, fabricated as transmission lines on the FR4 chip carrier. A sensitivity of -47 dBm with 200 kb/s on-off keying modulation is measured at a current consumption of 2.3 mu A from a 1 V supply. No trimming is used. The receiver accepts a dBm continuous wave blocking signal, or modulated blockers 6 dB below the sensitivity limit, with no loss of sensitivity.

  • 208.
    Nilsson, Felix
    et al.
    HMS Industrial Networks AB, Halmstad, Sweden.
    Jakobsen, Jens
    HMS Industrial Networks AB, Halmstad, Sweden.
    Alonso-Fernandez, Fernando
    Högskolan i Halmstad, Akademin för informationsteknologi, Halmstad Embedded and Intelligent Systems Research (EIS), CAISR Centrum för tillämpade intelligenta system (IS-lab).
    Detection and Classification of Industrial SignalLights for Factory Floors2020Konferensbidrag (Refereegranskat)
    Abstract [en]

    Industrial manufacturing has developed during the last decades from a labor-intensive manual control of machines to a fully-connected automated process. The next big leap is known as industry 4.0, or smart manufacturing. With industry 4.0 comes increased integration between IT systems and the factory floor from the customer order system to final delivery of the product. One benefit of this integration is mass production of individually customized products. However, this has proven challenging to implement into existing factories, considering that their lifetime can be up to 30 years. The single most important parameter to measure in a factory is the operating hours of each machine. Operating hours can be affected by machine maintenance as well as re-configuration for different products. For older machines without connectivity, the operating state is typically indicated by signal lights of green, yellow and red colours. Accordingly, the goal is to develop a solution which can measure the operational state using the input from a video camera capturing a factory floor. Using methods commonly employed for traffic light recognition in autonomous cars, a system with an accuracy of over 99% in the specified conditions is presented. It is believed that if more diverse video data becomes available, a system with high reliability that generalizes well could be developed using a similar methodology.

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  • 209.
    Nilsson, Kenneth
    et al.
    Högskolan i Halmstad, Akademin för informationsteknologi, Halmstad Embedded and Intelligent Systems Research (EIS).
    Bigun, Josef
    Högskolan i Halmstad, Akademin för informationsteknologi, Halmstad Embedded and Intelligent Systems Research (EIS), CAISR Centrum för tillämpade intelligenta system (IS-lab).
    Prominent symmetry points as landmarks in fingerprint images for alignment2002Ingår i: 16th International Conference on Pattern Recognition (ICPR'02) - Proceedings, Volume 3, Piscataway: IEEE Computer Society, 2002, Vol. III, s. 395-398Konferensbidrag (Övrigt vetenskapligt)
    Abstract [en]

    For the alignment of two fing erprints position of certain landmarks are needed. These should be automatically extracted with low misidentification rate. As landmarks we suggest the prominent symmetry points (core-points) in the fing erprint. They are extracted from the complex orientation field estimated from the global structure of the fingerprint, i.e. the overall pattern of the ridges and valleys. Complex filter s, applied to the orientation field in multiple resolution scales, are used to detect the symmetry and the type of symmetry. Experimental results are reported.

  • 210.
    Nytorpe Piledahl, Staffan
    et al.
    Högskolan i Halmstad, Akademin för informationsteknologi.
    Dahlberg, Daniel
    Högskolan i Halmstad, Akademin för informationsteknologi.
    Detektering av stress från biometrisk data i realtid2016Självständigt arbete på grundnivå (yrkesexamen), 20 poäng / 30 hpStudentuppsats (Examensarbete)
    Abstract [en]

    At the time of writing, stress and stress related disease have become the most common reasons for absence in the workplace in Sweden. The purpose of the work presented here is to identify and notify people managing unhealthy levels of stress. Since symptoms of mental stress manifest through functions of the Sympathetic Nervous System (SNS), they are best measured through monitoring of SNS changes and phenomena. In this study, changes in the sympathetic control of heart rate were recorded and analyzed using heart rate variability analysis and a simple runner’s heart rate sensor connected to a smartphone.

    Mental stress data was collected through stressful video gaming. This was compared to data from non-stressful activities, physical activity and extremely stressful activities such as public speaking events. By using the period between heartbeats and selecting features from the frequency domain, a simple machine learning algorithm could differentiate between the types of data and thus could effectively recognize mental stress.

    The study resulted in a collection of 100 data points, an algorithm to extract features and an application to continuously collect and classify sequences of heart periods. It also revealed an interesting relationship in the data between different subjects.

    The fact that continuous stress monitoring can be achieved using minimally intrusive sensors is the greatest benefit of these results, especially when connsidering its potential value in the identification and prevention of stress related disease.

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  • 211. Ortega-Garcia, Javier
    et al.
    Fierrez, Julian
    Alonso-Fernandez, Fernando
    Escuela Politecnica Superior, Univ. Autonoma de Madrid, Spain.
    Galbally, J.
    Freire, M. R.
    Gonzalez-Rodriguez, J.
    Garcia-Mateo, C.
    Alba-Castro, J. -L
    Gonzalez-Agulla, E.
    Otero-Muras, E.
    Garcia-Salicetti, S.
    Allano, L.
    Ly-Van, B.
    Dorizzi, B.
    Kittler, J.
    Bourlai, T.
    Poh, N.
    Deravi, F.
    Ng, M. W. R.
    Fairhurst, M.
    Hennebert, J.
    Humm, A.
    Tistarelli, M.
    Brodo, L.
    Richiardi, J.
    Drygajlo, A.
    Ganster, H.
    Sukno, F. M.
    Pavani, S. -K
    Frangi, A.
    Akarun, L.
    Savran, A.
    The Multi-Scenario Multi-Environment BioSecure Multimodal Database (BMDB)2009Ingår i: IEEE Trans. on Pattern Analysis and Machine Intelligence, Vol. 32, nr 6, s. 1097-1111Artikel i tidskrift (Refereegranskat)
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  • 212.
    Ortega-Garcia, Javier
    et al.
    Universidad Autonoma de Madrid, Madrid, Spain.
    Fierrez, Julian
    Universidad Autonoma de Madrid, Madrid, Spain.
    Alonso-Fernandez, Fernando
    Escuela Politecnica Superior, Univ. Autonoma de Madrid, Spain.
    Galbally, Javier
    Universidad Autonoma de Madrid, Madrid, Spain.
    Freire, Manuel R.
    Universidad Autonoma de Madrid, Madrid, Spain.
    Gonzalez-Rodriguez, Joaquin
    Universidad Autonoma de Madrid, Madrid, Spain.
    Garcia-Mateo, Carmen
    Universidad Autonoma de Madrid, Madrid, Spain.
    Alba-Castro, Jose-Luis
    Universidad Autonoma de Madrid, Madrid, Spain.
    Gonzalez-Agulla, Elisardo
    Universidad Autonoma de Madrid, Madrid, Spain.
    Otero-Muras, Enrique
    Universidad Autonoma de Madrid, Madrid, Spain.
    Garcia-Salicetti, Sonia
    Universidad Autonoma de Madrid, Madrid, Spain.
    Allano, Lorene
    Universidad Autonoma de Madrid, Madrid, Spain.
    Ly-Van, Bao
    Universidad Autonoma de Madrid, Madrid, Spain.
    Dorizzi, Bernadette
    Universidad Autonoma de Madrid, Madrid, Spain.
    Kittler, Josef
    Universidad Autonoma de Madrid, Madrid, Spain.
    Bourlai, Thirimachos
    Universidad Autonoma de Madrid, Madrid, Spain.
    Poh, Norman
    Universidad Autonoma de Madrid, Madrid, Spain.
    Deravi, Farzin
    Universidad Autonoma de Madrid, Madrid, Spain.
    Ng, Ming W. R.
    Universidad Autonoma de Madrid, Madrid, Spain.
    Fairhurst, Michael
    Universidad Autonoma de Madrid, Madrid, Spain.
    Hennebert, Jean
    Universidad Autonoma de Madrid, Madrid, Spain.
    Humm, Andreas
    Universidad Autonoma de Madrid, Madrid, Spain.
    Tistarelli, Massimo
    Universidad Autonoma de Madrid, Madrid, Spain.
    Brodo, Linda
    Universidad Autonoma de Madrid, Madrid, Spain.
    Richiardi, Jonas
    Universidad Autonoma de Madrid, Madrid, Spain.
    Drygajlo, Andrzej
    Universidad Autonoma de Madrid, Madrid, Spain.
    Ganster, Harald
    Universidad Autonoma de Madrid, Madrid, Spain.
    Sukno, Federico M.
    Universidad Autonoma de Madrid, Madrid, Spain.
    Pavani, Sri-Kaushik
    Universidad Autonoma de Madrid, Madrid, Spain.
    Frangi, Alejandro
    Universidad Autonoma de Madrid, Madrid, Spain.
    Akarun, Lale
    Universidad Autonoma de Madrid, Madrid, Spain.
    Savran, Arman
    Universidad Autonoma de Madrid, Madrid, Spain.
    The Multiscenario Multienvironment BioSecure Multimodal Database (BMDB)2010Ingår i: IEEE Transactions on Pattern Analysis and Machine Intelligence, ISSN 0162-8828, E-ISSN 1939-3539, Vol. 32, nr 6, s. 1097-1111Artikel i tidskrift (Refereegranskat)
    Abstract [en]

    A new multimodal biometric database designed and acquired within the framework of the European BioSecure Network of Excellence is presented. It is comprised of more than 600 individuals acquired simultaneously in three scenarios: 1) over the Internet, 2) in an office environment with desktop PC, and 3) in indoor/outdoor environments with mobile portable hardware. The three scenarios include a common part of audio/video data. Also, signature and fingerprint data have been acquired both with desktop PC and mobile portable hardware. Additionally, hand and iris data were acquired in the second scenario using desktop PC. Acquisition has been conducted by 11 European institutions. Additional features of the BioSecure Multimodal Database (BMDB) are: two acquisition sessions, several sensors in certain modalities, balanced gender and age distributions, multimodal realistic scenarios with simple and quick tasks per modality, cross-European diversity, availability of demographic data, and compatibility with other multimodal databases. The novel acquisition conditions of the BMDB allow us to perform new challenging research and evaluation of either monomodal or multimodal biometric systems, as in the recent BioSecure Multimodal Evaluation campaign. A description of this campaign including baseline results of individual modalities from the new database is also given. The database is expected to be available for research purposes through the BioSecure Association during 2008. © 2010 IEEE.

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  • 213.
    Pedrollo, Guilherme
    et al.
    Universidade Federal do Rio Grande do Sul, Porto Alegre, Brazil.
    Aparecida Konzen, Andréa
    Universidade Federal do Rio Grande do Sul, Porto Alegre, Brazil.
    Ourique de Morais, Wagner
    Högskolan i Halmstad, Akademin för informationsteknologi, Halmstad Embedded and Intelligent Systems Research (EIS), CAISR Centrum för tillämpade intelligenta system (IS-lab).
    Pignaton de Freitas, Edison
    Universidade Federal do Rio Grande do Sul, Porto Alegre, Brazil.
    Using smart virtual-sensor nodes to improve the robustness of indoor localization systems2021Ingår i: Sensors, E-ISSN 1424-8220, Vol. 21, nr 11, artikel-id 3912Artikel i tidskrift (Refereegranskat)
    Abstract [en]

    Young, older, frail, and disabled individuals can require some form of monitoring or assistance, mainly when critical situations occur, such as falling and wandering. Healthcare facilities are increasingly interested in e-health systems that can detect and respond to emergencies on time. Indoor localization is an essential function in such e-health systems, and it typically relies on wireless sensor networks (WSN) composed of fixed and mobile nodes. Nodes in the network can become permanently or momentarily unavailable due to, for example, power failures, being out of range, and wrong placement. Consequently, unavailable sensors not providing data can compromise the system’s overall function. One approach to overcome the problem is to employ virtual sensors as replacements for unavailable sensors and generate synthetic but still realistic data. This paper investigated the viability of modelling and artificially reproducing the path of a monitored target tracked by a WSN with unavailable sensors. Particularly, the case with just a single sensor was explored. Based on the coordinates of the last measured positions by the unavailable node, a neural network was trained with 4 min of not very linear data to reproduce the behavior of a sensor that become unavailable for about 2 min. Such an approach provided reasonably successful results, especially for areas close to the room’s entrances and exits, which are critical for the security monitoring of patients in healthcare facilities. © 2021 by the authors.

  • 214.
    Persson, Anton
    et al.
    Högskolan i Halmstad, Akademin för informationsteknologi.
    Dymne, Niklas
    Högskolan i Halmstad, Akademin för informationsteknologi.
    Alonso-Fernandez, Fernando
    Högskolan i Halmstad, Akademin för informationsteknologi, Halmstad Embedded and Intelligent Systems Research (EIS), CAISR Centrum för tillämpade intelligenta system (IS-lab).
    Classification of PS and ABS Black Plastics for WEEE Recycling Applications2021Ingår i: 2021 8th International Conference on Soft Computing & Machine Intelligence (ISCMI), Piscataway, NJ: IEEE, 2021, s. 219-224Konferensbidrag (Refereegranskat)
    Abstract [en]

    Pollution and climate change are some of the biggest challenges that humanity is facing. In such a context, efficient recycling is a crucial tool for a sustainable future. This work is aimed at creating a system that can classify different types of plastics by using picture analysis, in particular, black plastics of the type Polystyrene (PS) and Acrylonitrile Butadiene Styrene (ABS). They are two common plastics from Waste from Electrical and Electronic Equipment (WEEE). For this purpose, a Convolutional Neural Network has been tested and retrained, obtaining a validation accuracy of 95%. Using a separate test set, average accuracy goes down to 86.6%, but a further look at the results shows that the ABS type is correctly classified 100% of the time, so it is the PS type that accumulates all the errors. Overall, this demonstrates the feasibility of classifying black plastics using CNN machine learning techniques. It is believed that if a more diverse and extensive image dataset becomes available, a system with higher reliability that generalizes well could be developed using the proposed methodology.  © 2021 IEEE.

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  • 215.
    Pettersson, Stefan
    et al.
    RISE Viktoria, Gothenburg, Sweden.
    Bjärsvik, Susanne
    Volvo Car Corporation, Gothenburg, Sweden.
    Englund, Cristofer
    RISE Viktoria, Gothenburg, Sweden.
    Eriksson, Robert
    Volvo Car Corporation, Gothenburg, Sweden.
    Koponen, Veikko
    Volvo Car Corporation, Gothenburg, Sweden.
    Kristiansson, Urban
    RISE Viktoria, Gothenburg, Sweden.
    Milding, Hans-Göran
    Volvo Car Corporation, Gothenburg, Sweden.
    Sundström, Christofer
    RISE Viktoria, Gothenburg, Sweden.
    Wedlin, Johan
    RISE Viktoria, Gothenburg, Sweden.
    Driving style comparison of plug-in hybrids and fossil fueled vehicles based on data collection of fast sampled signals2018Konferensbidrag (Refereegranskat)
  • 216.
    Pirasteh, Parivash
    et al.
    Högskolan i Halmstad, Akademin för informationsteknologi, Halmstad Embedded and Intelligent Systems Research (EIS), CAISR Centrum för tillämpade intelligenta system (IS-lab).
    Nowaczyk, Sławomir
    Högskolan i Halmstad, Akademin för informationsteknologi, Halmstad Embedded and Intelligent Systems Research (EIS), CAISR Centrum för tillämpade intelligenta system (IS-lab).
    Pashami, Sepideh
    Högskolan i Halmstad, Akademin för informationsteknologi, Halmstad Embedded and Intelligent Systems Research (EIS), CAISR Centrum för tillämpade intelligenta system (IS-lab).
    Löwenadler, Magnus
    Aftermarket Solutions Department, Volvo Trucks, Gothenburg, Sweden.
    Thunberg, Klas
    Service Market Products, Volvo Buses, Gothenburg, Sweden.
    Ydreskog, Henrik
    Aftermarket Solutions Department, Volvo Trucks, Gothenburg, Sweden.
    Berck, Peter
    Högskolan i Halmstad, Akademin för informationsteknologi, Halmstad Embedded and Intelligent Systems Research (EIS), CAISR Centrum för tillämpade intelligenta system (IS-lab).
    Interactive feature extraction for diagnostic trouble codes in predictive maintenance: A case study from automotive domain2019Ingår i: Proceedings of the Workshop on Interactive Data Mining, New York, NY: Association for Computing Machinery (ACM), 2019, artikel-id 4Konferensbidrag (Refereegranskat)
    Abstract [en]

    Predicting future maintenance needs of equipment can be addressed in a variety of ways. Methods based on machine learning approaches provide an interesting platform for mining large data sets to find patterns that might correlate with a given fault. In this paper, we approach predictive maintenance as a classification problem and use Random Forest to separate data readouts within a particular time window into those corresponding to faulty and non-faulty component categories. We utilize diagnostic trouble codes (DTCs) as an example of event-based data, and propose four categories of features that can be derived from DTCs as a predictive maintenance framework. We test the approach using large-scale data from a fleet of heavy duty trucks, and show that DTCs can be used within our framework as indicators of imminent failures in different components.

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  • 217.
    Poh, Norman
    et al.
    University of Surrey, United Kingdom.
    Bourlai, Thirimachos
    West Virginia University, United States.
    Kittler, Josef
    University of Surrey, United Kingdom.
    Allano, Lorene
    CEA LIST, CEA Saclay, PC 72-91191 Gif-sur-Yvette Cedex, France.
    Alonso-Fernandez, Fernando
    Escuela Politecnica Superior, Univ. Autonoma de Madrid, Spain.
    Ambekar, Onkar
    Centrum Wiskunde and Informatica (CWI), 1098 XG, Amsterdam, Netherlands.
    Baker, John
    Johns Hopkins University, United States .
    Dorizzi, Bernadette
    Electronics and Physics Department, Institut Telecom, Telecom and Management SudParis, 91011 Evry, France.
    Fatukasi, Omolara
    University of Surrey, United Kingdom.
    Fierrez, Julian
    Universidad Autónoma de Madrid, Spain .
    Ganster, Harald
    Institute of Digital Image Processing, Joanneum Research, Austria.
    Ortega-Garcia, Javier
    Universidad Autónoma de Madrid, Spain .
    Maurer, Donald
    Johns Hopkins University, United States.
    Salah, Albert Ali
    ISLA-ISIS, University of Amsterdam, 1098 XG Amsterdam, Netherlands.
    Scheidat, Tobias
    Brandenburg University of Applied Sciences, Germany.
    Vielhauer, Claus
    Otto-von-Guericke-University of Magdeburg, Germany.
    Benchmarking Quality-dependent and Cost-sensitive Score-level Multimodal Biometric Fusion Algorithms2009Ingår i: IEEE Transactions on Information Forensics and Security, ISSN 1556-6013, E-ISSN 1556-6021, Vol. 4, nr 4, s. 849-866Artikel i tidskrift (Refereegranskat)
    Abstract [en]

    Automatically verifying the identity of a person by means of biometrics (e.g., face and fingerprint) is an important application in our day-to-day activities such as accessing banking services and security control in airports. To increase the system reliability, several biometric devices are often used. Such a combined system is known as a multimodal biometric system. This paper reports a benchmarking study carried out within the framework of the BioSecure DS2 (Access Control) evaluation campaign organized by the University of Surrey, involving face, fingerprint, and iris biometrics for person authentication, targeting the application of physical access control in a medium-size establishment with some 500 persons. While multimodal biometrics is a well-investigated subject in the literature, there exists no benchmark for a fusion algorithm comparison. Working towards this goal, we designed two sets of experiments: quality-dependent and cost-sensitive evaluation. The quality-dependent evaluation aims at assessing how well fusion algorithms can perform under changing quality of raw biometric images principally due to change of devices. The cost-sensitive evaluation, on the other hand, investigates how well a fusion algorithm can perform given restricted computation and in the presence of software and hardware failures, resulting in errors such as failure-to-acquire and failure-to-match. Since multiple capturing devices are available, a fusion algorithm should be able to handle this nonideal but nevertheless realistic scenario. In both evaluations, each fusion algorithm is provided with scores from each biometric comparison subsystem as well as the quality measures of both the template and the query data. The response to the call of the evaluation campaign proved very encouraging, with the submission of 22 fusion systems. To the best of our knowledge, this campaign is the first attempt to benchmark quality-based multimodal fusion algorithms. In the presence of changing image quality which may be due to a change of acquisition devices and/or device capturing configurations, we observe that the top performing fusion algorithms are those that exploit automatically derived quality measurements. Our evaluation also suggests that while using all the available biometric sensors can definitely increase the fusion performance, this comes at the expense of increased cost in terms of acquisition time, computation time, the physical cost of hardware, and its maintenance cost. As demonstrated in our experiments, a promising solution which minimizes the composite cost is sequential fusion, where a fusion algorithm sequentially uses match scores until a desired confidence is reached, or until all the match scores are exhausted, before outputting the final combined score. © 2009 IEEE.

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  • 218.
    Prytz, Rune
    Högskolan i Halmstad, Akademin för informationsteknologi, Halmstad Embedded and Intelligent Systems Research (EIS), CAISR Centrum för tillämpade intelligenta system (IS-lab). Volvo Group Trucks Technology, Malmö, Sweden.
    Machine learning methods for vehicle predictive maintenance using off-board and on-board data2014Licentiatavhandling, sammanläggning (Övrigt vetenskapligt)
    Abstract [en]

    Vehicle uptime is getting increasingly important as the transport solutions become more complex and the transport industry seeks new ways of being competitive. Traditional Fleet Management Systems are gradually extended with new features to improve reliability, such as better maintenance planning. Typical diagnostic and predictive maintenance methods require extensive experimentation and modelling during development. This is unfeasible if the complete vehicle is addressed as it would require too much engineering resources.

    This thesis investigates unsupervised and supervised methods for predicting vehicle maintenance. The methods are data driven and use extensive amounts of data, either streamed, on-board data or historic and aggregated data from off-board databases. The methods rely on a telematics gateway that enables vehicles to communicate with a back-office system. Data representations, either aggregations or models, are sent wirelessly to an off-board system which analyses the data for deviations. These are later associated to the repair history and form a knowledge base that can be used to predict upcoming failures on other vehicles that show the same deviations.

    The thesis further investigates different ways of doing data representations and deviation detection. The first one presented, COSMO, is an unsupervised and self-organised approach demonstrated on a fleet of city buses. It automatically comes up with the most interesting on-board data representations and uses a consensus based approach to isolate the deviating vehicle. The second approach outlined is a super-vised classification based on earlier collected and aggregated vehicle statistics in which the repair history is used to label the usage statistics. A classifier is trained to learn patterns in the usage data that precede specific repairs and thus can be used to predict vehicle maintenance. This method is demonstrated for failures of the vehicle air compressor and based on AB Volvo’s database of vehicle usage statistics.

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  • 219. Raeeji Yaneh Sari, Noorali
    et al.
    Fanaee Tork, Hadi
    Högskolan i Halmstad, Akademin för informationsteknologi.
    Rahat, Mahmoud
    Högskolan i Halmstad, Akademin för informationsteknologi.
    A Data-Driven Approach based on Tensor Completion for Replacing "Physical Sensors" with "Virtual Sensors"2021Ingår i: 2021 IEEE 8th International Conference on Data Science and Advanced Analytics (DSAA), IEEE conference proceedings, 2021Konferensbidrag (Refereegranskat)
    Abstract [en]

    Sensors are being used in many industrial applications for equipment health monitoring and anomaly detection. However, sometimes operation and maintenance of these sensors are costly. Thus companies are interested in reducing the number of required sensors as much as possible. The straightforward solution is to check the prediction power of sensors and eliminate those sensors with limited prediction capabilities. However, this is not an optimal solution because if we discard the identified sensors. As a result, their historical data also will not be utilized anymore. However, typically such historical data can help improve the remaining sensors' signal power, and abolishing them does not seem the right solution. Therefore, we propose the first data-driven approach based on tensor completion for re-utilizing data of removed sensors and the remaining sensors to create virtual sensors. We applied the proposed method on vibration sensors of high-speed separators, operating with five sensors. The producer company was interested in reducing the sensors to two. But with the aid of tensor completion-based virtual sensors, we show that we can safely keep only one sensor and use four virtual sensors that give almost equal detection power when we keep only two physical sensors.

  • 220.
    Ramos, D.
    et al.
    Universidad Autonoma de Madrid, Spain.
    Alonso-Fernandez, Fernando
    Universidad Autonoma de Madrid, Spain.
    A Practical Electronic Instrumentation Course for Engineering Students2010Ingår i: 2010 IEEE Education Engineering Conference, EDUCON 2010, Piscataway, N.J.: IEEE Press, 2010, s. 1181-1188Konferensbidrag (Refereegranskat)
    Abstract [en]

    A course on Electronic Instrumentation has recently been developed at the Universidad Autonoma de Madrid (Spain), which specifically emphasizes practical aspects. The objective of the course is to link theoretical principles with practical issues of electronic instrumentation through the development of a final project. First, students take practical work in several different scenarios, which are the basis for the design of an engineering project aimed to solve an electronic instrumentation problem which is set by the students. Students are exposed to a set of multidisciplinary aspects, both theoretical and practical, providing them with the ability of integrating blocks in which they have practically worked into a full instrumentation project. The course provides not only enhanced academic training but also increased student motivation, as students are encouraged to propose their own projects. © 2010 IEEE.

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  • 221.
    Ranftl, Andreas
    et al.
    Högskolan i Halmstad, Akademin för informationsteknologi.
    Alonso-Fernandez, Fernando
    Högskolan i Halmstad, Akademin för informationsteknologi, Halmstad Embedded and Intelligent Systems Research (EIS), CAISR Centrum för tillämpade intelligenta system (IS-lab).
    Karlsson, Stefan
    Högskolan i Halmstad, Akademin för informationsteknologi, Halmstad Embedded and Intelligent Systems Research (EIS), CAISR Centrum för tillämpade intelligenta system (IS-lab).
    Face Tracking Using Optical Flow: Development of a Real-Time AdaBoost Cascade Face Tracker2015Konferensbidrag (Refereegranskat)
    Abstract [en]

    In this paper a novel face tracking approach is presented where optical flow information is incorporated into the Viola-Jones face detection algorithm. In the original algorithm from Viola and Jones face detection is static as information from previous frames is not considered. In contrast to the Viola-Jones face detector and also to other known dynamic enhancements, the proposed facetracker preserves information about near-positives. The algorithm builds a likelihood map from the intermediate results of the Viola-Jones algorithm which is extrapolated using optical flow. The objects get extracted from the likelihood map using image segmentation techniques. All steps can be computed very efficiently in real-time. The tracker is verified on the Boston Head Tracking Database showing that the proposed algorithm outperforms the standard Viola-Jones face detector.

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  • 222.
    Ranftl, Andreas
    et al.
    Högskolan i Halmstad, Akademin för informationsteknologi.
    Alonso-Fernandez, Fernando
    Högskolan i Halmstad, Akademin för informationsteknologi, Halmstad Embedded and Intelligent Systems Research (EIS), CAISR Centrum för tillämpade intelligenta system (IS-lab).
    Karlsson, Stefan
    Högskolan i Halmstad, Akademin för informationsteknologi, Halmstad Embedded and Intelligent Systems Research (EIS), CAISR Centrum för tillämpade intelligenta system (IS-lab).
    Bigun, Josef
    Högskolan i Halmstad, Akademin för informationsteknologi, Halmstad Embedded and Intelligent Systems Research (EIS), CAISR Centrum för tillämpade intelligenta system (IS-lab).
    A Real-Time AdaBoost Cascade Face Tracker Based on Likelihood Map and Optical Flow2017Ingår i: IET Biometrics, ISSN 2047-4938, E-ISSN 2047-4946, Vol. 6, nr 6, s. 468-477Artikel i tidskrift (Refereegranskat)
    Abstract [en]

    We present a novel face tracking approach where optical flow information is incorporated into a modified version of the Viola-Jones detection algorithm. In the original algorithm, detection is static, as information from previous frames is not considered; in addition, candidate windows have to pass all stages of the classification cascade, otherwise they are discarded as containing no face. In contrast, the proposed tracker preserves information about the number of classification stages passed by each window. Such information is used to build a likelihood map, which represents the probability of having a face located at that position. Tracking capabilities are provided by extrapolating the position of the likelihood map to the next frame by optical flow computation. The proposed algorithm works in real time on a standard laptop. The system is verified on the Boston Head Tracking Database, showing that the proposed algorithm outperforms the standard Viola-Jones detector in terms of detection rate and stability of the output bounding box, as well as including the capability to deal with occlusions. We also evaluate two recently published face detectors based on Convolutional Networks and Deformable Part Models, with our algorithm showing a comparable accuracy at a fraction of the computation time.

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  • 223.
    Ribeiro, Eduardo
    et al.
    University of Salzburg, Salzburg, Austria & Federal University of Tocantins, Palmas, Brazil.
    Uhl, Andreas
    University of Salzburg, Salzburg, Austria.
    Alonso-Fernandez, Fernando
    Högskolan i Halmstad, Akademin för informationsteknologi, Halmstad Embedded and Intelligent Systems Research (EIS), CAISR Centrum för tillämpade intelligenta system (IS-lab).
    Iris Super-Resolution using CNNs: is Photo-Realism Important to Iris Recognition?2019Ingår i: IET Biometrics, ISSN 2047-4938, E-ISSN 2047-4946, Vol. 8, nr 1, s. 69-78Artikel i tidskrift (Refereegranskat)
    Abstract [en]

    The use of low-resolution images adopting more relaxed acquisition conditions such as mobile phones and surveillance videos is becoming increasingly common in Iris Recognition nowadays. Concurrently, a great variety of single image Super-Resolution techniques are emerging, specially with the use of convolutional neural networks. The main objective of these methods is to try to recover finer texture details generating more photo-realistic images based on the optimization of an objective function depending basically on the CNN architecture and the training approach. In this work, we explore single image Super-Resolution using CNNs for iris recognition. For this, we test different CNN architectures as well as the use of different training databases, validating our approach on a database of 1.872 near infrared iris images and on a mobile phone image database. We also use quality assessment, visual results and recognition experiments to verify if the photo-realism provided by the CNNs which have already proven to be effective for natural images can reflect in a better recognition rate for Iris Recognition. The results show that using deeper architectures trained with texture databases that provide a balance between edge preservation and the smoothness of the method can lead to good results in the iris recognition process. © The Institution of Engineering and Technology 2015

  • 224.
    Ribeiro, Eduardo
    et al.
    Federal University of Tocantins, Palmas, Brazil.
    Uhl, Andreas
    Department of Computer Sciences at Salzburg University, Salzburg, Austria.
    Alonso-Fernandez, Fernando
    Högskolan i Halmstad, Akademin för informationsteknologi, Halmstad Embedded and Intelligent Systems Research (EIS), CAISR Centrum för tillämpade intelligenta system (IS-lab).
    Super-Resolution and Image Re-Projection for Iris Recognition2019Ingår i: 2019 IEEE 5th International Conference on Identity, Security, and Behavior Analysis (ISBA), Piscataway, N.J.: IEEE, 2019, s. 1-7Konferensbidrag (Refereegranskat)
    Abstract [en]

    Several recent works have addressed the ability of deep learning to disclose rich, hierarchical and discriminative models for the most diverse purposes. Specifically in the super-resolution field, Convolutional Neural Networks (CNNs) using different deep learning approaches attempt to recover realistic texture and fine grained details from low resolution images. In this work we explore the viability of these approaches for iris Super-Resolution (SR) in an iris recognition environment. For this, we test different architectures with and without a so called image re-projection to reduce artifacts applying it to different iris databases to verify the viability of the different CNNs for iris super-resolution. Results show that CNNs and image re-projection can improve the results specially for the accuracy of recognition systems using a complete different training database performing the transfer learning successfully.

  • 225.
    Ribeiro, Eduardo
    et al.
    University of Salzburg, Salzburg, Austria.
    Uhl, Andreas
    University of Salzburg, Salzburg, Austria.
    Alonso-Fernandez, Fernando
    Högskolan i Halmstad, Akademin för informationsteknologi, Halmstad Embedded and Intelligent Systems Research (EIS), CAISR Centrum för tillämpade intelligenta system (IS-lab).
    Farrugia, Reuben A.
    University of Malta, Msida, Malta.
    Exploring Deep Learning Image Super-Resolution for Iris Recognition2017Ingår i: 2017 25th European Signal Processing Conference (EUSIPCO 2017), IEEE, 2017, s. 2240-2244Konferensbidrag (Refereegranskat)
    Abstract [en]

    In this work we test the ability of deep learning methods to provide an end-to-end mapping between low and high resolution images applying it to the iris recognition problem. Here, we propose the use of two deep learning single-image super-resolution approaches: Stacked Auto-Encoders (SAE) and Convolutional Neural Networks (CNN) with the most possible lightweight structure to achieve fast speed, preserve local in-formation and reduce artifacts at the same time. We validate the methods with a database of 1.872 near-infrared iris images with quality assessment and recognition experiments showing the superiority of deep learning approaches over the compared algorithms.  © EURASIP 2017

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  • 226.
    Rosberg, Felix
    Högskolan i Halmstad, Akademin för informationsteknologi. Engage Studios, Gothenburg, Sweden.
    Anonymizing Faces without Destroying Information2024Licentiatavhandling, sammanläggning (Övrigt vetenskapligt)
    Abstract [en]

    Anonymization is a broad term. Meaning that personal data, or rather data that identifies a person, is redacted or obscured. In the context of video and image data, the most palpable information is the face. Faces barely change compared to other aspect of a person, such as cloths, and we as people already have a strong sense of recognizing faces. Computers are also adroit at recognizing faces, with facial recognition models being exceptionally powerful at identifying and comparing faces. Therefore it is generally considered important to obscure the faces in video and image when aiming for keeping it anonymized. Traditionally this is simply done through blurring or masking. But this de- stroys useful information such as eye gaze, pose, expression and the fact that it is a face. This is an especial issue, as today our society is data-driven in many aspects. One obvious such aspect is autonomous driving and driver monitoring, where necessary algorithms such as object-detectors rely on deep learning to function. Due to the data hunger of deep learning in conjunction with society’s call for privacy and integrity through regulations such as the General Data Protection Regularization (GDPR), anonymization that preserve useful information becomes important.

    This Thesis investigates the potential and possible limitation of anonymizing faces without destroying the aforementioned useful information. The base approach to achieve this is through face swapping and face manipulation, where the current research focus on changing the face (or identity) while keeping the original attribute information. All while being incorporated and consistent in an image and/or video. Specifically, will this Thesis demonstrate how target-oriented and subject-agnostic face swapping methodologies can be utilized for realistic anonymization that preserves attributes. Thru this, this Thesis points out several approaches that is: 1) controllable, meaning the proposed models do not naively changes the identity. Meaning that what kind of change of identity and magnitude is adjustable, thus also tunable to guarantee anonymization. 2) subject-agnostic, meaning that the models can handle any identity. 3) fast, meaning that the models is able to run efficiently. Thus having the potential of running in real-time. The end product consist of an anonymizer that achieved state-of-the-art performance on identity transfer, pose retention and expression retention while providing a realism.

    Apart of identity manipulation, the Thesis demonstrate potential security issues. Specifically reconstruction attacks, where a bad-actor model learns convolutional traces/patterns in the anonymized images in such a way that it is able to completely reconstruct the original identity. The bad-actor networks is able to do this with simple black-box access of the anonymization model by constructing a pair-wise dataset of unanonymized and anonymized faces. To alleviate this issue, different defense measures that disrupts the traces in the anonymized image was investigated. The main take away from this, is that naively using what qualitatively looks convincing of hiding an identity is not necessary the case at all. Making robust quantitative evaluations important.

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  • 227.
    Rosberg, Felix
    et al.
    Berge Consulting, Gothenburg, Sweden.
    Aksoy, Eren
    Högskolan i Halmstad, Akademin för informationsteknologi, Centrum för forskning om tillämpade intelligenta system (CAISR).
    Alonso-Fernandez, Fernando
    Högskolan i Halmstad, Akademin för informationsteknologi, Centrum för forskning om tillämpade intelligenta system (CAISR).
    Englund, Cristofer
    Högskolan i Halmstad, Akademin för informationsteknologi, Centrum för forskning om tillämpade intelligenta system (CAISR).
    FaceDancer: Pose- and Occlusion-Aware High Fidelity Face Swapping2023Ingår i: Proceedings - 2023 IEEE Winter Conference on Applications of Computer Vision, WACV 2023, Piscataway: IEEE, 2023, s. 3443-3452Konferensbidrag (Refereegranskat)
    Abstract [en]

    In this work, we present a new single-stage method for subject agnostic face swapping and identity transfer, named FaceDancer. We have two major contributions: Adaptive Feature Fusion Attention (AFFA) and Interpreted Feature Similarity Regularization (IFSR). The AFFA module is embedded in the decoder and adaptively learns to fuse attribute features and features conditioned on identity information without requiring any additional facial segmentation process. In IFSR, we leverage the intermediate features in an identity encoder to preserve important attributes such as head pose, facial expression, lighting, and occlusion in the target face, while still transferring the identity of the source face with high fidelity. We conduct extensive quantitative and qualitative experiments on various datasets and show that the proposed FaceDancer outperforms other state-of-the-art networks in terms of identityn transfer, while having significantly better pose preservation than most of the previous methods. © 2023 IEEE.

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  • 228.
    Ruiz-Albacete, V.
    et al.
    Universidad Autonoma de Madrid, Spain.
    Tome-Gonzalez, P.
    Universidad Autonoma de Madrid, Spain.
    Alonso-Fernandez, Fernando
    Escuela Politecnica Superior, Univ. Autonoma de Madrid, Spain.
    Galbally, J.
    Universidad Autonoma de Madrid, Spain.
    Fierrez, J.
    Universidad Autonoma de Madrid, Spain.
    Ortega-Garcia, J.
    Universidad Autonoma de Madrid, Spain.
    Direct attacks using fake images in iris verification2008Ingår i: Biometrics and Identity Management / [ed] Ben Schouten, Berlin: Springer Berlin/Heidelberg, 2008, Vol. Springer LNCS-5372, s. 181-190Konferensbidrag (Refereegranskat)
    Abstract [en]

    In this contribution, the vulnerabilities of iris-based recognition systems to direct attacks are studied. A database of fake iris images has been created from real iris of the BioSec baseline database. Iris images are printed using a commercial printer and then, presented at the iris sensor. We use for our experiments a publicly available iris recognition system, which some modifications to improve the iris segmentation step. Based on results achieved on different operational scenarios, we show that the system is vulnerable to direct attacks, pointing out the importance of having countermeasures against this type of fraudulent actions. © 2008 Springer Berlin Heidelberg.

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  • 229.
    Rögnvaldsson, Thorsteinn
    Högskolan i Halmstad, Akademin för informationsteknologi, Halmstad Embedded and Intelligent Systems Research (EIS).
    A Simple trick for estimating the weight decay parameter2012Ingår i: Lecture Notes in Computer Science, ISSN 0302-9743, E-ISSN 1611-3349, Vol. 7700 LECTURE NO, s. 69-89Artikel i tidskrift (Refereegranskat)
    Abstract [en]

    We present a simple trick to get an approximate estimate of the weight decay parameter λ. The method combines early stopping and weight decay, into the estimate λ̂ = ||∇E(Wes)||/||2W es||, where Wes is the set of weights at the early stopping point, and E(W) is the training data fit error. The estimate is demonstrated and compared to the standard cross-validation procedure for λ selection on one synthetic and four real life data sets. The result is that is as good an estimator for the optimal weight decay parameter value as the standard search estimate, but orders of magnitude quicker to compute. The results also show that weight decay can produce solutions that are significantly superior to committees of networks trained with early stopping. © Springer-Verlag Berlin Heidelberg 2012.

  • 230.
    Rögnvaldsson, Thorsteinn
    et al.
    Högskolan i Halmstad, Akademin för informationsteknologi, Halmstad Embedded and Intelligent Systems Research (EIS), CAISR Centrum för tillämpade intelligenta system (IS-lab).
    Byttner, Stefan
    Högskolan i Halmstad, Akademin för informationsteknologi, Halmstad Embedded and Intelligent Systems Research (EIS), CAISR Centrum för tillämpade intelligenta system (IS-lab).
    Prytz, Rune
    Volvo Group Trucks Technology, Göteborg, Sweden.
    Nowaczyk, Sławomir
    Högskolan i Halmstad, Akademin för informationsteknologi, Halmstad Embedded and Intelligent Systems Research (EIS), CAISR Centrum för tillämpade intelligenta system (IS-lab).
    Svensson, Magnus
    Volvo Group Trucks Technology, Göteborg, Sweden.
    Wisdom of Crowds for Intelligent Monitoring of Vehicle FleetsManuskript (preprint) (Övrigt vetenskapligt)
    Abstract [en]

    An approach is presented and experimentally demonstrated where consensus among distributed self-organized agents is used for intelligent monitoring of mobile cyberphysical systems (in this case vehicles). The demonstration is done on test data from a 30 month long field test with a city bus fleet under real operating conditions. The self-organized models operate on-board the systems, like embedded agents, communicate their states over a wireless communication link, and their states are compared off-line to find systems that deviate from the consensus. In this way is the group (the fleet) of systems used to detect errors that actually occur. This can be used to build up a knowledge base that can be accumulated over the life-time of the systems.

  • 231.
    Saberi-Movahed, Farid
    et al.
    Graduate University Of Advanced Technology, Kerman, Iran; Institut Für Angewandte Informatik (infai), Dresden, Germany.
    Biswas, Bitasta
    Rwth Aachen University, Aachen, Germany.
    Tiwari, Prayag
    Högskolan i Halmstad, Akademin för informationsteknologi.
    Lehmann, Jens
    Technische Universität Dresden, Dresden, Germany; Amazon (work Done Outside Of Amazon), Dresden, Germany.
    Vahdati, Sahar
    Technische Universität Dresden, Dresden, Germany; Institut Für Angewandte Informatik (infai), Dresden, Germany.
    Deep Nonnegative Matrix Factorization with Joint Global and Local Structure Preservation2024Ingår i: Expert systems with applications, ISSN 0957-4174, E-ISSN 1873-6793, Vol. 249, nr B, s. 1-19, artikel-id 123645Artikel i tidskrift (Refereegranskat)
    Abstract [en]

    Deep Non-Negative Matrix Factorization (DNMF) methods provide an efficient low-dimensional representation of given data through their layered architecture. A limitation of such methods is that they cannot effectively preserve the local and global geometric structures of the data in each layer. Consequently, a significant amount of the geometrical information within the data, present in each layer of the employed deep framework, can be overlooked by the model. This can lead to an information loss and a subsequent drop in performance. In this paper, we propose a novel deep non-negative matrix factorization method, Deep Non-Negative Matrix Factorization with Joint Global and Local Structure Preservation (dubbed Dn2MFGL), that ensures the preservation of both global and local structures within the data space. Dn2MFGL performs representation learning through a sequential embedding procedure which involves both the global data structure by accounting for the data variance, and the local data relationships by utilizing information from neighboring data points. Moreover, a regularization term that promotes sparsity by utilizing the concept of the inner product is applied to the matrices representing the lower dimensions. This aims to retain the fundamental data structure while discarding less crucial features. Simultaneously, the residual matrix of Dn2MFGL is subjected to the L2,1 norm, which ensures the robustness of the model against noisy data samples. An effective and multiplicative updating process also facilitates Dn2MFGL in solving the employed objective function. The clustering performance of the proposed deep NMF method is explored across various benchmarks of face datasets. The results point to Dn2MFGL outperforming several existing classical and state-of-the-art NMF methods. The source code is available at https://github.com/FaridSaberi/Dn2MFGO.git. © 2024 Elsevier Ltd

  • 232.
    Sant'Anna, Anita
    Högskolan i Halmstad, Akademin för informationsteknologi, Halmstad Embedded and Intelligent Systems Research (EIS), Intelligenta system (IS-lab).
    Activity monitoring as a tool for person-centered care: preliminary report2014Ingår i: 2014 IEEE International Conference on Bioinformatics and Biomedicine (BIBM) / [ed] Huiru (Jane) Zheng, Werner Dubitzky, Xiaohua Hu, Jin-Kao Hao, Daniel Berrar, Kwang-Hyun Cho, Yadong Wang & David Gilbert, Piscataway, NJ: IEEE Press, 2014, s. 48-51Konferensbidrag (Refereegranskat)
    Abstract [en]

    The Person-Centered Care (PCC) paradigm advocates that instead of being the passive target of a medical intervention, patients should play an active part in their care and in the decision-making process, together with clinicians. Although new mobile and wearable technologies have created a new wave of personalized health-related applications, it is still unclear how these technologies can be used in health care institutions in order to support person-centered care. In order to investigate this matter, we undertook a pilot study aimed at determining if and how activity monitoring can support person-centered care routines for patients undergoing total hip replacement surgery. This is a preliminary report describing the methodology, preliminary results, and some practical challenges. We present here an orientation-invariant, accelerometer-based activity monitoring method, especially designed to address the requirements of monitoring in-patients in a real clinical setting. We also present and discuss some practical issues related to complying with hospital requirements and collaborating with hospital staff. © 2014 IEEE.

  • 233.
    Sant'Anna, Anita
    et al.
    Högskolan i Halmstad, Sektionen för Informationsvetenskap, Data– och Elektroteknik (IDE), Halmstad Embedded and Intelligent Systems Research (EIS), CAISR Centrum för tillämpade intelligenta system (IS-lab).
    Wickström, Nicholas
    Symbolic Approach to Motion Analysis: Framework and Gait Analysis Case Studies2013Ingår i: Telehealthcare Computing and Engineering: Principles and Design / [ed] Fei Hu, Boca Raton: CRC Press, 2013, 1, s. 561-606Kapitel i bok, del av antologi (Övrigt vetenskapligt)
  • 234.
    Sant'Anna, Anita
    et al.
    Högskolan i Halmstad, Sektionen för Informationsvetenskap, Data– och Elektroteknik (IDE), Halmstad Embedded and Intelligent Systems Research (EIS), Laboratoriet för intelligenta system.
    Wickström, Nicholas
    Högskolan i Halmstad, Sektionen för Informationsvetenskap, Data– och Elektroteknik (IDE), Halmstad Embedded and Intelligent Systems Research (EIS), Laboratoriet för intelligenta system.
    Symbolization of time series: an evaluation of SAX, persist, and ACA2011Ingår i: CISP 2011: Proceedings, the 4th International Congress on Image and Signal Processing, 15-17 October 2011, Shanghai, China / [ed] Peihua Qiu, Piscataway, N.J.: IEEE Press, 2011, s. 2223-2228Konferensbidrag (Refereegranskat)
    Abstract [en]

    Symbolization of time-series has successfully been used to extract temporal patterns from experimental data. Segmentation is an unavoidable step of the symbolization process, and it may be characterized on two domains: the amplitude and the temporal domain. These two groups of methods present advantages and disadvantages each. Can their performance be estimated a priori based on signal characteristics? This paper evaluates the performance of SAX, Persist and ACA on 47 different time-series, based on signal periodicity. Results show that SAX tends to perform best on random signals whereas ACA may outperform the other methods on highly periodic signals. However, results do not support that a most adequate method may be determined a priory.

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  • 235.
    Sant'Anna, Anita
    et al.
    Högskolan i Halmstad, Sektionen för Informationsvetenskap, Data– och Elektroteknik (IDE), Halmstad Embedded and Intelligent Systems Research (EIS), CAISR Centrum för tillämpade intelligenta system (IS-lab).
    Wickström, Nicholas
    Högskolan i Halmstad, Sektionen för Informationsvetenskap, Data– och Elektroteknik (IDE), Halmstad Embedded and Intelligent Systems Research (EIS), CAISR Centrum för tillämpade intelligenta system (IS-lab).
    Eklund, Helene
    Sahlgrenska Academy, Göteborg, Sweden.
    Zügner, Roland
    Sahlgrenska Academy, Göteborg, Sweden.
    Tranberg, Roy
    Sahlgrenska Academy, Göteborg, Sweden.
    Assessment of Gait Symmetry and Gait Normality Using Inertial Sensors: In-Lab and In-Situ Evaluation2013Ingår i: Biomedical Engineering Systems and Technologies: 5th International Joint Conference, BIOSTEC 2012, Vilamoura, Portugal, February 1-4, 2012, Revised Selected Papers / [ed] Joaquim Gabriel et al., Heidelberg: Springer Berlin/Heidelberg, 2013, s. 239-254Kapitel i bok, del av antologi (Refereegranskat)
    Abstract [en]

    Quantitative gait analysis is a powerful tool for the assessment of a number of physical and cognitive conditions. Unfortunately, the costs involved in providing in-lab 3D kinematic analysis to all patients is prohibitive. Inertial sensors such as accelerometers and gyroscopes may complement in-lab analysis by providing cheaper gait analysis systems that can be deployed anywhere. The present study investigates the use of inertial sensors to quantify gait symmetry and gait normality. The system was evaluated in-lab, against 3D kinematic measurements; and also in-situ, against clinical assessments of hip-replacement patients. Results show that the system not only correlates well with kinematic measurements but it also corroborates various quantitative and qualitative measures of recovery and health status of hip-replacement patients

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  • 236.
    Sarmadi, Hamid
    et al.
    Högskolan i Halmstad, Akademin för informationsteknologi, Centrum för forskning om tillämpade intelligenta system (CAISR).
    Rögnvaldsson, Thorsteinn
    Högskolan i Halmstad, Akademin för informationsteknologi, Centrum för forskning om tillämpade intelligenta system (CAISR).
    Carlsson, Nils Roger
    Högskolan i Halmstad, Akademin för informationsteknologi, Centrum för forskning om tillämpade intelligenta system (CAISR).
    Ohlsson, Mattias
    Högskolan i Halmstad, Akademin för informationsteknologi, Centrum för forskning om tillämpade intelligenta system (CAISR).
    Wahab, Ibrahim
    Lund University, Lund, Sweden.
    Hall, Ola
    Lund University, Lund, Sweden.
    Towards Explaining Satellite Based Poverty Predictions with Convolutional Neural Networks2023Ingår i: 2023 IEEE 10th International Conference on Data Science and Advanced Analytics (DSAA), IEEE, 2023Konferensbidrag (Refereegranskat)
    Abstract [en]

    Deep convolutional neural networks (CNNs) have been shown to predict poverty and development indicators from satellite images with surprising accuracy. This paper presents a first attempt at analyzing the CNNs responses in detail and explaining the basis for the predictions. The CNN model, while trained on relatively low resolution day- and night-time satellite images, is able to outperform human subjects who look at high-resolution images in ranking the Wealth Index categories. Multiple explainability experiments performed on the model indicate the importance of the sizes of the objects, pixel colors in the image, and provide a visualization of the importance of different structures in input images. A visualization is also provided of type images that maximize the network prediction of Wealth Index, which provides clues on what the CNN prediction is based on.

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  • 237.
    Savas, Süleyman
    et al.
    Högskolan i Halmstad, Akademin för informationsteknologi, Halmstad Embedded and Intelligent Systems Research (EIS), Centrum för forskning om inbyggda system (CERES). Department of Computers Science, Lund University, Lund, Sweden.
    Ul-Abdin, Zain
    Högskolan i Halmstad, Akademin för informationsteknologi, Halmstad Embedded and Intelligent Systems Research (EIS), Centrum för forskning om inbyggda system (CERES).
    Nordström, Tomas
    Umeå University, Umeå, Sweden.
    A Framework to Generate Domain-Specific Manycore Architectures from Dataflow Programs2020Ingår i: Microprocessors and microsystems, ISSN 0141-9331, E-ISSN 1872-9436, Vol. 72, artikel-id 102908Artikel i tidskrift (Refereegranskat)
    Abstract [en]

    In the last 15 years we have seen, as a response to power and thermal limits for current chip technologies, an explosion in the use of multiple and even many computer cores on a single chip. But now, to further improve performance and energy efficiency, when there are potentially hundreds of computing cores on a chip, we see a need for a specialization of individual cores and the development of heterogeneous manycore computer architectures.

    However, developing such heterogeneous architectures is a significant challenge. Therefore, we propose a design method to generate domain specific manycore architectures based on RISC-V instruction set architecture and automate the main steps of this method with software tools. The design method allows generation of manycore architectures with different configurations including core augmentation through instruction extensions and custom accelerators. The method starts from developing applications in a high-level dataflow language and ends by generating synthesizable Verilog code and cycle accurate emulator for the generated architecture.

    We evaluate the design method and the software tools by generating several architectures specialized for two different applications and measure their performance and hardware resource usages. Our results show that the design method can be used to generate specialized manycore architectures targeting applications from different domains. The specialized architectures show at least 3 to 4 times better performance than the general purpose counterparts. In certain cases, replacing general purpose components with specialized components saves hardware resources. Automating the method increases the speed of architecture development and facilitates the design space exploration of manycore architectures. © 2019 The Authors. Published by Elsevier B.V.

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  • 238.
    Sequeira, Ana F.
    et al.
    University of Reading, Reading, United Kingdom.
    Chen, Lulu
    University of Reading, Reading, United Kingdom.
    Ferryman, James
    University of Reading, Reading, United Kingdom.
    Wild, Peter
    Tecan Austria GmbH, Grödig, Austria.
    Alonso-Fernandez, Fernando
    Högskolan i Halmstad, Akademin för informationsteknologi, Halmstad Embedded and Intelligent Systems Research (EIS), CAISR Centrum för tillämpade intelligenta system (IS-lab).
    Bigun, Josef
    Högskolan i Halmstad, Akademin för informationsteknologi, Halmstad Embedded and Intelligent Systems Research (EIS), CAISR Centrum för tillämpade intelligenta system (IS-lab).
    Raja, Kiran B.
    Norwegian Biometrics Laboratory, NTNU, Gjøvik, Norway.
    Raghavendra, R.
    Norwegian Biometrics Laboratory, NTNU, Gjøvik, Norway.
    Busch, Christoph
    Norwegian Biometrics Laboratory, NTNU, Gjøvik, Norway.
    Freitas Pereira, Tiago
    Idiap Research Institute, Martigny, Switzerland.
    Marcel, Sébastien
    Idiap Research Institute, Martigny, Switzerland.
    Sangeeta Behera, Sushree
    Indian Institute of Technology Indore, Madhya Pradesh, India.
    Gour, Mahesh
    Indian Institute of Technology Indore, Madhya Pradesh, India.
    Kanhangad, Vivek
    Indian Institute of Technology Indore, Madhya Pradesh, India.
    Cross-Eyed 2017: Cross-Spectral Iris/Periocular Recognition Competition2017Konferensbidrag (Refereegranskat)
    Abstract [en]

    This work presents the 2nd Cross-Spectrum Iris/Periocular Recognition Competition (Cross-Eyed2017). The main goal of the competition is to promote and evaluate advances in cross-spectrum iris and periocular recognition. This second edition registered an increase in the participation numbers ranging from academia to industry: five teams submitted twelve methods for the periocular task and five for the iris task. The benchmark dataset is an enlarged version of the dual-spectrum database containing both iris and periocular images synchronously captured from a distance and within a realistic indoor environment. The evaluation was performed on an undisclosed test-set. Methodology, tested algorithms, and obtained results are reported in this paper identifying the remaining challenges in path forward. © 2017 IEEE

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  • 239.
    Sequeira, Ana F.
    et al.
    University of Reading, Reading, United Kingdom.
    Chen, Lulu
    University of Reading, Reading, United Kingdom.
    Wild, Peter
    AIT Austrian Institute of Technology, Vienna, Austria.
    Ferryman, James
    University of Reading, Reading, United Kingdom.
    Alonso-Fernandez, Fernando
    Högskolan i Halmstad, Akademin för informationsteknologi, Halmstad Embedded and Intelligent Systems Research (EIS), CAISR Centrum för tillämpade intelligenta system (IS-lab).
    Bigun, Josef
    Högskolan i Halmstad, Akademin för informationsteknologi, Halmstad Embedded and Intelligent Systems Research (EIS), CAISR Centrum för tillämpade intelligenta system (IS-lab).
    Raja, Kiran B.
    Norwegian Biometrics Laboratory, NTNU, Gjøvik, Norway.
    Raghavendra, R.
    Norwegian Biometrics Laboratory, NTNU, Gjøvik, Norway.
    Busch, Christoph
    Norwegian Biometrics Laboratory, NTNU, Gjøvik, Norway.
    Cross-Eyed: Cross-Spectral Iris/Periocular Recognition Database and Competition2016Ingår i: Proceedings of the 15th International Conference of the Biometrics Special Interest Group / [ed] Arslan Brömme, Christoph Busch, Christian Rathgeb & Andreas Uhl, Piscataway, N.J.: IEEE, 2016Konferensbidrag (Refereegranskat)
    Abstract [en]

    This work presents a novel dual-spectrum database containing both iris and periocular images synchronously captured from a distance and within a realistic indoor environment. This database was used in the 1st Cross-Spectrum Iris/Periocular Recognition Competition (Cross-Eyed 2016). This competition aimed at recording recent advances in cross- spectrum iris and periocular recognition. Six submissions were evaluated for cross-spectrum periocular recognition, and three for iris recognition. The submitted algorithms are briefly introduced. Detailed results are reported in this paper, and comparison of the results is discussed.

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  • 240.
    Simão, Miguel
    et al.
    Stratio Automotive, Lisbon, Portugal.
    Prytz, Rune
    Stratio Automotive, Lisbon, Portugal.
    Nowaczyk, Sławomir
    Högskolan i Halmstad, Akademin för informationsteknologi.
    Long-term Evaluation of the State-of-Health of Traction Lithium-ion Batteries in Operational Buses2022Ingår i: International Journal of Prognostics and Health Management, E-ISSN 2153-2648, Vol. 13, nr 1Artikel i tidskrift (Refereegranskat)
    Abstract [en]

    In this paper, we present and evaluate a novel methodology to estimate the usable capacity and state-of-health (SOH) of lithium-ion batteries in battery-electric buses (BEV). This methodology is designed to be applicable to any BEV in normal operation, independently of battery chemistry, and without requiring complex electrochemical models or large data sets. We have tested the proposed methodology on two vehicle fleets with a total of 105 vehicles, for which we have been acquiring data for up to three years. Additionally, we have analysed the operation of the fleets in terms of daily distance driven and the charging strategies chosen by the operators.

    The monitored vehicles are part of fleets currently in normal operation in Europe. The data collection is done with a third-party data logger that is connected to the vehicles’ Communication Area Network (CAN) buses, and no additional changes were made to the vehicle’s hardware or software. The results show that the proposed methodology shows significantly lower variance in SOH estimation than the alternative methodologies. It also shows similar accuracy in the long-term and smaller short-term deviations from the typical capacity fade model.

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  • 241.
    Skepetzis, Vasilios
    et al.
    Högskolan i Halmstad, Akademin för informationsteknologi.
    Hedman, Pontus
    Högskolan i Halmstad, Akademin för informationsteknologi.
    The Effect of Beautification Filters on Image Recognition: "Are filtered social media images viable Open Source Intelligence?"2021Självständigt arbete på avancerad nivå (magisterexamen), 10 poäng / 15 hpStudentuppsats (Examensarbete)
    Abstract [en]

    In light of the emergence of social media, and its abundance of facial imagery, facial recognition finds itself useful from an Open Source Intelligence standpoint. Images uploaded on social media are likely to be filtered, which can destroy or modify biometric features. This study looks at the recognition effort of identifying individuals based on their facial image after filters have been applied to the image. The social media image filters studied occlude parts of the nose and eyes, with a particular interest in filters occluding the eye region.

    Our proposed method uses a Residual Neural Network Model to extract features from images, with recognition of individuals based on distance measures, based on the extracted features. Classification of individuals is also further done by the use of a Linear Support Vector Machine and XGBoost classifier. In attempts to increase the recognition performance for images completely occluded in the eye region, we present a method to reconstruct this information by using a variation of a U-Net, and from the classification perspective, we also train the classifier on filtered images to increase the performance of recognition.

    Our experimental results showed good recognition of individuals when filters were not occluding important landmarks, especially around the eye region. Our proposed solution shows an ability to mitigate the occlusion done by filters through either reconstruction or training on manipulated images, in some cases, with an increase in the classifier’s accuracy of approximately 17% points with only reconstruction, 16% points when the classifier trained on filtered data, and  24% points when both were used at the same time. When training on filtered images, we observe an average increase in performance, across all datasets, of 9.7% points.

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  • 242.
    Stasiunas, Antanas
    et al.
    Kaunas University of Technology.
    Verikas, Antanas
    Högskolan i Halmstad, Akademin för informationsteknologi, Halmstad Embedded and Intelligent Systems Research (EIS).
    Bacauskiene, Marija
    Kaunas University of Technology.
    Miliauskas, As
    Kaunas University of Medicine.
    An adaptive functional model of the filtering system of the cochlea of the inner ear2011Ingår i: Proceedings of the 4th Imternational Symposium on Applied Sciences in Biomedical and Communication Technologies, ISABEL 2011, ACM Press, 2011, s. -6Konferensbidrag (Refereegranskat)
    Abstract [en]

    Outer hair cells (OHC) in the cochlea of the inner ear, together with the local structures of the basilar membrane, reticular lamina and tectorial membrane, constitute the adaptive primary filters (PF) of the second order. We used them for designing a serial-parallel signal filtering system. We determined a rational number of PF included in Gaussian channels of the system, summation weights of the output signals, and distribution of PF along the basilar membrane. A Gaussian channel consisting of five PF is presented as an example, and properties of the channel operating in the linear and non-linear mode are determined during adaptation and under efferent control. The results suggest that application of biological filtering principles can be useful for designing of cochlear implants with new strategies of speech encoding.

  • 243.
    Stasiunas, Antanas
    et al.
    Department of Applied Electronics, Kaunas University of Technology, Kaunas, Lithuania.
    Verikas, Antanas
    Högskolan i Halmstad, Akademin för informationsteknologi, Halmstad Embedded and Intelligent Systems Research (EIS), Intelligenta system (IS-lab). Department of Applied Electronics, Kaunas University of Technology, Kaunas, Lithuania.
    Kemesis, Povilas
    Department of Applied Electronics, Kaunas University of Technology, Kaunas, Lithuania.
    Bacauskiene, Marija
    Department of Applied Electronics, Kaunas University of Technology, Kaunas, Lithuania.
    Miliauskas, Rimvydas
    Department of Physiology, Kaunas University of Medicine, Kaunas, Lithuania.
    Stasiuniene, Natalija
    Department of Biochemistry, Kaunas University of Medicine, Kaunas, Lithuania.
    Malmqvist, Kerstin
    Högskolan i Halmstad, Akademin för informationsteknologi, Halmstad Embedded and Intelligent Systems Research (EIS), Intelligenta system (IS-lab).
    A multi-channel adaptive nonlinear filtering structure realizingsome properties of the hearing system2005Ingår i: Computers in Biology and Medicine, ISSN 0010-4825, E-ISSN 1879-0534, Vol. 35, nr 6, s. 495-510Artikel i tidskrift (Refereegranskat)
    Abstract [en]

    An adaptive nonlinear signal-filtering model of the cochlea is proposed based on the functional properties of the inner ear. The model consists of the cochlear filtering segments taking into account the longitudinal, transverse and radial pressure wave propagation. On the basis of an analytical description of different parts of the model and the results of computer modeling, the biological significance of the nonlinearity of signal transduction processes in the outer hair cells, their role in signal compression and adaptation, the efferent control over the characteristics of the filtering structures (frequency selectivity and sensitivity) are explained. © 2004 Elsevier Ltd. All rights reserved.

  • 244.
    Stozinic, Marko
    Högskolan i Halmstad, Akademin för informationsteknologi.
    Impedansanpassning vior: Analytisk studie av viors impedans, 0-10 GHz2020Självständigt arbete på grundnivå (yrkesexamen), 10 poäng / 15 hpStudentuppsats (Examensarbete)
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  • 245.
    Svanström, Fredrik
    et al.
    Högskolan i Halmstad, Akademin för informationsteknologi. Air Defence Regiment, Swedish Armed Forces, Halmstad, Sweden.
    Englund, Cristofer
    Högskolan i Halmstad, Akademin för informationsteknologi, Halmstad Embedded and Intelligent Systems Research (EIS), CAISR Centrum för tillämpade intelligenta system (IS-lab). RISE, Gothenburg, Sweden.
    Alonso-Fernandez, Fernando
    Högskolan i Halmstad, Akademin för informationsteknologi, Halmstad Embedded and Intelligent Systems Research (EIS), CAISR Centrum för tillämpade intelligenta system (IS-lab).
    Real-Time Drone Detection and Tracking With Visible, Thermal and Acoustic Sensors2021Ingår i: 2020 25th International Conference on Pattern Recognition (ICPR), IEEE, 2021, s. 7265-7272Konferensbidrag (Refereegranskat)
    Abstract [en]

    TThis paper explores the process of designing an automatic multi-sensor drone detection system. Besides the common video and audio sensors, the system also includes a thermal infrared camera, which is shown to be a feasible solution to the drone detection task. Even with slightly lower resolution, the performance is just as good as a camera in visible range. The detector performance as a function of the sensor-to-target distance is also investigated. In addition, using sensor fusion, the system is made more robust than the individual sensors, helping to reduce false detections. To counteract the lack of public datasets, a novel video dataset containing 650 annotated infrared and visible videos of drones, birds, airplanes and helicopters is also presented 1.1. https://github.com/DroneDetectionThesis/Drone-detection-dataset. The database is complemented with an audio dataset of the classes drones, helicopters and background noise. © 2020 IEEE

  • 246.
    Svensson, Joakim
    et al.
    Högskolan i Halmstad, Akademin för informationsteknologi.
    Yalda, Milad
    Högskolan i Halmstad, Akademin för informationsteknologi.
    Trådlös Övervakning av Inomhusklimat och PIR-baserad Passageräkning: En Demonstrationsanläggning åt Sweco Position AB2017Självständigt arbete på grundnivå (högskoleexamen), 10 poäng / 15 hpStudentuppsats (Examensarbete)
    Abstract [sv]

    Denna rapport är ett tekniskt komplement till Johanna Hernnäs och Linnea Martinssons rapport AerQ - Ett produktutvecklingsprojekt för att läsa av inomhusklimatet. I denna rapport beskrivs designen av en demonstrationsanläggning för inomhusklimat och passageräkning bestående av två trådlösa och batteridrivna enheter för inomhusklimat respektive passageräkning och en mobilapplikation till vilket datan presenteras. Klimatenheten mäter temperatur, relativ luftfuktighet och koldioxid samt kommunicerar via Wi-Fi och BLE och visuellt via RGB-LED:s. passageräknaren detekterar passager m h a en PIR-sensor och kommunicerar via BLE. I rapporten presenteras utförandet av demoanläggninen och en undersökning av PIR-sensorer. Resultatet visar att det är möjligt att detektera riktning med en PIR-sensor samt en lösning för långvarig batteridrift av en sensornod utrustad med ljusdiodslinga, Wi-Fi och VOC-sensor(MEMS metalloxidsensor). Systemets roll kan vara att återkoppla till behovsstyrd ventilation och/eller uppmärksamma användare om sitt inomhusklimat.

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  • 247.
    Svensson, Magnus
    et al.
    Volvo Technology.
    Forsberg, Magnus
    Högskolan i Halmstad, Akademin för informationsteknologi, Halmstad Embedded and Intelligent Systems Research (EIS).
    Byttner, Stefan
    Högskolan i Halmstad, Akademin för informationsteknologi, Halmstad Embedded and Intelligent Systems Research (EIS).
    Rögnvaldsson, Thorsteinn
    Högskolan i Halmstad, Akademin för informationsteknologi, Halmstad Embedded and Intelligent Systems Research (EIS).
    Deviation Detection by Self-Organized On-Line Models Simulated on a Feed-Back Controlled DC-Motor2009Ingår i: Proceeding Intelligent Systems and Control (ISC 2009) / [ed] M.H. Hamza, Cambridge, Mass.: ACTA Press, 2009Konferensbidrag (Refereegranskat)
    Abstract [en]

    A new approach to improve fault detection is proposed. The method takes benefit of using a population of systems to dynamically define a norm of how the system works. The norm is derived from self-organizing algorithms which generate a low dimensional representation of how selected feature data are correlated. The feature data is selected from the state variables and from the control signals. The self-organizing method and limited number of feature signals enable fast deviation detection and low computational footprint on each system to be analyzed. The comparison analysis between the systems is performed at a service centre, to where the low-dimensional representations of the systems are transmitted. The method is demonstrated on a simulated DC-motor and the results are promising for deviation detection.

  • 248.
    Svensson, Oskar
    et al.
    Högskolan i Halmstad, Akademin för informationsteknologi.
    Thelin, Simon
    Högskolan i Halmstad, Akademin för informationsteknologi.
    Byttner, Stefan
    Högskolan i Halmstad, Akademin för informationsteknologi, Halmstad Embedded and Intelligent Systems Research (EIS), CAISR Centrum för tillämpade intelligenta system (IS-lab).
    Fan, Yuantao
    Högskolan i Halmstad, Akademin för informationsteknologi, Halmstad Embedded and Intelligent Systems Research (EIS), CAISR Centrum för tillämpade intelligenta system (IS-lab).
    Indirect Tire Monitoring System - Machine Learning Approach2017Ingår i: IOP Conference Series: Materials Science and Engineering, Bristol: Institute of Physics Publishing (IOPP), 2017, Vol. 252, artikel-id 012018Konferensbidrag (Refereegranskat)
    Abstract [en]

    The heavy vehicle industry has today no requirement to provide a tire pressure monitoring system by law. This has created issues surrounding unknown tire pressure and thread depth during active service. There is also no standardization for these kind of systems which means that different manufacturers and third party solutions work after their own principles and it can be hard to know what works for a given vehicle type. The objective is to create an indirect tire monitoring system that can generalize a method that detect both incorrect tire pressure and thread depth for different type of vehicles within a fleet without the need for additional physical sensors or vehicle specific parameters. The existing sensors that are connected communicate through CAN and are interpreted by the Drivec Bridge hardware that exist in the fleet. By using supervised machine learning a classifier was created for each axle where the main focus was the front axle which had the most issues. The classifier will classify the vehicles tires condition and will be implemented in Drivecs cloud service where it will receive its data. The resulting classifier is a random forest implemented in Python. The result from the front axle with a data set consisting of 9767 samples of buses with correct tire condition and 1909 samples of buses with incorrect tire condition it has an accuracy of 90.54% (0.96%). The data sets are created from 34 unique measurements from buses between January and May 2017. This classifier has been exported and is used inside a Node.js module created for Drivecs cloud service which is the result of the whole implementation. The developed solution is called Indirect Tire Monitoring System (ITMS) and is seen as a process. This process will predict bad classes in the cloud which will lead to warnings. The warnings are defined as incidents. They contain only the information needed and the bandwidth of the incidents are also controlled so incidents are created within an acceptable range over a period of time. These incidents will be notified through the cloud for the operator to analyze for upcoming maintenance decisions. © 2017 Published under licence by IOP Publishing Ltd.

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  • 249.
    Svensson, Wolfgang
    et al.
    Högskolan i Halmstad, Akademin för informationsteknologi, Halmstad Embedded and Intelligent Systems Research (EIS), Intelligenta system (IS-lab).
    Holmberg, Ulf
    Högskolan i Halmstad, Akademin för informationsteknologi, Halmstad Embedded and Intelligent Systems Research (EIS), Intelligenta system (IS-lab).
    Estimating Ground Inclination Using Strain Sensors with Fourier Series Representation2010Ingår i: Journal of Robotics, ISSN 1687-9600, Vol. 2010, s. 1-8Artikel i tidskrift (Refereegranskat)
    Abstract [en]

    An embedded measurement system for foot orthosis during gait is proposed. Strain gauge sensors were mounted on a foot orthosis to give information about strain in the sagittal plane. The ankle angle of the orthosis was fixed and strain characteristics were therefore changed when walking on slopes. With a Fourier series representation of the strain during a gait cycle, ground angle at different walking speeds and inclinations could be estimated with similar accuracy as previous studies using kinematically based estimators. Furthermore, if the angle of the mechanical foot ankle was changed, the sensing technique still could estimate ground angle without need for recalibration as opposed to kinematical sensors. This indicates that embedded strain sensors can be used for online control of future orthoses with inclination adaptation. Also, there would be no need to recalibrate the sensing system when changing shoes with different heel heights.

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  • 250.
    Tackx, Esmee
    Högskolan i Halmstad, Akademin för informationsteknologi.
    Development of a real-time navigation and collisionavoidance system for an autonomous naval rover: A Contribution to the Environmental Initiative for Flexible andReal-time Water Monitoring of the CatFish team2023Självständigt arbete på avancerad nivå (magisterexamen), 20 poäng / 30 hpStudentuppsats (Examensarbete)
    Abstract [en]

    This thesis explores the design and optimisation of a sensor system for real-time position determination of the Fish component in the CatFish naval drone and the development of a sonar sensor system for accurate object localisation and safe navigation in dynamic and uncertain underwater conditions. Through comprehensive experimentation and analysis, this thesis demonstrates the effectiveness of the proposed sensor system and sonar sensing system in enhancing underwater navigation and obstacle avoidance capabilities. 

    Additionally, research will compare two sonar systems: one using more expensive MaxBotix sonar sensors that are plug-and-play and don't need a lot of extra interfacing and data analysis. The other system utilises the cheaper CUSA ultrasonic transducers which needs a surrounding circuit and extra data analysis to work. The findings help researchers select the right system and improve sonar technology by identifying limitations and proposing enhancements to the systems. The research offers insights into building a self-built beamforming sonar array and addresses the deficiencies of commonly used ultrasonic systems. It provides valuable guidance for engineers and contributes to the advancement of ultrasonic technologies. The study also offers a unique comparison between user-friendly systems and simple transducers, aiding engineers in choosing the appropriate system for their needs. Overall, it enriches the field's knowledge and guides future developments in sonar systems.

    This thesis contributes to the advancement of underwater navigation in autonomous naval vehicles, providing valuable insights and recommendations for the design and optimisation of sensor systems and sonar sensing systems. The findings have implications for improving the navigational capabilities of the CatFish drone and contribute to the scientific understanding of sonar system performance under diverse conditions.

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