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  • 151.
    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)2010In: IEEE Transaction on Pattern Analysis and Machine Intelligence, ISSN 0162-8828, E-ISSN 1939-3539, Vol. 32, no 6, p. 1097-1111Article in journal (Refereed)
    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.

  • 152.
    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 signals2018Conference paper (Refereed)
  • 153.
    Pirasteh, Parivash
    et al.
    Halmstad University, School of Information Technology, Halmstad Embedded and Intelligent Systems Research (EIS), CAISR - Center for Applied Intelligent Systems Research.
    Nowaczyk, Sławomir
    Halmstad University, School of Information Technology, Halmstad Embedded and Intelligent Systems Research (EIS), CAISR - Center for Applied Intelligent Systems Research.
    Pashami, Sepideh
    Halmstad University, School of Information Technology, Halmstad Embedded and Intelligent Systems Research (EIS), CAISR - Center for Applied Intelligent Systems Research.
    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
    Halmstad University, School of Information Technology, Halmstad Embedded and Intelligent Systems Research (EIS), CAISR - Center for Applied Intelligent Systems Research.
    Interactive feature extraction for diagnostic trouble codes in predictive maintenance: A case study from automotive domain2019In: Proceedings of the Workshop on Interactive Data Mining, New York, NY: Association for Computing Machinery (ACM), 2019, article id 4Conference paper (Refereed)
    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.

  • 154.
    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 Algorithms2009In: IEEE Transactions on Information Forensics and Security, ISSN 1556-6013, E-ISSN 1556-6021, Vol. 4, no 4, p. 849-866Article in journal (Refereed)
    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.

  • 155.
    Prytz, Rune
    Halmstad University, School of Information Technology, Halmstad Embedded and Intelligent Systems Research (EIS), CAISR - Center for Applied Intelligent Systems Research. Volvo Group Trucks Technology, Malmö, Sweden.
    Machine learning methods for vehicle predictive maintenance using off-board and on-board data2014Licentiate thesis, comprehensive summary (Other academic)
    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.

  • 156.
    Ramos, D.
    et al.
    Universidad Autonoma de Madrid, Spain.
    Alonso-Fernandez, Fernando
    Universidad Autonoma de Madrid, Spain.
    A Practical Electronic Instrumentation Course for Engineering Students2010In: 2010 IEEE Education Engineering Conference, EDUCON 2010, Piscataway, N.J.: IEEE Press, 2010, p. 1181-1188Conference paper (Refereed)
    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.

  • 157.
    Ranftl, Andreas
    et al.
    Halmstad University, School of Information Technology.
    Alonso-Fernandez, Fernando
    Halmstad University, School of Information Technology, Halmstad Embedded and Intelligent Systems Research (EIS), CAISR - Center for Applied Intelligent Systems Research.
    Karlsson, Stefan
    Halmstad University, School of Information Technology, Halmstad Embedded and Intelligent Systems Research (EIS), CAISR - Center for Applied Intelligent Systems Research.
    Face Tracking Using Optical Flow: Development of a Real-Time AdaBoost Cascade Face Tracker2015Conference paper (Refereed)
    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.

  • 158.
    Ranftl, Andreas
    et al.
    Halmstad University, School of Information Technology.
    Alonso-Fernandez, Fernando
    Halmstad University, School of Information Technology, Halmstad Embedded and Intelligent Systems Research (EIS), CAISR - Center for Applied Intelligent Systems Research.
    Karlsson, Stefan
    Halmstad University, School of Information Technology, Halmstad Embedded and Intelligent Systems Research (EIS), CAISR - Center for Applied Intelligent Systems Research.
    Bigun, Josef
    Halmstad University, School of Information Technology, Halmstad Embedded and Intelligent Systems Research (EIS), CAISR - Center for Applied Intelligent Systems Research.
    A Real-Time AdaBoost Cascade Face Tracker Based on Likelihood Map and Optical Flow2017In: IET Biometrics, ISSN 2047-4938, E-ISSN 2047-4946, Vol. 6, no 6, p. 468-477Article in journal (Refereed)
    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.

  • 159.
    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
    Halmstad University, School of Information Technology, Halmstad Embedded and Intelligent Systems Research (EIS), CAISR - Center for Applied Intelligent Systems Research.
    Iris Super-Resolution using CNNs: is Photo-Realism Important to Iris Recognition?2019In: IET Biometrics, ISSN 2047-4938, E-ISSN 2047-4946, Vol. 8, no 1, p. 69-78Article in journal (Refereed)
    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

  • 160.
    Ribeiro, Eduardo
    et al.
    Federal University of Tocantins, Palmas, Brazil.
    Uhl, Andreas
    Department of Computer Sciences at Salzburg University, Salzburg, Austria.
    Alonso-Fernandez, Fernando
    Halmstad University, School of Information Technology, Halmstad Embedded and Intelligent Systems Research (EIS), CAISR - Center for Applied Intelligent Systems Research.
    Super-Resolution and Image Re-Projection for Iris Recognition2019In: 2019 IEEE 5th International Conference on Identity, Security, and Behavior Analysis (ISBA), 2019, p. 1-7Conference paper (Refereed)
    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.

  • 161.
    Ribeiro, Eduardo
    et al.
    University of Salzburg, Salzburg, Austria.
    Uhl, Andreas
    University of Salzburg, Salzburg, Austria.
    Alonso-Fernandez, Fernando
    Halmstad University, School of Information Technology, Halmstad Embedded and Intelligent Systems Research (EIS), CAISR - Center for Applied Intelligent Systems Research.
    Farrugia, Reuben A.
    University of Malta, Msida, Malta.
    Exploring Deep Learning Image Super-Resolution for Iris Recognition2017In: 2017 25th European Signal Processing Conference (EUSIPCO 2017), 2017, p. 2240-2244Conference paper (Refereed)
    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

  • 162.
    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 verification2008In: Biometrics and Identity Management / [ed] Ben Schouten, Berlin: Springer Berlin/Heidelberg, 2008, Vol. Springer LNCS-5372, p. 181-190Conference paper (Refereed)
    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.

  • 163.
    Rögnvaldsson, Thorsteinn
    Halmstad University, School of Information Technology, Halmstad Embedded and Intelligent Systems Research (EIS).
    A Simple trick for estimating the weight decay parameter2012In: Lecture Notes in Computer Science, ISSN 0302-9743, E-ISSN 1611-3349, Vol. 7700 LECTURE NO, p. 69-89Article in journal (Refereed)
    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.

  • 164.
    Rögnvaldsson, Thorsteinn
    et al.
    Halmstad University, School of Information Technology, Halmstad Embedded and Intelligent Systems Research (EIS), CAISR - Center for Applied Intelligent Systems Research.
    Byttner, Stefan
    Halmstad University, School of Information Technology, Halmstad Embedded and Intelligent Systems Research (EIS), CAISR - Center for Applied Intelligent Systems Research.
    Prytz, Rune
    Volvo Group Trucks Technology, Göteborg, Sweden.
    Nowaczyk, Sławomir
    Halmstad University, School of Information Technology, Halmstad Embedded and Intelligent Systems Research (EIS), CAISR - Center for Applied Intelligent Systems Research.
    Svensson, Magnus
    Volvo Group Trucks Technology, Göteborg, Sweden.
    Wisdom of Crowds for Intelligent Monitoring of Vehicle FleetsManuscript (preprint) (Other academic)
    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.

  • 165.
    Sant'Anna, Anita
    Halmstad University, School of Information Technology, Halmstad Embedded and Intelligent Systems Research (EIS), Intelligent systems (IS-lab).
    Activity monitoring as a tool for person-centered care: preliminary report2014In: 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, p. 48-51Conference paper (Refereed)
    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.

  • 166.
    Sant'Anna, Anita
    et al.
    Halmstad University, School of Information Science, Computer and Electrical Engineering (IDE), Halmstad Embedded and Intelligent Systems Research (EIS), CAISR - Center for Applied Intelligent Systems Research.
    Wickström, Nicholas
    Symbolic Approach to Motion Analysis: Framework and Gait Analysis Case Studies2013In: Telehealthcare Computing and Engineering: Principles and Design / [ed] Fei Hu, Boca Raton: CRC Press, 2013, 1, p. 561-606Chapter in book (Other academic)
  • 167.
    Sant'Anna, Anita
    et al.
    Halmstad University, School of Information Science, Computer and Electrical Engineering (IDE), Halmstad Embedded and Intelligent Systems Research (EIS), Intelligent Systems´ laboratory.
    Wickström, Nicholas
    Halmstad University, School of Information Science, Computer and Electrical Engineering (IDE), Halmstad Embedded and Intelligent Systems Research (EIS), Intelligent Systems´ laboratory.
    Symbolization of time series: an evaluation of SAX, persist, and ACA2011In: 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, p. 2223-2228Conference paper (Refereed)
    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.

  • 168.
    Sant'Anna, Anita
    et al.
    Halmstad University, School of Information Science, Computer and Electrical Engineering (IDE), Halmstad Embedded and Intelligent Systems Research (EIS), CAISR - Center for Applied Intelligent Systems Research.
    Wickström, Nicholas
    Halmstad University, School of Information Science, Computer and Electrical Engineering (IDE), Halmstad Embedded and Intelligent Systems Research (EIS), CAISR - Center for Applied Intelligent Systems Research.
    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 Evaluation2013In: 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, p. 239-254Chapter in book (Refereed)
    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

  • 169.
    Savas, Süleyman
    et al.
    Halmstad University, School of Information Technology, Halmstad Embedded and Intelligent Systems Research (EIS), Centre for Research on Embedded Systems (CERES).
    Ul-Abdin, Zain
    Halmstad University, School of Information Technology, Halmstad Embedded and Intelligent Systems Research (EIS), Centre for Research on Embedded Systems (CERES).
    Nordström, Tomas
    Halmstad University, School of Information Technology, Halmstad Embedded and Intelligent Systems Research (EIS), Centre for Research on Embedded Systems (CERES).
    A Framework to Generate Domain-Specific Manycore Architectures from Dataflow Programs2019In: Microprocessors and microsystems, ISSN 0141-9331, E-ISSN 1872-9436Article in journal (Refereed)
    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.

  • 170.
    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
    Halmstad University, School of Information Technology, Halmstad Embedded and Intelligent Systems Research (EIS), CAISR - Center for Applied Intelligent Systems Research.
    Bigun, Josef
    Halmstad University, School of Information Technology, Halmstad Embedded and Intelligent Systems Research (EIS), CAISR - Center for Applied Intelligent Systems Research.
    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 Competition2017Conference paper (Refereed)
    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

  • 171.
    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
    Halmstad University, School of Information Technology, Halmstad Embedded and Intelligent Systems Research (EIS), CAISR - Center for Applied Intelligent Systems Research.
    Bigun, Josef
    Halmstad University, School of Information Technology, Halmstad Embedded and Intelligent Systems Research (EIS), CAISR - Center for Applied Intelligent Systems Research.
    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 Competition2016In: 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, 2016Conference paper (Refereed)
    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.

  • 172.
    Svensson, Joakim
    et al.
    Halmstad University, School of Information Technology.
    Yalda, Milad
    Halmstad University, School of Information Technology.
    Trådlös Övervakning av Inomhusklimat och PIR-baserad Passageräkning: En Demonstrationsanläggning åt Sweco Position AB2017Independent thesis Basic level (university diploma), 10 credits / 15 HE creditsStudent thesis
    Abstract [en]

    This report is a technical complement to Johanna Hernnäs and Linnea Martinssons bachelor work paper AerQ - Ett produktutvecklingsprojekt för att läsa av inomhusklimatet. This report describes the design of an indoor climate and people counting demo facility consisting of two wireless and battery-powered units for indoor climate and people counting and a mobile application to which the data is presented. The climate unit measures temperature, relative humidity and carbon dioxide, and communicates via Wi-Fi and BLE and visually via RGB LEDs. The people counter detects a passage with a PIR sensor and communicates via BLE. The report presents the implementation of the demo facility and a investigation of PIR sensors. The result shows that it is possible to detect direction with a PIR sensor as well as a long-term battery operation of a sensor node equipped with RGB LEDs, Wi-Fi and VOC Sensor (MEMS Metal Oxide Sensor). The system's role may be to feedback demand-driven ventilation and / or to alert users about their indoor climate.

  • 173.
    Svensson, Oskar
    et al.
    Halmstad University, School of Information Technology.
    Thelin, Simon
    Halmstad University, School of Information Technology.
    Byttner, Stefan
    Halmstad University, School of Information Technology, Halmstad Embedded and Intelligent Systems Research (EIS), CAISR - Center for Applied Intelligent Systems Research.
    Fan, Yuantao
    Halmstad University, School of Information Technology, Halmstad Embedded and Intelligent Systems Research (EIS), CAISR - Center for Applied Intelligent Systems Research.
    Indirect Tire Monitoring System - Machine Learning Approach2017In: IOP Conference Series: Materials Science and Engineering, Bristol: Institute of Physics Publishing (IOPP), 2017, Vol. 252, article id 012018Conference paper (Refereed)
    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.

  • 174.
    Svensson, Wolfgang
    et al.
    Halmstad University, School of Information Science, Computer and Electrical Engineering (IDE), Halmstad Embedded and Intelligent Systems Research (EIS), Intelligent systems (IS-lab).
    Holmberg, Ulf
    Halmstad University, School of Information Science, Computer and Electrical Engineering (IDE), Halmstad Embedded and Intelligent Systems Research (EIS), Intelligent systems (IS-lab).
    Estimating Ground Inclination Using Strain Sensors with Fourier Series Representation2010In: Journal of Robotics, ISSN 1687-9600, Vol. 2010, p. 1-8Article in journal (Refereed)
    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.

  • 175.
    Taheri, Tayebeh
    et al.
    Halmstad University, School of Information Science, Computer and Electrical Engineering (IDE).
    Sant'Anna, Anita
    Halmstad University, School of Information Technology, Halmstad Embedded and Intelligent Systems Research (EIS), Intelligent systems (IS-lab).
    Non-Invasive Breathing Rate Detection Using a Very Low Power Ultra-wide-band Radar2014In: 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, p. 78-83Conference paper (Refereed)
    Abstract [en]

    In this paper we present a novel method for remote breathing detection based on ultra-wide-band (UWB) radar. This is a method that does not require any wearable sensors, making it more comfortable and convenient for users. Furthermore, because of the wall penetrating characteristics of the transmitted signal, our system is useful in emergency situations such as monitoring people who may be trapped under earthquake rubble. For our investigation we used a Novelda UWB radar that provides high processing speed and low power consumption. In this paper we present two new convolution-based methods to extract breathing rate information from the received radar signal. This method was tested on several people who were monitored while laying down on a bed. The subject's position and breathing rate were calculated. Experimental results including 20 different subjects are provided, showing that this is a viable method for monitoring breathing rate using a low-power UWB radar.

  • 176.
    Tome, P.
    et al.
    Universidad Autonoma de Madrid, Spain.
    Alonso-Fernandez, Fernando
    Escuela Politecnica Superior, Univ. Autonoma de Madrid, Spain.
    Fierrez, J.
    Universidad Autonoma de Madrid, Spain.
    Ortega-Garcia, J.
    Universidad Autonoma de Madrid, Spain.
    On the effects of time variability in iris recognition2008In: BTAS 2008. 2nd IEEE International Conference on Biometrics: Theory, Applications and Systems, 2008, Piscataway, N.J.: IEEE Press, 2008, p. 411-416Conference paper (Refereed)
    Abstract [en]

    In this paper, we evaluate the effects of time separation between acquisitions in iris recognition. We use for our experiments a publicly available iris recognition system and the BiosecurlD database, containing 8128 iris images of 254 individuals acquired in four acquisition sessions, separated by one to four weeks between consecutive sessions. Reported results show that time separation between iris samples under comparison has severe impact on the recognition rates. An important degradation on the False Rejection Rate is observed, meaning that the intra-class variability is increased. All images in our database have been acquired under similar controlled conditions and with the same sensor. © 2008 IEEE.

  • 177.
    Tome, P.
    et al.
    Universidad Autonoma de Madrid, Spain.
    Fierrez, J.
    Universidad Autonoma de Madrid, Spain.
    Alonso-Fernandez, Fernando
    Escuela Politecnica Superior, Univ. Autonoma de Madrid, Spain.
    Ortega-Garcia, J.
    Universidad Autonoma de Madrid, Spain.
    Scenario-based score fusion for face recognition at a distance2010In: 2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Workshops, CVPRW 2010, Piscataway, N.J.: IEEE Press, 2010, p. 67-73Conference paper (Refereed)
    Abstract [en]

    The effect of different acquisition distances on the performance of face verification is studied. In particular, we evaluate two standard approaches using popular features (DCT and PCA) and matchers (GMM and SVM) under variation in the acquisition distance, as well as their score-level combination. The DCT-GMM-based system is found to be more robust to acquisition distance degradation than the PCASVM-based system. We exploit this fact by introducing an adaptive score fusion scheme based on a novel automatic scenario estimation which is shown to improve our system in uncontrolled environments. © 2010 IEEE.

  • 178.
    Vachkov, Gancho
    et al.
    Yamaguchi University, Yamaguchi, Japan.
    Byttner, Stefan
    Halmstad University, School of Information Technology, Halmstad Embedded and Intelligent Systems Research (EIS), Intelligent systems (IS-lab).
    Svensson, Magnus
    Volvo Group Trucks Technology, Göteborg, Sweden.
    Battery Aging Detection Based on Sequential Clustering and Similarity Analysis2012In: IS'2012: 2012 6th IEEE International Conference Intelligent Systems, Proceedings, Piscataway, N.J.: IEEE Press, 2012, p. 42-47, article id 6335112Conference paper (Refereed)
    Abstract [en]

    The battery cells are an important part of electric and hybrid vehicles and their deterioration due to aging directly affects the life cycle and performance of the whole battery system. Therefore an early aging detection of the battery cell is an important task and its correct solution could significantly improve the whole vehicle performance. This paper presents a computational strategy for battery aging detection, based on available data chunks from real operation of the vehicle. The first step is to aggregate (reduce) the original large amount of data by much smaller number of cluster centers. This is done by a newly proposed sequential clustering algorithm that arranges the clusters in decreasing order of their volumes. The next step is the proposed fuzzy inference procedure for weighed approximation of the cluster centers that creates comparable one dimensional fuzzy model for each available data set. Finally, the detection of the aged battery is treated as a similarity analysis problem, in which the pair distances between all battery cells are estimated by analyzing the predicted values from the respective fuzzy models. All these three steps of the computational procedure are explained in the paper and applied to real experimental data for battery aging detection. The results are positive and suggestions for further improvements are made in the conclusions. © 2012 IEEE.

  • 179.
    Vachkov, Gancho
    et al.
    School of Engineering and Physics, Faculty of Science, Technology and Environment, The University of the South Pacific, Laucala Bay, Suva, Fiji.
    Byttner, Stefan
    Halmstad University, School of Information Technology, Halmstad Embedded and Intelligent Systems Research (EIS), Intelligent Systems´ laboratory.
    Svensson, Magnus
    Volvo Technology, Göteborg, Sweden.
    Detection of Deviation in Performance of Battery Cells by Data Compression and Similarity Analysis2014In: International Journal of Intelligent Systems, ISSN 0884-8173, E-ISSN 1098-111X, Vol. 29, no 3, p. 207-222Article in journal (Refereed)
    Abstract [en]

    The battery cells are an important part of electric and hybrid vehicles, and their deterioration due to aging or malfunction directly affects the life cycle and performance of the whole battery system. Therefore, an early detection of deviation in performance of the battery cells is an important task and its correct solution could significantly improve the whole vehicle performance. This paper presents a computational strategy for the detection of deviation of battery cells, due to aging or malfunction. The detection is based on periodically processing a predetermined number of data collected in data blocks that are obtained during the real operation of the vehicle. The first step is data compression, when the original large amount of data is reduced to smaller number of cluster centers. This is done by a newly proposed sequential clustering algorithm that arranges the clusters in decreasing order of their volumes. The next step is using a fuzzy inference procedure for weighted approximation of the cluster centers to create one-dimensional models for each battery cell that represents the voltage–current relationship. This creates an equal basis for the further comparison of the battery cells. Finally, the detection of the deviated battery cells is treated as a similarity-analysis problem, in which the pair distances between all battery cells are estimated by analyzing the estimations for voltage from the respective fuzzy models. All these three steps of the computational procedure are explained in the paper and applied to real experimental data for the detection of deviation of five battery cells. Discussions and suggestions are made for a practical application aimed at designing a monitoring system for the detection of deviations. © 2013 Wiley Periodicals, Inc.

  • 180.
    Vaiciukynas, Evaldas
    et al.
    Kaunas University of Technology, Kaunas, Lithuania.
    Gelzinis, Adas
    Kaunas University of Technology, Kaunas, Lithuania.
    Verikas, Antanas
    Halmstad University, School of Information Technology, Halmstad Embedded and Intelligent Systems Research (EIS), CAISR - Center for Applied Intelligent Systems Research. Kaunas University of Technology, Kaunas, Lithuania.
    Bacauskiene, Marija
    Kaunas University of Technology, Kaunas, Lithuania.
    Parkinson’s Disease Detection from Speech Using Convolutional Neural Networks2018In: Smart objects and technologies for social good: Third International Conference, GOODTECHS 2017, Pisa, Italy, November 29-30, 2017, Proceedings / [ed] Guidi, B., Ricci, L., Calafate, C., Gaggi, O., Marquez-Barja, J., Cham: Springer, 2018, Vol. 233, p. 206-215Conference paper (Refereed)
    Abstract [en]

    Application of deep learning tends to outperform hand-crafted features in many domains. This study uses convolutional neural networks to explore effectiveness of various segments of a speech signal,? – text-dependent pronunciation of a short sentence, – in Parkinson’s disease detection task. Besides the common Mel-frequency spectrogram and its first and second derivatives, inclusion of various other input feature maps is also considered. Image interpolation is investigated as a solution to obtain a spectrogram of fixed length. The equal error rate (EER) for sentence segments varied from 20.3% to 29.5%. Fusion of decisions from sentence segments achieved EER of 14.1%, whereas the best result when using the full sentence exhibited EER of 16.8%. Therefore, splitting speech into segments could be recommended for Parkinson’s disease detection. © ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering 2018.

  • 181.
    Vaiciukynas, Evaldas
    et al.
    Kaunas University of Technology, Kaunas, Lithuania.
    Verikas, Antanas
    Halmstad University, School of Information Technology, Halmstad Embedded and Intelligent Systems Research (EIS), CAISR - Center for Applied Intelligent Systems Research. Kaunas University of Technology, Kaunas, Lithuania.
    Gelzinis, Adas
    Kaunas University of Technology, Kaunas, Lithuania.
    Bacauskiene, Marija
    Kaunas University of Technology, Kaunas, Lithuania.
    Detecting Parkinson's disease from sustained phonation and speech signals2017In: PLoS ONE, ISSN 1932-6203, E-ISSN 1932-6203, Vol. 12, no 10, article id e0185613Article in journal (Refereed)
    Abstract [en]

    This study investigates signals from sustained phonation and text-dependent speech modalities for Parkinson’s disease screening. Phonation corresponds to the vowel /a/ voicing task and speech to the pronunciation of a short sentence in Lithuanian language. Signals were recorded through two channels simultaneously, namely, acoustic cardioid (AC) and smart phone (SP) microphones. Additional modalities were obtained by splitting speech recording into voiced and unvoiced parts. Information in each modality is summarized by 18 well-known audio feature sets. Random forest (RF) is used as a machine learning algorithm, both for individual feature sets and for decision-level fusion. Detection performance is measured by the out-of-bag equal error rate (EER) and the cost of log-likelihood-ratio. Essentia audio feature set was the best using the AC speech modality and YAAFE audio feature set was the best using the SP unvoiced modality, achieving EER of 20.30% and 25.57%, respectively. Fusion of all feature sets and modalities resulted in EER of 19.27% for the AC and 23.00% for the SP channel. Non-linear projection of a RF-based proximity matrix into the 2D space enriched medical decision support by visualization. © 2017 Vaiciukynas et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

  • 182.
    Vaiciukynas, Evaldas
    et al.
    Kaunas University of Technology, Kaunas, Lithuania.
    Verikas, Antanas
    Halmstad University, School of Information Science, Computer and Electrical Engineering (IDE), Halmstad Embedded and Intelligent Systems Research (EIS), CAISR - Center for Applied Intelligent Systems Research. Kaunas University of Technology, Kaunas, Lithuania.
    Gelzinis, Adas
    Kaunas University of Technology, Kaunas, Lithuania.
    Bacauskiene, Marija
    Kaunas University of Technology, Kaunas, Lithuania.
    Sulcius, Sigitas
    Klaipeda University, Klaipeda, Lithuania.
    Paskauskas, Ricardas
    Klaipeda University, Klaipeda, Lithuania.
    Olenina, Irina
    Klaipeda University, Klaipeda, Lithuania.
    Prototype-Based Contour Detection Applied to Segmentation of Phytoplankton Images2013In: AWERProcedia Information Technology and Computer Science: 3rd World Conference on Information Technology (WCIT-2012) / [ed] Hafize Keser and Meltem Hakiz, 2013, p. 1285-1292Conference paper (Refereed)
    Abstract [en]

    Novel prototype-based framework for image segmentation is introduced and successfully applied for cell segmentation in microscopy imagery. This study is concerned with precise contour detection for objects representing the Prorocentrum minimum species in phytoplankton images. The framework requires a single object with the ground truth contour as a prototype to perform detection of the contour for the remaining objects. The level set method is chosen as a segmentation algorithm and its parameters are tuned by differential evolution. The fitness function is based on the distance between pixels near contour in the prototype image and pixels near detected contour in the target image. Pixels “of interest correspond to several concentric bands of various width in outer and inner areas, relative to the contour. Usefulness of the introduced approach was demonstrated by comparing it to the basic level set and advanced Weka segmentation techniques. Solving the parameter selection problem of the level set algorithm considerably improved segmentation accuracy.

  • 183.
    Varytimidis, Dimitrios
    et al.
    Halmstad University, School of Information Technology.
    Alonso-Fernandez, Fernando
    Halmstad University, School of Information Technology, Halmstad Embedded and Intelligent Systems Research (EIS), CAISR - Center for Applied Intelligent Systems Research.
    Englund, Cristofer
    RISE Viktoria, Gothenburg, Sweden.
    Duran, Boris
    RISE Viktoria, Gothenburg, Sweden.
    Action and intention recognition of pedestrians in urban traffic2018In: 2018 14th International Conference on Signal-Image Technology & Internet-Based Systems (SITIS) / [ed] Gabriella Sanniti di Baja, Luigi Gallo, Kokou Yetongnon, Albert Dipanda, Modesto Castrillón-Santana & Richard Chbeir, Piscataway, N.J.: IEEE, 2018, p. 676-682Conference paper (Refereed)
    Abstract [en]

    Action and intention recognition of pedestrians in urban settings are challenging problems for Advanced Driver Assistance Systems as well as future autonomous vehicles to maintain smooth and safe traffic. This work investigates a number of feature extraction methods in combination with several machine learning algorithms to build knowledge on how to automatically detect the action and intention of pedestrians in urban traffic. We focus on the motion and head orientation to predict whether the pedestrian is about to cross the street or not. The work is based on the Joint Attention for Autonomous Driving (JAAD) dataset, which contains 346 videoclips of various traffic scenarios captured with cameras mounted in the windshield of a car. An accuracy of 72% for head orientation estimation and 85% for motion detection is obtained in our experiments.

  • 184.
    Vinel, Alexey
    et al.
    Tampere University of Technology, Department of Communications Engineering.
    Belyaev, Evgeny
    Tampere University of Technology, Department of Signal Processing.
    Egiazarian, Karen
    Tampere University of Technology, Department of Signal Processing.
    Koucheryavy, Yevgeni
    Tampere University of Technology, Department of Communications Engineering.
    An Overtaking Assistance System Based on Joint Beaconing and Real-Time Video Transmission2012In: IEEE Transactions on Vehicular Technology, ISSN 0018-9545, E-ISSN 1939-9359, Vol. 61, no 5, p. 2319-2329Article in journal (Refereed)
    Abstract [en]

    Overtaking on rural roads often becomes dangerous when oncoming traffic is detected by the driver too late or its speed is underestimated. Recently proposed cooperative overtaking assistance systems, which are based on Vehicular Ad hoc NETworks (VANETs), rely on either real-time video transmission or the exchange of status messages (beacons). In the first case, a video stream captured by a camera installed at the windshield of a vehicle is compressed and broadcast to any vehicles driving behind it, where it is displayed to the driver. In the second case, beacons that include position, speed, and direction are frequently broadcast by all the vehicles to ensure detection of oncoming traffic as early as possible and to issue a warning to the driver whenever needed. In this paper, we demonstrate that the performance of a video-based overtaking assistant can be significantly improved if codec channel adaptation is undertaken by exploiting information from the beacons about any forthcoming increase in the load of the multiple access channel used. The theoretical framework presented describes the basic patterns of such a coupled overtaking assistant and can serve as a useful guideline for the future practical implementation of the system. The benefits of our approach are demonstrated in relation to the practical scenario of H.264/AVC video coding and IEEE 802.11p/Wireless Access in Vehicular Environments (WAVE) intervehicle communication standards. © 2012 IEEE.

  • 185.
    Vinel, Alexey
    et al.
    Halmstad University, School of Information Technology, Halmstad Embedded and Intelligent Systems Research (EIS), Centre for Research on Embedded Systems (CERES). Tampere University of Technology, Tampere, Finland.
    Belyaev, Evgeny
    Tampere University of Technology, Tampere, Finland.
    Lamotte, Olivier
    Hochschule für Technik Rapperswil, Rapperswil, Switzerland.
    Gabbouj, Moncef
    Tampere University of Technology, Tampere, Finland.
    Koucheryavy, Yevgeni
    Tampere University of Technology, Tampere, Finland.
    Egiazarian, Karen
    Tampere University of Technology, Tampere, Finland.
    Video transmission over IEEE 802.11p: Real-world measurements2013In: 2013 IEEE International Conference on Communications Workshops (ICC), Piscataway, NJ: IEEE Press, 2013, p. 505-509, article id 6649286Conference paper (Refereed)
    Abstract [en]

    IEEE 802.11p/ITG-G5 vehicle-to-vehicle communication technology, which enables the new class of safety and infotainment applications, is currently an emerging research topic in both industry and academia. The proposed spectrum allocation of 10 Mhz channels for DSRC (Dedicated Short Range Communication) in 5.9 GHz band for the USA and Europe allows considering the transmission of video information between vehicles as one of the grounding blocks for future automotive applications. Although several published works addressed the problems of video content delivery in VANETs (Vehicular Ad-hoc NETworks), no work has been reported on real-world measurements of visual quality for video being transmitted over the IEEE 802.11p vehicle-to-vehicle communication channel. This paper presents a real-time scalable video codec as well as the first results of visual quality measurements for the video information transmitted using the off-the-shelf Componentality FlexRoad DSRC equipment. © 2013 IEEE.

  • 186.
    Viteckova, Slavka
    et al.
    Faculty of Biomedical Engineering, Czech Technical University in Prague, Prague, Czech Republic.
    Khandelwal, Siddhartha
    Halmstad University, School of Information Technology, Halmstad Embedded and Intelligent Systems Research (EIS), CAISR - Center for Applied Intelligent Systems Research.
    Kutilek, Patrik
    Faculty of Biomedical Engineering, Czech Technical University in Prague, Prague, Czech Republic.
    Krupicka, Radim
    Faculty of Biomedical Engineering, Czech Technical University in Prague, Prague, Czech Republic.
    Szabo, Zoltan
    Faculty of Biomedical Engineering, Czech Technical University in Prague, Prague, Czech Republic.
    Gait symmetry methods: Comparison of waveform-based Methods and recommendation for use2020In: Biomedical Signal Processing and Control, ISSN 1746-8094, E-ISSN 1746-8108, Vol. 55, article id 101643Article in journal (Refereed)
    Abstract [en]

    Gait symmetry has been shown to be a relevant measure for differentiating between normal and pathological gait. Although a number of symmetry methods exist, it is not clear which of these methods should be used as they have been developed using data collected from varying experimental protocols. This paper presents a comparison of state-of-the-art waveform-based symmetry methods and tests them on walking data collected from different environments. Acceleration signals collected from the ankle are used to analyse symmetry methods under different signal circumstances, such as phase shift, waveform shape difference, signal length (i.e. number of gait cycles) and gait initiation phase. The cyclogram based method is invariant to signal phase shifts, signal length and the gait initiation phase. The trend symmetry method is not affected by signal scaling and the gait initiation phase but is affected by signal length depending on the environment. Similar to the trend method, the cross-correlation symmetry method is not responsive to signal scaling and the gait initiation phase. The results of the symbolic method are not influenced by signal scaling, gait initiation and depending on the environment by the signal phase shift. From the results of the performed analysis, we recommend the trend method to gait symmetry assessment. The comparison of waveform-based symmetry methods brings new knowledge that will help in selecting an appropriate method for gait symmetry assessment under different experimental protocols. © 2019 Elsevier Ltd. All rights reserved.

  • 187.
    Werner, Per
    Halmstad University, School of Information Science, Computer and Electrical Engineering (IDE).
    Radarmodul till cykel för upptäckt av bakomvarande trafik2014Independent thesis Basic level (university diploma), 10 credits / 15 HE creditsStudent thesis
    Abstract [en]

    The aim of this project has been to examine the possibility to implement a doppler radar for use by cyclists in traffic with the radar component TRX_024_06 from Silicon Radar. The main idea is that the radar component should be part of a warning system that makes the user aware of traffic in the direction behind the bicykle.

    A doppler radar has been implemented with TRX_024_06, an USB-connected A/D-converter (NI USB-6216), active filters, amplifiers and a laptop with a student version of LabView installed.

    The experiments performed shows the properties of TRX_024_06 when placed indoors and in traffic. The signal processing is done by a program made in LabView.

    The parameters of interest has been the speed and direction of the veichles located behind the bicykle.

    An attempt to design printed Yagi-Uda antennas for transmission and reception of radiowaves has also been made. These have been printed on a circuit board and tested with a network analyser and with the radar component.

  • 188.
    Zhang, Man
    et al.
    Institute of Automation Chinese Academy of Sciences, China.
    Liu, Jing
    University of Science and Technology of China, China.
    Sun, Zhenan
    Institute of Automation Chinese Academy of Sciences, China.
    Tan, Tieniu
    Institute of Automation Chinese Academy of Sciences, China.
    Su, Wu
    Zhuhai YiSheng Electronics Technology Co, Ltd, China.
    Alonso-Fernandez, Fernando
    Halmstad University, School of Information Technology, Halmstad Embedded and Intelligent Systems Research (EIS), CAISR - Center for Applied Intelligent Systems Research.
    Némesin, Valérian
    Aix-Marseilles University, Centrale Marseille, CNRS, Institut Fresnel, France.
    Othman, Nadia
    Institut Mines-Telecom, Télécom SudParis, France.
    Noda, Koichi
    Nihon System Laboratory, Ltd, Japan.
    Li, Peihua
    Dalian University of Technology, China.
    Hoyle, Edmundo
    University Federal of Rio de Janeiro, Brasil.
    Joshi, Akanksha
    Centre for Development of Advanced Computing, India.
    The First ICB Competition on Iris Recognition2014In: 2014 IEEE International Joint Conference on Biometrics (IJCB), Piscataway, NJ: IEEE Press, 2014, article id 6996292Conference paper (Refereed)
    Abstract [en]

    Iris recognition becomes an important technology in our society. Visual patterns of human iris provide rich texture information for personal identification. However, it is greatly challenging to match intra-class iris images with large variations in unconstrained environments because of noises, illumination variation, heterogeneity and so on. To track current state-of-the-art algorithms in iris recognition, we organized the first ICB∗ Competition on Iris Recognition in 2013 (or ICIR2013 shortly). In this competition, 8 participants from 6 countries submitted 13 algorithms totally. All the algorithms were trained on a public database (e.g. CASIA-Iris-Thousand [3]) and evaluated on an unpublished database. The testing results in terms of False Non-match Rate (FNMR) when False Match Rate (FMR) is 0.0001 are taken to rank the submitted algorithms. © 2014 IEEE.

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