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  • 1.
    Alonso-Fernandez, Fernando
    et al.
    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 survey on periocular biometrics research2016In: Pattern Recognition Letters, ISSN 0167-8655, E-ISSN 1872-7344, Vol. 82, part 2, p. 92-105Article in journal (Refereed)
    Abstract [en]

    Periocular refers to the facial region in the vicinity of the eye, including eyelids, lashes and eyebrows. While face and irises have been extensively studied, the periocular region has emerged as a promising trait for unconstrained biometrics, following demands for increased robustness of face or iris systems. With a surprisingly high discrimination ability, this region can be easily obtained with existing setups for face and iris, and the requirement of user cooperation can be relaxed, thus facilitating the interaction with biometric systems. It is also available over a wide range of distances even when the iris texture cannot be reliably obtained (low resolution) or under partial face occlusion (close distances). Here, we review the state of the art in periocular biometrics research. A number of aspects are described, including: (i) existing databases, (ii) algorithms for periocular detection and/or segmentation, (iii) features employed for recognition, (iv) identification of the most discriminative regions of the periocular area, (v) comparison with iris and face modalities, (vi) soft-biometrics (gender/ethnicity classification), and (vii) impact of gender transformation and plastic surgery on the recognition accuracy. This work is expected to provide an insight of the most relevant issues in periocular biometrics, giving a comprehensive coverage of the existing literature and current state of the art. © 2015 Elsevier B.V. All rights reserved.

  • 2.
    Alonso-Fernandez, Fernando
    et al.
    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.
    An Overview of Periocular Biometrics2017In: Iris and Periocular Biometric Recognition / [ed] Christian Rathgeb & Christoph Busch, London: The Institution of Engineering and Technology , 2017, p. 29-53Chapter in book (Refereed)
    Abstract [en]

    Periocular biometrics specifically refers to the externally visible skin region of the face that surrounds the eye socket. Its utility is specially pronounced when the iris or the face cannot be properly acquired, being the ocular modality requiring the least constrained acquisition process. It appears over a wide range of distances, even under partial face occlusion (close distance) or low resolution iris (long distance), making it very suitable for unconstrained or uncooperative scenarios. It also avoids the need of iris segmentation, an issue in difficult images. In such situation, identifying a suspect where only the periocular region is visible is one of the toughest real-world challenges in biometrics. The richness of the periocular region in terms of identity is so high that the whole face can even be reconstructed only from images of the periocular region. The technological shift to mobile devices has also resulted in many identity-sensitive applications becoming prevalent on these devices.

  • 3.
    Alonso-Fernandez, Fernando
    et al.
    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.
    Best Regions for Periocular Recognition with NIR and Visible Images2014In: 2014 IEEE International Conference on Image Processing (ICIP), Piscataway, NJ: IEEE Press, 2014, p. 4987-4991Conference paper (Refereed)
    Abstract [en]

    We evaluate the most useful regions for periocular recognition. For this purpose, we employ our periocular algorithm based on retinotopic sampling grids and Gabor analysis of the spectrum. We use both NIR and visible iris images. The best regions are selected via Sequential Forward Floating Selection (SFFS). The iris neighborhood (including sclera and eyelashes) is found as the best region with NIR data, while the surrounding skin texture (which is over-illuminated in NIR images) is the most discriminative region in visible range. To the best of our knowledge, only one work in the literature has evaluated the influence of different regions in the performance of periocular recognition algorithms. Our results are in the same line, despite the use of completely different matchers. We also evaluate an iris texture matcher, providing fusion results with our periocular system as well. © 2014 IEEE.

  • 4.
    Alonso-Fernandez, Fernando
    et al.
    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.
    Biometric Recognition Using Periocular Images2013Conference paper (Other academic)
    Abstract [en]

    We present a new system for biometric recognition using periocular images based on retinotopic sampling grids and Gabor analysis of the local power spectrum at different frequencies and orientations. A number of aspects are studied, including: 1) grid adaptation to dimensions of the target eye vs. grids of constant size, 2) comparison between circular- and rectangular-shaped grids, 3) use of Gabor magnitude vs. phase vectors for recognition, and 4) rotation compensation between query and test images. Results show that our system achieves competitive verification rates compared with other periocular recognition approaches. We also show that top verification rates can be obtained without rotation compensation, thus allowing to remove this step for computational efficiency. Also, the performance is not affected substantially if we use a grid of fixed dimensions, or it is even better in certain situations, avoiding the need of accurate detection of the iris region.

  • 5.
    Alonso-Fernandez, Fernando
    et al.
    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.
    Exploting Periocular and RGB Information in Fake Iris Detection2014In: 2014 37th International Conventionon Information and Communication Technology, Electronics and Microelectronics (MIPRO): 26 – 30 May 2014 Opatija, Croatia: Proceedings / [ed] Petar Biljanovic, Zeljko Butkovic, Karolj Skala, Stjepan Golubic, Marina Cicin-Sain, Vlado Sruk, Slobodan Ribaric, Stjepan Gros, Boris Vrdoljak, Mladen Mauher & Goran Cetusic, Rijeka: Croatian Society for Information and Communication Technology, Electronics and Microelectronics - MIPRO , 2014, p. 1354-1359Conference paper (Refereed)
    Abstract [en]

    Fake iris detection has been studied by several researchers. However, to date, the experimental setup has been limited to near-infrared (NIR) sensors, which provide grey-scale images. This work makes use of images captured in visible range with color (RGB) information. We employ Gray-Level CoOccurrence textural features and SVM classifiers for the task of fake iris detection. The best features are selected with the Sequential Forward Floating Selection (SFFS) algorithm. To the best of our knowledge, this is the first work evaluating spoofing attack using color iris images in visible range. Our results demonstrate that the use of features from the three color channels clearly outperform the accuracy obtained from the luminance (gray scale) image. Also, the R channel is found to be the best individual channel. Lastly, we analyze the effect of extracting features from selected (eye or periocular) regions only. The best performance is obtained when GLCM features are extracted from the whole image, highlighting that both the iris and the surrounding periocular region are relevant for fake iris detection. An added advantage is that no accurate iris segmentation is needed. This work is relevant due to the increasing prevalence of more relaxed scenarios where iris acquisition using NIR light is unfeasible (e.g. distant acquisition or mobile devices), which are putting high pressure in the development of algorithms capable of working with visible light. © 2014 MIPRO.

  • 6.
    Alonso-Fernandez, Fernando
    et al.
    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.
    Eye Detection by Complex Filtering for Periocular Recognition2014In: 2nd International Workshop on Biometrics and Forensics (IWBF2014): Valletta, Malta (27-28th March 2014), Piscataway, NJ: IEEE Press, 2014, article id 6914250Conference paper (Refereed)
    Abstract [en]

    We present a novel system to localize the eye position based on symmetry filters. By using a 2D separable filter tuned to detect circular symmetries, detection is done with a few ID convolutions. The detected eye center is used as input to our periocular algorithm based on retinotopic sampling grids and Gabor analysis of the local power spectrum. This setup is evaluated with two databases of iris data, one acquired with a close-up NIR camera, and another in visible light with a web-cam. The periocular system shows high resilience to inaccuracies in the position of the detected eye center. The density of the sampling grid can also be reduced without sacrificing too much accuracy, allowing additional computational savings. We also evaluate an iris texture matcher based on ID Log-Gabor wavelets. Despite the poorer performance of the iris matcher with the webcam database, its fusion with the periocular system results in improved performance. ©2014 IEEE.

  • 7.
    Alonso-Fernandez, Fernando
    et al.
    Halmstad University, School of Information Technology, Halmstad Embedded and Intelligent Systems Research (EIS), Intelligent Systems´ laboratory.
    Bigun, Josef
    Halmstad University, School of Information Technology, Halmstad Embedded and Intelligent Systems Research (EIS), CAISR - Center for Applied Intelligent Systems Research.
    Fake Iris Detection: A Comparison Between Near-Infrared and Visible Images2014In: Proceedings: 10th International Conference on Signal-Image Technology and Internet-Based Systems, SITIS 2014 / [ed] Kokou Yetongnon, Albert Dipanda & Richard Chbeir, Piscataway, NJ: IEEE Computer Society, 2014, p. 546-553Conference paper (Refereed)
    Abstract [en]

    Fake iris detection has been studied so far using near-infrared sensors (NIR), which provide grey scale-images, i.e. With luminance information only. Here, we incorporate into the analysis images captured in visible range, with color information, and perform comparative experiments between the two types of data. We employ Gray-Level Cocurrence textural features and SVM classifiers. These features analyze various image properties related with contrast, pixel regularity, and pixel co-occurrence statistics. We select the best features with the Sequential Forward Floating Selection (SFFS) algorithm. We also study the effect of extracting features from selected (eye or periocular) regions only. Our experiments are done with fake samples obtained from printed images, which are then presented to the same sensor than the real ones. Results show that fake images captured in NIR range are easier to detect than visible images (even if we down sample NIR images to equate the average size of the iris region between the two databases). We also observe that the best performance with both sensors can be obtained with features extracted from the whole image, showing that not only the eye region, but also the surrounding periocular texture is relevant for fake iris detection. An additional source of improvement with the visible sensor also comes from the use of the three RGB channels, in comparison with the luminance image only. A further analysis also reveals that some features are best suited to one particular sensor than the others. © 2014 IEEE

  • 8.
    Alonso-Fernandez, Fernando
    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.
    Bigun, Josef
    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.
    Halmstad University submission to the First ICB Competition on Iris Recognition (ICIR2013)2013Other (Other academic)
  • 9.
    Alonso-Fernandez, Fernando
    et al.
    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.
    Near-infrared and visible-light periocular recognition with Gabor features using frequency-adaptive automatic eye detection2015In: IET Biometrics, ISSN 2047-4938, E-ISSN 2047-4946, Vol. 4, no 2, p. 74-89Article in journal (Refereed)
    Abstract [en]

    Periocular recognition has gained attention recently due to demands of increased robustness of face or iris in less controlled scenarios. We present a new system for eye detection based on complex symmetry filters, which has the advantage of not needing training. Also, separability of the filters allows faster detection via one-dimensional convolutions. This system is used as input to a periocular algorithm based on retinotopic sampling grids and Gabor spectrum decomposition. The evaluation framework is composed of six databases acquired both with near-infrared and visible sensors. The experimental setup is complemented with four iris matchers, used for fusion experiments. The eye detection system presented shows very high accuracy with near-infrared data, and a reasonable good accuracy with one visible database. Regarding the periocular system, it exhibits great robustness to small errors in locating the eye centre, as well as to scale changes of the input image. The density of the sampling grid can also be reduced without sacrificing accuracy. Lastly, despite the poorer performance of the iris matchers with visible data, fusion with the periocular system can provide an improvement of more than 20%. The six databases used have been manually annotated, with the annotation made publicly available. © The Institution of Engineering and Technology 2015.

  • 10.
    Alonso-Fernandez, Fernando
    et al.
    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.
    Periocular Biometrics: Databases, Algorithms and Directions2016In: 2016 4th International Workshop on Biometrics and Forensics (IWBF): Proceedings : 3-4 March, 2016, Limassol, Cyprus, Piscataway, NJ: IEEE, 2016, article id 7449688Conference paper (Refereed)
    Abstract [en]

    Periocular biometrics has been established as an independent modality due to concerns on the performance of iris or face systems in uncontrolled conditions. Periocular refers to the facial region in the eye vicinity, including eyelids, lashes and eyebrows. It is available over a wide range of acquisition distances, representing a trade-off between the whole face (which can be occluded at close distances) and the iris texture (which do not have enough resolution at long distances). Since the periocular region appears in face or iris images, it can be used also in conjunction with these modalities. Features extracted from the periocular region have been also used successfully for gender classification and ethnicity classification, and to study the impact of gender transformation or plastic surgery in the recognition performance. This paper presents a review of the state of the art in periocular biometric research, providing an insight of the most relevant issues and giving a thorough coverage of the existing literature. Future research trends are also briefly discussed. © 2016 IEEE.

  • 11.
    Alonso-Fernandez, Fernando
    et al.
    Halmstad University, School of Information Technology, Halmstad Embedded and Intelligent Systems Research (EIS), Intelligent Systems´ laboratory.
    Bigun, Josef
    Halmstad University, School of Information Technology, Halmstad Embedded and Intelligent Systems Research (EIS), CAISR - Center for Applied Intelligent Systems Research.
    Periocular Recognition Using Retinotopic Sampling and Gabor Decomposition2012In: Computer Vision – ECCV 2012: Workshops and demonstrations : Florence, Italy, October 7-13, 2012, Proceedings. Part II / [ed] Fusiello, Andrea; Murino, Vittorio; Cucchiara, Rita, Berlin: Springer, 2012, Vol. 7584, p. 309-318Conference paper (Refereed)
    Abstract [en]

    We present a new system for biometric recognition using periocular images based on retinotopic sampling grids and Gabor analysis of the local power spectrum. A number of aspects are studied, including: 1) grid adaptation to dimensions of the target eye vs. grids of constant size, 2) comparison between circular- and rectangular-shaped grids, 3) use of Gabor magnitude vs. phase vectors for recognition, 4) rotation compensation between query and test images, and 5) comparison with an iris machine expert. Results show that our system achieves competitive verification rates compared with other periocular recognition approaches. We also show that top verification rates can be obtained without rotation compensation, thus allowing to remove this step for computational efficiency. Also, the performance is not affected substantially if we use a grid of fixed dimensions, or it is even better in certain situations, avoiding the need of accurate detection of the iris region. © 2012 Springer-Verlag.

  • 12.
    Alonso-Fernandez, Fernando
    et al.
    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.
    Quality Factors Affecting Iris Segmentation and Matching2013In: Proceedings – 2013 International Conference on Biometrics, ICB 2013 / [ed] Julian Fierrez, Ajay Kumar, Mayank Vatsa, Raymond Veldhuis & Javier Ortega-Garcia, Piscataway, N.J.: IEEE conference proceedings, 2013, article id 6613016Conference paper (Refereed)
    Abstract [en]

    Image degradations can affect the different processing steps of iris recognition systems. With several quality factors proposed for iris images, its specific effect in the segmentation accuracy is often obviated, with most of the efforts focused on its impact in the recognition accuracy. Accordingly, we evaluate the impact of 8 quality measures in the performance of iris segmentation. We use a database acquired with a close-up iris sensor and built-in quality checking process. Despite the latter, we report differences in behavior, with some measures clearly predicting the segmentation performance, while others giving inconclusive results. Recognition experiments with two matchers also show that segmentation and matching performance are not necessarily affected by the same factors. The resilience of one matcher to segmentation inaccuracies also suggest that segmentation errors due to low image quality are not necessarily revealed by the matcher, pointing out the importance of separate evaluation of the segmentation accuracy. © 2013 IEEE.

  • 13.
    Alonso-Fernandez, Fernando
    et al.
    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.
    Bigun, Josef
    Halmstad University, School of Information Technology, Halmstad Embedded and Intelligent Systems Research (EIS), CAISR - Center for Applied Intelligent Systems Research.
    Eigen-patch iris super-resolution for iris recognition improvement2015In: 2015 23rd European Signal Processing Conference (EUSIPCO), Piscataway, NJ: IEEE Press, 2015, p. 76-80, article id 7362348Conference paper (Refereed)
    Abstract [en]

    Low image resolution will be a predominant factor in iris recognition systems as they evolve towards more relaxed acquisition conditions. Here, we propose a super-resolution technique to enhance iris images based on Principal Component Analysis (PCA) Eigen-transformation of local image patches. Each patch is reconstructed separately, allowing better quality of enhanced images by preserving local information and reducing artifacts. We validate the system used a database of 1,872 near-infrared iris images. Results show the superiority of the presented approach over bilinear or bicubic interpolation, with the eigen-patch method being more resilient to image resolution reduction. We also perform recognition experiments with an iris matcher based 1D Log-Gabor, demonstrating that verification rates degrades more rapidly with bilinear or bicubic interpolation. ©2015 IEEE

  • 14.
    Alonso-Fernandez, Fernando
    et al.
    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.
    Bigun, Josef
    Halmstad University, School of Information Technology, Halmstad Embedded and Intelligent Systems Research (EIS), CAISR - Center for Applied Intelligent Systems Research.
    Improving Very Low-Resolution Iris Identification Via Super-Resolution Reconstruction of Local Patches2017In: 2017 International Conference of the Biometrics Special Interest Group (BIOSIG) / [ed] Arslan Brömme, Christoph Busch, Antitza Dantcheva, Christian Rathgeb & Andreas Uhl, Bonn: Gesellschaft für Informatik, 2017, Vol. P-270, article id 8053512Conference paper (Refereed)
    Abstract [en]

    Relaxed acquisition conditions in iris recognition systems have significant effects on the quality and resolution of acquired images, which can severely affect performance if not addressed properly. Here, we evaluate two trained super-resolution algorithms in the context of iris identification. They are based on reconstruction of local image patches, where each patch is reconstructed separately using its own optimal reconstruction function. We employ a database of 1,872 near-infrared iris images (with 163 different identities for identification experiments) and three iris comparators. The trained approaches are substantially superior to bilinear or bicubic interpolations, with one of the comparators providing a Rank-1 performance of ∼88% with images of only 15×15 pixels, and an identification rate of 95% with a hit list size of only 8 identities. © 2017 Gesellschaft fuer Informatik.

  • 15.
    Alonso-Fernandez, Fernando
    et al.
    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.
    Bigun, Josef
    Halmstad University, School of Information Technology, Halmstad Embedded and Intelligent Systems Research (EIS), CAISR - Center for Applied Intelligent Systems Research.
    Iris Super-Resolution Using Iterative Neighbor Embedding2017In: 2017 IEEE Conference on Computer Vision and Pattern Recognition Workshops / [ed] Lisa O’Conner, Los Alamitos: IEEE Computer Society, 2017, p. 655-663Conference paper (Refereed)
    Abstract [en]

    Iris recognition research is heading towards enabling more relaxed acquisition conditions. This has effects on the quality and resolution of acquired images, severely affecting the accuracy of recognition systems if not tackled appropriately. In this paper, we evaluate a super-resolution algorithm used to reconstruct iris images based on iterative neighbor embedding of local image patches which tries to represent input low-resolution patches while preserving the geometry of the original high-resolution space. To this end, the geometry of the low- and high-resolution manifolds are jointly considered during the reconstruction process. We validate the system with a database of 1,872 near-infrared iris images, while fusion of two iris comparators has been adopted to improve recognition performance. The presented approach is substantially superior to bilinear/bicubic interpolations at very low resolutions, and it also outperforms a previous PCA-based iris reconstruction approach which only considers the geometry of the low-resolution manifold during the reconstruction process. © 2017 IEEE

  • 16.
    Alonso-Fernandez, Fernando
    et al.
    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.
    Bigun, Josef
    Halmstad University, School of Information Technology, Halmstad Embedded and Intelligent Systems Research (EIS), CAISR - Center for Applied Intelligent Systems Research.
    Reconstruction of Smartphone Images for Low Resolution Iris Recognition2015In: 2015 IEEE International Workshop on Information Forensics and Security (WIFS), Piscataway, NJ: IEEE Press, 2015, article id 7368600Conference paper (Refereed)
    Abstract [en]

    As iris systems evolve towards a more relaxed acquisition, low image resolution will be a predominant issue. In this paper we evaluate a super-resolution method to reconstruct iris images based on Eigen-transformation of local image patches. Each patch is reconstructed separately, allowing better quality of enhanced images by preserving local information. We employ a database of 560 images captured in visible spectrum with two smartphones. The presented approach is superiorto bilinear or bicubic interpolation, especially at lower resolutions. We also carry out recognition experiments with six iris matchers, showing that better performance can be obtained at low-resolutions with the proposed eigen-patch reconstruction, with fusion of only two systems pushing the EER to below 5-8% for down-sampling factors up to a size of only 13x13. © 2015 IEEE.

  • 17.
    Alonso-Fernandez, Fernando
    et al.
    Halmstad University, School of Information Technology, Halmstad Embedded and Intelligent Systems Research (EIS), CAISR - Center for Applied Intelligent Systems Research.
    Farrugia, Reuben
    University of Malta, Msida, Malta.
    Bigun, Josef
    Halmstad University, School of Information Technology, Halmstad Embedded and Intelligent Systems Research (EIS), CAISR - Center for Applied Intelligent Systems Research.
    Learning-Based Local-Patch Resolution Reconstruction of Iris Smart-phone Images2017Conference paper (Refereed)
    Abstract [en]

    Application of ocular biometrics in mobile and at a distance environments still has several open challenges, with the lack quality and resolution being an evident issue that can severely affects performance. In this paper, we evaluate two trained image reconstruction algorithms in the context of smart-phone biometrics. They are based on the use of coupled dictionaries to learn the mapping relations between low and high resolution images. In addition, reconstruction is made in local overlapped image patches, where up-scaling functions are modelled separately for each patch, allowing to better preserve local details. The experimental setup is complemented with a database of 560 images captured with two different smart-phones, and two iris comparators employed for verification experiments. We show that the trained approaches are substantially superior to bilinear or bicubic interpolations at very low resolutions (images of 13×13 pixels). Under such challenging conditions, an EER of ∼7% can be achieved using individual comparators, which is further pushed down to 4-6% after the fusion of the two systems. © 2017 IEEE

  • 18.
    Alonso-Fernandez, Fernando
    et al.
    Halmstad University, School of Information Technology, Halmstad Embedded and Intelligent Systems Research (EIS), CAISR - Center for Applied Intelligent Systems Research.
    Farrugia, Reuben
    University of Malta, Msida, Malta.
    Bigun, Josef
    Halmstad University, School of Information Technology, Halmstad Embedded and Intelligent Systems Research (EIS), CAISR - Center for Applied Intelligent Systems Research.
    Very Low-Resolution Iris Recognition Via Eigen-Patch Super-Resolution and Matcher Fusion2016In: 2016 IEEE 8th International Conference on Biometrics Theory, Applications and Systems (BTAS), Piscataway: IEEE, 2016, article id 7791208Conference paper (Refereed)
    Abstract [en]

    Current research in iris recognition is moving towards enabling more relaxed acquisition conditions. This has effects on the quality of acquired images, with low resolution being a predominant issue. Here, we evaluate a super-resolution algorithm used to reconstruct iris images based on Eigen-transformation of local image patches. Each patch is reconstructed separately, allowing better quality of enhanced images by preserving local information. Contrast enhancement is used to improve the reconstruction quality, while matcher fusion has been adopted to improve iris recognition performance. We validate the system using a database of 1,872 near-infrared iris images. The presented approach is superior to bilinear or bicubic interpolation, especially at lower resolutions, and the fusion of the two systems pushes the EER to below 5% for down-sampling factors up to a image size of only 13×13.

  • 19.
    Alonso-Fernandez, Fernando
    et al.
    Halmstad University, School of Information Technology, Halmstad Embedded and Intelligent Systems Research (EIS), Intelligent Systems´ laboratory.
    Fierrez, Julian
    Universidad Autonoma de Madrid, Madrid, Spain.
    Bigun, Josef
    Halmstad University, School of Information Technology, Halmstad Embedded and Intelligent Systems Research (EIS), CAISR - Center for Applied Intelligent Systems Research.
    Quality Measures in Biometric Systems2015In: Encyclopedia of Biometrics / [ed] Stan Z. Li & Anil K. Jain, New York: Springer Science+Business Media B.V., 2015, 2, p. 1287-1297Chapter in book (Refereed)
    Abstract [en]

    This is an excerpt from the content

    Synonyms

    Quality assessment; Biometric quality; Quality-based processing

    Definition

    Since the establishment of biometrics as a specific research area in the late 1990s, the biometric community has focused its efforts in the development of accurate recognition algorithms [1]. Nowadays, biometric recognition is a mature technology that is used in many applications, offering greater security and convenience than traditional methods of personal recognition [2].

    During the past few years, biometric quality measurement has become an important concern after a number of studies and technology benchmarks that demonstrate how performance of biometric systems is heavily affected by the quality of biometric signals [3]. This operationally important step has been nevertheless under-researched compared to the primary feature extraction and pattern recognition tasks [4]. One of the main challenges facing biometric technologies is performance degradation in less controlled situations, and the problem of biometric quality measurement has arisen even stronger with the proliferation of portable handheld devices, with at-a-distance and on-the-move acquisition capabilities. These will require robust algorithms capable of handling a range of changing characteristics [2]. Another important example is forensics, in which intrinsic operational factors further degrade recognition performance.

    There are number of factors that can affect the quality of biometric signals, and there are numerous roles of a quality measure in the context of biometric systems. This section summarizes the state of the art in the biometric quality problem, giving an overall framework of the different challenges involved.

  • 20.
    Alonso-Fernandez, Fernando
    et al.
    ATVS, Escuela Politecnica Superior, Campus de Cantoblanco, Avda. Francisco Tomas y Valiente 11, 28049 Madrid, Spain.
    Fierrez-Aguilar, Julian
    ATVS, Escuela Politecnica Superior, Campus de Cantoblanco, Avda. Francisco Tomas y Valiente 11, 28049 Madrid, Spain.
    Fronthaler, Hartwig
    Halmstad University, School of Information Technology, Halmstad Embedded and Intelligent Systems Research (EIS).
    Kollreider, Klaus
    Halmstad University, School of Information Technology, Halmstad Embedded and Intelligent Systems Research (EIS).
    Ortega-Garcia, Javier
    ATVS, Escuela Politecnica Superior, Campus de Cantoblanco, Avda. Francisco Tomas y Valiente 11, 28049 Madrid, Spain.
    Gonzalez-Rodriguez, Joaquin
    ATVS, Escuela Politecnica Superior, Campus de Cantoblanco, Avda. Francisco Tomas y Valiente 11, 28049 Madrid, Spain.
    Bigun, Josef
    Halmstad University, School of Information Technology, Halmstad Embedded and Intelligent Systems Research (EIS), CAISR - Center for Applied Intelligent Systems Research.
    Combining multiple matchers for fingerprint verification: A case study in biosecure network of excellence2007In: Annales des télécommunications, ISSN 0003-4347, E-ISSN 1958-9395, Vol. 62, no 1-2, p. 62-82Article in journal (Refereed)
    Abstract [en]

    We report on experiments for the fingerprint modality conducted during the First BioSecure Residential Workshop. Two reference systems for fingerprint verification have been tested together with two additional non-reference systems. These systems follow different approaches of fingerprint processing and are discussed in detail. Fusion experiments involving different combinations of the available systems are presented. The experimental results show that the best recognition strategy involves both minutiae-based and correlation-based measurements. Regarding the fusion experiments, the best relative improvement is obtained when fusing systems that are based on heterogeneous strategies for feature extraction and/or matching. The best combinations of two/three/four systems always include the best individual systems whereas the best verification performance is obtained when combining all the available systems.

  • 21.
    Alonso-Fernandez, Fernando
    et al.
    Halmstad University, School of Information Technology, Halmstad Embedded and Intelligent Systems Research (EIS), CAISR - Center for Applied Intelligent Systems Research.
    Mikaelyan, Anna
    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.
    Compact Multi-scale Periocular Recognition Using SAFE Feature2016In: Proceedings - International Conference on Pattern Recognition, Washington: IEEE Communications Society, 2016, p. 1455-1460, article id 7899842Conference paper (Refereed)
    Abstract [en]

    In this paper, we present a new approach for periocular recognition based on the Symmetry Assessment by Feature Expansion (SAFE) descriptor, which encodes the presence of various symmetric curve families around image key points. We use the sclera center as single key point for feature extraction, highlighting the object-like identity properties that concentrates to this unique point of the eye. As it is demonstrated, such discriminative properties can be encoded with a reduced set of symmetric curves. Experiments are done with a database of periocular images captured with a digital camera. We test our system against reference periocular features, achieving top performance with a considerably smaller feature vector (given by the use of a single key point). All the systems tested also show a nearly steady correlation between acquisition distance and performance, and they are also able to cope well when enrolment and test images are not captured at the same distance. Fusion experiments among the available systems are also provided. © 2016 IEEE

  • 22.
    Alonso-Fernandez, Fernando
    et al.
    Halmstad University, School of Information Technology, Halmstad Embedded and Intelligent Systems Research (EIS), CAISR - Center for Applied Intelligent Systems Research.
    Mikaelyan, Anna
    Halmstad University, School of Information Technology, Halmstad Embedded and Intelligent Systems Research (EIS).
    Bigun, Josef
    Halmstad University, School of Information Technology, Halmstad Embedded and Intelligent Systems Research (EIS), CAISR - Center for Applied Intelligent Systems Research.
    Compact Multi-scale Periocular Recognition Using SAFE Features2017In: Proceedings of the 23rd International Conference On Pattern Recognition (Icpr), IEEE Computer Society, 2017, p. 1455-1460, article id 7899842Conference paper (Refereed)
    Abstract [en]

    In this paper, we present a new approach for periocular recognition based on the Symmetry Assessment by Feature Expansion (SAFE) descriptor, which encodes the presence of various symmetric curve families around image key points. We use the sclera center as single key point for feature extraction, highlighting the object-like identity properties that concentrates to this unique point of the eye. As it is demonstrated, such discriminative properties can be encoded with a reduced set of symmetric curves. Experiments are done with a database of periocular images captured with a digital camera. We test our system against reference periocular features, achieving top performance with a considerably smaller feature vector (given by the use of a single key point). All the systems tested also show a nearly steady correlation between acquisition distance and performance, and they are also able to cope well when enrolment and test images are not captured at the same distance. Fusion experiments among the available systems are also provided. © 2016 IEEE.

  • 23.
    Alonso-Fernandez, Fernando
    et al.
    Halmstad University, School of Information Technology, Halmstad Embedded and Intelligent Systems Research (EIS), CAISR - Center for Applied Intelligent Systems Research.
    Mikaelyan, Anna
    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.
    Comparison and Fusion of Multiple Iris and Periocular Matchers Using Near-Infrared and Visible Images2015In: 3rd International Workshop on Biometrics and Forensics, IWBF 2015, Piscataway, NJ: IEEE Press, 2015, p. Article number: 7110234-Conference paper (Refereed)
    Abstract [en]

    Periocular refers to the facial region in the eye vicinity. It can be easily obtained with existing face and iris setups, and it appears in iris images, so its fusion with the iris texture has a potential to improve the overall recognition. It is also suggested that iris is more suited to near-infrared (NIR) illu- mination, whereas the periocular modality is best for visible (VW) illumination. Here, we evaluate three periocular and three iris matchers based on different features. As experimen- tal data, we use five databases, three acquired with a close-up NIR camera, and two in VW light with a webcam and a dig- ital camera. We observe that the iris matchers perform better than the periocular matchers with NIR data, and the opposite with VW data. However, in both cases, their fusion can pro- vide additional performance improvements. This is specially relevant with VW data, where the iris matchers perform sig- nificantly worse (due to low resolution), but they are still able to complement the periocular modality. © 2015 IEEE.

  • 24.
    Alonso-Fernandez, Fernando
    et al.
    Halmstad University, School of Information Technology, Halmstad Embedded and Intelligent Systems Research (EIS), CAISR - Center for Applied Intelligent Systems Research.
    Mikaelyan, Anna
    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.
    Keypoint Description by Symmetry Assessment–Applications in BiometricsManuscript (preprint) (Other academic)
    Abstract [en]

    We present a model-based feature extractor to describe neighborhoods around keypoints by finite expansion, estimating the spatially varying orientation by harmonic functions. The iso-curves of such functions are highly symmetric w.r.t. the origin (a keypoint) and the estimated parameters have well defined geometric interpretations. The origin is also a unique singularity of all harmonic functions, helping to determine thel ocation of a keypoint precisely, whereas the functions describe the object shape of the neighborhood. This is novel and complementary to traditional texture features which describe texture shape properties i.e. they are purposively invariant to translation (within a texture). We report on experiments of verification and identification of keypoints in forensic fingerprints by using publicly available data (NIST SD27), and discuss the results in comparison to other studies. These support our conclusions that the novel features can equip single cores or single minutia with a significant verification power at 19% EER, and an identification power of 24-78% for ranks of 1-20. Additionally, we report verification results of periocular biometrics using near infrared images, reaching an EER performance of 13%, whichis comparable to the state of the art. More importantly, fusion of two systems, our and texture features (Gabor), result in a measurable performance improvement. We report reduction ofthe EER to 9%, supporting the view that the novel features capture relevant visual

  • 25.
    Alonso-Fernandez, Fernando
    et al.
    Halmstad University, School of Information Technology, Halmstad Embedded and Intelligent Systems Research (EIS), CAISR - Center for Applied Intelligent Systems Research.
    Raja, Kiran B.
    Norwegian University of Science and Technology, Gjøvik, Norway.
    Busch, Christoph
    Norwegian University of Science and Technology, Gjøvik, Norway.
    Bigun, Josef
    Halmstad University, School of Information Technology, Halmstad Embedded and Intelligent Systems Research (EIS), CAISR - Center for Applied Intelligent Systems Research.
    Log-Likelihood Score Level Fusion for Improved Cross-Sensor Smartphone Periocular Recognition2017In: 2017 25th European Signal Processing Conference (EUSIPCO), Piscataway: IEEE, 2017, p. 281-285, article id 8081211Conference paper (Refereed)
    Abstract [en]

    The proliferation of cameras and personal devices results in a wide variability of imaging conditions, producing large intra-class variations and a significant performance drop when images from heterogeneous environments are compared. However, many applications require to deal with data from different sources regularly, thus needing to overcome these interoperability problems. Here, we employ fusion of several comparators to improve periocular performance when images from different smartphones are compared. We use a probabilistic fusion framework based on linear logistic regression, in which fused scores tend to be log-likelihood ratios, obtaining a reduction in cross-sensor EER of up to 40% due to the fusion. Our framework also provides an elegant and simple solution to handle signals from different devices, since same-sensor and crosssensor score distributions are aligned and mapped to a common probabilistic domain. This allows the use of Bayes thresholds for optimal decision making, eliminating the need of sensor-specific thresholds, which is essential in operational conditions because the threshold setting critically determines the accuracy of the authentication process in many applications. © EURASIP 2017

  • 26.
    Aloulou, Hamdi
    et al.
    Institut Mines Telecom, Paris, France & Laboratory of Informatics, Robotics and Microelectronics, Montpellier, France.
    Abdulrazak, Bessam
    Laboratory of Informatics, Robotics and Microelectronics, Montpellier, France & University of Sherbrooke, Sherbrooke, Canada.
    Endelin, Romain
    Institut Mines Telecom, Paris, France & Laboratory of Informatics, Robotics and Microelectronics, Montpellier, France.
    Bentes, João
    Halmstad University, School of Information Technology, Halmstad Embedded and Intelligent Systems Research (EIS), CAISR - Center for Applied Intelligent Systems Research. Image and Pervasive Access Laboratory, Singapore, Singapore.
    Tiberghien, Thibaut
    Institut Mines Telecom, Paris, France & Image and Pervasive Access Laboratory, Singapore, Singapore.
    Bellmunt, Joaquim
    Institut Mines Telecom, Paris, France & Image and Pervasive Access Laboratory, Singapore, Singapore.
    Simplifying Installation and Maintenance of Ambient Intelligent Solutions Toward Large Scale Deployment2016In: Inclusive Smart Cities and Digital Health: 14th International Conference on Smart Homes and Health Telematics, ICOST 2016, Wuhan, China, May 25-27, 2016. Proceedings / [ed] Chang C.K., Jin H., Cao Y., Aloulou H., Mokhtari M., Chiari L., Heidelberg: Springer, 2016, p. 121-132Conference paper (Refereed)
    Abstract [en]

    Simplify deployment and maintenance of Ambient Intelligence solutions is important to enable large-scale deployment and maximize the use/benefit of these solutions. More mature Ambient Intelligence solutions emerge on the market as a result of an intensive investment in research. This research targets mainly the accuracy, usefulness, and usability aspects of the solutions. Still, possibility to adapt to different environments, ease of deployment and maintenance are ongoing problems of Ambient Intelligence. Existing solutions require an expert to move on-site in order to install or maintain systems. Therefore, we present in this paper our attempt to enable quick large scale deployment. We discuss lessons learned from our approach for automating the deployment process in order to be performed by ordinary people. We also introduce a solution for simplifying the monitoring and maintenance of installed systems. © Springer International Publishing Switzerland 2016.

  • 27.
    Andreasson, Henrik
    et al.
    Örebro University, Örebro, Sweden.
    Bouguerra, Abdelbaki
    Örebro University, Örebro, Sweden.
    Åstrand, Björn
    Halmstad University, School of Information Technology, Halmstad Embedded and Intelligent Systems Research (EIS), CAISR - Center for Applied Intelligent Systems Research.
    Rögnvaldsson, Thorsteinn
    Halmstad University, School of Information Technology, Halmstad Embedded and Intelligent Systems Research (EIS), CAISR - Center for Applied Intelligent Systems Research.
    Gold-fish SLAM: An application of SLAM to localize AGVs2014In: Field and Service Robotics: Results of the 8th International Conference / [ed] Kazuya Yoshida & Satoshi Tadokoro, Heidelberg: Springer, 2014, p. 585-598Conference paper (Refereed)
    Abstract [en]

    The main focus of this paper is to present a case study of a SLAM solution for Automated Guided Vehicles (AGVs) operating in real-world industrial environments. The studied solution, called Gold-fish SLAM, was implemented to provide localization estimates in dynamic industrial environments, where there are static landmarks that are only rarely perceived by the AGVs. The main idea of Gold-fish SLAM is to consider the goods that enter and leave the environment as temporary landmarks that can be used in combination with the rarely seen static landmarks to compute online estimates of AGV poses. The solution is tested and verified in a factory of paper using an eight ton diesel-truck retrofitted with an AGV control system running at speeds up to 3m/s. The paper includes also a general discussion on how SLAM can be used in industrial applications with AGVs. © Springer-Verlag Berlin Heidelberg 2014.

  • 28.
    Aramrattana, Maytheewat
    et al.
    Halmstad University, School of Information Technology, Halmstad Embedded and Intelligent Systems Research (EIS), Centre for Research on Embedded Systems (CERES). The Swedish National Road and Transport Research Institute (VTI), Linköping, Sweden.
    Detournay, Jérôme
    Halmstad University, School of Information Technology.
    Englund, Cristofer
    Halmstad University, School of Information Technology, Halmstad Embedded and Intelligent Systems Research (EIS), CAISR - Center for Applied Intelligent Systems Research. RISE Viktoria, Gothenburg, Sweden.
    Frimodig, Victor
    Halmstad University, School of Information Technology.
    Uddman Jansson, Oscar
    Halmstad University, School of Information Technology.
    Larsson, Tony
    Halmstad University, School of Information Technology, Halmstad Embedded and Intelligent Systems Research (EIS), Centre for Research on Embedded Systems (CERES).
    Mostowski, Wojciech
    Halmstad University, School of Information Technology, Halmstad Embedded and Intelligent Systems Research (EIS), Centre for Research on Embedded Systems (CERES).
    Díez Rodríguez, Víctor
    Halmstad University, School of Information Technology.
    Rosenstatter, Thomas
    The Swedish National Road and Transport Research Institute (VTI), Linköping, Sweden.
    Shahanoor, Golam
    Halmstad University, School of Information Technology.
    Mastering Cooperative Driving Challenges in a Competition Scenario2017In: IEEE transactions on intelligent transportation systems (Print), ISSN 1524-9050, E-ISSN 1558-0016Article in journal (Refereed)
  • 29.
    Aramrattana, Maytheewat
    et al.
    Halmstad University, School of Information Technology, Halmstad Embedded and Intelligent Systems Research (EIS), Centre for Research on Embedded Systems (CERES). The Swedish National Road and Transport Research Institute (VTI), Linköping, Sweden.
    Englund, Cristofer
    Halmstad University, School of Information Technology, Halmstad Embedded and Intelligent Systems Research (EIS), CAISR - Center for Applied Intelligent Systems Research. RISE Viktoria, Göteborg, Sweden.
    Jansson, Jonas
    The Swedish National Road and Transport Research Institute (VTI), Linköping, Sweden.
    Larsson, Tony
    Halmstad University, School of Information Technology, Halmstad Embedded and Intelligent Systems Research (EIS), Centre for Research on Embedded Systems (CERES).
    Nåbo, Arne
    The Swedish National Road and Transport Research Institute (VTI), Linköping, Sweden.
    Safety Analysis of Cooperative Adaptive Cruise Control in Vehicle Cut-in Situations2017In: Proceedings of 2017 4th International Symposium on Future Active Safety Technology towards Zero-Traffic-Accidents (FAST-zero), Society of Automotive Engineers of Japan , 2017, article id 20174621Conference paper (Refereed)
    Abstract [en]

    Cooperative adaptive cruise control (CACC) is a cooperative intelligent transport systems (C-ITS) function, which especially when used in platooning applications, possess many expected benefits including efficient road space utilization and reduced fuel consumption. Cut-in manoeuvres in platoons can potentially reduce those benefits, and are not desired from a safety point of view. Unfortunately, in realistic traffic scenarios, cut-in manoeuvres can be expected, especially from non-connected vehicles. In this paper two different controllers for platooning are explored, aiming at maintaining the safety of the platoon while a vehicle is cutting in from the adjacent lane. A realistic scenario, where a human driver performs the cut-in manoeuvre is used to demonstrate the effectiveness of the controllers. Safety analysis of CACC controllers using time to collision (TTC) under such situation is presented. The analysis using TTC indicate that, although potential risks are always high in CACC applications such as platooning due to the small inter-vehicular distances, dangerous TTC (TTC < 6 seconds) is not frequent. Future research directions are also discussed along with the results.

  • 30.
    Aramrattana, Maytheewat
    et al.
    Halmstad University, School of Information Technology, Halmstad Embedded and Intelligent Systems Research (EIS), Centre for Research on Embedded Systems (CERES). The Swedish National Road and Transport Research Institute (VTI), Linköping, Sweden.
    Larsson, Tony
    Halmstad University, School of Information Technology, Halmstad Embedded and Intelligent Systems Research (EIS), Centre for Research on Embedded Systems (CERES).
    Englund, Cristofer
    Halmstad University, School of Information Technology, Halmstad Embedded and Intelligent Systems Research (EIS), CAISR - Center for Applied Intelligent Systems Research. RISE Viktoria, Gothenburg, Sweden.
    Jansson, Jonas
    The Swedish National Road and Transport Research Institute (VTI), Linköping, Sweden.
    Nåbo, Arne
    The Swedish National Road and Transport Research Institute (VTI), Linköping, Sweden.
    Simulation of Cut-In by Manually Driven Vehicles in Platooning Scenarios2017In: 2017 IEEE 20th International Conference on Intelligent Transportation Systems (ITSC), 2017, p. 315-320Conference paper (Refereed)
    Abstract [en]

    In the near future, Cooperative Intelligent Transport System (C-ITS) applications are expected to be deployed. To support this, simulation is often used to design and evaluate the applications during the early development phases. Simulations of C-ITS scenarios often assume a fleet of homogeneous vehicles within the transportation system. In contrast, once C-ITS is deployed, the traffic scenarios will consist of a mixture of connected and non-connected vehicles, which, in addition, can be driven manually or automatically. Such mixed cases are rarely analysed, especially those where manually driven vehicles are involved. Therefore, this paper presents a C-ITS simulation framework, which incorporates a manually driven car through a driving simulator interacting with a traffic simulator, and a communication simulator, which together enable modelling and analysis of C-ITS applications and scenarios. Furthermore, example usages in the scenarios, where a manually driven vehicle cut-in to a platoon of Cooperative Adaptive Cruise Control (CACC) equipped vehicles are presented.

  • 31.
    Aramrattana, Maytheewat
    et al.
    Halmstad University, School of Information Technology, Halmstad Embedded and Intelligent Systems Research (EIS), Centre for Research on Embedded Systems (CERES). The Swedish National Road and Transport Research Institute (VTI), Linköping, Sweden.
    Larsson, Tony
    Halmstad University, School of Information Technology, Halmstad Embedded and Intelligent Systems Research (EIS), Centre for Research on Embedded Systems (CERES).
    Jansson, Jonas
    The Swedish National Road and Transport Research Institute (VTI), Linköping, Sweden.
    Englund, Cristofer
    Halmstad University, School of Information Technology, Halmstad Embedded and Intelligent Systems Research (EIS), CAISR - Center for Applied Intelligent Systems Research. Viktoria Swedish ICT, Gothenburg, Sweden.
    Dimensions of Cooperative Driving, ITS and Automation2015In: 2015 IEEE Intelligent Vehicles Symposium (IV), Piscataway, NJ: IEEE Press, 2015, p. 144-149Conference paper (Refereed)
    Abstract [en]

    Wireless technology supporting vehicle-to-vehicle (V2V), and vehicle-to-infrastructure (V2I) communication, allow vehicles and infrastructures to exchange information, and cooperate. Cooperation among the actors in an intelligent transport system (ITS) can introduce several benefits, for instance, increase safety, comfort, efficiency. Automation has also evolved in vehicle control and active safety functions. Combining cooperation and automation would enable more advanced functions such as automated highway merge and negotiating right-of-way in a cooperative intersection. However, the combination have influences on the structure of the overall transport systems as well as on its behaviour. In order to provide a common understanding of such systems, this paper presents an analysis of cooperative ITS (C-ITS) with regard to dimensions of cooperation. It also presents possible influence on driving behaviour and challenges in deployment and automation of C-ITS.

  • 32.
    Ashfaq, Awais
    et al.
    Halmstad University, School of Information Technology, Halmstad Embedded and Intelligent Systems Research (EIS), CAISR - Center for Applied Intelligent Systems Research. KTH Royal Institute of Technology, Stockholm, Sweden.
    Adler, Jonas
    KTH Royal Institute of Technology, Stockholm, Sweden & Elekta Instrument AB, Stockholm, Sweden.
    A modified fuzzy C means algorithm for shading correction in craniofacial CBCT images2017In: CMBEBIH 2017: Proceedings of the International Conference on Medical and Biological Engineering 2017 / [ed] Almir Badnjevic, Singapore: Springer, 2017, Vol. 62, p. 531-538Conference paper (Refereed)
    Abstract [en]

    CBCT images suffer from acute shading artifacts primarily due to scatter. Numerous image-domain correction algorithms have been proposed in the literature that use patient-specific planning CT images to estimate shading contributions in CBCT images. However, in the context of radiosurgery applications such as gamma knife, planning images are often acquired through MRI which impedes the use of polynomial fitting approaches for shading correction. We present a new shading correction approach that is independent of planning CT images. Our algorithm is based on the assumption that true CBCT images follow a uniform volumetric intensity distribution per material, and scatter perturbs this uniform texture by contributing cupping and shading artifacts in the image domain. The framework is a combination of fuzzy C-means coupled with a neighborhood regularization term and Otsu’s method. Experimental results on artificially simulated craniofacial CBCT images are provided to demonstrate the effectiveness of our algorithm. Spatial non-uniformity is reduced from 16% to 7% in soft tissue and from 44% to 8% in bone regions. With shading-correction, thresholding based segmentation accuracy for bone pixels is improved from 85% to 91% when compared to thresholding without shading-correction. The proposed algorithm is thus practical and qualifies as a plug and play extension into any CBCT reconstruction software for shading correction. © Springer Nature Singapore Pte Ltd. 2017.

  • 33.
    Bangalore Girijeswara, Karthik
    Halmstad University, School of Information Technology, Halmstad Embedded and Intelligent Systems Research (EIS), CAISR - Center for Applied Intelligent Systems Research. Mr..
    Peer group recognition based on Vehicle operation and behavior: Supervised and unsupervised approach towards peer group recognition and feature space exploration2017Independent thesis Advanced level (degree of Master (Two Years)), 20 credits / 30 HE creditsStudent thesis
    Abstract [en]

    Behavior recognition provides an interesting perspective for understandingthe different modes of a system and the influence of eachmode under varying conditions. In most of the systems, prior knowledgeof different expected behavior is available. Whereas, in an automotivedomain, a fleet of vehicle with many external factors influencingeach vehicle and an asynchronous performance of each vehicleon road, creates the complexity on analyzing and predicting the exacttime segments of vehicles in a fleet exhibiting similar behavior. Thisthesis focuses on recognizing time segments of vehicles that exhibitsimilar behavior based on supervised and unsupervised approaches.In supervised approach, classifiers are trained to predict two distinctiveoperations(highway and in-city). In unsupervised approach, featurespace is explored for identification of consistent features and existenceof other operations. An unsupervised approach to recognizepeer cluster groups is combined with supervised classification resultsto achieve lower computational complexity.

  • 34.
    Bellone, Mauro
    et al.
    Chalmers University of Technology, Göteborg, Sweden.
    Qutteineh, Jafar
    Halmstad University, School of Information Technology, Halmstad Embedded and Intelligent Systems Research (EIS), CAISR - Center for Applied Intelligent Systems Research.
    Extension of Trajectory Planning in Parameterized Spaces to Articulated Vehicles2017In: Proceedings of the 2017 22nd IEEE International Conference on Emerging Technologies and Factory Automation, 2017Conference paper (Refereed)
    Abstract [en]

    The main objective of this research is to study a novel method for safe maneuvering of articulated vehicles in warehouses. The presented method extends the concept of probabilistic planning on manifolds to articulated vehicles, which will be capable of driving, maneuvering and performing obstacle avoidance in any scenario. The proposed technique involves the extension of a parameterized space, developed for the reactive navigation of differential driven vehicles, to include an additional degree of freedom and use a probabilistic planner to calculate kinematically feasible trajectories. As a result, the algorithm is able to successfully generate maneuvers for an articulated truck and to navigate towards specific target points. The approach was validated using three problems representing different driving scenarios, demonstrating the possible utilization of the method in real-case scenarios. The solutions have been further benchmarked on multiple runs to evaluate success rate and to demonstrate the validity of the algorithm.

  • 35.
    Bengtsson, Hoai
    et al.
    Viktoria Swedish ICT, Gothenburg, Sweden.
    Chen, Lei
    Viktoria Swedish ICT, Gothenburg, Sweden.
    Voronov, Alexey
    Viktoria Swedish ICT, Gothenburg, Sweden.
    Englund, Cristofer
    Halmstad University, School of Information Technology, Halmstad Embedded and Intelligent Systems Research (EIS), CAISR - Center for Applied Intelligent Systems Research. Viktoria Swedish ICT, Gothenburg, Sweden.
    Interaction Protocol for Highway Platoon Merge2015In: 2015 IEEE 18th International Conference on Intelligent Transportation Systems / [ed] Lisa O’Conner, Los Alamitos: IEEE, 2015, p. 1971-1976, article id 7313411Conference paper (Refereed)
    Abstract [en]

    An interaction protocol for cooperative platoon merge on highways is proposed. The interaction protocol facilitates a challenge scenario for the Grand Cooperative Driving Challenge (GCDC) 2016, where two platoons running on separate lanes merge into one platoon due to a roadwork in one of the lanes. Detailed interaction procedures, described with state machines of each vehicle are presented. A communication message set is designed to support platoon controllers to perform safe and efficient manoeuvres. © 2015 IEEE.

  • 36.
    Bentes, João
    Halmstad University, School of Information Technology, Halmstad Embedded and Intelligent Systems Research (EIS), CAISR - Center for Applied Intelligent Systems Research.
    Service platform for continuous delivery of assisted living systems2016In: Middleware Doctoral Symposium'16: Proceedings of the Doctoral Symposium of the 17th International Middleware Conference, New York: Association for Computing Machinery (ACM), 2016, article id 6Conference paper (Refereed)
    Abstract [en]

    A smart home has potential to support independent living of elderly people in their preferred living environments. However, smart home systems do not fully address the aims of Ambient assisted living (AAL), mainly due to limited support outside the home. This need of continuous delivery of assistance for elderly people on the go require technology which extends the home into the society. This ongoing work proposes to identify the architectural requirements for a service platform being able to continuously deliver assistive services at home and beyond. © 2016 Copyright held by the owner/author(s). Publication rights licensed to ACM.

  • 37.
    Bentes, João
    et al.
    Halmstad University, School of Information Technology, Halmstad Embedded and Intelligent Systems Research (EIS), CAISR - Center for Applied Intelligent Systems Research.
    Khandelwal, Siddhartha
    Halmstad University, School of Information Technology, Halmstad Embedded and Intelligent Systems Research (EIS), CAISR - Center for Applied Intelligent Systems Research.
    Carlsson, Hampus
    Halmstad University, School of Information Technology.
    Kärrman, Marcus
    Halmstad University, School of Information Technology.
    Svensson, Tim
    Halmstad University, School of Information Technology.
    Wickström, Nicholas
    Halmstad University, School of Information Technology, Halmstad Embedded and Intelligent Systems Research (EIS), CAISR - Center for Applied Intelligent Systems Research.
    Novel System Architecture for Online Gait Analysis2017Conference paper (Refereed)
    Abstract [en]

    Although wearable devices can be used to perform continuous gait analysis in daily life, existing platforms only support short-term analysis in quasi-controlled environments. This paper proposes a novel system architecture that is designed for long-term, online gait analysis in free-living environments. Various aspects related to the feasibility and scalability of the proposed system are presented.

  • 38.
    Bigun, Josef
    et al.
    Halmstad University, School of Information Technology, Halmstad Embedded and Intelligent Systems Research (EIS), CAISR - Center for Applied Intelligent Systems Research.
    Mikaelyan, Anna
    Halmstad University, School of Information Technology, Halmstad Embedded and Intelligent Systems Research (EIS), CAISR - Center for Applied Intelligent Systems Research.
    Dense frequency maps by Structure Tensor and logarithmic scale space: application to forensic fingerprintsManuscript (preprint) (Other academic)
    Abstract [en]

    Increasingly, reliable absolute frequency and orientation maps are needed, e.g. for image enhancement. Less studied is however the mutual dependence of both maps, and how to estimate them when none is known initially. We introduce a logarithmic scale space generated by the trace of Structure Tensor to study the relationship. The scale space is non-linear and absolute frequency estimation is reduced to an orientation estimation in it. We show that this offers significant advantages, including construction of efficient estimation methods, using Structure Tensor yielding dense maps of absolute frequency as well as orientation. In fingerprints, both maps can successively improve each other, combined in an image enhancement scheme via Gabor filtering. We verify that the suggested method compares favorably with state of the art, using forensic fingerprints recognition as test bed, and using test images where the ground truth is known. Furthermore, we suggest a novel continuous ridge counting method, relying only on dense absolute frequency and orientation maps, without ridge detection, thinning, etc. We present new evidence that the neighborhoods of the absolute frequency map are useful attributes of minutiae. In experiments, we use public data sets to support the conclusions.

  • 39.
    Bigun, Josef
    et al.
    Halmstad University, School of Information Technology, Halmstad Embedded and Intelligent Systems Research (EIS), CAISR - Center for Applied Intelligent Systems Research.
    Mikaelyan, Anna
    Halmstad University, School of Information Technology, Halmstad Embedded and Intelligent Systems Research (EIS).
    Frequency map by Structure Tensor in Logarithmic Scale Space and Forensic Fingerprints2016In: PROCEEDINGS OF 29TH IEEE CONFERENCE ON COMPUTER VISION AND PATTERN RECOGNITION WORKSHOPS, (CVPRW 2016), Piscataway, NJ: IEEE, 2016, p. 204-213, article id 7789522Conference paper (Refereed)
    Abstract [en]

    Increasingly, absolute frequency and orientation maps are needed, e.g. for forensics. We introduce a non-linear scale space via the logarithm of trace of the Structure Tensor. Therein, frequency estimation becomes an orientation estimation problem. We show that this offers significant advantages, including construction of efficient isotropic estimations of dense maps of frequency. In fingerprints, both maps are shown to improve each other in an enhancement scheme via Gabor filtering. We suggest a novel continuous ridge counting method, relying only on dense absolute frequency and orientation maps, without ridge detection, thinning, etc. Furthermore, we present new evidence that frequency maps are useful attributes of minutiae. We verify that the suggested method compares favorably with state of the art using forensic fingerprints as test bed, and test images where the ground truth is known. In evaluations, we use public data sets and published methods only.

  • 40.
    Bosshard, Pascal Fabian
    Halmstad University, School of Information Technology, Halmstad Embedded and Intelligent Systems Research (EIS), CAISR - Center for Applied Intelligent Systems Research.
    Investigation of Trajectory Optimization for Multiple Car-Like Vehicles2015Report (Other academic)
  • 41.
    Bouguelia, Mohamed-Rafik
    et al.
    Halmstad University, School of Information Technology, Halmstad Embedded and Intelligent Systems Research (EIS), CAISR - Center for Applied Intelligent Systems Research.
    Gonzalez, Ramon
    Robotic Mobility Group, Massachusetts Institute of Technology, Cambridge, USA.
    Iagnemma, Karl
    Robotic Mobility Group, Massachusetts Institute of Technology, Cambridge, USA.
    Byttner, Stefan
    Halmstad University, School of Information Technology, Halmstad Embedded and Intelligent Systems Research (EIS), CAISR - Center for Applied Intelligent Systems Research.
    Unsupervised classification of slip events for planetary exploration rovers2017In: Journal of terramechanics, ISSN 0022-4898, E-ISSN 1879-1204, Vol. 73, p. 95-106Article in journal (Refereed)
    Abstract [en]

    This paper introduces an unsupervised method for the classification of discrete rovers' slip events based on proprioceptive signals. In particular, the method is able to automatically discover and track various degrees of slip (i.e. low slip, moderate slip, high slip). The proposed method is based on aggregating the data over time, since high level concepts, such as high and low slip, are concepts that are dependent on longer time perspectives. Different features and subsets of the data have been identified leading to a proper clustering, interpreting those clusters as initial models of the prospective concepts. Bayesian tracking has been used in order to continuously improve the parameters of these models, based on the new data. Two real datasets are used to validate the proposed approach in comparison to other known unsupervised and supervised machine learning methods. The first dataset is collected by a single-wheel testbed available at MIT. The second dataset was collected by means of a planetary exploration rover in real off-road conditions. Experiments prove that the proposed method is more accurate (up to 86% of accuracy vs. 80% for K-means) in discovering various levels of slip while being fully unsupervised (no need for hand-labeled data for training). © 2017 ISTVS

  • 42.
    Bouguelia, Mohamed-Rafik
    et al.
    Halmstad University, School of Information Technology, Halmstad Embedded and Intelligent Systems Research (EIS), CAISR - Center for Applied Intelligent Systems Research.
    Karlsson, Alexander
    University of Skövde, Skövde, Sweden.
    Pashami, Sepideh
    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.
    Holst, Anders
    Swedish Institute of Computer Science, Kista, Sweden.
    Mode tracking using multiple data streams2018In: Information Fusion, ISSN 1566-2535, E-ISSN 1872-6305, Vol. 43, p. 33-46Article in journal (Refereed)
    Abstract [en]

    Most existing work in information fusion focuses on combining information with well-defined meaning towards a concrete, pre-specified goal. In contradistinction, we instead aim for autonomous discovery of high-level knowledge from ubiquitous data streams. This paper introduces a method for recognition and tracking of hidden conceptual modes, which are essential to fully understand the operation of complex environments. We consider a scenario of analyzing usage of a fleet of city buses, where the objective is to automatically discover and track modes such as highway route, heavy traffic, or aggressive driver, based on available on-board signals. The method we propose is based on aggregating the data over time, since the high-level modes are only apparent in the longer perspective. We search through different features and subsets of the data, and identify those that lead to good clusterings, interpreting those clusters as initial, rough models of the prospective modes. We utilize Bayesian tracking in order to continuously improve the parameters of those models, based on the new data, while at the same time following how the modes evolve over time. Experiments with artificial data of varying degrees of complexity, as well as on real-world datasets, prove the effectiveness of the proposed method in accurately discovering the modes and in identifying which one best explains the current observations from multiple data streams. © 2017 Elsevier B.V. All rights reserved.

  • 43.
    Bouguelia, Mohamed-Rafik
    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.
    Payberah, Amir H.
    Swedish Institute of Computer Science, Stockholm, Sweden.
    An adaptive algorithm for anomaly and novelty detection in evolving data streams2018In: Data mining and knowledge discovery, ISSN 1384-5810, E-ISSN 1573-756X, Vol. 32, no 6, p. 1597-1633Article in journal (Refereed)
    Abstract [en]

    In the era of big data, considerable research focus is being put on designing efficient algorithms capable of learning and extracting high-level knowledge from ubiquitous data streams in an online fashion. While, most existing algorithms assume that data samples are drawn from a stationary distribution, several complex environments deal with data streams that are subject to change over time. Taking this aspect into consideration is an important step towards building truly aware and intelligent systems. In this paper, we propose GNG-A, an adaptive method for incremental unsupervised learning from evolving data streams experiencing various types of change. The proposed method maintains a continuously updated network (graph) of neurons by extending the Growing Neural Gas algorithm with three complementary mechanisms, allowing it to closely track both gradual and sudden changes in the data distribution. First, an adaptation mechanism handles local changes where the distribution is only non-stationary in some regions of the feature space. Second, an adaptive forgetting mechanism identifies and removes neurons that become irrelevant due to the evolving nature of the stream. Finally, a probabilistic evolution mechanism creates new neurons when there is a need to represent data in new regions of the feature space. The proposed method is demonstrated for anomaly and novelty detection in non-stationary environments. Results show that the method handles different data distributions and efficiently reacts to various types of change. © 2018 The Author(s)

  • 44.
    Bouguelia, Mohamed-Rafik
    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.
    Santosh, K. C.
    The University of South Dakota, Vermillion, South Dakota, USA.
    Verikas, Antanas
    Halmstad University, School of Information Technology, Halmstad Embedded and Intelligent Systems Research (EIS), CAISR - Center for Applied Intelligent Systems Research.
    Agreeing to disagree: active learning with noisy labels without crowdsourcing2018In: International Journal of Machine Learning and Cybernetics, ISSN 1868-8071, E-ISSN 1868-808X, Vol. 9, no 8, p. 1307-1319Article in journal (Refereed)
    Abstract [en]

    We propose a new active learning method for classification, which handles label noise without relying on multiple oracles (i.e., crowdsourcing). We propose a strategy that selects (for labeling) instances with a high influence on the learned model. An instance x is said to have a high influence on the model h, if training h on x (with label y = h(x)) would result in a model that greatly disagrees with h on labeling other instances. Then, we propose another strategy that selects (for labeling) instances that are highly influenced by changes in the learned model. An instance x is said to be highly influenced, if training h with a set of instances would result in a committee of models that agree on a common label for x but disagree with h(x). We compare the two strategies and we show, on different publicly available datasets, that selecting instances according to the first strategy while eliminating noisy labels according to the second strategy, greatly improves the accuracy compared to several benchmarking methods, even when a significant amount of instances are mislabeled. © Springer-Verlag Berlin Heidelberg 2017

  • 45.
    Bouguelia, Mohamed-Rafik
    et al.
    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.
    Nowaczyk, Sławomir
    Halmstad University, School of Information Technology, Halmstad Embedded and Intelligent Systems Research (EIS), CAISR - Center for Applied Intelligent Systems Research.
    Multi-Task Representation Learning2017In: 30th Annual Workshop ofthe Swedish Artificial Intelligence Society SAIS 2017: May 15–16, 2017, Karlskrona, Sweden / [ed] Niklas Lavesson, Linköping: Linköping University Electronic Press, 2017, p. 53-59Conference paper (Refereed)
    Abstract [en]

    The majority of existing machine learning algorithms assume that training examples are already represented with sufficiently good features, in practice ones that are designed manually. This traditional way of preprocessing the data is not only tedious and time consuming, but also not sufficient to capture all the different aspects of the available information. With big data phenomenon, this issue is only going to grow, as the data is rarely collected and analyzed with a specific purpose in mind, and more often re-used for solving different problems. Moreover, the expert knowledge about the problem which allows them to come up with good representations does not necessarily generalize to other tasks. Therefore, much focus has been put on designing methods that can automatically learn features or representations of the data instead of learning from handcrafted features. However, a lot of this work used ad hoc methods and the theoretical understanding in this area is lacking.

  • 46.
    Bouguerra, Abdelbaki
    et al.
    Örebro University, Sweden.
    Andreasson, Henrik
    Örebro University, Sweden.
    Lilienthal, Achim J.
    Örebro University, Sweden.
    Åstrand, Björn
    Halmstad University, School of Information Technology, Halmstad Embedded and Intelligent Systems Research (EIS), CAISR - Center for Applied Intelligent Systems Research.
    Rögnvaldsson, Thorsteinn
    Halmstad University, School of Information Technology, Halmstad Embedded and Intelligent Systems Research (EIS), CAISR - Center for Applied Intelligent Systems Research.
    An Autonomous Robotic System for Load Transportation2009In: 2009 IEEE CONFERENCE ON EMERGING TECHNOLOGIES & FACTORY AUTOMATION (EFTA 2009), Piscataway, N.J.: IEEE, 2009Conference paper (Refereed)
    Abstract [en]

    This paper presents an overview of an autonomous robotic system for material handling. The system is being developed by extending the functionalities of traditional AGVs to be able to operate reliably and safely in highly dynamic environments. Traditionally, the reliable functioning of AGVs relies on the availability of adequate infrastructure to support navigation. In the target environments of our system, such infrastructure is difficult to setup in an efficient way. Additionally, the location of objects to handle are unknown, which requires runtime object detection and tracking. Another requirement to be fulfilled by the system is the ability to generate trajectories dynamically, which is uncommon in industrial AGV systems.

  • 47.
    Byttner, Stefan
    et al.
    Halmstad University, School of Information Technology, Halmstad Embedded and Intelligent Systems Research (EIS), CAISR - Center for Applied Intelligent Systems Research.
    Holmberg, Ulf
    Halmstad University, School of Information Technology, Halmstad Embedded and Intelligent Systems Research (EIS).
    Ion Current Based Control of Combustion Variability2003Conference paper (Other academic)
  • 48.
    Byttner, Stefan
    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.
    Prytz, Rune
    Volvo Group Trucks Technology, Gothenburg, Sweden.
    Rögnvaldsson, Thorsteinn
    Halmstad University, School of Information Technology, Halmstad Embedded and Intelligent Systems Research (EIS), CAISR - Center for Applied Intelligent Systems Research.
    A field test with self-organized modeling for knowledge discovery in a fleet of city buses2013In: 2013 IEEE International Conference on Mechatronics and Automation (ICMA 2013) / [ed] Shuxiang Guo, Piscataway, NJ: IEEE Press, 2013, p. 896-901, article id 6618034Conference paper (Refereed)
    Abstract [en]

    Fleets of commercial vehicles represent an excellent real life setting for ubiquitous knowledge discovery. There are many electronic control units onboard a modern bus or truck, with hundreds of signals being transmitted between them on the controller area network. The growing complexity of the vehicles has lead to a significant desire to have systems for fault detection, remote diagnostics and maintenance prediction. This paper aims to show that it is possible to discover useful diagnostic knowledge by a self-organized algorithm in the scenario of a fleet of city buses. The approach is demonstrated as a process consisting of two parts; Unsupervised modeling (where interesting features are discovered) and Guided search (where the previously found features are coupled to additional information sources). The modeling part searches for simple linear models in a group of vehicles, where interesting features are selected based on both non-randomness in relations and variability in the group. It is shown in an eight months long data collection study that this approach was able to discover features related to broken wheelspeed sensors. Strikingly, deviations in these features (for the vehicles with broken sensors) can be observed up to several months before a breakdown occur. This potentially allows for sufficient time to schedule the vehicle for maintenance and prepare the workshop with relevant components. © 2013 IEEE.

  • 49.
    Byttner, Stefan
    et al.
    Halmstad University, School of Information Technology, Halmstad Embedded and Intelligent Systems Research (EIS), CAISR - Center for Applied Intelligent Systems Research.
    Rögnvaldsson, Thorsteinn
    Halmstad University, School of Information Technology, Halmstad Embedded and Intelligent Systems Research (EIS), CAISR - Center for Applied Intelligent Systems Research.
    Wickström, Nicholas
    Halmstad University, School of Information Technology, Halmstad Embedded and Intelligent Systems Research (EIS), CAISR - Center for Applied Intelligent Systems Research.
    Strategies for handling the fuel additive problem in neural network based ion current interpretation2001Conference paper (Refereed)
    Abstract [en]

    With the introduction of unleaded gasoline, special fuel agents have appeared on the market for lubricating and cleaning the valve seats. These fuel agents often contain alkali metals that have a significant impact on the ion current signal, thus affecting strategies that use the ion current for engine control and diagnosis, e.g., for estimating the location of the pressure peak. This paper introduces a method for making neural network algorithms robust to expected disturbances in the input signal and demonstrates how well this method applies to the case of disturbances to the ion current signal due to fuel additives containing sodium. The performance of the neural estimators is compared to a Gaussian fit algorithm, which they outperform. It is also shown that using a fuel additive significantly improves the estimation of the location of the pressure peak. © 2001 Society of Automotive Engineers, Inc.

  • 50.
    Calikus, Ece
    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.
    Pinheiro Sant'Anna, Anita
    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.
    Ranking Abnormal Substations by Power Signature Dispersion2018In: Energy Procedia, ISSN 1876-6102, E-ISSN 1876-6102, Vol. 149, p. 345-353Article in journal (Refereed)
    Abstract [en]

    The relation between heat demand and outdoor temperature (heat power signature) is a typical feature used to diagnose abnormal heat demand. Prior work is mainly based on setting thresholds, either statistically or manually, in order to identify outliers in the power signature. However, setting the correct threshold is a difficult task since heat demand is unique for each building. Too loose thresholds may allow outliers to go unspotted, while too tight thresholds can cause too many false alarms.

    Moreover, just the number of outliers does not reflect the dispersion level in the power signature. However, high dispersion is often caused by fault or configuration problems and should be considered while modeling abnormal heat demand.

    In this work, we present a novel method for ranking substations by measuring both dispersion and outliers in the power signature. We use robust regression to estimate a linear regression model. Observations that fall outside of the threshold in this model are considered outliers. Dispersion is measured using coefficient of determination R2 which is a statistical measure of how close the data are to the fitted regression line.

    Our method first produces two different lists by ranking substations using number of outliers and dispersion separately. Then, we merge the two lists into one using the Borda Count method. Substations appearing on the top of the list should indicate higher abnormality in heat demand compared to the ones on the bottom. We have applied our model on data from substations connected to two district heating networks in the south of Sweden. Three different approaches i.e. outlier-based, dispersion-based and aggregated methods are compared against the rankings based on return temperatures. The results show that our method significantly outperforms the state-of-the-art outlier-based method. © 2018 The Authors. Published by Elsevier Ltd.

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