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  • 101.
    Gelzinis, Adas
    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.
    Vaiciukynas, Evaldas
    Kaunas University of Technology, Kaunas, Lithuania.
    Bacauskiene, Marija
    Kaunas University of Technology, Kaunas, Lithuania.
    Šulčius, Sigitas
    Marine Science and Technology Center, Klaipeda University, Klaipeda, Lithuania & Open Access Centre for Nature Research, Nature Research Centre, Vilnius, Lithuania.
    Staniulis, Juozas
    Laboratory of Plant Viruses, Nature Research Centre, Institute of Botany, Vilnius, Lithuania.
    Paškauskas, Ričardas
    Marine Science and Technology Center, Klaipeda University, Klaipeda, Lithuania & Laboratory of Algology and Microbial Ecology, Nature Research Centre, Vilnius, Lithuania.
    Automatic detection and morphological delineation of bacteriophages in electron microscopy images2015In: Computers in Biology and Medicine, ISSN 0010-4825, E-ISSN 1879-0534, Vol. 64, p. 101-116Article in journal (Refereed)
    Abstract [en]

    Automatic detection, recognition and geometric characterization of bacteriophages in electron microscopy images was the main objective of this work. A novel technique, combining phase congruency-based image enhancement, Hough transform-, Radon transform- and open active contours with free boundary conditions-based object detection was developed to detect and recognize the bacteriophages associated with infection and lysis of cyanobacteria Aphanizomenon flos-aquae. A random forest classifier designed to recognize phage capsids provided higher than 99% accuracy, while measurable phage tails were detected and associated with a correct capsid with 81.35% accuracy. Automatically derived morphometric measurements of phage capsids and tails exhibited lower variability than the ones obtained manually. The technique allows performing precise and accurate quantitative (e.g. abundance estimation) and qualitative (e.g. diversity and capsid size) measurements for studying the interactions between host population and different phages that infect the same host. © 2015 Elsevier Ltd.

  • 102.
    Gholami Shahbandi, Saeed
    Halmstad University, School of Information Technology, Halmstad Embedded and Intelligent Systems Research (EIS), CAISR - Center for Applied Intelligent Systems Research.
    Interpretation and Alignment of 2D Indoor Maps: Towards a Heterogeneous Map Representation2018Doctoral thesis, comprehensive summary (Other academic)
    Abstract [en]

    Mobile robots are increasingly being used in automation solutions with notable examples in service robots, such as home-care, and warehouses. Autonomy of mobile robots is particularly challenging, since their work space is not deterministic, known a priori, or fully predictable. Accordingly, the ability to model the work space, that is robotic mapping, is among the core technologies that are the backbone of autonomous mobile robots. However, for some applications the abilities of mapping and localization do not meet all the requirements, and robots with an enhanced awareness of their surroundings are desired. For instance, a map augmented with semantic labels is instrumental to support Human-Robot Interaction and high-level task planning and reasoning.This thesis addresses this requirement through an interpretation and integration of multiple input maps into a semantically annotated heterogeneous representation. The heterogeneity of the representation should to contain different interpretations of an input map, establish and maintain associations among different input sources, and construct a hierarchy of abstraction through model-based representation. The structuring and construction of this representation are at the core of this thesis, and the main objectives are: a) modeling, interpretation, semantic annotation, and association of the different data sources into a heterogeneous representation, and b) improving the autonomy of the aforementioned processes by curtailing the dependency of the methods on human input, such as domain knowledge.This work proposes map interpretation techniques, such as abstract representation through modeling and semantic annotation, in an attempt to enrich the final representation. In order to associate multiple data sources, this work also proposes a map alignment method. The contributions and general observations that result from the studies included in this work could be summarized as: i) manner of structuring the heterogeneous representation, ii) underlining the advantages of modeling and abstract representations, iii) several approaches to semantic annotation, and iv) improved extensibility of methods by lessening their dependency on human input.The scope of the work has been focused on 2D maps of well-structured indoor environments, such as warehouses, home, and office buildings.

  • 103.
    Gholami Shahbandi, Saeed
    Halmstad University, School of Information Technology, Halmstad Embedded and Intelligent Systems Research (EIS), CAISR - Center for Applied Intelligent Systems Research.
    Semantic Mapping in Warehouses2016Licentiate thesis, comprehensive summary (Other academic)
    Abstract [en]

    This thesis and appended papers present the process of tacking the problem of environment modeling for autonomous agent. More specifically, the focus of the work has been semantic mapping of warehouses. A semantic map for such purpose is expected to be layout-like and support semantics of both open spaces and infrastructure of the environment. The representation of the semantic map is required to be understandable by all involved agents (humans, AGVs and WMS.) And the process of semantic mapping is desired to lean toward full-autonomy, with minimum input requirement from human user. To that end, we studied the problem of semantic annotation over two kinds of spatial map from different modalities. We identified properties, structure, and challenges of the problem. And we have developed representations and accompanied methods, while meeting the set criteria. The overall objective of the work is “to develop and construct a layer of abstraction (models and/or decomposition) for structuring and facilitate access to salient information in the sensory data. This layer of abstraction connects high level concepts to low-level sensory pattern.” Relying on modeling and decomposition of sensory data, we present our work on abstract representation for two modalities (laser scanner and camera) in three appended papers. Feasibility and the performance of the proposed methods are evaluated over data from real warehouse. The thesis conclude with summarizing the presented technical details, and drawing the outline for future work.

  • 104.
    Gholami Shahbandi, Saeed
    et al.
    Halmstad University, School of Information Technology, Halmstad Embedded and Intelligent Systems Research (EIS), CAISR - Center for Applied Intelligent Systems Research.
    Magnusson, Martin
    Örebro University, Örebro, Sweden.
    2D Map Alignment With Region Decomposition2019In: Autonomous Robots, ISSN 0929-5593, E-ISSN 1573-7527, Vol. 43, no 5, p. 1117-1136Article in journal (Refereed)
    Abstract [en]

    In many applications of autonomous mobile robots the following problem is encountered. Two maps of the same environment are available, one a prior map and the other a sensor map built by the robot. To benefit from all available information in both maps, the robot must find the correct alignment between the two maps. There exist many approaches to address this challenge, however, most of the previous methods rely on assumptions such as similar modalities of the maps, same scale, or existence of an initial guess for the alignment. In this work we propose a decomposition-based method for 2D spatial map alignment which does not rely on those assumptions. Our proposed method is validated and compared with other approaches, including generic data association approaches and map alignment algorithms. Real world examples of four different environments with thirty six sensor maps and four layout maps are used for this analysis. The maps, along with an implementation of the method, are made publicly available online. © 2018, The Author(s).

  • 105.
    Gholami Shahbandi, Saeed
    et al.
    Halmstad University, School of Information Technology, Halmstad Embedded and Intelligent Systems Research (EIS), CAISR - Center for Applied Intelligent Systems Research.
    Magnusson, Martin
    Örebro University, Örebro, Sweden .
    Iagnemma, Karl
    Massachusetts Institute of Technology, Cambridge, MA, USA.
    Nonlinear Optimization of Multimodal Two-Dimensional Map Alignment With Application to Prior Knowledge Transfer2018In: IEEE Robotics and Automation Letters, ISSN 2377-3766, E-ISSN 1949-3045, Vol. 3, no 3, p. 2040-2047Article in journal (Refereed)
    Abstract [en]

    We propose a method based on a nonlinear transformation for nonrigid alignment of maps of different modalities, exemplified with matching partial and deformed two-dimensional maps to layout maps. For two types of indoor environments, over a dataset of 40 maps, we have compared the method to state-of-the-art map matching and nonrigid image registration methods and demonstrate a success rate of 80.41% and a mean point-to-point alignment error of 1.78 m, compared to 31.9% and 10.7 m for the best alternative method. We also propose a fitness measure that can quite reliably detect bad alignments. Finally, we show a use case of transferring prior knowledge (labels/segmentation), demonstrating that map segmentation is more consistent when transferred from an aligned layout map than when operating directly on partial maps (95.97% vs. 81.56%). © 2018 IEEE.

  • 106.
    Gholami Shahbandi, Saeed
    et al.
    Halmstad University, School of Information Technology, Halmstad Embedded and Intelligent Systems Research (EIS), CAISR - Center for Applied Intelligent Systems Research.
    Åstrand, Björn
    Halmstad University, School of Information Technology, Halmstad Embedded and Intelligent Systems Research (EIS), CAISR - Center for Applied Intelligent Systems Research.
    Modeling of a Large Structured Environment: With a Repetitive Canonical Geometric-Semantic Model2014In: Advances in Autonomous Robotics Systems: 15th Annual Conference, TAROS 2014, Birmingham, UK, September 1-3, 2014. Proceedings / [ed] Michael Mistry, Aleš Leonardis, Mark Witkowski & Chris Melhuish, Heidelberg: Springer, 2014, Vol. 8717, p. 1-12Conference paper (Refereed)
    Abstract [en]

    AIMS project attempts to link the logistic requirements of an intelligent warehouse and state of the art core technologies of automation, by providing an awareness of the environment to the autonomous systems and vice versa. In this work we investigate a solution for modeling the infrastructure of a structured environment such as warehouses, by the means of a vision sensor. The model is based on the expected pattern of the infrastructure, generated from and matched to the map. Generation of the model is based on a set of tools such as closed-form Hough transform, DBSCAN clustering algorithm, Fourier transform and optimization techniques. The performance evaluation of the proposed method is accompanied with a real world experiment. © 2014 Springer International Publishing.

  • 107.
    Gholami Shahbandi, Saeed
    et al.
    Halmstad University, School of Information Technology, Halmstad Embedded and Intelligent Systems Research (EIS), CAISR - Center for Applied Intelligent Systems Research.
    Åstrand, Björn
    Halmstad University, School of Information Technology, Halmstad Embedded and Intelligent Systems Research (EIS), CAISR - Center for Applied Intelligent Systems Research.
    Philippsen, Roland
    Halmstad University, School of Information Technology, Halmstad Embedded and Intelligent Systems Research (EIS), CAISR - Center for Applied Intelligent Systems Research.
    Semi-Supervised Semantic Labeling of Adaptive Cell Decomposition Maps in Well-Structured Environments2015In: 2015 European Conference on Mobile Robots (ECMR), Piscataway, NJ: IEEE Press, 2015, article id 7324207Conference paper (Refereed)
    Abstract [en]

    We present a semi-supervised approach for semantic mapping, by introducing human knowledge after unsupervised place categorization has been combined with an adaptive cell decomposition of an occupancy map. Place categorization is based on clustering features extracted from raycasting in the occupancy map. The cell decomposition is provided by work we published previously, which is effective for the maps that could be abstracted by straight lines. Compared to related methods, our approach obviates the need for a low-level link between human knowledge and the perception and mapping sub-system, or the onerous preparation of training data for supervised learning. Application scenarios include intelligent warehouse robots which need a heightened awareness in order to operate with a higher degree of autonomy and flexibility, and integrate more fully with inventory management systems. The approach is shown to be robust and flexible with respect to different types of environments and sensor setups. © 2015 IEEE

  • 108.
    Gholami Shahbandi, Saeed
    et al.
    Halmstad University, School of Information Technology, Halmstad Embedded and Intelligent Systems Research (EIS), CAISR - Center for Applied Intelligent Systems Research.
    Åstrand, Björn
    Halmstad University, School of Information Technology, Halmstad Embedded and Intelligent Systems Research (EIS), CAISR - Center for Applied Intelligent Systems Research.
    Philippsen, Roland
    Halmstad University, School of Information Technology, Halmstad Embedded and Intelligent Systems Research (EIS), CAISR - Center for Applied Intelligent Systems Research.
    Sensor Based Adaptive Metric-Topological Cell Decomposition Method for Semantic Annotation of Structured Environments2014In: 2014 13th International Conference on Control Automation Robotics & Vision (ICARCV), Piscataway, NJ: IEEE Press, 2014, p. 1771-1777, article id 7064584Conference paper (Refereed)
    Abstract [en]

    A fundamental ingredient for semantic labeling is a reliable method for determining and representing the relevant spatial features of an environment. We address this challenge for planar metric-topological maps based on occupancy grids. Our method detects arbitrary dominant orientations in the presence of significant clutter, fits corresponding line features with tunable resolution, and extracts topological information by polygonal cell decomposition. Real-world case studies taken from the target application domain (autonomous forklift trucks in warehouses) demonstrate the performance and robustness of our method, while results from a preliminary algorithm to extract corridors, and junctions, demonstrate its expressiveness. Contribution of this work starts with the formulation of metric-topological surveying of environment, and a generic n-direction planar representation accompanied with a general method for extracting it from occupancy map. The implementation also includes some semantic labels specific to warehouse like environments. © 2014 IEEE.

  • 109.
    Gonzalez, Ramon
    et al.
    Massachusetts Institute of Technology, Cambridge, MA, USA.
    Byttner, Stefan
    Halmstad University, School of Information Technology, Halmstad Embedded and Intelligent Systems Research (EIS), CAISR - Center for Applied Intelligent Systems Research.
    Iagnemma, Karl
    Massachusetts Institute of Technology, Cambridge, MA, USA.
    Comparison of Machine Learning Approaches for Soil Embedding Detection of Planetary Exploration Rovers2016In: Proceedings of the 8th ISTVS Americas Conference, Detroit, September 12-14, 2016., 2016Conference paper (Refereed)
    Abstract [en]

    This paper analyzes the advantages and limitations of known machine learning approaches to cope with the problem of incipient rover embedding detection based on propioceptive signals. In particular, two supervised learning approaches (Support Vector Machines and Feed-forward Neural Networks) are compared to two unsupervised learning approaches (K-means and Self-Organizing Maps) in order to identify various degrees of slip (e.g. low slip, moderate slip, high slip). A real dataset collected by a single-wheel testbed available at MIT has been used to validate each strategy. The SVM algorithm achieves the best performance (accuracy >95 %). However, the SOM algorithm represents a better solution in terms of accuracy and the need of hand-labeled data for training the classifier (accuracy >84 %).

  • 110.
    Gonzalez-Sosa, Ester
    et al.
    Nokia Bell-Labs, Madrid, Spain & Universidad Autonoma de Madrid, Madrid, Spain.
    Fierrez, Julian
    Universidad Autonoma de Madrid, Madrid, Spain.
    Vera-Rodriguez, Ruben
    Universidad Autonoma de Madrid, Madrid, Spain.
    Alonso-Fernandez, Fernando
    Halmstad University, School of Information Technology, Halmstad Embedded and Intelligent Systems Research (EIS), CAISR - Center for Applied Intelligent Systems Research.
    Facial Soft Biometrics for Recognition in the Wild: Recent Works, Annotation and Evaluation2018In: IEEE Transactions on Information Forensics and Security, ISSN 1556-6013, E-ISSN 1556-6021, Vol. 13, no 8, p. 2001-2014Article in journal (Refereed)
    Abstract [en]

    The role of soft biometrics to enhance person recognition systems in unconstrained scenarios has not been extensively studied. Here, we explore the utility of the following modalities: gender, ethnicity, age, glasses, beard, and moustache. We consider two assumptions: 1) manual estimation of soft biometrics and 2) automatic estimation from two commercial off-the-shelf systems (COTS). All experiments are reported using the labeled faces in the wild (LFW) database. First, we study the discrimination capabilities of soft biometrics standalone. Then, experiments are carried out fusing soft biometrics with two state-of-the-art face recognition systems based on deep learning. We observe that soft biometrics is a valuable complement to the face modality in unconstrained scenarios, with relative improvements up to 40%/15% in the verification performance when using manual/automatic soft biometrics estimation. Results are reproducible as we make public our manual annotations and COTS outputs of soft biometrics over LFW, as well as the face recognition scores. © 2018 IEEE.

  • 111.
    Gottschlich, Carsten
    et al.
    University of Göttingen, Göttingen, Germany.
    Mikaelyan, Anna
    Halmstad University, School of Information Technology, Halmstad Embedded and Intelligent Systems Research (EIS), CAISR - Center for Applied Intelligent Systems Research.
    Olsen, Martin Aastrup
    Norwegian Biometrics Laboratory, Gjøvik University College, Gjøvik, Norway.
    Bigun, Josef
    Halmstad University, School of Information Technology, Halmstad Embedded and Intelligent Systems Research (EIS), CAISR - Center for Applied Intelligent Systems Research.
    Busch, Christoph
    Norwegian Biometrics Laboratory, Gjøvik University College, Gjøvik, Norway.
    Improving Fingerprint Alteration Detection2015In: 2015 9th International Symposium on Image and Signal Processing and Analysis (ISPA) / [ed] Sven Lončarić, Dick Lerski, Hannu Eskola, Robert Bregović, Zagreb: University of Zagreb , 2015, p. 83-86Conference paper (Refereed)
    Abstract [en]

    Fingerprint alteration is a type of presentation attack in which the attacker strives to avoid identification, e.g. at border control or in forensic investigations. As a countermeasure, fingerprint alteration detection aims to automatically discover the occurrence of such attacks by classifying fingerprint images as ’normal’ or ’altered’. In this paper, we propose four new features for improving the performance of fingerprint alteration detection modules. We evaluate the usefulness of these features on a benchmark and compare them to four existing features from the literature. © Copyright 2015 IEEE - All rights reserved.

  • 112.
    Hedenberg, Klas
    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. University of Skövde, Skövde, Sweden.
    Obstacle Detection for Driverless Trucks in Industrial Environments2014Licentiate thesis, comprehensive summary (Other academic)
    Abstract [en]

    With an increased demand on productivity and safety in industry, new issues in terms of automated material handling arise. This results in industries not having a homogenous fleet of trucks and driven and driverless trucks are mixed in a dynamic environment. Driven trucks are more flexible than driverless trucks, but are also involved in more accidents. A transition from driven to driverless trucks can increase safety, but also productivity in terms of fewer accidents and more accurate delivery. Hence, reliable and standardized solutions that avoid accidents are important to achieve high productivity and safety. There are two different safety standards for driverless trucks for Europe (EN1525) and U.S. (B56.5–2012) and they have developed differently. In terms of obstacles, they both consider contact with humans. However, a machinery-shaped object has recently been added to the U.S. standard (B56.5–2012). The U.S. standard also considers different materials for different sensors and non-contact sensors. For obstacle detection, the historical contact-sensitive mechanical bumpers as well as the traditional laser scanner used today both have limitations – they do not detect hanging objects. In this work we have identified several thin objects that are of interest in an industrial environment. A test apparatus with a thin structure is introduced for a more uniform way to evaluate sensors. To detect thin obstacles, we used a standard setup of a stereo system and developed this further to a trinocular system (a stereo system with three cameras). We also propose a method to evaluate 3D sensors based on the information from a 2D range sensor. The 3D model is created by measuring the position of a reflector with known position to an object with a known size. The trinocular system, a 3D TOF camera and a Kinect sensor are evaluated with this method. The results showed that the method can be used to evaluate sensors. It also showed that 3D sensor systems have potential to be used on driverless trucks to detect obstacles, initially as a complement to existing safety classed sensors. To improve safety and productivity, there is a need for harmonization of the European and the U.S. safety standards. Furthermore, parallel development of sensor systems and standards is needed to make use of state-of-the-art technology for sensors.

  • 113.
    Hedenberg, Klas
    et al.
    Skövde University, Skövde, Sweden.
    Åstrand, Björn
    Halmstad University, School of Information Technology, Halmstad Embedded and Intelligent Systems Research (EIS), CAISR - Center for Applied Intelligent Systems Research.
    3D Sensors on Driverless Trucks for Detection of Overhanging Objects in the Pathway2015In: Autonomous Industrial Vehicles: From the Laboratory to the Factory Floor / [ed] Roger Bostelman & Elena Messina, Conshohocken: ASTM International, 2015, p. 41-56Chapter in book (Refereed)
    Abstract [en]

    Human-operated and driverless trucks often collaborate in a mixed work space in industries and warehouses. This is more efficient and flexible than using only one kind of truck. However, since driverless trucks need to give way to trucks, a reliable detection system is required. Several challenges exist in the development of an obstacle detection system in an industrial setting. The first is to select interesting situations and objects. Overhanging objects are often found in industrial environments, e.g. tines on a forklift. Second is choosing a detection system that has the ability to detect those situations. The traditional laser scanner situated two decimetres above the floor does not detect overhanging objects. Third is to ensure that the perception system is reliable. A solution used on trucks today is to mount a 2D laser scanner on the top of the truck and tilt the scanner towards the floor. However, objects at the top of the truck will be detected too late and a collision cannot always be avoided. Our aim is to replace the upper 2D laser scanner with a 3D camera, structural light or time-of-flight (TOF) camera. It is important to maximize the field of view in the desired detection volume. Hence, the placement of the sensor is important. We conducted laboratory experiments to check and compare the various sensors’ capabilities for different colors, used tines and a model of a tine in a controlled industrial environment. We also conducted field experiments in a warehouse. The conclusion is that both the tested structural light and TOF sensors have problems to detect black items that is nonperpendicular to the sensor and at the distance of interest. It is important to optimize the light economy, meaning the illumination power, field of view and exposure time in order to detect as many different objects as possible. Copyright © 2016 by ASTM International

  • 114.
    Helldin, Tove
    et al.
    University of Skövde, Skövde, Sweden.
    Riveiro, Maria
    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.
    Falkman, Göran
    University of Skövde, Skövde, Sweden.
    Byttner, Stefan
    Halmstad University, School of Information Technology, Halmstad Embedded and Intelligent Systems Research (EIS), CAISR - Center for Applied Intelligent Systems Research.
    Slawomir, Nowaczyk
    Halmstad University, School of Information Technology, Halmstad Embedded and Intelligent Systems Research (EIS), CAISR - Center for Applied Intelligent Systems Research.
    Supporting Analytical Reasoning: A Study from the Automotive Industry2016In: Human Interface and the Management of Information: Applications and Services: 18th International Conference, HCI International 2016: Toronto, Canada, July 17-22, 2016. Proceedings, Part II / [ed] Sakae Yamamoto, Cham: Springer, 2016, Vol. 9735, p. 20-31Conference paper (Refereed)
    Abstract [en]

    In the era of big data, it is imperative to assist the human analyst in the endeavor to find solutions to ill-defined problems, i.e. to “detect the expected and discover the unexpected” (Yi et al., 2008). To their aid, a plethora of analysis support systems is available to the analysts. However, these support systems often lack visual and interactive features, leaving the analysts with no opportunity to guide, influence and even understand the automatic reasoning performed and the data used. Yet, to be able to appropriately support the analysts in their sense-making process, we must look at this process more closely. In this paper, we present the results from interviews performed together with data analysts from the automotive industry where we have investigated how they handle the data, analyze it and make decisions based on the data, outlining directions for the development of analytical support systems within the area. © Springer International Publishing Switzerland 2016.

  • 115.
    Hernandez-Diaz, Kevin
    et al.
    Halmstad University, School of Information Technology, Halmstad Embedded and Intelligent Systems Research (EIS), CAISR - Center for Applied Intelligent Systems Research.
    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.
    Cross Spectral Periocular Matching using ResNet Features2019Conference paper (Refereed)
  • 116.
    Hernandez-Diaz, Kevin
    et al.
    Halmstad University, School of Information Technology, Halmstad Embedded and Intelligent Systems Research (EIS), CAISR - Center for Applied Intelligent Systems Research.
    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.
    Periocular Recognition Using CNN Features Off-the-Shelf2018In: 2018 International Conference of the Biometrics Special Interest Group (BIOSIG), Piscataway, N.J.: IEEE, 2018Conference paper (Refereed)
    Abstract [en]

    Periocular refers to the region around the eye, including sclera, eyelids, lashes, brows and skin. With a surprisingly high discrimination ability, it is the ocular modality requiring the least constrained acquisition. Here, we apply existing pre-trained architectures, proposed in the context of the ImageNet Large Scale Visual Recognition Challenge, to the task of periocular recognition. These have proven to be very successful for many other computer vision tasks apart from the detection and classification tasks for which they were designed. Experiments are done with a database of periocular images captured with a digital camera. We demonstrate that these offthe-shelf CNN features can effectively recognize individuals based on periocular images, despite being trained to classify generic objects. Compared against reference periocular features, they show an EER reduction of up to ~40%, with the fusion of CNN and traditional features providing additional improvements.

  • 117.
    Hofbauer, Heinz
    et al.
    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.
    Bigun, Josef
    Halmstad University, School of Information Technology, Halmstad Embedded and Intelligent Systems Research (EIS), CAISR - Center for Applied Intelligent Systems Research.
    Uhl, Andreas
    University of Salzburg, Salzburg, Austria.
    Experimental Analysis Regarding the Influence of Iris Segmentation on the Recognition Rate2016In: IET Biometrics, ISSN 2047-4938, E-ISSN 2047-4946, Vol. 5, no 3, p. 200-211Article in journal (Refereed)
    Abstract [en]

    In this study the authors will look at the detection and segmentation of the iris and its influence on the overall performance of the iris-biometric tool chain. The authors will examine whether the segmentation accuracy, based on conformance with a ground truth, can serve as a predictor for the overall performance of the iris-biometric tool chain. That is: If the segmentation accuracy is improved will this always improve the overall performance? Furthermore, the authors will systematically evaluate the influence of segmentation parameters, pupillary and limbic boundary and normalisation centre (based on Daugman's rubbersheet model), on the rest of the iris-biometric tool chain. The authors will investigate if accurately finding these parameters is important and how consistency, that is, extracting the same exact region of the iris during segmenting, influences the overall performance. © The Institution of Engineering and Technology 2016

  • 118.
    Hofbauer, Heinz
    et al.
    Department of Computer Sciences, 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.
    Wild, Peter
    Department of Computer Sciences, University of Salzburg, Salzburg, Austria.
    Bigun, Josef
    Halmstad University, School of Information Technology, Halmstad Embedded and Intelligent Systems Research (EIS), CAISR - Center for Applied Intelligent Systems Research.
    Uhl, Andreas
    Department of Computer Sciences, University of Salzburg, Salzburg, Austria.
    A Ground Truth for Iris Segmentation2014In: 2014 22nd International Conference on Pattern Recognition (ICPR) / [ed] Lisa O’Conner, Los Alamitos: IEEE Computer Society, 2014, p. 527-532Conference paper (Refereed)
    Abstract [en]

    Classical iris biometric systems assume ideal environmental conditions and cooperative users for image acquisition. When conditions are less ideal or users are uncooperative or unaware of their biometrics being taken the image acquisition quality suffers. This makes it harder for iris localization and segmentation algorithms to properly segment the acquired image into iris and non-iris parts. Segmentation is a critical part in iris recognition systems, since errors in this initial stage are propagated to subsequent processing stages. Therefore, the performance of iris segmentation algorithms is paramount to the performance of the overall system. In order to properly evaluate and develop iris segmentation algorithm, especially under difficult conditions like off angle and significant occlusions or bad lighting, it is beneficial to directly assess the segmentation algorithm. Currently, when evaluating the performance of iris segmentation algorithms this is mostly done by utilizing the recognition rate, and consequently the overall performance of the biometric system. In order to streamline the development and assessment of iris segmentation algorithms with the dependence on the whole biometric system we have generated a iris segmentation ground truth database. We will show a method for evaluating iris segmentation performance base on this ground truth database and give examples of how to identify problematic cases in order to further analyse the segmentation algorithms. ©2014 IEEE.

  • 119.
    Jonsson, Magnus
    et al.
    Halmstad University, School of Information Technology, Halmstad Embedded and Intelligent Systems Research (EIS), Centre for Research on Embedded Systems (CERES).
    Wiberg, Per-Arne
    Halmstad University, School of Information Technology, Halmstad Embedded and Intelligent Systems Research (EIS), Centre for Research on Embedded Systems (CERES).
    Wickström, Nicholas
    Halmstad University, School of Information Technology, Halmstad Embedded and Intelligent Systems Research (EIS), CAISR - Center for Applied Intelligent Systems Research.
    Vision-based low-level navigation using a feed-forward neural network1997In: Proc. International Workshop on Mechatronical Computer Systems for Perception and Action (MCPA'97), Pisa, Italy, Feb. 10-12, 1997, p. 105-111Conference paper (Refereed)
    Abstract [en]

    In this paper we propose a simple method for low-level navigation for autonomous mobile robots, employing an artificial neural network. Both corridor following and obstacle avoidance in indoor environments are managed by the same network. Raw grayscale images of size 32 x 23 pixels are processed one at a time by a feed-forward neural network. The output signals from the network directly control the motor control system of the robot. The feed-forward network is trained using the RPROP algorithm. Experiments in both familiar and unfamiliar environments are reported.

  • 120.
    Järpe, Eric
    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.
    Räkna med rester: Matematik att tillämpas inom kryptologi2013Book (Other academic)
    Abstract [sv]

    Ämnet kryptologi, dvs. kryptering, dekryptering och kodknäckning ­omfattar såväl matematik, dator­programmering som allmän finurlighet. Denna bok behandlar Caesarkrypto, substitutionskrypto, Vignèrekrypto, RSA-krypto och den bakom­liggande ­matematiken (ekvations­lösning, räkning med exponential­uttryck, resträkning, primtalsteori och rekursion) samt angränsande matematik (såsom kombinatorik, statisti­ska metoder för t.ex. detektering av krypterad kod och beräkning av överföringskvalitet). Boken innehåller även en så pass bred ­genomgång av elementär algebra (mängdlära, logik, trigono­metri, komplexa tal och rekurrensekva­tioner) och analys (funktioner i en variabel, derivata och integraler) att den kan användas vid inledande studier i matematik inom en mängd olika utbildningar. Boken syftar till att ge en introduktion till kryptologisk problem­lösning och visa på de stora synergieffekter som uppnås genom att tillämpa en väl­balanserad ­kombination av grundläggande matematik och elementär programmering inom området. Förhoppningen är också att läsaren, sporrad av de nyvunna insikterna om kryptologi, lockas till vidare kunskapsfördjupning. Boken vänder sig i första hand till blivande IT-forensiker som kan behöva kompetensen att kryptera och knäcka krypton i sin yrkesroll men som inte har en omfattande matematisk förkunskap. Den ­vänder sig även till studenter på landets tekniska högskolor och ­universitet.

  • 121.
    Järpe, Eric
    Halmstad University, School of Information Technology, Halmstad Embedded and Intelligent Systems Research (EIS), CAISR - Center for Applied Intelligent Systems Research.
    Visit to care center Angeles Cobo Lopez, Alcaudete, Andalucia, Spain: A secondment within the REMIND project2019Report (Other (popular science, discussion, etc.))
  • 122.
    Kalsyte, Zivile
    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.
    A novel approach to exploring company’s financial soundness: Investor’s perspective2013In: Expert systems with applications, ISSN 0957-4174, E-ISSN 1873-6793, Vol. 40, no 13, p. 5085-5092Article in journal (Refereed)
    Abstract [en]

    Prediction of company's life cycle stage change; creation of an ordered 2D map allowing to explore company's financial soundness from a rating agency perspective; and prediction of trends of main valuation attributes usually used by investors are the main objectives of this article. The developed algorithms are based on a random forest (RF) and a nonlinear data mapping technique ''t-distributed stochastic neighbor embedding''. Information from five different perspectives, namely balance, income, cash flow, stock price, and risk indicators was aggregated via proximity matrices of RF to enable exploration of company's financial soundness from a rating agency perspective. The proposed use of information not only from companies' financial statements but also from the stock price and risk indicators perspectives has proved useful in creating ordered 2D maps of rated companies. The companies were well ordered according to the credit risk rating assigned by the Moody's rating agency. Results of experimental investigations substantiate that the developed models are capable of predicting short term trends of the main valuation attributes, providing valuable information for investors, with low error. The models reflect financial soundness of actions taken by company's management team. It was also found that company's life cycle stage change can be determined with the average accuracy of 72.7%. Bearing in mind fuzziness of the transition moment, the obtained prediction accuracy is rather encouraging. © 2013 Elsevier Ltd. All rights reserved.

  • 123.
    Kalsyte, Zivile
    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.
    Bacauskiene, Marija
    Kaunas University of Technology, Kaunas, Lithuania.
    Gelzinis, Adas
    Kaunas University of Technology, Kaunas, Lithuania.
    A Novel Technique to Design an Adaptive Committee of Models Applied to Predicting Company’s Future Performance2013In: International Conference on Computer Research and Development: ICCRD 2013 / [ed] Fan Yama, New York, NY: ASME Press, 2013, p. 65-70Conference paper (Refereed)
    Abstract [en]

    This article presents an approach to designing an adaptive, data dependent, committee of models applied to prediction of several financial attributes for assessing company’s future performance. A self-organizing map (SOM) used for data mapping and analysis enables building committees, which are specific (committee size and aggregation weights) for each SOM node. The number of basic models aggregated into a committee and the aggregation weights depend on accuracy of basic models and their ability to generalize in the vicinity of the SOM node. The proposed technique led to a statistically significant increase in prediction accuracy if compared to other types of committees.

  • 124.
    Khandelwal, Siddhartha
    Halmstad University, School of Information Technology, Halmstad Embedded and Intelligent Systems Research (EIS), CAISR - Center for Applied Intelligent Systems Research.
    Gait Event Detection in the Real World2018Doctoral thesis, comprehensive summary (Other academic)
    Abstract [en]

    Healthy gait requires a balance between various neuro-physiological systems and is considered an important indicator of a subject's physical and cognitive health status. As such, health-related applications would immensely benefit by performing long-term or continuous monitoring of subjects' gait in their natural environment and everyday lives. In contrast to stationary sensors such as motion capture systems and force plates, inertial sensors provide a good alternative for such gait analysis applications as they are miniature, cheap, mobile and can be easily integrated into wearable systems.

    This thesis focuses on improving overall gait analysis using inertial sensors by providing a methodology for detecting gait events in real-world settings. Although the experimental protocols for such analysis have been restricted to only highly-controlled lab-like indoor settings; this thesis presents a new gait database that consists of data from gait activities carried out in both, indoor and outdoor environments. The thesis shows how domain knowledge about gait could be formulated and utilized to develop methods that are robust and can tackle real-world challenges. It also shows how the proposed approach can be generalized to estimate gait events from multiple body locations. Another aspect of this thesis is to demonstrate that the traditionally used temporal error metrics are not enough for presenting the overall performance of gait event detection methods. The thesis introduces how non-parametric tests can be used to complement them and provide a better overview.

    The results of comparing the proposed methodology to state-of-the-art methods showed that the approach of incorporating domain knowledge into the time-frequency analysis of the signal was robust across different real-world scenarios and outperformed other methods, especially for the scenario involving variable gait speeds in outdoor settings. The methodology was also benchmarked on publicly available gait databases yielding good performance for estimating events from different body locations. To conclude, this thesis presents a road map for the development of gait analysis systems in real-world settings.

  • 125.
    Khandelwal, Siddhartha
    et al.
    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.
    Evaluation of the performance of accelerometer-based gait event detection algorithms in different real-world scenarios using the MAREA gait database2017In: Gait & Posture, ISSN 0966-6362, E-ISSN 1879-2219, Vol. 51, p. 84-90Article in journal (Refereed)
    Abstract [en]

    Numerous gait event detection (GED) algorithms have been developed using accelerometers as they allow the possibility of long-term gait analysis in everyday life. However, almost all such existing algorithms have been developed and assessed using data collected in controlled indoor experiments with pre-defined paths and walking speeds. On the contrary, human gait is quite dynamic in the real-world, often involving varying gait speeds, changing surfaces and varying surface inclinations. Though portable wearable systems can be used to conduct experiments directly in the real-world, there is a lack of publicly available gait datasets or studies evaluating the performance of existing GED algorithms in various real-world settings.

    This paper presents a new gait database called MAREA (n=20 healthy subjects) that consists of walking and running in indoor and outdoor environments with accelerometers positioned on waist, wrist and both ankles. The study also evaluates the performance of six state-of-the-art accelerometer-based GED algorithms in different real-world scenarios, using the MAREA gait database. The results reveal that the performance of these algorithms is inconsistent and varies with changing environments and gait speeds. All algorithms demonstrated good performance for the scenario of steady walking in a controlled indoor environment with a combined median F1score of 0.98 for Heel-Strikes and 0.94 for Toe-Offs. However, they exhibited significantly decreased performance when evaluated in other lesser controlled scenarios such as walking and running in an outdoor street, with a combined median F1score of 0.82 for Heel-Strikes and 0.53 for Toe-Offs. Moreover, all GED algorithms displayed better performance for detecting Heel-Strikes as compared to Toe-Offs, when evaluated in different scenarios. © 2016 Elsevier B.V.

  • 126.
    Khandelwal, Siddhartha
    et al.
    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.
    Gait Event Detection in Real-World Environment for Long-Term Applications: Incorporating Domain Knowledge into Time-Frequency Analysis2016In: IEEE transactions on neural systems and rehabilitation engineering, ISSN 1534-4320, E-ISSN 1558-0210, Vol. 24, no 12, p. 1363-1372Article in journal (Refereed)
    Abstract [en]

    Detecting gait events is the key to many gait analysis applications that would benefit from continuous monitoring or long-term analysis. Most gait event detection algorithms using wearable sensors that offer a potential for use in daily living have been developed from data collected in controlled indoor experiments. However, for real-word applications, it is essential that the analysis is carried out in humans’ natural environment; that involves different gait speeds, changing walking terrains, varying surface inclinations and regular turns among other factors. Existing domain knowledge in the form of principles or underlying fundamental gait relationships can be utilized to drive and support the data analysis in order to develop robust algorithms that can tackle real-world challenges in gait analysis. This paper presents a novel approach that exhibits how domain knowledge about human gait can be incorporated into time-frequency analysis to detect gait events from longterm accelerometer signals. The accuracy and robustness of the proposed algorithm are validated by experiments done in indoor and outdoor environments with approximately 93,600 gait events in total. The proposed algorithm exhibits consistently high performance scores across all datasets in both, indoor and outdoor environments. © Copyright 2016 IEEE

  • 127.
    Khandelwal, Siddhartha
    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.
    Identification of Gait Events using Expert Knowledge and Continuous Wavelet Transform Analysis2014In: BIOSIGNALS 2014: Proceedings of the International Conference on Bio-inspired Systems and Signal Processing / [ed] Harald Loose, Guy Plantier, Tanja Schultz, Ana Fred & Hugo Gamboa, [S.l.]: SciTePress, 2014, p. 197-204Conference paper (Refereed)
    Abstract [en]

    Many gait analysis applications involve long-term or continuous monitoring which require gait measurements to be taken outdoors. Wearable inertial sensors like accelerometers have become popular for such applications as they are miniature, low-powered and inexpensive but with the drawback that they are prone to noise and require robust algorithms for precise identification of gait events. However, most gait event detection algorithms have been developed by simulating physical world environments inside controlled laboratories. In this paper, we propose a novel algorithm that robustly and efficiently identifies gait events from accelerometer signals collected during both, indoor and outdoor walking of healthy subjects. The proposed method makes adept use of prior knowledge of walking gait characteristics, referred to as expert knowledge, in conjunction with continuous wavelet transform analysis to detect gait events of heel strike and toe off. It was observed that in comparison to indoor, the outdoor walking acceleration signals were of poorer quality and highly corrupted with noise. The proposed algorithm presents an automated way to effectively analyze such noisy signals in order to identify gait events.

  • 128.
    Khandelwal, Siddhartha
    et al.
    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), Centre for Research on Embedded Systems (CERES).
    Novel methodology for estimating Initial Contact events from accelerometers positioned at different body locations2018In: Gait & Posture, ISSN 0966-6362, E-ISSN 1879-2219, Vol. 59, p. 278-285Article in journal (Refereed)
    Abstract [en]

    Identifying Initial Contact events (ICE) is essential in gait analysis as they segment the walking pattern into gait cycles and facilitate the computation of other gait parameters. As such, numerous algorithms have been developed to identify ICE by placing the accelerometer at a specific body location. Simultaneously, many researchers have studied the effects of device positioning for participant or patient compliance, which is an important factor to consider especially for long-term studies in real-life settings. With the adoption of accelerometery for long-term gait analysis in daily living, current and future applications will require robust algorithms that can either autonomously adapt to changes in sensor positioning or can detect ICE from multiple sensors locations.

    This study presents a novel methodology that is capable of estimating ICE from accelerometers placed at different body locations. The proposed methodology, called DK-TiFA, is based on utilizing domain knowledge about the fundamental spectral relationships present between the movement of different body parts during gait to drive the time-frequency analysis of the acceleration signal. In order to assess the performance, DK-TiFA is benchmarked on four large publicly available gait databases, consisting of a total of 613 subjects and 7 unique body locations, namely, ankle, thigh, center waist, side waist, chest, upper arm and wrist. The DK-TiFA methodology is demonstrated to achieve high accuracy and robustness for estimating ICE from data consisting of different accelerometer specifications, varying gait speeds and different environments. © 2017 Elsevier B.V.

  • 129.
    Krish, Ram P.
    et al.
    School of Electronic Engineering, Dublin City University, Ireland.
    Fierrez, Julian
    School of Engineering, Universidad Autonoma de Madrid, Spain.
    Ramos, Daniel
    School of Engineering, Universidad Autonoma de Madrid, Spain.
    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.
    Improving Automated Latent Fingerprint Identification Using Extended Minutia Types2019In: Information Fusion, ISSN 1566-2535, E-ISSN 1872-6305, Vol. 50, p. 9-19Article in journal (Refereed)
    Abstract [en]

    Latent fingerprints are usually processed with Automated Fingerprint Identification Systems (AFIS) by law enforcement agencies to narrow down possible suspects from a criminal database. AFIS do not commonly use all discriminatory features available in fingerprints but typically use only some types of features automatically extracted by a feature extraction algorithm. In this work, we explore ways to improve rank identification accuracies of AFIS when only a partial latent fingerprint is available. Towards solving this challenge, we propose a method that exploits extended fingerprint features (unusual/rare minutiae) not commonly considered in AFIS. This new method can be combined with any existing minutiae-based matcher. We first compute a similarity score based on least squares between latent and tenprint minutiae points, with rare minutiae features as reference points. Then the similarity score of the reference minutiae-based matcher at hand is modified based on a fitting error from the least square similarity stage. We use a realistic forensic fingerprint casework database in our experiments which contains rare minutiae features obtained from Guardia Civil, the Spanish law enforcement agency. Experiments are conducted using three minutiae-based matchers as a reference, namely: NIST-Bozorth3, VeriFinger-SDK and MCC-SDK. We report significant improvements in the rank identification accuracies when these minutiae matchers are augmented with our proposed algorithm based on rare minutiae features. © 2018 Elsevier B.V.

  • 130.
    Krish, Ram P.
    et al.
    Universidad Autonoma de Madrid, Madrid, Spain.
    Fierrez, Julian
    Universidad Autonoma de Madrid, Madrid, Spain.
    Ramos, Daniel
    Universidad Autonoma de Madrid, Madrid, Spain.
    Ortega-Garcia, Javier
    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.
    Partial Fingerprint Registration for Forensics using Minutiae-generated Orientation Fields2014In: 2nd International Workshop on Biometrics and Forensics (IWBF2014): Valletta, Malta (27-28th March 2014), Piscataway, NJ: IEEE Press, 2014Conference paper (Refereed)
    Abstract [en]

    Minutia based matching scheme is the most widely accepted method for both automated as well as manual (forensic) fingerprint matching. The scenario of comparing a partial fingerprint minutia set against a full fingerprint minutia set is a challenging problem. In this work, we propose a method to register the orientation field of the partial fingerprint minutia set to that of the orientation field of full fingerprint minutia set. As a consequence of registering the partial fingerprint orientation field, we obtain extra information that can augment a minutia based matcher by reducing the search space of minutiae in the full fingerprint. We present the accuracy of our registration algorithm on NIST-SD27 database, reporting separately for both subjective and quantitative quality classification of NIST-SD27. The registration performance accuracy is measured in terms of percentage of ground truth minutiae present in the reduced minutiae search space generated by our algorithm. ©2014 IEEE.

  • 131.
    Krish, Ram P.
    et al.
    Universidad Autonóma de Madrid, Madrid, Spain.
    Fierrez, Julian
    Universidad Autonóma de Madrid, Madrid, Spain.
    Ramos, Daniel
    Universidad Autonóma de Madrid, Madrid, Spain.
    Ortega-Garcia, Javier
    Universidad Autonóma 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.
    Pre-registration for Improved Latent Fingerprint Identification2014In: 2014 22nd International Conference on Pattern Recognition (ICPR) / [ed] Lisa O’Conner, Los Alamitos: IEEE Computer Society, 2014, p. 696-701Conference paper (Refereed)
    Abstract [en]

    Comparing a latent fingerprint minutiae set against a ten print fingerprint minutiae set using an automated fingerprint identification system is a challenging problem. This is mainly because latent fingerprints obtained from crime scenes are mostly partial fingerprints, and most automated systems expect approximately the same number of minutiae between query and the reference fingerprint under comparison for good performance. In this work, we propose a methodology to reduce the minutiae set of ten print with respect to that of query latent minutiae set by registering the orientation field of latent fingerprint with the ten print orientation field. By reducing the search space of minutiae from the ten print, we can improve the performance of automated identification systems for latent fingerprints. We report the performance of our registration algorithm on the NIST-SD27 database as well as the improvement in the Rank Identification accuracy of a standard minutiae-based automated system. © 2014 IEEE.

  • 132.
    Krish, Ram Prasad
    et al.
    Universidad Autonóma de Madrid, Madrid, Spain.
    Fierrez, Julian
    Universidad Autonóma de Madrid, Madrid, Spain.
    Ramos, Daniel
    Universidad Autonóma de Madrid, Madrid, Spain.
    Ortega-Garcia, Javier
    Universidad Autonóma 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.
    Pre-registration of latent fingerprints based on orientation field2015In: IET Biometrics, ISSN 2047-4938, E-ISSN 2047-4946, Vol. 4, no 2, p. 42-52Article in journal (Refereed)
    Abstract [en]

    In this study, the authors present a hierarchical algorithm to register a partial fingerprint against a full fingerprint using only the orientation fields. In the first level, they shortlist possible locations for registering the partial fingerprint in the full fingerprint using a normalised correlation measure, taking various rotations into account. As a second level, on those candidate locations, they calculate three other similarity measures. They then perform score fusion for all the estimated similarity scores to locate the final registration. By registering a partial fingerprint against a full fingerprint, they can reduce the search space of the minutiae set in the full fingerprint, thereby improving the result of partial fingerprint identification, particularly for poor quality latent fingerprints. They report the rank identification improvements of two minutiae-based automated fingerprint identification systems on the National Institute of Standards and Technology (NIST)-Special Database 27 database when they use the authors hierarchical registration as a pre-alignment. © The Institution of Engineering and Technology 2015.

  • 133.
    Lundström, Jens
    Halmstad University, School of Information Technology, Halmstad Embedded and Intelligent Systems Research (EIS), CAISR - Center for Applied Intelligent Systems Research.
    Situation Awareness in Colour Printing and Beyond2014Doctoral thesis, comprehensive summary (Other academic)
    Abstract [en]

    Machine learning methods are increasingly being used to solve real-world problems in the society. Often, the complexity of the methods are well hidden for users. However, integrating machine learning methods in real-world applications is not a straightforward process and requires knowledge both about the methods and domain knowledge of the problem. Two such domains are colour print quality assessment and anomaly detection in smart homes, which are currently driven by manual monitoring of complex situations. The goal of the presented work is to develop methods, algorithms and tools to facilitate monitoring and understanding of the complex situations which arise in colour print quality assessment and anomaly detection for smart homes. The proposed approach builds on the use and adaption of supervised and unsupervised machine learning methods.

    Novel algorithms for computing objective measures of print quality in production are proposed in this work. Objective measures are also modelled to study how paper and press parameters influence print quality. Moreover, a study on how print quality is perceived by humans is presented and experiments aiming to understand how subjective assessments of print quality relate to objective measurements are explained. The obtained results show that the objective measures reflect important aspects of print quality, these measures are also modelled with reasonable accuracy using paper and press parameters. The models of objective  measures are shown to reveal relationships consistent to known print quality phenomena.

    In the second part of this thesis the application area of anomaly detection in smart homes is explored. A method for modelling human behaviour patterns is proposed. The model is used in order to detect deviating behaviour patterns using contextual information from both time and space. The proposed behaviour pattern model is tested using simulated data and is shown to be suitable given four types of scenarios.

    The thesis shows that parts of offset lithographic printing, which traditionally is a human-centered process, can be automated by the introduction of image processing and machine learning methods. Moreover, it is concluded that in order to facilitate robust and accurate anomaly detection in smart homes, a holistic approach which makes use of several contextual aspects is required.

  • 134.
    Lundström, Jens
    et al.
    Halmstad University, School of Information Technology, Halmstad Embedded and Intelligent Systems Research (EIS), CAISR - Center for Applied Intelligent Systems Research.
    Järpe, Eric
    Halmstad University, School of Information Technology, Halmstad Embedded and Intelligent Systems Research (EIS), CAISR - Center for Applied Intelligent Systems Research.
    Verikas, Antanas
    Halmstad University, School of Information Technology, Halmstad Embedded and Intelligent Systems Research (EIS), CAISR - Center for Applied Intelligent Systems Research.
    Detecting and exploring deviating behaviour of people in their own homesManuscript (preprint) (Other academic)
    Abstract [en]

    A system for detecting deviating human behaviour in a smart home environment is the long-term goal of this work. It is believed that such systems will be very important in ambient assisted living services. Three types of deviations are considered in this work: deviation in activity intensity, deviation in time and deviation in space. Detection of deviations in activity intensity is formulated as the on-line quickest detection of a parameter shift in a sequence of independent Poisson random variables. Random forests trained in an unsupervised fashion are used to learn the spatial and temporal structure of data representing normal behaviour and are thereafter utilised to find deviations.The experimental investigations have shown that the Page and Shiryaev change-point detection methods are preferable in terms of expected delay of motivated alarm. Interestingly only a little is lost when the methods are specified with estimated intensity parameters rather than the true intensity values which are not available in a real situation. As to the spatial and temporal deviations, they can be revealed through analysis of a 2D map of high dimensional data. It was demonstrated that such a map is stable in terms of the number of clusters formed. We have shown that the data clusters can be understood/explored by finding the most important variables and by analysing the structure of the most representative tree.

  • 135.
    Lundström, Jens
    et al.
    Halmstad University, School of Information Technology, Halmstad Embedded and Intelligent Systems Research (EIS), CAISR - Center for Applied Intelligent Systems Research.
    Järpe, Eric
    Halmstad University, School of Information Technology, Halmstad Embedded and Intelligent Systems Research (EIS), CAISR - Center for Applied Intelligent Systems Research.
    Verikas, Antanas
    Halmstad University, School of Information Technology, Halmstad Embedded and Intelligent Systems Research (EIS), CAISR - Center for Applied Intelligent Systems Research.
    Detecting and exploring deviating behaviour of smart home residents2016In: Expert systems with applications, ISSN 0957-4174, E-ISSN 1873-6793, Vol. 55, p. 429-440Article in journal (Refereed)
    Abstract [en]

    A system for detecting deviating human behaviour in a smart home environment is the long-term goal of this work. Clearly, such systems will be very important in ambient assisted living services. A new approach to modelling human behaviour patterns is suggested in this paper. The approach reveals promising results in unsupervised modelling of human behaviour and detection of deviations by using such a model. Human behaviour/activity in a short time interval is represented in a novel fashion by responses of simple non-intrusive sensors. Deviating behaviour is revealed through data clustering and analysis of associations between clusters and data vectors representing adjacent time intervals (analysing transitions between clusters). To obtain clusters of human behaviour patterns, first, a random forest is trained without using beforehand defined teacher signals. Then information collected in the random forest data proximity matrix is mapped onto the 2D space and data clusters are revealed there by agglomerative clustering. Transitions between clusters are modelled by the third order Markov chain.

    Three types of deviations are considered: deviation in time, deviation in space and deviation in the transition between clusters of similar behaviour patterns.

    The proposed modelling approach does not make any assumptions about the position, type, and relationship of sensors but is nevertheless able to successfully create and use a model for deviation detection-this is claimed as a significant result in the area of expert and intelligent systems. Results show that spatial and temporal deviations can be revealed through analysis of a 2D map of high dimensional data. It is demonstrated that such a map is stable in terms of the number of clusters formed. We show that the data clusters can be understood/explored by finding the most important variables and by analysing the structure of the most representative tree. © 2016 Elsevier Ltd. All rights reserved.

  • 136.
    Lundström, Jens
    et al.
    Halmstad University, School of Information Technology, Halmstad Embedded and Intelligent Systems Research (EIS), Intelligent Systems´ laboratory.
    Ourique de Morais, Wagner
    Halmstad University, School of Information Technology, Halmstad Embedded and Intelligent Systems Research (EIS), CAISR - Center for Applied Intelligent Systems Research.
    Cooney, Martin
    Halmstad University, School of Information Technology, Halmstad Embedded and Intelligent Systems Research (EIS), CAISR - Center for Applied Intelligent Systems Research.
    A Holistic Smart Home Demonstrator for Anomaly Detection and Response2015In: 2015 IEEE International Conference on Pervasive Computing and Communication Workshops (PerCom Workshops), Piscataway, NJ: IEEE Press, 2015, p. 330-335Conference paper (Refereed)
    Abstract [en]

    Applying machine learning methods in scenarios involving smart homes is a complex task. The many possible variations of sensors, feature representations, machine learning algorithms, middle-ware architectures, reasoning/decision schemes, and interactive strategies make research and development tasks non-trivial to solve.In this paper, the use of a portable, flexible and holistic smart home demonstrator is proposed to facilitate iterative development and the acquisition of feedback when testing in regard to the above-mentioned issues. Specifically, the focus in this paper is on scenarios involving anomaly detection and response. First a model for anomaly detection is trained with simulated data representing a priori knowledge pertaining to a person living in an apartment. Then a reasoning mechanism uses the trained model to infer and plan a reaction to deviating activities. Reactions are carried out by a mobile interactive robot to investigate if a detected anomaly constitutes a true emergency. The implemented demonstrator was able to detect and respond properly in 18 of 20 trials featuring normal and deviating activity patterns, suggesting the feasibility of the proposed approach for such scenarios. © IEEE 2015

  • 137.
    Lundström, Jens
    et al.
    Halmstad University, School of Information Technology, Halmstad Embedded and Intelligent Systems Research (EIS), CAISR - Center for Applied Intelligent Systems Research.
    Ourique de Morais, Wagner
    Halmstad University, School of Information Technology, Halmstad Embedded and Intelligent Systems Research (EIS), CAISR - Center for Applied Intelligent Systems Research.
    Menezes, Maria Luiza Recena
    Halmstad University, School of Information Technology, Halmstad Embedded and Intelligent Systems Research (EIS).
    Gabrielli, C.
    Bentes, João
    School of Computing and Mathematics, University of Ulster, Shore Road, Jordanstown, Newtownabbey, Co. Antrim, United Kingdom.
    Pinheiro Sant'Anna, Anita
    Halmstad University, School of Information Technology, Halmstad Embedded and Intelligent Systems Research (EIS), CAISR - Center for Applied Intelligent Systems Research.
    Synnott, Jonathan
    School of Computing and Mathematics, University of Ulster, Shore Road, Jordanstown, Newtownabbey, Co. Antrim, United Kingdom.
    Nugent, Christopher
    Halmstad University, School of Information Technology, Halmstad Embedded and Intelligent Systems Research (EIS), CAISR - Center for Applied Intelligent Systems Research.
    Halmstad intelligent home - Capabilities and opportunities2016In: Internet of Things Technologies for HealthCare: Third International Conference, HealthyIoT 2016, Västerås, Sweden, October 18-19, 2016, Revised Selected Papers / [ed] Mobyen Uddin AhmedShahina BegumWasim Raad, Berlin: Springer Berlin/Heidelberg, 2016, Vol. 187, p. 9-15Conference paper (Refereed)
    Abstract [en]

    Research on intelligent environments, such as smart homes, concerns the mechanisms that intelligently orchestrate the pervasive technical infrastructure in the environment. However, significant challenges are to build, configure, use and maintain these systems. Providing personalized services while preserving the privacy of the occupants is also difficult. As an approach to facilitate research in this area, this paper presents the Halmstad Intelligent Home and a novel approach for multioccupancy detection utilizing the presented environment. This paper also presents initial results and ongoing work. © ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering 2016.

  • 138.
    Lundström, Jens
    et al.
    Halmstad University, School of Information Technology, Halmstad Embedded and Intelligent Systems Research (EIS), CAISR - Center for Applied Intelligent Systems Research.
    Synnott, Jonathan
    Ulster University, Jordanstown, United Kingdom.
    Järpe, Eric
    Halmstad University, School of Information Technology, Halmstad Embedded and Intelligent Systems Research (EIS), CAISR - Center for Applied Intelligent Systems Research.
    Nugent, Christopher
    Halmstad University, School of Information Technology, Halmstad Embedded and Intelligent Systems Research (EIS), CAISR - Center for Applied Intelligent Systems Research. Ulster University, Jordanstown, United Kingdom.
    Smart Home Simulation using Avatar Control and Probabilistic Sampling2015In: 2015 IEEE International Conference on Pervasive Computing and Communication Workshops (PerCom Workshops), Piscataway, NJ: IEEE Press, 2015, p. 336-341Conference paper (Refereed)
    Abstract [en]

    Development, testing and validation of algorithms for smart home applications are often complex, expensive and tedious processes. Research on simulation of resident activity patterns in Smart Homes is an active research area and facilitates development of algorithms of smart home applications. However, the simulation of passive infrared (PIR) sensors is often used in a static fashion by generating equidistant events while an intended occupant is within sensor proximity. This paper suggests the combination of avatar-based control and probabilistic sampling in order to increase realism of the simulated data. The number of PIR events during a time interval is assumed to be Poisson distributed and this assumption is used in the simulation of Smart Home data. Results suggest that the proposed approach increase realism of simulated data, however results also indicate that improvements could be achieved using the geometric distribution as a model for the number of PIR events during a time interval. © IEEE 2015

  • 139.
    Manasa, Justen
    et al.
    Division of Infectious Diseases, Department of Medicine Stanford University, Stanford, CA, USA.
    Varghese, Vici
    Division of Infectious Diseases, Department of Medicine Stanford University, Stanford, CA, USA.
    Kosakovsky Pond, Sergei
    Department of Biology, Temple University, Philadelphia, PA, USA.
    Rhee, Soo-Yon
    Division of Infectious Diseases, Department of Medicine Stanford University, Stanford, CA, USA.
    Tzou, Philip
    Division of Infectious Diseases, Department of Medicine Stanford University, Stanford, CA, USA.
    Fessel, Jeffrey
    Department of Internal Medicine, Kaiser Permanente Northern California, San Francisco Medical Center, San Francisco, CA, USA.
    Jang, Karen
    Division of Infectious Diseases, Department of Medicine Stanford University, Stanford, CA, USA.
    White, Elizabeth
    Division of Infectious Diseases, Department of Medicine Stanford University, Stanford, CA, USA.
    Rögnvaldsson, Thorsteinn
    Halmstad University, School of Information Technology, Halmstad Embedded and Intelligent Systems Research (EIS), CAISR - Center for Applied Intelligent Systems Research.
    Katzenstein, David A.
    Division of Infectious Diseases, Department of Medicine Stanford University, Stanford, CA, USA.
    Shafer, Robert A.
    Division of Infectious Diseases, Department of Medicine Stanford University, Stanford, CA, USA.
    Evolution of gag and gp41 in Patients Receiving Ritonavir-Boosted Protease Inhibitors2017In: Scientific Reports, ISSN 2045-2322, E-ISSN 2045-2322, Vol. 7, no 1, article id 11559Article in journal (Refereed)
    Abstract [en]

    Several groups have proposed that genotypic determinants in gag and the gp41 cytoplasmic domain (gp41-CD) reduce protease inhibitor (PI) susceptibility without PI-resistance mutations in protease. However, no gag and gp41-CD mutations definitively responsible for reduced PI susceptibility have been identified in individuals with virological failure (VF) while receiving a boosted PI (PI/r)-containing regimen. To identify gag and gp41 mutations under selective PI pressure, we sequenced gag and/or gp41 in 61 individuals with VF on a PI/r (n = 40) or NNRTI (n = 20) containing regimen. We quantified nonsynonymous and synonymous changes in both genes and identified sites exhibiting signal for directional or diversifying selection. We also used published gag and gp41 polymorphism data to highlight mutations displaying a high selection index, defined as changing from a conserved to an uncommon amino acid. Many amino acid mutations developed in gag and in gp41-CD in both the PI- and NNRTI-treated groups. However, in neither gene, were there discernable differences between the two groups in overall numbers of mutations, mutations displaying evidence of diversifying or directional selection, or mutations with a high selection index. If gag and/or gp41 encode PI-resistance mutations, they may not be confined to consistent mutations at a few sites. © 2017 The Author(s).

  • 140.
    Mashad Nemati, Hassan
    Halmstad University, School of Information Technology, Halmstad Embedded and Intelligent Systems Research (EIS), CAISR - Center for Applied Intelligent Systems Research.
    Data analytics for weak spot detection in power distribution grids2019Doctoral thesis, comprehensive summary (Other academic)
    Abstract [en]

    This research aims to develop data-driven methods that extract information from the available data in distribution grids for detecting weak spots, including the components with degraded reliability and areas with power quality problems. The results enable power distribution companies to change from reactive maintenance to predictive maintenance by deriving benefits from available data. In particular, the data is exploited for three purposes: (a) failure pattern discovery, (b) reliability evaluation of power cables, and (c) analyzing and modeling propagation of power quality disturbances (PQDs) in low-voltage grids.

    To analyze failure characteristics it is important to discover which failures share common features, e.g., if there are any types of failures that happen mostly in certain parts of the grid or at certain times. This analysis provides information about correlation between different features and identifying the most vulnerable components. In this case, we applied statistical analysis and association rules to discover failure patterns. Furthermore, we propose a visualization of the correlations between different factors representing failures by using an approximated Bayesian network. We show that the Bayesian Network constructed based on the interesting rules of two items is a good approximation of the real dataset.

    The main focus of reliability evaluation is on failure rate estimation and reliability ranking. In case of power cables, the limited amount of recorded events makes it difficult to perform failure rate modeling. Therefore, we propose a method for interpreting the results of goodness-of-fit measures with confidence intervals, estimated using synthetic data.

    To perform reliability ranking of power cables, in addition to the age of cables, we consider other factors. Then, we use the proportional hazard model (PHM) to assess the impact of the factors and calculate the failure rate of each individual cable. In reliability evaluation, it is important to consider the fact that power cables are repairable components. We discuss that the conclusions about different factors in PHM and cables ranking will be misleading if one considers the cables as non-repairable components.

    In low-voltage distribution grids, analyzing PQDs is important as we are moving towards smart grids with the next generation of producers and consumers. Installing Power Quality and Monitoring Systems (PQMS) at all the nodes in the network, for monitoring the impacts of the new consumer/producer, is prohibitively expensive. Instead, we demonstrate that power companies can utilize the available smart meters, which are widely deployed in the low-voltage grids, for monitoring power quality events and identifying areas with power quality problems. In particular, several models for propagation of PQDs, within neighbor customers in different levels of the grid topology, are investigated. The results show that meters data can be used to detect and describe propagation in low-voltage grids.

    The developed methods of (a) failure pattern discovery are applied on data from Halmstad Energi och Miljö (HEM Nät), Öresundskraft, Göteborg Energy, and Växjö Energy, four different distribution system operators in Sweden. The developed methods of (b) reliability evaluation of power cables and (c) analyzing and modeling propagation of PQDs are applied on data from HEM Nät.

  • 141.
    Mashad Nemati, Hassan
    Halmstad University, School of Information Technology, Halmstad Embedded and Intelligent Systems Research (EIS), CAISR - Center for Applied Intelligent Systems Research.
    Data-Driven Methods for Reliability Evaluation of Power Cables in Smart Distribution Grids2017Licentiate thesis, comprehensive summary (Other academic)
    Abstract [en]

    This research aims to develop data-driven methods that automatically exploit historical data in smart distribution grids for reliability evaluation, i.e., analyzing frequency of failures, and modeling components’ lifetime. The results enable power distribution companies to change from reactive maintenance to predictive maintenance by deriving benefits from historical data. In particular, the data is exploited for two purposes: (a) failure pattern discovery, and (b) reliability evaluation of power cables. To analyze failure characteristics it is important to discover which failures share common features, e.g., if there are any types of failures that happen mostly in certain parts of the grid or at certain times. This analysis provides information about correlation between different features and identifying the most vulnerable components. In this case, we applied statistical analysis and association rules to discover failure patterns. Furthermore, we propose an easy-to-understand visualization of the correlations between different factors representing failures by using an approximated Bayesian network. We show that the Bayesian Network constructed based on the interesting rules of two items is a good approximation of the real dataset. The main focus of reliability evaluation is on failure rate estimation and reliability ranking. In case of power cables, the limited amount of recorded events makes it difficult to perform failure rate modeling, i.e., estimating the function that describes changes in the rate of failure depending on age. Therefore, we propose a method for interpreting the results of goodness-of-fit measures with confidence intervals, estimated using synthetic data. To perform reliability ranking of power cables, in addition to the age of cables, we consider other factors. Then, we use the Cox proportional hazard model (PHM) to assess the impact of the factors and calculate the failure rate of each individual cable. In reliability evaluation, it is important to consider the fact that power cables are repairable components. We show that the conclusions about different factors in PHM and cables ranking will be misleading if one considers the cables as non-repairable components. The developed methods of (a) are applied on data from Halmstad Energi och Miljö (HEM Nät), Öresundskraft, Göteborg Energy, and Växjö Energy, four different distribution system operators in Sweden. The developed methods of (b) are applied on data from HEM Nät.

  • 142.
    Mashad Nemati, Hassan
    et al.
    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.
    Alonso-Fernandez, Fernando
    Halmstad University, School of Information Technology, Halmstad Embedded and Intelligent Systems Research (EIS), CAISR - Center for Applied Intelligent Systems Research.
    Hand Detection and Gesture Recognition Using Symmetric Patterns2016In: Studies in Computational Intelligence, ISSN 1860-949X, E-ISSN 1860-9503, Vol. 642, p. 365-375Article in journal (Refereed)
    Abstract [en]

    Hand detection and gesture recognition is one of the challenging issues in human-robot interaction. In this paper we proposed a novel method to detect human hands and recognize gestures from video stream by utilizing a family of symmetric patterns: log-spiral codes. In this case, several log-family spirals mounted on a hand glove were extracted and utilized for positioning the palm and fingers. The proposed method can be applied in real time and even on a low quality camera stream. The experiments are implemented in different conditions to evaluatethe illumination, scale, and rotation invariance of the proposed method. The results show that using the proposed technique we can have a precise and reliable detection and tracking of the hand and fingers with accuracy about 98 %.

  • 143.
    Mashad Nemati, Hassan
    et al.
    Halmstad University, School of Information Technology, Halmstad Embedded and Intelligent Systems Research (EIS), CAISR - Center for Applied Intelligent Systems Research.
    Gholami Shahbandi, Saeed
    Halmstad University, School of Information Technology, Halmstad Embedded and Intelligent Systems Research (EIS), CAISR - Center for Applied Intelligent Systems Research.
    Åstrand, Björn
    Halmstad University, School of Information Technology, Halmstad Embedded and Intelligent Systems Research (EIS), CAISR - Center for Applied Intelligent Systems Research.
    Human Tracking in Occlusion based on Reappearance Event Estimation2016In: ICINCO 2016: 13th International Conference on Informatics in Control, Automation and Robotics: Proceedings, Volume 2 / [ed] Oleg Gusikhin, Dimitri Peaucelle & Kurosh Madani, SciTePress, 2016, Vol. 2, p. 505-512Conference paper (Refereed)
    Abstract [en]

    Relying on the commonsense knowledge that the trajectory of any physical entity in the spatio-temporal domain is continuous, we propose a heuristic data association technique. The technique is used in conjunction with an Extended Kalman Filter (EKF) for human tracking under occlusion. Our method is capable of tracking moving objects, maintain their state hypothesis even in the period of occlusion, and associate the target reappeared from occlusion with the existing hypothesis. The technique relies on the estimation of the reappearance event both in time and location, accompanied with an alert signal that would enable more intelligent behavior (e.g. in path planning). We implemented the proposed method, and evaluated its performance with real-world data. The result validates the expected capabilities, even in case of tracking multiple humans simultaneously.

  • 144.
    Mashad Nemati, Hassan
    et al.
    Halmstad University, School of Information Technology, Halmstad Embedded and Intelligent Systems Research (EIS), CAISR - Center for Applied Intelligent Systems Research.
    Laso, A.
    Department of Electrical and Energy Engineering, University of Cantabria, Santander, Spain.
    Manana, M.
    Department of Electrical and Energy Engineering, University of Cantabria, Santander, Spain.
    Pinheiro Sant'Anna, Anita
    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.
    Stream Data Cleaning for Dynamic Line Rating Application2018In: Energies, ISSN 1996-1073, E-ISSN 1996-1073, Vol. 11, no 8, article id 2007Article in journal (Refereed)
    Abstract [en]

    The maximum current that an overhead transmission line can continuously carry depends on external weather conditions, most commonly obtained from real-time streaming weather sensors. The accuracy of the sensor data is very important in order to avoid problems such as overheating. Furthermore, faulty sensor readings may cause operators to limit or even stop the energy production from renewable sources in radial networks. This paper presents a method for detecting and replacing sequences of consecutive faulty data originating from streaming weather sensors. The method is based on a combination of (a) a set of constraints obtained from derivatives in consecutive data, and (b) association rules that are automatically generated from historical data. In smart grids, a large amount of historical data from different weather stations are available but rarely used. In this work, we show that mining and analyzing this historical data provides valuable information that can be used for detecting and replacing faulty sensor readings. We compare the result of the proposed method against the exponentially weighted moving average and vector autoregression models. Experiments on data sets with real and synthetic errors demonstrate the good performance of the proposed method for monitoring weather sensors.

  • 145.
    Mashad Nemati, Hassan
    et al.
    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.
    Nowaczyk, Sławomir
    Halmstad University, School of Information Technology, Halmstad Embedded and Intelligent Systems Research (EIS), CAISR - Center for Applied Intelligent Systems Research.
    Analyzing and Modeling Propagation of SmartMeters' Alarms in Low-Voltage GridsIn: Data mining and knowledge discovery, ISSN 1384-5810, E-ISSN 1573-756XArticle in journal (Refereed)
    Abstract [en]

    In low-voltage energy distribution networks, analyzing and modelingpropagation of disturbances is important as we are moving towards smartgrids. Today, smart meters are generally deployed at all customers and continuouslymeasure several features related to power consumption and quality.However, the data collected from such low-cost devices has been, until now,considered unsuitable for analysis of disturbance propagation, mainly due to itsvery low time resolution. This paper demonstrates that the existence of propagationin the low-voltage grids can be detected using smart meters alarm data.In particular, several models for propagation of disturbances, within neighborcustomers in different levels of the grid topology, are investigated. A methodfor measuring how the reality corresponds to each of the models, by measuringthe similarity between real data and synthetic data, is proposed. Theresults show that the models which include propagation within both deliverypointsand branches are better representation of the disturbances in the realdata, compared to other models. Furthermore, the paper presents smart metersalarm dataset (SMAData), an open-source dataset containing power qualitydisturbances of over 1000 customers.

  • 146.
    Mashad Nemati, Hassan
    et al.
    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.
    Nowaczyk, Sławomir
    Halmstad University, School of Information Technology, Halmstad Embedded and Intelligent Systems Research (EIS), CAISR - Center for Applied Intelligent Systems Research.
    Jürgensen, Jan Henning
    KTH Royal Institute of Technology, Stockholm, Sweden.
    Hilber, Patrik
    KTH Royal Institute of Technology, Stockholm, Sweden.
    Reliability Evaluation of Power Cables Considering the Restoration Characteristic2019In: International Journal of Electrical Power & Energy Systems, ISSN 0142-0615, E-ISSN 1879-3517, Vol. 105, p. 622-631Article in journal (Refereed)
    Abstract [en]

    In this paper Weibull parametric proportional hazard model (PHM) is used to estimate the failure rate of every individual cable based on its age and a set of explanatory factors. The required information for the proposed method is obtained by exploiting available historical cable inventory and failure data. This data-driven method does not require any additional measurements on the cables, and allows the cables to be ranked for maintenance prioritization and repair actions.

    Furthermore, the results of reliability analysis of power cables are compared when the cables are considered as repairable or non-repairable components. The paper demonstrates that the methods which estimate the time-to-the-first failure (for non-repairable components) lead to incorrect conclusions about reliability of repairable power cables.

    The proposed method is used to evaluate the failure rate of each individual Paper Insulated Lead Cover (PILC) underground cables in a distribution grid in the south of Sweden. © 2018 Elsevier Ltd

  • 147.
    Mashad Nemati, Hassan
    et al.
    Halmstad University, School of Information Technology, Halmstad Embedded and Intelligent Systems Research (EIS), CAISR - Center for Applied Intelligent Systems Research.
    Sant´Anna, Anita
    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.
    Bayesian Network Representation of Meaningful Patterns in Electricity Distribution Grids2016In: 2016 IEEE International Energy Conference (ENERGYCON), 2016Conference paper (Refereed)
    Abstract [en]

    The diversity of components in electricity distribution grids makes it impossible, or at least very expensive, to deploy monitoring and fault diagnostics to every individual element. Therefore, power distribution companies are looking for cheap and reliable approaches that can help them to estimate the condition of their assets and to predict the when and where the faults may occur. In this paper we propose a simplified representation of failure patterns within historical faults database, which facilitates visualization of association rules using Bayesian Networks. Our approach is based on exploring the failure history and detecting correlations between different features available in those records. We show that a small subset of the most interesting rules is enough to obtain a good and sufficiently accurate approximation of the original dataset. A Bayesian Network created from those rules can serve as an easy to understand visualization of the most relevant failure patterns. In addition, by varying the threshold values of support and confidence that we consider interesting, we are able to control the tradeoff between accuracy of the model and its complexity in an intuitive way. © 2016 IEEE

  • 148.
    Mashad Nemati, Hassan
    et al.
    Halmstad University, School of Information Technology, Halmstad Embedded and Intelligent Systems Research (EIS), CAISR - Center for Applied Intelligent Systems Research.
    Sant'Anna, Anita
    Halmstad University, School of Information Technology, Halmstad Embedded and Intelligent Systems Research (EIS).
    Nowaczyk, Sławomir
    Halmstad University, School of Information Technology, Halmstad Embedded and Intelligent Systems Research (EIS), Intelligent Systems´ laboratory.
    Overview of Smart Grid Challenges in Sweden2014In: The SAIS Workshop 2014 Proceedings, Swedish Artificial Intelligence Society (SAIS) , 2014, p. 155-164Conference paper (Refereed)
    Abstract [en]

    Smart grids are advanced power grids that use modern hardware and software technologies to provide clean, safe, secure, reliable, ecient and sustainable energy. However, there are many challenges in the eld of smart grids in terms of communication, reliability, interoperability, and big data that should be considered. In this paper we present a brief overview of some of the challenges and solutions in the smart grids, focusing especially on the Swedish point of view. We discuss thirty articles, from 2006 until 2013, with the main interest on datarelated challenges.

  • 149.
    Mashad Nemati, Hassan
    et al.
    Halmstad University, School of Information Technology, Halmstad Embedded and Intelligent Systems Research (EIS), CAISR - Center for Applied Intelligent Systems Research.
    Sant'Anna, Anita
    Halmstad University, School of Information Technology, Halmstad Embedded and Intelligent Systems Research (EIS).
    Nowaczyk, Sławomir
    Halmstad University, School of Information Technology, Halmstad Embedded and Intelligent Systems Research (EIS), Intelligent Systems´ laboratory.
    Reliability Evaluation of Underground Power Cables with Probabilistic Models2015In: DMIN'15: The 2015 International Conference on Data Mining, 2015, p. 37-43Conference paper (Refereed)
    Abstract [en]

    Underground power cables are one of the fundamental elements in power grids, but also one of the more difficult ones to monitor. Those cables are heavily affected by ionization, as well as thermal and mechanical stresses. At the same time, both pinpointing and repairing faults is very costly and time consuming. This has caused many power distribution companies to search for ways of predicting cable failures based on available historical data.

    In this paper, we investigate five different models estimating the probability of failures for in-service underground cables. In particular, we focus on a methodology for evaluating how well different models fit the historical data. In many practical cases, the amount of data available is very limited, and it is difficult to know how much confidence should one have in the goodness-of-fit results.

    We use two goodness-of-fit measures, a commonly used one based on mean square error and a new one based on calculating the probability of generating the data from a given model. The corresponding results for a real data set can then be interpreted by comparing against confidence intervals obtained from synthetic data generated according to different models.

    Our results show that the goodness-of-fit of several commonly used failure rate models, such as linear, piecewise linear and exponential, are virtually identical. In addition, they do not explain the data as well as a new model we introduce: piecewise constant.

  • 150.
    Masood, Jawad
    et al.
    Halmstad University, School of Information Technology, Halmstad Embedded and Intelligent Systems Research (EIS).
    Philippsen, Roland
    Halmstad University, School of Information Technology, Halmstad Embedded and Intelligent Systems Research (EIS), CAISR - Center for Applied Intelligent Systems Research.
    Duracz, Jan
    Halmstad University, School of Information Technology, Halmstad Embedded and Intelligent Systems Research (EIS), Centre for Research on Embedded Systems (CERES).
    Taha, Walid
    Halmstad University, School of Information Technology, Halmstad Embedded and Intelligent Systems Research (EIS), Centre for Research on Embedded Systems (CERES). Rice University, Houston, Texas, USA.
    Eriksson, Henrik
    SP Technical Research Institute, Borås, Sweden.
    Grante, Christian
    Volvo Group Trucks Technology, Göteborg, Sweden.
    Domain Analysis for Standardised Functional Safety: A Case Study on Design-Time Verification of Automatic Emergency Breaking2014In: FISITA World Automotive Congress 2014: Maastricht, The Netherlands 2-6 June 2014: Volume 2 of 5, Hague: Royal Netherlands Society of Engineers (KIVI) , 2014, p. 845-854Conference paper (Refereed)
    Abstract [en]

    Simulation traditionally computes individual trajectories, which severely limits the assessment of overall system behaviour. To address this fundamental shortcoming, we rely on computing enclosures to determine bounds on system behaviour instead of individual traces. In the present case study, we investigate the enclosures of a generic Automatic Emergency Braking (AEB) system and demonstrate how this creates a direct link between requirement specification and standardized safety criteria as put forward by ISO 26262. The case study strongly supports that a methodology based on enclosures can provide a missing link across the engineering process, from design to compliance testing. This result is highly relevant for ongoing efforts to virtualize testing and create a unified tool-chain for the development of next generation Advanced Driver Assistance Systems.

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