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  • 151.
    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

  • 152.
    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.

  • 153.
    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.

  • 154.
    Khoshkangini, Reza
    et al.
    Halmstad University, School of Information Technology, Halmstad Embedded and Intelligent Systems Research (EIS), CAISR - Center for Applied Intelligent Systems Research.
    Pashami, Sepideh
    Halmstad University, School of Information Technology, Halmstad Embedded and Intelligent Systems Research (EIS), CAISR - Center for Applied Intelligent Systems Research.
    Nowaczyk, Sławomir
    Halmstad University, School of Information Technology, Halmstad Embedded and Intelligent Systems Research (EIS), CAISR - Center for Applied Intelligent Systems Research.
    Warranty Claim Rate Prediction using Logged Vehicle Data2019In: Lecture Notes in Computer Science, ISSN 0302-9743, E-ISSN 1611-3349, Vol. 11804, p. 663-674Article in journal (Refereed)
    Abstract [en]

    Early detection of anomalies, trends and emerging patterns can be exploited to reduce the number and severity of quality problems in vehicles. This is crucially important since having a good understanding of the quality of the product leads to better designs in the future, and better maintenance to solve the current issues. To this end, the integration of large amounts of data that are logged during the vehicle operation can be used to build the model of usage patterns for early prediction. In this study, we have developed a machine learning system for warranty claims by integrating available information sources: Logged Vehicle Data (LVD) and Warranty Claims (WCs). The experimental results obtained from a large data set of heavy duty trucks are used to demonstrate the effectiveness of the proposed system to predict the warranty claims. © Springer Nature Switzerland AG 2019.

  • 155.
    Khoshkangini, Reza
    et al.
    Halmstad University, School of Information Technology, Halmstad Embedded and Intelligent Systems Research (EIS), CAISR - Center for Applied Intelligent Systems Research.
    Pini, M. S.
    Department of Information Engineering, University of Padova, Padua, Italy.
    Rossi, F.
    IBM T. J. Watson Research Center, Yorktown Heights, NY, United States.
    Constructing CP-Nets from Users Past Selection2019In: Lecture Notes in Computer Science: Volume 11919 LNAI, Springer , 2019, p. 130-142Conference paper (Refereed)
    Abstract [en]

    Although recommender systems have been significantly developed for providing customized services to users in various domains, they still have some limitations regarding the extraction of users’ conditional preferences from their past selections when they are in a dynamic context. We propose a framework to automatically extract and learn users’ conditional and qualitative preferences in a gamified system taking into consideration the players’ past behaviour, without asking any information from the players. To do that, we construct CP-nets modeling users preferences via a procedure that employs multiple Information Criterion score functions within an heuristic algorithm to learn a Bayesian network. The approach has been validated experimentally in the challenge recommendation domain in an urban mobility gamified system. © 2019, Springer Nature Switzerland AG.

  • 156.
    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.

  • 157.
    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.

  • 158.
    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.

  • 159.
    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.

  • 160.
    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.

  • 161.
    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.

  • 162.
    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.

  • 163.
    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

  • 164.
    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.

  • 165.
    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

  • 166.
    Magnusson, Martin
    et al.
    MRO lab, Centre for Applied Autonomous Sensor Systems (AASS), Örebro University, Örebro, Sweden.
    Kucner, Tomasz Piotr
    MRO lab, Centre for Applied Autonomous Sensor Systems (AASS), Örebro University, Örebro, Sweden.
    Gholami Shahbandi, Saeed
    Halmstad University, School of Information Technology, Halmstad Embedded and Intelligent Systems Research (EIS), CAISR - Center for Applied Intelligent Systems Research.
    Andreasson, Henrik
    MRO lab, Centre for Applied Autonomous Sensor Systems (AASS), Örebro University, Örebro, Sweden.
    Lilienthal, Achim J.
    MRO lab, Centre for Applied Autonomous Sensor Systems (AASS), Örebro University, Örebro, Sweden.
    Semi-Supervised 3D Place Categorisation by Descriptor Clustering2017In: IROS Vancouver 2017: Conference Digest, Piscataway: Institute of Electrical and Electronics Engineers (IEEE), 2017, p. 620-625Conference paper (Refereed)
    Abstract [en]

    Place categorisation; i.e., learning to group perception data into categories based on appearance; typically uses supervised learning and either visual or 2D range data. This paper shows place categorisation from 3D data without any training phase. We show that, by leveraging the NDT histogram descriptor to compactly encode 3D point cloud appearance, in combination with standard clustering techniques, it is possible to classify public indoor data sets with accuracy comparable to, and sometimes better than, previous supervised training methods. We also demonstrate the effectiveness of this approach to outdoor data, with an added benefit of being able to hierarchically categorise places into sub-categories based on a user-selected threshold. This technique relieves users of providing relevant training data, and only requires them to adjust the sensitivity to the number of place categories, and provide a semantic label to each category after the process is completed. © 2017 IEEE.

  • 167.
    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).

  • 168.
    Marcos, Mar
    et al.
    Universitat Jaume I, Castellón, Spain.
    Juarez, Jose M.University of Murcia, Murcia, Spain.Lenz, RichardFriedrich-Alexander-University of Erlangen-Nuremberg, Erlangen, Germany.Nalepa, Grzegorz J.Jagiellonian University and AGH University of Science and Technology, Kraków, Poland.Nowaczyk, SławomirHalmstad University, School of Information Technology, Halmstad Embedded and Intelligent Systems Research (EIS), CAISR - Center for Applied Intelligent Systems Research.Peleg, MorUniversity of Haifa, Haifa, Israel.Stefanowski, JerzyPoznań University of Technology, Poznan, Poland.Stiglic, GregorUniversity of Maribor, Maribor, Slovenia.
    Artificial Intelligence in Medicine: Knowledge Representation and Transparent and Explainable Systems2020Conference proceedings (editor) (Refereed)
  • 169.
    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.

  • 170.
    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.

  • 171.
    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 %.

  • 172.
    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.

  • 173.
    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.

  • 174.
    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 GridsManuscript (preprint) (Other (popular science, discussion, etc.))
    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.

  • 175.
    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

  • 176.
    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

  • 177.
    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.

  • 178.
    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.

  • 179.
    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.

  • 180.
    Mendoza-Palechor, Fabio
    et al.
    Department of Electronic and Systems Engineering, Universidad de la Costa, CUC, Barranquilla, Colombia.
    Menezes, Maria Luiza Recena
    Halmstad University, School of Information Technology, Halmstad Embedded and Intelligent Systems Research (EIS).
    Pinheiro Sant'Anna, Anita
    Halmstad University, School of Information Technology, Halmstad Embedded and Intelligent Systems Research (EIS), CAISR - Center for Applied Intelligent Systems Research.
    Ortiz-Barrios, Miguel
    Department of Industrial Management, Agroindustry and Operations, Universidad de la Costa, CUC, Barranquilla, Colombia.
    Samara, Anas
    School of Computing, Computer Science Research Institute, Ulster University, Belfast, United Kingdom.
    Galway, Leo
    School of Computing, Computer Science Research Institute, Ulster University, Belfast, United Kingdom.
    Affective recognition from EEG signals: an integrated data-mining approach2019In: Journal of Ambient Intelligence and Humanized Computing, ISSN 1868-5137, E-ISSN 1868-5145, Vol. 10, no 10, p. 3955-3974Article in journal (Refereed)
    Abstract [en]

    Emotions play an important role in human communication, interaction, and decision making processes. Therefore, considerable efforts have been made towards the automatic identification of human emotions, in particular electroencephalogram (EEG) signals and Data Mining (DM) techniques have been then used to create models recognizing the affective states of users. However, most previous works have used clinical grade EEG systems with at least 32 electrodes. These systems are expensive and cumbersome, and therefore unsuitable for usage during normal daily activities. Smaller EEG headsets such as the Emotiv are now available and can be used during daily activities. This paper investigates the accuracy and applicability of previous affective recognition methods on data collected with an Emotiv headset while participants used a personal computer to fulfill several tasks. Several features were extracted from four channels only (AF3, AF4, F3 and F4 in accordance with the 10–20 system). Both Support Vector Machine and Naïve Bayes were used for emotion classification. Results demonstrate that such methods can be used to accurately detect emotions using a small EEG headset during a normal daily activity. © 2018, Springer-Verlag GmbH Germany, part of Springer Nature.

  • 181.
    Menezes, Maria Luiza Recena
    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.
    Alonso-Fernandez, Fernando
    Halmstad University, School of Information Technology, Halmstad Embedded and Intelligent Systems Research (EIS), CAISR - Center for Applied Intelligent Systems Research.
    Methodology for Subject Authentification and Identification through EEG signal: equipment's and positioning artifacts2018In: A2IC 2018: Artificial Intelligence International Conference: Book of Abstract, 2018, p. 37-37Conference paper (Refereed)
  • 182.
    Menezes, Maria Luiza Recena
    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.
    Pavel, Misha
    Northeastern University, Boston, USA.
    Jimison, Holly
    Northeastern University, Boston, USA.
    Alonso-Fernandez, Fernando
    Halmstad University, School of Information Technology, Halmstad Embedded and Intelligent Systems Research (EIS), CAISR - Center for Applied Intelligent Systems Research.
    Affective Ambient Intelligence: from Domotics to Ambient Intelligence2018In: A2IC 2018: Artificial Intelligence International Conference: Book of Abstract, 2018, p. 25-25Conference paper (Refereed)
  • 183.
    Menezes, Maria Luiza Recena
    et al.
    Halmstad University, School of Information Technology, Halmstad Embedded and Intelligent Systems Research (EIS).
    Samara, A.
    School of Computing and Mathematics, Ulster University Belfast, Belfast, United Kingdom.
    Galway, L.
    School of Computing and Mathematics, Ulster University Belfast, Belfast, 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.
    Verikas, Antanas
    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.
    Wang, H.
    School of Computing and Mathematics, Ulster University Belfast, Belfast, United Kingdom.
    Bond, R.
    School of Computing and Mathematics, Ulster University Belfast, Belfast, United Kingdom.
    Towards emotion recognition for virtual environments: an evaluation of eeg features on benchmark dataset2017In: Personal and Ubiquitous Computing, ISSN 1617-4909, E-ISSN 1617-4917, Vol. 21, no 6, p. 1003-1013Article in journal (Refereed)
    Abstract [en]

    One of the challenges in virtual environments is the difficulty users have in interacting with these increasingly complex systems. Ultimately, endowing machines with the ability to perceive users emotions will enable a more intuitive and reliable interaction. Consequently, using the electroencephalogram as a bio-signal sensor, the affective state of a user can be modelled and subsequently utilised in order to achieve a system that can recognise and react to the user’s emotions. This paper investigates features extracted from electroencephalogram signals for the purpose of affective state modelling based on Russell’s Circumplex Model. Investigations are presented that aim to provide the foundation for future work in modelling user affect to enhance interaction experience in virtual environments. The DEAP dataset was used within this work, along with a Support Vector Machine and Random Forest, which yielded reasonable classification accuracies for Valence and Arousal using feature vectors based on statistical measurements and band power from the and waves and High Order Crossing of the EEG signal. © 2017, The Author(s).

  • 184.
    Midtiby, Henrik Skov
    et al.
    The Maersk Mc-Kinney Moller Institute, University of Southern Denmark, Odense, Denmark.
    Åstrand, Björn
    Halmstad University, School of Information Technology, Halmstad Embedded and Intelligent Systems Research (EIS), CAISR - Center for Applied Intelligent Systems Research.
    Jørgensen, Ole
    Operations Management, Aarhus University, Tjele, Denmark.
    Jørgensen, Rasmus Nyholm
    Signal Processing, Aarhus University, Aarhus, Denmark.
    Upper limit for context-based crop classification in robotic weeding applications2016In: Biosystems Engineering, ISSN 1537-5110, E-ISSN 1537-5129, Vol. 146, p. 183-192Article in journal (Refereed)
    Abstract [en]

    Knowledge of the precise position of crop plants is a prerequisite for effective mechanical weed control in robotic weeding application such as in crops like sugar beets which are sensitive to mechanical stress. Visual detection and recognition of crop plants based on their shapes has been described many times in the literature. In this paper the potential of using knowledge about the crop seed pattern is investigated based on simulated output from a perception system. The reliability of position–based crop plant detection is shown to depend on the weed density (ρ, measured in weed plants per square metre) and the crop plant pattern position uncertainty (σx and σy, measured in metres along and perpendicular to the crop row, respectively). The recognition reliability can be described with the positive predictive value (PPV), which is limited by the seeding pattern uncertainty and the weed density according to the inequality: PPV ≤ (1 + 2πρσxσy)−1. This result matches computer simulations of two novel methods for position–based crop recognition as well as earlier reported field–based trials. © 2016 IAgrE

  • 185.
    Mikaelyan, Anna
    Halmstad University, School of Information Technology, Halmstad Embedded and Intelligent Systems Research (EIS), CAISR - Center for Applied Intelligent Systems Research.
    Compact orientation and frequency estimation with applications in biometrics: Biometrics on the orientation express2015Doctoral thesis, comprehensive summary (Other academic)
    Abstract [en]

    Automatic feature extraction still remains a relevant image and signal processing problem even tough both the field and technologies are developing rapidly. Images of low quality, where it is extremely difficult to reliably process image information automatically, are of special interest. To such images we can refer forensic fingerprints, which are left unintentionally on different surfaces andare contaminated by several of the most difficult noise types. For this reason, identification of fingerprints is mainly based on the visual skills of forensic examiners. We address the problem caused by low quality in fingerprints by connecting different sources of information together, yielding dense frequency and orientation maps in an iterative scheme. This scheme comprises smoothing ofthe original, but only along, ideally never across, the ridges. Reliable estimation of dense maps allows to introduce a continuous fingerprint ridge counting technique. In fingerprint scenario the collection of irrefutable tiny details, e.g. bifurcation of ridges, called minutiae, is used to tie the pattern of such points and their tangential directions to the finger producing the pattern. This limited feature set, location and direction of minutiae, is used in current AFIS systems, while fingerprint examiners use the extended set of features, including the image information between the points. With reasonably accurate estimationsof dense frequency and orientation maps at hand, we have been able to propose a novel compact feature descriptor of arbitrary points. We have used these descriptors to show that the image information between minutiae can be extracted automatically and be valuable for identity establishment of forensic images even if the underlying images are noisy. We collect and compress the image information in the neighborhoods of the fine details, such as minutiae, to vectors, one per minutia, and use the vectors to "color" the minutiae. When matching two patterns (of minutiae) even the color of the minutia must match to conclude that they come from the same identity. This feature development has been concentrated and tested on forensic fingerprint images. However, we have also studied an extension of its application area to other biometrics, periocular regions of faces. This allowed us to test the persistence of automatically extracted features across different types of imagesand image qualities, supporting its generalizability.

  • 186.
    Mikaelyan, Anna
    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 by Detection of Local Symmetry Patterns2014In: Proceedings: Tenth International Conference on Signal-Image Technology and Internet-Based System: 23–27 November 2014: Marrakech, Morocco / [ed] Kokou Yetongnon, Albert Dipanda & Richard Chbeir, Los Alamitos, CA: IEEE Computer Society, 2014, p. 584-591Conference paper (Refereed)
    Abstract [en]

    We present a new system for biometric recognition using periocular images. The feature extraction method employed describes neighborhoods around keypoints by projection onto harmonic functions which estimates the presence of a series of various symmetric curve families around such keypoints. The iso-curves of such functions are highly symmetric w.r.t. the keypoints and the estimated coefficients have well defined geometric interpretations. The descriptors used are referred to as Symmetry Assessment by Feature Expansion (SAFE). Extraction is done across a set of discrete points of the image, uniformly distributed in a rectangular-shaped grid positioned in the eye center. Experiments are done with two databases of iris data, one acquired with a close-up iris camera, and another in visible light with a webcam. The two databases have been annotated manually, meaning that the radius and center of the pupil and sclera circles are available, which are used as input for the experiments. Results show that this new system has a performance comparable with other periocular recognition approaches. We particularly carry out comparative experiments with another periocular system based on Gabor features extracted from the same set of grid points, with the fusion of the two systems resulting in an improved performance. We also evaluate an iris texture matcher, providing fusion results with the periocular systems as well.

  • 187.
    Mikaelyan, Anna
    et al.
    Halmstad University, School of Information Science, Computer and Electrical Engineering (IDE), Halmstad Embedded and Intelligent Systems Research (EIS), CAISR - Center for Applied Intelligent Systems Research.
    Bigun, Josef
    Halmstad University, School of Information Science, Computer and Electrical Engineering (IDE), Halmstad Embedded and Intelligent Systems Research (EIS), CAISR - Center for Applied Intelligent Systems Research.
    Frequency and ridge estimation using structure tensor2013In: Proceedings of Biometric Technologies in Forensic Science: Nijmegen, 14–15 October 2013, Nijmegen: Radboud University Nijmegen , 2013, p. 58-59Conference paper (Refereed)
  • 188.
    Mikaelyan, Anna
    et al.
    Halmstad University, School of Information Technology, Halmstad Embedded and Intelligent Systems Research (EIS), CAISR - Center for Applied Intelligent Systems Research.
    Bigun, Josef
    Halmstad University, School of Information Technology, Halmstad Embedded and Intelligent Systems Research (EIS), CAISR - Center for Applied Intelligent Systems Research.
    SAFE features for matching fingermarks by neighbourhoods of single minutiae2014In: 2014 14th International Symposium on Communications and Information Technologies (ISCIT), Piscataway, N.J.: IEEE Press, 2014, p. 181-185, article id 7011896Conference paper (Refereed)
    Abstract [en]

    Symmetry Assessment by Finite Expansion (SAFE) is a novel description of image information by means of Generalized Structure Tensor. It represents orientation data in neighbourhood of key points projected onto the space of harmonic functions creating a geometrically interpretable feature of low dimension. The proposed feature has built in quality metrics reflecting accuracy of the extracted feature and ultimately the quality of the key point. The feature vector is orientation invariant in that it is orientation steerable with low computational cost. We provide experiments on minutia key points of forensic fingerprints to demonstrate its usefulness. Matching is performed based on minutia in regions with high orientation variance, e.g. in proximity of core points. Performance of single matching minutia equals to 20% EER and Rank-20 CMC 69% on the only publicly available annotated forensic fingerprint SD27 database.

    Further, we complement SAFE descriptors of orientation maps with SAFE descriptors of frequency features in a similar manner. In case of combined features the performance is improved further to 19% EER and 74% Rank-20 CMC. © 2014 IEEE.

  • 189.
    Mikaelyan, Anna
    et al.
    Halmstad University, School of Information Technology, Halmstad Embedded and Intelligent Systems Research (EIS), CAISR - Center for Applied Intelligent Systems Research.
    Bigun, Josef
    Halmstad University, School of Information Technology, Halmstad Embedded and Intelligent Systems Research (EIS), CAISR - Center for Applied Intelligent Systems Research.
    Symmetry Assessment by Finite Expansion: application to forensic fingerprints2014In: 2014 International Conference of the Biometrics Special Interest Group (BIOSIG) / [ed] Arslan Brömme & Christoph Busch, Bonn: Gesellschaft für Informatik, 2014, p. 87-98Conference paper (Refereed)
    Abstract [en]

    Common image features have too poor information for identification of forensic images of fingerprints, where only a small area of the finger is imaged and hence a small amount of key points are available. Noise, nonlinear deformation, and unknown rotation are additional issues that complicate identification of forensic fingerprints. We propose a feature extraction method which describes image information around key points: Symmetry Assessment by Finite Expansion (SAFE). The feature set has built-in quality estimates as well as a rotation invariance property. The theory is developed for continuous space, allowing compensation for features directly in the feature space when images undergo such rotation without actually rotating them. Experiments supporting that use of these features improves identification of forensic fingerprint images of the public NIST SD27 database are presented. Performance of matching orientation information in a neighborhood of core points has an EER of 24% with these features alone, without using minutiae constellations, in contrast to 36% when using minutiae alone. Rank-20 CMC is 58%, which is lower than 67% when using notably more manually collected minutiae information.

  • 190.
    Minelga, Jonas
    et al.
    Department of Electric Power Systems, 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.
    Vaiciukynas, Evaldas
    Department of Electric Power Systems, Kaunas University of Technology, Kaunas, Lithuania.
    Gelzinis, Adas
    Department of Electric Power Systems, Kaunas University of Technology, Kaunas, Lithuania.
    Bacauskiene, Marija
    Department of Electric Power Systems, Kaunas University of Technology, Kaunas, Lithuania.
    A Transparent Decision Support Tool in Screening for Laryngeal Disorders Using Voice and Query Data2017In: Applied Sciences: APPS, ISSN 1454-5101, E-ISSN 1454-5101, Vol. 7, no 10, p. 1-15, article id 1096Article in journal (Refereed)
    Abstract [en]

    The aim of this study is a transparent tool for analysis of voice (sustained phonation /a/) and query data capable of providing support in screening for laryngeal disorders. In this work, screening is concerned with identification of potentially pathological cases by classifying subject’s data into ’healthy’ and ’pathological’ classes as well as visual exploration of data and automatic decisions. A set of association rules and a decision tree, techniques lending themselves for exploration, were generated for pathology detection. Data pairwise similarities, estimated in a novel way, were mapped onto a 2D metric space for visual inspection and analysis. Accurate identification of pathological cases was observed on unseen subjects using the most discriminative query parameter and six audio parameters routinely used by otolaryngologists in a clinical practice: equal error rate (EER) of 11.1% was achieved using association rules and 10.2% using the decision tree. The EER was further reduced to 9.5% by combining results from these two classifiers. The developed solution can be a useful tool for Otolaryngology departments in diagnostics, education and exploratory tasks. © 2017 by the authors.

  • 191.
    Miyasaka, Hiroyuki
    et al.
    Fujita Hlth Univ, Dept Rehabil, Nanakuri Mem Hosp, Tsu, Mie, Japan..
    Kondo, Izumi
    Fujita Hlth Univ, Fujita Mem Nanakuri Inst, Dept Rehabil, Tsu, Mie, Japan.;Natl Ctr Geriatr & Gerontol, Dept Rehabil Med, Obu, Aichi, Japan..
    Yamamura, Chihiro
    Fujita Hlth Univ, Dept Rehabil, Nanakuri Mem Hosp, Tsu, Mie, Japan..
    Fujita, Naoko
    Fujita Hlth Univ, Dept Rehabil, Nanakuri Mem Hosp, Tsu, Mie, Japan.;Kariya Toyota Gen Hosp, Takahama Branch Hosp, Kariya, Aichi, Japan..
    Orand, Abbas
    Halmstad University, School of Information Technology, Halmstad Embedded and Intelligent Systems Research (EIS), CAISR - Center for Applied Intelligent Systems Research.
    Sonoda, Shigeru
    Fujita Hlth Univ, Sch Med, Dept Rehabil Med 2, Tsu, Mie, Japan..
    The quantification of task-difficulty of upper limb motor function skill based on Rasch analysis2020In: Topics in Stroke Rehabilitation, ISSN 1074-9357, E-ISSN 1945-5119, Vol. 27, no 1, p. 49-56Article in journal (Refereed)
    Abstract [en]

    Background: The degree of difficulty of skills of paretic upper limbs in daily life has not been investigated. Objective: To determine the internal validity and level of difficulty of items of the Functional Skills Measure After Paralysis (FSMAP), which can be used to evaluate the functional skills of daily living for stroke patients. Method: A total of 105 first-stroke patients were assessed using the FSMAP. The evaluation system consists of 65 items in 15 categories. We examined the internal validity and level of difficulty of these items using Rasch analysis. In this study, an item with either infit or outfit of >= 1.5 was defined as underfit. Results: Rasch analysis showed that 8 items were underfit. The highest infit and outfit logits were 2.47 for "Trouser donning/doffing" and 8.44 for "Paper manipulation". "Shirt donning/doffing" was the easiest item and "Coin manipulation" was the most difficult, with difficulty logits of -35.8 and 41.5, respectively. Conclusion: The therapist can confirm items that the patient can or cannot perform. By understanding the level of difficulty of each item, the most appropriate functional skill to focus on acquiring next can be identified.

  • 192.
    Muhammad, Naveed
    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.
    Intention Estimation Using Set of Reference Trajectories as Behaviour Model2018In: Sensors, ISSN 1424-8220, E-ISSN 1424-8220, Vol. 18, no 12, article id 4423Article in journal (Refereed)
    Abstract [en]

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

  • 193.
    Muhammad, Naveed
    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.
    Predicting Agent Behaviour and State for Applications in a Roundabout-Scenario Autonomous Driving2019In: Sensors, ISSN 1424-8220, E-ISSN 1424-8220, Vol. 19, no 19, article id 4279Article in journal (Refereed)
    Abstract [en]

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

  • 194.
    Mühlfellner, Peter
    Halmstad University, School of Information Technology, Halmstad Embedded and Intelligent Systems Research (EIS), CAISR - Center for Applied Intelligent Systems Research. Volkswagen AG.
    Lifelong Visual Localization for Automated Vehicles2015Doctoral thesis, comprehensive summary (Other academic)
    Abstract [en]

    Automated driving can help solve the current and future problems of individualtransportation. Automated valet parking is a possible approach to help with overcrowded parking areas in cities and make electric vehicles more appealing. In an automated valet system, drivers are able to drop off their vehicle close to a parking area. The vehicle drives to a free parking spot on its own, while the driver is free to perform other tasks — such as switching the mode of transportation. Such a system requires the automated car to navigate unstructured, possibly three dimensional areas. This goes beyond the scope ofthe tasks performed in the state of the art for automated driving.

    This thesis describes a visual localization system that provides accuratemetric pose estimates. As sensors, the described system uses multiple monocular cameras and wheel-tick odometry. This is a sensor set-up that is close to what can be found in current production cars. Metric pose estimates with errors in the order of tens of centimeters enable maneuvers such as parking into tight parking spots. This system forms the basis for automated navigationin the EU-funded V-Charge project.

    Furthermore, we present an approach to the challenging problem of life-long mapping and localization. Over long time spans, the visual appearance ofthe world is subject to change due to natural and man-made phenomena. The effective long-term usage of visual maps requires the ability to adapt to these changes. We describe a multi-session mapping system, that fuses datasets intoiiia single, unambiguous, metric representation. This enables automated navigation in the presence of environmental change. To handle the growing complexityof such a system we propose the concept of Summary Maps, which contain a reduced set of landmarks that has been selected through a combination of scoring and sampling criteria. We show that a Summary Map with bounded complexity can achieve accurate localization under a wide variety of conditions.

    Finally, as a foundation for lifelong mapping, we propose a relational database system. This system is based on use-cases that are not only concerned with solving the basic mapping problem, but also with providing users with a better understanding of the long-term processes that comprise a map. We demonstrate that we can pose interesting queries to the database, that help us gain a better intuition about the correctness and robustness of the created maps. This is accomplished by answering questions about the appearance and distribution of visual landmarks that were used during mapping. This thesis takes on one of the major unsolved challenges in vision-based localization and mapping: long-term operation in a changing environment. We approach this problem through extensive real world experimentation, as well as in-depth evaluation and analysis of recorded data. We demonstrate that accurate metric localization is feasible both during short term changes, as exemplified by the transition between day and night, as well as longer term changes, such as due to seasonal variation.

  • 195.
    Mühlfellner, Peter
    et al.
    Halmstad University, School of Information Technology, Halmstad Embedded and Intelligent Systems Research (EIS), CAISR - Center for Applied Intelligent Systems Research.
    Bürki, Mathias
    ETH, Zürich, Switzerland.
    Bosse, Mike
    ETH, Zürich, Switzerland.
    Derendarz, Wojciech
    Volkswagen AG, Wolfsburg, Germany.
    Philippsen, Roland
    Halmstad University, School of Information Technology, Halmstad Embedded and Intelligent Systems Research (EIS), CAISR - Center for Applied Intelligent Systems Research.
    Furgale, Paul
    ETH, Zürich, Switzerland.
    Summary Maps for Lifelong Visual Localization2016In: Journal of Field Robotics, ISSN 1556-4959, E-ISSN 1556-4967, Vol. 33, no 5, p. 561-590Article in journal (Refereed)
    Abstract [en]

    Robots that use vision for localization need to handle environments which are subject to seasonal and structural change, and operate under changing lighting and weather conditions. We present a framework for lifelong localization and mapping designed to provide robust and metrically accurate online localization in these kinds of changing environments. Our system iterates between offline map building, map summary, and online localization. The offline mapping fuses data from multiple visually varied datasets, thus dealing with changing environments by incorporating new information. Before passing this data to the online localization system, the map is summarized, selecting only the landmarks that are deemed useful for localization. This Summary Map enables online localization that is accurate and robust to the variation of visual information in natural environments while still being computationally efficient.

    We present a number of summary policies for selecting useful features for localization from the multi-session map and explore the tradeoff between localization performance and computational complexity. The system is evaluated on 77 recordings, with a total length of 30 kilometers, collected outdoors over sixteen months. These datasets cover all seasons, various times of day, and changing weather such as sunshine, rain, fog, and snow. We show that it is possible to build consistent maps that span data collected over an entire year, and cover day-to-night transitions. Simple statistics computed on landmark observations are enough to produce a Summary Map that enables robust and accurate localization over a wide range of seasonal, lighting, and weather conditions. © 2015 Wiley Periodicals, Inc.

  • 196.
    Mühlfellner, Peter
    et al.
    Halmstad University, School of Information Technology, Halmstad Embedded and Intelligent Systems Research (EIS), CAISR - Center for Applied Intelligent Systems Research. Volkswagen AG.
    Furgale, Paul
    Autonomous Systems Lab, ETH Z¨urich Leonhardstrasse 21, Z¨urich, Switzerland.
    Derendarz, Wojciech
    Volkswagen AG, Wolfsburg, Germany .
    Philippsen, Roland
    Halmstad University, School of Information Technology, Halmstad Embedded and Intelligent Systems Research (EIS), CAISR - Center for Applied Intelligent Systems Research.
    Designing a Relational Database for Long-Term Visual MappingManuscript (preprint) (Other academic)
    Abstract [en]

    We present a map architecture based on a relational database that helps tackle the challenge of lifelong visuallocalization and mapping. The proposed design is rooted in a set of use-cases that describe the processes necessary for creating, using and analyzing visual maps. Our database and software architecture effectively expresses the requiredinteractions between map elements, such as visual frames generated by multi-camera systems. One of the major strengths of the proposed system is the ease of formulating pertinent and novel queries. We show how these queries can help us gaina better intuition about the map contents, taking into account complex data associations, even as session upon session is added to the map. Furthermore, we demonstrate how referential integrity checks, rollbacks and similar features of relational database management systems are beneficial for building long-term maps. Based on our experience with the proposed system during one year of intensive data collection and analysis, we discuss key lessons learned and indicate directions for evolving its design. These lessons show the importance of using higher relational normal forms to make the database schema even more useful for querying, as well as the need for a distributed, versioned system.

  • 197.
    Najmeh, Abiri
    et al.
    Department of Astronomy and Theoretical Physics, Lund University, Lund, Sweden.
    Ohlsson, Mattias
    Halmstad University, School of Information Technology, Halmstad Embedded and Intelligent Systems Research (EIS), CAISR - Center for Applied Intelligent Systems Research.
    Variational auto-encoders with Student’s t-prior2019In: ESANN 2019 Proceedings, 27th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning: Bruges – 24-26 April 2019, Bruges: ESANN , 2019, p. 415-420Conference paper (Refereed)
    Abstract [en]

    We propose a new structure for the variational auto-encoders (VAEs) prior, with the weakly informative multivariate Student’s t-distribution. In the proposed model all distribution parameters are trained, thereby allowing for a more robust approximation of the underlying data distribution. We used Fashion-MNIST data in two experiments to compare the proposed VAEs with the standard Gaussian priors. Both experiments showed a better reconstruction of the images with VAEs using Student’s t-prior distribution. © 2019 ESANN (i6doc.com). All rights reserved.

  • 198.
    Nemati, Hassan
    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.
    Tracking of People in Paper Mill Warehouse Using Laser Range Sensor2014In: UKSim-AMSS Eighth European Modelling Symposium on Computer Modelling and Simulation, EMS 2014 / [ed] David Al-Dabass, Valentina Colla, Marco Vannucci & Athanasios Pantelous, Los Alamitos, CA: IEEE Computer Society, 2014, p. 52-57, article id 7153974Conference paper (Refereed)
    Abstract [en]

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

  • 199.
    Nilsson, Kenneth
    et al.
    Halmstad University, School of Information Technology, Halmstad Embedded and Intelligent Systems Research (EIS).
    Bigun, Josef
    Halmstad University, School of Information Technology, Halmstad Embedded and Intelligent Systems Research (EIS), CAISR - Center for Applied Intelligent Systems Research.
    Complex Filters Applied to Fingerprint Images Detecting Prominent Symmetry Points Used for Alignment2002In: Biometric Authentication: International ECCV 2002 Workshop Copenhagen, Denmark, June 1, 2002 Proceedings, Berlin: Springer Berlin/Heidelberg, 2002, p. 39-47Chapter in book (Other academic)
    Abstract [en]

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

  • 200.
    Nilsson, Kenneth
    et al.
    Halmstad University, School of Information Technology, Halmstad Embedded and Intelligent Systems Research (EIS).
    Bigun, Josef
    Halmstad University, School of Information Technology, Halmstad Embedded and Intelligent Systems Research (EIS), CAISR - Center for Applied Intelligent Systems Research.
    Localization of corresponding points in fingerprints by complex filtering2003In: Pattern Recognition Letters, ISSN 0167-8655, E-ISSN 1872-7344, Vol. 24, no 13, p. 2135-2144Article in journal (Refereed)
    Abstract [en]

    For the alignment of two fingerprints certain landmark points are needed. These should be automaticaly extracted with low misidentification rate. As landmarks we suggest the prominent symmetry points (singular points, SPs) in the fingerprints. We identify an SP by its symmetry properties. SPs are extracted from the complex orientation field estimated from the global structure of the fingerprint, i.e. the overall pattern of the ridges and valleys. Complex filters, applied to the orientation field in multiple resolution scales, are used to detect the symmetry and the type of symmetry. Experimental results are reported.

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