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  • 101.
    Fan, Yuantao
    Högskolan i Halmstad, Akademin för informationsteknologi, Halmstad Embedded and Intelligent Systems Research (EIS), CAISR Centrum för tillämpade intelligenta system (IS-lab).
    Wisdom of the Crowd for Fault Detection and Prognosis2020Doktoravhandling, med artikler (Annet vitenskapelig)
    Abstract [en]

    Monitoring and maintaining the equipment to ensure its reliability and availability is vital to industrial operations. With the rapid development and growth of interconnected devices, the Internet of Things promotes digitization of industrial assets, to be sensed and controlled across existing networks, enabling access to a vast amount of sensor data that can be used for condition monitoring. However, the traditional way of gaining knowledge and wisdom, by the expert, for designing condition monitoring methods is unfeasible for fully utilizing and digesting this enormous amount of information. It does not scale well to complex systems with a huge amount of components and subsystems. Therefore, a more automated approach that relies on human experts to a lesser degree, being capable of discovering interesting patterns, generating models for estimating the health status of the equipment, supporting maintenance scheduling, and can scale up to many equipment and its subsystems, will provide great benefits for the industry. 

    This thesis demonstrates how to utilize the concept of "Wisdom of the Crowd", i.e. a group of similar individuals, for fault detection and prognosis. The approach is built based on an unsupervised deviation detection method, Consensus Self-Organizing Models (COSMO). The method assumes that the majority of a crowd is healthy; individual deviates from the majority are considered as potentially faulty. The COSMO method encodes sensor data into models, and the distances between individual samples and the crowd are measured in the model space. This information, regarding how different an individual performs compared to its peers, is utilized as an indicator for estimating the health status of the equipment. The generality of the COSMO method is demonstrated with three condition monitoring case studies: i) fault detection and failure prediction for a commercial fleet of city buses, ii) prognosis for a fleet of turbofan engines and iii) finding cracks in metallic material. In addition, the flexibility of the COSMO method is demonstrated with: i) being capable of incorporating domain knowledge on specializing relevant expert features; ii) able to detect multiple types of faults with a generic data- representation, i.e. Echo State Network; iii) incorporating expert feedback on adapting reference group candidate under an active learning setting. Last but not least, this thesis demonstrated that the remaining useful life of the equipment can be estimated from the distance to a crowd of peers. 

  • 102.
    Fan, Yuantao
    et al.
    Högskolan i Halmstad, Akademin för informationsteknologi, Halmstad Embedded and Intelligent Systems Research (EIS), CAISR Centrum för tillämpade intelligenta system (IS-lab).
    Aramrattana, Maytheewat
    Högskolan i Halmstad, Akademin för informationsteknologi, Halmstad Embedded and Intelligent Systems Research (EIS), CAISR Centrum för tillämpade intelligenta system (IS-lab).
    Shahbandi, Saeed Gholami
    Högskolan i Halmstad, Akademin för informationsteknologi, Halmstad Embedded and Intelligent Systems Research (EIS), CAISR Centrum för tillämpade intelligenta system (IS-lab).
    Nemati, Hassan Mashad
    Högskolan i Halmstad, Akademin för informationsteknologi, Halmstad Embedded and Intelligent Systems Research (EIS), CAISR Centrum för tillämpade intelligenta system (IS-lab).
    Åstrand, Björn
    Högskolan i Halmstad, Akademin för informationsteknologi, Halmstad Embedded and Intelligent Systems Research (EIS), CAISR Centrum för tillämpade intelligenta system (IS-lab).
    Infrastructure Mapping in Well-Structured Environments Using MAV2016Inngår i: Lecture Notes in Computer Science, ISSN 0302-9743, E-ISSN 1611-3349, Vol. 9716, s. 116-126Artikkel i tidsskrift (Fagfellevurdert)
    Abstract [en]

    In this paper, we present a design of a surveying system for warehouse environment using low cost quadcopter. The system focus on mapping the infrastructure of surveyed environment. As a unique and essential parts of the warehouse, pillars from storing shelves are chosen as landmark objects for representing the environment. The map are generated based on fusing the outputs of two different methods, point cloud of corner features from Parallel Tracking and Mapping (PTAM) algorithm with estimated pillar position from a multi-stage image analysis method. Localization of the drone relies on PTAM algorithm. The system is implemented in Robot Operating System(ROS) and MATLAB, and has been successfully tested in real-world experiments. The result map after scaling has a metric error less than 20 cm. © Springer International Publishing Switzerland 2016.

  • 103.
    Fan, Yuantao
    et al.
    Högskolan i Halmstad, Akademin för informationsteknologi, Halmstad Embedded and Intelligent Systems Research (EIS), CAISR Centrum för tillämpade intelligenta system (IS-lab).
    Nowaczyk, Sławomir
    Högskolan i Halmstad, Akademin för informationsteknologi, Halmstad Embedded and Intelligent Systems Research (EIS), Laboratoriet för intelligenta system.
    Rögnvaldsson, Thorsteinn
    Högskolan i Halmstad, Akademin för informationsteknologi, Halmstad Embedded and Intelligent Systems Research (EIS), CAISR Centrum för tillämpade intelligenta system (IS-lab).
    Evaluation of Self-Organized Approach for Predicting Compressor Faults in a City Bus Fleet2015Inngår i: Procedia Computer Science, ISSN 1877-0509, E-ISSN 1877-0509, Vol. 53, s. 447-456Artikkel i tidsskrift (Fagfellevurdert)
    Abstract [en]

    Managing the maintenance of a commercial vehicle fleet is an attractive application domain of ubiquitous knowledge discovery. Cost effective methods for predictive maintenance are progressively demanded in the automotive industry. The traditional diagnostic paradigm that requires human experts to define models is not scalable to today's vehicles with hundreds of computing units and thousands of control and sensor signals streaming through the on-board controller area network. A more autonomous approach must be developed. In this paper we evaluate the performance of the COSMO approach for automatic detection of air pressure related faults on a fleet of city buses. The method is both generic and robust. Histograms of a single pressure signal are collected and compared across the fleet and deviations are matched against workshop maintenance and repair records. It is shown that the method can detect several of the cases when compressors fail on the road, well before the failure. The work is based on data from a three year long field study involving 19 buses operating in and around a city on the west coast of Sweden. © The Authors. Published by Elsevier B.V.

  • 104.
    Fan, Yuantao
    et al.
    Högskolan i Halmstad, Akademin för informationsteknologi, Halmstad Embedded and Intelligent Systems Research (EIS), CAISR Centrum för tillämpade intelligenta system (IS-lab).
    Nowaczyk, Sławomir
    Högskolan i Halmstad, Akademin för informationsteknologi, Halmstad Embedded and Intelligent Systems Research (EIS), Laboratoriet för intelligenta system.
    Rögnvaldsson, Thorsteinn
    Högskolan i Halmstad, Akademin för informationsteknologi, Halmstad Embedded and Intelligent Systems Research (EIS), CAISR Centrum för tillämpade intelligenta system (IS-lab).
    Incorporating Expert Knowledge into a Self-Organized Approach for Predicting Compressor Faults in a City Bus Fleet2015Inngår i: Frontiers in Artificial Intelligence and Applications, ISSN 0922-6389, E-ISSN 1879-8314, Vol. 278, s. 58-67Artikkel i tidsskrift (Fagfellevurdert)
    Abstract [en]

    In the automotive industry, cost effective methods for predictive maintenance are increasingly in demand. The traditional approach for developing diagnostic methods on commercial vehicles is heavily based on knowledge of human experts, and thus it does not scale well to modern vehicles with many components and subsystems. In previous work we have presented a generic self-organising approach called COSMO that can detect, in an unsupervised manner, many different faults. In a study based on a commercial fleet of 19 buses operating in Kungsbacka, we have been able to predict, for example, fifty percent of the compressors that break down on the road, in many cases weeks before the failure.

    In this paper we compare those results with a state of the art approach currently used in the industry, and we investigate how features suggested by experts for detecting compressor failures can be incorporated into the COSMO method. We perform several experiments, using both real and synthetic data, to identify issues that need to be considered to improve the accuracy. The final results show that the COSMO method outperforms the expert method.

  • 105.
    Fan, Yuantao
    et al.
    Högskolan i Halmstad, Akademin för informationsteknologi, Halmstad Embedded and Intelligent Systems Research (EIS), CAISR Centrum för tillämpade intelligenta system (IS-lab).
    Nowaczyk, Sławomir
    Högskolan i Halmstad, Akademin för informationsteknologi, Halmstad Embedded and Intelligent Systems Research (EIS), CAISR Centrum för tillämpade intelligenta system (IS-lab).
    Rögnvaldsson, Thorsteinn
    Högskolan i Halmstad, Akademin för informationsteknologi, Halmstad Embedded and Intelligent Systems Research (EIS), CAISR Centrum för tillämpade intelligenta system (IS-lab).
    Transfer learning for remaining useful life prediction based on consensus self-organizing modelsManuskript (preprint) (Annet vitenskapelig)
    Abstract [en]

    The traditional paradigm for developing machine prognostics usually relies on generalization from data acquired in experiments under controlled conditions prior to deployment of the equipment. Detecting or predicting failures and estimating machine health in this way assumes that future field data will have a very similar distribution to the experiment data. However, many complex machines operate under dynamic environmental conditions and are used in many different ways. This makes collecting comprehensive data very challenging, and the assumption that pre-deployment data and post-deployment data follow very similar distributions is unlikely to hold. Transfer Learning (TL) refers to methods for transferring knowledge learned in one setting (the source domain) to another setting (the target domain). In this work, we present a TL method for predicting Remaining Useful Life (RUL) of equipment, under the assumption that labels are available only for the source domain and not the target domain. This setting corresponds to generalizing from a limited number of run-to-failure experiments performed prior to deployment into making prognostics with data coming from deployed equipment that is being used under multiple new operating conditions and experiencing previously unseen faults. We employ a deviation detection method, Consensus Self-Organizing Models (COSMO), to create transferable features for building the RUL regression model. These features capture how different target equipment is in comparison to its peers. The efficiency of the proposed TL method is demonstrated using the NASA Turbofan Engine Degradation Simulation Data Set. Models using the COSMO transferable features show better performance than other methods on predicting RUL when the target domain is more complex than the source domain.

  • 106.
    Fan, Yuantao
    et al.
    Högskolan i Halmstad, Akademin för informationsteknologi, Halmstad Embedded and Intelligent Systems Research (EIS), CAISR Centrum för tillämpade intelligenta system (IS-lab).
    Nowaczyk, Sławomir
    Högskolan i Halmstad, Akademin för informationsteknologi, Halmstad Embedded and Intelligent Systems Research (EIS), CAISR Centrum för tillämpade intelligenta system (IS-lab).
    Rögnvaldsson, Thorsteinn
    Högskolan i Halmstad, Akademin för informationsteknologi, Halmstad Embedded and Intelligent Systems Research (EIS), CAISR Centrum för tillämpade intelligenta system (IS-lab).
    Using Histograms to Find Compressor Deviations in Bus Fleet Data2014Inngår i: The SAIS Workshop 2014 Proceedings, Swedish Artificial Intelligence Society (SAIS) , 2014, s. 123-132Konferansepaper (Fagfellevurdert)
    Abstract [en]

    Cost effective methods for predictive maintenance are increasingly demanded in the automotive industry. One solution is to utilize the on-board signals streams on each vehicle and build self-organizing systems that discover data deviations within a fleet. In this paper we evaluate histograms as features for describing and comparing individual vehicles. The results are based on a long-term field test with nineteen city buses operating around Kungsbacka in Halland. The purpose of this work is to investigate ways of discovering abnormal behaviors and irregularities between histograms of on-board signals, here specifically focusing on air pressure. We compare a number of distance measures and analyze the variability of histograms collected over different time spans. Clustering algorithms are used to discover structure in the data and track how this changes over time. As data are compared across the fleet, observed deviations should be matched against (often imperfect) reference data coming from workshop maintenance and repair databases.

  • 107.
    Fan, Yuantao
    et al.
    Högskolan i Halmstad, Akademin för informationsteknologi, Halmstad Embedded and Intelligent Systems Research (EIS), CAISR Centrum för tillämpade intelligenta system (IS-lab).
    Nowaczyk, Sławomir
    Högskolan i Halmstad, Akademin för informationsteknologi, Halmstad Embedded and Intelligent Systems Research (EIS), CAISR Centrum för tillämpade intelligenta system (IS-lab).
    Rögnvaldsson, Thorsteinn
    Högskolan i Halmstad, Akademin för informationsteknologi, Halmstad Embedded and Intelligent Systems Research (EIS), CAISR Centrum för tillämpade intelligenta system (IS-lab).
    Antonelo, Eric Aislan
    Federal University of Santa Catarina, Florianópolis, Brazil.
    Predicting Air Compressor Failures with Echo State Networks2016Inngår i: PHME 2016: Proceedings of the Third European Conference of the Prognostics and Health Management Society 2016 / [ed] Ioana Eballard, Anibal Bregon, PHM Society , 2016, s. 568-578Konferansepaper (Fagfellevurdert)
    Abstract [en]

    Modern vehicles have increasing amounts of data streaming continuously on-board their controller area networks. These data are primarily used for controlling the vehicle and for feedback to the driver, but they can also be exploited to detect faults and predict failures. The traditional diagnostics paradigm, which relies heavily on human expert knowledge, scales poorly with the increasing amounts of data generated by highly digitised systems. The next generation of equipment monitoring and maintenance prediction solutions will therefore require a different approach, where systems can build up knowledge (semi-)autonomously and learn over the lifetime of the equipment.

    A key feature in such systems is the ability to capture and encode characteristics of signals, or groups of signals, on-board vehicles using different models. Methods that do this robustly and reliably can be used to describe and compare the operation of the vehicle to previous time periods or to other similar vehicles. In this paper two models for doing this, for a single signal, are presented and compared on a case of on-road failures caused by air compressor faults in city buses. One approach is based on histograms and the other is based on echo state networks. It is shown that both methods are sensitive to the expected changes in the signal's characteristics and work well on simulated data. However, the histogram model, despite being simpler, handles the deviations in real data better than the echo state network.

  • 108.
    Farouq, Shiraz
    Högskolan i Halmstad, Akademin för informationsteknologi, Halmstad Embedded and Intelligent Systems Research (EIS), CAISR Centrum för tillämpade intelligenta system (IS-lab).
    Towards large-scale monitoring of operationally diverse thermal energy systems with data-driven techniques2019Licentiatavhandling, med artikler (Annet vitenskapelig)
    Abstract [en]

    The core of many typical large-scale industrial infrastructure consists of hundreds or thousands of systems that are similar in their basic design and purpose. For instance, District Heating (DH) utilities rely on a large network of substations to deliver heat to their customers. Similarly, a factory may require a large fleet of specialized robots for manufacturing a certain product. Monitoring these systems is important for maintaining the overall efficiency of industrial operations by detecting various problems due to faults and misconfiguration. However, this can be challenging since a well-understood prior model for each system is rarely available. In most cases, each system in a fleet or network is fitted with a set of sensors to measure its state at different time intervals. Typically, a data-driven model for each system can be used for their monitoring. However, not all factors that can possibly influence the operations of each system in a fleet or network has an associated sensor. Moreover, sufficient instances of normal, atypical and faulty behavior are rarely available to train such a model. These issues can impede the effectiveness of a system level data-driven model. Alternatively, it can be assumed that since all the systems in a fleet or network are working on a similar task, they should all behave in a homogeneous manner. Any system that behaves differently from the majority is then considered as an outlier. This is referred to as the global model at the fleet or network level. While the approach is simple, it is less effective in the presence of non-stationary working conditions. Hence, both system level and global modeling approaches have their limitations. 

    This thesis investigates system level and fleet or network level (global) models for large-scale monitoring, and proposes an alternative way which is referred to as a reference-group based approach. Herein, the operational monitoring of each system, referred to as a target, is delegated to a reference-group, which consists of systems experiencing a comparable operating regime along with the target. Thus, the definition of a normal, atypical or faulty operational behavior in a target system is described relative to its reference-group. In this sense, if the target system is not behaving operationally in consort with the systems in its reference-group, then it can be inferred that this is either due to a fault or because of some atypical operation arising at the target system due to its local peculiarities. The application area for these investigations is the large-scale operational monitoring of thermal energy systems: networks of district heating (DH) substations and fleets of heatpumps. The current findings indicate three advantages of a reference-group based approach. The first is that the reference operational behavior of any system in the fleet or network does not need to be predefined. The second is that it provides a basis for what a system’s operational behavior should have been and what it is. In this respect, each system in the reference-group provides an evidence about a particular behavior during a particular time period. This can be very useful when the description of a normal, atypical and faulty operational behavior is not available. The third is that it can detect potential atypical and faulty operational behavior quicker compared to global models of outlier detection at the fleet or network level.

  • 109.
    Farouq, Shiraz
    et al.
    Högskolan i Halmstad, Akademin för informationsteknologi, Halmstad Embedded and Intelligent Systems Research (EIS), CAISR Centrum för tillämpade intelligenta system (IS-lab).
    Byttner, Stefan
    Högskolan i Halmstad, Akademin för informationsteknologi, Halmstad Embedded and Intelligent Systems Research (EIS), CAISR Centrum för tillämpade intelligenta system (IS-lab).
    Bouguelia, Mohamed-Rafik
    Högskolan i Halmstad, Akademin för informationsteknologi, Halmstad Embedded and Intelligent Systems Research (EIS), CAISR Centrum för tillämpade intelligenta system (IS-lab).
    On monitoring heat-pumps with a group-based conformal anomaly detection approach2018Inngår i: ICDATA' 18: Proceedings of the 2018 International Conference on Data Science / [ed] Robert Stahlbock, Gary M. Weiss, Mahmoud Abou-Nasr, CSREA Press, 2018, s. 63-69Konferansepaper (Fagfellevurdert)
    Abstract [en]

    The ever increasing complexity of modern systems and equipment make the task of monitoring their health quite challenging. Traditional methods such as expert defined thresholds, physics based models and process history based techniques have certain drawbacks. Thresholds defined by experts require deep knowledge about the system and are often too conservative. Physics driven approaches are costly to develop and maintain. Finally, process history based models require large amount of data that may not be available at design time of a system. Moreover, the focus of these traditional approaches has been system specific. Hence, when industrial systems are deployed on a large scale, their monitoring becomes a new challenge. Under these conditions, this paper demonstrates the use of a group-based selfmonitoring approach that learns over time from similar systems subject to similar conditions. The approach is based on conformal anomaly detection coupled with an exchangeability test that uses martingales. This allows setting a threshold value based on sound theoretical justification. A hypothesis test based on this threshold is used to decide on if a system has deviated from its group. We demonstrate the feasibility of this approach through a real case study of monitoring a group of heat-pumps where it can detect a faulty hot-water switch-valve and a broken outdoor temperature sensor without previously observing these faults.

  • 110.
    Farouq, Shiraz
    et al.
    Högskolan i Halmstad, Akademin för informationsteknologi, Halmstad Embedded and Intelligent Systems Research (EIS), CAISR Centrum för tillämpade intelligenta system (IS-lab).
    Byttner, Stefan
    Högskolan i Halmstad, Akademin för informationsteknologi, Halmstad Embedded and Intelligent Systems Research (EIS), CAISR Centrum för tillämpade intelligenta system (IS-lab).
    Bouguelia, Mohamed-Rafik
    Högskolan i Halmstad, Akademin för informationsteknologi, Halmstad Embedded and Intelligent Systems Research (EIS), CAISR Centrum för tillämpade intelligenta system (IS-lab).
    Nord, Natasa
    Department of Energy and Process Engineering, Norwegian University of Science and Technology, Trondheim, Norway.
    Gadd, Henrik
    Öresundskraft, Helsingborg, Sweden.
    Large-scale monitoring of operationally diverse district heating substations: A reference-group based approach2020Inngår i: Engineering applications of artificial intelligence, ISSN 0952-1976, E-ISSN 1873-6769, Vol. 90, artikkel-id 103492Artikkel i tidsskrift (Fagfellevurdert)
    Abstract [en]

    A typical district heating (DH) network consists of hundreds, sometimes thousands, of substations. In the absence of a well-understood prior model or data labels about each substation, the overall monitoring of such large number of substations can be challenging. To overcome the challenge, an approach based on the collective operational monitoring of each substation by a local group (i.e., the reference-group) of other similar substations in the network was formulated. Herein, if a substation of interest (i.e., the target) starts to behave differently in comparison to those in its reference-group, then it was designated as an outlier. The approach was demonstrated on the monitoring of the return temperature variable for atypical and faulty operational behavior in 778 substations associated with multi-dwelling buildings. The choice of an appropriate similarity measure along with its size k were the two important factors that enables a reference-group to effectively detect an outlier target. Thus, different similarity measures and size k for the construction of the reference-groups were investigated, which led to the selection of the Euclidean distance with = 80. This setup resulted in the detection of 77 target substations that were outliers, i.e., the behavior of their return temperature changed in comparison to the majority of those in their respective reference-groups. Of these, 44 were detected due to the local construction of the reference-groups. In addition, six frequent patterns of deviating behavior in the return temperature of the substations were identified using the reference-group based approach, which were then further corroborated by the feedback from a DH domain expert. © 2020 Elsevier Ltd

  • 111.
    Farouq, Shiraz
    et al.
    Högskolan i Halmstad, Akademin för informationsteknologi, Halmstad Embedded and Intelligent Systems Research (EIS).
    Byttner, Stefan
    Högskolan i Halmstad, Akademin för informationsteknologi, Halmstad Embedded and Intelligent Systems Research (EIS), CAISR Centrum för tillämpade intelligenta system (IS-lab).
    Gadd, Henrik
    Öresundskraft AB, Ängelholm, Sweden.
    Towards understanding district heating substation behavior using robust first difference regression2018Inngår i: Energy Procedia, Amsterdam: Elsevier, 2018, Vol. 149, s. 236-245Konferansepaper (Fagfellevurdert)
    Abstract [en]

    The behavior of a district heating (DH) substation has a social and operational context. The social context comes from its general usage pattern and personal requirements of building inhabitants. The operational context comes from its configuration settings which considers both the weather conditions and social requirements. The parameter estimating thermal energy demand response with respect to change in outdoor temperature conditions along with the strength of the relationship between these variables are two important measures of operational efficiency of a substation. In practice, they can be estimated using a regression model where the slope parameter measures the average response and R2 measures the strength of the relationship. These measures are also important from a monitoring perspective. However, factors related to the social context of a building and the presence of unexplained outliers can make the estimation of these measures a challenging task. Social context of a data point in DH, in many cases appears as an outlier. Data efficiency is also required if these measures are to be estimated in a timely manner. Under these circumstances, methods that can isolate and reduce the effect of outliers in a principled and data efficient manner are required. We therefore propose to use Huber regression, a robust method based on M-estimator type loss function. This method can not only identify possible outliers present in the data of each substation but also reduce their effect on the estimated slope parameter. Moreover, substations that are comparable according to certain criteria, for instance, those with almost identical energy demand levels, should have relatively similar slopes. This provides an opportunity to observe deviating substations under the assumption that comparable substations should show homogeneity in their behavior. Furthermore, the slope parameter can be compared across time to observe if the dynamics of a substation has changed. Our analysis shows that Huber regression in combination with ordinary least squares can provide reliable estimates on the operational efficiency of DH substations. © 2018 The Authors. Published by Elsevier Ltd.

  • 112.
    Femling, Frida
    et al.
    Högskolan i Halmstad, Akademin för informationsteknologi.
    Olsson, Adam
    Högskolan i Halmstad, Akademin för informationsteknologi.
    Alonso-Fernandez, Fernando
    Högskolan i Halmstad, Akademin för informationsteknologi, Halmstad Embedded and Intelligent Systems Research (EIS), CAISR Centrum för tillämpade intelligenta system (IS-lab).
    Fruit and Vegetable Identification Using Machine Learning for Retail Application2018Inngår i: 2018 14th International Conference on Signal-Image Technology & Internet-Based Systems (SITIS) / [ed] Gabriella Sanniti di Baja, Luigi Gallo, Kokou Yetongnon, Albert Dipanda, Modesto Castrillón-Santana & Richard Chbeir, Los Alamitos: IEEE Computer Society, 2018, s. 9-15Konferansepaper (Fagfellevurdert)
    Abstract [en]

    This paper describes an approach of creating a system identifying fruit and vegetables in the retail market using images captured with a video camera attached to the system. The system helps the customers to label desired fruits and vegetables with a price according to its weight. The purpose of the system is to minimize the number of human computer interactions, speed up the identification process and improve the usability of the graphical user interface compared to existing manual systems. The hardware of the system is constituted by a Raspberry Pi, camera, display, load cell and a case. To classify an object, different convolutional neural networks have been tested and retrained. To test the usability, a heuristic evaluation has been performed with several users, concluding that the implemented system is more user friendly compared to existing systems.

  • 113.
    Fierrez, Julian
    et al.
    Universidad Autonoma de Madrid, Madrid, Spain.
    Li, Stan Z.Chinese Academy of Sciences, Beijing, China.Ross, ArunMichigan State University, East Lansing, USA.Veldhuis, RaymondUniversity of Twente, Enschede, Netherlands.Alonso-Fernandez, FernandoHögskolan i Halmstad, Akademin för informationsteknologi, Halmstad Embedded and Intelligent Systems Research (EIS), CAISR Centrum för tillämpade intelligenta system (IS-lab).Bigun, JosefHögskolan i Halmstad, Akademin för informationsteknologi, Halmstad Embedded and Intelligent Systems Research (EIS), CAISR Centrum för tillämpade intelligenta system (IS-lab).
    2016 International Conference on Biometrics (ICB): Proceedings: 13-16 June 2016, Halmstad, Sweden2016Konferanseproceedings (Fagfellevurdert)
  • 114.
    Galozy, Alexander
    Högskolan i Halmstad, Akademin för informationsteknologi, Halmstad Embedded and Intelligent Systems Research (EIS), CAISR Centrum för tillämpade intelligenta system (IS-lab).
    Towards Understanding ICU Procedures using Similarities in Patient Trajectories: An exploratory study on the MIMIC-III intensive care database2018Independent thesis Advanced level (degree of Master (Two Years)), 20 poäng / 30 hpOppgave
    Abstract [en]

    Recent advancements in Artificial Intelligence has prompted a shearexplosion of new research initiatives and applications, improving notonly existing technologies, but also opening up opportunities for newand exiting applications. This thesis explores the MIMIC-III intensive care unit database and conducts experiment on an interpretable feature space based on sever-ty scores, defining a patient health state, commonly used to predict mortality in an ICU setting. Patient health state trajectories are clustered and correlated with administered medication and performed procedures to get a better understanding of the potential usefulness in evaluating treatments on their effect on said health state, where commonalities and deviations in treatment can be understood. Furthermore, medication and procedure classification is carried out to explore their predictability using the severity subscore feature space.

  • 115.
    Galozy, Alexander
    et al.
    Högskolan i Halmstad.
    Nowaczyk, Sławomir
    Högskolan i Halmstad, Akademin för informationsteknologi, Halmstad Embedded and Intelligent Systems Research (EIS), CAISR Centrum för tillämpade intelligenta system (IS-lab).
    Pinheiro Sant'Anna, Anita
    Högskolan i Halmstad, Akademin för informationsteknologi, Halmstad Embedded and Intelligent Systems Research (EIS), CAISR Centrum för tillämpade intelligenta system (IS-lab).
    Towards Understanding ICU Treatments Using Patient Health Trajectories2019Inngår i: Lect. Notes Comput. Sci., Springer, 2019, s. 67-81Konferansepaper (Fagfellevurdert)
    Abstract [en]

    Overtreatment or mistreatment of patients is a phenomenon commonly encountered in health care and especially in the Intensive Care Unit (ICU) resulting in increased morbidity and mortality. We explore the MIMIC-III intensive care unit database and conduct experiments on an interpretable feature space based on the fusion of severity subscores, commonly used to predict mortality in an ICU setting. Clustering of medication and procedure context vectors based on a semantic representation has been performed to find common and individual treatment patterns. Two-day patient health state trajectories of a cohort of congestive heart failure patients are clustered and correlated with the treatment and evaluated based on an increase or reduction of probability of mortality on the second day of stay. Experimental results show differences in treatments and outcomes and the potential for using patient health state trajectories as a starting point for further evaluation of medical treatments and interventions. © Springer Nature Switzerland AG 2019.

  • 116.
    Galozy, Alexander
    et al.
    Högskolan i Halmstad, Akademin för informationsteknologi, Halmstad Embedded and Intelligent Systems Research (EIS), CAISR Centrum för tillämpade intelligenta system (IS-lab).
    Nowaczyk, Sławomir
    Högskolan i Halmstad, Akademin för informationsteknologi, Halmstad Embedded and Intelligent Systems Research (EIS), CAISR Centrum för tillämpade intelligenta system (IS-lab).
    Pinheiro Sant'Anna, Anita
    Högskolan i Halmstad, Akademin för informationsteknologi, Halmstad Embedded and Intelligent Systems Research (EIS), CAISR Centrum för tillämpade intelligenta system (IS-lab).
    Ohlsson, Mattias
    Lund University, Lund, Sweden.
    Lingman, Markus
    Halland Hospital, Region Halland, Sweden & Institute of Medicine, Department of Molecular and Clinical Medicine/Cardiology, Sahlgrenska Academy, University of Gothenburg, Gothenburg, Sweden.
    Pitfalls of medication adherence approximation through EHR and pharmacy records: Definitions, data and computation2020Inngår i: International Journal of Medical Informatics, ISSN 1386-5056, E-ISSN 1872-8243, Vol. 136, artikkel-id 104092Artikkel i tidsskrift (Fagfellevurdert)
    Abstract [en]

    Background and purpose: Patients’ adherence to medication is a complex, multidimensional phenomenon. Dispensation data and electronic health records are used to approximate medication-taking through refill adherence. In-depth discussions on the adverse effects of data quality and computational differences are rare. The purpose of this article is to evaluate the impact of common pitfalls when computing medication adherence using electronic health records.

    Procedures: We point out common pitfalls associated with the data and operationalization of adherence measures. We provide operational definitions of refill adherence and conduct experiments to determine the effect of the pitfalls on adherence estimations. We performed statistical significance testing on the impact of common pitfalls using a baseline scenario as reference.

    Findings: Slight changes in definition can significantly skew refill adherence estimates. Pickup patterns cause significant disagreement between measures and the commonly used proportion of days covered. Common data related issues had a small but statistically significant (p < 0.05) impact on population-level and significant effect on individual cases.

    Conclusion: Data-related issues encountered in real-world administrative databases, which affect various operational definitions of refill adherence differently, can significantly skew refill adherence values, leading to false conclusions about adherence, particularly when estimating adherence for individuals. © 2020 The Authors. Published by Elsevier B.V. 

  • 117.
    Gangwar, Abhishek
    et al.
    Centre for Development of Advanced Computing (CDAC), Mumbai, India.
    Joshi, Akanksha
    Centre for Development of Advanced Computing (CDAC), Mumbai, India.
    Singh, Ashutosh
    Centre for Development of Advanced Computing (CDAC), Mumbai, India.
    Alonso-Fernandez, Fernando
    Högskolan i Halmstad, Akademin för informationsteknologi, Halmstad Embedded and Intelligent Systems Research (EIS), CAISR Centrum för tillämpade intelligenta system (IS-lab).
    Bigun, Josef
    Högskolan i Halmstad, Akademin för informationsteknologi, Halmstad Embedded and Intelligent Systems Research (EIS), CAISR Centrum för tillämpade intelligenta system (IS-lab).
    IrisSeg: A Fast and Robust Iris Segmentation Framework for Non-Ideal Iris Images2016Inngår i: 2016 INTERNATIONAL CONFERENCE ON BIOMETRICS (ICB) / [ed] J. Fierrez, S.Z. Li, A. Ross, R. Veldhuis, F. Alonso-Fernandez, J. Bigun, Piscataway: IEEE, 2016Konferansepaper (Fagfellevurdert)
    Abstract [en]

    This paper presents a state-of-the-art iris segmentation framework specifically for non-ideal irises. The framework adopts coarse-to-fine strategy to localize different boundaries. In the approach, pupil is coarsely detected using an iterative search method exploiting dynamic thresholding and multiple local cues. The limbic boundary is first approximated in polar space using adaptive filters and then refined in Cartesianspace. The framework is quite robust and unlike the previously reported works, does notrequire tuning of parameters for different databases. The segmentation accuracy (SA) is evaluated using well known measures; precision, recall and F-measure, using the publicly available ground truth data for challenging iris databases; CASIAV4-Interval, ND-IRIS-0405, and IITD. In addition, the approach is also evaluated on highly challenging periocular images of FOCS database. The validity of proposed framework is also ascertained by providing comprehensive comparisons with classical approaches as well asstate-of-the-art methods such as; CAHT, WAHET, IFFP, GST and Osiris v4.1. The results demonstrate that our approach provides significant improvements in segmentation accuracy as well as in recognition performance that too with lower computational complexity. © 2016 IEEE.

  • 118.
    Gelzinis, Adas
    et al.
    Kaunas University of Technology, Kaunas, Lithuania.
    Verikas, Antanas
    Högskolan i Halmstad, Akademin för informationsteknologi, Halmstad Embedded and Intelligent Systems Research (EIS), CAISR Centrum för tillämpade intelligenta system (IS-lab). Kaunas University of Technology, Kaunas, Lithuania.
    Bacauskiene, Marija
    Kaunas University of Technology, Kaunas, Lithuania.
    Vaiciukynas, Evaldas
    Kaunas University of Technology, Kaunas, Lithuania.
    Learning Accurate Active Contours2013Inngår i: Engineering Applications of Neural Networks: 14th International Conference, EANN 2013, Halkidiki, Greece, September 13-16, 2013 Proceedings, Part I / [ed] Lazaros Iliadis, Harris Papadopoulos & Chrisina Jayne, Berlin Heidelberg: Springer Berlin/Heidelberg, 2013, Vol. 383, s. 396-405Konferansepaper (Fagfellevurdert)
    Abstract [en]

    Focus of research in Active contour models (ACM) area is mainly on development of various energy functions based on physical intuition. In this work, instead of designing a new energy function, we generate a multitude of contour candidates using various values of ACM parameters, assess their quality, and select the most suitable one for an object at hand. A random forest is trained to make contour quality assessments. We demonstrate experimentally superiority of the developed technique over three known algorithms in the P. minimum cells detection task solved via segmentation of phytoplankton images. © Springer-Verlag Berlin Heidelberg 2013.

  • 119.
    Gelzinis, Adas
    et al.
    Department of Electric Power Systems, Kaunas University of Technology, Kaunas, Lithuania.
    Verikas, Antanas
    Högskolan i Halmstad, Akademin för informationsteknologi, Halmstad Embedded and Intelligent Systems Research (EIS), CAISR Centrum för tillämpade intelligenta system (IS-lab). Department of Electric Power Systems, Kaunas University of Technology, Kaunas, Lithuania.
    Vaiciukynas, Evaldas
    Department of Electric Power Systems, Kaunas University of Technology, Kaunas, Lithuania.
    Bacauskiene, Marija
    Department of Electric Power Systems, Kaunas University of Technology, Kaunas, Lithuania.
    Minelga, Jonas
    Department of Electric Power Systems, Kaunas University of Technology, Kaunas, Lithuania.
    Hållander, Magnus
    Högskolan i Halmstad, Akademin för informationsteknologi, Halmstad Embedded and Intelligent Systems Research (EIS), CAISR Centrum för tillämpade intelligenta system (IS-lab).
    Uloza, Virgilijus
    Department of Otolaryngology, Lithuanian University of Health Sciences, Kaunas, Lithuania.
    Padervinskis, Evaldas
    Department of Otolaryngology, Lithuanian University of Health Sciences, Kaunas, Lithuania.
    Exploring sustained phonation recorded with acoustic and contact microphones to screen for laryngeal disorders2014Inngår i: 2014 IEEE Symposium on Computational Intelligence in Healthcare and e-health (CICARE), Piscataway, NJ: IEEE Press, 2014, s. 125-132Konferansepaper (Fagfellevurdert)
    Abstract [en]

    Exploration of various features and different structures of data dependent random forests in screening for laryngeal disorders through analysis of sustained phonation recorded by acoustic and contact microphones is the main objective of this study. To obtain a versatile characterization of voice samples, 14 different sets of features were extracted and used to build an accurate classifier to distinguish between normal and pathological cases. We proposed a new, data dependent random forest-based, way to combine information available from the different feature sets. An approach to exploring data and decisions made by a random forest was also presented. Experimental investigations using a mixed gender database of 273 subjects have shown that the Perceptual linear predictive cepstral coefficients (PLPCC) was the best feature set for both microphones. However, the LP-coefficients and LPCT-coefficients feature sets exhibited good performance in the acoustic microphone case only. Models designed using the acoustic microphone data significantly outperformed the ones built using data recorded by the contact microphone. The contact microphone did not bring any additional information useful for classification. The proposed data dependent random forest significantly outperformed traditional designs. © 2014 IEEE.

  • 120.
    Gelzinis, Adas
    et al.
    Kaunas University of Technology, Kaunas, Lithuania.
    Verikas, Antanas
    Högskolan i Halmstad, Akademin för informationsteknologi, Halmstad Embedded and Intelligent Systems Research (EIS), CAISR Centrum för tillämpade intelligenta system (IS-lab). Kaunas University of Technology, Kaunas, Lithuania.
    Vaiciukynas, Evaldas
    Kaunas University of Technology, Kaunas, Lithuania.
    Bacauskiene, Marija
    Kaunas University of Technology, Kaunas, Lithuania.
    Šulčius, Sigitas
    Marine Science and Technology Center, Klaipeda University, Klaipeda, Lithuania & Open Access Centre for Nature Research, Nature Research Centre, Vilnius, Lithuania.
    Staniulis, Juozas
    Laboratory of Plant Viruses, Nature Research Centre, Institute of Botany, Vilnius, Lithuania.
    Paškauskas, Ričardas
    Marine Science and Technology Center, Klaipeda University, Klaipeda, Lithuania & Laboratory of Algology and Microbial Ecology, Nature Research Centre, Vilnius, Lithuania.
    Automatic detection and morphological delineation of bacteriophages in electron microscopy images2015Inngår i: Computers in Biology and Medicine, ISSN 0010-4825, E-ISSN 1879-0534, Vol. 64, s. 101-116Artikkel i tidsskrift (Fagfellevurdert)
    Abstract [en]

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

  • 121.
    Gholami Shahbandi, Saeed
    Högskolan i Halmstad, Akademin för informationsteknologi, Halmstad Embedded and Intelligent Systems Research (EIS), CAISR Centrum för tillämpade intelligenta system (IS-lab).
    Interpretation and Alignment of 2D Indoor Maps: Towards a Heterogeneous Map Representation2018Doktoravhandling, med artikler (Annet vitenskapelig)
    Abstract [en]

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

  • 122.
    Gholami Shahbandi, Saeed
    Högskolan i Halmstad, Akademin för informationsteknologi, Halmstad Embedded and Intelligent Systems Research (EIS), CAISR Centrum för tillämpade intelligenta system (IS-lab).
    Semantic Mapping in Warehouses2016Licentiatavhandling, med artikler (Annet vitenskapelig)
    Abstract [en]

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

  • 123.
    Gholami Shahbandi, Saeed
    et al.
    Högskolan i Halmstad, Akademin för informationsteknologi, Halmstad Embedded and Intelligent Systems Research (EIS), CAISR Centrum för tillämpade intelligenta system (IS-lab).
    Magnusson, Martin
    Örebro University, Örebro, Sweden.
    2D Map Alignment With Region Decomposition2019Inngår i: Autonomous Robots, ISSN 0929-5593, E-ISSN 1573-7527, Vol. 43, nr 5, s. 1117-1136Artikkel i tidsskrift (Fagfellevurdert)
    Abstract [en]

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

  • 124.
    Gholami Shahbandi, Saeed
    et al.
    Högskolan i Halmstad, Akademin för informationsteknologi, Halmstad Embedded and Intelligent Systems Research (EIS), CAISR Centrum för tillämpade intelligenta system (IS-lab).
    Magnusson, Martin
    Örebro University, Örebro, Sweden .
    Iagnemma, Karl
    Massachusetts Institute of Technology, Cambridge, MA, USA.
    Nonlinear Optimization of Multimodal Two-Dimensional Map Alignment With Application to Prior Knowledge Transfer2018Inngår i: IEEE Robotics and Automation Letters, ISSN 2377-3766, E-ISSN 1949-3045, Vol. 3, nr 3, s. 2040-2047Artikkel i tidsskrift (Fagfellevurdert)
    Abstract [en]

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

  • 125.
    Gholami Shahbandi, Saeed
    et al.
    Högskolan i Halmstad, Akademin för informationsteknologi, Halmstad Embedded and Intelligent Systems Research (EIS), CAISR Centrum för tillämpade intelligenta system (IS-lab).
    Åstrand, Björn
    Högskolan i Halmstad, Akademin för informationsteknologi, Halmstad Embedded and Intelligent Systems Research (EIS), CAISR Centrum för tillämpade intelligenta system (IS-lab).
    Modeling of a Large Structured Environment: With a Repetitive Canonical Geometric-Semantic Model2014Inngår i: Advances in Autonomous Robotics Systems: 15th Annual Conference, TAROS 2014, Birmingham, UK, September 1-3, 2014. Proceedings / [ed] Michael Mistry, Aleš Leonardis, Mark Witkowski & Chris Melhuish, Heidelberg: Springer, 2014, Vol. 8717, s. 1-12Konferansepaper (Fagfellevurdert)
    Abstract [en]

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

  • 126.
    Gholami Shahbandi, Saeed
    et al.
    Högskolan i Halmstad, Akademin för informationsteknologi, Halmstad Embedded and Intelligent Systems Research (EIS), CAISR Centrum för tillämpade intelligenta system (IS-lab).
    Åstrand, Björn
    Högskolan i Halmstad, Akademin för informationsteknologi, Halmstad Embedded and Intelligent Systems Research (EIS), CAISR Centrum för tillämpade intelligenta system (IS-lab).
    Philippsen, Roland
    Högskolan i Halmstad, Akademin för informationsteknologi, Halmstad Embedded and Intelligent Systems Research (EIS), CAISR Centrum för tillämpade intelligenta system (IS-lab).
    Semi-Supervised Semantic Labeling of Adaptive Cell Decomposition Maps in Well-Structured Environments2015Inngår i: 2015 European Conference on Mobile Robots (ECMR), Piscataway, NJ: IEEE Press, 2015, artikkel-id 7324207Konferansepaper (Fagfellevurdert)
    Abstract [en]

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

  • 127.
    Gholami Shahbandi, Saeed
    et al.
    Högskolan i Halmstad, Akademin för informationsteknologi, Halmstad Embedded and Intelligent Systems Research (EIS), CAISR Centrum för tillämpade intelligenta system (IS-lab).
    Åstrand, Björn
    Högskolan i Halmstad, Akademin för informationsteknologi, Halmstad Embedded and Intelligent Systems Research (EIS), CAISR Centrum för tillämpade intelligenta system (IS-lab).
    Philippsen, Roland
    Högskolan i Halmstad, Akademin för informationsteknologi, Halmstad Embedded and Intelligent Systems Research (EIS), CAISR Centrum för tillämpade intelligenta system (IS-lab).
    Sensor Based Adaptive Metric-Topological Cell Decomposition Method for Semantic Annotation of Structured Environments2014Inngår i: 2014 13th International Conference on Control Automation Robotics & Vision (ICARCV), Piscataway, NJ: IEEE Press, 2014, s. 1771-1777, artikkel-id 7064584Konferansepaper (Fagfellevurdert)
    Abstract [en]

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

  • 128.
    Gonzalez, Ramon
    et al.
    Massachusetts Institute of Technology, Cambridge, MA, USA.
    Byttner, Stefan
    Högskolan i Halmstad, Akademin för informationsteknologi, Halmstad Embedded and Intelligent Systems Research (EIS), CAISR Centrum för tillämpade intelligenta system (IS-lab).
    Iagnemma, Karl
    Massachusetts Institute of Technology, Cambridge, MA, USA.
    Comparison of Machine Learning Approaches for Soil Embedding Detection of Planetary Exploration Rovers2016Inngår i: Proceedings of the 8th ISTVS Americas Conference, Detroit, September 12-14, 2016., 2016Konferansepaper (Fagfellevurdert)
    Abstract [en]

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

  • 129.
    Gonzalez-Sosa, Ester
    et al.
    Nokia Bell-Labs, Madrid, Spain & Universidad Autonoma de Madrid, Madrid, Spain.
    Fierrez, Julian
    Universidad Autonoma de Madrid, Madrid, Spain.
    Vera-Rodriguez, Ruben
    Universidad Autonoma de Madrid, Madrid, Spain.
    Alonso-Fernandez, Fernando
    Högskolan i Halmstad, Akademin för informationsteknologi, Halmstad Embedded and Intelligent Systems Research (EIS), CAISR Centrum för tillämpade intelligenta system (IS-lab).
    Facial Soft Biometrics for Recognition in the Wild: Recent Works, Annotation and Evaluation2018Inngår i: IEEE Transactions on Information Forensics and Security, ISSN 1556-6013, E-ISSN 1556-6021, Vol. 13, nr 8, s. 2001-2014Artikkel i tidsskrift (Fagfellevurdert)
    Abstract [en]

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

  • 130.
    Gonzalez-Sosa, Ester
    et al.
    Universidad Autonoma de Madrid, Madrid, Spain.
    Vera-Rodriguez, Ruben
    Universidad Autonoma de Madrid, Madrid, Spain.
    Fierrez, Julian
    Universidad Autonoma de Madrid, Madrid, Spain.
    Alonso-Fernandez, Fernando
    Högskolan i Halmstad, Akademin för informationsteknologi, Halmstad Embedded and Intelligent Systems Research (EIS), CAISR Centrum för tillämpade intelligenta system (IS-lab).
    Patel, Vishal M.
    Rutgers University, NJ, USA.
    Exploring Body Texture From mmW Images for Person Recognition2019Inngår i: IEEE Transactions on Biometrics, Behavior, and Identity Science, E-ISSN 2637-6407, Vol. 1, nr 2, s. 139-151Artikkel i tidsskrift (Fagfellevurdert)
    Abstract [en]

    Imaging using millimeter waves (mmWs) has many advantages including the ability to penetrate obscurants, such as clothes and polymers. After having explored shape information retrieved from mmW images for person recognition, in this paper we aim to gain some insight about the potential of using mmW texture information for the same task, considering not only the mmW face, but also mmW torso and mmW wholebody. We report experimental results using the mmW TNO database consisting of 50 individuals based on both hand-crafted and learned features from Alexnet and VGG-face pretrained convolutional neural networks (CNNs) models. First, we analyze the individual performance of three mmW body parts, concluding that: 1) mmW torso region is more discriminative than mmW face and the whole body; 2) CNN features produce better results compared to hand-crafted features on mmW faces and the entire body; and 3) hand-crafted features slightly outperform CNN features on mmW torso. In the second part of this paper, we analyze different multi-algorithmic and multi-modal techniques, including a novel CNN-based fusion technique, improving verification results to 2% EER and identification rank-1 results up to 99%. Comparative analyses with mmW body shape information and face recognition in the visible and NIR spectral bands are also reported.

  • 131.
    Gottschlich, Carsten
    et al.
    University of Göttingen, Göttingen, Germany.
    Mikaelyan, Anna
    Högskolan i Halmstad, Akademin för informationsteknologi, Halmstad Embedded and Intelligent Systems Research (EIS), CAISR Centrum för tillämpade intelligenta system (IS-lab).
    Olsen, Martin Aastrup
    Norwegian Biometrics Laboratory, Gjøvik University College, Gjøvik, Norway.
    Bigun, Josef
    Högskolan i Halmstad, Akademin för informationsteknologi, Halmstad Embedded and Intelligent Systems Research (EIS), CAISR Centrum för tillämpade intelligenta system (IS-lab).
    Busch, Christoph
    Norwegian Biometrics Laboratory, Gjøvik University College, Gjøvik, Norway.
    Improving Fingerprint Alteration Detection2015Inngår i: 2015 9th International Symposium on Image and Signal Processing and Analysis (ISPA) / [ed] Sven Lončarić, Dick Lerski, Hannu Eskola, Robert Bregović, Zagreb: University of Zagreb , 2015, s. 83-86Konferansepaper (Fagfellevurdert)
    Abstract [en]

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

  • 132.
    Haglund, Emma
    et al.
    Högskolan i Halmstad, Akademin för ekonomi, teknik och naturvetenskap, Rydberglaboratoriet för tillämpad naturvetenskap (RLAS). RandD Spenshult, Halmstad, Sweden.
    Pinheiro Sant'Anna, Anita
    Högskolan i Halmstad, Akademin för informationsteknologi, Halmstad Embedded and Intelligent Systems Research (EIS), CAISR Centrum för tillämpade intelligenta system (IS-lab).
    Andersson, Maria
    RandD Spenshult, Halmstad, Sweden & Department of Clinical Sciences, Department of Rheumatology, Lund University, Lund,, Sweden.
    Bremander, Ann
    Högskolan i Halmstad, Akademin för ekonomi, teknik och naturvetenskap, Rydberglaboratoriet för tillämpad naturvetenskap (RLAS). Department of Regional Health Research, University of Southern Denmark, Odense, Denmark.
    Aili, Katarina
    Högskolan i Halmstad, Akademin för hälsa och välfärd, Centrum för forskning om välfärd, hälsa och idrott (CVHI). RandD Spenshult, Halmstad, Sweden & Institute of Environmental Medicine, Karolinska Institutet, Stockholm, Sweden.
    Dynamic joint stability measured as gait symmetry in people with symptomatic knee osteoarthritis2019Inngår i: Annals of the Rheumatic Diseases, ISSN 0003-4967, E-ISSN 1468-2060, Vol. 78, nr Suppl. 2, s. -1458Artikkel i tidsskrift (Fagfellevurdert)
    Abstract [en]

    Background: Modern strategies for knee osteoarthritis (OA) treatment and prevention includes early detection and analyses about pain, gait and lower extremity muscle function including both strength and stability. The very first sign of knee OA is pain or perceived knee instability, often experienced during weight bearing activities e.g. walking. Increased muscle strength will provide dynamic joint stability, reduce pain, and disability. Specific measures of gait symmetry (GS) can be assessed objectively by using accelerometers, which potentially is a feasible method when evaluating early symptoms of symptomatic knee OA.

    Objectives: The aim was to study if symptoms of early knee pain affected gait symmetry, and the association between lower extremity muscles function and gait symmetry in patients with symptomatic knee OA.

    Methods: Thirty-five participants (mean age 52 SD 9 years, 66% women) with uni- or bilateral symptomatic knee OA, and without signs of an inflammatory rheumatic disease or knee trauma were included. Pain was assessed by a numeric rating scale (NRS, range 0-10 best to worse), tests of lower extremity muscle function with the maximum number of one leg rises. Dynamic stability was measured as GS by using wearable inertial sensors (PXNordic senseneering platform), during the 6 min walking test to obtain spatio-temporal gait parameters. GS was computed based on stride time (temporal symmetry, TS) and stride length (spatial symmetry, SS). Stride length was normalized by height. Kruskal-Wallis and Spearman’s correlation coefficient were used for analyses.

    Results: Reports of knee pain did not differ between gender (women 4.7, SD 2.4 vs. men 3.9, SD 2.4, p= 0.362), neither did one leg rises or gait symmetry. Participants who reported unilateral knee pain (left/right side n=9/13), had a shorter stride length on the painful side. The mean difference in stride length was 0.7% of the subject’s height (SD 1.3). Participants with unilateral pain also presented less SS gait than those who reported bilateral pain (p=0.005). The higher number of one-leg rises performed, the better TS was observed. We found a significant relationship between TS and one-leg rise for the right r s =-0.39, p=0.006, and left r s =-0.40, p=0.004 left side). No significant relationship was observed between SS and one-leg rises.

    Conclusion: Our results is in line with earlier findings stating that knee pain affects GS negatively and that lower extremity muscle function is an important feature for symmetry and dynamic joint stability in patients with symptomatic knee OA. We also found that pain in one leg was related to impaired GS. Bilateral knee pain was however more symmetrical and will need healthy controls for comparison to better understand the negative impact of symptomatic knee OA.

  • 133.
    Hedenberg, Klas
    Högskolan i Halmstad, Sektionen för Informationsvetenskap, Data– och Elektroteknik (IDE), Halmstad Embedded and Intelligent Systems Research (EIS), CAISR Centrum för tillämpade intelligenta system (IS-lab). University of Skövde, Skövde, Sweden.
    Obstacle Detection for Driverless Trucks in Industrial Environments2014Licentiatavhandling, med artikler (Annet vitenskapelig)
    Abstract [en]

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

  • 134.
    Hedenberg, Klas
    et al.
    Skövde University, Skövde, Sweden.
    Åstrand, Björn
    Högskolan i Halmstad, Akademin för informationsteknologi, Halmstad Embedded and Intelligent Systems Research (EIS), CAISR Centrum för tillämpade intelligenta system (IS-lab).
    3D Sensors on Driverless Trucks for Detection of Overhanging Objects in the Pathway2015Inngår i: Autonomous Industrial Vehicles: From the Laboratory to the Factory Floor / [ed] Roger Bostelman & Elena Messina, Conshohocken: ASTM International, 2015, s. 41-56Kapittel i bok, del av antologi (Fagfellevurdert)
    Abstract [en]

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

  • 135.
    Heikkilä, Mikko A.
    et al.
    Helsinki Institute for Information Technology HIIT, Department of Mathematics and Statistics, University of Helsinki, Helsinki, Finland.
    Jälkö, Joonas
    Helsinki Institute for Information Technology HIIT, Department of Computer Science, Aalto University, Esbo, Finland.
    Dikmen, Onur
    Högskolan i Halmstad, Akademin för informationsteknologi, Halmstad Embedded and Intelligent Systems Research (EIS), CAISR Centrum för tillämpade intelligenta system (IS-lab).
    Honkela, Antti
    Helsinki Institute for Information Technology HIIT, Department of Computer Science, University of Helsinki, Helsinki, Finland & Department of Public Health, University of Helsinki, Helsinki, Finland.
    Differentially Private Markov Chain Monte Carlo2019Konferansepaper (Fagfellevurdert)
    Abstract [en]

    Recent developments in differentially private (DP) machine learning and DP Bayesian learning have enabled learning under strong privacy guarantees for the training data subjects. In this paper, we further extend the applicability of DP Bayesian learning by presenting the first general DP Markov chain Monte Carlo (MCMC) algorithm whose privacy-guarantees are not subject to unrealistic assumptions on Markov chain convergence and that is applicable to posterior inference in arbitrary models. Our algorithm is based on a decomposition of the Barker acceptance test that allows evaluating the Renyi DP privacy cost of the accept-reject choice. We further show how to improve the DP guarantee through data subsampling and approximate acceptance tests.

  • 136.
    Helldin, Tove
    et al.
    University of Skövde, Skövde, Sweden.
    Riveiro, Maria
    University of Skövde, Skövde, Sweden.
    Pashami, Sepideh
    Högskolan i Halmstad, Akademin för informationsteknologi, Halmstad Embedded and Intelligent Systems Research (EIS), CAISR Centrum för tillämpade intelligenta system (IS-lab).
    Falkman, Göran
    University of Skövde, Skövde, Sweden.
    Byttner, Stefan
    Högskolan i Halmstad, Akademin för informationsteknologi, Halmstad Embedded and Intelligent Systems Research (EIS), CAISR Centrum för tillämpade intelligenta system (IS-lab).
    Slawomir, Nowaczyk
    Högskolan i Halmstad, Akademin för informationsteknologi, Halmstad Embedded and Intelligent Systems Research (EIS), CAISR Centrum för tillämpade intelligenta system (IS-lab).
    Supporting Analytical Reasoning: A Study from the Automotive Industry2016Inngår i: Human Interface and the Management of Information: Applications and Services: 18th International Conference, HCI International 2016: Toronto, Canada, July 17-22, 2016. Proceedings, Part II / [ed] Sakae Yamamoto, Cham: Springer, 2016, Vol. 9735, s. 20-31Konferansepaper (Fagfellevurdert)
    Abstract [en]

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

  • 137.
    Hernandez-Diaz, Kevin
    et al.
    Högskolan i Halmstad, Akademin för informationsteknologi, Halmstad Embedded and Intelligent Systems Research (EIS), CAISR Centrum för tillämpade intelligenta system (IS-lab).
    Alonso-Fernandez, Fernando
    Högskolan i Halmstad, Akademin för informationsteknologi, Halmstad Embedded and Intelligent Systems Research (EIS), CAISR Centrum för tillämpade intelligenta system (IS-lab).
    Bigun, Josef
    Högskolan i Halmstad, Akademin för informationsteknologi, Halmstad Embedded and Intelligent Systems Research (EIS), CAISR Centrum för tillämpade intelligenta system (IS-lab).
    Cross Spectral Periocular Matching using ResNet Features2019Inngår i: 2019 International Conference on Biometrics (ICB), 2019Konferansepaper (Fagfellevurdert)
    Abstract [en]

    Periocular recognition has gained attention in the last years thanks to its high discrimination capabilities in less constraint scenarios than other ocular modalities. In this paper we propose a method for periocular verification under different light spectra using CNN features with the particularity that the network has not been trained for this purpose. We use a ResNet-101 pretrained model for the ImageNet Large Scale Visual Recognition Challenge to extract features from the IIITD Multispectral Periocular Database. At each layer the features are compared using χ 2 distance and cosine similitude to carry on verification between images, achieving an improvement in the EER and accuracy at 1% FAR of up to 63.13% and 24.79% in comparison to previous works that employ the same database. In addition to this, we train a neural network to match the best CNN feature layer vector from each spectrum. With this procedure, we achieve improvements of up to 65% (EER) and 87% (accuracy at 1% FAR) in cross-spectral verification with respect to previous studies.

  • 138.
    Hernandez-Diaz, Kevin
    et al.
    Högskolan i Halmstad, Akademin för informationsteknologi, Halmstad Embedded and Intelligent Systems Research (EIS), CAISR Centrum för tillämpade intelligenta system (IS-lab).
    Alonso-Fernandez, Fernando
    Högskolan i Halmstad, Akademin för informationsteknologi, Halmstad Embedded and Intelligent Systems Research (EIS), CAISR Centrum för tillämpade intelligenta system (IS-lab).
    Bigun, Josef
    Högskolan i Halmstad, Akademin för informationsteknologi, Halmstad Embedded and Intelligent Systems Research (EIS), CAISR Centrum för tillämpade intelligenta system (IS-lab).
    Cross-Spectral Biometric Recognition with Pretrained CNNs as Generic Feature Extractors2019Konferansepaper (Annet vitenskapelig)
    Abstract [en]

    Periocular recognition has gained attention in the last years thanks to its high discrimination capabilities in less constraint scenarios than face or iris. In this paper we propose a method for periocular verification under different light spectra using CNN features with the particularity that the network has not been trained for this purpose. We use a ResNet-101 pretrained model for the ImageNet Large Scale Visual Recognition Challenge to extract features from the IIITD Multispectral Periocular Database. At each layer the features are compared using χ 2 distance and cosine similitude to carry on verification between images, achieving an improvement in the EER and accuracy at 1% FAR of up to 63.13% and 24.79% in comparison to previous works that employ the same database. In addition to this, we train a neural network to match the best CNN feature layer vector from each spectrum. With this procedure, we achieve improvements of up to 65% (EER) and 87% (accuracy at 1% FAR) in cross-spectral verification with respect to previous studies.

  • 139.
    Hernandez-Diaz, Kevin
    et al.
    Högskolan i Halmstad, Akademin för informationsteknologi, Halmstad Embedded and Intelligent Systems Research (EIS), CAISR Centrum för tillämpade intelligenta system (IS-lab).
    Alonso-Fernandez, Fernando
    Högskolan i Halmstad, Akademin för informationsteknologi, Halmstad Embedded and Intelligent Systems Research (EIS), CAISR Centrum för tillämpade intelligenta system (IS-lab).
    Bigun, Josef
    Högskolan i Halmstad, Akademin för informationsteknologi, Halmstad Embedded and Intelligent Systems Research (EIS), CAISR Centrum för tillämpade intelligenta system (IS-lab).
    Periocular Recognition Using CNN Features Off-the-Shelf2018Inngår i: 2018 International Conference of the Biometrics Special Interest Group (BIOSIG), Piscataway, N.J.: IEEE, 2018Konferansepaper (Fagfellevurdert)
    Abstract [en]

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

  • 140.
    Hofbauer, Heinz
    et al.
    University of Salzburg, Salzburg, Austria.
    Alonso-Fernandez, Fernando
    Högskolan i Halmstad, Akademin för informationsteknologi, Halmstad Embedded and Intelligent Systems Research (EIS), CAISR Centrum för tillämpade intelligenta system (IS-lab).
    Bigun, Josef
    Högskolan i Halmstad, Akademin för informationsteknologi, Halmstad Embedded and Intelligent Systems Research (EIS), CAISR Centrum för tillämpade intelligenta system (IS-lab).
    Uhl, Andreas
    University of Salzburg, Salzburg, Austria.
    Experimental Analysis Regarding the Influence of Iris Segmentation on the Recognition Rate2016Inngår i: IET Biometrics, ISSN 2047-4938, E-ISSN 2047-4946, Vol. 5, nr 3, s. 200-211Artikkel i tidsskrift (Fagfellevurdert)
    Abstract [en]

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

  • 141.
    Hofbauer, Heinz
    et al.
    Department of Computer Sciences, University of Salzburg, Salzburg, Austria.
    Alonso-Fernandez, Fernando
    Högskolan i Halmstad, Akademin för informationsteknologi, Halmstad Embedded and Intelligent Systems Research (EIS), CAISR Centrum för tillämpade intelligenta system (IS-lab).
    Wild, Peter
    Department of Computer Sciences, University of Salzburg, Salzburg, Austria.
    Bigun, Josef
    Högskolan i Halmstad, Akademin för informationsteknologi, Halmstad Embedded and Intelligent Systems Research (EIS), CAISR Centrum för tillämpade intelligenta system (IS-lab).
    Uhl, Andreas
    Department of Computer Sciences, University of Salzburg, Salzburg, Austria.
    A Ground Truth for Iris Segmentation2014Inngår i: 2014 22nd International Conference on Pattern Recognition (ICPR) / [ed] Lisa O’Conner, Los Alamitos: IEEE Computer Society, 2014, s. 527-532Konferansepaper (Fagfellevurdert)
    Abstract [en]

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

  • 142.
    Holst, Anders
    et al.
    RISE SICS, Stockholm, Sweden.
    Bouguelia, Mohamed-Rafik
    Högskolan i Halmstad, Akademin för informationsteknologi, Halmstad Embedded and Intelligent Systems Research (EIS), CAISR Centrum för tillämpade intelligenta system (IS-lab).
    Görnerup, Olof
    RISE SICS, Stockholm, Sweden.
    Pashami, Sepideh
    Högskolan i Halmstad, Akademin för informationsteknologi, Halmstad Embedded and Intelligent Systems Research (EIS), CAISR Centrum för tillämpade intelligenta system (IS-lab).
    Al-Shishtawy, Ahmad
    RISE SICS, Stockholm, Sweden.
    Falkman, Göran
    University of Skövde, Skövde, Sweden.
    Karlsson, Alexander
    University of Skövde, Skövde, Sweden.
    Said, Alan
    University of Skövde, Skövde, Sweden.
    Bae, Juhee
    University of Skövde, Skövde, Sweden.
    Girdzijauskas, Sarunas
    RISE SICS, Stockholm, Sweden.
    Nowaczyk, Sławomir
    Högskolan i Halmstad, Akademin för informationsteknologi, Halmstad Embedded and Intelligent Systems Research (EIS), CAISR Centrum för tillämpade intelligenta system (IS-lab).
    Soliman, Amira
    RISE SICS, Stockholm, Sweden.
    Eliciting Structure in Data2019Inngår i: Joint Proceedings of the ACM IUI 2019 Workshops, Los Angeles, USA, March 20, 2019 / [ed] Christoph Trattner, Denis Parra & Nathalie Riche, Aachen: Rheinisch-Westfaelische Technische Hochschule Aachen , 2019Konferansepaper (Fagfellevurdert)
    Abstract [en]

    This paper demonstrates how to explore and visualize different types of structure in data, including clusters, anomalies, causal relations, and higher order relations. The methods are developed with the goal of being as automatic as possible and applicable to massive, streaming, and distributed data. Finally, a decentralized learning scheme is discussed, enabling finding structure in the data without collecting the data centrally.

  • 143.
    Holst, Anders
    et al.
    RISE SICS, Stockholm, Sweden.
    Karlsson, Alexander
    School of Informatics, University of Skövde, Sweden.
    Bae, Juhee
    School of Informatics, University of Skövde, Sweden.
    Bouguelia, Mohamed-Rafik
    Högskolan i Halmstad, Akademin för informationsteknologi, Halmstad Embedded and Intelligent Systems Research (EIS), CAISR Centrum för tillämpade intelligenta system (IS-lab). Department of Intelligent Systems and Digital Design, Halmstad University, Sweden.
    Interactive clustering for exploring multiple data streams at different time scales and granularity2019Inngår i: Proceedings of the Workshop on Interactive Data Mining, WIDM 2019, Association for Computing Machinery (ACM), 2019Konferansepaper (Fagfellevurdert)
    Abstract [en]

    We approach the problem of identifying and interpreting clusters over different time scales and granularity in multivariate time series data. We extract statistical features over a sliding window of each time series, and then use a Gaussian mixture model to identify clusters which are then projected back on the data streams. The human analyst can then further analyze this projection and adjust the size of the sliding window and the number of clusters in order to capture the different types of clusters over different time scales. We demonstrate the effectiveness of our approach in two different application scenarios: (1) fleet management and (2) district heating, wherein each scenario, several different types of meaningful clusters can be identified when varying over these dimensions. © 2019 Copyright held by the owner/author(s). Publication rights licensed to ACM.

  • 144.
    Holst, Anders
    et al.
    RISE SICS, Stockholm, Sweden.
    Pashami, Sepideh
    Högskolan i Halmstad, Akademin för informationsteknologi, Halmstad Embedded and Intelligent Systems Research (EIS), CAISR Centrum för tillämpade intelligenta system (IS-lab).
    Bae, Juhee
    School of Informatics, University of Skövde, Skövde, Sweden.
    Incremental causal discovery and visualization2019Inngår i: Proceedings of the Workshop on Interactive Data Mining, WIDM 2019, Association for Computing Machinery (ACM), 2019Konferansepaper (Fagfellevurdert)
    Abstract [en]

    Discovering causal relations from limited amounts of data can be useful for many applications. However, all causal discovery algorithms need huge amounts of data to estimate the underlying causal graph. To alleviate this gap, this paper proposes a novel visualization tool which incrementally discovers causal relations as more data becomes available. That is, we assume that stronger causal links will be detected quickly and weaker links revealed when enough data is available. In addition to causal links, the correlation between variables and the uncertainty of the strength of causal links are visualized in the same graph. The tool is illustrated on three example causal graphs, and results show that incremental discovery works and that the causal structure converges as more data becomes available. © 2019 Copyright held by the owner/author(s). Publication rights licensed to ACM.

  • 145.
    Jonsson, Magnus
    et al.
    Högskolan i Halmstad, Akademin för informationsteknologi, Halmstad Embedded and Intelligent Systems Research (EIS), Centrum för forskning om inbyggda system (CERES).
    Wiberg, Per-Arne
    Högskolan i Halmstad, Akademin för informationsteknologi, Halmstad Embedded and Intelligent Systems Research (EIS), Centrum för forskning om inbyggda system (CERES).
    Wickström, Nicholas
    Högskolan i Halmstad, Akademin för informationsteknologi, Halmstad Embedded and Intelligent Systems Research (EIS), CAISR Centrum för tillämpade intelligenta system (IS-lab).
    Vision-based low-level navigation using a feed-forward neural network1997Inngår i: Proc. International Workshop on Mechatronical Computer Systems for Perception and Action (MCPA'97), Pisa, Italy, Feb. 10-12, 1997, s. 105-111Konferansepaper (Fagfellevurdert)
    Abstract [en]

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

  • 146.
    Järpe, Eric
    Högskolan i Halmstad, Akademin för informationsteknologi, Halmstad Embedded and Intelligent Systems Research (EIS), CAISR Centrum för tillämpade intelligenta system (IS-lab).
    An alternative Diffie-Hellman protocol2020Inngår i: Cryptography, E-ISSN 2410-387X, Vol. 4, nr 1, artikkel-id 5Artikkel i tidsskrift (Fagfellevurdert)
    Abstract [en]

    The Diffie–Hellman protocol, ingenious in its simplicity, is still the major solution in protocols for generating a shared secret in cryptography for e-trading and many other applications after an impressive number of decades. However, lately, the threat from a future quantum computer has prompted successors resilient to quantum computer-based attacks. Here, an algorithm similar to Diffie–Hellman is presented. In contrast to the classic Diffie–Hellman, it involves floating point numbers of arbitrary size in the generation of a shared secret. This can, in turn, be used for encrypted communication based on symmetric cyphers. The validity of the algorithm is verified by proving that a vital part of the algorithm satisfies a one-way property. The decimal part is deployed for the one-way function in a way that makes the protocol a post-quantum key generation procedure. This is concluded from the fact that there is, as of yet, no quantum computer algorithm reverse engineering the one-way function. An example illustrating the use of the protocol in combination with XOR encryption is given. © 2020 MDPI (Basel, Switzerland)

  • 147.
    Järpe, Eric
    Högskolan i Halmstad, Sektionen för Informationsvetenskap, Data– och Elektroteknik (IDE), Halmstad Embedded and Intelligent Systems Research (EIS), CAISR Centrum för tillämpade intelligenta system (IS-lab).
    Räkna med rester: Matematik att tillämpas inom kryptologi2013Bok (Annet vitenskapelig)
    Abstract [sv]

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

  • 148.
    Järpe, Eric
    Högskolan i Halmstad, Akademin för informationsteknologi, Halmstad Embedded and Intelligent Systems Research (EIS), CAISR Centrum för tillämpade intelligenta system (IS-lab).
    Visit to care center Angeles Cobo Lopez, Alcaudete, Andalucia, Spain: A secondment within the REMIND project2019Rapport (Annet (populærvitenskap, debatt, mm))
  • 149.
    Kalsyte, Zivile
    et al.
    Kaunas University of Technology, Kaunas, Lithuania.
    Verikas, Antanas
    Högskolan i Halmstad, Sektionen för Informationsvetenskap, Data– och Elektroteknik (IDE), Halmstad Embedded and Intelligent Systems Research (EIS), CAISR Centrum för tillämpade intelligenta system (IS-lab).
    A novel approach to exploring company’s financial soundness: Investor’s perspective2013Inngår i: Expert systems with applications, ISSN 0957-4174, E-ISSN 1873-6793, Vol. 40, nr 13, s. 5085-5092Artikkel i tidsskrift (Fagfellevurdert)
    Abstract [en]

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

  • 150.
    Kalsyte, Zivile
    et al.
    Kaunas University of Technology, Kaunas, Lithuania.
    Verikas, Antanas
    Högskolan i Halmstad, Sektionen för Informationsvetenskap, Data– och Elektroteknik (IDE), Halmstad Embedded and Intelligent Systems Research (EIS), CAISR Centrum för tillämpade intelligenta system (IS-lab). Kaunas University of Technology, Kaunas, Lithuania.
    Bacauskiene, Marija
    Kaunas University of Technology, Kaunas, Lithuania.
    Gelzinis, Adas
    Kaunas University of Technology, Kaunas, Lithuania.
    A Novel Technique to Design an Adaptive Committee of Models Applied to Predicting Company’s Future Performance2013Inngår i: International Conference on Computer Research and Development: ICCRD 2013 / [ed] Fan Yama, New York, NY: ASME Press, 2013, s. 65-70Konferansepaper (Fagfellevurdert)
    Abstract [en]

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

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