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  • 151.
    Balador, Ali
    et al.
    Polytechnic University of Valencia, Valencia, Spain.
    Böhm, Annette
    Halmstad Universit, Sweden.
    Uhlemann, Elisabeth
    Mälardalen University, School of Innovation, Design and Engineering, Embedded Systems.
    Calafate, Carlos T.
    Polytechnic University of Valencia, Valencia, Spain.
    Ji, Yusheng
    National Institute of Informatics, Tokyo, Japan.
    Cano, Juan-Carlos
    Polytechnic University of Valencia, Valencia, Spain.
    Manzoni, Pietro
    Polytechnic University of Valencia, Valencia, Spain.
    An Efficient MAC Protocol for vehicle platooning in automated highway systems2015In: Jornadas Sarteco 2015 JS 2015, Cordoba, Spain, 2015Conference paper (Refereed)
    Abstract [en]

    Lately, all the top truck manufacturers are investing considerable resources in the research and development of platooning systems which would allow vehicles to save fuel and improve safety by travelling in a close-following manner. The platoon-ing system requires frequent and reliable vehicle-to-vehicle communications. As platooning takes place in a vehicular ad hoc network, the use of IEEE 802.11p is close to mandatory. However, the 802.11p medium access method suffers from packet collisions and random delays. Most ongoing research suggests using TDMA on top of 802.11p as a potential remedy. However , TDMA requires synchronization and is not very flexible if the beacon frequency needs to be updated, the number of platoon members changes, or if re-transmissions for increased reliability are required. We therefore suggest a token-passing medium access method where the next token holder is selected based on beacon data age. This has the advantage of allowing beacons to be re-broadcasted in each beacon interval whenever time and bandwidth are available. We show that our token-based method is able to reduce the data age and considerably increase reliability considerably compared to pure 802.11p.

  • 152.
    Balador, Ali
    et al.
    Mälardalen University, School of Innovation, Design and Engineering, Embedded Systems.
    Ericsson, Niclas
    Bakhshi, Zeynab
    Communication Middleware Technologies for Industrial Distributed Control Systems: A Literature Review2017In: International Conference on Emerging Technologies And Factory Automation ETFA'17, 2017Conference paper (Refereed)
    Abstract [en]

    Industry 4.0 is the German vision for the future of manufacturing, where smart factories use information and communication technologies to digitise their processes to achieve improved quality, lower costs, and increased efficiency. It is likely to bring a massive change to the way control systems function today. Future distributed control systems are expected to have an increased connectivity to the Internet, in order to capitalize on new offers and research findings related to digitalization, such as cloud, big data, and machine learning. A key technology in the realization of distributed control systems is middleware, which is usually described as a reusable software layer between operating system and distributed applications. Various middleware technologies have been proposed to facilitate communication in industrial control systems and hide the heterogeneity amongst the subsystems, such as OPC UA, DDS, and RT-CORBA. These technologies can significantly simplify the system design and integration of devices despite their heterogeneity. However, each of these technologies has its own characteristics that may work better for particular applications. Selection of the best middleware for a specific application is a critical issue for system designers. In this paper, we conduct a survey on available standard middleware technologies, including OPC UA, DDS, and RT-CORBA, and show new trends for different industrial domains.

  • 153.
    Balador, Ali
    et al.
    Mälardalen University, School of Innovation, Design and Engineering, Embedded Systems.
    Kouba, A.
    Polytechnic Institute of Porto, Porto 4249-015, Portugal.
    Cassioli, D.
    University of L'Aquila, L'Aquila, 67100, Italy.
    Foukalas, F.
    Technical University of Denmark, Kongens Lyngby, 2800, Denmark.
    Severino, R.
    Polytechnic Institute of Porto, Porto 4249-015, Portugal.
    Stepanova, D.
    Finnish Meteorological Institute, 99600 Sodankylä, Finland.
    Agosta, G.
    Politecnico di Milano ,Via G. Ponzio 32, Milano, I-20133, Italy.
    Xie, J.
    Group Technology & Research, DNV GL, Veritasveien 1, Norway.
    Pomante, L.
    University of L'Aquila, L'Aquila, 67100, Italy.
    Mongelli, M.
    CNR-IEIIT ,via De Marini 6, Genova, 16149, Italy.
    Pierini, P.
    Intecs S.p.A., Pisa, 56121, Italy.
    Petersen, S.
    SINTEF ICT, Trondheim, 7465, Norway.
    Sukuvaara, T.
    Finnish Meteorological Institute, 99600 Sodankylä, Finland.
    Wireless Communication Technologies for Safe Cooperative Cyber Physical Systems2018In: Sensors, ISSN 1424-8220, E-ISSN 1424-8220, Vol. 18, no 11, article id 4075Article in journal (Refereed)
    Abstract [en]

    Cooperative Cyber-Physical Systems (Co-CPSs) can be enabled using wireless communication technologies, which in principle should address reliability and safety challenges. Safety for Co-CPS enabled by wireless communication technologies is a crucial aspect and requires new dedicated design approaches. In this paper, we provide an overview of five Co-CPS use cases, as introduced in our SafeCOP EU project, and analyze their safety design requirements. Next, we provide a comprehensive analysis of the main existing wireless communication technologies giving details about the protocols developed within particular standardization bodies. We also investigate to what extent they address the non-functional requirements in terms of safety, security and real time, in the different application domains of each use case. Finally, we discuss general recommendations about the use of different wireless communication technologies showing their potentials in the selected real-world use cases. The discussion is provided under consideration in the 5G standardization process within 3GPP, whose current efforts are inline to current gaps in wireless communications protocols for Co-CPSs including many future use cases.

  • 154.
    Balador, Ali
    et al.
    Mälardalen University, School of Innovation, Design and Engineering, Embedded Systems.
    Uhlemann, Elisabeth
    Mälardalen University, School of Innovation, Design and Engineering, Embedded Systems.
    Calafate, C. T.
    Universitat Politècnica de València, València, Spain.
    Cano, J. -C
    Universitat Politècnica de València, València, Spain.
    Supporting beacon and event-driven messages in vehicular platoons through token-based strategies2018In: Sensors, ISSN 1424-8220, E-ISSN 1424-8220, Vol. 18, no 4, article id 955Article in journal (Refereed)
    Abstract [en]

    Timely and reliable inter-vehicle communications is a critical requirement to support traffic safety applications, such as vehicle platooning. Furthermore, low-delay communications allow the platoon to react quickly to unexpected events. In this scope, having a predictable and highly effective medium access control (MAC) method is of utmost importance. However, the currently available IEEE 802.11p technology is unable to adequately address these challenges. In this paper, we propose a MAC method especially adapted to platoons, able to transmit beacons within the required time constraints, but with a higher reliability level than IEEE 802.11p, while concurrently enabling efficient dissemination of event-driven messages. The protocol circulates the token within the platoon not in a round-robin fashion, but based on beacon data age, i.e., the time that has passed since the previous collection of status information, thereby automatically offering repeated beacon transmission opportunities for increased reliability. In addition, we propose three different methods for supporting event-driven messages co-existing with beacons. Analysis and simulation results in single and multi-hop scenarios showed that, by providing non-competitive channel access and frequent retransmission opportunities, our protocol can offer beacon delivery within one beacon generation interval while fulfilling the requirements on low-delay dissemination of event-driven messages for traffic safety applications. 

  • 155.
    Balasubramanian, S.M.N
    et al.
    Technische Universiteit Eindhoven, Eindhoven, Netherlands.
    Afshar, Sara Zargari
    Mälardalen University, School of Innovation, Design and Engineering, Embedded Systems.
    Behnam, Moris
    Mälardalen University, School of Innovation, Design and Engineering, Embedded Systems.
    Gai, Paolo
    Evidence Srl, Pisa, Italy.
    Bril, Reinder J.
    Mälardalen University, School of Innovation, Design and Engineering, Embedded Systems. Technische Universiteit Eindhoven, Eindhoven, Netherlands.
    A dual shared stack for FSLM in Erika enterprise2017In: The 23rd IEEE International Conference on Embedded and Real-Time Computing Systems and Applications - WiP Session RTCSA'17, 2017Conference paper (Refereed)
    Abstract [en]

    Recently, the flexible spin-lock model (FSLM) has been introduced, unifying spin-based and suspension-based resource sharing protocols for real-time multi-core platforms. Unlike the multiprocessor stack resource policy (MSRP), FSLM doesn’t allow tasks on a core to share a single stack, however. In this paper, we present a hypothesis claiming that for a restricted range of spin-lock priorities, FSLM requires only two stacks. We briefly describe our implementation of a dual stack for FSLM in the Erika Enterprise RTOS as instantiated on an Altera Nios II platform using 4 soft-core processors.

  • 156.
    Balasubramanian, S.M.N
    et al.
    Tech Univ Eindhoven, Eindhoven, Netherlands.
    Afshar, Sara Zargari
    Mälardalen University, School of Innovation, Design and Engineering, Embedded Systems.
    Gai, Paolo
    Evidence Srl, Pisa, Italy.
    Behnam, Moris
    Mälardalen University, School of Innovation, Design and Engineering, Embedded Systems.
    J. Bril, Reinder
    Mälardalen University, School of Innovation, Design and Engineering, Embedded Systems. Tech Univ Eindhoven, Eindhoven, Netherlands.
    Incorporating implementation overheads in the analysis for the flexible spin-lock model2017In: IECON 2017 - 43RD ANNUAL CONFERENCE OF THE IEEE INDUSTRIAL ELECTRONICS SOCIETY, 2017, p. 411-8418Conference paper (Refereed)
    Abstract [en]

    The flexible spin-lock model (FSLM) unifies suspension-based and spin-based resource sharing protocols for partitioned fixed-priority preemptive scheduling based real-time multiprocessor platforms. Recent work has been done in defining the protocol for FSLM and providing a schedulability analysis without accounting for the implementation overheads. In this paper, we extend the analysis for FSLM with implementation overheads. Utilizing an initial implementation of FSLM in the OSEK/VDX-compliant Erika Enterprise RTOS on an Altera Nios II platform using 4 soft-core processors, we present an improved implementation. Given the design of the implementation, the overheads are characterized and incorporated in specific terms of the existing analysis. The paper also supplements the analysis with measurement results, enabling an analytical comparison of FSLM with the natively provided multiprocessor stack resource policy (MSRP), which may serve as a guideline for the choice of FSLM or MSRP for a specific application.

  • 157.
    Ballesteros, A.
    et al.
    DMI, Universitat de les Illes Balears, Spain.
    Proenza, J.
    DMI, Universitat de les Illes Balears, Spain.
    Gessner, D.
    DMI, Universitat de les Illes Balears, Spain.
    Rodriguez-Navas, Guillermo
    Mälardalen University, School of Innovation, Design and Engineering, Embedded Systems.
    Sauter, T.
    Danube University Krems, Austria.
    Achieving elementary cycle synchronization between masters in the flexible time-triggered replicated star for ethernet2014In: 19th IEEE International Conference on Emerging Technologies and Factory Automation, ETFA 2014, 2014, p. Article number 7005335-Conference paper (Refereed)
    Abstract [en]

    For a distributed embedded system (DES) to operate continuously in a dynamic environment, it must be flexible and highly reliable. This applies in particular to its communication subsystem. The Flexible Time-Triggered Replicated Star for Ethernet (FTTRS) aims at providing such a subsystem by means of a highly-reliable switched-Ethernet architecture based on the Flexible Time-Triggered paradigm (FTT), a master/slave communication paradigm where the master periodically polls the slaves using so-called trigger messages (TMs). In particular, FTTRS interconnects nodes by redundant communication paths provided by two switches, each embedding an FTT master that manages the communication. This allows FTTRS to tolerate the failure of one switch without interrupting the communication as long as the masters are replica determinate, i.e., provide identical service to the slaves. The master replica determinism entails the masters broadcasting their TMs in a lockstep fashion: when one master broadcasts a TM, the other should do the same quasi-simultaneously. In this paper we present a solution inspired by the Precision Time Protocol (PTP) for achieving this lockstep transmission and preliminary results showing the precision with which we can synchronize the masters on a software prototype.

  • 158.
    Banaee, Hadi
    et al.
    Örebro University, Sweden.
    Ahmed, Mobyen Uddin
    Mälardalen University, School of Innovation, Design and Engineering, Embedded Systems. Örebro University, Sweden.
    Loutfi, Amy
    Örebro University, Sweden.
    Descriptive Modelling of Clinical Conditions with Data-driven Rule Mining in Physiological Data2015In: 8th International Conference on Health Informatics HEALTHINF, Lisbon, Portugal, 2015Conference paper (Refereed)
    Abstract [en]

    This paper presents an approach to automatically mine rules in time series data representing physiological parameters in clinical conditions. The approach is fully data driven, where prototypical patterns are mined for each physiological time series data. The generated rules based on the prototypical patterns are then described in a textual representation which captures trends in each physiological parameter and their relation to the other physiological data. In this paper, a method for measuring similarity of rule sets is introduced in order to validate the uniqueness of rule sets. This method is evaluated on physiological records from clinical classes in the MIMIC online database such as angina, sepsis, respiratory failure, etc.. The results show that the rule mining technique is able to acquire a distinctive model for each clinical condition, and represent the generated rules in a human understandable textual representation.

  • 159.
    Barua, Shaibal
    Mälardalen University, School of Innovation, Design and Engineering, Embedded Systems.
    Intelligent Driver Mental State Monitoring System Using Physiological Sensor Signals2015Licentiate thesis, comprehensive summary (Other academic)
    Abstract [en]

    Driving a vehicle involves a series of events, which are related to and evolve with the mental state (such as sleepiness, mental load, and stress) of the driv- er. These states are also identified as causal factors of critical situations that can lead to road accidents and vehicle crashes. These driver impairments need to be detected and predicted in order to reduce critical situations and road accidents. In the past years, physiological signals have become conven- tional measures in driver impairment research. Physiological signals have been applied in various studies to identify different levels of mental load, sleepiness, and stress during driving.

    This licentiate thesis work has investigated several artificial intelligence algorithms for developing an intelligent system to monitor driver mental state using physiological signals. The research aims to measure sleepiness and mental load using Electroencephalography (EEG). EEG signals, if pro- cessed correctly and efficiently, have potential to facilitate advanced moni- toring of sleepiness, mental load, fatigue, stress etc. However, EEG signals can be contaminated with unwanted signals, i.e., artifacts. These artifacts can lead to serious misinterpretation. Therefore, this work investigates EEG arti- fact handling methods and propose an automated approach for EEG artifact handling. Furthermore, this research has also investigated how several other physiological parameters (Heart Rate (HR) and Heart Rate Variability (HRV) from the Electrocardiogram (ECG), Respiration Rate, Finger Tem- perature (FT), and Skin Conductance (SC)) to quantify drivers’ stress. Dif- ferent signal processing methods have been investigated to extract features from these physiological signals. These features have been extracted in the time domain, in the frequency domain as well as in the joint time-frequency domain using wavelet analysis. Furthermore, data level signal fusion has been proposed using Multivariate Multiscale Entropy (MMSE) analysis by combining five physiological sensor signals. Primarily Case-Based Reason- ing (CBR) has been applied for drivers’ mental state classification, but other Artificial intelligence (AI) techniques such as Fuzzy Logic, Support Vector Machine (SVM) and Artificial Neural Network (ANN) have been investigat- ed as well.

    For drivers’ stress classification, using the CBR and MMSE approach, the system has achieved 83.33% classification accuracy compared to a human expert. Moreover, three classification algorithms i.e., CBR, an ANN, and a SVM were compared to classify drivers’ stress. The results show that CBR has achieved 80% and 86% accuracy to classify stress using finger tempera- ture and heart rate variability respectively, while ANN and SVM reached an accuracy of less than 80%. 

  • 160.
    Barua, Shaibal
    Mälardalen University, School of Innovation, Design and Engineering, Embedded Systems.
    Multivariate Data Analytics to Identify Driver’s Sleepiness, Cognitive load, and Stress2019Doctoral thesis, comprehensive summary (Other academic)
    Abstract [en]

    Driving a vehicle in a dynamic traffic environment requires continuous adaptation of a complex manifold of physiological and cognitive activities. Impaired driving due to, for example, sleepiness, inattention, cognitive load or stress, affects one’s ability to adapt, predict and react to upcoming traffic events. In fact, human error has been found to be a contributing factor in more than 90% of traffic crashes. Unfortunately, there is no robust, objective ground truth for determining a driver’s state, and researchers often revert to using subjective self-rating scales when assessing level of sleepiness, cognitive load or stress. Thus, the development of better tools to understand, measure and monitor human behaviour across diverse scenarios and states is crucial. The main objective of this thesis is to develop objective measures of sleepiness, cognitive load and stress, which can later be used as research tools, either to benchmark unobtrusive sensor solutions or when investigating the influence of other factors on sleepiness, cognitive load, and stress.

    This thesis employs multivariate data analysis using machine learning to detect and classify different driver states based on physiological data. The reason for using rather intrusive sensor data, such as electroencephalography (EEG), electrooculography (EOG), electrocardiography (ECG), skin conductance, finger temperature, and respiration is that these methods can be used to analyse how the brain and body respond to internal and external changes, including those that do not generate overt behaviour. Moreover, the use of physiological data is expected to grow in importance when investigating human behaviour in partially automated vehicles, where active driving is replaced by passive supervision.

    Physiological data, especially the EEG is sensitive to motion artifacts and noise, and when recorded in naturalistic environments such as driving, artifacts are unavoidable. An automatic EEG artifact handling method ARTE (Automated aRTifacts handling in EEG) was therefore developed. When used as a pre-processing step in the classification of driver sleepiness, ARTE increased classification performance by 5%. ARTE is data-driven and does not rely on additional reference signals or manually defined thresholds, making it well suited for use in dynamic settings where unforeseen and rare artifacts are commonly encountered. In addition, several machine-learning algorithms have been developed for sleepiness, cognitive load, and stress classification. Regarding sleepiness classification, the best achieved accuracy was achieved using a Support Vector Machine (SVM) classifier. For multiclass, the obtained accuracy was 79% and for binary class it was 93%. A subject-dependent classification exhibited a 10% improvement in performance compared to the subject-independent classification, suggesting that much can be gained by using personalized classifiers. Moreover, by embedding contextual information, classification performance improves by approximately 5%. In regard to cognitive load classification, a 72% accuracy rate was achieved using a random forest classifier. Combining features from several data sources may improve performance, and indeed, we observed classification performance improvement by 10%-20% compared to using features from a single data source. To classify drivers’ stress, using the Case-based reasoning (CBR) and data fusion approach, the system achieved an 83.33% classification accuracy rate.

    This thesis work encourages the use of multivariate data for detecting and classifying driver states, including sleepiness, cognitive load, and stress. A univariate data source often presents challenges, since features from a single source or one just aspect of the feature are not entirely reliable; Therefore, multivariate information requires accurate driver state detection. Often, driver states are a subjective experience, in which other contextual data plays a vital role. Thus, the implication of incorporating contextual information in the classification scheme is presented in this thesis work. Although there are several commonalities, physiological signals are modulated differently in different driver states; Hence, multivariate data could help detect multiple driver states simultaneously – for example, cognitive load detection when a person is under the influence of different levels of stress.

  • 161.
    Barua, Shaibal
    et al.
    Mälardalen University, School of Innovation, Design and Engineering, Embedded Systems.
    Ahmed, Mobyen Uddin
    Mälardalen University, School of Innovation, Design and Engineering, Embedded Systems.
    Ahlström, Christer
    The Swedish National Road and Transport Research Institute (VTI), Linköping, SE, Sweden.
    Begum, Shahina
    Mälardalen University, School of Innovation, Design and Engineering, Embedded Systems.
    Automatic driver sleepiness detection using EEG, EOG and contextual information2019In: Expert systems with applications, ISSN 0957-4174, E-ISSN 1873-6793, Vol. 115, p. 121-135Article in journal (Refereed)
    Abstract [en]

    The many vehicle crashes that are caused by driver sleepiness each year advocates the development of automated driver sleepiness detection (ADSD) systems. This study proposes an automatic sleepiness classification scheme designed using data from 30 drivers who repeatedly drove in a high-fidelity driving simulator, both in alert and in sleep deprived conditions. Driver sleepiness classification was performed using four separate classifiers: k-nearest neighbours, support vector machines, case-based reasoning, and random forest, where physiological signals and contextual information were used as sleepiness indicators. The subjective Karolinska sleepiness scale (KSS) was used as target value. An extensive evaluation on multiclass and binary classifications was carried out using 10-fold cross-validation and leave-one-out validation. With 10-fold cross-validation, the support vector machine showed better performance than the other classifiers (79% accuracy for multiclass and 93% accuracy for binary classification). The effect of individual differences was also investigated, showing a 10% increase in accuracy when data from the individual being evaluated was included in the training dataset. Overall, the support vector machine was found to be the most stable classifier. The effect of adding contextual information to the physiological features improved the classification accuracy by 4% in multiclass classification and by and 5% in binary classification.

  • 162.
    Barua, Shaibal
    et al.
    Mälardalen University, School of Innovation, Design and Engineering, Embedded Systems.
    Ahmed, Mobyen Uddin
    Mälardalen University, School of Innovation, Design and Engineering, Embedded Systems.
    Ahlström, Christer
    MFT, Linköping Sweden.
    Begum, Shahina
    Mälardalen University, School of Innovation, Design and Engineering, Embedded Systems.
    Funk, Peter
    Mälardalen University, School of Innovation, Design and Engineering, Embedded Systems.
    Automated EEG Artifact Handling with Application in Driver Monitoring2017In: IEEE journal of biomedical and health informatics, ISSN 2168-2194, E-ISSN 2168-2208, Vol. 22, no 5, p. 1350-1361Article in journal (Refereed)
    Abstract [en]

    Automated analyses of electroencephalographic (EEG) signals acquired in naturalistic environments is becoming increasingly important in areas such as brain computer interfaces and behaviour science. However, the recorded EEG in such environments is often heavily contaminated by motion artifacts and eye movements. This poses new requirements on artifact handling. The objective of this paper is to present an automated EEG artifacts handling algorithm which will be used as a pre-processing step in a driver monitoring application. The algorithm, named ARTE (Automated aRTifacts handling in EEG), is based on wavelets, independent component analysis and hierarchical clustering. The algorithm is tested on a dataset obtained from a driver sleepiness study including 30 drivers and 540 30-minute 30-channel EEG recordings. The algorithm is evaluated by a clinical neurophysiologist, by quantitative criteria (signal quality index, mean square error, relative error and mean absolute error), and by demonstrating its usefulness as a preprocessing step in driver monitoring, here exemplified with driver sleepiness classification. All results are compared with a state of the art algorithm called FORCe. The quantitative and expert evaluation results show that the two algorithms are comparable and that both algorithms significantly reduce the impact of artifacts in recorded EEG signals. When artifact handling is used as a pre-processing step in driver sleepiness classification, the classification accuracy increased by 5% when using ARTE and by 2% when using FORCe. The advantage with ARTE is that it is data driven and does not rely on additional reference signals or manually defined thresholds, making it well suited for use in dynamic settings where unforeseen and rare artifacts are commonly encountered.

  • 163.
    Barua, Shaibal
    et al.
    Mälardalen University, School of Innovation, Design and Engineering, Embedded Systems.
    Ahmed, Mobyen Uddin
    Mälardalen University, School of Innovation, Design and Engineering, Embedded Systems.
    Begum, Shahina
    Mälardalen University, School of Innovation, Design and Engineering, Embedded Systems.
    Classifying drivers' cognitive load using EEG signals2017In: Studies in Health Technology and Informatics, ISSN 0926-9630, E-ISSN 1879-8365, Vol. 237, p. 99-106Article in journal (Refereed)
    Abstract [en]

    A growing traffic safety issue is the effect of cognitive loading activities on traffic safety and driving performance. To monitor drivers' mental state, understanding cognitive load is important since while driving, performing cognitively loading secondary tasks, for example talking on the phone, can affect the performance in the primary task, i.e. driving. Electroencephalography (EEG) is one of the reliable measures of cognitive load that can detect the changes in instantaneous load and effect of cognitively loading secondary task. In this driving simulator study, 1-back task is carried out while the driver performs three different simulated driving scenarios. This paper presents an EEG based approach to classify a drivers' level of cognitive load using Case-Based Reasoning (CBR). The results show that for each individual scenario as well as using data combined from the different scenarios, CBR based system achieved approximately over 70% of classification accuracy. 

  • 164.
    Barua, Shaibal
    et al.
    Mälardalen University, School of Innovation, Design and Engineering, Embedded Systems.
    Ahmed, Mobyen Uddin
    Mälardalen University, School of Innovation, Design and Engineering, Embedded Systems.
    Begum, Shahina
    Mälardalen University, School of Innovation, Design and Engineering, Embedded Systems.
    Distributed Multivariate Physiological Signal Analytics for Driver´s Mental State Monitoring2018In: Lecture Notes of the Institute for Computer Sciences, Social-Informatics and Telecommunications Engineering, LNICST, Volume 225, 2018, p. 26-33Conference paper (Refereed)
    Abstract [en]

    This paper presents a distributed data analytics approach for drivers’ mental state monitoring using multivariate physiological signals. Driver’s mental states such as cognitive distraction, sleepiness, stress, etc. can be fatal contributing factors and to prevent car crashes these factors need to be understood. Here, a cloud-based approach with heterogeneous sensor sources that generates extremely large data sets of physiological signals need to be handled and analyzed in a big data scenario. In the proposed physiological big data analytics approach, for driver state monitoring, heterogeneous data coming from multiple sources i.e., multivariate physiological signals are used, processed and analyzed to aware impaired vehicle drivers. Here, in a distributed big data environment, multi-agent case-based reasoning facilitates parallel case similarity matching and handles data that are coming from single and multiple physiological signal sources.

  • 165.
    Barua, Shaibal
    et al.
    Mälardalen University, School of Innovation, Design and Engineering, Embedded Systems.
    Ahmed, Mobyen Uddin
    Mälardalen University, School of Innovation, Design and Engineering, Embedded Systems.
    Begum, Shahina
    Mälardalen University, School of Innovation, Design and Engineering, Embedded Systems.
    Drivers' Sleepiness Classification using Machine Learning with Physiological and Contextual dataIn: First International Conference on Advances in Signal Processing and Artificial Intelligence ASPAI' 2019Conference paper (Refereed)
    Abstract [en]

    Analysing physiological parameters together with contextual information of car drivers to identify drivers’ sleepiness is a challenging issue. Machine learning algorithms show high potential in data analysis and classification tasks in many domains. This paper presents a use case of machine learning approach for drivers’ sleepiness classification. The classifications are conducted based on drivers’ physiological parameters and contextual information. The sleepiness classification shows receiver operating characteristic (ROC) curves for KNN, SVM and RF were 0.98 on 10-fold cross-validation and 0.93 for leave-one-out (LOO) for all classifiers.

  • 166.
    Barua, Shaibal
    et al.
    Mälardalen University, School of Innovation, Design and Engineering, Embedded Systems.
    Begum, Shahina
    Mälardalen University, School of Innovation, Design and Engineering, Embedded Systems.
    A Review on Machine Learning Algorithms in Handling EEG Artifacts2014In: The Swedish AI Society (SAIS) Workshop SAIS, 14, 2014Conference paper (Refereed)
    Abstract [en]

    Brain waves obtained by Electroencephalograms (EEG) recording are an important research area in medical and health and brain computer interface (BCI). Due to the nature of EEG signal, noises and artifacts can contaminate it, which leads to a serious misinterpretation in EEG signal analysis. These contaminations are referred to as artifacts, which are signals of other than brain activity. Moreover, artifacts can cause significant miscalculation of the EEG measurements that reduces the clinical usefulness of EEG signals. Therefore, artifact handling is one of the cornerstones in EEG signal analysis. This paper provides a review of machine learning algorithms that have been applied in EEG artifacts handling such as artifacts identification and removal. In addition, an analysis of these methods has been reported based on their performance.

  • 167.
    Barua, Shaibal
    et al.
    Mälardalen University, School of Innovation, Design and Engineering, Embedded Systems.
    Begum, Shahina
    Mälardalen University, School of Innovation, Design and Engineering, Embedded Systems.
    Ahmed, Mobyen Uddin
    Mälardalen University, School of Innovation, Design and Engineering, Embedded Systems.
    Clustering based Approach for Automated EEG Artifacts Handling2015In: Frontiers in Artificial Intelligence and Applications, vol. 278, 2015, p. 7-16Conference paper (Refereed)
    Abstract [en]

    Electroencephalogram (EEG), measures the neural activity of the central nervous system, which is widely used in diagnosing brain activity and therefore plays a vital role in clinical and Brain-Computer Interface application. However, analysis of EEG signal is often complex since the signal recoding often contaminates with noises or artifacts such as ocular and muscle artifacts, which could mislead the diagnosis result. Therefore, to identify the artifacts from the EEG signal and handle it in a proper way is becoming an important and interesting research area. This paper presents an automated EEG artifacts handling approach, where it combines Independent Component Analysis (ICA) with a 2nd order clustering approach. Here, the 2nd order clustering approach combines the Hierarchical and Gaussian Picture Model clustering algorithm. The effectiveness of the proposed approach has been examined and observed on real EEG recording. According to result, the artifacts in the EEG signals are identified and removed successfully where the clean EEG signal shows acceptable considering visual inspection.

  • 168.
    Barua, Shaibal
    et al.
    Mälardalen University, School of Innovation, Design and Engineering, Embedded Systems. IS (Embedded Systems).
    Begum, Shahina
    Mälardalen University, School of Innovation, Design and Engineering, Embedded Systems. IS (Embedded Systems).
    Ahmed, Mobyen Uddin
    Mälardalen University, School of Innovation, Design and Engineering, Embedded Systems. IS (Embedded Systems).
    Driver’s State Monitoring: A Case Study on Big Data Analytics2016In: The 3rd EAI International Conference on IoT Technologies for HealthCare HealthyIoT'16, 2016, Vol. 187, p. 145-147Conference paper (Refereed)
    Abstract [en]

    Driver's distraction, inattention, sleepiness, stress, etc. are identified as causal factors of vehicle crashes and accidents. Today, we know that physiological signals are convenient and reliable measures of driver’s impairments. Heterogeneous sensors are generating vast amount of signals, which need to be handled and analyzed in a big data scenario. Here, we propose a big data analytics approach for driver state monitoring using heterogeneous data that are coming from multiple sources, i.e., physiological signals along with vehicular data and contextual information. These data are processed and analyzed to aware impaired vehicle drivers.

  • 169.
    Barua, Shaibal
    et al.
    Mälardalen University, School of Innovation, Design and Engineering, Embedded Systems.
    Begum, Shahina
    Mälardalen University, School of Innovation, Design and Engineering, Embedded Systems.
    Ahmed, Mobyen Uddin
    Mälardalen University, School of Innovation, Design and Engineering, Embedded Systems.
    Intelligent automated eeg artifacts handling using wavelet transform, independent component analysis and hierarchal clustering2017In: Lect. Notes Inst. Comput. Sci. Soc. Informatics Telecommun. Eng., Springer Verlag , 2017, p. 144-148Conference paper (Refereed)
    Abstract [en]

    Billions of interconnected neurons are the building block of the human brain. For each brain activity these neurons produce electrical signals or brain waves that can be obtained by the Electroencephalogram (EEG) recording. Due to the characteristics of EEG signals, recorded signals often contaminate with undesired physiological signals other than the cerebral signal that is referred to as the EEG artifacts such as the ocular or the muscle artifacts. Therefore, identification and handling of artifacts in the EEG signals in a proper way is becoming an important research area. This paper presents an automated EEG artifacts handling approach, combining Wavelet transform, Independent Component Analysis (ICA), and Hierarchical clustering. The effectiveness of the proposed approach has been examined and observed on real EEG recording. According to the result, the proposed approach identified artifacts in the EEG signals effectively and after handling artifacts EEG signals showed acceptable considering visual inspection. © ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering 2017.

  • 170.
    Barua, Shaibal
    et al.
    Mälardalen University, School of Innovation, Design and Engineering, Embedded Systems.
    Begum, Shahina
    Mälardalen University, School of Innovation, Design and Engineering, Embedded Systems.
    Ahmed, Mobyen Uddin
    Mälardalen University, School of Innovation, Design and Engineering, Embedded Systems.
    Intelligent Automated EEG Artifacts Handling Using Wavelet Transform, Independent Component Analysis and Hierarchical clustering2015Conference paper (Refereed)
    Abstract [en]

    Billions of interconnected neurons are the building block of human brain. For each brain activity these neurons produce electrical signals or brain waves that can be obtained by the Electroencephalogram (EEG) recording. Due to the characteristics of EEG signal, recorded signal often contaminate with undesired physiological signals other than cerebral signal that refers to as EEG artifacts such as ocular or muscle artifacts. Therefore, identification of artifacts from the EEG signal and handle it in a proper way is becoming an important research area. This paper presents an automated EEG artifacts handling approach, where it combines Wavelet transform, Independent Component Analysis (ICA) with Hierarchical clustering method. The effectiveness of the proposed approach has been examined and observed on real EEG recording. According to result, the artifacts in the EEG signals are identified and removed successfully where after handling artifacts EEG signals show acceptable considering visual inspection.

  • 171.
    Barua, Shaibal
    et al.
    Mälardalen University, School of Innovation, Design and Engineering, Embedded Systems.
    Begum, Shahina
    Mälardalen University, School of Innovation, Design and Engineering, Embedded Systems.
    Ahmed, Mobyen Uddin
    Mälardalen University, School of Innovation, Design and Engineering, Embedded Systems.
    Scalable Framework for Distributed Case-based Reasoning for Big data analytics2018In: Lecture Notes of the Institute for Computer Sciences, Social-Informatics and Telecommunications Engineering, LNICST, Volume 225, 2018, p. 111-114Conference paper (Refereed)
    Abstract [en]

    This paper proposes a scalable framework for distributed case-based reasoning methodology to provide actionable knowledge based on historical big amount of data. The framework addresses several challenges, i.e., promptly analyse big data, cross-domain, use-case specific data processing, multi-source case representation, dynamic case-management, uncertainty, check the plausibility of solution after adaptation etc. through its’ five modules architectures. The architecture allows the functionalities with distributed data analytics and intended to provide solutions under different conditions, i.e. data size, velocity, variety etc.

  • 172.
    Barua, Shaibal
    et al.
    Mälardalen University, School of Innovation, Design and Engineering, Embedded Systems.
    Begum, Shahina
    Mälardalen University, School of Innovation, Design and Engineering, Embedded Systems.
    Ahmed, Mobyen Uddin
    Mälardalen University, School of Innovation, Design and Engineering, Embedded Systems.
    Supervised Machine Learning Algorithms to Diagnose Stress for Vehicle Drivers Based on Physiological Sensor Signals2015In: Studies in Health Technology and Informatics, Volume 211: Proceedings of the 12th International Conference on Wearable Micro and Nano Technologies for Personalized Health, 2–4 June 2015, Västerås, Sweden, 2015, Vol. 211, p. 241-248Conference paper (Refereed)
    Abstract [en]

    Machine learning algorithms play an important role in computer science research. Recent advancement in sensor data collection in clinical sciences lead to a complex, heterogeneous data processing and analysis for patient diagnosis and prognosis. Diagnosis and treatment of patients based on manual analysis of these sensor data is difficult and time consuming. Therefore, development of Knowledge-based systems to support clinicians in decision-making is important. However, it is necessary to perform experimental work to compare performances of different machine learning methods to help to select appropriate method for a specific characteristic of data sets. This paper compares classification performance of three popular machine learning methods i.e., case-based reasoning, neutral networks and support vector machine to diagnose stress of vehicle drivers using finger temperature and heart rate variability. The experimental results show that case-based reasoning outperforms other two methods in terms of classification accuracy. Case-based reasoning has achieved 80% and 86% accuracy to classify stress using finger temperature and heart rate variability. On contrary, both neural network and support vector machine have achieved less than 80% accuracy by using both physiological signals.

  • 173.
    Barua, Shaibal
    et al.
    Mälardalen University, School of Innovation, Design and Engineering, Embedded Systems.
    Begum, Shahina
    Mälardalen University, School of Innovation, Design and Engineering, Embedded Systems.
    Ahmed, Mobyen Uddin
    Mälardalen University, School of Innovation, Design and Engineering, Embedded Systems.
    Towards Distributed k-NN similarity for Scalable Case Retrieval2018In: ICCBR 2018: The 26th International Conference on Case-Based Reasoning July, 09th-12th 2018 in Stockholm, Sweden, Workshop Proceedings, 2018, p. 151-160Conference paper (Refereed)
    Abstract [en]

    In Big data era, the demand of processing large amount of data posing several challenges. One biggest challenge is that it is no longer possible to process the data in a single machine. Similar challenges can be assumed for case-based reasoning (CBR) approach, where the size of a case library is increasing and constructed using heterogenous data sources. To deal with the challenges of big data in CBR, a distributed CBR system can be developed, where case libraries or cases are distributed over clusters. MapReduce programming framework has the facilities of parallel processing massive amount of data through a distributed system. This paper proposes a scalable case-representation and retrieval approach using distributed k-NN similarity. The proposed approach is considered to be developed using MapReduce programming framework, where cases are distributed in many clusters.

  • 174.
    Barua, Shaibal
    et al.
    Mälardalen University, School of Innovation, Design and Engineering, Embedded Systems.
    Begum, Shahina
    Mälardalen University, School of Innovation, Design and Engineering, Embedded Systems.
    Ahmed, Mobyen Uddin
    Mälardalen University, School of Innovation, Design and Engineering, Embedded Systems.
    Ahlström, Christer
    The Swedish National Road and Transport Research Institute (VTI), Sweden.
    AUTOMATED EEG ARTIFACTS HANDLING FOR DRIVER SLEEPINESS MONITORING2016In: 2nd International Symposium on Somnolence, Vigilance, and Safety SomnoSafe2016, 2016Conference paper (Refereed)
  • 175.
    Bashir, Shariq
    et al.
    Mohammad Ali Jinnah University, Islamabad, Pakistan.
    Afzal, Wasif
    Mälardalen University, School of Innovation, Design and Engineering, Embedded Systems.
    Baig, Rauf
    Al Imam Mohammad Ibn Saud Islamic University (IMSIU), Riyadh, Saudi Arabia.
    Opinion-based entity ranking using learning to rank2016In: Applied Soft Computing, ISSN 1568-4946, E-ISSN 1872-9681, Vol. 38, no 1, p. 151-163Article in journal (Refereed)
    Abstract [en]

    As social media and e-commerce on the Internet continue to grow, opinions have become one of the most important sources of information for users to base their future decisions on. Unfortunately, the large quantities of opinions make it difficult for an individual to comprehend and evaluate them all in a reasonable amount of time. The users have to read a large number of opinions of different entities before making any decision. Recently a new retrieval task in information retrieval known as Opinion-Based Entity Ranking (OpER) has emerged. OpER directly ranks relevantentities based on how well opinions on them are matched with a user's preferences that are given in the form of queries. With such a capability, users do not need to read a large number of opinions available for the entities. Previous research on OpER does not take into account the importance and subjectivity of query keywords in individual opinions of an entity. Entity relevance scores are computed primarily on the basis of occurrences of query keywords match, by assuming all opinions of an entity as a single field of text. Intuitively, entities that have positive judgments and strong relevance with query keywords should be ranked higher than those entities that have poor relevance and negative judgments. This paper outlines several ranking features and develops an intuitive framework for OpER in which entities are ranked according to how well individual opinions of entities are matched with the user's query keywords. As a useful ranking model may be constructed from many rankingfeatures, we apply learning to rank approach based on genetic programming (GP) to combine features in order to develop an effective retrieval model for OpER task. The proposed approach is evaluated on two collections and is found to be significantly more effective than the standard OpER approach.

  • 176.
    Bate, Iain
    et al.
    Univ York, Dept Comp Sci, York YO10 5DD, N Yorkshire, England..
    Hansson, Hans
    Mälardalen University, School of Innovation, Design and Engineering, Embedded Systems.
    Punnekkat, Sasikumar
    Mälardalen University, School of Innovation, Design and Engineering, Embedded Systems. Mälardalen University, School of Innovation, Design and Engineering, Innovation and Product Realisation.
    Better, Faster, Cheaper, and Safer Too - Is This Really Possible?2012In: 2012 IEEE 17TH CONFERENCE ON EMERGING TECHNOLOGIES & FACTORY AUTOMATION (ETFA), IEEE , 2012Conference paper (Refereed)
    Abstract [en]

    Increased levels of automation together with increased complexity of automation systems brings increased responsibility on the system developers in terms of quality demands from the legal perspectives as well as company reputation. Component based development of software systems provides a viable and cost-effective alternative in this context provided one can address the quality and safety certification demands in an efficient manner. In this paper we present our vision, challenges and a brief outline of various research themes in which our team is engaged currently within two major projects.

  • 177.
    Baumgart, S.
    et al.
    Volvo Construction Equipment, Eskilstuna, Sweden.
    Zhang, X.
    Volvo Construction Equipment, Eskilstuna, Sweden.
    Fröberg, Joakim
    Mälardalen University, School of Innovation, Design and Engineering, Embedded Systems.
    Punnekkat, Sasikumar
    Mälardalen University, School of Innovation, Design and Engineering, Embedded Systems.
    Variability management in product lines of safety critical embedded systems2014In: International Conference on Embedded Systems, ICES 2014, 2014, p. 98-103Conference paper (Refereed)
    Abstract [en]

    The product line engineering approach is a promising concept to identify and manage reuse in a structured and efficient way and is even applied for the development of safety critical embedded systems. Managing the complexity of variability and addressing functional safety at the same time is challenging and is not yet solved. Variability management is an enabler to both establish traceability and making necessary information visible for safety engineers. We identify a set of requirements for such a method and evaluate existing variability management methods. We apply the most promising method to an industrial case and study its suitability for developing safety critical product family members. This study provides positive feedback on the potential of the model-based method PLUS in supporting the development of functional safety critical embedded systems in product lines. As a result of our analysis we suggest potential improvements for it.

  • 178.
    Baumgart, Stephan
    Mälardalen University, School of Innovation, Design and Engineering, Embedded Systems. Volvo Construction Equipment.
    Incorporating Functional Safety in Model-based Development of Product Lines2016Licentiate thesis, comprehensive summary (Other academic)
    Abstract [en]

    Product lines in industry are often based on an engineer’s focus on fast and feasible product instantiation rather than a precise product line development method and process as described in literature. When considering functional safety, we need a precise model that includes evidence for the safety of each variant of the product.Functional safety standards provide guidance to develop safety critical products and require that evidence is collected to prove the safety of the product. But today’s functional safety standards do not provide guidance on how to achieve functional safety in product lines. At the same time arguments need to be collected during development so that each product configuration is safe and is fulfilling the requirements of the standards. Providing these arguments requires tracing safety-related requirements and dependencies through the development process taking the impact of variability in different development artifacts into consideration.

    In this thesis, we study the challenges of developing safety critical products in product lines. We explore industrial practices to achieve functional safety standard compliance in product lines by interviewing practitioners from different companies and by collecting the reported challenges and practices. This information helps us to identify improvement areas and we derive requirements that a product line engineering method needs to fulfill. Based on these findings we analyze variability management methods from the software product line engineering research domain to identify potential candidate solutions that can be adapted to support safety critical products. We provide an approach for capturing functional safety related characteristics in a model-based product line engineering method. We apply our method in an industrial case demonstrating the applicability.

  • 179.
    Baumgart, Stephan
    et al.
    E&E System Architecture Department, Volvo Construction Equipment, Eskilstuna, Sweden.
    Fröberg, Joakim
    Mälardalen University, School of Innovation, Design and Engineering, Embedded Systems.
    Functional Safety in Product Lines - A Systematic Mapping Study2016In: 42nd Euromicro Conference series on Software Engineering and Advanced Applications SEAA 2016, 2016, p. 313-322Conference paper (Refereed)
    Abstract [en]

    Software product line engineering is a widely used approach to plan and manage reuse of software. When safety critical products are developed, achieving functional safety standard compliance must be shown. The requirements stated in the functional safety standards also apply when safety critical products are developed in product lines. Managing functional safety in industrial product lines is challenging and work around solutions are applied in practice. The objective of this research is to collect and review reported research publications focusing on achieving safety in product lines and to identify gaps in todays research. We conduct a systematic mapping study of research publications reported until January 2016.We identify 39 research articles to be included in a list of primary studies and analyze how product lines are documented, which safety-related topics are covered and which evaluation method the studies apply. Generally, we find that the area of how to achieve functional safety in product lines needs more attention. Our study provides an overview on which topics have been discussed until now and which safety-related topics need more attention.

  • 180.
    Baumgart, Stephan
    et al.
    Volvo Construction Equipment, Eskilstuna, Sweden.
    Fröberg, Joakim
    Mälardalen University, School of Innovation, Design and Engineering, Embedded Systems. RISE ICT/SICS Västerås, Sweden.
    Punnekkat, Sasikumar
    Mälardalen University, School of Innovation, Design and Engineering, Embedded Systems.
    Can STPA be used for a System-of-Systems? Experiences from an Automated Quarry Site2018In: 2018 IEEE International Symposium on Systems Engineering ISSE2018, 2018, no 4Conference paper (Refereed)
    Abstract [en]

    Automation is becoming prevalent in more and more industrial domains due to the potential benefits in cost reduction as well as the new approaches/solutions they enable. When machines are automated and utilized in system-of-systems, a thorough analysis of potential critical scenarios is necessary to derive appropriate design solutions that are safe as well. Hazard analysis methods like PHA, FTA or FMEA help to identify and follow up potential risks for the machine operators or bystanders and are well-established in the development process for safety critical machinery. However, safety certified individual machines can no way guarantee safety in the context of system-of-systems since their integration and interactions could bring forth newer hazards. Hence it is paramount to understand the application sce- narios of the system-of-systems and to apply a structured method to identify all potential hazards. In this paper, we 1) provide an overview of proposed hazard analysis methods for system-of- systems, 2) describe a case from construction equipment domain, and 3) apply the well-known System-Theoretic Process Analysis (STPA)f to our case. Our experiences during the case study and the analysis of results clearly point out certain inadequacies of STPA in the context of system-of-systems and underlines the need for the development of improved techniques for safety analysis of system-of-systems.

  • 181.
    Baumgart, Stephan
    et al.
    Volvo Construction Equipment, Eskilstuna, Sweden.
    Fröberg, Joakim
    Mälardalen University, School of Innovation, Design and Engineering, Innovation and Product Realisation.
    Punnekkat, Sasikumar
    Mälardalen University, School of Innovation, Design and Engineering, Embedded Systems. BIT-Pilani KK Birla Goa Campus, India.
    Enhancing Model-Based Engineering of Product Lines by Adding Functional Safety2015In: CEUR Workshop Proceedings, vol. 1487, 2015, p. 53-62Conference paper (Refereed)
    Abstract [en]

    Today's industrial product lines in the automotive and construction equipment domain face the challenge to show functional safety standard compliance and argue for the absence of failures for all derived product variants. The product line approaches are not su cient to support practitioners to trace safety-related characteristics through development. We aim to provide aid in creating a safety case for a certain con guration in a product line such that overall less e ort is necessary for each con guration. In this paper we 1) discuss the impact of functional safety on product line development, 2) propose a model-based approach to capture safety-related characteristics during concept phase for product lines and 3) analyze the usefulness of our proposal.

  • 182.
    Baumgart, Stephan
    et al.
    Volvo Construction Equipment, Eskilstuna, Sweden.
    Fröberg, Joakim
    Mälardalen University, School of Innovation, Design and Engineering, Embedded Systems.
    Punnekkat, Susikumar
    Mälardalen University, School of Innovation, Design and Engineering, Embedded Systems.
    Analyzing Hazards in System-of-Systems: Described in a Quarry Site Automation Context2017In: 11th Annual IEEE International Systems conference SysCon, 2017, p. 544-551Conference paper (Refereed)
    Abstract [en]

    Methods for analyzing hazards related to individual systems are well studied and established in industry today. When system-of-systems are set up to achieve new emergent behavior, hazards specifically caused by malfunctioning behavior of the complex interactions between the involved systems may not be revealed by just analyzing single system hazards. A structured process is required to reduce the complexity to enable identification of hazards when designing system-of-systems. In this paper we first present how hazards are identified and analyzed using hazard and risk assessment (HARA) methodology by the industry in the context of single systems. We describe systems-of-systems and provide a quarry site automation example from the construction equipment domain. We propose a new structured process for identifying potential hazards in systems-of-systems (HISoS), exemplified in the context of the provided example. Our approach helps to streamline the hazard analysis process in an efficient manner thus helping faster certification of system-of-systems.

  • 183.
    Baumgart, Stephan
    et al.
    Volvo Construction Equipment, Eskilstuna, Sweden.
    Parmeza, Ditmar
    Mälardalen University, School of Innovation, Design and Engineering, Embedded Systems.
    Predicting the Effort for Functional Safety in Product Lines2015In: The 41st Euromicro Conference on Software Engineering and Advanced Applications SEAA'15, 2015Conference paper (Refereed)
  • 184.
    Becker, Matthias
    Mälardalen University, School of Innovation, Design and Engineering, Embedded Systems. IS (Embedded Systems).
    Consolidating Automotive Applications on Clustered Many-Core Platforms2017In: SIGDA Student Research Forum at ASP-DAC ASP-DAC SRF'17, 2017Conference paper (Refereed)
    Abstract [en]

    The increased proliferation of automotive systems is leading to a paradigm shift in the automotive system architecture. Several, now distributed, applications will be consolidated on fewer, more powerful platforms, containing tens or hundreds of compute cores. Clustered many-core processors are a promising candidate for such systems, since each cluster provides enough computational power to host complex applications, while their intrinsic hardware architecture isolates different cluster from each other. The described PhD project works towards methods that allow the consolidation of automotive applications on clustered many-core architectures, while all their timing requirements are maintained. A contention-free execution framework is proposed that successfully diminishes the access-delays due to contention on shared resources within a cluster. In order to integrate complex end-to-end constraints on multi-rate chains, a method is proposed that allows the analysis of such chains and generates job-level dependencies. Such job-level dependencies can then be used to integrate the end-to-end constraints into the proposed execution framework. The applicability of the proposed methods to industrial problems is demonstrated via industrial case studies.

  • 185.
    Becker, Matthias
    Mälardalen University, School of Innovation, Design and Engineering, Embedded Systems.
    Consolidating Automotive Real-Time Applications on Many-Core Platforms2017Doctoral thesis, comprehensive summary (Other academic)
    Abstract [en]

    Automotive systems have transitioned from basic transportation utilities to sophisticated systems. The rapid increase in functionality comes along with a steep increase in software complexity. This manifests itself in a surge of the number of functionalities as well as the complexity of existing functions. To cope with this transition, current trends shift away from today’s distributed architectures towards integrated architectures, where previously distributed functionality is consolidated on fewer, more powerful, computers. This can ease the integration process, reduce the hardware complexity, and ultimately save costs.

    One promising hardware platform for these powerful embedded computers is the many-core processor. A many-core processor hosts a vast number of compute cores, that are partitioned on tiles which are connected by a Network-on-Chip. These natural partitions can provide exclusive execution spaces for different applications, since most resources are not shared among them. Hence, natural building blocks towards temporally and spatially separated execution spaces exist as a result of the hardware architecture.

    Additionally to the traditional task local deadlines, automotive applications are often subject to timing constraints on the data propagation through a chain of semantically related tasks. Such requirements pose challenges to the system designer as they are only able to verify them after the system synthesis (i.e. very late in the design process).

    In this thesis, we present methods that transform complex timing constraints on the data propagation delay to precedence constraints between individual jobs. An execution framework for the cluster of the many-core is proposed that allows access to cluster external memory while it avoids contention on shared resources by design. A partitioning and configuration of the Network-on-Chip provides isolation between the different applications and reduces the access time from the clusters to external memory. Moreover, methods that facilitate the verification of data propagation delays in each development step are provided. 

  • 186.
    Becker, Matthias
    Mälardalen University, School of Innovation, Design and Engineering, Embedded Systems.
    Efficient Resource Management for Many-Core based Industrial Real-Time Systems2015Licentiate thesis, comprehensive summary (Other academic)
    Abstract [en]

    The increased complexity of today’s industrial embedded systems stands inneed for more computational power while most systems must adhere to a restrictedenergy consumption, either to prolong the battery lifetime or to reduceoperational costs. The many-core processor is therefore a natural fit. Due tothe simple architecture of the compute cores, and therefore their good analyzability,such processors are additionally well suited for real-time applications.In our research, we focus on two particular problems which need to be addressedin order to pave the way into the many-core era. The first area is powerand thermal aware execution frameworks, where we present different energyaware extensions to well known load balancing algorithms, allowing them todynamically scale the number of active cores depending on their workload.In contrast, an additional framework is presented which balances workloadsto minimize temperature gradients on the die. The second line of works focuseson industrial standards in the face of massively parallel platforms, wherewe address the automotive and automation domain. We present an executionframework for IEC 61131-3 applications, allowing the consolidation of severalIEC 61131-3 applications on the same platform. Additionally, we discussseveral architectural options for the AUTOSAR software architecture on suchmassively parallel platforms.

  • 187.
    Becker, Matthias
    et al.
    Mälardalen University, School of Innovation, Design and Engineering, Embedded Systems.
    Dasari, D.
    Research and Technology Centre, Robert Bosch, India.
    Nelis, V.
    CISTER/INESC-TEC, ISEP, Portugal.
    Behnam, Moris
    Mälardalen University, School of Innovation, Design and Engineering, Embedded Systems.
    Pinho, L. M.
    CISTER/INESC-TEC, ISEP, Portugal.
    Nolte, Thomas
    Mälardalen University, School of Innovation, Design and Engineering, Embedded Systems.
    Investigation on AUTOSAR-Compliant solutions for many-core architectures2015In: Proceedings - 18th Euromicro Conference on Digital System Design, DSD 2015, 2015, p. 95-103Conference paper (Refereed)
    Abstract [en]

    As of today, AUTOSAR is the de facto standard in the automotive industry, providing a common software architecture and development process for automotive applications. While this standard is originally written for singlecore operated Electronic Control Units (ECU), new guidelines and recommendations have been added recently to provide support for multicore architectures. This update came as a response to the steady increase of the number and complexity of the software functions embedded in modern vehicles, which call for the computing power of multicore execution environments. In this paper, we enumerate and analyze the design options and the challenges of porting AUTOSAR-based automotive applications onto multicore platforms. In particular, we investigate those options when considering the emerging many-core architectures that provide a more 'scalable' environment than the traditional multicore systems. Such platforms are suitable to enable massive parallel execution, and their design is more suitable for partitioning and isolating the software components.

  • 188.
    Becker, Matthias
    et al.
    Mälardalen University, School of Innovation, Design and Engineering, Embedded Systems.
    Dasari, Dakshina
    Research and Technology Centre, Robert Bosch, India.
    Mubeen, Saad
    Mälardalen University, School of Innovation, Design and Engineering, Embedded Systems.
    Behnam, Moris
    Mälardalen University, School of Innovation, Design and Engineering, Embedded Systems.
    Nolte, Thomas
    Mälardalen University, School of Innovation, Design and Engineering, Embedded Systems.
    Analyzing End-to-End Delays in Automotive Systems at Various Levels of Timing Information2016In: IEEE 4th International Workshop on Real-Time Computing and Distributed systems in Emerging Applications REACTION'16, Porto, Portugal, 2016Conference paper (Refereed)
    Abstract [en]

    Software design for automotive systems is highly complex due to the presence of strict data age constraints for event chains in addition to task specific requirements. These age constraints define the maximum time for the propagation of data through an event chain consisting of independently triggered tasks. Tasks in event chains can have different periods, introducing over- and under-sampling effects, which additionally aggravates their timing analysis. Furthermore, different functionality in these systems, is developed by different suppliers before the final system integration on the ECU. The software itself is developed in a hardware agnostic manner and this uncertainty and limited information at the early design phases may not allow effective analysis of end-to-end delays during that phase. In this paper, we present a method to compute end-to-end delays given the information available in the design phases, thereby enabling timing analysis throughout the development process. The presented methods are evaluated with extensive experiments where the decreasing pessimism with increasing system information is shown.

  • 189.
    Becker, Matthias
    et al.
    Mälardalen University, School of Innovation, Design and Engineering, Embedded Systems.
    Dasari, Dakshina
    Robert Bosch GmbH, Renningen, Germany.
    Mubeen, Saad
    Mälardalen University, School of Innovation, Design and Engineering, Embedded Systems.
    Behnam, Moris
    Mälardalen University, School of Innovation, Design and Engineering, Embedded Systems.
    Nolte, Thomas
    Mälardalen University, School of Innovation, Design and Engineering, Embedded Systems.
    Analyzing end-to-end delays in automotive systems at various levels of timing information2017In: ACM SIGBED Review, E-ISSN 1551-3688, Vol. 14, no 4, p. 8-13Article in journal (Refereed)
    Abstract [en]

    Software design for automotive systems is highly complex due to the presence of strict data age constraints for event chains in addition to task specific requirements. These age constraints define the maximum time for the propagation of data through an event chain consisting of independently triggered tasks. Tasks in event chains can have different periods, introducing over- and under-sampling effects, which additionally aggravates their timing analysis. Furthermore, different functionality in these systems, is developed by different suppliers before the final system integration on the ECU. The software itself is developed in a hardware agnostic manner and this uncertainty and limited information at the early design phases may not allow effective analysis of end-to-end delays during that phase. In this paper, we present a method to compute end-to-end delays given the information available in the design phases, thereby enabling timing analysis throughout the development process. The presented methods are evaluated with extensive experiments where the decreasing pessimism with increasing system information is shown.

  • 190.
    Becker, Matthias
    et al.
    Mälardalen University, School of Innovation, Design and Engineering, Embedded Systems.
    Dasari, Dakshina
    Robert Bosch GmbH, Renningen, Germany.
    Mubeen, Saad
    Mälardalen University, School of Innovation, Design and Engineering, Embedded Systems. Arcticus Systems AB, Järfälla, Sweden.
    Behnam, Moris
    Mälardalen University, School of Innovation, Design and Engineering, Embedded Systems.
    Nolte, Thomas
    Mälardalen University, School of Innovation, Design and Engineering, Embedded Systems.
    End-to-End Timing Analysis of Cause-Effect Chains in Automotive Embedded Systems2017In: Journal of systems architecture, ISSN 1383-7621, E-ISSN 1873-6165, Vol. 80, no Supplement C, p. 104-113Article in journal (Refereed)
    Abstract [en]

    Automotive embedded systems are subjected to stringent timing requirements that need to be verified. One of the most complex timing requirement in these systems is the data age constraint. This constraint is specified on cause- effect chains and restricts the maximum time for the propagation of data through the chain. Tasks in a cause-effect chain can have different activation patterns and different periods, that introduce over- and under-sampling effects, which additionally aggravate the end-to-end timing analysis of the chain. Furthermore, the level of timing information available at various development stages (from modeling of the software architecture to the software implementation) varies a lot, the complete timing information is available only at the implementation stage. This uncertainty and limited timing information can restrict the end-to-end timing analysis of these chains. In this paper, we present methods to compute end-to-end delays based on different levels of system information. The characteristics of different communication semantics are further taken into account, thereby enabling timing analysis throughout the development process of such heterogeneous software systems. The presented methods are evaluated with extensive experiments. As a proof of concept, an industrial case study demonstrates the applicability of the proposed methods following a state-of-the-practice development process.

  • 191.
    Becker, Matthias
    et al.
    Mälardalen University, School of Innovation, Design and Engineering, Embedded Systems.
    Dasari, Dakshina
    Research and Technology Centre, Robert Bosch, India.
    Mubeen, Saad
    Mälardalen University, School of Innovation, Design and Engineering, Embedded Systems.
    Behnam, Moris
    Mälardalen University, School of Innovation, Design and Engineering, Embedded Systems.
    Nolte, Thomas
    Mälardalen University, School of Innovation, Design and Engineering, Embedded Systems.
    MECHAniSer - A Timing Analysis and Synthesis Tool for Multi-Rate Effect Chains with Job-Level Dependencies2016In: 7th International Workshop on Analysis Tools and Methodologies for Embedded and Real-time Systems WATERS'16, 2016Conference paper (Refereed)
    Abstract [en]

    Many industrial embedded systems have timing con- straints on the data propagation through a chain of independent tasks. These tasks can execute at different periods which leads to under and oversampling of data. In such situations, understand- ing and validating the temporal correctness of end-to-end delays is not trivial. Many industrial areas further face distributed development where different functionalities are integrated on the same platform after the development process. The large effect of scheduling decisions on the end-to-end delays can lead to expensive redesigns of software parts due to the lack of analysis at early design stages. Job-level dependencies is one solution for this challenge and means of scheduling such systems are available. In this paper we present MECHAniSer, a tool targeting the early analysis of end-to-end delays in multi-rate cause effect chains with specified job-level dependencies. The tool further provides the possibility to synthesize job-level dependencies for a set of cause-effect chains in a way such that all end-to-end requirements are met. The usability and applicability of the tool to industrial problems is demonstrated via a case study.

  • 192.
    Becker, Matthias
    et al.
    Mälardalen University, School of Innovation, Design and Engineering, Embedded Systems.
    Dasari, Dakshina
    Research and Technology Centre, Robert Bosch, India.
    Mubeen, Saad
    Mälardalen University, School of Innovation, Design and Engineering, Embedded Systems.
    Behnam, Moris
    Mälardalen University, School of Innovation, Design and Engineering, Embedded Systems.
    Nolte, Thomas
    Mälardalen University, School of Innovation, Design and Engineering, Embedded Systems.
    Synthesizing Job-Level Dependencies for Automotive Multi-Rate Effect Chains2016In: The 22th IEEE International Conference on Embedded and Real-Time Computing Systems and Applications RTCSA'16, 2016, Vol. sept, p. 159-169, article id 579951Conference paper (Refereed)
    Abstract [en]

    Today’s automotive embedded systems comprise a multitude of functionalities, many with complex timing re- quirements. Besides task specific timing requirements, such ap- plications often have timing requirements for the propagation of data through a chain of tasks. An important metric for control applications is the data age, which is addressed in this work. The analysis of such systems is non-trivial because tasks involved in the data propagation may execute at different periods, which leads to over and undersampling within one chain. This work presents a novel method to compute worst- and best-case end-to-end latencies for such systems. A second contribution synthesizes job-level dependencies for such task sets in a way that data paths which exceed the age constraint are eliminated. An extensive evaluation is performed on synthetic task sets and the applicability to industrial applications is demonstrated in a case study.

  • 193.
    Becker, Matthias
    et al.
    Mälardalen University, School of Innovation, Design and Engineering, Embedded Systems.
    Dasari, Dakshina
    Robert Bosch GmbH, Renningen, Germany.
    Mubeen, Saad
    Mälardalen University, School of Innovation, Design and Engineering, Embedded Systems.
    Behnam, Moris
    Mälardalen University, School of Innovation, Design and Engineering, Embedded Systems.
    Nolte, Thomas
    Mälardalen University, School of Innovation, Design and Engineering, Embedded Systems.
    Timing Analysis and Synthesis of Mixed Multi-Rate Effect Chains in MECHAniSer2016In: Open Demo Session of Real-Time Systems located at Real Time Systems Symposium (RTSS) RTSS@Work 2016, 2016Conference paper (Refereed)
    Abstract [en]

    The majority of embedded control systems are modeled with several chains of independently triggered tasks, also known as multi-rate effect chains. These chains have often stringent end-to-end timing requirements that should be satisfied before running the system. MECHAniSer is one of the tools that supports end-to-end timing analysis of such chains. In addition, the tool provides the possibility to synthesize job-level dependencies for these chains such that all end-to-end timing requirements are satisfied. In this paper we showcase an extension of MECHAniSer that supports the analysis of mixed chains that contain a mix of independent and dependent tasks.

  • 194.
    Becker, Matthias
    et al.
    Mälardalen University, School of Innovation, Design and Engineering, Embedded Systems.
    Dasari, Dakshina
    Research and Technology Centre, Robert Bosch, India.
    Nelis, Vincent
    CISTER/INESC-TEC, ISEP, Portugal.
    Behnam, Moris
    Mälardalen University, School of Innovation, Design and Engineering, Embedded Systems.
    Pinho, Luis Miguel
    CISTER/INESC-TEC, ISEP, Portugal.
    Nolte, Thomas
    Mälardalen University, School of Innovation, Design and Engineering, Embedded Systems.
    Investigation on AUTOSAR-Compliant Solutionsfor Many-Core Architectures2015In: Proceedings of the 18th Euromicro Conference on Digital System Design (DSD 2015), 2015Conference paper (Refereed)
    Abstract [en]

    As of today, AUTOSAR is the de facto standard inthe automotive industry, providing a common software architectureand development process for automotive applications. Whilethis standard is originally written for singlecore operated ElectronicControl Units (ECU), new guidelines and recommendationshave been added recently to provide support for multicore architectures.This update came as a response to the steady increase ofthe number and complexity of the software functions embedded inmodern vehicles, which call for the computing power of multicoreexecution environments. In this paper, we enumerate and analyzethe design options and the challenges of porting AUTOSAR-basedautomotive applications onto multicore platforms. In particular,we investigate those options when considering the emerging manycorearchitectures that provide a more scalable environment thanthe traditional multicore systems. Such platforms are suitableto enable massive parallel execution, and their design is moresuitable for partitioning and isolating the software components.

  • 195.
    Becker, Matthias
    et al.
    Mälardalen University, School of Innovation, Design and Engineering, Embedded Systems.
    Dasari, Dakshina
    Research and Technology Centre, Robert Bosch, India.
    Nicolic, Borislav
    CISTER, INESC-TEC, ISEP, Portugal .
    Åkesson, Benny
    CISTER, INESC-TEC, ISEP, Portugal .
    Nélis, Vincent
    CISTER/INESC-TEC, ISEP, Portugal.
    Nolte, Thomas
    Mälardalen University, School of Innovation, Design and Engineering, Embedded Systems.
    Contention-Free Execution of Automotive Applications on a Clustered Many-Core Platform2016In: 28th Euromicro Conference on Real-Time Systems ECRTS'16, Toulouse, France, 2016, p. 14-24Conference paper (Refereed)
    Abstract [en]

    Next generations of compute-intensive real-time applications in automotive systems will require more powerful computing platforms. One promising power-efficient solution for such applications is to use clustered many-core architectures. However, ensuring that real-time requirements are satisfied in the presence of contention in shared resources, such as memories, remains an open issue. This work presents a novel contention-free execution framework to execute automotive applications on such platforms. Privatization of memory banks together with defined access phases to shared memory resources is the backbone of the framework. An Integer Linear Programming (ILP) formulation is presented to find the optimal time-triggered schedule for the on-core execution as well as for the access to shared memory. Additionally a heuristic solution is presented that generates the schedule in a fraction of the time required by the ILP. Extensive evaluations show that the proposed heuristic performs only 0.5% away from the optimal solution while it outperforms a baseline heuristic by 67%. The applicability of the approach to industrially sized problems is demonstrated in a case study of a software for Engine Management Systems.

  • 196.
    Becker, Matthias
    et al.
    Mälardalen University, School of Innovation, Design and Engineering, Embedded Systems.
    Dasari, Dakshina
    Research and Technology Centre, Robert Bosch, India.
    Nélis, Vincent
    CISTER/INESC-TEC, ISEP, Portugal.
    Behnam, Moris
    Mälardalen University, School of Innovation, Design and Engineering, Embedded Systems.
    Nolte, Thomas
    Mälardalen University, School of Innovation, Design and Engineering, Embedded Systems.
    Partitioning the Network-on-Chip to Enable Virtualization on Many-Core Processors2015In: The 6th International Real-Time Scheduling Open Problems Seminar RTSOPS'15, 2015Conference paper (Refereed)
    Abstract [en]

    Technological advances have increased the transistor density, thereby ushering in multi- and more recently many-core systems, distinguished by the presence of hundreds of cores on a single chip. For such a platform, the Network-on-Chip (NoC) has emerged as a scalable and efficient interconnect fabric to realize the communication across an ever increasing number of processor cores, memories, and specialized IP blocks both on- and off-chip. In this paper, we highlighted some key problems in NoC based architectures that must be addressed before the deployment of real-time applications onto these platforms becomes possible. A paradigm shift from function centric to data and communication centric approaches is required. Combining hardware and software based flow-regulation seems to be the only way to ensure that NoCs go beyond the best-effort service and address the requirements of diverse applications.

  • 197.
    Becker, Matthias
    et al.
    Mälardalen University, School of Innovation, Design and Engineering, Embedded Systems.
    Khalilzad, Nima
    Mälardalen University, School of Innovation, Design and Engineering, Embedded Systems.
    Bril, Reinder J.
    Mälardalen University, School of Innovation, Design and Engineering, Embedded Systems. Technische Universiteit Eindhoven, Eindhoven, Netherlands.
    Nolte, Thomas
    Mälardalen University, School of Innovation, Design and Engineering, Embedded Systems.
    Extended support for limited preemption fixed priority scheduling for OSEK/AUTOSAR-compliant operating systems2015In: 2015 10th IEEE International Symposium on Industrial Embedded Systems, SIES 2015 - Proceedings, 2015, p. 207-217Conference paper (Refereed)
    Abstract [en]

    Fixed Priority Scheduling (FPS) is the de facto standard in industry and it is the scheduling algorithm used in OSEK/AUTOSAR. Applications in such systems are compositions of so called runnables, the functional entities of the system. Runnables are mapped to operating system tasks during system synthesis. In order to improve system performance it is proposed to execute runnables non-preemptively while varying the tasks threshold between runnables. This allows simpler resource access, can reduce the stack usage of the system, and improve the schedulability of the task sets. FPDS , as a special case of fixed-priority scheduling with deferred preemptions, executes subjobs non-preemptively and preemption points have preemption thresholds, providing exactly the proposed behavior. However OSEK/AUTOSAR-conform systems cannot execute such schedules. In this paper we present an approach allowing the execution of FPDS schedules. In our approach we exploit pseudo resources in order to implement FPDS . It is further shown that our optimal algorithm produces a minimum number of resource accesses. In addition, a simulation based evaluation is presented in which the number of resource accesses as well as the number of required pseudo-resources by the proposed algorithms are investigated. Finally, we report the overhead of resource access primitives using our measurements performed on an AUTOSARcompliant operating system.

  • 198.
    Becker, Matthias
    et al.
    Mälardalen University, School of Innovation, Design and Engineering, Embedded Systems.
    Liu, Meng
    Mälardalen University, School of Innovation, Design and Engineering, Embedded Systems.
    Behnam, Moris
    Mälardalen University, School of Innovation, Design and Engineering, Embedded Systems.
    Nolte, Thomas
    Mälardalen University, School of Innovation, Design and Engineering, Embedded Systems.
    Adaptive Routing of Real-Time Traffic on a 2D-Mesh Based NoC2015In: The 21st IEEE International Conference on Embedded and Real-Time Computing Systems and Applications, WiP RTCSA-wip'15, 2015Conference paper (Refereed)
  • 199.
    Becker, Matthias
    et al.
    Mälardalen University, School of Innovation, Design and Engineering, Embedded Systems. KTH, Sweden.
    Mubeen, Saad
    Mälardalen University, School of Innovation, Design and Engineering, Embedded Systems.
    Timing Analysis Driven Design-Space Exploration of Cause-Effect Chains in Automotive Systems2018In: 44th Annual Conference of the IEEE Industrial Electronics Society IECON'18, Washington DC, United States, 2018Conference paper (Refereed)
    Abstract [en]

    Model-based development and component-based software engineering have emerged as a promising approach to deal with enormous software complexity in automotive systems. This approach supports the development of software architectures by interconnecting (and reusing) software components (SWCs) at various abstraction levels. Automotive software architectures are often modeled with chains of SWCs, also called cause-effect chains that are constrained by timing requirements. Based on the variations in activation patterns of SWCs, a single model of a cause-effect chain at a higher abstraction level can conform to several valid refined models of the chain at a lower abstraction level, which is closer to the system implementation. As a consequence, the total number of valid implementation-level models generated by the existing techniques increases exponentially, thereby significantly increasing the runtime of the timing analysis engines and liming the scalability of the existing techniques. This paper computes an upper bound on the activation pattern combinations that may result from a system of cause-effect chains in a given high-level model of the software architecture. An efficient algorithm is presented that traverses only a reduced number of possible combinations of the cause-effect chains, resulting in the timing analysis of significantly lower number of implementation-level models of the software architecture. A proof of concept is provided by conducting a case study that shows significant reduction in the runtime of timing analysis engines, i.e., the timing behavior of the considered system is verified by performing the timing analysis of only 27% of all possible combinations of the cause-effect chains.

  • 200.
    Becker, Matthias
    et al.
    Mälardalen University, School of Innovation, Design and Engineering, Embedded Systems.
    Mubeen, Saad
    Mälardalen University, School of Innovation, Design and Engineering, Embedded Systems. Arcticus Systems AB, Järfälla, Sweden.
    Behnam, Moris
    Mälardalen University, School of Innovation, Design and Engineering, Embedded Systems.
    Nolte, Thomas
    Mälardalen University, School of Innovation, Design and Engineering, Embedded Systems.
    Extending Automotive Legacy Systems with Existing End-to-End Timing Constraints2018In: 14th International Conference on Information Technology : New Generations ITNG'17, 2018, Vol. 558, p. 597-605Conference paper (Refereed)
    Abstract [en]

    Developing automotive software is becoming in- creasingly challenging due to continuous increase in its size and complexity. The development challenge is amplified when the industrial requirements dictate extensions to the legacy (previously developed) automotive software while requiring to meet the existing timing requirements. To cope with these challenges, sufficient techniques and tooling to support the modeling and timing analysis of such systems at earlier development phases is needed. Within this context, we focus on the extension of software component chains in the software architectures of automotive legacy systems. Selecting the sampling frequency, i.e. period, for newly added software components is crucial to meet the timing requirements of the chains. The challenges in selecting periods are identified. It is further shown how to automatically assign periods to software components, such that the end-to-end timing requirements are met while the runtime overhead is minimized. An industrial case study is presented that demonstrates the applicability of the proposed solution to industrial problems.

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