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  • 151.
    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.

  • 152.
    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.

  • 153.
    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.

  • 154.
    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.

  • 155.
    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.

  • 156.
    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.

  • 157.
    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.

  • 158.
    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.

  • 159.
    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)
  • 160.
    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.

  • 161.
    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.

  • 162.
    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.

  • 163.
    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.

  • 164.
    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.

  • 165.
    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.

  • 166.
    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)
  • 167.
    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.

  • 168.
    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. 

  • 169.
    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.

  • 170.
    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.

  • 171.
    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.

  • 172.
    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.

  • 173.
    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.

  • 174.
    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.

  • 175.
    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.

  • 176.
    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.

  • 177.
    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.

  • 178.
    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.

  • 179.
    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.

  • 180.
    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)
  • 181.
    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.

  • 182.
    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, Järfälla, Sweden.
    Dasari, Dakshina
    Research and Technology Centre, Robert Bosch, India.
    Behnam, Moris
    Mälardalen University, School of Innovation, Design and Engineering, Embedded Systems.
    Nolte, Thomas
    Mälardalen University, School of Innovation, Design and Engineering, Embedded Systems.
    A Generic Framework Facilitating Early Analysis of Data Propagation Delays in Multi-Rate Systems2017In: The 23th IEEE International Conference on Embedded and Real-Time Computing Systems and Applications RTCSA'17, 2017, article id 8046323Conference paper (Refereed)
    Abstract [en]

    A majority of multi-rate real-time systems are constrained by a multitude of timing requirements, in addition to the traditional deadlines on well-studied response times. This means, the timing predictability of these systems not only depends on the schedulability of certain task sets but also on the timely propagation of data through the chains of tasks from sensors to actuators. In the automotive industry, four different timing constraints corresponding to various data propagation delays are commonly specified on the systems. This paper identifies and addresses the source of pessimism as well as optimism in the calculations for one such delay, namely the reaction delay, in the state-of-the-art analysis that is already implemented in several industrial tools. Furthermore, a generic framework is proposed to compute all the four end-to-end data propagation delays, complying with the established delay semantics, in a scheduler and hardware-agnostic manner. This allows analysis of the system models already at early development phases, where limited system information is present. The paper further introduces mechanisms to generate job-level dependencies, a partial ordering of jobs, which need to be satisfied by any execution platform in order to meet the data propagation timing requirements. The job-level dependencies are first added to all task chains of the system and then reduced to its minimum required set such that the job order is not affected. Moreover, a necessary schedulability test is provided, allowing for varying the number of CPUs. The experimental evaluations demonstrate the tightness in the reaction delay with the proposed framework as compared to the existing state-of-the-art and practice solutions.

  • 183.
    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.
    Dasari, Dakshina
    Research and Technology Centre, Robert Bosch, India.
    Behnam, Moris
    Mälardalen University, School of Innovation, Design and Engineering, Embedded Systems.
    Nolte, Thomas
    Mälardalen University, School of Innovation, Design and Engineering, Embedded Systems.
    Scheduling Multi-Rate Real-Time Applications on Clustered Many-Core Architectures with Memory Constraints2018In: 2018 23RD ASIA AND SOUTH PACIFIC DESIGN AUTOMATION CONFERENCE (ASP-DAC), 2018, p. 560-567Conference paper (Refereed)
    Abstract [en]

    Access to shared memory is one of the main chal- lenges for many-core processors. One group of scheduling strategies for such platforms focuses on the division of tasks’ access to shared memory and code execution. This allows to orchestrate the access to shared local and off-chip memory in a way such that access contention between different compute cores is avoided by design. In this work, an execution framework is introduced that leverages local memory by statically allocating a subset of tasks to cores. This reduces the access times to shared memory, as off-chip memory access is avoided, and in turn improves the schedulability of such systems. A Constrained Programming (CP) formulation is presented to selects the statically allocated tasks and generates the complete system schedule. Evaluations show that the pro- posed approach yields an up to 21% higher schedulability ratio than related work, and a case study demonstrates its applicability to industrial problems.

  • 184.
    Becker, Matthias
    et al.
    Mälardalen University, School of Innovation, Design and Engineering, Embedded Systems.
    Nicolic, Borislav
    Technische Universität Braunschweig, Germany.
    Dasari, Dakshina
    Robert Bosch GmbH, Renningen, Germany.
    Åkesson, Benny
    CISTER/INESC-TEC, ISEP, Portugal.
    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 and Analysis of the Network-on-Chip on a COTS Many-Core Platform2017In: 23rd IEEE Real-Time and Embedded Technology and Applications Symposium RTAS'17, 2017, p. 101-112Conference paper (Refereed)
    Abstract [en]

    Many-core processors can provide the computational power required by future complex embedded systems. However, their adoption is not trivial, since several sources of interference on COTS many-core platforms have adverse effects on the resulting performance. One main source of performance degradation is the contention on the Network-on-Chip, which is used for communication among the compute cores via the off- chip memory. Available analysis techniques for the traversal time of messages on the NoC do not consider many of the architectural features found on COTS platforms. In this work, we target a state-of-the-art many-core processor, the Kalray MPPA R . A novel partitioning strategy for reducing the contention on the NoC is proposed. Further, we present an analysis technique dedicated to the proposed partitioning strategy, which considers all architectural features of the COTS NoC. Additionally, it is shown how to configure the parameters for flow-regulation on the NoC, such that the Worst-Case Traversal Time (WCTT) is minimal and buffers never overflow. The benefits of our approach are evaluated based on extensive experiments that show that contention is significantly reduced compared to the unconstrained case, while the proposed analysis outperforms a state-of-the-art analysis for the same platform. An industrial case study shows the tightness of the proposed analysis.

  • 185.
    Becker, Matthias
    et al.
    Mälardalen University, School of Innovation, Design and Engineering, Embedded Systems.
    Sandström, K.
    ABB Corporate Research, Västerås, 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.
    Limiting temperature gradients on many-cores by adaptive reallocation of real-time workloads2014In: 19th IEEE International Conference on Emerging Technologies and Factory Automation, ETFA 2014, 2014, p. Article number 7005241-Conference paper (Refereed)
    Abstract [en]

    The advent of many-core processors came with the increase in computational power needed for future applications. However new challenges arrived at the same time, especially for the real-time community. Each core on such a processor is a heat source and uneven usage can lead to hot spots on the processor, affecting its lifetime and reliability. For real-time systems, it is therefore of paramount importance to keep the temperature differences between the individual cores below critical values, in order to prevent premature failure of the system. We argue that this problem can not be solved by traditional approaches, since the growing number of cores makes them intractable. We rather argue to split the problem in the spacial domain and control the temperature on core level. The cores control their temperature by rearranging the load in a predictable manner during runtime. To achieve this, a feedback controller is implemented on each core. We conclude our work with a simulation based evaluation of the proposed approach comparing its performance against a previously presented algorithm. 

  • 186.
    Becker, Matthias
    et al.
    Mälardalen University, School of Innovation, Design and Engineering, Embedded Systems.
    Sandström, Kristian
    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.
    A Many-Core based Execution Framework for IEC 61131-32015In: IECON 2015 - 41st Annual Conference of the IEEE Industrial Electronics Society, 2015, p. 4525-4530, article id 7392805Conference paper (Refereed)
    Abstract [en]

    Programmable logic controllers are widely used for the control of automationsystems. The standard IEC 61131-3 defines the execution model as well as theprogramming languages for such systems. Nowadays, actuators and sensorsconnect to the programmable logic controller via automation buses. While suchbuses, as well as the sensors and actuators, become more and more powerful, ashift away from the current distributed operation of automation systems, closeto the field level, becomes possible. Instead, execution of complex controlfunctions can be relocated to more powerful hardware, and technologies. Thispaper presents an execution framework for IEC 61131-3, based on a many-coreprocessors. The presented execution model exploits the characteristics of theIEC 61131-3 applications as well as the characteristics of the many-core processor,yielding a predictable execution. We present the platform architectureand an algorithm to allocate a number of IEC 61131-3 conform applications.Experimental as well as simulation based evaluation is provided.

  • 187.
    Becker, Matthias
    et al.
    Mälardalen University, School of Innovation, Design and Engineering, Embedded Systems.
    Sandström, Kristian
    ABB Corporate Research, Västerås, 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.
    Dynamic Power Management for Thermal Control of Many-Core Real-Time Systems2014In: Sigbed Review, ISSN 1551-3688, Vol. 11, no 3, p. 26-29Article in journal (Refereed)
    Abstract [en]

    Many-Core systems, processors incorporating numerous cores interconnected by a Network on Chip (NoC), provide the computing power needed by future applications. High power density caused by the steadily shrinking transistor size, which is still following Moore's law, leads to a number of problems such as overheating cores, affecting processor reliability and lifetime. Embedded real-time systems are exposed to a changing ambient temperature and thus need to adapt their configuration in order to keep the individual core temperature below critical values. %Targeting embedded real-time systems, systems need to adapt to changing environments. In our approach a hysteresis controller is implemented on each core, triggering a redistribution of the cores and the transition into idle state allowing the core to cool down. We propose two approaches, one global and one local approach, to redistribute the tasks and relive overheating cores during runtime. We evaluate the two proposed approaches by comparing them against each other based on simulations.

  • 188.
    Becker, Matthias
    et al.
    Mälardalen University, School of Innovation, Design and Engineering, Embedded Systems.
    Sandström, Kristian
    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.
    Increased Reliability of Many-Core Platforms through Thermal Feedback Control2014In: Performance, Power and Predictability of Many-Core Embedded Systems 3PMCES'14, Dresden, Germany, 2014Conference paper (Refereed)
    Abstract [en]

    In this paper we present a low overhead thermal management approach to increase reliability of many-core embedded real-time systems. Each core is controlled by a feedback controller. We adapt the utilization of the core in order to decrease the dynamic power consumption and thus the corresponding heat development. Sophisticated control mechanisms allow us to migrate the load in advance, before reaching critical temperature values and thus we can migrate in a safe way with a guarantee to meet all deadlines.

  • 189.
    Becker, Matthias
    et al.
    Mälardalen University, School of Innovation, Design and Engineering, Embedded Systems.
    Sandström, Kristian
    ABB Corporate Research.
    Behnam, Moris
    Mälardalen University, School of Innovation, Design and Engineering, Embedded Systems.
    Nolte, Thomas
    Mälardalen University, School of Innovation, Design and Engineering, Embedded Systems.
    Mapping Real-Time Tasks onto Many-Core Systems considering Message Flows2014In: Proceedings of the Work-in-Progress Session of the 20th IEEE Real-Time and Embedded Technology and Applications Symposium, Berlin, Germany, 2014, p. 17-18Conference paper (Refereed)
    Abstract [en]

    In this work we focus on the task mapping problem for many-core real-time systems. The growing number of cores connected by a Network-on-Chip (NoC) calls for sophisticated mapping techniques to meet the growing demands of real-time applications. Hardware should be used in an efficient way such that unnecessary resource usage is avoided. Because of the NP-hardness of the problem, heuristic and meta-heuristic techniques are used to find good solutions. We further consider periodic communication between tasks and we focus on a static mapping solution.

  • 190.
    Becker, Matthias
    et al.
    Mälardalen University, School of Innovation, Design and Engineering, Embedded Systems.
    Schmidt, Adriaan
    Fraunhofer Institute for Embedded Systems and Communication Technologies ESK, Germany.
    Orehek, Martin
    University of Applied Sciences Munich, Germany.
    Nolte, Thomas
    Mälardalen University, School of Innovation, Design and Engineering, Embedded Systems.
    Saving Energy by Means of Dynamic Load Management in Embedded Multicore Systems2014In: Proceedings of the 9th IEEE International Symposium on Industrial Embedded Systems, SIES 2014, 2014, p. 11-20Conference paper (Refereed)
    Abstract [en]

    Load balancing is widely used to optimize response times and throughput of software systems. When considering embedded systems, however, additional optimization goals like energy consumption become relevant. In this paper, we explore the use of load balancing in embedded multicore applications. We present extensions to three prominent load balancing schemes, enabling them to dynamically scale the number of active cores. We integrated the algorithms in a proprietary operating system targeting multicore embedded systems. Our evaluation, which is based on a telecommunication (VoIP) scenario, shows that a significant reduction in energy consumption is possible.

  • 191.
    Begum, Shahina
    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.
    Funk, Peter
    Mälardalen University, School of Innovation, Design and Engineering, Embedded Systems.
    Physiological Sensor Signals Analysis to Represent Cases in a Case-based Diagnostic System2013In: Innovations in Knowledge-based Systems in Biomedicine, vol. 250 / [ed] Pham T.D,Jain L.C., Springer, 2013, p. 1-25Chapter in book (Other academic)
    Abstract [en]

    Today, medical knowledge is expanding so rapidly that clinicians cannot follow all progress any more. This is one reason for making knowledge- based systems desirable in medicine. Such systems can give a clinician a second opinion and give them access to new experience and knowledge. Recent advances in Artificial Intelligence (AI) offers methods and techniques with the potential of solving tasks previously difficult to solve with computer-based systems in medical domains. This chapter is especially concerned with diagnosis of stress-related dysfunctions using AI methods and techniques. Since there are large individual variations between people when looking at biological sensor signals to diagnose stress, this is a worthy challenge. Stress is an inevitable part of our human life. No one can live without stress. However, long-term exposure to stress may in the worst case cause severe mental and/or physical problems that are often related to different kind of psychosomatic disorders, coronary heart disease etc. So, diagnosis of stress is an important issue for health and well-being. Diagnosis of stress often involves acquisition of biological signals for example finger temperature, electrocardiogram (ECG), electromyography (EMG) signal, skin conductance (SC) signals etc. and is followed by a careful analysis by an expert. However, the number of experts to diagnose stress in psycho-physiological domain is limited. Again, responses to stress are different for different persons. So, interpreting a particular curve and diagnosing stress levels is difficult even for experts in the domain due to large individual variations. It is a highly complex and partly intuitive process which experienced clinicians use when manually inspecting biological sensor signals and classifying a patient. Clinical studies show that the pattern of variation within heart rate i.e., HRV signal and finger temperature can help to determine stress-related disorders. This chapter presents a signal pre-processing and feature extraction approach based on electrocardiogram (ECG) and finger temperature sensor signals. The extracted features are used to formulate cases in a case-based reasoning system to develop a personalized stress diagnosis system. The results obtained from the evaluation show a performance close to an expert in the domain in diagnosing stress.

  • 192.
    Begum, Shahina
    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.
    Funk, Peter
    Mälardalen University, School of Innovation, Design and Engineering, Embedded Systems.
    Filla, Reno
    Volvo.
    Mental State Monitoring System for the Professional Drivers Based on Heart Rate Variability Analysis and Case-based Reasoning2012In: 2012 FEDERATED CONFERENCE ON COMPUTER SCIENCE AND INFORMATION SYSTEMS (FEDCSIS), NEW YORK: IEEE , 2012, p. 35-42Conference paper (Refereed)
    Abstract [en]

    The consequences of tiredness, drowsiness, stress and lack of concentration caused by a variety of different factors such as illness, sleep depletion, drugs and alcohol is a serious problem in traffic and when operating industrial equipment. A system that recognizes the state of the driver and e. g. suggests breaks when stress level is too high or driver is too tired would enable large savings and reduces accident. So, the aim of the project is to develop an intelligent system that can monitor drivers' stress depending on psychological and behavioral conditions/status using Heart Rate Variability (HRV). Here, we have proposed a solution using Case-Based Reasoning (CBR) to diagnose individual driver's level of stress. The system also considers feedback from the driver's on how well the test was performed. The validation of the approach is based on close collaboration with experts and measurements from 18 drivers from Volvo Construction Equipment (Volvo CE) are used as reference.

  • 193.
    Begum, Shahina
    et al.
    Mälardalen University, School of Innovation, Design and Engineering, Embedded Systems.
    Barua, Shaibal
    Mälardalen University, School of Innovation, Design and Engineering, Embedded Systems.
    Ahmed, Mobyen Uddin
    Mälardalen University, School of Innovation, Design and Engineering, Embedded Systems.
    In-Vehicle Stress Monitoring Based on EEG Signal2017In: International Journal of Engineering Research and Applications, ISSN 2248-9622, E-ISSN 2248-9622, Vol. 7, no 7, p. 55-71Article in journal (Refereed)
    Abstract [en]

    In recent years, improved road safety by monitoring human factors i.e., stress, mental load, sleepiness, fatigue etc. of vehicle drivers has been addressed in a number of studies. Due to the individual variations and complex dynamic in-vehicle environment systems that can monitor such factors of a driver while driving is challenging. This paper presents a drivers’ stress monitoring system based on electroencephalography (EEG) signals enabling individual-focused computational approach that can generate automatic decision. Here, a combination of different signal processing i.e., discrete wavelet transform, largest Lyapunov exponent (LLE) and modified covariance have been applied to extract key features from the EEG signals. Hybrid classification approach Fuzzy-CBR (case-based reasoning) is used for decision support. The study has focused on both long and short-term temporal assessment of EEG signals enabling monitoring in different time intervals. In short time interval, which requires complex computations, the classification accuracy using the proposed approach is 79% compare to a human expert. Accuracy of EEG in developing such system is also compared with other reference signals e.g., Electrocardiography (ECG), Finger temperature, Skin conductance, and Respiration. The results show that in decision making the system can handle individual variations and provides decision in each minute time interval.

  • 194.
    Begum, Shahina
    et al.
    Mälardalen University, School of Innovation, Design and Engineering, Embedded Systems.
    Barua, Shaibal
    Mälardalen University, School of Innovation, Design and Engineering, Embedded Systems.
    Ahmed, Mobyen Uddin
    Mälardalen University, School of Innovation, Design and Engineering, Embedded Systems.
    Physiological sensor signals classification for healthcare using sensor data fusion and case-based reasoning2014In: Sensors (Switzerland), ISSN 1424-8220, Vol. 14, no 7, p. 11770-11785Article in journal (Refereed)
    Abstract [en]

    Today, clinicians often do diagnosis and classification of diseases based on information collected from several physiological sensor signals. However, sensor signal could easily be vulnerable to uncertain noises or interferences and due to large individual variations sensitivity to different physiological sensors could also vary. Therefore, multiple sensor signal fusion is valuable to provide more robust and reliable decision. This paper demonstrates a physiological sensor signal classification approach using sensor signal fusion and case-based reasoning. The proposed approach has been evaluated to classify Stressed or Relaxed individuals using sensor data fusion. Physiological sensor signals i.e., Heart Rate (HR), Finger Temperature (FT), Respiration Rate (RR), Carbon dioxide (CO2) and Oxygen Saturation (SpO2) are collected during the data collection phase. Here, sensor fusion has been done in two different ways: (i) decision-level fusion using features extracted through traditional approaches; and (ii) data-level fusion using features extracted by means of Multivariate Multiscale Entropy (MMSE). Case-Based Reasoning (CBR) is applied for the classification of the signals. The experimental result shows that the proposed system could classify Stressed or Relaxed individual 87.5% accurately compare to an expert in the domain. So, it shows promising result in the psychophysiological domain and could be possible to adapt this approach to other relevant healthcare systems. 

  • 195.
    Begum, Shahina
    et al.
    Mälardalen University, School of Innovation, Design and Engineering, Embedded Systems.
    Barua, Shaibal
    Mälardalen University, School of Innovation, Design and Engineering, Embedded Systems.
    Ahmed, Mobyen Uddin
    Mälardalen University, School of Innovation, Design and Engineering, Embedded Systems.
    Funk, Peter
    Mälardalen University, School of Innovation, Design and Engineering, Embedded Systems.
    A Fusion Based System for Physiological Sensor Signal Classification2014In: Medicinteknikdagarna 2014 MTD10, 2014Conference paper (Refereed)
    Abstract [en]

    Today, usage of physiological sensor signals is essential in medical applications for diagnoses and classification of diseases. Clinicians often rely on information collected from several physiological sensor signals to diagnose a patient. However, sensor signals are mostly non-stationary and noisy, and single sensor signal could easily be contaminated by uncertain noises and interferences that could cause miscalculation of measurements and reduce clinical usefulness. Therefore, an apparent choice is to use multiple sensor signals that could provide more robust and reliable decision. Therefore, a physiological signal classification approach is presented based on sensor signal fusion and case-based reasoning. To classify Stressed and Relaxed individuals from physiological signals, data level and decision level fusion are performed and case-based reasoning is applied as classification algorithm. Five physiological sensor signals i.e., Heart Rate (HR), Finger Temperature (FT), Respiration Rate (RR), Carbon dioxide (CO2) and Oxygen Saturation (SpO2) are collected during the data collection phase. Here, data level fusion is performed using Multivariate Multiscale Entropy (MMSE) and extracted features are then used to build a case- library. Decision level fusion is performed on the features extracted using traditional time and frequency domain analysis. Case-Based Reasoning (CBR) is applied for the classification of the signals. The experimental result shows that the proposed system could classify Stressed or Relaxed individual 87.5% accurately compare to an expert in the domain. So, it shows promising result in the psychophysiological domain and could be possible to adapt this approach to other relevant healthcare systems.

  • 196.
    Begum, Shahina
    et al.
    Mälardalen University, School of Innovation, Design and Engineering, Embedded Systems.
    Barua, Shaibal
    Mälardalen University, School of Innovation, Design and Engineering, Embedded Systems.
    Filla, Reno
    Volvo Construction Equipment, Sweden.
    Ahmed, Mobyen Uddin
    Mälardalen University, School of Innovation, Design and Engineering, Embedded Systems. Örebro University, Sweden.
    Classification of physiological signals for wheel loader operators using Multi-scale Entropy analysis and case-based reasoning2014In: Expert systems with applications, ISSN 0957-4174, E-ISSN 1873-6793, Vol. 41, no 2, p. 295-305Article in journal (Refereed)
    Abstract [en]

    Sensor signal fusion is becoming increasingly important in many areas including medical diagnosis and classification. Today, clinicians/experts often do the diagnosis of stress, sleepiness and tiredness on the basis of information collected from several physiological sensor signals. Since there are large individual variations when analyzing the sensor measurements and systems with single sensor, they could easily be vulnerable to uncertain noises/interferences in such domain; multiple sensors could provide more robust and reliable decision. Therefore, this paper presents a classification approach i.e. Multivariate Multiscale Entropy Analysis–Case-Based Reasoning (MMSE–CBR) that classifies physiological parameters of wheel loader operators by combining CBR approach with a data level fusion method named Multivariate Multiscale Entropy (MMSE). The MMSE algorithm supports complexity analysis of multivariate biological recordings by aggregating several sensor measurements e.g., Inter-beat-Interval (IBI) and Heart Rate (HR) from Electrocardiogram (ECG), Finger Temperature (FT), Skin Conductance (SC) and Respiration Rate (RR). Here, MMSE has been applied to extract features to formulate a case by fusing a number of physiological signals and the CBR approach is applied to classify the cases by retrieving most similar cases from the case library. Finally, the proposed approach i.e. MMSE–CBR has been evaluated with the data from professional drivers at Volvo Construction Equipment, Sweden. The results demonstrate that the proposed system that fuses information at data level could classify ‘stressed’ and ‘healthy’ subjects 83.33% correctly compare to an expert’s classification. Furthermore, with another data set the achieved accuracy (83.3%) indicates that it could also classify two different conditions ‘adapt’ (training) and ‘sharp’ (real-life driving) for the wheel loader operators. Thus, the new approach of MMSE–CBR could support in classification of operators and may be of interest to researchers developing systems based on information collected from different sensor sources.

  • 197.
    Begum, Shahina
    et al.
    Mälardalen University, School of Innovation, Design and Engineering, Embedded Systems.
    Behnam, Moris
    Mälardalen University, School of Innovation, Design and Engineering, Embedded Systems.
    Larsson, Thomas B
    Mälardalen University, School of Innovation, Design and Engineering, Embedded Systems.
    Nolte, Thomas
    Mälardalen University, School of Innovation, Design and Engineering, Embedded Systems.
    Sandström, Kristian
    Mälardalen University, School of Innovation, Design and Engineering, Embedded Systems.
    Towards a Compositional Service Architecture for Real-Time Cloud Robotics2016In: ACM SIGBED Review, E-ISSN 1551-3688, p. 63-64Article in journal (Refereed)
    Abstract [en]

    In this paper we present our ongoing work towards a compositional service architecture that integrates cloud technology for computational capacity targeting real-time robotics applications. In particular we take a look at the challenges inherent within the data center where the services are executing. We outline characteristics of the services used in the real-time cloud robotics application, along with the service management and corresponding task model used to execute services. We identify several key central challenges that must be addressed towards integrating cloud technology in real-time robotics.

  • 198.
    Begum, Shahina
    et al.
    Mälardalen University, School of Innovation, Design and Engineering, Embedded Systems.
    Kerstis, Birgitta
    Mälardalen University, School of Health, Care and Social Welfare, Health and Welfare.
    Barua, Shaibal
    Mälardalen University, School of Innovation, Design and Engineering, Embedded Systems.
    Westerlund, Hanna
    Camanio Care AB, Sweden.
    Hjortsberg, Cecilia
    Västerås stad, Sweden.
    Food4You: A Personalized System for Adaptive Mealtime Situations for Elderly2017In: Medicinteknikdagarna 2017 MTD 2017, 2017Conference paper (Refereed)
  • 199.
    Behnam, Moris
    et al.
    Mälardalen University, School of Innovation, Design and Engineering, Embedded Systems.
    Ciccozzi, Federico
    Mälardalen University, School of Innovation, Design and Engineering, Embedded Systems.
    Sjödin, Mikael
    Mälardalen University, School of Innovation, Design and Engineering, Embedded Systems.
    Bruhn, Fredrik
    Bruhnspace AB, Uppsala, Sweden .
    Software architecture for next generation hyperparallel cyber-physical hardware platforms: challenges and opportunities2015In: ECSAW '15 Proceedings of the 2015 European Conference on Software Architecture Workshops, 2015, Vol. Article No. 19Conference paper (Refereed)
    Abstract [en]

    We present what is destined to become the de-facto standard for hardware platforms for next generation cyber-physical systems. Heterogeneous System Architecture (HSA) is an initiative to harmonize the industry around a common architecture which is easier to program and is an open standard defining the key interfaces for parallel computation. Since HSA is supported by virtually all major players in the silicon market we can conjecture that HSA, with its capabilities and quirks, will highly influence both the hardware and software for next generation cyber-physical systems. In this paper we describe HSA and discuss how its nature will influence architectures of system software and application software. Specifically, we believe that the system software needs to both leverage the hyperparallel nature of HSA while providing predictable and efficient resource allocation to different parallel activities. The application software, on the other hand, should be isolated from the complexity of the hardware architecture but yet be able to efficiently use the full potential of the hyperparallel nature of HSA.

  • 200.
    Belli, Fevzi
    et al.
    University of Paderborn, Germany.
    Seceleanu, Cristina
    Mälardalen University, School of Innovation, Design and Engineering, Embedded Systems.
    Panel Description: 40 Years of Software Engineering2008In: Proceedings 32nd Annual IEEE International Computer Software and Applications Conference, COMPSAC2008, 2008, p. 7-7Conference paper (Other academic)
    Abstract [en]

    In the fall of 1968, NATO hosted in Garmisch- Partenkirchen, close to Munich, a conference devoted to the problems of the computer industry that was having a great deal of trouble in producing large and complex programs. The term Software Engineering (SE) was not in general use at that time, its adoption for the title of this conference was deliberately provocative. As a result, the conference and its report have played a major role in gaining general acceptance of the term SE.

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