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A Survey of Recent Developments in Testability, Safety and Security of RISC-V Processors

A Survey of Recent Developments in Testability, Safety and S...

引用

IEEE European Test Symposium (ETS)

作者： Jens Anders Pablo Andreu Bernd Becker Steffen Becker Riccardo Cantoro Nikolaos I. Deligiannis Nourhan Elhamawy Tobias Faller Carles Hernandez Nele Mentens Mahnaz Namazi Rizi Ilia Polian Abolfazl Sajadi Mathias Sauer Denis Schwachhofer Matteo Sonza Reorda Todor Stefanov Ilya Tuzov Stefan Wagner Nuša Zidarič Institute of Smart Sensors University of Stuttgart Stuttgart Germany Universitat Politècnica de València Spain Dpt. of Computer Science University of Freiburg Freiburg Germany Institute of Software Engineering University of Stuttgart Stuttgart Germany Dpt. of Control and Computer Engineering Politecnico di Torino Turin Italy Institute of Computer Architecture and Computer Engineering University of Stuttgart Stuttgart Germany LIACS Leiden University Leiden Netherlands Advantest Europe Boeblingen Germany

With the continued success of the open RISC-V architecture, practical deployment of RISC-V processors necessitates an in-depth consideration of their testability, safety and security aspects. This survey provides an overview of recent developments in this quickly-evolving field. We start with discussing the application of state-of-the-art functional and system-level test solutions to RISC-V processors. Then, we discuss the use of RISC-V processors for safety-related applications; to this end, we outline the essential techniques necessary to obtain safety both in the functional and in the timing domain and review recent processor designs with safety features. Finally, we survey the different aspects of security with respect to RISC-V implementations and discuss the relationship between cryptographic protocols and primitives on the one hand and the RISC-V processor architecture and hardware implementation on the other. We also comment on the role of a RISC-V processor for system security and its resilience against side-channel attacks.

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Online em monitoring of 802.11n networks using self adaptive kernel machine 18

Online em monitoring of 802.11n networks using self adaptive...

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18th IEEE International Conference on Machine Learning and Applications, ICMLA 2019

作者： Villain, Jonathan Fleury, Anthony Deniau, Virginie Gransart, Christophe Simon, Eric Computer Sciences and Automatic Control Dpt. Uria Imt Lille Douai and Univ. Lille Douai France COSYS-LEOST Ifsttar Villeneuve d'Ascq France Iemn Univ. Lille Lille France

ISBN: (纸本)9781728145495

In this work, we evaluated the performances of an adaptive and online clustering algorithm (Self-Adaptive Kernel Machine - SAKM) adjusted for the automatic and online recognition of attacks on wi-fi communication (802.11n protocol). The results presented here are part of a wider project dealing with wi-fi system monitoring. The radio waves are easy to listen. Due to the quick evolution in the available attacks, the use of learning algorithm cannot cover all configurations. Online clustering constructs evolving models without knowledge of the different cases to discriminate and is therefore well suited to this type of problematic. Based on SVM and kernel methods, the SAKM algorithm uses a fast adaptive learning procedure to take into account variations over time. © 2019 IEEE.

关键词： Wireless local area networks (WLAN)

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Online EM Monitoring of 802.11n Networks using Self Adaptive Kernel Machine

Online EM Monitoring of 802.11n Networks using Self Adaptive...

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International Conference on Machine Learning and Applications (ICMLA)

作者： Jonathan Villain Anthony Fleury Virginie Deniau Christophe Gransart Eric Simon Computer Sciences & Automatic Control dpt. URIA IMT Lille Douai and Univ. Lille Douai France COSYS-LEOST IFSTTAR Villeneuve d’Ascq France IEMN Univ. Lille Lille France

关键词： Kernel Monitoring Support vector machines Wireless fidelity Jamming IEEE 802.11n Standard Protocols

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System Design for Coordinated Multi-robot Assistance Deployment in Smart Spaces

System Design for Coordinated Multi-robot Assistance Deploym...

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IEEE International Conference on Robotic Computing (IRC)

作者： Panagiotis Papadakis Christophe Lohr Marin Lujak Abir Karami Ioannis Kanellos Guillaume Lozenguez Anthony Fleury IMT Atlantique Bretagne-Pays de la Loire France IMT Lille Douai Computer Sciences and Automatic Control dpt. and Univ. Lille Lille France University of Lyon 2 Lyon France

In this work, we set the bases of the integration of ambient intelligence (AmI) with mobile robot teams (MRT), aiming to enhance ambient assisted living services addressing a variety of tasks. We argue that people with reduced mobility can benefit from a synergy between AmI and MRT in various aspects. Towards this direction, we identify principal functionalities such an integrated system should provide in connection to relevant previous works and the way by which synergy could be accomplished, from low-level behavioural to higher-level task planning of a multi-layered system architecture.

关键词： Robot kinematics Task analysis Monitoring Semantics Logic gates Interoperability

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Human Action Recognition from Body-Part Directional Velocity using Hidden Markov Models

Human Action Recognition from Body-Part Directional Velocity...

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IEEE International Conference on Machine Learning and Applications

作者： Sid Ahmed Walid Talha Anthony Fleury Sebastien Ambellouis Computer Sciences and Automatic Control Dpt. Univ. Lille Lille France Cosys Dept. Leost IFSTTAR Villeneuve d'Ascq France

This paper introduces a novel approach for early recognition of human actions using 3D skeleton joints extracted from 3D depth data. We propose a novel, frame-by-frame and real-time descriptor called Body-part Directional Velocity (BDV) calculated by considering the algebraic velocity produced by different body-parts. A real-time Hidden Markov Models algorithm with Gaussian Mixture Models state-output distributions is used to carry out the classification. We show that our method outperforms various state-of-the-art skeleton-based human action recognition approaches on MSRAction3D and Florence3D datasets. We also proved the suitability of our approach for early human action recognition by deducing the decision from a partial analysis of the sequence.

关键词： Human Action Recognition RGB-D Sensor Hidden Markov models (HMMs) Gaussian mixture models (GMMs)

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Autotuning of an in-line pH control system

Autotuning of an in-line pH control system

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International Conference on Emerging Technologies and Factory Automation (ETFA)

作者： Pedro Mercader Kristian Soltesz Alfonso Baños Dpt. Computer and Systems Engineering University of Murcia Murcia Spain Dpt. Automatic Control Lund University Lund Sweden

A novel autotuning procedure is presented through application to an industrial in-line pH control system. The procedure has three advantages over classical relay auto-tuners: experiment duration is very short (no need for limit-cycle convergence); all data is used for identification (instead of only peaks and switch instances); a parameter uncertainty model is identified and utilized for robust controller synthesis.

关键词： Relays Process control Robustness Switches Limit-cycles Convergence

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Feature extractionand Incremental Learning to Improve Activity Recognition on Streaming Data

Feature extractionand Incremental Learning to Improve Activi...

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IEEE International Conference on Evolving and Adaptive Intelligent Systems

作者： Nawel Yala Belkacem Fergani Anthony Fleury LISIC Laboratory USTHB Faculty of Electronics and Computer Sciences Computer Sciences and Automatic Control Dpt. Ecole des Mines de Douai

ISBN: (纸本)9781467366991

In this paper, we propose an approach for an online human daily activity recognition system using motion sensor data. From the sensor readings, the system decides which activity is performed when the values change. It uses the previous measurements to interpret the current ones, without the need to wait for future information. The contributions of this study relies on the presentation of two methods to extract features from the sequence of sensor events, a clustering method to handle missing activity labels in dataset and an incremental learning method to deal with complexity and time spent in training since our system works on streaming data. Our methods are evaluated on publicly available real environment datasets.

关键词： Activity Recognition Streaming data Incremental SVM Activity recognition learning (artificial intelligence) Streaming Dataset Clustering methods motion sensor

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Multiobjective optimization of the transport in oil pipelines networks

Multiobjective optimization of the transport in oil pipeline...

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2003 IEEE Conference on Emerging Technologies and Factory Automation, ETFA 2003

作者： De La Cruz, J.M. De Andres-Toro, B. Herran, A. Porta, E.B. Blanco, P.F. Dpt. Computer Architecture and Automatic Control Universidad Complutense de Madrid Madrid28040 Spain

ISBN: (纸本)0780379373

Heuristic methods are specially well suited to solve combinatorial problems. One of these problems is the distribution of petroleum products through oil pipelines networks. In this paper the problem is stated and solved using a multiobjective and constraint evolutionary optimization algorithm. Several objective functions has been defined to express the goals of the solutions as well as the preferences among them. Some constraints are included as hard objective functions and some has been evaluated through a repairing function to avoid infeasible solutions. An example of working is given. © 2003 IEEE.

关键词： Heuristic methods

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Multiobjective optimization of the transport in oil pipelines networks

Multiobjective optimization of the transport in oil pipeline...

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International Conference on Emerging Technologies and Factory Automation (ETFA)

作者： J.M. de la Cruz B. de Andres-Toro A. Herran E.B. Porta P.F. Blanco Dpt. Computer Architecture and Automatic Control Universidad Complutense de Madrid Madrid Spain

关键词： Intelligent networks Petroleum Pipelines Constraint optimization Evolutionary computation computer architecture automatic control Fuel economy Refining Transportation

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