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arXiv 2020年

作者： Dey, Somdip Singh, Amit K. Wang, Xiaohang McDonald-Maier, Klaus D. Embedded and Intelligent Systems Laboratory University of Essex United Kingdom School of Software Engineering South China University of Technology China

Given the constant rise in utilizing embedded devices in daily life, side channels remain a challenge to information flow control and security in such systems. One such important security flaw could be exploited through temperature side-channel attacks, where heat dissipation and propagation from the processing elements are observed over time in order to deduce security flaws. In our proposed methodology, DATE: Defense Against TEmperature side-channel attacks, we propose a novel approach of reducing spatial and temporal thermal gradient, which makes the system more secure against temperature side-channel attacks, and at the same time increases the reliability of the device in terms of lifespan. In this paper, we have also introduced a new metric, Thermal-Security-in-Multi-Processors (TSMP), which is capable of quantifying the security against temperature side-channel attacks on computing systems, and DATE is evaluated to be 139.24% more secure at the most for certain applications than the state-of-the-art, while reducing thermal cycle by 67.42% at the most. Copyright © 2020, The Authors. All rights reserved.

关键词： Machine learning

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Highway Traffic Classification for the Perception Level of Situation Awareness 15th

Highway Traffic Classification for the Perception Level of S...

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15th International Symposium on Visual Computing, ISVC 2020

作者： Nine, Julkar Manoharan, Shanmugapriyan Sapkota, Manoj Saleh, Shadi Hardt, Wolfram Department of Computer Engineering Technische Universität Chemnitz Straße der Nationen 62 Chemnitz09111 Germany Embedded Systems Technische Universität Chemnitz Straße der Nationen 62 Chemnitz09111 Germany Automotive Software Engineering Technische Universität Chemnitz Straße der Nationen 62 Chemnitz09111 Germany

ISBN: (纸本)9783030645588

The automotive industry is rapidly moving towards the highest level of autonomy. However, one of the major challenges for highly autonomous vehicles is the differentiation between driving modes according to different driving situations. Different driving zones have different driving safety regulations. For example, German traffic regulations require a higher degree of safety measurements for highway driving. Therefore, a classification of the different driving scenarios on a highway is necessary to regulate these safety assessments. This paper presents a novel vision-based approach to the classification of German highway driving scenarios. We develop three different and precise algorithms utilizing image processing and machine learning approaches to recognize speed signs, traffic lights, and highway traffic signs. Based on the results of these algorithms, a weight-based classification process is performed, which determines the current driving situation either as a highway driving mode or not. The main goal of this research work is to maintain and to ensure the high safety specifications required for the German highway. Finally, the result of this classification process is provided as an extracted driving scenario-based feature on the perceptual level of a system known as situation awareness to provide a high level of driving safety. This study was realized on a custom-made hardware unit called "CE-Box", which was developed at the Department of Computer engineering at TU Chemnitz as an automotive test solution for testing automotive software applications on an embedded hardware unit. © 2020, Springer Nature Switzerland AG.

关键词： Machine learning

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Evaluating software Diversity in Branch Prediction Analyses for static WCET Estimation

Evaluating Software Diversity in Branch Prediction Analyses ...

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IEEE International Conference on embedded and Real-Time Computing systems and Applications (RTCSA)

作者： Joachim Fellmuth Jonas Zell Sabine Glesner Software and Embedded Systems Engineering Technical University Berlin Technical University Berlin Software and Embedded Systems Technical University Berlin

Static worst-case execution time analysis enables to obtain guaranteed timing bounds for programs, which is required for safety-critical hard real-time systems. This comprises micro-architectural analyses that rely on full knowledge of the executed program. An example are current approaches to statically bound the time penalty of mispredicted branches in systems using static or dynamic branch predictors. On the other hand, in artificial software diversity, uncertainty in program aspects is used to render code-reuse attacks useless, making the system considerably more secure. We solve this conflict by proposing adapted static analyses for static and dynamic branch prediction that are able to cope with diversity, and by quantifying the impact of diversity onto the analysis results through extensive evaluation.

关键词： History software Real-time systems Indexing Timing Static analysis Layout

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A C Code Generator for Fast Inference and Simple Deployment of Convolutional Neural Networks on Resource Constrained systems

A C Code Generator for Fast Inference and Simple Deployment ...

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IOT, Electronics and Mechatronics Conference (IEMTRONICS), IEEE International

作者： Oliver Urbann Simon Camphausen Arne Moos Ingmar Schwarz Sören Kerner Maximilian Otten Automation and Embedded Systems Fraunhofer IML Dortmund Germany Robotics Research Institute TU Dortmund University Dortmund Germany Software and Information Engineering Fraunhofer IML Dortmund Germany

ISBN: (数字)9781728196152

ISBN: (纸本)9781728196169

Inference of Convolutional Neural Networks in time critical applications usually requires a GPU. In robotics or embedded devices these are often not available due to energy, space and cost constraints. Furthermore, installation of a deep learning framework or even a native compiler on the target platform is not possible. This paper presents a neural network code generator (NNCG) that generates from a trained CNN a plain ANSI C code file that encapsulates the inference in single a function. It can easily be included in existing projects and due to lack of dependencies, cross compilation is usually possible. Additionally, the code generation is optimized based on the known trained CNN and target platform following four design principles. The system is evaluated utilizing small CNN designed for this application. Compared to TensorFlow XLA and Glow speed-ups of up to 11.81 can be shown and even GPUs are outperformed regarding latency.

关键词： Robots Artificial neural networks Machine learning Libraries Generators embedded systems Optimization

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Organic computing doctoral dissertation colloquium 2019

Lecture Notes in Informatics (LNI), Proceedings - Series of ...

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Lecture Notes in Informatics (LNI), Proceedings - Series of the Gesellschaft fur Informatik (GI) 2019年 295卷 467-467页

作者： Krupitzer, Christian Tomforde, Sven Software Engineering Group Universitat Wurzburg Germany Intelligent Embedded Systems Universitat Kassel Germany

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Distributed nonlinear model predictive control and metric learning for heterogeneous vehicle platooning with cut-in/cut-out maneuvers

arXiv

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arXiv 2020年

作者： Basiri, Mohammad Hossein Ghojogh, Benyamin Azad, Nasser L. Fischmeister, Sebastian Karray, Fakhri Crowley, Mark The Department of Systems Design Engineering University of Waterloo ON Canada The Department of Electrical and Computer Engineering University of Waterloo ON Canada The Smart Hybrid and Electric Vehicles Systems Lab The Real-Time Embedded Software Lab The Machine Learning Lab The Centre for Pattern Analysis and Machine Intelligence

Vehicle platooning has been shown to be quite fruitful in the transportation industry to enhance fuel economy, road throughput, and driving comfort. Model Predictive Control (MPC) is widely used in literature for platoon control to achieve certain objectives, such as safely reducing the distance among consecutive vehicles while following the leader vehicle. In this paper, we propose a Distributed Nonlinear MPC (DNMPC), based upon an existing approach, to control a heterogeneous dynamic platoon with unidirectional topologies, handling possible cut-in/cut-out maneuvers. The introduced method addresses a collision-free driving experience while tracking the desired speed profile and maintaining a safe desired gap among the vehicles. The time of convergence in the dynamic platooning is derived based on the time of cut-in and/or cut-out maneuvers. In addition, we analyze the improvement level of driving comfort, fuel economy, and absolute and relative convergence of the method by using distributed metric learning and distributed optimization with Alternating Direction Method of Multipliers (ADMM). Simulation results on a dynamic platoon with cut-in and cut-out maneuvers and with different unidirectional topologies show the effectiveness of the introduced method. Copyright © 2020, The Authors. All rights reserved.

关键词： Topology

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6G Internet-of-Things assisted smart homes and buildings: Enabling technologies, opportunities and challenges

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Internet of Things 2025年 32卷

作者： Arif Ullah Fawad Aamir Nadeem Muhammad Arif Muhammad Mehran Bashir Wooyeol Choi School of Electronics Electrical Engineering and Computer Science Queen’s University Belfast United Kingdom Gwangju Institute of Science and Technology Gwangju Republic of Korea Chonnam National University Gwangju Republic of Korea Institute of Networked and Embedded Systems University of Klagenfurt 9020 Klagenfurt Austria Ubiquitous Sensing Systems Lab University of Klagenfurt-Silicon Austria Labs 9020 Klagenfurt Austria Department of Electrical Engineering Muhammad Nawaz Sharif University of Engineering and Technology Multan Pakistan School of Computer Science and Engineering College of Software Chung-Ang University Seoul Republic of Korea

Smart homes (SHs) and smart buildings (SBs) play a crucial role in addressing global urbanization and are key elements of smart cities (SCs). They utilize integrated systems to process large volumes of data, enabling intelligent responses and improved quality of life. However, the growing number of connected devices and increasing data demands have exceeded the capabilities of existing network technologies, reducing service effectiveness. This article provides a comprehensive survey of emerging technologies, especially those underexplored in SH and SB contexts. It highlights the potential of sixth-generation (6G) wireless networks and the Internet of Things (IoT) to transform smart indoor environments through greater automation, intelligence, and adaptability. The authors first analyze the limitations of current IoT-based systems and then examine advanced 6G technologies, including integrated sensing and communication (ISAC), machine learning (ML), visible light communication (VLC), reconfigurable intelligent surfaces (RIS), and blockchain (BC). These technologies aim to resolve issues related to scalability, connectivity, and interoperability that challenge current IoT/5G models. Unlike previous studies focusing on narrow areas such as energy or security, this survey takes a holistic approach. It aligns 6G capabilities with SH/SB needs like human-device interaction, energy efficiency, and robust security, and introduces new applications such as AI-driven digital twins and edge intelligence. The article also identifies research gaps, including resource allocation in diverse networks and ethical AI deployment. It offers guidance for future research and urges collaboration, standardization, and human-centric design to enable next-generation intelligent living environments.

关键词： 6G AI Blockchain Integrated sensing and communication IoT Smart buildings Smart homes VLC

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Information flow analysis of combined simulink/stateflow models 27

Information flow analysis of combined simulink/stateflow mod...

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27th IEEE International Conference on Enabling Technologies: Infrastructure for Collaborative Enterprises, WETICE 2018

作者： Mikulcak, Marcus Herber, Paula Gothel, Thomas Glesner, Sabine Software and Embedded Systems Engineering Group Technische Universitat Berlin Germany

ISBN: (纸本)9781538669167

Simulink and Stateflow are widely-used industrial tools for the development of embedded systems, e.g. in the automotive domain. In modern automotive control systems, multiple components are typically interconnected, and, nowadays, also have a connection to the internet. This poses severe threats, as safety-critical components may be subject to remote attacks, which divert control or information flow from non-critical to safety-critical components. In this paper, we present a novel approach for the analysis of information flow in combined Simulink/Stateflow models. The key idea of our approach is that we analyze the information flow in a given model by computing an over-approximation of the control flow and deduce whether all control flow conditions on a given path combined permit information flow or not. With our approach, we safely rule out the existence of information flow on specific paths. Thus, it enables us to reason about non-interference and the compliance with security policies. © 2018 IEEE.

关键词： Simulink

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Dynamic residential load scheduling based on an adaptive consumption level pricing scheme

arXiv

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arXiv 2020年

作者： Haider, Haider Tarish See, Ong Hang Elmenreich, Wilfried Department of Electronics and Communication Engineering Universiti Tenaga Nasional Jalan IKRAMUNITEN Kajang Selangor43000 Malaysia Department of Computer and Software Engineering University of Al-Mustansiriyah Baghdad10001 Iraq Institute of Networked & Embedded Systems/Lakeside Labs Alpen-Adria-Universität Klagenfurt Klagenfurt9020 Austria

Demand response (DR) for smart grids, which intends to balance the required power demand with the available supply resources, has been gaining widespread attention. The growing demand for electricity has presented new opportunities for residential load scheduling systems to improve energy consumption by shifting or curtailing the demand required with respect to price change or emergency cases. In this paper, a dynamic residential load scheduling system (DRLS) is proposed for optimal scheduling of household appliances on the basis of an adaptive consumption level (CL) pricing scheme (ACLPS). The proposed load scheduling system encourages customers to manage their energy consumption within the allowable consumption allowance (CA) of the proposed DR pricing scheme to achieve lower energy bills. Simulation results show that employing the proposed DRLS system benefits the customers by reducing their energy bill and the utility companies by decreasing the peak load of the aggregated load demand. For a given case study, the proposed residential load scheduling system based on ACLPS allows customers to reduce their energy bills by up to 53% and to decrease the peak load by up to 35%. Copyright © 2020, The Authors. All rights reserved.

关键词： Housing

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Deep Learning based Edge defect detection 1

Deep Learning based Edge defect detection

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1st International Conference on Emerging Trends and Innovations In engineering And Technological Research, ICETIETR 2018

作者： Vidheesha, P.K. Antony, Jerin K Jacob, Jobymol VLSI and Embedded Systems Model Engineering College Thrikkakara India Embedded Software Ignitarium Technology Solutions Pvt. Ltd. Kalamassery India

ISBN: (纸本)9781538657447

An important aspect in the process of production is the difficulty to predict all possible problems or defects before initializing the product. It is necessary to have a continuous inspection process of products to ensure their qualities. In addition, some defects transcend the product quality issues. Many of the inspection processes of products, are still performed manually. The drawbacks of such human inspection is that it is time consuming and costlier. Moreover, the efficiency of human labor can be easily affected by environmental and personal factors. In this context, automated inspection processes are highly advantageous. In this work, a system to automatically identify edge defects in planar objects eg: Razor blades is implemented. © 2018 IEEE.

关键词： Defects

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