Metamodeling Techniques Applied to the Design of Reconfigurable Control Applications

In order to realize autonomous manufacturing systems in environments characterized by high dynamics and high complexity of task, it is necessary to improve the control system modelling and performance. This requires the use of better and reusable abstractions. In this paper, we explore the metamodel techniques as a foundation to the solution of this problem. The increasing popularity of model-driven approaches and a new generation of tools to support metamodel techniques are changing software engineering landscape, boosting the adoption of new methodologies for control application development

Transparency in Global Production Networks: Improving Disruption Management by Increased Information Exchange

Modern companies operate in global production networks. The operational performance of production networks is hampered by disrupting events. Digitalization and the horizontal interlinkage of production networks may increase information exchange and lead to more transparency. It is propagated as being an enabler for a faster identification and reaction to disruptions. This paper presents a metamodeling approach that maps disruptions as systematic parameter variations and analyzes their impact on the performance of production networks under different level of information exchange. The method aims for the determination of cause-effect relationships and contributes to the determination of the appropriate level of information exchange in production networks

Building accurate radio environment maps from multi-fidelity spectrum sensing data

In cognitive wireless networks, active monitoring of the wireless environment is often performed through advanced spectrum sensing and network sniffing. This leads to a set of spatially distributed measurements which are collected from different sensing devices. Nowadays, several interpolation methods (e.g., Kriging) are available and can be used to combine these measurements into a single globally accurate radio environment map that covers a certain geographical area. However, the calibration of multi-fidelity measurements from heterogeneous sensing devices, and the integration into a map is a challenging problem. In this paper, the auto-regressive co-Kriging model is proposed as a novel solution. The algorithm is applied to model measurements which are collected in a heterogeneous wireless testbed environment, and the effectiveness of the new methodology is validated

Transmitter and Receiver Equalizers Optimization Methodologies for High-Speed Links in Industrial Computer Platforms Post-Silicon Validation

As microprocessor design scales to nanometric technology, traditional post-silicon validation techniques are inappropriate to get a full system functional coverage. Physical complexity and extreme technology process variations introduce design challenges to guarantee performance over process, voltage, and temperature conditions. In addition, there is an increasingly higher number of mixed-signal circuits within microprocessors. Many of them correspond to high-speed input/output (HSIO) links. Improvements in signaling methods, circuits, and process technology have allowed HSIO data rates to scale beyond 10 Gb/s, where undesired effects can create multiple signal integrity problems. With all of these elements, post-silicon validation of HSIO links is tough and time-consuming. One of the major challenges in electrical validation of HSIO links lies in the physical layer (PHY) tuning process, where equalization techniques are used to cancel these undesired effects. Typical current industrial practices for PHY tuning require massive lab measurements, since they are based on exhaustive enumeration methods. In this work, direct and surrogate-based optimization methods, including space mapping, are proposed based on suitable objective functions to efficiently tune the transmitter and receiver equalizers. The proposed methodologies are evaluated by lab measurements on realistic industrial post-silicon validation platforms, confirming dramatic speed up in PHY tuning and substantial performance improvement

A Model-Based Approach to Managing Feature Binding Time in Software Product Line Engineering

Software Product Line Engineering (SPLE) is a software reuse paradigm for developing software products, from managed reusable assets, based on analysis of commonality and variability (C & V) of a product line. Many approaches of SPLE use a feature as a key abstraction to capture the C&V. Recently, there have been increasing demands for the provision of flexibility about not only the variability of features but also the variability of when features should be selected (i.e., variability on feature binding times). Current approaches to support variations of feature binding time mostly focused on ad hoc implementation mechanisms. In this paper, we first identify the challenges of feature binding time management and then propose an approach to analyze the variation of feature binding times and use the results to specify model-based architectural components for the product line. Based on the specification, components implementing variable features are parameterized with the binding times and the source codes for the components and the connection between them are generated

High Level Design of adaptive distributed controller for Partial Dynamic reconfiguration in FPGA

International audienceControlling dynamic and partial reconfigurations becomes one of the most important key issues in modern embedded systems design. In fact, in such systems, the reconfiguration controller can significantly affect the system performances. Indeed, the controller has to handle efficiently three major tasks during runtime: observation (monitoring), taking reconfiguration decisions and notify decisions to the rest of the system in order to realize it. We present in this paper a novel high level approach permitting to model, using MARTE UML profile, modular and flexible distributed controllers for dynamic reconfiguration management. This approach permits components/ models reuse and allows systematic code generation. It consequently makes reconfigurable systems design less tedious and reduces time to market

Post-silicon Receiver Equalization Metamodeling by Artificial Neural Networks

As microprocessor design scales to the 10 nm technology and beyond, traditional pre- and post-silicon validation techniques are unsuitable to get a full system functional coverage. Physical complexity and extreme technology process variations severely limits the effectiveness and reliability of pre-silicon validation techniques. This scenario imposes the need of sophisticated post-silicon validation approaches to consider complex electromagnetic phenomena and large manufacturing fluctuations observed in actual physical platforms. One of the major challenges in electrical validation of high-speed input/output (HSIO) links in modern computer platforms lies in the physical layer (PHY) tuning process, where equalization techniques are used to cancel undesired effects induced by the channels. Current industrial practices for PHY tuning in HSIO links are very time consuming since they require massive lab measurements. An alternative is to use machine learning techniques to model the PHY, and then perform equalization using the resultant surrogate model. In this paper, a metamodeling approach based on neural networks is proposed to efficiently simulate the effects of a receiver equalizer PHY tuning settings. We use several design of experiments techniques to find a neural model capable of approximating the real system behavior without requiring a large amount of actual measurements. We evaluate the models performance by comparing with measured responses on a real server HSIO link

Digital-Twins towards Cyber-Physical Systems: A Brief Survey

Cyber-Physical Systems (CPS) are integrations of computation and physical processes. Physical processes are monitored and controlled by embedded computers and networks, which frequently have feedback loops where physical processes affect computations and vice versa. To ease the analysis of a system, the costly physical plants can be replaced by the high-fidelity virtual models that provide a framework for Digital-Twins (DT). This paper aims to briefly review the state-of-the-art and recent developments in DT and CPS. Three main components in CPS, including communication, control, and computation, are reviewed. Besides, the main tools and methodologies required for implementing practical DT are discussed by following the main applications of DT in the fourth industrial revolution through aspects of smart manufacturing, sixth wireless generation (6G), health, production, energy, and so on. Finally, the main limitations and ideas for future remarks are talked about followed by a short guideline for real-world application of DT towards CPS