Autonomous CPSoS for cognitive large manufacturing industries.

The general aim of a cognitive Cyber Physical System of Systems (CPSoS) is to provide managed access to data in a smart fashion such that sensing and actuation capabilities are connected. Whilst there is significant funding and research devoted to this area, focus remains purely on creating bespoke systems. This paper presents a novel approach, based on a set of components to leverage Situational Awareness and Smart Actuation in large manufacturing industries with the focus on enabling predictive maintenance for asset and abnormal situation management. This paper presents a novel generic platform, named AtiCoS, that combines case-based and common-sense reasoning, as the enabling methodologies for enhancing CPSoS with cognitive capabilities

Facilitating Preemptive Hardware System Design Using Partial Reconfiguration Techniques

In FPGA-based control system design, partial reconfiguration is especially well suited to implement preemptive systems. In real-time systems, the deadline for critical task can compel the preemption of noncritical one. Besides, an asynchronous event can demand immediate attention and, then, force launching a reconfiguration process for high-priority task implementation. If the asynchronous event is previously scheduled, an explicit activation of the reconfiguration process is performed. If the event cannot be previously programmed, such as in dynamically scheduled systems, an implicit activation to the reconfiguration process is demanded. This paper provides a hardware-based approach to explicit and implicit activation of the partial reconfiguration process in dynamically reconfigurable SoCs and includes all the necessary tasks to cope with this issue. Furthermore, the reconfiguration service introduced in this work allows remote invocation of the reconfiguration process and then the remote integration of off-chip components. A model that offers component location transparency is also presented to enhance and facilitate system integration

FPGA-Based Hyperspectral Lossy Compressor With Adaptive Distortion Feature for Unexpected Scenarios

Lossy compression solutions have grown up during the past decades because of the increment of the data rate in the new-generation hyperspectral sensors; however, linear compression techniques include useless information on regions of little interest for the final application and, at the same time, scarce information on areas of interest. In this article, a transform-based lossy compressor, HyperLCA, has been extended to include a runtime adaptive distortion feature that brings multiple compression ratios in the same scenario. The solution has been designed to keep the same hardware-friendly feature, just as its previous version, specifically conceived to ease the deployment of the solution on reconfigurable hardware devices (FPGAs). The experiments demonstrate that the new version of the compressor is able to process 1024 × 1024 hyperspectral images and 180 spectral bands (377.5 MB) in 0.935 s with a power consumption of 1.145 W. In addition, experimental results also reveal that our architecture features high throughput (MSamples/s) and remarkable energy-efficiency (MB/s/W) tradeoffs, $10\times$ and $6\times$ greater than the best state-of-the-art solution, respectively