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Register a new userAI-Augmented Multi Function Radar Engineering with Digital Twin: Towards Proactivity
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Thales new generation digital multi-missions radars, fully-digital and software-defined, like the Sea Fire and Ground Fire radars, benefit from a considerable increase of accessible degrees of freedoms to optimally design their operational modes. To effectively leverage these design choices and turn them into operational capabilities, it is necessary to develop new engineering tools, using artificial intelligence. Innovative optimization algorithms in the discrete and continuous domains, coupled with a radar Digital Twins, allowed construction of a generic tool for search mode design (beam synthesis, waveform and volume grid) compliant with the available radar time budget. The high computation speeds of these algorithms suggest tool application in a Proactive Radar configuration, which would dynamically propose to the operator, operational modes better adapted to environment, threats and the equipment failure conditions.
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In the way towards Industry 4.0, the complexity of the industrial systems increases due to the presence of multiple agents, Cyber-Physical Systems, distributed sensing, and big data introducing unknown dynamics that affect the production goals of the manufacturing processes. Thus, Digital Twin is a breaking technology corresponding to the capacity of developing a virtual representation of any complex system in order to perform design, analysis, and behavior prediction tasks that enhance the understanding of these systems through new enabling capabilities like real-time analytics, parallel sensing, or Smart Control Engineering. In this paper, a novel framework is proposed for the design and implementation of Digital Twin applications to the development of Smart Control Engineering. The steps of this framework involve system documentation, multidomain simulation, behavioral matching, and real-time monitoring. This framework is applied to develop the Digital Twin for a real-time vision feedback infrared temperature uniformity control. The obtained results show that Digital Twin is a fundamental part of the transformation into Industry 4.0.
Digital pathology is not only one of the most promising fields of diagnostic medicine, but at the same time a hot topic for fundamental research. Digital pathology is not just the transfer of histopathological slides into digital representations. The combination of different data sources (images, patient records, and *omics data) together with current advances in artificial intelligence/machine learning enable to make novel information accessible and quantifiable to a human expert, which is not yet available and not exploited in current medical settings. The grand goal is to reach a level of usable intelligence to understand the data in the context of an application task, thereby making machine decisions transparent, interpretable and explainable. The foundation of such an augmented pathologist needs an integrated approach: While machine learning algorithms require many thousands of training examples, a human expert is often confronted with only a few data points. Interestingly, humans can learn from such few examples and are able to instantly interpret complex patterns. Consequently, the grand goal is to combine the possibilities of artificial intelligence with human intelligence and to find a well-suited balance between them to enable what neither of them could do on their own. This can raise the quality of education, diagnosis, prognosis and prediction of cancer and other diseases. In this paper we describe some (incomplete) research issues which we believe should be addressed in an integrated and concerted effort for paving the way towards the augmented pathologist.
Digital Engineering, the digital transformation of engineering to leverage digital technologies, is coming globally. This paper explores digital systems engineering, which aims at developing theory, methods, models, and tools to support the emerging digital engineering. A critical task is to digitalize engineering artifacts, thus enabling information sharing across platform, across life cycle, and across domains. We identify significant challenges and enabling digital technologies; analyze the transition from traditional engineering to digital engineering; define core concepts, including digitalization, unique identification, digitalized artifacts, digital augmentation, and others; present a big picture of digital systems engineering in four levels: vision, strategy, action, and foundation; briefly discuss each of main areas of research issues. Digitalization enables fast infusing and leveraging novel digital technologies; unique identification enables information traceability and accountability in engineering lifecycle; provenance enables tracing dependency relations among engineering artifacts; supporting model reproducibility and replicability; helping with trustworthiness evaluation of digital engineering artifacts.
Robust indoor ego-motion estimation has attracted significant interest in the last decades due to the fast-growing demand for location-based services in indoor environments. Among various solutions, frequency-modulated continuous-wave (FMCW) radar sensors in millimeter-wave (MMWave) spectrum are gaining more prominence due to their intrinsic advantages such as penetration capability and high accuracy. Single-chip low-cost MMWave radar as an emerging technology provides an alternative and complementary solution for robust ego-motion estimation, making it feasible in resource-constrained platforms thanks to low-power consumption and easy system integration. In this paper, we introduce Milli-RIO, an MMWave radar-based solution making use of a single-chip low-cost radar and inertial measurement unit sensor to estimate six-degrees-of-freedom ego-motion of a moving radar. Detailed quantitative and qualitative evaluations prove that the proposed method achieves precisions on the order of few centimeters for indoor localization tasks.
To demystify the Digital Twin concept, we built a simple yet representative thermal incubator system. The incubator is an insulated box fitted with a heatbed, and complete with a software system for communication, a controller, and simulation models. We developed two simulation models to predict the temperature inside the incubator, one with two free parameters and one with four free parameters. Our experiments showed that the latter model was better at predicting the thermal inertia of the heatbed itself, which makes it more appropriate for further development of the digital twin. The hardware and software used in this case study are available open source, providing an accessible platform for those who want to develop and verify their own techniques for digital twins.
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