Model based code generation for distributed embedded systems

Embedded systems are becoming increasingly complex and more distributed. Cost and quality requirements necessitate reuse of the functional software components for multiple deployment architectures. An important step is the allocation of software components to hardware. During this process the differences between the hardware and application software architectures must be reconciled. In this paper we discuss an architecture driven approach involving model-based techniques to resolve these differences and integrate hardware and software components. The system architecture serves as the underpinning based on which distributed real-time components can be generated. Generation of various embedded system architectures using the same functional architecture is discussed. The approach leverages the following technologies – IME (Integrated Modeling Environment), the SAE AADL (Architecture Analysis and Design Language), and Ocarina. The approach is illustrated using the electronic throttle control system as a case study

Real-Time Semantic Segmentation using Hyperspectral Images for Mapping Unstructured and Unknown Environments

Autonomous navigation in unstructured off-road environments is greatly improved by semantic scene understanding. Conventional image processing algorithms are difficult to implement and lack robustness due to a lack of structure and high variability across off-road environments. The use of neural networks and machine learning can overcome the previous challenges but they require large labeled data sets for training. In our work we propose the use of hyperspectral images for real-time pixel-wise semantic classification and segmentation, without the need of any prior training data. The resulting segmented image is processed to extract, filter, and approximate objects as polygons, using a polygon approximation algorithm. The resulting polygons are then used to generate a semantic map of the environment. Using our framework. we show the capability to add new semantic classes in run-time for classification. The proposed methodology is also shown to operate in real-time and produce outputs at a frequency of 1Hz, using high resolution hyperspectral images

FUELCELL2006-97257 DEVELOPMENT OF AN AUTOMATED CONTROL SYSTEM VERIFICATION PLATFORM FOR A SOLID OXIDE FUEL CELL

ABSTRACT The fuel cell industry is currently undergoing rapid development, and applications of fuel cell based power sources are diversifying. The advent of new and more sophisticated application areas and the expanding market necessitates development of efficient and robust fuel cell based power supplies that are reliable in their performance. These demands are answered not only by improved plant designs and innovations, but also by developing high-quality control algorithms. Quality and reliability of the complete system are ensured through extensive and varied testing. To this end an automated Hardware-in-the-Loop based control code verification and validation platform for the Delphi Solid Oxide Fuel Cell plant and control system has been developed. Verification activities are managed using the System Verification Manager tool. This paper outlines the application of this platform for safety and diagnostics verification and validation for a Solid Oxide Fuel Cell system. INTRODUCTION Control algorithms for complex industrial systems require thorough verification and validation against a variety of different operating conditions to ensure robustness and reliability of the design. Further, algorithm verification and validation becomes more important in the context of safety critical features of a system. Manual comprehensive testing of a development control algorithm can be a tedious, human-errorprone, time and resource intensive process, which is often infeasible

Response of Autonomous Vehicles to Emergency Response Vehicles (RAVEV)

69A3551747115The objective of this project was to explore how an autonomous vehicle identifies and safely responds to emergency vehicles using visual and other onboard sensors. Emergency vehicles can include police, fire, hospital and other responders\u2019 vehicles. An autonomous vehicle in the presence of an emergency vehicle must have the ability to accurately sense its surroundings in real-time and be able to safely yield to the emergency vehicle. This project used machine learning algorithms to identify the presence of emergency vehicles, mainly through onboard vision, and then maneuver an in-path non-emergency autonomous vehicle to a stop on the curbside. Two image processing frameworks were tested to identify the best combination of vision-based detection algorithms, and a novel lateral control algorithm was developed for maneuvering the autonomous vehicle

Global overview of the management of acute cholecystitis during the COVID-19 pandemic (CHOLECOVID study)

Background: This study provides a global overview of the management of patients with acute cholecystitis during the initial phase of the COVID-19 pandemic. Methods: CHOLECOVID is an international, multicentre, observational comparative study of patients admitted to hospital with acute cholecystitis during the COVID-19 pandemic. Data on management were collected for a 2-month study interval coincident with the WHO declaration of the SARS-CoV-2 pandemic and compared with an equivalent pre-pandemic time interval. Mediation analysis examined the influence of SARS-COV-2 infection on 30-day mortality. Results: This study collected data on 9783 patients with acute cholecystitis admitted to 247 hospitals across the world. The pandemic was associated with reduced availability of surgical workforce and operating facilities globally, a significant shift to worse severity of disease, and increased use of conservative management. There was a reduction (both absolute and proportionate) in the number of patients undergoing cholecystectomy from 3095 patients (56.2 per cent) pre-pandemic to 1998 patients (46.2 per cent) during the pandemic but there was no difference in 30-day all-cause mortality after cholecystectomy comparing the pre-pandemic interval with the pandemic (13 patients (0.4 per cent) pre-pandemic to 13 patients (0.6 per cent) pandemic; P = 0.355). In mediation analysis, an admission with acute cholecystitis during the pandemic was associated with a non-significant increased risk of death (OR 1.29, 95 per cent c.i. 0.93 to 1.79, P = 0.121). Conclusion: CHOLECOVID provides a unique overview of the treatment of patients with cholecystitis across the globe during the first months of the SARS-CoV-2 pandemic. The study highlights the need for system resilience in retention of elective surgical activity. Cholecystectomy was associated with a low risk of mortality and deferral of treatment results in an increase in avoidable morbidity that represents the non-COVID cost of this pandemic

Guest Editorial Focused Section on Mechatronics in Cyber-Physical Systems

Wireless networking, sensing, computing, and control advances have changed the way in which society interacts with the physical world. The computational processes are deeply embedded in the physical world, creating a highly coupled system that has the potential to enhance human safety, mission objectives, and system efficiency. In the context of cyber-physical systems (CPSs), mechatronic systems, which are becoming highly multidisciplinary, require an ever-increasing combination of mechanical, electrical/electronic, control, and information disciplines. This further offers ample prospects for the integration of various mechatronic components/subsystems, toward enhanced system safety, performance, energy, and intelligence. The objective of this focused Section is to compile recent research and development efforts contributing to advances in mechatronics in the CPS, including the state-of-the-art in associated developments and methodologies, and the perspectives on future developments and applications of mechatronics in the CPS