ArchGenTool: a System-Independent Collaborative Tool for Robotic Architecture Design

Complex robotic architectures require a collaborative effort in design and adherence to the design in the implementation phse. ArchGentTool is a collaborative architecture generation tool which supports the design of the robotic architecture in a multi-level fashion. It comprises high-level conceptual analysis of the system to be designed, as well as low-level implementation breakdown of its functional components, acting complementary to the ROS framework. The tool facilitates reusability and expandability of the architecture to any robotic system, as it can be adapted to different specifications. A case study with the RAMCIP service robot is presente

Complicated Gallstones after Laparoscopic Sleeve Gastrectomy

Background. The natural history of gallstone formation after laparoscopic sleeve gastrectomy (LSG), the incidence of symptomatic gallstones, and timing of cholecystectomy are not well established. Methods. A retrospective review of prospectively collected database of 150 patients that underwent LSG was reviewed. Results. Preoperatively, gallbladder disease was identified in 32 of the patients (23.2%). Postoperatively, eight of 138 patients (5.8%) became symptomatic. Namely, three of 23 patients (13%) who had evident cholelithiasis preoperatively developed complicated cholelithiasis. From the cohort of patients without preoperative cholelithiasis, five of 106 patients (4.7%) experienced complicated gallstones after LSG. Total cumulative incidence of complicated gallstones was 4.7% (95% CI: 1.3–8.1%). The gallbladder disease-free survival rate was 92.2% at 2 years. No patient underwent cholecystectomy earlier than 9 months or later than 23 months indicating the post-LSG effect. Conclusion. A significant proportion of bariatric patients compared to the general population became symptomatic and soon developed complications after LSG, thus early cholecystectomy is warranted. Routine concomitant cholecystectomy could be considered because the proportion of patients who developed complications especially those with potentially significant morbidities is high and the time to develop complications is short and because of the real technical difficulties during subsequent cholecystectomy

Identifying 5G system enhancements: enabling technologies for multi-service networks

Proceeding of: 2018 IEEE Conference on Standards for Communications and Networking (CSCN)The fifth generation (5G) of mobile and wireless communications networks aims at addressing a diverse set of use cases, services, and applications with a particular focus on enabling new business cases via network slicing. The development of 5G has thus advanced quickly with research projects and standardization efforts resulting in the 5G baseline architecture. Nevertheless, for the realization of native end-to-end (E2E) network slicing, further features and optimizations shall still be introduced. In this paper, we provide a gap analysis of current 5G system (5GS) with respect to some specific enhancements and detail our insights on the enabling innovations that can fill the identified gaps. We will then discuss the essential building blocks and design principles of an evolved 5G baseline architecture capitalizing on the innovations that are being developed.This work has been performed in the framework of the H2020 project 5G-MoNArch co-funded by the EU

Developing an infrastructure for secure patient summary exchange in the EU context: Lessons learned from the KONFIDO project:

Background: The increase of healthcare digitalization comes along with potential information security risks. Thus, the EU H2020 KONFIDO project aimed to provide a toolkit supporting secure cross-border health data exchange. Methods: KONFIDO focused on the so-called "User Goals", while also identifying barriers and facilitators regarding eHealth acceptance. Key user scenarios were elaborated both in terms of threat analysis and legal challenges. Moreover, KONFIDO developed a toolkit aiming to enhance the security of OpenNCP, the reference implementation framework. Results: The main project outcomes are highlighted and the "Lessons Learned," the technical challenges and the EU context are detailed. Conclusions: The main "Lessons Learned" are summarized and a set of recommendations is provided, presenting the position of the KONFIDO consortium toward a robust EU-wide health data exchange infrastructure. To this end, the lack of infrastructure and technical capacity is highlighted, legal and policy challenges are identified and the need to focus on usability and semantic interoperability is emphasized. Regarding technical issues, an emphasis on transparent and standards-based development processes is recommended, especially for landmark software projects. Finally, promoting mentality change and knowledge dissemination is also identified as key step toward the development of secure cross-border health data exchange services

An Approach to Development of System Architecture in Large Collaborative Projects

Part 2: Product and Asset Life Cycle Management in Smart Factories of Industry 4.0International audienceInnovation projects in manufacturing domain often include several end users with different use cases that require a special approach for converging to one architecture solution, which addresses the needs of all end users. The communication between end users and developers in different research and software development projects should be supported correspondingly. This paper describes an approach to development of software intensive system architecture in large collaborative projects that extends traditional approaches with different architecture viewpoints and additional iterative steps aiming to design a main platform integrating project solutions. The approach is applied and validated in a large collaborative EU-funded H2020 research project entitled Z-Factor, i.e. Zero-defect manufacturing strategies towards on-line production management for European factories. Based on the standard ISO/IEC/IEEE 42010 that implies a process based on a set of relevant architecture viewpoints and following the architecture development approach introduced in this study, Z-Factor platform is defined by the following viewpoints: conceptual, functional, information, and deployment

Real-Time Abnormal Event Detection for Enhanced Security in Autonomous Shuttles Mobility Infrastructures

Autonomous vehicles (AVs) are already operating on the streets of many countries around the globe. Contemporary concerns about AVs do not relate to the implementation of fundamental technologies, as they are already in use, but are rather increasingly centered on the way that such technologies will affect emerging transportation systems, our social environment, and the people living inside it. Many concerns also focus on whether such systems should be fully automated or still be partially controlled by humans. This work aims to address the new reality that is formed in autonomous shuttles mobility infrastructures as a result of the absence of the bus driver and the increased threat from terrorism in European cities. Typically, drivers are trained to handle incidents of passengers’ abnormal behavior, incidents of petty crimes, and other abnormal events, according to standard procedures adopted by the transport operator. Surveillance using camera sensors as well as smart software in the bus will maximize the feeling and the actual level of security. In this paper, an online, end-to-end solution is introduced based on deep learning techniques for the timely, accurate, robust, and automatic detection of various petty crime types. The proposed system can identify abnormal passenger behavior such as vandalism and accidents but can also enhance passenger security via petty crimes detection such as aggression, bag-snatching, and vandalism. The solution achieves excellent results across different use cases and environmental conditions

Using activity-related behavioural features towards more effective automatic stress detection

This paper introduces activity-related behavioural features that can be automatically extracted from a computer system, with the aim to increase the effectiveness of automatic stress detection. The proposed features are based on processing of appropriate video and accelerometer recordings taken from the monitored subjects. For the purposes of the present study, an experiment was conducted that utilized a stress-induction protocol based on the stroop colour word test. Video, accelerometer and biosignal (Electrocardiogram and Galvanic Skin Response) recordings were collected from nineteen participants. Then, an explorative study was conducted by following a methodology mainly based on spatiotemporal descriptors (Motion History Images) that are extracted from video sequences. A large set of activity-related behavioural features, potentially useful for automatic stress detection, were proposed and examined. Experimental evaluation showed that several of these behavioural features significantly correlate to self-reported stress. Moreover, it was found that the use of the proposed features can significantly enhance the performance of typical automatic stress detection systems, commonly based on biosignal processing