Design Pillars for Medical Cyber-Physical System Middleware

Our goal is to improve patient outcomes and safety through medical device interoperability. To achieve this, it is not enough to build a technically perfect system. We present here our work toward the validation of middleware for use in interoperable medical cyber-physical systems. This includes clinical requirements, together with our methodology for collecting them, and a set of eighteen `design pillars\u27 that document the non-functional requirements and design goals that we believe are necessary to build a successful interoperable medical device system. We discuss how the clinical requirements and design pillars are involved in the selection of a middleware for our OpenICE implementation

Software Architecture Trends and Promising Technology for Ambient Assisted Living Systems

Driven by the ongoing demographical, structural, and social changes in all modern, industrialized countries, there is a huge interest in IT-based equipment and services these days that enable independent living of people with specific needs. Despite of promising concepts, approaches and technology, those systems are still rather a vision than reality. In order to pave the way towards a common understanding of the problem and overall software solution approaches, this paper (i) characterizes the Ambient Assisted Living domain, (ii) briefly presents relevant software architecture trends, esp. applicable styles and patterns and (iii) discusses promising software technology already available to solve the problems

Intelligent Embedded Software: New Perspectives and Challenges

Intelligent embedded systems (IES) represent a novel and promising generation of embedded systems (ES). IES have the capacity of reasoning about their external environments and adapt their behavior accordingly. Such systems are situated in the intersection of two different branches that are the embedded computing and the intelligent computing. On the other hand, intelligent embedded software (IESo) is becoming a large part of the engineering cost of intelligent embedded systems. IESo can include some artificial intelligence (AI)-based systems such as expert systems, neural networks and other sophisticated artificial intelligence (AI) models to guarantee some important characteristics such as self-learning, self-optimizing and self-repairing. Despite the widespread of such systems, some design challenging issues are arising. Designing a resource-constrained software and at the same time intelligent is not a trivial task especially in a real-time context. To deal with this dilemma, embedded system researchers have profited from the progress in semiconductor technology to develop specific hardware to support well AI models and render the integration of AI with the embedded world a reality

Modeling medical devices for plug-and-play interoperability

Thesis (M. Eng.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2007.Includes bibliographical references (p. 181-187).One of the challenges faced by clinical engineers is to support the connectivity and interoperability of medical-electrical point-of-care devices. A system that could enable plug-and-play connectivity and interoperability for medical devices would improve patient safety, save hospitals time and money, and provide data for electronic medical records. However, existing medical device connectivity standards, such as IEEE 11073, have not been widely adopted by medical device manufacturers. This lack of adoption is likely due to the complexity of the existing standards and their poor support for legacy devices. We attempted to design a simpler, more flexible standard for an integrated clinical environment manager. Our standard, called the ICEMAN standard, provides a meta-model for describing medical devices and a communication protocol to enable plug-and-play connectivity for compliant devices. To demonstrate the capabilities of ICEMAN standard, we implemented a service-oriented system that can pair application requirements with device capabilities, based on the ICEMAN device meta-model. This system enables medical devices to interoperate with the manager in a driverless fashion. The system was tested using simulated medical devices.by Robert Matthew Hofmann.M.Eng

Scalable fleet monitoring and visualization for smart machine maintenance and industrial IoT applications

The wide adoption of smart machine maintenance in manufacturing is blocked by open challenges in the Industrial Internet of Things (IIoT) with regard to robustness, scalability and security. Solving these challenges is of uttermost importance to mission-critical industrial operations. Furthermore, effective application of predictive maintenance requires well-trained machine learning algorithms which on their turn require high volumes of reliable data. This paper addresses both challenges and presents the Smart Maintenance Living Lab, an open test and research platform that consists of a fleet of drivetrain systems for accelerated lifetime tests of rolling-element bearings, a scalable IoT middleware cloud platform for reliable data ingestion and persistence, and a dynamic dashboard application for fleet monitoring and visualization. Each individual component within the presented system is discussed and validated, demonstrating the feasibility of IIoT applications for smart machine maintenance. The resulting platform provides benchmark data for the improvement of machine learning algorithms, gives insights into the design, implementation and validation of a complete architecture for IIoT applications with specific requirements concerning robustness, scalability and security and therefore reduces the reticence in the industry to widely adopt these technologies

The role of a manufacturing execution system during a lean improvement project

Observation is a key aspect within a Lean improvement project. The project team starts from scratch and analyses the current situation by walking through the production process. During the last decade, the digitization of manufacturing operations has had its share of attention. Different kinds of software tools collect and analyze real-time data and turn them into valuable knowledge to support and optimize manufacturing operations. These systems are commonly referred to as Manufacturing Execution Systems (MES). The historical data – incorporated in these systems – can be used to support or validate the Lean efforts. As MES enforces the standard way of working on the production floor, it is also crucial to (re)align the system with the Lean improvements. A case study within a food and beverage company illustrates this dual role of an MES during a Lean improvement project

Experts Knowledge Sharing System In Diagnosing Proton Car Engines

The proton car engine diagnosis system provides a broad range of technical expertise from top engines diagnosticians assembled in Proton cars to the mechanics or foremen at all Proton dealerships. The expertise provided by the system includes problem identification, analysis and solution. Current scenario that happened in service centre or at any dealership workshop is they still don‟t have a proper system to share and keep the knowledge of expertise so that the knowledge can be reused by others as well as can be retained in the company for future use. Experts‟ mechanics that have highly experienced skills and nonexperts mechanics that are still new and less experienced working together in a certain location to do services and repair any problems happened to the cars. However, there is time when the experts are not available and the non –experts don‟t have referees (experts) to be referred to about certain issues and then the problems arise. When non experts do not know how to fix things correctly, thus the mechanical faults will not being properly rectified thus leading to the creation of another fault which will significantly cause Proton customers to spend unnecessarily in getting their vehicle fixed. After recognising this problem matters, study had been conducted in order to produce a proper system that can be used as the knowledge sharing centre for the users at every level. Findings based on the survey and interviews had gave the system developer more ideas to further understand on the system functionalities and system development processes. Right after the development phase, the system had been tested by the users and the feedback was very impressive and there were few recommendations given by the users for the system improvements

Strategic Intelligence Monitor on Personal Health Systems (SIMPHS): Report on Typology/Segmentation of the PHS Market

This market segmentation reports for Personal Health Systems (PHS) describes the methodological background and illustrates the principles of classification and typology regarding different fragments forming this market. It discusses different aspects of the market for PHS and highlights challenges towards a stringent and clear-cut typology or defining market segmentation. Based on these findings a preliminary hybrid typology and indications and insights are created in order to be used in the continuation of the SIMPHS project. It concludes with an annex containing examples and cases studies.JRC.DDG.J.4-Information Societ

From specialists to generalists : inductive biases of deep learning for higher level cognition

Les réseaux de neurones actuels obtiennent des résultats de pointe dans une gamme de domaines problématiques difficiles. Avec suffisamment de données et de calculs, les réseaux de neurones actuels peuvent obtenir des résultats de niveau humain sur presque toutes les tâches. En ce sens, nous avons pu former des spécialistes capables d'effectuer très bien une tâche particulière, que ce soit le jeu de Go, jouer à des jeux Atari, manipuler le cube Rubik, mettre des légendes sur des images ou dessiner des images avec des légendes. Le prochain défi pour l'IA est de concevoir des méthodes pour former des généralistes qui, lorsqu'ils sont exposés à plusieurs tâches pendant l'entraînement, peuvent s'adapter rapidement à de nouvelles tâches inconnues. Sans aucune hypothèse sur la distribution génératrice de données, il peut ne pas être possible d'obtenir une meilleure généralisation et une meilleure adaptation à de nouvelles tâches (inconnues). Les réseaux de neurones actuels obtiennent des résultats de pointe dans une gamme de domaines problématiques difficiles. Une possibilité fascinante est que l'intelligence humaine et animale puisse être expliquée par quelques principes, plutôt qu'une encyclopédie de faits. Si tel était le cas, nous pourrions plus facilement à la fois comprendre notre propre intelligence et construire des machines intelligentes. Tout comme en physique, les principes eux-mêmes ne suffiraient pas à prédire le comportement de systèmes complexes comme le cerveau, et des calculs importants pourraient être nécessaires pour simuler l'intelligence humaine. De plus, nous savons que les vrais cerveaux intègrent des connaissances a priori détaillées spécifiques à une tâche qui ne pourraient pas tenir dans une courte liste de principes simples. Nous pensons donc que cette courte liste explique plutôt la capacité des cerveaux à apprendre et à s'adapter efficacement à de nouveaux environnements, ce qui est une grande partie de ce dont nous avons besoin pour l'IA. Si cette hypothèse de simplicité des principes était correcte, cela suggérerait que l'étude du type de biais inductifs (une autre façon de penser aux principes de conception et aux a priori, dans le cas des systèmes d'apprentissage) que les humains et les animaux exploitent pourrait aider à la fois à clarifier ces principes et à fournir source d'inspiration pour la recherche en IA. L'apprentissage en profondeur exploite déjà plusieurs biais inductifs clés, et mon travail envisage une liste plus large, en se concentrant sur ceux qui concernent principalement le traitement cognitif de niveau supérieur. Mon travail se concentre sur la conception de tels modèles en y incorporant des hypothèses fortes mais générales (biais inductifs) qui permettent un raisonnement de haut niveau sur la structure du monde. Ce programme de recherche est à la fois ambitieux et pratique, produisant des algorithmes concrets ainsi qu'une vision cohérente pour une recherche à long terme vers la généralisation dans un monde complexe et changeant.Current neural networks achieve state-of-the-art results across a range of challenging problem domains. Given enough data, and computation, current neural networks can achieve human-level results on mostly any task. In the sense, that we have been able to train \textit{specialists} that can perform a particular task really well whether it's the game of GO, playing Atari games, Rubik's cube manipulation, image caption or drawing images given captions. The next challenge for AI is to devise methods to train \textit{generalists} that when exposed to multiple tasks during training can quickly adapt to new unknown tasks. Without any assumptions about the data generating distribution it may not be possible to achieve better generalization and adaption to new (unknown) tasks. A fascinating possibility is that human and animal intelligence could be explained by a few principles (rather than an encyclopedia). If that was the case, we could more easily both understand our own intelligence and build intelligent machines. Just like in physics, the principles themselves would not be sufficient to predict the behavior of complex systems like brains, and substantial computation might be needed to simulate human intelligence. In addition, we know that real brains incorporate some detailed task-specific a priori knowledge which could not fit in a short list of simple principles. So we think of that short list rather as explaining the ability of brains to learn and adapt efficiently to new environments, which is a great part of what we need for AI. If that simplicity of principles hypothesis was correct it would suggest that studying the kind of inductive biases (another way to think about principles of design and priors, in the case of learning systems) that humans and animals exploit could help both clarify these principles and provide inspiration for AI research. Deep learning already exploits several key inductive biases, and my work considers a larger list, focusing on those which concern mostly higher-level cognitive processing. My work focuses on designing such models by incorporating in them strong but general assumptions (inductive biases) that enable high-level reasoning about the structure of the world. This research program is both ambitious and practical, yielding concrete algorithms as well as a cohesive vision for long-term research towards generalization in a complex and changing world