Divide and Conquer - Organizing Component-based Adaptation

This paper introduces a divide and conquer approach for organizing the adaptation of distributed applications in a potentially large number of interacting middleware instances. In such an environment, a centralistic and static adaptation reasoning i) is inadequate and ii) gives the same priority to all applications. The divide and conquer method aims at minimizing the interference between running applications, allowing users to weight the priority of applications, and organizing the adaptation and the reasoning about the adaptation in a decentralized and flexible way

Proceedings of the First International DisCoTec Workshop on Context-aware Adaptation Mechanisms for Pervasive and Ubiquitous Services (CAMPUS 2008) Divide and Conquer -Organizing Component-based Adaptation in Distributed Environments Divide and Conquer -O

Abstract: This paper introduces a divide and conquer approach for organizing the adaptation of distributed applications in a potentially large number of interacting middleware instances. In such an environment, a centralistic and static adaptation reasoning i) is inadequate and ii) gives the same priority to all applications. The divide and conquer method aims at minimizing the interference between running applications, allowing users to weight the priority of applications, and organizing the adaptation and the reasoning about the adaptation in a decentralized and flexible way

Learning-based Coordination of Distributed Component Deployment

Self-organizing and resource-aware component deployment is an important feature of mobile pervasive systems. Distributed resources must be dynamically allocated to software components to ensure QoS demands and not distracting the user. In this paper, we propose a Reinforcement Learning technique to optimize distributed component deployment and migration. We argue that the approach meets some main requirements demanded by applications running on mobile systems. A motivating scenario is presented in which a distributed application server allows users to share content and run applications in mobile ad-hoc networks

Dynamic Adaptive Point Cloud Streaming

High-quality point clouds have recently gained interest as an emerging form of representing immersive 3D graphics. Unfortunately, these 3D media are bulky and severely bandwidth intensive, which makes it difficult for streaming to resource-limited and mobile devices. This has called researchers to propose efficient and adaptive approaches for streaming of high-quality point clouds. In this paper, we run a pilot study towards dynamic adaptive point cloud streaming, and extend the concept of dynamic adaptive streaming over HTTP (DASH) towards DASH-PC, a dynamic adaptive bandwidth-efficient and view-aware point cloud streaming system. DASH-PC can tackle the huge bandwidth demands of dense point cloud streaming while at the same time can semantically link to human visual acuity to maintain high visual quality when needed. In order to describe the various quality representations, we propose multiple thinning approaches to spatially sub-sample point clouds in the 3D space, and design a DASH Media Presentation Description manifest specific for point cloud streaming. Our initial evaluations show that we can achieve significant bandwidth and performance improvement on dense point cloud streaming with minor negative quality impacts compared to the baseline scenario when no adaptations is applied.Comment: 6 pages, 23rd ACM Packet Video (PV'18) Workshop, June 12--15, 2018, Amsterdam, Netherland

Technology-Enhanced Learning (TEL) tools to improve computational thinking skills

The common and easy access to technological devices has led to the rapid inclusion of technology into the learning process. The development of technical skills, as well as the increasing confidence in computer attitudes, seems to be obvious. We therefore propose to go beyond and advocate the use of TEL to provide specific leadership, multi-tasking and other organizational skills, known as computational thinking, as precisely the main contributions provided by TEL. To support this hypothesis, we present two different experiences. The first, based on high-school students, to introduce young people to technology at the same time as they acquire other demanding skills. The second, with undergraduate Computer Science students, is focused on technology itself to enhance and improve computational thinking skills. A comparison is also made between two populations with different digital profiles in their user skills (general in the first case and engineering biased in the second).Peer ReviewedPostprint (author’s final draft

Integrated performance prediction and quality control in manufacturing systems

textPredicting the condition of a degrading dynamic system is critical for implementing successful control and designing the optimal operation and maintenance strategies throughout the lifetime of the system. In many situations, especially in manufacturing, systems experience multiple degradation cycles, failures, and maintenance events throughout their lifetimes. In such cases, historical records of sensor readings observed during the lifecycle of a machine can yield vital information about degradation patterns of the monitored machine, which can be used to formulate dynamic models for predicting its future performance. Besides the ability to predict equipment failures, another major component of cost effective and high-throughput manufacturing is tight control of product quality. Quality control is assured by taking periodic measurements of the products at various stages of production. Nevertheless, quality measurements of the product require time and are often executed on costly measurement equipment, which increases the cost of manufacturing and slows down production. One possible way to remedy this situation is to utilize the inherent link between the manufacturing equipment condition, mirrored in the readings of sensors mounted on that machine, and the quality of products coming out of it. The concept of Virtual Metrology (VM) addresses the quality control problem by using data-driven models that relate the product quality to the equipment sensors, enabling continuous estimation of the quality characteristics of the product, even when physical measurements of product quality are not available. VM can thus bring significant production benefits, including improved process control, reduced quality losses and higher productivity. In this dissertation, new methods are formulated that will combine long-term performance prediction of sensory signatures from a degrading manufacturing machine with VM quality estimation, which enables integration of predictive condition monitoring (prediction of sensory signatures) with predictive manufacturing process control (predictive VM model). The recently developed algorithm for prediction of sensory signatures is capable of predicting the system condition by comparing the similarity of the most recent performance signatures with the known degradation patterns available in the historical records. The method accomplishes the prediction of non-Gaussian and non-stationary time-series of relevant performance signatures with analytical tractability, which enables calculations of predicted signature distributions with significantly greater speeds than what can be found in literature. VM quality estimation is implemented using the recently introduced growing structure multiple model system paradigm (GSMMS), based on the use of local linear dynamic models. The concept of local models enables representation of complex, non-linear dependencies with non-Gaussian and non-stationary noise characteristics, using a locally tractable model representation. Localized modeling enables a VM that can detect situations when the VM model is not adequate and needs to be improved, which is one of the main challenges in VM. Finally, uncertainty propagation with Monte Carlo simulation is pursued in order to propagate the predicted distributions of equipment signatures through the VM model to enable prediction of distributions of the quality variables using the readily available sensor readings streaming from the monitored manufacturing machine. The newly developed methods are applied to long-term production data coming from an industrial plasma-enhanced chemical vapor deposition (PECVD) tool operating in a major semiconductor manufacturing fab.Mechanical Engineerin

Towards Automotive Embedded Systems with Self-X Properties

With self-adaptation and self-organization new paradigms for the management of distributed systems have been introduced. By enhancing the automotive software system with self-X capabilities, e.g. self-healing, self-configuration and self-optimization, the complexity is handled while increasing the flexibility, scalability and dependability of these systems. In this chapter we present an approach for enhancing automotive systems with self-X properties. At first, we discuss the benefits of providing automotive software systems with self-management capabilities and outline concrete use cases. Afterwards, we will discuss requirements and challenges for realizing adaptive automotive embedded systems

Equipment for artificial intelligence

Issued as Final report, Project G-36-66