Prototype of Fault Adaptive Embedded Software for Large-Scale Real-Time Systems

This paper describes a comprehensive prototype of large-scale fault adaptive embedded software developed for the proposed Fermilab BTeV high energy physics experiment. Lightweight self-optimizing agents embedded within Level 1 of the prototype are responsible for proactive and reactive monitoring and mitigation based on specified layers of competence. The agents are self-protecting, detecting cascading failures using a distributed approach. Adaptive, reconfigurable, and mobile objects for reliablility are designed to be self-configuring to adapt automatically to dynamically changing environments. These objects provide a self-healing layer with the ability to discover, diagnose, and react to discontinuities in real-time processing. A generic modeling environment was developed to facilitate design and implementation of hardware resource specifications, application data flow, and failure mitigation strategies. Level 1 of the planned BTeV trigger system alone will consist of 2500 DSPs, so the number of components and intractable fault scenarios involved make it impossible to design an `expert system' that applies traditional centralized mitigative strategies based on rules capturing every possible system state. Instead, a distributed reactive approach is implemented using the tools and methodologies developed by the Real-Time Embedded Systems group.Comment: 2nd Workshop on Engineering of Autonomic Systems (EASe), in the 12th Annual IEEE International Conference and Workshop on the Engineering of Computer Based Systems (ECBS), Washington, DC, April, 200

From piles to tiles: designing for overview and control in case handling systems

Poor overview and control of workload in electronic case handling systems is a potential health risk factor which affects the users. Case handling systems must therefore be designed to give the users a better overview and maximum control over their workload. In an earlier study, we developed a prototype interface for managing cases, based on the piles metaphor. This paper introduces a second prototype, which is designed to incorporate the findings of an evaluation of the piles metaphor prototype. In this second prototype cases are visualized as “tiles”, reflecting the number and complexity of the cases. This paper also describes some the results of the evaluation of the tiles prototype

Dynamic Influence Networks for Rule-based Models

We introduce the Dynamic Influence Network (DIN), a novel visual analytics technique for representing and analyzing rule-based models of protein-protein interaction networks. Rule-based modeling has proved instrumental in developing biological models that are concise, comprehensible, easily extensible, and that mitigate the combinatorial complexity of multi-state and multi-component biological molecules. Our technique visualizes the dynamics of these rules as they evolve over time. Using the data produced by KaSim, an open source stochastic simulator of rule-based models written in the Kappa language, DINs provide a node-link diagram that represents the influence that each rule has on the other rules. That is, rather than representing individual biological components or types, we instead represent the rules about them (as nodes) and the current influence of these rules (as links). Using our interactive DIN-Viz software tool, researchers are able to query this dynamic network to find meaningful patterns about biological processes, and to identify salient aspects of complex rule-based models. To evaluate the effectiveness of our approach, we investigate a simulation of a circadian clock model that illustrates the oscillatory behavior of the KaiC protein phosphorylation cycle.Comment: Accepted to TVCG, in pres

NMOS-based integrated modular bypass for use in solar systems (NIMBUS): intelligent bypass for reducing partial shading power loss in solar panel applications

NMOS-based Integrated Modular Bypass for Use in Solar systems (NIMBUS) is designed as a replacement for the traditional bypass diode, used in common solar panels. Because of the series connection between the individual solar cells, the power output of a photovoltaic (PV) panel will drop disproportionally under partial shading. Currently, this is solved by dividing the PV panel into substrings, each with a diode bypass placed in parallel. This allows an alternative current path. However, the diodes still have a significant voltage drop (about 350 mV), and due to the fairly large currents in a panel, the diodes are dissipating power that we would rather see at the output of the panel. The NIMBUS chip, being a low-voltage-drop switch, aims to replace these diodes and, thus, reduce that power loss. NIMBUS is a smart bypass: a completely stand-alone system that detects the failing of one or more cells and activates when necessary. It is designed for a 100-mV voltage drop under a 5-A load current. When two or more NIMBUS chips are placed in parallel, an internal synchronization circuit ensures proper operation to provide for larger load currents. This paper will elaborate on the operation, design and implementation of the NIMBUS chip, as well as on the first measurements

Business Process Management Education in Academia: Status, challenges, and Recommendations

In response to the growing proliferation of Business Process Management (BPM) in industry and the demand this creates for BPM expertise, universities across the globe are at various stages of incorporating knowledge and skills in their teaching offerings. However, there are still only a handful of institutions that offer specialized education in BPM in a systematic and in-depth manner. This article is based on a global educators’ panel discussion held at the 2009 European Conference on Information Systems in Verona, Italy. The article presents the BPM programs of five universities from Australia, Europe, Africa, and North America, describing the BPM content covered, program and course structures, and challenges and lessons learned. The article also provides a comparative content analysis of BPM education programs illustrating a heterogeneous view of BPM. The examples presented demonstrate how different courses and programs can be developed to meet the educational goals of a university department, program, or school. This article contributes insights on how best to continuously sustain and reshape BPM education to ensure it remains dynamic, responsive, and sustainable in light of the evolving and ever-changing marketplace demands for BPM expertise

Private-Sector Credit in Central & Eastern Europe: New (Over) Shooting Stars?

This paper analyzes the equilibrium level of private credit to GDP in 11 Central and Eastern European countries in order to see whether the high credit growth recently observed in some of these countries led to above equilibrium private credit to- GDP levels. We use estimation results obtained for a panel of small open OECD economies (out-of-sample sample) to derive the equilibrium credit level for a panel of transition economies (in-sample panel). We opt for this (out-of-sample) approach because the coefficient estimates for transition economies are fairly unstable. We show that there is a large amount of uncertainty to determine the equilibrium level of private credit. Yet our results indicate that a number of countries are very close or even above the estimated equilibrium levels, whereas others are still well below the equilibrium level.http://deepblue.lib.umich.edu/bitstream/2027.42/57232/1/wp852 .pd

Wireless sensors and IoT platform for intelligent HVAC control

Energy consumption of buildings (residential and non-residential) represents approximately 40% of total world electricity consumption, with half of this energy consumed by HVAC systems. Model-Based Predictive Control (MBPC) is perhaps the technique most often proposed for HVAC control, since it offers an enormous potential for energy savings. Despite the large number of papers on this topic during the last few years, there are only a few reported applications of the use of MBPC for existing buildings, under normal occupancy conditions and, to the best of our knowledge, no commercial solution yet. A marketable solution has been recently presented by the authors, coined the IMBPC HVAC system. This paper describes the design, prototyping and validation of two components of this integrated system, the Self-Powered Wireless Sensors and the IOT platform developed. Results for the use of IMBPC in a real building under normal occupation demonstrate savings in the electricity bill while maintaining thermal comfort during the whole occupation schedule.QREN SIDT [38798]; Portuguese Foundation for Science & Technology, through IDMEC, under LAETA [ID/EMS/50022/2013

Monitoring spatial sustainable development: Semi-automated analysis of satellite and aerial images for energy transition and sustainability indicators

Solar panels are installed by a large and growing number of households due to the convenience of having cheap and renewable energy to power house appliances. In contrast to other energy sources solar installations are distributed very decentralized and spread over hundred-thousands of locations. On a global level more than 25% of solar photovoltaic (PV) installations were decentralized. The effect of the quick energy transition from a carbon based economy to a green economy is though still very difficult to quantify. As a matter of fact the quick adoption of solar panels by households is difficult to track, with local registries that miss a large number of the newly built solar panels. This makes the task of assessing the impact of renewable energies an impossible task. Although models of the output of a region exist, they are often black box estimations. This project's aim is twofold: First automate the process to extract the location of solar panels from aerial or satellite images and second, produce a map of solar panels along with statistics on the number of solar panels. Further, this project takes place in a wider framework which investigates how official statistics can benefit from new digital data sources. At project completion, a method for detecting solar panels from aerial images via machine learning will be developed and the methodology initially developed for BE, DE and NL will be standardized for application to other EU countries. In practice, machine learning techniques are used to identify solar panels in satellite and aerial images for the province of Limburg (NL), Flanders (BE) and North Rhine-Westphalia (DE).Comment: This document provides the reader with an overview of the various datasets which will be used throughout the project. The collection of satellite and aerial images as well as auxiliary information such as the location of buildings and roofs which is required to train, test and validate the machine learning algorithm that is being develope