3D Data Processing Toward Maintenance and Conservation. The Integrated Digital Documentation of Casa de Vidro

During the last decade, 3D integrated surveys and BIM modelling procedures have greatly improved the overall knowledge on some Brazilian Modernist buildings. In this framework, the Casa de Vidro 3D survey carried out by DIAPReM centre at Ferrara University, beside the important outputs, analysis and researches achieved from the point cloud database processing, was also useful to test several awareness increasing activities in cooperation with local stakeholders. The first digital documentation test of the Casa de Vidro allowed verifying the feasibility of a full survey on the building towards the restoration and possible placement of new architectures into the garden as an archive-museum of the Lina Bo and P.M. Bardi Foundation. Later, full 3D integrated survey and diagnostic analysis were carried out to achieve the total digital documentation of the house sponsored by the Keeping it Modern initiative of Getty Foundation (Los Angeles). Following its characteristics, the survey had to take into consideration the different architectural features, up to the relationship of architecture and nature. These 3D documentation activities and the point cloud processing allowed several analysis in a multidisciplinary framework

Cross-layer system reliability assessment framework for hardware faults

System reliability estimation during early design phases facilitates informed decisions for the integration of effective protection mechanisms against different classes of hardware faults. When not all system abstraction layers (technology, circuit, microarchitecture, software) are factored in such an estimation model, the delivered reliability reports must be excessively pessimistic and thus lead to unacceptably expensive, over-designed systems. We propose a scalable, cross-layer methodology and supporting suite of tools for accurate but fast estimations of computing systems reliability. The backbone of the methodology is a component-based Bayesian model, which effectively calculates system reliability based on the masking probabilities of individual hardware and software components considering their complex interactions. Our detailed experimental evaluation for different technologies, microarchitectures, and benchmarks demonstrates that the proposed model delivers very accurate reliability estimations (FIT rates) compared to statistically significant but slow fault injection campaigns at the microarchitecture level.Peer ReviewedPostprint (author's final draft

Multi-agent systems for power engineering applications - part 1 : Concepts, approaches and technical challenges

This is the first part of a 2-part paper that has arisen from the work of the IEEE Power Engineering Society's Multi-Agent Systems (MAS) Working Group. Part 1 of the paper examines the potential value of MAS technology to the power industry. In terms of contribution, it describes fundamental concepts and approaches within the field of multi-agent systems that are appropriate to power engineering applications. As well as presenting a comprehensive review of the meaningful power engineering applications for which MAS are being investigated, it also defines the technical issues which must be addressed in order to accelerate and facilitate the uptake of the technology within the power and energy sector. Part 2 of the paper explores the decisions inherent in engineering multi-agent systems for applications in the power and energy sector and offers guidance and recommendations on how MAS can be designed and implemented

Distributed computing methodology for training neural networks in an image-guided diagnostic application

Distributed computing is a process through which a set of computers connected by a network is used collectively to solve a single problem. In this paper, we propose a distributed computing methodology for training neural networks for the detection of lesions in colonoscopy. Our approach is based on partitioning the training set across multiple processors using a parallel virtual machine. In this way, interconnected computers of varied architectures can be used for the distributed evaluation of the error function and gradient values, and, thus, training neural networks utilizing various learning methods. The proposed methodology has large granularity and low synchronization, and has been implemented and tested. Our results indicate that the parallel virtual machine implementation of the training algorithms developed leads to considerable speedup, especially when large network architectures and training sets are used

A candidate architecture for monitoring and control in chemical transfer propulsion systems

To support the exploration of space, a reusable space-based rocket engine must be developed. This engine must sustain superior operability and man-rated levels of reliability over several missions with limited maintenance or inspection between flights. To meet these requirements, an expander cycle engine incorporating a highly capable control and health monitoring system is planned. Alternatives for the functional organization and the implementation architecture of the engine's monitoring and control system are discussed. On the basis of this discussion, a decentralized architecture is favored. The trade-offs between several implementation options are outlined and future work is proposed