Innovating the Construction Life Cycle through BIM/GIS Integration: A Review

The construction sector is in continuous evolution due to the digitalisation and integration into daily activities of the building information modelling approach and methods that impact on the overall life cycle. This study investigates the topic of BIM/GIS integration with the adoption of ontologies and metamodels, providing a critical analysis of the existing literature. Ontologies and metamodels share several similarities and could be combined for potential solutions to address BIM/GIS integration for complex tasks, such as asset management, where heterogeneous sources of data are involved. The research adopts a systematic literature review (SLR), providing a formal approach to retrieve scientific papers from dedicated online databases. The results found are then analysed, in order to describe the state of the art and suggest future research paths, which is useful for both researchers and practitioners. From the SLR, it emerged that several studies address ontologies as a promising way to overcome the semantic barriers of the BIM/GIS integration. On the other hand, metamodels (and MDE and MDA approaches, in general) are rarely found in relation to the integration topic. Moreover, the joint application of ontologies and metamodels for BIM/GIS applications is an unexplored field. The novelty of this work is the proposal of the joint application of ontologies and metamodels to perform BIM/GIS integration, for the development of software and systems for asset management

Development of a grid-dispersion model in a large-eddy-simulation–generated planetary boundary layer

Numerical simulations of dispersion experiments within the planetary boundary layer are actually feasible making use of Large Eddy Simulations (LES). In Eulerian framework, a conservation equation for a passive scalar may be superimposed on LES wind/turbulence fields to get a realistic description of timevarying concentration field. Aim of this work is to present a numerical technique to solve the Eulerian conservation equation. The technique is based on Fractional Step/Locally One-Dimensional (LOD) methods. Advection terms are calculated with a semi-Lagrangian cubic-spline technique, while diffusive terms are calculated with Crank-Nicholson implicit scheme. To test the grid model, the dispersion of contaminants emitted from an elevated continuous point source in a convective boundary layer is simulated. Results show that the calculated concentration distributions agree quite well with numerical and experimental data found in the literature

Adapted Compressed Sensing: A Game Worth Playing

Despite the universal nature of the compressed sensing mechanism, additional information on the class of sparse signals to acquire allows adjustments that yield substantial improvements. In facts, proper exploitation of these priors allows to significantly increase compression for a given reconstruction quality. Since one of the most promising scopes of application of compressed sensing is that of IoT devices subject to extremely low resource constraint, adaptation is especially interesting when it can cope with hardware-related constraint allowing low complexity implementations. We here review and compare many algorithmic adaptation policies that focus either on the encoding part or on the recovery part of compressed sensing. We also review other more hardware-oriented adaptation techniques that are actually able to make the difference when coming to real-world implementations. In all cases, adaptation proves to be a tool that should be mastered in practical applications to unleash the full potential of compressed sensing

An architecture for ultra-low-voltage ultra-low-power compressed sensing-based acquisition systems

Compressed Sensing (CS) has been addressed as a paradigm capable of lowering energy requirements in acquisition systems. Furthermore, the capability of simultaneously acquiring and compressing an input signal makes this paradigm perfectly suitable for low-power devices. However, the need for analog hardware blocks makes the adoption of most of standard solutions proposed so far in the literature problematic when an aggressive voltage and energy scaling is considered, as in the case of ultra-low-power IoT devices that need to be battery-powered or energy harvesting-powered. Here, we investigate a recently proposed architecture that, due to the lack of any analog block (except for the comparator required in the following A/D stage) is compatible with the aggressive voltage scaling required by IoT devices. Feasibility and expected performance of this architecture are investigated according to the most recent state-of-the-art literature

Subspace Energy Monitoring for Anomaly Detection @Sensor or @Edge

The amount of data generated by distributed monitoring systems that can be exploited for anomaly detection, along with real time, bandwidth, and scalability requirements leads to the abandonment of centralized approaches in favor of processing closer to where data are generated. This increases the interest in algorithms coping with the limited computational resources of gateways or sensor nodes. We here propose two dual and lightweight methods for anomaly detection based on generalized spectral analysis. We monitor the signal energy laying along with the principal and anti-principal signal subspaces, and call for an anomaly when such energy changes significantly with respect to normal conditions. A streaming approach for the online estimation of the needed subspaces is also proposed. The methods are tested by applying them to synthetic data and real-world sensor readings. The synthetic setting is used for design space exploration and highlights the tradeoff between accuracy and computational cost. The real-world example deals with structural health monitoring and shows how, despite the extremely low computations costs, our methods are able to detect permanent and transient anomalies that would classically be detected by full spectral analysis

A Deep Learning Method for Optimal Undersampling Patterns and Image Recovery for MRI Exploiting Losses and Projections

Compressed Sensing was recently proposed to reduce the long acquisition time of Magnetic Resonance Imaging by undersampling the signal frequency content and then algorithmically reconstructing the original image. We propose a way to significantly improve the above method by exploiting a deep neural network to tackle both problems of frequency sub-sampling and image reconstruction simultaneously, thanks to the introduction of a new loss function to drive the training and the addition of a post-processing non-neural stage. Furthermore, we highlight how some of the quantities along the processing chain can be used as a proxy of the quality of the recovered image, thus allowing a self-assessment of the whole technique. All improvements hinge on the possibility of identifying constraints to which the final image must obey and suitably enforce them. The effectiveness of our approach is tested on real-world MRI acquisitions from the fastMRI public database and achieves an appreciable improvement in Peak Signal-to-Noise Ratio with respect to the original CS-based proposal with speed-up factors 4 and 8

The great denial of the monstrous in organization theory

When we began this review of The monstrous organization, we encountered an uncommon reading of organizational theory and life, populated by monsters, fantastical creatures and deviant bodies. The novel account of Thanem\u2019s Monstrous organizational theory caused us to reflect as we approached this text. The according of relevance to monstrosity has not aroused the same curiosity in Western authors, with certain fields of studies allocating greater prominence to monsters and Monstrous aspects of life than others. The guiding question for our analysis was inspired by this disparity: why, within Western culture, do images of monstrosity abound in literature, paintings, architecture, and cinema, whilst scant interest has been directed towards Monstrous bodies and creatures in organizational theory and management studies

A model for the estimation of standard deviation of air pollution concentration in different stability conditions

We propose to estimate the standard deviations of the air pollution concentration in the horizontal and vertical direction, σy and σz, based on Pasquill’s well-known equation, in terms of the wind variance and the Lagrangian integral time scales, on the basis of an atmospheric turbulence spectra model. The main advantage of the spectral model is its treatment of turbulent kinetic energy spectra as the sum of buoyancy and a shear produced part, modelling each one separately. The formulation represents both shear and buoyant turbulent mechanisms characterizing the various regimes of the Planetary Boundary Layer, and gives continuous values at any elevation and all stability conditions from unstable to stable. As a consequence, both the wind variance and the Lagrangian integral time scales in the dispersion parameters are more general than those found in literature, because they are not derived from diffusion experiments as most parameterizations. Furthermore, they provide a formulation continuous for the whole boundary layer resulting more physically consistent. The σy, σz parameters, included in a Gaussian model have been tested and compared with a dispersion scheme reported in the literature, using experimental data in different emission conditions (low and tall stacks) and in several meteorological conditions ranging from stable to convective. Results show that the dispersion model with the sigmas parameterisation included, produces a good fitting of the measured ground-level concentration data in all the experimental conditions considered, performing slightly better than other state-of-art models

Low-power fixed-point compressed sensing decoder with support oracle

Approaches for reconstructing signals encoded with Compressed Sensing (CS) techniques, and based on Deep Neural Networks (DNNs) are receiving increasing interest in the literature. In a recent work, a new DNN-based method named Trained CS with Support Oracle (TCSSO) is introduced, relying the signal reconstruction on the two separate tasks of support identification and measurements decoding. The aim of this paper is to improve the TCSSO framework by considering actual implementations using a finite-precision hardware. Solutions with low memory footprint and low computation requirements by employing fixed-point notation and by reducing the number of bits employed are considered. Results using synthetic electrocardiogram (ECG) signals as a case study show that this approach, even when used in a constrained-resources scenario, still outperform current state-of-art CS approaches

Streaming Algorithms for Subspace Analysis: Comparative Review and Implementation on IoT Devices

Subspace analysis is a widely used technique for coping with high-dimensional data and is becoming a fundamental step in the early treatment of many signal processing tasks. However, traditional subspace analysis often requires a large amount of memory and computational resources, as it is equivalent to eigenspace determination. To address this issue, specialized streaming algorithms have been developed, allowing subspace analysis to be run on low-power devices such as sensors or edge devices. Here, we present a classification and a comparison of these methods by providing a consistent description and highlighting their features and similarities. We also evaluate their performance in the task of subspace identification with a focus on computational complexity and memory footprint for different signal dimensions. Additionally, we test the implementation of these algorithms on common hardware platforms typically employed for sensors and edge devices