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

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

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

Deep Neural Oracles for Short-Window Optimized Compressed Sensing of Biosignals

The recovery of sparse signals given their linear mapping on lower-dimensional spaces can be partitioned into a support estimation phase and a coefficient estimation phase. We propose to estimate the support with an oracle based on a deep neural network trained jointly with the linear mapping at the encoder. The divination of the oracle is then used to estimate the coefficients by pseudo-inversion. This architecture allows the definition of an encoding-decoding scheme with state-of-the-art recovery capabilities when applied to biological signals such as ECG and EEG, thus allowing extremely low-complex encoders. As an additional feature, oracle-based recovery is able to self-assess, by indicating with remarkable accuracy chunks of signals that may have been reconstructed with a non-satisfactory quality. This self-assessment capability is unique in the CS literature and paves the way for further improvements depending on the requirements of the specific application. As an example, our scheme is able to satisfyingly compress by a factor of 2.67 an ECG or EEG signal with a complexity equivalent to only 24 signed sums per processed sample

Inertial Sensors in Swimming: Detection of Stroke Phases through 3D Wrist Trajectory.

Monitoring the upper arm propulsion is a crucial task for swimmer performance. The swimmer indeed can produce displacement of the body by modulating the upper limb kinematics. The present study proposes an approach for automatically recognize all stroke phases through three-dimensional (3D) wrist\u2019s trajectory estimated using inertial devices. Inertial data of 14 national-level male swimmer were collected while they performed 25 m front-crawl trial at intensity range from 75% to 100% of their 25 m maximal velocity. The 3D coordinates of the wrist were computed using the inertial sensors orientation and considering the kinematic chain of the upper arm biomechanical model. An algorithm that automatically estimates the duration of entry, pull, push, and recovery phases result from the 3D wrist\u2019s trajectory was tested using the bi-dimensional (2D) video-based systems as temporal reference system. A very large correlation (r = 0.87), low bias (0.8%), and reasonable Root Mean Square error (2.9%) for the stroke phases duration were observed using inertial devices versus 2D video-based system methods. The 95% limits of agreement (LoA) for each stroke phase duration were always lower than 7.7% of cycle duration. The mean values of entry, pull, push and recovery phases duration in percentage of the complete cycle detected using 3D wrist\u2019s trajectory using inertial devices were 34.7 (\ub1 6.8)%, 22.4 (\ub1 5.8)%, 14.2 (\ub1 4.4)%, 28.4 (\ub1 4.5)%. The swimmer\u2019s velocity and arm coordination model do not affect the performance of the algorithm in stroke phases detection. The 3D wrist trajectory can be used for an accurate and complete identification of the stroke phases in front crawl using inertial sensors. Results indicated the inertial sensor device technology as a viable option for swimming arm-stroke phase assessment

Training Binary Layers by Self-Shrinking of Sigmoid Slope: Application to Fast MRI Acquisition

Deep Neural Networks (DNN) have become popular and widespread because they combine computational power and flexibility, but they may present critical hyper-parameters that need to be tuned before the model can be trained. Recently, the use of trainable binary masks in the field of Magnetic Resonance Imaging (MRI) acquisition brought new state-of-the-art results, but with the disadvantage of introducing a bulky hyper-parameter, which tuning is usually time-consuming. We present a novel callback-based method that is applied during training and turns the tuning problem into a triviality, also bringing non-negligible performance improvements. We test our method on the fastMRI dataset