Senspectra

Both digital and physical 3D models of buildings as well as historical photographs of architecture are used for a wide range of needs, from research in humanities and information technologies, museum contexts and library studies, to touristic applications. Spatially oriented photographs play an important role in visualizing and browsing contemporary as well as historical architecture, starting with the ground-breaking Photo Tourism project [4]. We present a technique to combine physical 3D models of buildings with spatially registered historical photographic documents in a hand-held Augmented Reality (AR) environment. Users are enabled to spatially explore historical views of architecture by selecting photos from a collection of images which are then utilized as textures for the physical model rendered on their respective mobile device. We compare different methods to select photos registered to a physical model in hand-held AR.

Until recent years, detailed studies of hydraulic structures were made almost systematically by hydraulic physical models offering great capabilities to understand the hydraulic behaviour of the studied flows. On the other hand, physical models are in general rather expensive and time consuming, and can only represent some hydraulic phenomenon of interest. This depending on the similarity law for which they have been calibrated. Nowadays, given the increasing computational capabilities and further development of user-friendly interfaces, 3D CFD modelling can balance these issues and provide a flexible and powerful tool to support physical modelling, or even replace it in certain cases. The numerical modelling of complex hydraulic flows is becoming more and more efficient and cost effective, providing a powerful tool for hydraulic analysis, from preliminary to detailed studies. This article describes how the 3D CFD tool has been first validated on a large range of hydraulic phenomena, then how it is exploited by Tractebel on several large projects to analyse and optimize the design or operation of hydraulic structures. Several way to exploit 3D CFD models are developed, either at early stages of the design process, ahead or together with a physical model in order to optimize it exploitation. Application examples are presented, among which the modelling of large gated overflow and orifice spillways … Possibilities and limitations of the 3D CFD tool on these various topics is discussed. Simplified modelling methods has been developed to address specific issues, in an optimized way, among which applications on vertical 2D, and hybrid 2D planar/3D models are presented, the advantages provided by these approaches are highlighted with respect to the addressed issues. The procedures developed for each particular case is described so as to provide indicative frameworks for deploying 3D CFD tools in engineering projects, and provide added value. Based on Tractebel experience, the challenges that have still to be overcome are discussed, with an emphasis put on the modelling of air entrainment. Physical phenomena involved in monophasic flows, however complex, were proven to be well simulated by 3D CFD software. This as long as the modeller has sufficient hydraulic knowledge and experience to adapt a priori the model parameters, mesh and boundary conditions and to carry out an a posteriori critical analysis of the results. In spillway design, this statement applies in particular to the control and conveyance structures, such as channels, weirs, spillway chutes, or flip buckets. When it comes to energy dissipation structures, such as stepped spillways, stilling basins or plunge pools, careful calibration tests of the air entrainment parameters must be performed for each case, questioning the practical and standalone use of current 3D biphasic models in hydraulic engineering. Nevertheless, once the air entrainment parameters have been calibrated, hybrid approach for such complex analyses, combining physical and numerical models, is of main interest, leading to time savings, better physical understanding and enhanced flexibility in the design activities.

Energy consumption of data center has increased dramatically due to the massive computing demands driven from every sector of the economy. Hence, data center energy management has become very important for operating data centers within environmental standards while achieving low energy cost. In order to advance the understanding of thermal management in data centers, relevant environmental information such as temperature, humidity and air quality are gathered through a network of real-time sensors or simulated via sophisticated physical models (e.g. computational fluid dynamics models). However, sensor readings of environmental parameters are collected only at sparse locations and thus cannot provide a detailed map of temperature distribution for the entire data center. While the physics models yield high resolution temperature maps, it is often not feasible, due to computational complexity of these models, to run them in real-time, which is ideally required for optimum data center operation and management. In this work, we propose a novel statistical modeling approach to updating physical model outputs in real-time and providing automatic scheduling for re-computing physical model outputs. The proposed method dynamically corrects the discrepancy between a steady-state output of the physical model and real-time thermal sensor data. We show that the proposed method can provide valuable information for data center energy management such as real-time high-resolution thermal maps. Moreover, it can efficiently detect systematic changes in a data center thermal environment, and automatically schedule physical models to be re-executed whenever significant changes are detected.

Macroscale and multi-functional flexible sensor networks have been widely proposed for the Internet of Things (IoT) and the trillion sensor networks. In this report, our recent progress of flexible sensors fabricated by some printing methods on user-defined non-planer substrates is presented. Especially, flexible strain sensor and temperature sensors are discussed to monitor human condition as a health monitoring device and to detect an object for a robotic prosthesis skin. In addition, flexible digital and analog circuits for the future fully integrated flexible device system are introduced.

Flexible sensors are increasingly employed in soft robotics and wearable devices to provide proprioception of freeform deformations. Although supervised learning can train shape predictors from sensor signals, prediction accuracy strongly depends on sensor layout, which is typically determined heuristically or through trial-and-error. This work introduces a model-free, data-driven computational pipeline that jointly optimizes the number, length, and placement of flexible length-measurement sensors together with the parameters of a shape prediction network for large freeform deformations. Unlike model-based approaches, the proposed method relies solely on datasets of deformed shapes, without requiring physical simulation models, and is therefore broadly applicable to diverse robotic sensing tasks. The pipeline incorporates differentiable loss functions that account for both prediction accuracy and manufacturability constraints. By co-optimizing sensor layouts and network parameters, the method significantly improves deformation prediction accuracy over unoptimized layouts while ensuring practical feasibility. The effectiveness and generality of the approach are validated through numerical and physical experiments on multiple soft robotic and wearable systems.

In this paper, we address the issue of collaborative information processing for diffusive source localization and tracking using wireless sensor networks capable of sensing in dispersive medium/environment. We first determine the space-time concentration distribution of the dispersion from physical modeling and mathematical formulations of an underwater oil spill scenario, considering the effect of laminar water velocity as an external force. For static diffusive source localization, we propose two parametric estimation techniques based on maximum-likelihood (ML) and best linear unbiased estimator for the special case of our physical dispersion model. We prove the consistency and asymptotic normality of the obtained ML solution when the number of sensor nodes and samples approach infinity, and derive the Cramer-Rao lower bound on its performance. We also propose a particle filter-based target tracking scheme for moving diffusive source and derive the posterior Cramer-Rao lower bound for the moving source state estimates as a theoretical performance bound. The performance of the proposed schemes are shown through numerical simulations and compared with the derived theoretical bounds.

Lattice vibrations are the microscopic mechanism responsible for a large, if not dominant, contribution to heat transport in crystalline insulators. These vibrations are described in terms of phonons, collective excitations (or quasiparticles) in the form of waves of atomic displacements inside a crystal. Phonons are traditionally considered to be the quasiparticles responsible for carrying heat through the material. Heat transport is considered as a flux of a phonon gas, diffusing from hot areas (high phonon densities) to cold areas (low phonon densities) in an attempt to reestablish equilibrium, with phonon collisions being the source of heat flux dissipation. However, as dimensionality is reduced, the motion of phonons stimulated by temperature perturbations becomes correlated and this gas-like picture of thermal transport in terms of phonons becomes invalid. In this Thesis, we lay out an interpretation of thermal transport in 2D materials based on the Boltzmann transport equation in the form of collective excitations of phonons. These collective phonon excitations give raise to complex phenomena, such as high thermal conductivities, that are otherwise unexplained. As another example, collective excitations, at variance with conventional diffusive transport, can induce wave-like heat propagation, or second sound. This had been found only in a few exotic materials at cryogenic temperatures, but is present instead routinely in 2D materials at room temperature. The correlated-phonon description of heat transfer can be rationalised by introducing a new collective excitation, called 'relaxon', which is defined as the eigenstate of the collision operator. Whereas only oversimplifying assumptions endow phonons with well defined relaxation times (the average interval of time between collisions), relaxons have always well defined relaxation times and permit an exact description of thermal transport. The complex dynamic of heat transport in 2D is thus greatly simplified and a kinetic gas theory of thermal transport still applies, provided that the gas is not constituted by phonons, but by relaxons. Our work on thermal transport is part of a larger effort, aiming at the creation of a database of numerically computed properties of materials. The high-throughput production of simulated properties is a challenging task, since it necessitates the understanding of a physical model, but it also needs to face a myriad of technicalities and problems that hinder the execution of a large number of calculations. In order to allow the creation of computational materials databases, we developed AiiDA, an open-source automated interactive infrastructure and database for computational science. This platform tackles the problems of creation, management, analysis and sharing of data and simulations, summarized in the pillars of Automation, Data, Environment and Sharing. Automation is achieved by management of remote computational resources and the encoding of workflows, that allows the execution of complex sequences of calculations. The tight coupling between automation and data storage, handled by the platform, enables full reproducibility of the results and a suitable database design allows for efficient data analysis tools. Sharing of scientific knowledge is addressed by providing tools for distribution of data and of the underlying workflows that generated them, creating an ecosystem for computational materials science.

Wearable biosensors have received tremendous attention over the past decade owing to their great potential in predictive analytics and treatment toward personalized medicine. Flexible electronics could serve as an ideal platform for personalized wearable devices because of their unique properties such as light weight, low cost, high flexibility and great conformability. Unlike most reported flexible sensors that mainly track physical activities and vital signs, the new generation of wearable and flexible chemical sensors enables real-time, continuous and fast detection of accessible biomarkers from the human body, and allows for the collection of large-scale information about the individual's dynamic health status at the molecular level. In this article, we review and highlight recent advances in wearable and flexible sensors toward continuous and non-invasive molecular analysis in sweat, tears, saliva, interstitial fluid, blood, wound exudate as well as exhaled breath. The flexible platforms, sensing mechanisms, and device and system configurations employed for continuous monitoring are summarized. We also discuss the key challenges and opportunities of the wearable and flexible chemical sensors that lie ahead.

A highly sensitive and flexible gas sensor that can detect a wide range of chemicals is crucial for wearable applications. However, conventional single resistance-based flexible sensors face challenges in maintaining chemical sensitivity under mechanical stress and can be affected by interfering gases. This study presents a versatile approach for fabricating a micropyramidal flexible ion gel sensor, which accomplishes sub-ppm sensitivity (<80 ppb) at room temperature and discrimination capability between various analytes, including toluene, isobutylene, ammonia, ethanol, and humidity. The discrimination accuracy of our flexible sensor is as high as 95.86%, enhanced by using machine learning-based algorithms. Moreover, its sensing capability remains stable with only a 2.09% change from the flat state to a 6.5 mm bending radius, further amplifying its universal usage for wearable chemical sensing. Therefore, we envision that a micropyramidal flexible ion gel sensor platform assisted by machine learning-based algorithms will provide a new strategy toward next-generation wearable sensing technology.

This paper presents some of the results and experiences in monitoring the hydraulic response of downscaled slope models under simulated rainfall in 1 g. The downscaled slope model platform was developed as part of a four-year research project, “Physical modeling of landslide remediation constructions’ behavior under static and seismic actions”, and its main components are briefly described with the particular focus on the sensor network that allows monitoring changes in soil moisture and pore-water pressure (pwp). The technical characteristics of the sensors and the measurement methods used to provide the metrics are described in detail. Some data on the hydraulic and mechanical responses obtained from the conducted tests on slope models built from different soil types under different test conditions are presented and interpreted in the context of rainfall-induced landslides. The results show that the sensor network used is suitable for monitoring changes in the soil moisture and pwp in the model, both in terms of the transient rainfall infiltration through partially saturated soil and in terms of the rise in the water table and pwp build-up under fully saturated conditions. It is shown how simultaneous monitoring of soil moisture and pwp can be used to reconstruct stress paths that the monitored points undergo during different test phases. Finally, some peculiarities related to hydraulic hysteresis and surface erosion that were observed in some of tests are discussed, as well as possible difficulties in achieving and maintaining the targeted initial moisture distribution in slope models.

Abstract. Innovation Technologies and Applications for Coastal Archaeological sites project (ITACA) aims to develop and test a management system for underwater archaeological sites in coastal regions. The discovering and monitoring service will use innovative satellite remote sensing techniques combined with image processing algorithms. The project will develop a set of applications integrated in a system pursuing the following objectives: – Search and location of ancient ship wrecks; – Monitoring of ship wrecks, ruins and historical artefacts that are now submerged; – Integration of resulting search and monitoring data with on-site data into a management tool for underwater sites; – Demonstration of the system’s suitability for a service. High resolution synthetic aperture radar (TerraSAR-X, Cosmo-SkyMed) and multispectral satellite data (WorldView) will be combined to derive the relative bathymetry of the bottom of the sea up to the depth of 50 meters. The resulting data fusion will be processed using shape detection algorithms specific for archaeological items. The new algorithms, the physical modelling and the computational capabilities will be integrated into the Web-GIS, together with data recorded from surface (2D and 3D modelling) and from underwater surveys. Additional specific archaeological layers will be included into the WebGIS to facilitate the object identification through shape detection techniques and mapping. The system will be verified and validated through an extensive onground (sea) campaign carried out with both cutting edge technologies (side-scan sonar, multi beam echo sounder) and traditional means (professional scuba divers) in two test sites in Italy and Greece. The project is leaded by Planetek Hellas E.P.E. and include ALMA Sistemi sas for the “shape detection” and dissemination tasks, DHI-GRAS and Kell Srl for multispectral and SAR bathymetry. The complete consortium is composed by eleven partners and the project Kick-Off has been held in January 2014. The present contribution aims to present the project research achievements and finding at the mid-term review.

ABSTRACTThe hydrodynamic performance of a gas-stirred ladle is analyzed experimentally, through a factorial experimental design, through physical modeling and particle image velocimetry techniques, to find the effects of the nozzle position, and its number, the gas flow rate and the presence or not of an oil layer on the flow velocity field, the turbulent kinetic energy distribution, and the open eye area, A. Flow patterns changed drastically in the presence of one or two plugs and as a result of the nozzle’s positions. The turbulent flow fields showed significant differences under the presence or absence of an oil layer and the open eye area strongly depends on the gas flow rate, the position and the number of nozzles. Responses , , and A were statistically processed to identify the relevant effects of each variable on , , and A. These responses were used to calculate the optimum process conditions that minimize A and maximize both and , and then improve mixing efficiency in ladles using multi-objective optimization through the genetic algorithm (GAmultiobj). A Pareto characterization and its analysis were performed to reveal the embedded operation rules allowing optimum performances of the ladle for specific production goals.

Concern for the environment and rational management of resources requires the development of recoverable methods of obtaining metallic materials. This also applies to the production of aluminium and its alloys. The quality requirements of the market drive aluminium producers to use effective refining methods, and one of the most commonly used is blowing an inert gas into liquid aluminium via a rotating impeller. The efficiency and cost of this treatment depends largely on the application of the correct ratios between the basic parameters of the process, which are the flow rate of the inert gas, the speed of the rotor and the duration of the process. Determining these ratios in production conditions is expensive and difficult. This article presents the results of research aimed at determining the optimal ratio of the inert gas flow rate to the rotary impeller speed, using physical modeling techniques for the rotor as used in industrial conditions. The tests were carried out for rotary impeller speeds from 150 to 550 rpm and gas flow rates of 12, 17 and 22 dm3/min. The research was carried out on a 1:1 scale physical model, and the results, in the form of visualization of the degree of gas-bubble dispersion, were assessed on the basis of the five typical dispersion patterns. The removal of oxygen from water was carried out analogously to the process of removing hydrogen from aluminium. The curves of the rate of oxygen removal from the model liquid were determined, showing the course of oxygen reduction during refining with the same inert gas flows and rotor speeds mentioned above.

Several methods of device and circuit modelling are reviewed, followed by a detailed outline of state-of-the-art equivalent circuit and physical modeling techniques applicable to both device and circuit design. It is noted that the requirement for CAD (computer-aided design) utilities which extend available linear design techniques into the large-signal regime is being met by both equivalent circuit and physical device models, coupled with powerful algorithms such as the harmonic balance technique. The advent of fast physical device models allows the designer to optimize both devices and circuits based on the actual device structure, without resorting to electrical measurements at the design stage. This is particularly important in the design of integrated circuits, where it is essential to minimize the cost of the design-fabrication cycle.< <ETX xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">&gt;</ETX>

Gimbals are mechatronic systems well known for their use in the stabilization of cameras which are under the effect of sudden movements. Gimbals help keeping cameras at previously defined fixed orientations, so that the captured images have the highest quality. This paper focuses on the design of a Linear Quadratic Gaussian, LQG, controller, based on the physical modeling of a commercial Gimbal with two degrees of freedom (2DOF), which is used for first-person applications in unmanned aerial vehicle (UAV). This approach is proposed to make a more realistic representation of the system under study, since it guarantees high accuracy in the simulation of the dynamic response, as compared to the prediction of the mathematical model of the same system. The development of the model starts by sectioning the Gimbal into a series of interconnected links. Subsequently, a fixed reference system is assigned to each link body and the corresponding homogeneous transformation matrices are established, which will allow the calculation of the orientation of each link and the displacement of their centers of mass. Once the total kinetic and potential energy of the mechanical components are obtained, Lagrange’s method is utilized to establish the mathematical model of the mechanical structure of the Gimbal. The equations of motion of the system are then expressed in state space form, with two inputs, two outputs and four states, where the inputs are the torques produced by each one of the motors, the outputs are the orientation of the first two links, and the states are the aforementioned orientations along with their time derivatives. The state space model was implemented in MATLAB’s Simulink environment to compare its prediction of the transient response with the prediction obtained with the representation of the same system using MATLAB’s SimMechanics physical modelling interface. The mathematical model of each one of the three-phase Brushless DC motors is also expressed in state space form, where the three inputs of each motor model are the voltages of the corresponding motor phases, its two outputs are the angular position and angular velocity, and its four states are the currents in two of the phases, the orientation of the motor shaft and its rate of change. This model is experimentally validated by performing a switching sequence in both the simulation model and the physical system and observing that the transient response of the angular position of the motor shaft is in accordance with the theoretical model. The control system design process starts with the interconnection of the models of the mechanical components and the models of the Brushless DC Motor, using their corresponding state space representations. The resulting model features six inputs, two outputs and eight states. The inputs are the voltages in each phase of the two motors in the Gimbal, the outputs are the angular positions of the first two links, and the states are the currents in two of the phases for each motor and the orientations of the first two links, along with their corresponding time derivatives. An optimal LQG control system is designed using MATLAB’s dlqr and Kalman functions, which calculate the gains for the control system and the gains for the states estimated by the observer. The external excitation in each of the phases is carried out by pulse width modulation. Finally, the transient response of the overall system is evaluated for different reference points. The simulation results show very good agreement with the experimental measurements.