Comparison between 5G private network and Wifi6 in industrial environment

Industry 4.0 refers to the 4th industrial revolution and at its bases, we can see the digitalization and the automation of the assembly line. The whole production process has improved and evolved thanks to the advances made in networking, and AI studies, which include of course machine learning, cloud computing, IoT, and other technologies that are finally being implemented into the industrial scenario. All these technologies have in common a need for faster, more secure, robust, and reliable communication. One of the many solutions for these demands is the use of mobile communication technologies in the industrial environment, but which technology is better suited for these demands? Of course, the answer isn’t as simple as it seems. The 4th industrial revolution has a never seen incomparable potential with respect to the previous ones, every factory, enterprise, or company have different network demands, and even in each of these infrastructures, the demands may diversify by sector, or by application. For example, in the health care industry, there may be e a need for increased bandwidth for the analysis of high-definition videos or, faster speeds in order to have analytics occur in real-time, and again another application might be higher security and reliability to protect patients’ data. As seen above, choosing the right technology for the right environment and application, considers many things, and the ones just stated are but a speck of dust with respect to the overall picture. In this thesis, we will investigate a comparison between the use of two of the available technologies in use for the industrial environment: Wi-Fi 6 and 5G Private Networks in the specific case of a steel factory.

Trasformazione digitale nei processi di misura e fatturazione - il caso azienda Alfa

Con il lancio di nuove applicazioni tecnologiche come l'Internet of Things, Big Data, Cloud computing e tecnologie mobili che stanno accelerando in maniera spropositata la velocità di cambiamento, i comportamenti, le abitudini e i modi di vivere sono completamente mutati nel favorire un mondo di tecnologie digitali che agevolino le operazioni quotidiane. Questi progressi stanno velocemente cambiando il modo in cui le aziende fanno business, con grandi ripercussioni in tutto quello che è il contesto aziendale, ma non solo. L’avvento della Digital Transformation ha incrementato questi fenomeni e la si potrebbe definire come causa scatenante di tutti i mutamenti che stiamo vivendo. La velocità e l’intensità del cambiamento ha effetti disruptive rispetto al passato, colpendo numerosi settori economici ed abitudini dei consumatori. L’obiettivo di questo elaborato è di analizzare la trasformazione digitale applicata al caso dell’azienda Alfa, comprendendone le potenzialità. In particolare, si vogliono studiare i principali risvolti portati da tale innovazione, le più importanti iniziative adottate in merito alle nuove tecnologie implementate e i benefici che queste portano in campo strategico, di business e cultura aziendale.

Middleware per integrazione di dati eterogenei per sistemi SAP su Openshift

Questo lavoro di tesi è incentrato sullo sviluppo di una soluzione applicativa nell'ambito dell'integrazione di sistemi software basati su tecnologie considerate legacy. In particolar modo è stato studiata una soluzione integrativa per il popolare ERP gestionale Sap su piattaforma Cloud OpenShift. La soluzione è articolata su diversi livelli basati sull'architettura proposta da Gartner nell'ambito della Digital Integration Hub. È stata sviluppata tramite tecnologie open source leader nel settore e tecnologie cloud avanzate.

Generative Neural Networks for image iper-resolution and improvement of Optical Character Recognition's performances

The usage of Optical Character Recognition’s (OCR, systems is a widely spread technology into the world of Computer Vision and Machine Learning. It is a topic that interest many field, for example the automotive, where becomes a specialized task known as License Plate Recognition, useful for many application from the automation of toll road to intelligent payments. However, OCR systems need to be very accurate and generalizable in order to be able to extract the text of license plates under high variable conditions, from the type of camera used for acquisition to light changes. Such variables compromise the quality of digitalized real scenes causing the presence of noise and degradation of various type, which can be minimized with the application of modern approaches for image iper resolution and noise reduction. Oneclass of them is known as Generative Neural Networks, which are very strong ally for the solution of this popular problem.

Towards high-available and energy-efficient virtual computing environments in the cloud

Empowered by virtualisation technology, cloud infrastructures enable the construction of flexi- ble and elastic computing environments, providing an opportunity for energy and resource cost optimisation while enhancing system availability and achieving high performance. A crucial re- quirement for effective consolidation is the ability to efficiently utilise system resources for high- availability computing and energy-efficiency optimisation to reduce operational costs and carbon footprints in the environment. Additionally, failures in highly networked computing systems can negatively impact system performance substantially, prohibiting the system from achieving its initial objectives. In this paper, we propose algorithms to dynamically construct and readjust vir- tual clusters to enable the execution of users’ jobs. Allied with an energy optimising mechanism to detect and mitigate energy inefficiencies, our decision-making algorithms leverage virtuali- sation tools to provide proactive fault-tolerance and energy-efficiency to virtual clusters. We conducted simulations by injecting random synthetic jobs and jobs using the latest version of the Google cloud tracelogs. The results indicate that our strategy improves the work per Joule ratio by approximately 12.9% and the working efficiency by almost 15.9% compared with other state-of-the-art algorithms.

Introducing mobile edge computing capabilities through distributed 5G Cloud Enabled Small Cells

Current trends in broadband mobile networks are addressed towards the placement of different capabilities at the edge of the mobile network in a centralised way. On one hand, the split of the eNB between baseband processing units and remote radio headers makes it possible to process some of the protocols in centralised premises, likely with virtualised resources. On the other hand, mobile edge computing makes use of processing and storage capabilities close to the air interface in order to deploy optimised services with minimum delay. The confluence of both trends is a hot topic in the definition of future 5G networks. The full centralisation of both technologies in cloud data centres imposes stringent requirements to the fronthaul connections in terms of throughput and latency. Therefore, all those cells with limited network access would not be able to offer these types of services. This paper proposes a solution for these cases, based on the placement of processing and storage capabilities close to the remote units, which is especially well suited for the deployment of clusters of small cells. The proposed cloud-enabled small cells include a highly efficient microserver with a limited set of virtualised resources offered to the cluster of small cells. As a result, a light data centre is created and commonly used for deploying centralised eNB and mobile edge computing functionalities. The paper covers the proposed architecture, with special focus on the integration of both aspects, and possible scenarios of application.

Logarithmically proportional objective function for planar surfaces recognition in 3D point cloud

3D laser scanning is becoming a standard technology to generate building models of a facility's as-is condition. Since most constructions are constructed upon planar surfaces, recognition of them paves the way for automation of generating building models. This paper introduces a new logarithmically proportional objective function that can be used in both heuristic and metaheuristic (MH) algorithms to discover planar surfaces in a point cloud without exploiting any prior knowledge about those surfaces. It can also adopt itself to the structural density of a scanned construction. In this paper, a metaheuristic method, genetic algorithm (GA), is used to test this introduced objective function on a synthetic point cloud. The results obtained show the proposed method is capable to find all plane configurations of planar surfaces (with a wide variety of sizes) in the point cloud with a minor distance to the actual configurations. © 2014 IEEE.

Mobile device-to-device distributed computing using data sets

The rapidly increasing computing power, available storage and communication capabilities of mobile devices makes it possible to start processing and storing data locally, rather than offloading it to remote servers; allowing scenarios of mobile clouds without infrastructure dependency. We can now aim at connecting neighboring mobile devices, creating a local mobile cloud that provides storage and computing services on local generated data. In this paper, we describe an early overview of a distributed mobile system that allows accessing and processing of data distributed across mobile devices without an external communication infrastructure. Copyright © 2015 ICST.

Simulation of a-Si PV system linked to the grid by DC boost and three-level inverter under cloud scope

This paper is about a PV system linked to the electric grid through power converters under cloud scope. The PV system is modeled by the five parameters equivalent circuit and a MPPT procedure is integrated into the modeling. The modeling for the converters models the association of a DC-DC boost with a three-level inverter. PI controllers are used with PWM by sliding mode control associated with space vector modulation controlling the booster and the inverter. A case study addresses a simulation to assess the performance of a PV system linked to the electric grid. Conclusions regarding the integration of the PV system into the electric grid are presented. © IFIP International Federation for Information Processing 2015.

Simulation of offshore wind system with three-level converters: HVDC power transmission in cloud scope

This paper is on a simulation for offshore wind systems in deep water under cloud scope. The system is equipped with a permanent magnet synchronous generator and a full-power three-level converter, converting the electric energy at variable frequency in one at constant frequency. The control strategies for the three-level are based on proportional integral controllers. The electric energy is injected through a HVDC transmission submarine cable into the grid. The drive train is modeled by a three-mass model taking into account the resistant stiffness torque, structure and tower in the deep water due to the moving surface elevation. Conclusions are taken on the influence of the moving surface on the energy conversion. © IFIP International Federation for Information Processing 2015.

Monitorització d'alt nivell per a el computing de l'experiment ATLAS (LHC)

Aquest projecte descriu la fusió de les necessitats diaries de monitorització del experiment ATLAS des del punt de vista del cloud. La idea principal es desenvolupar un conjunt de col·lectors que recullin informació de la distribució i processat de les dades i dels test de wlcg (Service Availability Monitoring), emmagatzemant-la en BBDD específiques per tal de mostrar els resultats en una sola pàgina HLM (High Level Monitoring). Un cop aconseguit, l’aplicació ha de permetre investigar més enllà via interacció amb el front-end, el qual estarà alimentat per les estadístiques emmagatzemades a la BBDD.

Computing the persistent homology of range images with alpha shapes

Laser scanning is becoming an increasingly popular method for measuring 3D objects in industrial design. Laser scanners produce a cloud of 3D points. For CAD software to be able to use such data, however, this point cloud needs to be turned into a vector format. A popular way to do this is to triangulate the assumed surface of the point cloud using alpha shapes. Alpha shapes start from the convex hull of the point cloud and gradually refine it towards the true surface of the object. Often it is nontrivial to decide when to stop this refinement. One criterion for this is to do so when the homology of the object stops changing. This is known as the persistent homology of the object. The goal of this thesis is to develop a way to compute the homology of a given point cloud when processed with alpha shapes, and to infer from it when the persistent homology has been achieved. Practically, the computation of such a characteristic of the target might be applied to power line tower span analysis.

Big Data Processing in the Cloud: a Hydra/Sufia Experience

Presentation at Open Repositories 2014, Helsinki, Finland, June 9-13, 2014

A 3D cloud-construction algorithm for the EarthCARE satellite mission

This article presents and assesses an algorithm that constructs 3D distributions of cloud from passive satellite imagery and collocated 2D nadir profiles of cloud properties inferred synergistically from lidar, cloud radar and imager data. It effectively widens the active–passive retrieved cross-section (RXS) of cloud properties, thereby enabling computation of radiative fluxes and radiances that can be compared with measured values in an attempt to perform radiative closure experiments that aim to assess the RXS. For this introductory study, A-train data were used to verify the scene-construction algorithm and only 1D radiative transfer calculations were performed. The construction algorithm fills off-RXS recipient pixels by computing sums of squared differences (a cost function F) between their spectral radiances and those of potential donor pixels/columns on the RXS. Of the RXS pixels with F lower than a certain value, the one with the smallest Euclidean distance to the recipient pixel is designated as the donor, and its retrieved cloud properties and other attributes such as 1D radiative heating rates are consigned to the recipient. It is shown that both the RXS itself and Moderate Resolution Imaging Spectroradiometer (MODIS) imagery can be reconstructed extremely well using just visible and thermal infrared channels. Suitable donors usually lie within 10 km of the recipient. RXSs and their associated radiative heating profiles are reconstructed best for extensive planar clouds and less reliably for broken convective clouds. Domain-average 1D broadband radiative fluxes at the top of theatmosphere(TOA)for (21 km)2 domains constructed from MODIS, CloudSat andCloud–Aerosol Lidar and Infrared Pathfinder Satellite Observations (CALIPSO) data agree well with coincidental values derived from Clouds and the Earth’s Radiant Energy System (CERES) radiances: differences betweenmodelled and measured reflected shortwave fluxes are within±10Wm−2 for∼35% of the several hundred domains constructed for eight orbits. Correspondingly, for outgoing longwave radiation∼65% are within ±10Wm−2.

CDASH: a cloud-enabled program for structure solution from powder diffraction data

The simulated annealing approach to crystal structure determination from powder diffraction data, as implemented in the DASH program, is readily amenable to parallelization at the individual run level. Very large scale increases in speed of execution can be achieved by distributing individual DASH runs over a network of computers. The CDASH program delivers this by using scalable on-demand computing clusters built on the Amazon Elastic Compute Cloud service. By way of example, a 360 vCPU cluster returned the crystal structure of racemic ornidazole (Z0 = 3, 30 degrees of freedom) ca 40 times faster than a typical modern quad-core desktop CPU. Whilst used here specifically for DASH, this approach is of general applicability to other packages that are amenable to coarse-grained parallelism strategies.