Implementation of an Ultrasound Measurement System

Tässä diplomityössä toteutettiin ultraäänimittausjärjestelmän prototyyppi. Sen tarkoituksena oli tehostaa jätehuoltoyrityksen säiliöiden etävalvontaa. Tutkimuskysymyksenä oli, kuinka luotettavia mittaustuloksia järjestelmä tuottaa. Tutkimus koostuu teoriaosuudesta, jossa käsitellään äänen ominaisuuksia, ultraäänen muodostamista, sen käyttöä etäisyysmittauksissa ja siihen liittyviä rajoituksia. Teoriaosuuteen kuuluu myös aiempi tutkimus, joka sisältää kolme tieteellistä artikkelia samasta aihepiiristä. Käytännön osuudessa esitetään ultraäänimittausjärjestelmän kokoonpano sekä mittaustulokset. Ultraäänimittausjärjestelmä koottiin edullisista komponenteista kuten Arduino Uno -mikrokontrollerista, Raspberry Pi 3 -tietokoneesta, HC-SR04-ultraäänianturista ja Boschin BME280-anturiyksiköstä. Järjestelmään kuuluu myös Huawei E5377 langaton 3G/4G-modeemi, johon Raspberry Pi 3 -tietokone on langattomassa WLAN-yhteydessä ja 3G/4G-modeemi välittää mittaustulokset pilvipalveluun. Järjestelmässä hyödynnettiin avoimen lähdekoodin ratkaisuja. Mitattavat etäisyydet vaihtelivat 18,0 - 3791,0 mm välillä. Mittaustulosten systemaattisten virheiden keskiarvo on -2,0 mm ja satunnaisten virheiden keskiarvo on ±0,9 mm. Suurin systemaattinen virhe on -8,3 mm 1089,0 mm etäisyydellä. Welchin t-testin perusteella ultraäänianturin valmistajan antama tarkkuus 3,0 mm toteutuu parhaiten riskitasolla 0,05 75,0 % tapauksista mitatuista etäisyyksistä. Jos halutaan, että mittausvirhe mahtuisi riskitasolla 0,05 sallitun toleranssin sisälle kaikilla mittausetäisyyksillä, toleranssia olisi kasvatettava 9,0 millimetriin. Kun otetaan huomioon tämän tutkimuksen alkuperäinen tavoite, voidaan todeta mittausjärjestelmän tuottavan riittävän tarkkoja tuloksia

Categorization of Digital Twins : A Literature Review of IoT and Industry

In recent years, the digital twin concept has gained traction in both academia and industry. But what is a digital twin? It is quite common to see many kinds of publications from scientific research to news articles on digital twins mentioning that there is no exact definition for the term. In this paper, we will go through the digital database of IEEE Xplore in an attempt to find out how the publications on digital twins use the term, and how the twins could be categorized and defined more clearly. Our focus is on literature that studies the digital twins within the context of Internet-of-Things (IoT) and industry. Our studies will show that there is indeed a need for a more standardized definition for the term, and that digital twin is often used as a blanket term to cover many systems, prototypes and implementations that may or may not be actual digital twins.Peer reviewe

Classification of Masonry Bricks using Convolutional Neural Networks : a Case Study in a University-Industry Collaboration Project

This paper presents a case study – developing a computer-based classification framework to classify masonry bricks into three quality categories – carried out as a part of the Robocoast R&D Center project. The project aims at better collaboration between universities and industry by establishing an innovation platform where companies can bring their challenges to be addressed together with university experts. The project also promotes collaboration between universities being a part of the RoboAI Competence Centre – a joint research and innovation platform of Satakunta University of Applied Sciences (SAMK) and Tampere University, Pori unit. Automatic classification of bricks is important as it is foreseen that a robotic arm, powered by an automatic classifier, could replace the heavy and tedious work currently performed by humans in brick factories. A convolutional neural network-based solution, using a pretrained VGG-16 deep learning architecture, is proposed. Overall accuracy of 88 % was obtained when considering all three quality classes.When only discarding class 3 bricks, i.e., those that are not suitable for any construction work, the accuracy was 93 %.acceptedVersionPeer reviewe

Interpretation, Modeling and Visualization of Crowdsourced Road Condition Data

Nowadays almost everyone has a mobile phone and even the most basic smartphones often come embedded with a variety of sensors. These sensors, in combination with a large user base, offer huge potential in the realization of crowdsourcing applications. The crowdsourcing aspect is of interest especially in situations where users’ everyday actions can generate data usable in more complex scenarios. The research goal in this paper is to introduce a combination of models for data gathering and analysis of the gathered data, enabling effective data processing of large data sets. Both models are applied and tested in the developed prototype system. In addition, the paper presents the test setup and results of the study, including a description of the web user interface used to illustrate road condition data. The data were collected by a group of users driving on roads in western Finland. Finally, it provides a discussion on the challenges faced in the implementation of the prototype system and a look at the problems related to the analysis of the collected data. In general, the collected data were discovered to be more useful in the assessment of the overall condition of roads, and less useful for finding specific problematic spots on roads, such as potholes.acceptedVersionPeer reviewe

Challenges of Combining Open and Commercial Data Sources in Visitor Mobility Estimations

This paper explores the opportunities and challenges of open data. Open and free interfaces can provide useful data, but sometimes there are challenges with data sufficiency and quality. Open data can be enriched by acquiring commercial data, and by merging multiple data sources, a more comprehensive understanding of a situation can be obtained. The focus of this paper is on nature parks, but the technical solutions can be applied to other venues. In the case presented by this paper, the parks are equipped with on-location visitor counters, and with the help of commercial data provided by a mobile network operator, and open sources, such as the weather and road traffic services, we are attempting to gain a better view of visitor movements. The motivation for this research originated from the cities located in the Satakunta region of Finland. For the purpose of developing recreational and business opportunities in the region, the cities require a better understanding of movement of people - which locations and services are popular, and which locations need improvement. To help achieve this goal, this paper presents a technical solution of how analysis and visualization of combined data can be used to estimate visitor amounts within a geographical area.Peer reviewe

Implementation of an Ultrasound Measurement System

Tässä diplomityössä toteutettiin ultraäänimittausjärjestelmän prototyyppi. Sen tarkoituksena oli tehostaa jätehuoltoyrityksen säiliöiden etävalvontaa. Tutkimuskysymyksenä oli, kuinka luotettavia mittaustuloksia järjestelmä tuottaa. Tutkimus koostuu teoriaosuudesta, jossa käsitellään äänen ominaisuuksia, ultraäänen muodostamista, sen käyttöä etäisyysmittauksissa ja siihen liittyviä rajoituksia. Teoriaosuuteen kuuluu myös aiempi tutkimus, joka sisältää kolme tieteellistä artikkelia samasta aihepiiristä. Käytännön osuudessa esitetään ultraäänimittausjärjestelmän kokoonpano sekä mittaustulokset. Ultraäänimittausjärjestelmä koottiin edullisista komponenteista kuten Arduino Uno -mikrokontrollerista, Raspberry Pi 3 -tietokoneesta, HC-SR04-ultraäänianturista ja Boschin BME280-anturiyksiköstä. Järjestelmään kuuluu myös Huawei E5377 langaton 3G/4G-modeemi, johon Raspberry Pi 3 -tietokone on langattomassa WLAN-yhteydessä ja 3G/4G-modeemi välittää mittaustulokset pilvipalveluun. Järjestelmässä hyödynnettiin avoimen lähdekoodin ratkaisuja. Mitattavat etäisyydet vaihtelivat 18,0 - 3791,0 mm välillä. Mittaustulosten systemaattisten virheiden keskiarvo on -2,0 mm ja satunnaisten virheiden keskiarvo on ±0,9 mm. Suurin systemaattinen virhe on -8,3 mm 1089,0 mm etäisyydellä. Welchin t-testin perusteella ultraäänianturin valmistajan antama tarkkuus 3,0 mm toteutuu parhaiten riskitasolla 0,05 75,0 % tapauksista mitatuista etäisyyksistä. Jos halutaan, että mittausvirhe mahtuisi riskitasolla 0,05 sallitun toleranssin sisälle kaikilla mittausetäisyyksillä, toleranssia olisi kasvatettava 9,0 millimetriin. Kun otetaan huomioon tämän tutkimuksen alkuperäinen tavoite, voidaan todeta mittausjärjestelmän tuottavan riittävän tarkkoja tuloksia

An Open-Source Solution for Mobile Robot based Environmental Sensing

Internet of Things-based devices are equipped with sensors and network connections to collect data and store this data to cloud services. This collected data allows operational decision-making processes to be based on a more accurate picture of the current state of physical environment than before. Existing data collection methods often rely on sensors in fixed locations to obtain environmental measurements. Such a solution is not very scalable in terms of the density of the measuring points. In a dynamic environment, the cost of maintaining such fixedbased solutions may also be high. A more flexible solution will be achieved by equipping an autonomous mobile robot with sensors. Mobile robots are increasingly being used for data-gathering in a wide range of environmental applications. However, a robotic solution can be expensive. In this paper, we present a low-cost solution based on opensource components. The solution utilizes a robot operating system, sensor and IoT-board based on open-source implementation. By implementing a pilot project, we concretize the potential of our approach to environmental monitoring. Potential application areas of this solution include the microclimate control of greenhouses and warehouses, for example.acceptedVersionPeer reviewe

Lessons Learned from Developing Prototypes for Customer Complaint Validation

This research introduces two prototypes installed in vehicles and a cloud service for autonomous collection of data. The prototypes utilize camera, location data, and timestamps to help those responsible for managing customer complaints, and to improve the overall quality of the provided customer service. The use of the system is illustrated by two cases: tracking and photographing bus stops, and tracking and photographing recycling areas. The first prototype is implemented for the Android mobile platform and the second one for the Raspberry Pi single-board computer. This paper discusses the differences and challenges faced in designing and implementing the two prototypes for different platforms.publishedVersionPeer reviewe

Experimenting with Means to Store and Monitor IoT based Measurement Results for Energy Saving

Nowadays, energy consumption and especially energy saving are important issues. The news of global warming has increased the need to save energy in many areas of our living community. Therefore, several research projects on various aspects have been established. This study is part of our ongoing project with the purpose of developing a means of reducing energy consumption in houses and apartments. In particular, the project aims to find ways to increase energy savings without compromising comfortable living. This requires the constant measuring of living conditions, which produces a huge amount of data that has to be monitored continuously and stored for later analysis. In this paper, we report our experiments to select appropriate tools for storing, monitoring, and visualizing data on living conditions. In our test arrangements, the data is gathered by IoT sensors in real locations. Different types of database systems and dashboard software have been used in the trials. Each of these is discussed from the IoT data processing perspective and is presented with examples of gathered data. Finally, we present the toolset that we considered a suitable choice for our context.acceptedVersionPeer reviewe

Conceptual design of an autonomous rover with ground penetrating radar : Application in characterizing soils using deep learning

In the pursuit to make agricultural production efficient, the earliest farmers used data in the form of notes of observations. In the current age of data,it has become easier to collect data over a wide spectrum of parameters. There are numerous sensing technologies for measuring processes and parameters over the field surface, typically mounted on satellites, aerial (drone), ground vehicle and static platforms. In the latest understanding, soil is gaining increasing attention and recognition for its significance in not only increasing productivity but also stabilizing the environment. However, characterizing soil in a field is not trivial, especially when required toaccess the deeper layers and quantifying the essential contents –water, nutrients and organic matter. This paper presents a short review of applications of ground penetrating radars (GPR) in measuring soil content and structure. The focusis ondeeplearning constructs that have been used for interpreting and establishing correlations. The review serves to inform design considerations for a planned autonomous rover that will be used for surveying field soils in the Satakunta region of FinlandacceptedVersionPeer reviewe