Hyvinvointia draamasta : teatteri-ilmaisun ohjaajan ja terveydenhoitajan työparityöskentely

Tämän opinnäytetyön tavoitteena on edistää moniammatillista yhteistyötä terveys- ja teatterialojen välillä. Tutkimustehtävänä on kuvata teatteri-ilmaisun ohjaajan ja terveydenhoitajan dialogista työparityöskentelyä. Opinnäytetyön tarkoituksena oli suunnitella ja toteuttaa Hyvinvointia draamasta -työpajat kahdessa pirkanmaalaisessa yläkoulussa. Draamatyöpajojen tarkoituksena oli osallistujien terveyden ja hyvinvoinnin edistäminen erilaisia soveltavia draamamenetelmiä käyttäen. Työpajojen tavoitteena oli etenkin oppilaiden sosioemotionaalisten taitojen ja ryhmätyötaitojen kehittyminen sekä koulun yhteistyöryhmien kanssa yhteisesti mietittyjen luokkakohtaisten tavoitteiden toteutuminen. Tämä opinnäytetyö toteutettiin pääosin toimintaan painottuvana opinnäytetyönä. Työpajojen toteutuksessa ja raportoinnissa on käytetty laadulliselle tutkimukselle tyypillisiä keinoja. Oppilailta kerättiin työpajojen lopuksi kirjallinen palaute. Suuntaa antavasti voidaan todeta, että jo neljän tapaamiskerran aikana, oppilaiden ja opettajien palautteeseen sekä työpajojen ohjaajien arviointiin perustuen, yhteishenki ja ryhmätyötaidot draamatyöpajoihin osallistuneissa luokissa parantuivat jonkin verran. Tuloksia ei voida kuitenkaan yleistää. Teatteri-ilmaisun ohjaajan ja terveydenhoitajan työparityötä voitaisiin kehittää terveyden edistämisen näkökulmasta laajemminkin. Yhteistyötä kaikilla terveydenhoitajan työsektoreilla, kuten neuvoloissa ja kouluterveydenhuollossa, voitaisiin tutkia ja kehittää. Teatteri-ilmaisunohjaajan ja terveydenhoitajan yhteistyötä voitaisiin hyödyntää myös terveydenhoitajien koulutuksessa. Yhteistyönä voitaisiin toteuttaa esimerkiksi terveydenhoitajien ryhmänohjaustaitoja tukeva draamakurssi.The aim of this thesis is to promote collaboration between health sector and the field of theatre. The aim of the present research is to describe the dialogical pair work between a public health nurse and a drama instructor. Therefore, the purpose of this thesis was to plan and organize "Drama and Well-Being" workshops in two secondary schools in the area of Pirkanmaa. The aim of the workshops was to increase the students' well-being and to help students to develop their social and emotional skills. The method of the present study is mainly functional. Some devices of qualitative research have been used in the context of the workshops and in the written part of this thesis. The workshops included four meetings that lasted for 105 minutes. Based on the evaluation of the group leaders, students and teachers, the general atmosphere in the groups enhanced and some development in the group skills was also noticed. The results cannot be generalized. The pair work of a drama instructor and a public health nurse could be examined and expanded even more. It would be interesting to develop and study this pair work in other public health nurse's working fields, such as maternity clinics. The collaboration of drama instructors and public health nurses could also be valuable in the education of public health nurses. For example a drama course which would support and develop the group director's skills could be organized

A CNN Approach for 5G mmWave Positioning Using Beamformed CSI Measurements

The advent of Artificial Intelligence (AI) has impacted all aspects of human life. One of the concrete examples of AI impact is visible in radio positioning. In this article, for the first time we utilize the power of AI by training a Convolutional Neural Network (CNN) using 5G New Radio (NR) fingerprints consisting of beamformed Channel State Information (CSI). By observing CSI, it is possible to characterize the multipath channel between the transmitter and the receiver, and thus provide a good source of spatiotemporal data to find the position of a User Equipment (UE). We collect ray-tracing-based 5G NR CSI from an urban area. The CSI data of the signals from one Base Station (BS) is collected at the reference points with known positions to train a CNN. We evaluate our work by testing: a) the robustness of the trained network for estimating the positions for the new measurements on the same reference points and b) the accuracy of the CNN-based position estimation while the UE is on points other than the reference points. The results prove that our trained network for a specific urban environment can estimate the UE position with a minimum mean error of 0.98 m.Peer reviewe

Improved deep depth estimation for environments with sparse visual cues

Most deep learning-based depth estimation models that learn scene structure self-supervised from monocular video base their estimation on visual cues such as vanishing points. In the established depth estimation benchmarks depicting, for example, street navigation or indoor offices, these cues can be found consistently, which enables neural networks to predict depth maps from single images. In this work, we are addressing the challenge of depth estimation from a real-world bird’s-eye perspective in an industry environment which contains, conditioned by its special geometry, a minimal amount of visual cues and, hence, requires incorporation of the temporal domain for structure from motion estimation. To enable the system to incorporate structure from motion from pixel translation when facing context-sparse, i.e., visual cue sparse, scenery, we propose a novel architecture built upon the structure from motion learner, which uses temporal pairs of jointly unrotated and stacked images for depth prediction. In order to increase the overall performance and to avoid blurred depth edges that lie in between the edges of the two input images, we integrate a geometric consistency loss into our pipeline. We assess the model’s ability to learn structure from motion by introducing a novel industry dataset whose perspective, orthogonal to the floor, contains only minimal visual cues. Through the evaluation with ground truth depth, we show that our proposed method outperforms the state of the art in difficult context-sparse environments.Peer reviewe

Disruptive GNSS Signal detection and classification at different Power levels Using Advanced Deep-Learning Approach

Although the Global Navigation Satellite System (GNSS) technology provides an excellent benefit in different critical areas such as civilian, aviation, military, and commercial applications, it is highly vulnerable to various signal disruptions causing significant positioning errors. One of the major threats to a GNSS receiver is the intentional interference known as jamming. A Jammer significantly disrupts the normal functioning of a GNSS receiver, at the acquisition, tracking, and positioning stages. The foremost important step to combat against jamming of GNSS signals is the early detection and characterization of the interfering signals to guarantee the Quality of Service (QoS). This paper presents a robust Deep-Learning (DL) based technique using transfer learning to characterize the type of disruption in GNSS signal based on time-frequency analysis. To this end, a pretrained Convolutional Neural Network (CNN) is used to extract the informative features from the scalogram of the received signals. Further, a fully connected layer followed by a Soft-Max activation function is deployed to classify the signals. In this work, the Signal of Interest (SoI) is a synthetic GPS signal generated by a GNSS simulator. In our experiment, the GPS signal is combined with different kinds of jamming, spoofing, and multipath signals. Moreover, the proposed classification approach can recognize not only the various kinds of jammers such as ones producing Continuous Wave Interference (CWI), Multi-CWI (MCWI), Chirp Interference (CI), and Pulse interference (PI) but also the inclusion of Additive White Gaussian Noise (AWGN). Besides that, the effect of five pre-trained CNNs, namely, AlexNet, GoogleNet, ResNet-18, VGG-16, and MobileNet-V2, is evaluated on classification accuracy. The GNSS signal and its seven disruptive variants are recorded at three different power levels such as low, medium, and high. The medium power level signal is used for training and the testing has been carried out for unseen data set of low, high, and mixed power level. From the simulation results, it has been observed that MobileNet-V2 has performed better than other techniques with an accuracy of 99.8%. Finally, the trained MobileNet-V2 is used to predict the unseen data type generated at different Jamming to signal Ratios (JSRs).Peer reviewe

Vaimosta on kehkeytynyt aito ranskatar. Naistenlehtiin kirjoittuvan naiskuvan tarkastelua kriittisen diskurssianalyysin ja systeemis-funktionaalisen kieliopin keinoin

Tutkimuksen aiheena on naistenlehtiin kirjoittuvan maailman tarkastelu. Tavoitteena on selvittää, millainen naiskuva eri-ikäisille naisille suunnattujen aikakauslehtien henkilöartikkeleihin kirjoittuu: minkälaisina toimijoina ja kokijoina naiset kirjoittuvat ja minkälaisina heidät kuvataan. Tavoitteena on myös selvittää, onko eri-ikäisille naisille suunnattujen lehtien naiskuvassa eroja, ja minkälaista ideologiaa tekstit ilmentävät. Tutkielman teoreettinen viitekehys on kriittinen diskurssianalyysi ja menetelmänä käytetään systeemis-funktionaalista kielioppia. Diskurssianalyysin pohjalta pystytään kommentoimaan lehtiin kirjoittuvaa ideologiaa. Systeemis-funktionaalisessa kieliopissa kielellä katsotaan olevan monia eri funktioita, ja edelleen kolme metafunktiota. Metafunktiot ovat ideationaalinen, interpersoonainen ja tekstuaalinen. Työssä tutkitaan ideationaalista metafunktiota, joka on kielenulkoisesta maailmasta kertova metafunktio: näin pystytään tutkimaan artikkeleihin kirjoittuvaa maailmaa. Ideationaalinen metafunktio jakautuu kuuteen eri prosessiluokkaan, joista tutkimuksessa keskitytään pääprosessiluokkiin eli materiaalisiin, mentaalisiin ja relationaalisiin. Aineistona on yhdeksän henkilöjuttua Demistä, Trendistä ja Eevasta, kolme jokaisesta. Artikkelit ovat vuosilta 2016−2017. Analyysi on kvalitatiivista, ja sen perusteella naiset kuvautuvat osin tavallisina, osin ihanteellisina. Materiaalisissa prosesseissa naiset kuvautuvat aktiivisina toimijoina, myös tunteista puhutaan materiaalisten prosessien avulla. Mentaalisissa prosesseissa naiset kuvautuvat analyyttisinä ja kognitiivisina, ja relationaalisissa prosesseissa he kuvautuvat joko suhteessa johonkin tai tulevat määritellyiksi jollain tavalla. Eevassa nainen tulee kuvatuksi miehen asennoitumisen kautta, ja esimerkiksi tässä näkyy ero nuorille ja vanhoille naisille suunnattujen aikakauslehtien välillä. Artikkeleissa on myös prosessien analyysin kautta havaittavissa erilaisia ideologisia asetelmia.fi=Opinnäytetyö kokotekstinä PDF-muodossa.|en=Thesis fulltext in PDF format.|sv=Lärdomsprov tillgängligt som fulltext i PDF-format

RSS-Based Fusion of UWB and WiFi-Based Ranging for Indoor Positioning

Publisher Copyright: © 2021 CEUR-WS. All rights reserved.WiFi positioning with estimated ranges using Round Trip Time (RTT) measurements based on IEEE 802.11 Wireless Local Area Network (WLAN) has become well known since Fine Timing Measurement (FTM) protocol has been characterized. However, the multipath effect is one of the barriers to accurate time-based range measurement. On the other hand, Ultra Wide Band (UWB)-based range measurement has fair resistance to multipath effects but its accuracy is highly dependant on the orientation of the antennas in the transmitter and the receiver and its transmit power is also limited due to the applied regulations. This paper utilizes a Received Signal Strength (RSS)-based fusion of both UWB and WiFi-based range measurements to increase the indoor positioning accuracy. The proposed method takes the advantage of WiFi FTM protocol as well as Two-Way Ranging (TWR) for UWB devices. The empirical range measurement campaign is done at the University of Helsinki premises. Test points with known positions are considered as the ground truth to evaluate the results. The outcome proves that fusing UWB and WiFi ranges for indoor positioning, improves the accuracy in comparison with using the UWB or WiFi alone.Peer reviewe

Vision-Aided Pedestrian Navigation for Challenging GNSS Environments

There is a strong need for an accurate pedestrian navigation system, functional also in GNSS challenging environments, namely urban areas and indoors, for improved safety and to enhance everyday life. Pedestrian navigation is mainly needed in these environments that are challenging for GNSS but also for other RF positioning systems and some non-RF systems such as the magnetometry used for heading due to the presence of ferrous material. Indoor and urban navigation has been an active research area for years. There is no individual system at this time that can address all needs set for pedestrian navigation in these environments, but a fused solution of different sensors can provide better accuracy, availability and continuity. Self-contained sensors, namely digital compasses for measuring heading, gyroscopes for heading changes and accelerometers for the user speed, constitute a good option for pedestrian navigation. However, their performance suffers from noise and biases that result in large position errors increasing with time. Such errors can however be mitigated using information about the user motion obtained from consecutive images taken by a camera carried by the user, provided that its position and orientation with respect to the user’s body are known. The motion of the features in the images may then be transformed into information about the user’s motion. Due to its distinctive characteristics, this vision-aiding complements other positioning technologies in order to provide better pedestrian navigation accuracy and reliability. This thesis discusses the concepts of a visual gyroscope that provides the relative user heading and a visual odometer that provides the translation of the user between the consecutive images. Both methods use a monocular camera carried by the user. The visual gyroscope monitors the motion of virtual features, called vanishing points, arising from parallel straight lines in the scene, and from the change of their location that resolves heading, roll and pitch. The method is applicable to the human environments as the straight lines in the structures enable the vanishing point perception. For the visual odometer, the ambiguous scale arising when using the homography between consecutive images to observe the translation is solved using two different methods. First, the scale is computed using a special configuration intended for indoors. Secondly, the scale is resolved using differenced GNSS carrier phase measurements of the camera in a method aimed at urban environments, where GNSS can’t perform alone due to tall buildings blocking the required line-of-sight to four satellites. However, the use of visual perception provides position information by exploiting a minimum of two satellites and therefore the availability of navigation solution is substantially increased. Both methods are sufficiently tolerant for the challenges of visual perception in indoor and urban environments, namely low lighting and dynamic objects hindering the view. The heading and translation are further integrated with other positioning systems and a navigation solution is obtained. The performance of the proposed vision-aided navigation was tested in various environments, indoors and urban canyon environments to demonstrate its effectiveness. These experiments, although of limited durations, show that visual processing efficiently complements other positioning technologies in order to provide better pedestrian navigation accuracy and reliability

Dissemination of GNSS RTK using MQTT

Precise positioning using Global Navigation Satellite System (GNSS) requires the GNSS receivers to compensate for the errors caused by distortion in the GNSS signal's path due to atmospheric conditions. The Real Time Kinematics (RTK) technique uses terrestrial reference stations that continuously monitor the quality of GNSS signals and provide information that can be be used by the GNSS receivers in the vicinity of a reference station to compensate for the errors. In this paper, we explore the performance of disseminating the RTK correction information using the Message Queuing Telemetry Transport (MQTT) protocol over 5G. We also compare the indirection costs (latency overheads) of using MQTT over 5G to Ethernet and Wi-Fi, our baselines for high-speed and wireless connectivity respectively, and we highlight the impact of 5G power savings when disseminating GNSS RTK using MQTT.Peer reviewe

On the Use of an Ultra-Tight Integration for Robust Navigation in Jammed Scenarios

Peer reviewe

Vector-tracking-based GNSS/INS Deep Coupling and Experiment Platform for Urban Scenarios

Peer reviewe