• Refine Query
  • Source
  • Publication year
  • to
  • Language
  • 2
  • Tagged with
  • 3
  • 2
  • 2
  • 2
  • 2
  • 1
  • 1
  • 1
  • 1
  • 1
  • 1
  • 1
  • 1
  • 1
  • 1
  • About
  • The Global ETD Search service is a free service for researchers to find electronic theses and dissertations. This service is provided by the Networked Digital Library of Theses and Dissertations.
    Our metadata is collected from universities around the world. If you manage a university/consortium/country archive and want to be added, details can be found on the NDLTD website.
1

Metoden TAKK som inkludering i förskolan

Blohmé, Evelina, Hedengård, Erika January 2019 (has links)
The purpose with this study is to examine how educators work with ACC (KWS) as inclusion in preschools. As future educators it is in our interest to contribute with more knowledge about the method and its ability for inclusion for all children. The study was carried out as a qualitative investigation. The empirical material was gathered thru semi-structured interviews in form of interviews with different educators and with different experience of the method. We chose the sociocultural theory, the communication theory and the didactic theory for our analysis of the material. The result of the study shows that although the educators have the proper education with the method, the use of it is sometimes sparse due to time, will or effort of the co-workers, themselfs or families of the children. It is important to work with ACC (KWS) from both an organization, group and individual perspective, as it takes time to build up effective communication methods. All the educators we interviewed are comfortable with the use of the ACC (KWS) method and see the positive outcome of the method. The result of our study also shows that the educators who regularly use the method see more opportunities for inclusion of all children.
2

TAKK och bildstöd i förskolan : En kvalitativ studie om förskollärares syn på TAKK och bildstöds påverkan på barns språkutveckling / TAKK and visual support in preschool : A qualitative study about preschool teachers view on the effect of TAKK and visual support on childrens language development

Olménius, Sara, Lundin, Marie January 2023 (has links)
Språk och lärande har nära anknytning till varandra och varje barn ska få möjlighet att uttrycka sig med hjälp av olika uttrycksformer (Läroplan för förskolan [Lpfö18], 2018). Förskollärare är ansvariga för att ge barnen i förskolan de förutsättningar som behövs för att språkutveckling ska ske (Lpfö18, 2018). Syftet med denna studie var att få kunskap om hur förskollärare resonerar om sitt arbete med TAKK och bildstöd och hur detta påverkar barns språkutveckling. Den teori som användes var det sociokulturella perspektivet som utgår från Lev Vygotskijs teorier om att samspel och sammanhang och att lärande sker när människor ingår i sociala interaktioner. Inom sociokulturellt perspektiv lades fokus på begreppen: redskap, mediering och den närmaste utvecklingszonen. Metoden som valdes var semistrukturerade intervjuer vilket valdes för att få reda på förskollärarnas resonemang om TAKK och bildstöd. Intervjuer genomfördes med sex legitimerade och verksamma förskollärare. Resultatet visade att förskollärare använder TAKK och bildstöd som redskap för att stödja barns språkutveckling, både i spontana och rutinsituationer i förskolan. TAKK och bildstöd används för att skapa ett sammanhang i barns vardag, bidra till språkutveckling och hjälpa barn i deras kommunikation. Förskollärare anpassar användandet av TAKK och bildstöd efter det individuella barnets förutsättningar och behov. Då förskollärarna anser att TAKK och bildstöd gynnar alla barns språkutveckling används det med alla barn på förskolan. Förskollärare samverkar med vårdnadshavare för att främja barns språkutveckling både i hemmet och på förskolan
3

Hardware/Software Co-Design for Keyword Spotting on Edge Devices

Jacob Irenaeus M Bushur (15360553) 29 April 2023 (has links)
<p>The introduction of artificial neural networks (ANNs) to speech recognition applications has sparked the rapid development and popularization of digital assistants. These digital assistants perform keyword spotting (KWS), constantly monitoring the audio captured by a microphone for a small set of words or phrases known as keywords. Upon recognizing a keyword, a larger audio recording is saved and processed by a separate, more complex neural network. More broadly, neural networks in speech recognition have popularized voice as means of interacting with electronic devices, sparking an interest in individuals using speech recognition in their own projects. However, while large companies have the means to develop custom neural network architectures alongside proprietary hardware platforms, such development precludes those lacking similar resources from developing efficient and effective neural networks for embedded systems. While small, low-power embedded systems are widely available in the hobbyist space, a clear process is needed for developing a neural network that accounts for the limitations of these resource-constrained systems. In contrast, a wide variety of neural network architectures exists, but often little thought is given to deploying these architectures on edge devices. </p> <p><br></p> <p>This thesis first presents an overview of audio processing techniques, artificial neural network fundamentals, and machine learning tools. A summary of a set of specific neural network architectures is also discussed. Finally, the process of implementing and modifying these existing neural network architectures and training specific models in Python using TensorFlow is demonstrated. The trained models are also subjected to post-training quantization to evaluate the effect on model performance. The models are evaluated using metrics relevant to deployment on resource-constrained systems, such as memory consumption, latency, and model size, in addition to the standard comparisons of accuracy and parameter count. After evaluating the models and architectures, the process of deploying one of the trained and quantized models is explored on an Arduino Nano 33 BLE using TensorFlow Lite for Microcontrollers and on a Digilent Nexys 4 FPGA board using CFU Playground.</p>

Page generated in 0.0124 seconds