Browsing by Author "Vanhala, Janne"

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Implementing an Offline First Web Application
(2017-12-11) Vanhala, Janne
Perustieteiden korkeakoulu | Master's thesis
The Internet usage of mobile and tablet devices combined overtook desktop for the first time in October 2016. Unlike desktop computers, which usually have physical access to the Internet, mobile devices' connectivity is not guaranteed even in a highly connected area such as a large city in a well-developed country. Dead zones can be encountered in many situations such as while commuting to work, standing in the wrong corner of a room, or attending an event with a large crowd. Despite this, web developers often build apps under the assumption that everyone has a fast and fixed broadband connection. Unfortunately, this leads to a horrible user experience for many people. A recent design paradigm, offline-first, aims to tackle this issue by focusing on the offline experience first. The goal of this thesis is to investigate how a web application can be implemented with the offline-first mindset. The study consists of two parts: a literature study, which reviews the browser features that enable offline web applications, and a constructive study, in which a prototype web application using the offline-first approach is designed and implemented. The prototype developed in this thesis presents solutions to problems such as accessing the application offline, persisting application data to the browser storage and synchronizing the local state to a remote server. However, it was revealed that there are a lot more aspects to consider in the scope of offline web applications. Challenges including conflict management and background synchronization were left to be solved in the future. Nevertheless, the prototype should still serve as a valuable reference to anyone who wants to build an offline-first web application.
Ohjelmistopäivitysten mekanismit mobiililaitteissa
(2009) Vanhala, Janne
Informaatio- ja luonnontieteiden tiedekunta | Bachelor's thesis
Perceptual parsing for extracting semantic information from images.
(2021-01-25) Donayorov, Maksad
Perustieteiden korkeakoulu | Master's thesis
Extracting semantic information from images is a challenge that is hard to achieve with the traditional software engineering approach. However, recent development in deep neural networks allows extracting semantic annotations affordably and efficiently. This work covers building a semantic segmentation model using a multi-task learning approach. The model is able to classify an image based on a scene and extract semantic information about objects present in an image, parts of those objects, as well as annotations about material and texture at a pixel level. A heterogeneous dataset is used to successfully train the model, which unifies five different datasets. Unlike conventional training of segmentation models, this research implements a concept called super-convergence, in which training time is reduced by at least 50%, yet the performance is not compromised. Many points discussed in this research paper are investigative. They aim to reach the main goal: building a segmentation model, efficiently training it, and producing a good semantic annotation of a given image. This work includes extensive information about the sequential progress to achieve this goal, starting with a literature review and ending with the model's implementation.