Sökning: "Edge Computing"

Visar resultat 21 - 25 av 78 avhandlingar innehållade orden Edge Computing.

  1. 21. Improving Soft Real-time Performance of Fog Computing

    Författare :Vaclav Struhar; Moris Behnam; Seyed Mohammad Hossein Ashjaei; Alessandro Papadopoulos; Silviu Craciunas; Ivona Brandic; Mälardalens högskola; []
    Nyckelord :TEKNIK OCH TEKNOLOGIER; ENGINEERING AND TECHNOLOGY; Fog computing; real-time systems; cloud; virtualization; Computer Science; datavetenskap;

    Sammanfattning : Fog computing is a distributed computing paradigm that brings data processing from remote cloud data centers into the vicinity of the edge of the network. The computation is performed closer to the source of the data, and thus it decreases the time unpredictability of cloud computing that stems from (i) the computation in shared multi-tenant remote data centers, and (ii) long distance data transfers between the source of the data and the data centers. LÄS MER

  3. 22. On linear graph invariants related to Ramsey and edge numbers : or how I learned to stop worrying and love the alien invasion

    Författare :Oliver Krüger; Jörgen Backelin; Alexander Engström; Stockholms universitet; []
    Nyckelord :NATURVETENSKAP; NATURAL SCIENCES; NATURVETENSKAP; NATURAL SCIENCES; Ramsey number; edge number; minimal Ramsey graph; independence number; clique number; Turán s theorem; crochet pattern; H13-pattern; linear graph invariant; triangle-free graph; Mathematics; matematik;

    Sammanfattning : In this thesis we study the Ramsey numbers, R(l,k), the edge numbers, e(l,k;n) and graphs that are related to these. The edge number e(l,k;n) may be defined as the least natural number m for which all graphs on n vertices and less than m edges either contains a complete subgraph of size l or an independent set of size k. LÄS MER

  4. 23. Deep Learning on the Edge : A Flexible Multi-level Optimization Approach

    Författare :Nesma Rezk; Magnus Jonsson; Mahdi Fazeli; Antonio Carlos Schneider Beck; Högskolan i Halmstad; []
    Nyckelord :TEKNIK OCH TEKNOLOGIER; ENGINEERING AND TECHNOLOGY;

    Sammanfattning : Recent advances in Deep Learning (DL) research have been adopted in a wide variety of applications, including autonomous driving, AI in health care, and smart homes. In parallel, research in high-performance embedded computing has resulted in advanced hardware platforms that offer enhanced performance and energy efficiency for demanding computations. LÄS MER

  5. 24. Discrete Scale-Space Theory and the Scale-Space Primal Sketch

    Författare :Tony Lindeberg; Jan-Olof Eklundh; Jan. J Koenderink; KTH; []
    Nyckelord :NATURVETENSKAP; NATURAL SCIENCES; NATURVETENSKAP; NATURAL SCIENCES; NATURVETENSKAP; NATURAL SCIENCES; Computer vision; low-level processing; scale-space; diffusion; Gaussian filtering; discrete smoothing; primal sketch; segmentation; descriptive elements; scale detection; image structure; focus-of-attention; tuning low-level processing; blob detection; edge detection; edge focusing; histogram analysis; junction classification; perceptual grouping; texture analysis; critical points; classification of blob events; bifurcations; drift velocity; density of local extrema; multi-scale representation; digital signal processing; Computer Science; Datalogi;

    Sammanfattning : This thesis, within the subfield of computer science known as computer vision, deals with the use of scale-space analysis in early low-level processing of visual information. The main contributions comprise the following five subjects:The formulation of a scale-space theory for discrete signals. LÄS MER

  7. 25. Energy Efficiency in Machine Learning : Approaches to Sustainable Data Stream Mining

    Författare :Eva García Martín; Håkan Grahn; Veselka Boeva; Emiliano Casalicchio; Jesse Read; Blekinge Tekniska Högskola; []
    Nyckelord :NATURVETENSKAP; NATURAL SCIENCES; machine learning; energy efficiency; data stream mining; green machine learning; edge computing; Computer Science; Datavetenskap;

    Sammanfattning : Energy efficiency in machine learning explores how to build machine learning algorithms and models with low computational and power requirements. Although energy consumption is starting to gain interest in the field of machine learning, still the majority of solutions focus on obtaining the highest predictive accuracy, without a clear focus on sustainability. LÄS MER