Sökning: "Cloud Execution Environment"

Visar resultat 1 - 5 av 31 uppsatser innehållade orden Cloud Execution Environment.

1. 1. Optimizing Realistic 3D Facial Models for VR Avatars through Mesh Simplification

   Master-uppsats, KTH/Skolan för elektroteknik och datavetenskap (EECS)

   Författare :Beiqian Liu; [2023]
   Nyckelord :Mesh Simplification; Virtual Reality; Realistic Avatars; 3D Face Reconstruction;

   Sammanfattning : The use of realistic 3D avatars in Virtual Reality (VR) has gained significant traction in applications such as telecommunication and gaming, offering immersive experiences and face-to-face interactions. However, standalone VR devices often face limitations in computational resources and real-time rendering requirements, necessitating the optimization of 3D models through mesh simplification to enhance performance and ensure a smooth user experience. LÄS MER

3. 2. Attack Surface Management : Principles for simplifying the complexity of OT security

   Master-uppsats, Luleå tekniska universitet/Institutionen för system- och rymdteknik

   Författare :Jyotirmay Veshne; [2023]
   Nyckelord :OT; ICS; SCADA; CPS; IIoT; IoT; Attack Surface; MES; Attack Surface Management; Security; Remote connectivity;

   Sammanfattning : Purpose: Operational technology (OT) environments face significant risks and threats stemming from Industry 4.0. The security landscape for OT is confronted with unprecedented challenges due to the expanding attack surface resulting from factors like cloud adoption, Industrial Internet of Things, and increased mobility. LÄS MER

4. 3. Confidential Federated Learning with Homomorphic Encryption

   Master-uppsats, KTH/Skolan för elektroteknik och datavetenskap (EECS)

   Författare :Zekun Wang; [2023]
   Nyckelord :Cloud Technology; Confidential Computing; Federated Learning; Homomorphic Encryption; Trusted Execution Environment; Molnteknik; Konfidentiell databehandling; Federerad inlärning; Homomorfisk kryptering; Betrodd körningsmiljö;

   Sammanfattning : Federated Learning (FL), one variant of Machine Learning (ML) technology, has emerged as a prevalent method for multiple parties to collaboratively train ML models in a distributed manner with the help of a central server normally supplied by a Cloud Service Provider (CSP). Nevertheless, many existing vulnerabilities pose a threat to the advantages of FL and cause potential risks to data security and privacy, such as data leakage, misuse of the central server, or the threat of eavesdroppers illicitly seeking sensitive information. LÄS MER

5. 4. Computation Offloading for Real-Time Applications : Server Time Reservation for Periodic Tasks

   Master-uppsats, KTH/Skolan för elektroteknik och datavetenskap (EECS)

   Författare :Lizzy Tengana Hurtado; [2023]
   Nyckelord :Computation Offloading; Real-Time Applications; Resource Reservation; Beräkningsavlastning; realtidsapplikationer; resursreservation;

   Sammanfattning : Edge computing is a distributed computing paradigm where computing resources are located physically closer to the data source compared to the traditional cloud computing paradigm. Edge computing enables computation offloading from resource-constrained devices to more powerful servers in the edge and cloud. LÄS MER

7. 5. Low-power Acceleration of Convolutional Neural Networks using Near Memory Computing on a RISC-V SoC

   Master-uppsats, Lunds universitet/Institutionen för elektro- och informationsteknik

   Författare :Kristoffer Westring; Linus Svensson; [2023]
   Nyckelord :FPGA; ASIC; Near Memory Computing; RISC-V; Convolutional Neural Network; Technology and Engineering;

   Sammanfattning : The recent peak in interest for artificial intelligence, partly fueled by language models such as ChatGPT, is pushing the demand for machine learning and data processing in everyday applications, such as self-driving cars, where low latency is crucial and typically achieved through edge computing. The vast amount of data processing required intensifies the existing performance bottleneck of the data movement. LÄS MER