Sökning: "mobil inlärning"

Visar resultat 1 - 5 av 9 uppsatser innehållade orden mobil inlärning.

1. 1. Multi-Scale Task Dynamics in Transfer and Multi-Task Learning : Towards Efficient Perception for Autonomous Driving

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

   Författare :Simon Ekman von Huth; [2023]
   Nyckelord :Autonomous Driving; Computer Vision; Deep Learning; Machine Learning; Multi-Task Learning; Transfer Learning; Task Relationships; Task Dynamics; Python; Multi-Scale Representation Learning; Fuss-Free Network; Självkörande Fordon; Datorseende; Djupinlärning; Maskininlärning; Multiuppgiftsinlärning; Överföringsinlärning; Uppgiftsrelationer; Uppgiftsdynamik; Python; Flerskalig Representationsinlärning; Fuss-Free Nätverk;

   Sammanfattning : Autonomous driving technology has the potential to revolutionize the way we think about transportation and its impact on society. Perceiving the environment is a key aspect of autonomous driving, which involves multiple computer vision tasks. LÄS MER

3. 2. Deep Reinforcement Learning on Social Environment Aware Navigation based on Maps

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

   Författare :Victor Sanchez; [2023]
   Nyckelord :Deep Reinforcement Learning; Environment-aware navigation; Robotics; Artificial Intelligence; Apprentissage par renforcement profond; Navigation consciente de l’humain; Intelligence Artificielle; Robotique; Djup Förstärkande Inlärning; Människomedveten navigering; Robotik; Artificiell Intelligens;

   Sammanfattning : Reinforcement learning (RL) has seen a fast expansion in recent years of its successful application to a range of decision-making and complex control tasks. Moreover, deep learning offers RL the opportunity to enlarge its spectrum of complex fields. LÄS MER

4. 3. Unsupervised Domain Adaptation for 3D Object Detection Using Adversarial Adaptation : Learning Transferable LiDAR Features for a Delivery Robot

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

   Författare :Mattias Hansson; [2023]
   Nyckelord :Unsupervised Domain Adaptation; 3D Object Detection; Mobile Robotics; Adversarial Adaptation; Computer Vision; Oövervakad Domänanpassning; 3D Objektigenkänning; Mobila Robotar; Motspelaranpassning; Datorseende;

   Sammanfattning : 3D object detection is the task of detecting the full 3D pose of objects relative to an autonomous platform. It is an important perception system that can be used to plan actions according to the behavior of other dynamic objects in an environment. LÄS MER

5. 4. Initial access in 5G mmWave networks with different base station parameters

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

   Författare :Xiao Yang; [2022]
   Nyckelord :Initial Access; Millimeter-Wave Network; Cell Search; Random Search; Genetic Algorithm; Machine Learning; Första åtkomst; Millimetervågsnätverk; Cell sökning; Slumpmässig sökning; Genetisk algoritm; Maskininlärning;

   Sammanfattning : Nowadays in the fifth generation (5G) communication systems, millimeter wave (mmWave) has aroused interest to not only industrial use but also network operators due to the massive amount of bandwidth available at mmWave frequencies. Initial access in cellular systems is an essential procedure in which new mobile user equipment (UE) establishes a connection with a base station (BS). LÄS MER

7. 5. Optimizing web camera based eye tracking system : An investigating of the effect of network pruning and image resolution

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

   Författare :Olle Svensson; [2021]
   Nyckelord :Neural network; deep learning; eye tracking; pruning; computer vision; optimization; Neurala nätvärk; djup inlärning; blickspårning; beskärning; datorseende; optimering;

   Sammanfattning : Deep learning has opened new doors to things that were only imaginable before. When it comes to eye tracking, the advances in deep learning have made it possible to predict gaze using the integrated camera that most mobile and desktop devices have nowadays. LÄS MER