Sökning: "Simultaneous Learning"

Visar resultat 1 - 5 av 40 uppsatser innehållade orden Simultaneous Learning.

  1. 1. Deep Learning-Based Depth Estimation Models with Monocular SLAM : Impacts of Pure Rotational Movements on Scale Drift and Robustness

    Master-uppsats, Linköpings universitet/Datorseende

    Författare :Daniel Bladh; [2023]
    Nyckelord :Deep Learning; Computer Vision; Monocular; SLAM; Depth Estimation;

    Sammanfattning : This thesis explores the integration of deep learning-based depth estimation models with the ORB-SLAM3 framework to address challenges in monocular Simultaneous Localization and Mapping (SLAM), particularly focusing on pure rotational movements. The study investigates the viability of using pre-trained generic depth estimation networks, and hybrid combinations of these networks, to replace traditional depth sensors and improve scale accuracy in SLAM systems. LÄS MER

  3. 2. Digital Front End Algorithms for Sub-Band Full Duplex

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

    Författare :Midhat Rizvi; Khaled Al-Khateeb; [2023]
    Nyckelord :Adjacent Channel Leakage Ratio; Bit Error Rate; Clipping and Filtering; Crest Factor Reduction; Digital front end; Digital Pre-Distortion Error Vector Magnitude; Frequency Division Duplex; Power Amplifier; Peak to Average Power Ratio; Peak Cancellation Crest Factor Reduction; Sub Band Full Duplex; Self-Interference Cancellation; Signal-to-Interference Noise Ratio; Signal-to-Noise Ratio; Turbo Clipping; Time Division Duplex; Technology and Engineering;

    Sammanfattning : Sub-band full duplex is a new communication scheme technology, where a single frequency band is partitioned into sub-bands for downlink (DL) and up-link(UL) transmissions, and both can take place simultaneously. The idea behind the sub-band full duplex development is to improve the throughput, and coverage and reduce the latency of the UL communication by allowing the UL reception during the DL transmission. LÄS MER

  4. 3. Nobody Takes It All: Analysis of dynamic propagation of knowledge in a social network

    D-uppsats, Handelshögskolan i Stockholm/Institutionen för nationalekonomi

    Författare :Filippo Allegri; Urja Jain; [2023]
    Nyckelord :Knoweldge; Network; Game Theory; Machine Learning;

    Sammanfattning : In this paper, we analyse how knowledge is shared in a social network, with the purpose of defining recurring patterns and equilibria. We develop a formal model, the Know-It-All game, based on the PageRank algorithm and other related game theoretical models, such as the Buck-holding game. LÄS MER

  5. 4. Through the Blur with Deep Learning : A Comparative Study Assessing Robustness in Visual Odometry Techniques

    Uppsats för yrkesexamina på avancerad nivå, Uppsala universitet/Avdelningen för systemteknik

    Författare :Alexander Berglund; [2023]
    Nyckelord :artificial intelligence; AI; machine learning; ML; deep learning; DL; computer vision; neural networks; NN; convolutional neural networks; CNN; visual odometry; VO; robustness; motion blur; AirForestry; localization; navigation; ego-motion; pose estimation; SLAM; DF-VO; DytanVO; ORB-SLAM3; artificiell intelligens; maskininlärning; datorseende;

    Sammanfattning : In this thesis, the robustness of deep learning techniques in the field of visual odometry is investigated, with a specific focus on the impact of motion blur. A comparative study is conducted, evaluating the performance of state-of-the-art deep convolutional neural network methods, namely DF-VO and DytanVO, against ORB-SLAM3, a well-established non-deep-learning technique for visual simultaneous localization and mapping. LÄS MER

  7. 5. 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