Essays about: "tracking applications"

Showing result 1 - 5 of 270 essays containing the words tracking applications.

  1. 1. Machine Learning for Spatial Positioning for XR Environments

    University essay from Stockholms universitet/Institutionen för data- och systemvetenskap

    Author : Khaled Alraas; [2024]
    Keywords : Extended Reality; Machine Learning; Sensor Fusion; Spatial Data Accuracy Virtual Productions; Augmented Reality; Virtual Reality; Real-time Camera Tracking; Location-Based Services; Gaming Platforms; Sensor Integration;

    Abstract : This bachelor's thesis explores the integration of machine learning (ML) with sensor fusion techniques to enhance spatial data accuracy in Extended Reality (XR) environments. With XR's revolutionary impact across various sectors, accurate localization in virtual environments becomes imperative. READ MORE

  3. 2. The Cost of Convenience: An Exploration of the Privacy Aspects in Period-Tracking Applications : A mixed-method study of perceived privacy by users in period-tracking applications

    University essay from Malmö universitet/Fakulteten för teknik och samhälle (TS)

    Author : Linnéa Beramand; [2023]
    Keywords : Femtech; Period-tracking; Actor-Network Theory; Privacy; Sceptical Design;

    Abstract : The increasing demand for female-centred services and devices in the digital era has challenged traditional notions of privacy. This thesis explores how users perceive privacy issues in period- tracking applications provided by various developers. READ MORE

  4. 3. Detection and Analysis of Anomalies in Tactical Sensor Systems through Structured Hypothesis Testing

    University essay from KTH/Skolan för elektroteknik och datavetenskap (EECS)

    Author : Fredrik Ohlson; [2023]
    Keywords : Tactical sensor systems; Sensor fusion; Model based diagnostics; Hypothesis testing; Taktiska sensor system; Sensor fusion; Modellbaserad diagnostisering; Hypotesprövning;

    Abstract : The project explores the domain of tactical sensor systems, focusing on SAAB Gripen’s sensor technologies such as radar, RWR (Radar Warning Receiver), and IRST (InfraRed Search and Track). The study employs structured hypothesis testing and model based diagnostics to examine the effectiveness of identifying and isolating deviations within these systems. READ MORE

  5. 4. Deep Learning Model Deployment for Spaceborne Reconfigurable Hardware : A flexible acceleration approach

    University essay from KTH/Skolan för elektroteknik och datavetenskap (EECS)

    Author : Javier Ferre Martin; [2023]
    Keywords : Space Situational Awareness; Deep Learning; Convolutional Neural Networks; FieldProgrammable Gate Arrays; System-On-Chip; Computer Vision; Dynamic Partial Reconfiguration; High-Level Synthesis; Rymdsituationstänksamhet; Djupinlärning; Konvolutionsnätverk; Omkonfigurerbara Field-Programmable Gate Arrays FPGAs ; System-On-Chip SoC ; Datorseende; Dynamisk partiell omkonfigurering; Högnivåsyntes.;

    Abstract : Space debris and space situational awareness (SSA) have become growing concerns for national security and the sustainability of space operations, where timely detection and tracking of space objects is critical in preventing collision events. Traditional computer-vision algorithms have been used extensively to solve detection and tracking problems in flight, but recently deep learning approaches have seen widespread adoption in non-space related applications for their high accuracy. READ MORE

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

    University essay from Linköpings universitet/Datorseende

    Author : Daniel Bladh; [2023]
    Keywords : Deep Learning; Computer Vision; Monocular; SLAM; Depth Estimation;

    Abstract : 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. READ MORE