Sökning: "Input filter"

Visar resultat 16 - 20 av 192 uppsatser innehållade orden Input filter.

  1. 16. Probabilistic Multi-Modal Data Fusion and Precision Coordination for Autonomous Mobile Systems Navigation : A Predictive and Collaborative Approach to Visual-Inertial Odometry in Distributed Sensor Networks using Edge Nodes

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

    Författare :Isabella Luppi; [2023]
    Nyckelord :Distributed Sensor Networks; Point Cloud Processing; Bounding Box Fitting; Trajectory Tracking; Distributed Estimation; Predictive Estimation; Edge-Computing; Reti di Sensori Distribuiti; Elaborazione di Nuvole di Punti; Riquadri di Delimitazione; Tracciamento della Traiettoria; Stima Distribuita; Stima Predittiva; Calcolo Distribuito.; Distribuerade Sensornätverk; Bearbetning av Punktmoln; Anpassning av Begränsningsruta; Trajektorieuppföljning; Distribuerad Uppskattning; Prediktiv Uppskattning; Edge-datorbehandling;

    Sammanfattning : This research proposes a novel approach for improving autonomous mobile system navigation in dynamic and potentially occluded environments. The research introduces a tracking framework that combines data from stationary sensing units and on-board sensors, addressing challenges of computational efficiency, reliability, and scalability. LÄS MER

  3. 17. Enhancing GNSS Precision for Mobile Devices with Sensor Fusion Techniques: A Case Study on eBike Tracking Using State Estimation

    Uppsats för yrkesexamina på avancerad nivå, Lunds universitet/Institutionen för reglerteknik

    Författare :Richard Byström; William Sjödin; [2023]
    Nyckelord :Technology and Engineering;

    Sammanfattning : Electric bicycles have over time become a common method of transportation. With a rapidly increasing user base and expensive prices, there is also an increasing demand for safety and insurance. Bike safety can be used to notify the bicycle owner of a potential theft, or to locate the position of the bike when it is lost. LÄS MER

  4. 18. GPS-Free UAV Geo-Localization Using a Reference 3D Database

    Master-uppsats, Linköpings universitet/Institutionen för systemteknik

    Författare :Justus Karlsson; [2022]
    Nyckelord :Deep Learning; Machine Learning; ML; AI; UAV; GPS-Free; CNN; 3D CNN; GCNN; 3D Database; geolocalization; geo-localization; georegistration; Hidden Markov Model; HMM; satellite; satellite database; Batch-Hard; triplet loss; PyTorch Geometric;

    Sammanfattning : The goal of this thesis has been global geolocalization using only visual input and a 3D database for reference. In recent years Convolutional Neural Networks (CNNs) have seen huge success in the task of classifying images. The flattened tensors at the final layers of a CNN can be viewed as vectors describing different input image features. LÄS MER

  5. 19. Beamformed Channel Matrix Positioning using 5G Testbench CSI data with a Deep-Learning Pipeline

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

    Författare :Andre Ráth; [2022]
    Nyckelord :5G; Deep Learning; Positioning; Channel State Information; CSI; Deep-learning; neural-network; particle-filter; particle; filter; Channel Matrix; Beamformed Channel Matrix; localization; real-world; data-driven; 5G testbench; basestation; Technology and Engineering;

    Sammanfattning : Within the telecommunications industry, a positioning system for estimating user equipment (UE) location using purely information available for the basestation has an enormous number of potential uses. The link between physical position and the network channel state enables potential positioning systems to function by under- standing the network channel state dependency on location, using a model-based, data-based, or a combined approach. LÄS MER

  7. 20. Estimating the risk of insurance fraud based on tonal analysis

    Master-uppsats, Lunds universitet/Matematisk statistik

    Författare :Henrik Steneld; [2022]
    Nyckelord :Spectral analysis; Speaker recognition; Tonal analysis; Speaker Diarization; Machine Learning; LSTM; ResNet; Fraud detection; Mathematics and Statistics;

    Sammanfattning : Insurance companies utilize various methods for identifying claims that are of potential fraudulent nature. With the ever progressing field of artificial intelligence and machine learning models, great interest can be found within the industry to evaluate the use of new methods that may arise as a result of new advanced models in combination with the rich data that is being gathered. LÄS MER