Sökning: "computational implementation"

Visar resultat 1 - 5 av 404 uppsatser innehållade orden computational implementation.

  1. 1. Movement Estimation with SLAM through Multimodal Sensor Fusion

    Master-uppsats, Linköpings universitet/Medie- och Informationsteknik; Linköpings universitet/Tekniska fakulteten

    Författare :Jimmy Cedervall Lamin; [2024]
    Nyckelord :slam; discrete-slam; continuous-slam; synchronous; asynchronous; computer vision; BRISK; opencv; ceres; visual; inertial; sensor fusion; multimodal; Simultaneous Localization and Mapping; time offset; pose estimation; quaternions; movement estimation;

    Sammanfattning : In the field of robotics and self-navigation, Simultaneous Localization and Mapping (SLAM) is a technique crucial for estimating poses while concurrently creating a map of the environment. Robotics applications often rely on various sensors for pose estimation, including cameras, inertial measurement units (IMUs), and more. LÄS MER

  3. 2. Real-Time Certified MPC for a Nano Quadcopter

    Master-uppsats, Linköpings universitet/Institutionen för systemteknik

    Författare :Arvid Linder; [2024]
    Nyckelord :MPC; Model predictive control; quadcopter; control system;

    Sammanfattning : There is a constant demand to use more advanced control methods in a wider field of applications. Model Predictive Control (MPC) is one such control method, based on recurrently solving an optimization problem for determining the optimal control signal. LÄS MER

  4. 3. Utilizing energy-saving techniques to reduce energy and memory consumption when training machine learning models : Sustainable Machine Learning

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

    Författare :Khalid El Yaacoub; [2024]
    Nyckelord :Sustainable AI; Machine learning; Quantization-Aware Training; Model Distillation; Quantized Distillation; Siamese Neural Networks; Continual Learning; Experience Replay; Data Efficient AI; Energy Consumption; Energy-Savings; Sustainable ML; Computation resources; Hållbar maskin inlärning; Hållbar AI; Maskininlärning; Quantization-Aware Training; Model Distillation; Quantized Distillation; siamesiska neurala nätverk; Continual Learning; Experience Replay; Dataeffektiv AI; Energiförbrukning; Energibesparingar; Beräkningsresurser;

    Sammanfattning : Emerging machine learning (ML) techniques are showing great potential in prediction performance. However, research and development is often conducted in an environment with extensive computational resources and blinded by prediction performance. LÄS MER

  5. 4. Leveraging Large Language Models for Firm-Intelligence: A RAG Framework Approach

    Magister-uppsats, Lunds universitet/Statistiska institutionen

    Författare :Niclas Wölner-Hanssen; [2024]
    Nyckelord :Artificial Intelligence; Large Language Models; Retrieval Augmented Generation; Retrieval Augmented Generation Assessment; Contrastive Learning; Mathematics and Statistics;

    Sammanfattning : In the wake of OpenAI's release of ChatGPT in November 2022, powered by the 175 billion parameter neural network GPT-3, the potential applications of Large Language Models (LLMs) in various sectors have become evident. One such application lies in hedge funds and trading desks where knowledge sharing is paramount. LÄS MER

  7. 5. Big Data and Analytics with Driving  Data : Implementation and Analysis of Data Pipeline and Data Processing Resources

    Master-uppsats, Uppsala universitet/Institutionen för informationsteknologi

    Författare :Ivar Blohm; Erik Jarvis; [2023]
    Nyckelord :;

    Sammanfattning : This thesis project was conducted in cooperation with Zenseact for the purpose of investigating the possible usage of Google BigQuery and its capabilities to store and provide insights of large time-series data. An end-to-end data pipeline was built to facilitate the movement of data from Zenseact's local servers and ingestion into BigQuery. LÄS MER