Property:OneLineSummary

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C
CoopSim +
Simulation of cooperative systems behavior in the presence of faults  +
Coordinated Multi-Drone Pattern Formation with Crazyflie & Lighthouse +
Design and implement synchronized multi-pattern formation (circle, square, hexagon, spiral) for 2–6 Crazyflie drones using Lighthouse positioning and ROS 2/Crazyswarm2, with smooth formation transitions and quantified accuracy/synchronization.  +
Courteous robot guide for visitors to an intelligent home +
Courteous robot guide for visitors to an intelligent home  +
Cross-Spectrum Ocular Identity Recognition via Deep Learning +
Cross-Spectrum Ocular Identity Recognition via Deep Learning  +
D
Data Heterogeneity in Federated Learning +
Addressing the challenges of data imbalance in Federated Learning  +
Data Mining In a Warehouse Inventory +
A study of feature selection and distance measures for clustering big number of categories (>1000) and novelty detection in warehouse environment.  +
Data analysis in collaboration with WirelessCar +
Data analysis in collaboration with WirelessCar  +
Data mining for fault diagnostics in cyberphysical systems +
Data mining for fault diagnostics in cyberphysical systems  +
Data muling services over a constellation of aircraft +
Data muling services over a constellation of aircraft  +
Data sharing and 3D coordination control between couple of mobile robots and a drone +
Develop a coordination control between a mobile robot and drone for movement and sharing data of the environment  +
Data-Driven Activity Recognition and Energy Consumption Forecasting for Heavy-Duty Vehicles +
develop a machine learning framework for activity recognition and energy consumption forecasting, in collaboration with Volvo Group  +
Deep Active Learning for LiDAR Point Cloud Segmentation +
Active Learning to improve data efficiency for LiDAR point Cloud Segmentation  +
Deep Decision Forest +
Designing a deep model that uses decision trees instead of artificial neurons  +
Deep Graph Networks for Future Graph Prediction +
In this project, the candidate is supposed to implement a deep graph network that receives a set of graphs as input and returns the predicted next upcoming graph(s).  +
Deep Networks for Semantic Scene Understanding +
The candidate will implement a neural network to detect spatial relations between objects in the scene. For instance, the book is on the table or the spoon is in the cup.  +
Deep Recurrent Networks for Machine Prognostics +
Construct and optimise Recurrent Neural Networks for industrial applications on machine prognostics; Augmenting industrial data for supervised learning  +
Deep clustering for vehicle operation type +
In this project, deep clustering will be used on the logged vehicle data (LVD) to find the best representation of vehicles’ operation to explain the behavior of the vehicles over time.  +
Deep feature analysis and extraction on Logged Vehicle data for the task of predictive maintenance +
This project is about applying supervised/unsupervised methods of feature selection on Logged Vehicle data (LVD) from Volvo trucks and investigate the contribution in model construction for different predictive maintenance tasks  +
Deep learning and Back Order Solutions +
Deep learning and Back Order Solutions  +
Deep neural network optimization for path prediction in vessels! +
The purpose of this thesis is analyzing a ferry dataset to identify the most optimal path using deep-net.  +
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