Sökning: "stationary applications"
Visar resultat 1 - 5 av 178 avhandlingar innehållade orden stationary applications.
1. Computer Vision for Traffic Surveillance Systems : Methods and Applications
Sammanfattning : Computer vision solutions play a significant role in intelligent transportation systems (ITS) by improving traffic flow, safety and management. In addition, they feature prominently in autonomous vehicles and their future development. The main advantages of vision-based systems are their flexibility, coverage and accessibility. LÄS MER
2. On the use of traffic flows for improved transportation systems : Mathematical modeling and applications
Sammanfattning : This thesis concerns the mathematical modeling of transportation systems for improved decision support and analysis of transportation-related problems. The main purpose of this thesis is to develop and evaluate models and methods that exploit link flows. LÄS MER
3. Economic Aspects of Fuel Cell-Based Stationary Energy Systems
Sammanfattning : It is evident that human activity has an important impact on climate. Constantly increasing energy demand is one of the biggest causes of climate change. The fifth assessment report of the Inter-governmental panel on climate change states that decarbonisation of electricity generation is a key component of climate change mitigation. LÄS MER
4. Modelling and Inference using Locally Stationary Processes : Biomedical applications
Sammanfattning : This thesis considers statistical methods for non-stationary signals, specifically stochastic modelling, inference on the model parameters and optimal spectral estimation. The models are based on Silverman’s definition of Locally Stationary Processes (LSPs). LÄS MER
5. Statistical inference and time-frequency estimation for non-stationary signal classification
Sammanfattning : This thesis focuses on statistical methods for non-stationary signals. The methods considered or developed address problems of stochastic modeling, inference, spectral analysis, time-frequency analysis, and deep learning for classification. LÄS MER