Avancerad sökning
Visar resultat 1 - 5 av 53 avhandlingar som matchar ovanstående sökkriterier.
1. Model Error Compensation in ODE and DAE Estimators : with Automotive Engine Applications
Sammanfattning : Control and diagnosis of complex systems demand accurate information of the system state to enable efficient control and to detect system malfunction. Physical sensors are expensive and some quantities are hard or even impossible to measure with physical sensors. This has made model-based estimation an attractive alternative. LÄS MER
2. Digital Compensation Techniques for Transmitters inWireless Communications Networks
Sammanfattning : Since they appeared, wireless technologies have deeply transformed our society. Today, wireless internet access and other wireless applications demandincreasingly more traffic. However, the continuous traffic increase can be unbearableand requires rethinking and redesigning the wireless technologies inmany different aspects. LÄS MER
3. On Nonlinear Compensation Techniques for Coherent Fiber-Optical Channel
Sammanfattning : Fiber-optical communication systems form the backbone of the internet, enabling global broadband data services. Over the past decades, the demand for high-speed communications has grown exponentially. One of the key techniques for the efficient use of existing bandwidth is the use of higher order modulation formats along with coherent detection. LÄS MER
4. First-Order Algorithms for Communication Efficient Distributed Learning
Sammanfattning : Technological developments in devices and storages have made large volumes of data collections more accessible than ever. This transformation leads to optimization problems with massive data in both volume and dimension. LÄS MER
5. First-Order Algorithms for Communication Efficient Distributed Learning
Sammanfattning : Innovations in numerical optimization, statistics and high performance computing have enabled tremendous advances in machine learning algorithms, fuelling applications from natural language processing to autonomous driving.To deal with increasing data volumes, and to keep the training times of increasingly complex machine learning models reasonable, modern optimization algorithms distribute both data and computations over a large number of machines. LÄS MER