Wykaz obszarów badawczych związanych z tagiem Konwolucyjne-sieci-neuronowe:
| # | Obszar badawczy | Dziedzina naukowa |
|---|---|---|
| 1 |
The research topic includes the development of algorithms for the processing and analysis of physiological signals, including EEG, for the purpose of the detection and prediction of epileptic seizures. The research will also cover the location of the sources of epilepsy. An important aspect of the research will be the development of algorithms that can be used in medical practice. The EEG signals database collected as part of cooperation with the Medical University of Warsaw for over 50 people is available for use. The pre-processing research is expected to develop an EMG / EEA / ECG artifact elimination method. The task involves the development of effective trait extraction methods for the detection and prediction of epileptic seizures. As an important research novelty, it is worth considering the use of deep learning, including autoencoders and convolutional neural networks.
|
|
| 2 |
The use of machine learning in the analysis and processing of remote sensing data, mainly optical and radar. In particular, the application of deep machine learning and transfer learning in selected areas related to computer vision. The main applications of the described processing and analysis are the classification of the content of satellite, aerial and terrestial imagery, as well as, in general, image recognition. The topi is related to the automation of remote sensing image processing, which is gaining importance with the development of remote sensing: an increase in the number of Earth observation satellites and of image data itself, as well as the increasing use of image data in various types of decision-making processes, analysis of time changes, climate monitoring, etc.
|
|
| 3 |
Using deep machine learning to segment and classify landforms on Mars. The research will use satellite imagery and imagery from different generations of Mars rovers. The result of the work will be the development of a methodology for multi-resolution classification of geomorphological formations on Mars.
|