As the number of sensors increases, it becomes increasingly difficult to make the right conclusions from the flood of available information. Methods of data analysis, such as Machine learning can help identify essential patterns in the data and ultimately use them to provide better products and better sensors that ultimately optimize business processes.
Our basic motivation is to design and harness robust tracking algorithms and data analysis techniques using both machine learning and statistical methods. The focus is also on hybrid methods that take advantage of both aspects.