DSP applications have different requirements on data precision and dynamic range.
GDPR goes into effect on May 25th, and places requirements on data controllers, forcing them to explain to people what personal data they intend to collect and why.
In recent years, the NASPI was working on synchrophasor application classification, and attempted to define the applications' requirements on data accuracy, data loss, and latency.
However, such a purely data driven bottom-up modelling of façade structures is only feasible if the available observations meet considerable requirements on data quality.
The full range of orbital, atmospheric, and surface exploration will drive requirements on data return, energy-efficient communications, connectivity, and positioning.
Future mobile communication systems need to efficiently support fully packet-based services with largely different requirements on data rates, ranging from a few kbps to hundreds of Mbps, and largely varying Quality of Service (QoS) requirements.
The experimental comparison studies show that the proposed method has higher performance, lower requirements on data (small number of sampling points in learning instance), and more reliable performance (consistent diagnosis accuracy for different fault modes) than existing methods.
And indeed transparency is a key underlying principle of GDPR, which places requirements on data controllers to clearly explain to people what personal data they intend to collect and for what exact purpose — in order to gain informed consent for processing the data (or, if not consent, another valid basis is required for the data processing to be legal).
The aim of this paper is to identify minimal requirements on data sets to be used with this algorithm and to characterize optimal data sets.
