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We analyze sparse frame based regularization of inverse problems by means of a diagonal frame decomposition (DFD) for the forward operator, which generalizes the SVD. The DFD allows to define a non-iterative (direct) operator-adapted frame thresholding approach which we show to provide a convergent regularization method with linear convergence rates. These results will be compared to the well-known analysis and synthesis variants of sparse ℓ1-regularization which are usually implemented thorough iterative schemes. If the frame is a basis (non-redundant case), the three versions of sparse regularization, namely synthesis and analysis variants of ℓ1-regularization as well as the DFD thresholding are equivalent. However, in the redundant case, those three approaches are pairwise different.
Cybersecurity in Health Care
(2020)
Ethical questions have always been crucial in health care; the rapid dissemination of ICT makes some of those questions even more pressing and also raises new ones. One of these new questions is cybersecurity in relation to ethics in health care. In order to more closely examine this issue, this chapter introduces Beauchamp and Childress’ four principles of biomedical ethics as well as additional ethical values and technical aims of relevance for health care. Based on this, two case studies—implantable medical devices and electronic Health Card—are presented, which illustrate potential conflicts between ethical values and technical aims as well as between ethical values themselves. It becomes apparent that these conflicts cannot be eliminated in general but must be reconsidered on a case-by-case basis. An ethical debate on cybersecurity regarding the design and implementation of new (digital) technologies in health care is essential.