A new image processing method for discriminating internal layers from radio echo sounding data of ice sheets via a combined robust principal component analysis and total variation approach

Discriminating internal layers by radio echo sounding is important in analyzing the thickness and ice deposits in the Antarctic ice sheet.The signal processing method of synthesis aperture radar(SAR)has been widely used for improving the signal to noise ratio(SNR)and discriminating internal layers by radio echo sounding data of ice sheets.This method is not efficient when we use edge detection operators to obtain accurate information of the layers,especially the ice-bed interface.This paper presents a new image processing method via a combined robust principal component analysis-total variation(RPCA-TV)approach for discriminating internal layers of ice sheets by radio echo sounding data.The RPCA-based method is adopted to project the high-dimensional observations to low-dimensional subspace structure to accelerate the operation of the TV-based method,which is used to discriminate the internal layers.The efficiency of the presented method has been tested on simulation data and the dataset of the Institute of Electronics,Chinese Academy of Sciences,collected during CHINARE 28.The results show that the new method is more efficient than the previous method in discriminating internal layers of ice sheets by radio echo sounding data.

On the Legal Regulation of Algorithms

Increasingly,algorithms challenge legal regulations,and also challenge the right to explanation,personal privacy and freedom,and individual equal protection.As decision-making mechanisms for human-machine interaction,algorithms are not value-neutral and should be legally regulated.Algorithm disclosure,personal data empowerment,and anti-algorithmic discrimination are traditional regulatory methods relating to algorithms,but mechanically using these methods presents difficulties in feasibility and desirability.Algorithm disclosure faces difficulties such as technical infeasibility,meaningless disclosure,user gaming and intellectual property right infringement.And personal data empowerment faces difficulties such as personal difficulty in exercising data rights and excessive personal data empowerment,making it difficult for big data and algorithms to operate effectively.Anti-algorithmic discrimination faces difficulties such as non-machine algorithmic discrimination,impossible status neutrality,and difficult realization of social equality.Taking scenarios of algorithms lightly is the root cause of the traditional algorithm regulation path dilemma.Algorithms may differ in attributes due to specific algorithmic subjects,objects and domains involved.Therefore,algorithm regulation should be developed and employed based on a case-by-case approach to the development of accountable algorithms.Following these development principles,specific rules can be enacted to regulate algorithm disclosure,data empowerment,and anti-algorithmic discrimination.