Learning Entropy as a Learning-Based Information Concept

Recently, a novel concept of a non-probabilistic novelty detection measure, based on a multi-scale quantification of unusually large learning efforts of machine learning systems, was introduced as learning entropy (LE). The key finding with LE is that the learning effort of learning systems is quantifiable as a novelty measure for each individually observed data point of otherwise complex dynamic systems, while the model accuracy is not a necessary requirement for novelty detection. This brief paper extends the explanation of LE from the point of an informatics approach towards a cognitive (learning-based) information measure emphasizing the distinction from Shannon’s concept of probabilistic information. Fundamental derivations of learning entropy and of its practical estimations are recalled and further extended. The potentials, limitations, and, thus, the current challenges of LE are discussed

Wavelet Transform based compression techniques for Raw SAR Data

International audienceSynthetic aperture radar (SAR) is a sophisticated technique of all-weather radar imaging capable of producing fine detailed images from a moving platform. When such a radar is placed on-board a satellite, compression of the raw SAR signal is necessary to reduce the large amount of collected data for downlink to a ground station within the bandwidth constraints. We present a transform-based compression system using Haar wavelet, Battle-Lemarie wavelets (linear and quadratic) and Daubechies wavelets (D-4 and D-20). The transformed data are then quantized using a bit allocation strategy. We take advantage of the multiresolution analysis to use different quantizers in each frequency band of wavelet coefficients. Since the wavelets considered here form orthonormal bases, the reconstruction is guaranteed in each case. Experimental results point out the advantages and drawbacks of this approac

IEEE Symbiotic Autonomous Systems White Paper II

This White Paper follows the first one produced in 2017 by the IEEE Symbiotic Autonomous Systems Initiative (SAS)1 , extending it to address updated technologies and cover additional topics due to the evolution of science and technology. Additional white papers will follow because this is an area in continuous development.   The first examples of symbioses are already available in a number of areas, and even now, these are impacting our economic system and way of life. The IEEE SAS Initiative takes a 360° view based on technology and standardization—the foundation of IEEE—and invites all interested constituencies to contribute complementary point of views, including economic, regulatory, and sociocultural perspectives. The transformation fostered by technology evolution in all paths of life requires planning and education by current and future players. Another goal of the initiative is to consider the future of education, given that these symbioses transform its meaning, making it both shared and distributed.   In this respect, the aims of this White Paper are to further develop the ideas presented in the first white paper: (1) to highlight impacts that are clearly identifiable today, and (2) to indicate emerging issues, thus providing a starting point to those involved in making public policy to understand the technical fundamentals, their evolution and their potential implications.   Note that this White Paper is intended to be self-contained, without requiring the reader to read the previous white paper