Classical methods use statistical-moments to determine the type of modulation in question. Thisessentially correct approach for discerning amplitude modulation (AM) from frequency modulation(FM) fails for more demanding cases such as AM vs. AM-LSB (lower side-band rejection) - radiosignals being richer in information than statistical moments. Parameters with good discriminatingpower were selected in a data conditioning phase and binary deep-learning classifiers were trained forAM-LSB vs. AM-USB, FM vs. AM, AM vs. AM-LSB, etc. The parameters were formed asfeatures, from wave reconstruction primary parameters: rolling pedestal, amplitude, frequency andphase. Very encouraging results were obtained for AM-LSB vs. AM-USB with stochastic training,showing that this particularly difficult case (inaccessible with stochastic moments) is well solvablewith multi-layer perceptron (MLP) neuromorphic software.

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The Lorentz Transformation is traditionally derived requiring the Principle of Relativity and light-speed universality. While the latter can be relaxed, the Principle of Relativity is seen as core to the transformation. The present letter relaxes both statements to the weaker, Symmetry of Reference Principle. Thus the resulting Lorentz transformation and its consequences (time dilatation, length contraction) are, in turn, effects of how we manage space and time.Comment: 2 page

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