In analogy with analog speech transmission, simultaneous two-way (" full-duplex")
transmission of data signals over two-wire circuits can, in principle, be achieved by the …

The reconstruction of a continuous-time signal s (t) from the sign of its (deliberately)
contaminated samples is considered. Sequential, generally nonlinear estimates of s (t) are …

Neural networks can reach their true potential only when they are implemented in hardware
as massively parallel processors. This paper presents the random-pulse machine concept …

A universal source coding system with vector quantizer codebook transmissions is studied
using high resolution quantization theory. Conditions are derived for the optimal tradeoff …

Extrapolating the von Neumann's random-pulse machine concept, the paper discusses the
multibit random-data representation and shows how it can be used for the modular design of …

Abstract Neural Networks (NNs), which are able to learn nonlinear behaviors from a limited
set of measurement data, can provide efficient modeling solutions for many virtual reality …

Geometric precision in digital images refers to the ability to determine parameters such as
position and orientation for an object in an image. This precision is funda-mentally limited by …

The paper is devoted to the theory of linearization of the quantization, by means of the
Random Quantization principle. In the first part, the author defines this principle and …

The paper approaches the effect of domain unstability in random reference in the case of
Random Reference Quantizer (RRQ) with uniformly distributed reference and a constant …

The estimation of analog waveforms s (t) from their clipped contaminated samples is
considered. For differentiable signals s (t), appropriate estimates are established and their …