In this paper we show that any sequence of infinite lattice constellations which is good for the unconstrained Gaussian channel can be shaped into a capacity-achieving sequence of codes for the power-constrained Gaussian channel under lattice decoding and non-uniform signalling. Unlike previous results in the literature, our scheme holds with no extra condition on the lattices (e.g. quantizatio...

We consider the upper bounds of the finite block length capacity Cn,FB(P ) of the discrete time Gaussian channel with feedback. We also let Cn(p) the nonfeedback capacity. We prove the relations Cn(P ) ≤ Cn,FB(P ) ≤ Cn(αP ) + 1 2 ln(1 + 1 α ) and Cn(P ) ≤ Cn,FB(P ) ≤ (1 + 1 α )Cn(αP ) for any P > 0 and any α > 0, which induce the half-bit and factor-of two bounds given by Cover and Pombra in th...

This article presents a new descriptor dedicated to Audio Identification (audioID), based on sinusoidal modeling. The core idea is an appropriate selection of the sinusoidal components of the signal to be detected. This new descriptor is robust against usual distortions found in audioID tasks. It has several advantages compared to classical subband-based descriptors including an increased robus...

| Non-coherent detectors for initial code synchronization (acquisition) of BPSK direct sequence spread spectrum signals on an AWGN channel are analyzed. In addition to the thermal noise, in many applications such detectors are faced with the \self-noise", due to the partial period correlations. Under the random code sequences assumption, in this paper an exact analysis of the non-coherent corre...

The symmetric equilibrium resulting from the celebrated Tournament model of Lazear and Rosen has a range of compensation between winner and loser which is inversely proportional to E[f(X)], the expectation of the additive noise’s density. There seems to be a belief that this functional is always increasing in the noise’s variability, which would agree with economic intuition when output is nois...

without (a) and with (b) the proposed regularization. Noise level = 5%. (c) RMSE vs. regularization factor at various noise levels. λopt is defined in Eqn. (5). Fig. 1 RMSE for GRAPPA images with different Tikhonov regularization factors (a) and additive noise level in the ACS region (b). λopt and σopt are defined in Eqn. (5) and (6), respectively. Image matrix 256 x 256, reduction factor R = 4...

ANCHOR is an integrated memory-based scaling model that accounts for a wide range of phenomena in category rating and absolute identification. The model uses anchors stored in memory that serve as prototypes for each response category. The stimuli are represented by magnitudes. Two alternative formulations of the magnitude variability are considered: additive noise, which leads to logarithmic s...

We analyze a family of methods for statistical causal inference from sample under the so-called Additive Noise Model. While most work on the subject has concentrated on establishing the soundness of the Additive Noise Model, the statistical consistency of the resulting inference methods has received little attention. We derive general conditions under which the given family of inference methods...

Many digital steganographic systems employ the capacity of an additive white Gaussian noise (WGN) channel as a performance metric. We present results that show that this capacity metric understates the capacity potential of these systems and propose a more accurate measure based on the arbitrary noise channel. This measure is compared to the capacity of the additive WGN channel and the performa...