Maria Gkizeli

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For any given host image or group of host images and any (block) transform domain of interest, we find the signature vector that when used for spread-spectrum (SS) message embedding maximizes the signal-to-interference-plus-noise ratio (SINR) at the output of the corresponding maximum-SINR linear filter. We establish that, under a (colored) Gaussian(More)
We propose an iterative generalized least squares procedure to recover unknown messages hidden in image hosts via spread-spectrum embedding. Neither the original host nor the embedding signature is assumed available. We demonstrate that for hidden messages of sufficient length (data sample support), recovery can be achieved with probability of error close(More)
For any given host image and (block) transform domain of interest, we derive the signature vector that when used for spread−spectrum (SS) message embedding maximizes the signal−to−interference−plus−noise ratio (SINR) at the output of the maximum SINR linear filter receiver. Under a (colored) Gaussian assumption on the transform domain host data, we see that(More)
—The recent increased interest in large-scale multiple-input multiple-output systems, combined with the cost of analog radio-frequency (RF) chains, necessitates the use of efficient antenna selection (AS) schemes. Capacity or signal-to-noise ratio (SNR) optimal AS has been considered to require an exhaustive search among all possible antenna subsets. In(More)
—In amplify-and-forward single-relay systems that employ an average relay power constraint, one-shot detection at the destination terminal is not optimal when the channel between the source and relay terminals is unknown. In this work, we derive the maximum-likelihood (ML) block noncoherent detector and show that it can be expressed as a reduced-rank(More)