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We introduce it compressed training adaptive equalization as a novel approach for reducing number of training symbols in a communication packet. The proposed semi-blind approach is based on the exploitation of the special magnitude boundedness of communication symbols. The algorithms are derived from a special convex optimization setting based on l_\infty norm. The corresponding framework has a direct link with the compressive sensing literature established by invoking the duality between l_1 and l_\infty norms.

In the state of the art, a single frame of DFT transform is commonly used as a basis for building amplitude spectral envelopes.
Multiple Frame Analysis (MFA) has already been suggested for envelope estimation, but often with excessive complexity.
In this paper, two MFA-based methods are presented: one simplifying an existing Least Square (LS) solution, and another one based on a simple linear interpolation.

It transpires that the irregularity in the structure of the ionospheric plasma plays a significant role on the ionospherically-propagated HF signals. In this paper, special attention has been paid to derive a simulator that can explicate the perturbed phase influence imposed by the ionosphere irregularities. This has been achieved by studying the space-time correlation as well as statistics of the perturbed phases so that the problem of perturbed phase simulation is recast as generating particular time series satisfying specific power spectrum and statistical distribution.

Runtime-reconfigurable, mixed-radix FFT/IFFT engines are essential for modern wireless communication systems. To comply with varying standards requirements, these engines are customized for each modem. The Chisel hardware construction language has been used in this work to create a generator of runtime-reconfigurable 2n3m5k(7l...) FFT engines targeting software-defined radios (SDR) for modern communications, but with flexibility to support a wide range of applications.

In medical imaging, the acquired images are usually analyzed by a human observer and rated with respect to a diagnostic question. However, this procedure is time-demanding and expensive. Furthermore, the lack of a reference image makes this task challenging. In order to support the human observer in assessing image quality and to ensure an objective evaluation, we extend in this paper our previous no-reference magnetic resonance (MR) image quality assessment system with an active learning loop to reduce the amount of necessary labeled training data.