Design and Validation of a Software Defined Radio Testbed for DVB-T Transmission

This paper describes the design and validation of a Software Defined Radio (SDR) testbed, which can be used for Digital Television transmission using the Digital Video Broadcasting - Terrestrial (DVB-T) standard. In order to generate a DVB-T-compliant signal with low computational complexity, we design an SDR architecture that uses the C/C++ language and exploits multithreading and vectorized instructions. Then, we transmit the generated DVB-T signal in real time, using a common PC equipped with multicore central processing units (CPUs) and a commercially available SDR modem board. The proposed SDR architecture has been validated using fixed TV sets, and portable receivers. Our results show that the proposed SDR architecture for DVB-T transmission is a low-cost low-complexity solution that, in the worst case, only requires less than 22% of CPU load and less than 170 MB of memory usage, on a 3.0 GHz Core i7 processor. In addition, using the same SDR modem board, we design an off-line software receiver that also performs time synchronization and carrier frequency offset estimation and compensation

Peak-tracking for stepwise perturbed NMR spectra: development of a new analysis method

Stepwise perturbed NMR spectra analysis is a powerful tool capable of describing kinetic, thermodynamic, structural aspects of proteins at a residue level and of following the physical and chemical changes of the system. The analysis of an NMR spectrum still offers compelling challenges to the automatic identification of the chemical shift evolution. We designed and developed a data-analysis method which allows automatic peak detection in every spectrum, peak tracking between spectra and peak reconstruction for BLUU-Tramp sessions, a stepwise isotopic exchange experiment producing few hundreds of 2D NMR spectra. The method has been named TinT (Trace in Track), referring to the idea that a gaussian decomposition traces peaks within the tracks recognized through 3D mathematical morphology. TinT is capable of determining the evolution of the chemical shifts, intensity and linewidths of each tracked peak. The performances obtained in term of track reconstruction and correct assignment on realistic synthetic spectra were high above 90% when a noise level similar to that of experimental data were considered. TinT was applied successfully to several protein systems during a temperature ramp in isotope exchange experiments. The comparison with a state-of-the-art algorithm showed very good results for great numbers of spectra and low signal to noise ratios, when the graduality of the perturbation is appropriate. In the thesis, in addition to the description of the current version of TinT, some observations and considerations that can allow future revisions or improvements on BLUU-Tramp protocol or its analysis are also described.Co-supervisore: Andrea Fusiello - Dottorato di ricerca cofinanziato FSE: progetto S.H.A.R.M. per la realizzazione di attività di ricerca in collaborazione con imprese; Impresa tutor: Bruker Italia S.R.L.openDottorato di ricerca in Scienze biomediche e biotecnologicheopenBanelli, Tommas

Bayesian Estimation of a Gaussian source in Middleton's Class-A Impulsive Noise

The paper focuses on minimum mean square error (MMSE) Bayesian estimation for a Gaussian source impaired by additive Middleton's Class-A impulsive noise. In addition to the optimal Bayesian estimator, the paper considers also the soft-limiter and the blanker, which are two popular suboptimal estimators characterized by very low complexity. The MMSE-optimum thresholds for such suboptimal estimators are obtained by practical iterative algorithms with fast convergence. The paper derives also the optimal thresholds according to a maximum-SNR (MSNR) criterion, and establishes connections with the MMSE criterion. Furthermore, closed form analytic expressions are derived for the MSE and the SNR of all the suboptimal estimators, which perfectly match simulation results. Noteworthy, these results can be applied to characterize the receiving performance of any multicarrier system impaired by a Gaussian-mixture noise, such as asymmetric digital subscriber lines (ADSL) and power-line communications (PLC).Comment: 30 pages, 13 figures, part of this work has been submitted to IEEE Signal Processing Letter

Decoding (Pseudo)-Scalar Operators in Leptonic and Semileptonic BB Decays

We consider leptonic $B^-\to \ell^- \bar\nu_\ell$ and semileptonic $\bar B \to \pi \ell^- \bar\nu_\ell$, $\bar B \to \rho \ell^- \bar\nu_\ell$ decays and present a strategy to determine short-distance coefficients of New-Physics operators and the CKM element $|V_{ub}|$. As the leptonic channels play a central role, we illustrate this method for (pseudo)-scalar operators which may lift the helicity suppression of the corresponding transition amplitudes arising in the Standard Model. Utilising a new result by the Belle collaboration for the branching ratio of $B^-\to \mu^- \bar\nu_\mu$, we explore theoretically clean constraints and correlations between New Physics coefficients for leptonic final states with $\mu$ and $\tau$ leptons. In order to obtain stronger bounds and to extract $|V_{ub}|$, we employ semileptonic $\bar B \to \pi \ell^- \bar\nu_\ell$ and $\bar B \to \rho \ell^- \bar\nu_\ell$ decays as an additional ingredient, involving hadronic form factors which are determined through QCD sum rule and lattice calculations. In addition to a detailed analysis of the constraints on the New Physics contributions following from current data, we make predictions for yet unmeasured decay observables, compare them with experimental constraints and discuss the impact of CP-violating phases of the New-Physics coefficients.Comment: 35 pages, 19 figures, matches published versio

Distributed Adaptive Learning of Graph Signals

The aim of this paper is to propose distributed strategies for adaptive learning of signals defined over graphs. Assuming the graph signal to be bandlimited, the method enables distributed reconstruction, with guaranteed performance in terms of mean-square error, and tracking from a limited number of sampled observations taken from a subset of vertices. A detailed mean square analysis is carried out and illustrates the role played by the sampling strategy on the performance of the proposed method. Finally, some useful strategies for distributed selection of the sampling set are provided. Several numerical results validate our theoretical findings, and illustrate the performance of the proposed method for distributed adaptive learning of signals defined over graphs.Comment: To appear in IEEE Transactions on Signal Processing, 201