Search

The need for underwater reliable connections requested to provide different services as sea monitoring, devices control and camera images/videos may lead to require also large rate values. Due to the very limited bandwidth achieving few Khz, space diversity techniques can aid in this twofold mission. In this paper we investigate the use of an underwater acoustic MIMO technique, and jointly adopt the Pulse Position Modulation (PPM), in order to improve the data rates without reducing the reliability of the link. PPM is known to be power efficient modulation format while it is bandwidth inefficient so the use of MIMO can compensate that drawback with reasonable complexity. The performance has been evaluated on the basis of signal recordings for what concerns interference.

Vehicle to Infrastructure (V2I) networks can help managing different on-the-road applications and services, especially where the vehicles population is low and Internet access is needed for messaging, alerts, entertainment. Accessing a network through a proper technology requires the possibility of establishing connections with a minimum quality level; besides, a mechanism for switching from a network with poor performance to a better one by means of a so-called Vertical HandOver (VHO) may result of paramount importance. In this work we propose a switching mechanism based on sensing and processing of some physical/access/networking parameters in order to choose the technologies allowing the minimization of outages.

In compressive sensing, the Restricted Isometry Property is an analytical condition on the measurement matrix that assures reconstruction of a signal which is sparse either in the spatial or in a transformed domain given an undersampled measurements' set. In this paper, we demonstrate the RIP for a sparse, structured measurements matrix, referred to as Radon-like CS matrix. The sparse Radon-Like CS matrix favorably applies to real sensing problems, since it significantly reduces the energy/bandwidth cost of actually collecting each and every sensed values contributing to the CS measurements. Simulation results confirm the feasibility of field reconstruction from undersampled CS measurements set obtained using the Radon-Like CS matrix.

This paper introduces a compressive sampling (CS) scheme for pulse stream images. We adopt a particular sparse sampling matrix, that we call Radon-like CS matrix. Whilst the Radon transform evaluates projections of a given image along different directions, the Radon-like CS matrix evaluates randomly weighted projections of the image along a few directions. We demonstrate that the such CS measurements are invertible and assess the reconstruction accuracy of CS with the Radonlike sampling matrix by numerical trials. The Radon-like CS performs almost as well as state of the art techniques, with a reduced number of operations. As an application example, we show that, when implemented in a resource limited framework such as a Smart Sensors Grid, the sampling matrix significantly reduces inter-node signaling and then the associated energy consumption.

In this paper we introduce a novel, reduced dimension, Polynomial Regression based Classifier (PRC). The classical PRC expands the observed feature data set by considering higher order data statistics. The herein presented novel PRC preliminary performs projections of the data on suitable subspaces associated with the different classes. The projection operation is followed by discarding the contributions due to the higher order mixed sample moments evaluated on the data. Thereby, the overall polynomial approximation order is maintained while the dimensionality of the expanded feature space exploited by the reduced dimension classifier is drastically reduced. We assess the performance of both the full and the reduced PRC by numerical simulations on different scenarios. The reduced dimension PRC performs at least as well as the classical PRC with a significantly lower number of involved terms. This paves the way for extensively exploiting the PRC flexibility and applicability to complex classification problem although in resource limited system environments, such as, for instance, real-time applications on FPGAs.

Power Line Communications (PLC) are one of the most recent technologies proposed to allow the InHome Internet access, in competition with different Wireless Standards as Wi-Fi. The advantages offered by PLC deal with network availability since each modem connected to the plug is able, in principle, to access the Internet while in the Wi-Fi scenario, the propagation environment strongly influences the received signal strength and consequently connection performance. The conventional approach followed in planning the physical layer of PLC systems, like for example in HomePlug solution, is based on Orthogonal Frequency Division Multiplexing (OFDM), able to combat the frequency selective nature of the channel frequency response. However, OFDM presents some drawbacks as, for example, Peak to Average Power Ratio (PAPR) and Inter Carrier Interference (ICI) while a different approach based on constrained signal shaping (single carrier) can solve these problems. In this direction, this work shows a frequency shaping approach where the frequency occupancy is minimized through constraints on spectral emission and link quality level.

The problem of image restoration is considered, where the goal is to recover the original image starting from its blurred and noisy degraded version. A Bayesian restoration procedure is introduced based on modeling the image given the measurements as a Markov Random Fields characterized by spatially variant local priors. A suitable complex valued line process is introduced, generalizing previous literature works, to account for both the intensity and the orientation of image edges. The presence and the orientation of image edges are locally estimated by a computationally efficient filtering stage especially tuned to visually relevant image features, namely a first-order Circular Harmonic Function filter. Simulation results shows the effectiveness of the complex line process in describing local image discontinuities.

In this paper we introduce a novel edge directed image interpolation algorithm so as to obtain an high-resolution image, given a low-resolution image. The interpolation is based on the local image directionality features estimated on the low-resolution image. The in depth analysis of the local edge features is accomplished at a low computational cost by filtering the low-resolution image by means of the first order filter belonging to the class of the Circular Harmonic Functions (CHF). The interpolation algorithm shows low computational complexity. Numerical results show that the CHF-driven interpolation outperforms state of the art estimators from both a subjective and objective point of view, in several simulation conditions.

In this paper a blind computationally efficient Maximum Likelihood (ML) phase offset estimator for QAM constellations is introduced. The estimator is based on the computation of a weighted phase histogram named Constellation Phase Signature (CPS). The CPS is defined as the phase-dependent distribution of the received signal modulus after the application of a non-linear transformation. Here we show how the CPS can be exploited to derive a sub-optimal Maximum Likelihood (ML) estimator that approaches the CRLB for medium-high SNR. Specifically, the values of the measured CPS can be recognized as frequencies of recurrence of the received samples phases. Stemming on this observation, the CPS values can be regarded as a multinomial distributed random variable for which we are able to conduct the maximization of the likelihood function. The theoretical analysis of the estimator performance is carried out in close form. The theoretical estimator performance is compared with those of state-of-art estimators and of the Cramer-Rao lower bound (CRLB). The performance improvement is clearly appreciated, since, at medium to high SNR, the herein described estimator approaches the Cramer-Rao lower bound on all constellations.

Fast and bit-saving video bit-rate switching is an important issue in video streaming systems on a time varying channel as the one offered by a wireless mesh network, or the one sensed during a vertical handover. The recent H.264 video coding standard supports the seamless switching among bitstreams coded at different bit-rates by means of suitably coded frames, named switching pictures. This work addresses the modelling of the traffic generated by a H.264 source performing bit-rate switching using SP frames. The H.264 source is modelled by a Markov chain where each state models the generation of an entire group of pictures (GOP), and is characterized by the kind of SP frame encoded in the GOP. Inter- frame correlation, typical of video sources, is suitably taken into account by the interstate dependence. The accuracy of the model is assessed by comparison of the cell loss rate of a fixed size buffer filled with a synthetic source according to the model herein proposed, with a state of the art AR model and with a real H.264 video codec.

In this paper we present theoretical performance analysis for a blind frequency offset estimator for cross quadrature amplitude modulated constellations. The estimator is based on applying a tentative frequency offset compensation by means of a nonlinear transformation of the received signal samples and on estimating an accumulation function in different angular windows. For perfect frequency offset compensation, the measurements are suitably clustered and their accumulation, named "constellation phase signature" (CPS), is a peaked function of the window orientation. Hence, the frequency offset estimator is selected by maximization of the peakness of the accumulation function. The performance analysis is shown to match the numerical simulations for medium to high values of SNR.

This paper introduces a novel blind frequency offset estimator for signaling using cross Quadrature Amplitude Modulated constellations. The estimator is based on the tentative frequency offset compensation of nonlinearly transformed received signal samples and on their accumulation in different angular windows. When the compensation perfectly removes the frequency offset, the measurements are suitably clustered and their accumulation, that we call the "Constellation Phase Signature" (CPS), is a peaked function of the window orientation. Hence, the frequency offset estimator is obtained by adjusting the compensation so that the peakness of the sample CPS is maximized. The performance of the estimator has been evaluated by numerical simulations. Comparison of the CPS based estimator with a selected existing estimator shows that it performs well for medium to high values of SNR.

This paper addresses the problem of semiblind equalization for long sparse channels using a sparse equalizer. We formulate the equalization problem following a Bussgang approach, derive the coherent MMSE input synbol estimator, and assess the performances of the analyzed semi-blind Bussgang equalizer in typical sparse channels. The simulations show that the semi-blind linear equalization algorithm allows flexible integration of the training in the eqaulization procedure while the sparse, not connected, support favourably copes with channels characterized by long delayed echoes.