Stefano Rinauro

Fast communication: Fast near-maximum likelihood phase estimation of X-ray pulsars

This letter addresses the problem of X-ray pulsar radiation phase estimation, encountered in research works concerning autonomous deep space navigation systems. Autonomous navigation systems represent an intriguing solution to be employed when Earth-assisted navigation is not viable for long range missions. In such applications, X-ray pulsars, as well as other celestial objects, may be employed as peculiar beacons to allow the spacecrafts to adjust their own route. State of the art techniques for estimation of X-ray pulsar radiation phase involve maximization of generally non-convex objective functions, thus resulting in computationally onerous procedures. Here, we show how the problem of pulsar phase estimation can be recast as a cyclic shift parameter estimation problem under multinomial distributed observations, whose maximum likelihood solution can be implemented by means of a fast, Discrete Fourier Transform based procedure. Numerical results show how the herein described fast, near maximum likelihood, estimator favorably compares with selected state of the art estimators, while presenting a significantly reduced computational complexity.

Generalized Method of Moments Estimation of Location Parameters: Application to Blind Phase Acquisition

In this paper, we address the problem of location parameter estimation via a Generalized Method of Moments (GMM) approach. The general framework for the GMM estimation requires the minimization of a suitable, generally nonconvex, elliptic norm. Here we show that, if the estimandum is a shift parameter for a suitable statistic of the observations, a fast, DFT-based, computationally efficient procedure can be employed to perform the estimation. Besides we discuss the relation between the GMM estimation and the maximum likelihood (ML) estimation, showing that the GMM estimation rule provides a closed form ML estimator for shift parameters when the observations are multinomially distributed. As a case study, we analyze a GMM blind phase offset estimator for general quadrature amplitude modulation constellations. Simulation results and theoretical performance analysis show that the GMM estimator outperforms selected state of the art estimators, approaching the Cramér-Rao lower bound for a wide range of signal-to-noise ratio values.

Gain-Control-Free Near-Efficient Phase Acquisition for QAM Constellations

This paper introduces a novel, not data aided, phase-offset estimator for quadrature amplitude modulated (QAM) signals. Contrarily to near-efficient existing phase acquisition techniques, this estimator does not require a preliminary gain adjustment stage while its accuracy preserves the slope of Cramer-Rao bound for medium-high signal-to-noise ratio (SNR) ranges, where it typically outperforms existing blind estimators, with significant improvement for dense and cross QAM constellations. Moreover, it needs only a very rough estimate of the SNR. Like other gain-control-free blind phase-offset estimators, it measures the amount of the cyclic shift by which the (four-folded) phase probability density function (pdf) is rotated under an unknown phase-offset. Estimation of the phase-offset-induced cyclic shift is conducted first by measuring the received data phase pdf by a canonical phase histogram procedure, then by estimating the phase-offset-induced cyclic shift through a cyclic cross correlation-based procedure between the measured phase histogram and a reference phase pdf evaluated within the zero phase-offset hypothesis. Actually, the estimation procedure is presented in a generalized version that considers a tomographic projection of the bidimensional (magnitude/phase) pdf of suitable nonlinear transformations of the received data. The tomographic projection performs a magnitude weighing on the pdf, and this, in turn, results in an improved overall estimation accuracy, as shown by theoretical analysis and numerical simulations here performed to assess the estimator performance.

Merging Color Shift Keying and complementary Pulse Position Modulation for visible light illumination and communication

The ever increasing need to be able to take advantage of broadband services without the need to increase the electromagnetic pollution, has led the scientific community, in recent years, to look for alternatives to the use of a radio frequency communication. From this, it stems the need of budding paradigm of visible light communications. In the context of the activities of the IEEE 802.15.7 Task Group, a new modulation format named Color Shift Keying (CSK), based on sending signals spaced in the domain of the wavelength able to both support the communication and the illumination of indoor environments has been tackled. In this paper, a transmission scheme based on the use of the CSK modulation which also makes use of a modulation format that descends from the Pulse Position Modulation (PPM) has been proposed. The aim of this contribution is also proposing the receiver architecture for that kind of transmission and then evaluate its performance in terms of Bit Error Rate (BER) of Transmission Rate by performing also comparisons with the literature. The proposed scheme is robust with respect to optical interference and presents high rate and low BER at the cost of a bit complexity increasing with respect to other approaches.

Gain-Control-Free Blind Carrier Frequency Offset Acquisition for QAM Constellations

This paper introduces a novel blind frequency offset estimator for quadrature amplitude modulated (QAM) signals. Specifically, after a preliminary frequency compensation, the estimator is based on the ¿/2-folded phase histogram of the received data. Then, the frequency offset estimate is taken as the frequency compensation value that minimizes the mean square error between the phase histogram measured on the received samples and the reference phase probability density function analytically calculated in the case of zero frequency offset. The ¿/2 -folded phase histogram of the received data is here called Constellation Phase Signature, since it definitively characterizes the phase distribution of signal samples belonging to a particular QAM constellation, and it has already been employed to develop a gain-control-free phase estimator that well performs both for square and cross constellations. Also the here described frequency offset estimator has the remarkable property to be gain-control-free and, thus, it can be fruitfully employed in frequency acquisition stages. The asymptotic performance of the estimator has been analytically evaluated and assessed by numerical simulations. Theoretical analysis and numerical results show that the novel frequency offset estimator outperforms state-of-the art estimators in a wide range of signal-to-noise ratio (SNR) values.

Fast image interpolation using Circular Harmonic Functions

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.

Bayesian image interpolation using Markov random fields driven by visually relevant image features

Abstract In this paper we present a Markov Random Field (MRF) based image interpolation procedure suited to both noise-free and noisy measurements. Specifically, after introducing a MRF characterized by means of a novel complex line process representing the visually relevant image features, we derive the global Maximum A Posteriori (MAP) interpolator under the hypothesis of spatially variant additive Gaussian noise. Besides, we derive a closed form local Bayesian MAP interpolator, on the base of which we develop a suboptimal, computationally efficient, single pass interpolation procedure. Numerical simulations demonstrate that the interpolation procedure outperforms state-of-the-art techniques, from both a subjective and objective point of view, in the case of noise-free and noisy measurements.

Joint source and sending rate modeling in adaptive video streaming

This work addresses the modeling of traffic generated by a video source operating in the context of adaptive streaming services. Traffic modeling is a key in several network design issues, such as dimensioning of core and access network resources, developing pricing procedures, carrying out cost-revenue studies. The actual traffic generated during a video streaming session depends on both the video source and the bandwidth variations imposed by lower communication layers. We propose a new traffic model that jointly encompasses these two effects. Specifically, we consider the modeling of the sequence of frame sizes generated by a video streaming source that dynamically adapts its rate to the available communication channel bandwidth using bitstream switching techniques. In order to represent the source rate adaptation to the random network bandwidth variations on the communication channel, we resort to a framework based on Hidden Markov Processes (HMPs). Our HMP model represents the first joint source and sending rate model in adaptive streaming literature. Thanks to effective modeling assumptions on the frame size probability density function (pdf), the HMP parameters can be estimated by means of the Expectation Maximization algorithm. The traffic model is validated by numerical simulations of a mobile adaptive video streaming scenario. We study the models ability to predict several traffic statistics, including the traffic load of a video streaming source in different network points. Besides, we evaluate the model accuracy in characterizing aggregate video traffic resulting from multiplexing various video sources. In all experiments, we show that the proposed model is able to accurately capture the traffic characteristics.

Efficient compressive sampling of spatially sparse fields in wireless sensor networks

Wireless sensor networks (WSNs), i.e., networks of autonomous, wireless sensing nodes spatially deployed over a geographical area, are often faced with acquisition of spatially sparse fields. In this paper, we present a novel bandwidth/energy-efficient compressive sampling (CS) scheme for the acquisition of spatially sparse fields in a WSN. The paper contribution is twofold. Firstly, we introduce a sparse, structured CS matrix and analytically show that it allows accurate reconstruction of bidimensional spatially sparse signals, such as those occurring in several surveillance application. Secondly, we analytically evaluate the energy and bandwidth consumption of our CS scheme when it is applied to data acquisition in a WSN. Numerical results demonstrate that our CS scheme achieves significant energy and bandwidth savings with respect to state-of-the-art approaches when employed for sensing a spatially sparse field by means of a WSN.

Markov Random Fields using complex line process: An application to Bayesian image restoration

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.