Search

Cognitive radio (CR) is currently one of the most promising solutions for designing spectrum-efficient wireless com- munication systems. In this paper, we present a greedy cognitive access policy that handles spectrum sensing with the tools of image processing. By means of the Wigner-Ville transform (WVT), we are able to analyze the time and frequency features of the received interference, and represent them on a time-frequency grid as an image in which brightness reveals the spectrum occupancy. Ac- cording to sensed interference, each secondary user autonomously tries to access the grid following a multiple-division principle, i.e., by properly shaping the signal in the code, time, and frequency domains. In detail, this is realized by processing the WVT to derive the shaping mask (and the matching algorithm) that allows the filling up of the "time spectrum holes." Numerical simulations and a test-bed implementation show how the proposed scheme outper- forms selected state-of-the-art approaches while still presenting an affordable computational cost. Index Terms—Cognitive radio (CR), edge detection (ED), multiple access, Wigner-Ville transform (WVT). I. INTRODUCTION developed here fully embraces the cognitive paradigm and, following (3), is characterized by awareness (through interfer- ence shape detection), the ability to make decisions (shaping is based on the learning/acquisition phases), adaptivity (to con- dition evolutions), reliability (i.e., robustness to impairments), and efficiency (from smart energy consumption). A possible application of this kind of device ranges from medical body area networks (MBANs) or smart grids (not power-lined) to public safety networks. Establishing a connection easily is the common denominator of these networks. In fact, smart grids allow the connection of new devices in an unlicensed band (e.g., electrical vehicles needing to be charged). In the area of public safety, CR was identified in 2008 by both a National Emergency Communications Plan report and the National Broadband Plan (4) as one of the emerging technologies for increasing effi- ciency and effectiveness of spectrum usage. Finally, MBANs need devices able to work in some spectrum bands subject to constraints related to forbidden bands, as specified by different hospitals. In this regard, flexibility and self-configuring ability are two relevant issues. The proposed approach answers those requests.

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.

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.

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.

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.

In this paper we investigate how to rephrase the concepts of cognitive access, originally developed for radio communications, in the framework of underwater acoustic communications. Differently from classical cognitive radio access, where each signal at the receiver is generated by a communication source, in the underwater scenario acoustic signals at the general receive node are likely to be due to communication source as well as natural/artificial acoustic sources (e.g mammals, ship engines and so forth). In order to maximize the access probability for cognitive acoustic nodes, we then focus on understanding the nature of the sensed interference via the tools of image classification, being the image the spectrogram of the acquired signal.

This paper addresses the problem of texture images recovery from compressively sampled measurements. Texture images hardly present a sparse, or even compressible, representation in transformed domains (e.g. wavelet) and are therefore difficult to deal with in the Compressive Sampling (CS) framework. Herein, we resort to the recently defined Graph-based transform (GBT), formerly introduced for depth map coding, as a sparsifying transform for classes of textures sharing the similar spatial patterns. Since GBT proves to be a good candidate for compact representation of some classes of texture, we leverage it for CS texture recovery. To this aim, we resort to a modified version of a state-of-the-art recovery algorithm to reconstruct the texture representation in the GBT domain. Numerical simulation results show that this approach outperforms state-of-the-art CS recovery algorithms on texture images.

The goal of this chapter is twofold. Firstly, we provide the reader with a summary of the state-of-the-art depth map interpolation techniques. Secondly, we present recent results making use of markovian priors for depth map interpolation. Specifically, we provide insight into Markovian mathematical models recently presented in the literature, and we show that such priors allow interpolation without making use of contemporaneously acquired luminance views. This feature extends the scope of the interpolation procedure and decouple its performances from those of the luminance/depth images registration stage.

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.

UnderWater Acoustic (UWA) channels are narrow bandwidth and typically affected by some interference due to propagation, Doppler effect, noise and possible disturbing signals originated by different acoustic sources. Orthogonal Frequency Division Modulation (OFDM) is an effective answer to such impairments. Resource management in the framework of OFDM usually makes use of channel knowledge without taking care of its cost. This work tackles with the dualism between channel estimation and channel prediction and its impact on adaptive modulation, both from a bandwidth and power point of view also in the presence of acoustic interference. The main result is that adaptivity is worth even if requires some processing both at the transmit and receive sides.

Cognitive access is growing in importance in radio frequency wireless applications for occupying spectrum holes leaved free by licensed and unlicensed primary services. Recently the cognitive paradigm involved underwater communications aspects since this propagation scenario is, generally, rich of acoustic sources. This justifies the curiosity of the scientific community to investigate the possibility of using some of the concept borrowed by the cognitive paradigm. Specifically, we consider an underwater active sensor network where each cognitive node applies a Wigner-Ville image processing based pattern analysis procedure not only for evaluating the presence of interference but also its nature. In fact, if the source of interference is a network node, coexistence issues should be taken into account, while, when the interference source is external (mammals, bubbles or ship engines), in principle the transmission is possible in the interfered bands, provided the presence of the interference does not affect the overall transmission quality.

Wireless sensor networks (WSN), 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 CS scheme for acquisition of spatially sparse fields in a WSN. The paper contribution is twofold. Firstly, we introduce a sparse, structured CS matrix and we 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 wrt state-of-the-art approaches when employed for sensing a spatially sparse field by means of a WSN., Comment: Submitted to EURASIP Journal on Advances in Signal Processing

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.

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 we develop a strategy for visual relevancy evaluation using Rate Distortion analysis in the Circular Harmonic Functions (CHF) Domain. In the CHF domain, the different visual characteristics of the transformed frames - edges, lines, forks, crosses, etc - are emphasized. Resorting to this domain we analyze a new approach to evaluate the distortion due to an event of video data loss accounting for the visual relevance of individual lost data. In a real video streaming session, distortion measurements taking into account the visual impact of error events can be stored along with pre-coded video sequence so as to drive the strategies for ad-hoc error protection. Numerical simulations show that the approach herein proposed to evaluate the visual impact of loss events in the decoded sequences captures the visual distortion much better than commonly adopted mean square error criteria between the decoded and the original sequence.

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.