Silvia Liberata Ullo

The role of pervasive and cooperative Sensor Networks in Smart Grids communication

The cornerstone of a Smart Grid is the ability for multiple entities to interact via communication networks. A scalable and pervasive communication infrastructure represents a crucial issue in both structuring and operating smart networks. In addressing this problem this paper figures out the potential role of cooperative Wireless Sensor Networks (WSNs). In detail, it analyses the performance of IEEE 802.15.4 based WSNs in order to establish their suitability for a typical set of monitoring and supervision functionalities required by urban-scale Smart Grids applications. The results obtained show that the application of this technology may be very promising in several Smart Grids applications as far as automation, remote monitoring and supervision are concerned.

The role of cooperative information spreading paradigms for Smart Grid monitoring

The large-scale deployment of the Smart Grid paradigm will support the evolution of conventional electrical power systems toward active, flexible and self-healing web energy networks composed of distributed and cooperative energy resources. In this field, the application of traditional hierarchical monitoring paradigms has some disadvantages that could hinder their application in Smart Grids where the constant growth of grid complexity and the need for massive pervasion of Distribution Generation Systems (DGS) require more scalable, more flexible control and regulation paradigms. To overcome these challenges, this paper proposes the concept of a decentralized non-hierarchical monitoring architecture based on intelligent and cooperative smart entities. These devices employ traditional sensors to acquire local bus variables and run information spreading algorithms in order to assess the main variables describing the global grid state. Two average consensus algorithms are compared : Kuramoto and Gossiping respectively and important remarks are underlined.

Image Registration using Non-Linear Diffusion

An image registration algorithm based on mutual information maximization and non-linear diffusion is presented. It relies on a non-parametric estimation of the degree of dependency between the reference image and the template to be registered, which is intrinsically more robust against possible deformations due to imaging geometry and propagation disturbances. The approach based on non linear diffusion, nonetheless, has the advantage of producing a non-parametric discrete warping model which does not rely on a particular set of basis functions, and is therefore as much general as possible. The experimental results on simulated images have quantitatively shown the accuracy of the proposed method.

Multiband CFAR detection of thermal anomalies using principal component analysis

This paper deals with the problem of CFAR detection of thermal anomalies in multispectral satellite data. The goal is to extend the algorithm proposed in [1], and successfully applied to MODIS data from band 21, to the case of multiband investigation. A multiple-channel model has been designed, where data from MODIS bands 21 and 31 are projected into a new coordinates system by adopting the principal component analysis (PCA). A preliminary statistical analysis has been performed on both the principal components of data to verify that the Weibull distribution can be adopted for background. Subsequently, a Kendall test has been used to check the level of dependency of the projected data and it has shown that channels independence can be assumed with high significance level. After PCA, a CFAR detection is applied to projected data and thanks to data independence the single detections are combined with an AND rule. The outcome of the AND operation gives the thermal anomalies detected in both channels with an assigned overall probability of false alarm (PFA). The Multiband CFAR algorithm has been applied to a 256 x 256 MODIS image from bands 21 and 31 and results have been compared with those from NASA-DAAC MOD14.

Constant false alarm rate in fire detection for MODIS data

Abstract : This paper introduces the concept of Constant False Alarm Rate (CFAR) in fire detection for multispectral satellite data. A new algorithm is proposed, based on a technique successfully applied for detection of extended objects in High Resolution SAR images. It compares the pixel under analysis with an adaptive threshold, suitably estimated from the pixels surrounding the one under test, in order to ensure the CFAR property. The proposed approach requires that the background distribution is of Location Scale (LS) type or amenable to such a distribution by a suitable transformation. MODIS data from the 4-micrometer channel are considered. A preliminary statistical analysis is performed to verify if the Weibull distribution, compliant with LS representation, can be adopted for background. MODIS cloud and water masking are applied to identify those pixels to be discarded before implementing the statistical analysis. Results of fire detection are presented for different values of the system parameters (censoring depth and false alarm rate) and compared with the algorithm implemented in the NASA-DAAC MOD14.

The Hyperspectral Unmixing of Trace-Gases From ESA SCIAMACHY Reflectance Data

Atmospheric concentrations of trace-gases are retrieved from hyperspectral data using a blind source separation method. The algorithm relies on the assumption that the absorption cross sections of the gas components are weakly dependent on the overall atmospheric background. The unmixing of contributions from the logarithm of the spectral reflectance provides estimates of both individual trace-gas absorption cross sections and their concentrations. In the experimental analysis, nadir reflectances received by SCanning Imaging Absorption spectroMeter for Atmospheric CHartographY are considered in two scenarios: the sulfur dioxide emissions from a volcanic eruption and the nitrogen dioxide production from anthropogenic pollution. In both cases, it is demonstrated that the algorithm performs very similarly to the Differential Optical Absorption Spectroscopy algorithm but with very little ancillary information.

Land cover classification and monitoring through multisensor image and data combination

Authors in this work aim to present new analysis methods for Earth Observation, developed by processing Sentinel-1 and Landsat-8 satellite data and combining them in an original way. Comparing SAR and Optical/Multispectral data is a procedure already in use because they are two acquisition systems that provide very different and therefore complementary and useful information. Even if the combination of such different data is not a simple process, the overall information greatly improves when both satellite data are jointly used, as application of our procedure to some case studies demonstrates.

A new algorithm for noise reduction and quality improvement in SAR interferograms using inpainting and diffusion

A high-contrast inpainting scheme, based on the Complex Ginzburg-Landau equation, is successfully applied to restoration of SAR interferograms. The algorithm demonstrates quite effective in recovering the phase values in low coherence regions. The validation setup has been carried out in the presence of additive phase noise with a suitable probability density function. Results show that the application of the proposed algorithm to small regions produces very good results both in terms of Signal-to-Noise Ratio (SNR) and in terms of Mean Square Error (MSE).

Phase retrieval in SAR interferograms using diffusion and inpainting

A high-contrast inpainting scheme based on the Complex Ginzburg-Landau equation recently applied successfully to image restoration is applied to SAR interferograms to improve their quality and therefore final quality of Digital Elevation Models (DEMs). The new technique attempts to recover the phase values in low coherence regions through diffusion and inpainting. After phase unwrapping low coherence regions are masked and discarded and a Complex Ginzburg-Landau (CGL) inpainting scheme is applied to regions where phase values are missing. We demonstrate that the residues reduce and the proposed algorithm leads to a higher Signal-to-Noise Ratio (SNR) if compared with MCF algorithm. The restoration technique has been applied to ERS-1 and ERS-2 data sets acquired on July 1995. Results appear to be very promising: the proposed algorithm provides good performances especially in presence of strong noise level and low coherence areas with relatively small dimensions.

Analysis of GPS signals backscattered from a target on the sea surface

In this paper the Two-Scale Model has been used to derive the theoretical Normalized Radar Cross Section (NRCS) for sea clutter in the L-band, taking into account also the circular polarization of GPS signals. Using this theoretical model and the theoretical formula for NRCS, authors aim to investigate the possibility to extend target detection through GPS signals in backscattering configuration by varying the incidence angle and the wind speed, whereas results obtained previously were related only to a single value of the incidence angle and to two values of the wind speed. Target Scattered Power and Sea Clutter Power are derived and compared finding that: 1) smaller targets can be detected as the incidence angle increases; 2) the Sea Clutter Power is lower than the one estimated when the “worst case” of a VV-polarization signal is chosen to derive the NRCS value from experimental data. Some final considerations are made as future work.