Fourier independent component analysis of radar micro-Doppler features

The capability of discriminating radar targets exhibiting multiple moving parts has become of great interest for both aerospace and ground-based target recognition and analysis. In particular, helicopters and other targets with rotors, as for instance miniature Unmanned Aerial Vehicles, exhibit peculiar characteristics in the radar return that can be used for their recognition. In this paper a novel algorithm to address the problem of micro-Doppler signature unmixing is proposed, exploiting the signal separation capabilities of the Independent Component Analysis (ICA). The core of the algorithm is represented precisely by the use of the ICA procedure, that has been already proved to be a very effective technique for separating hidden information in mixtures of observations. ICA has been successfully employed in several applications such as wireless communications, radar beamforming, trace-gases unmixing and medical imaging processing. The helicopter's rotor blade signature unmixing from a multi-static radar system is considered as case study and results obtained through the application of ICA to simulated multi-component micro-Doppler signatures show the capability of the proposed approach to successfully accomplish the unmixing operation

Integration of Sentinel-1 and Sentinel-2 data for Earth surface classification using Machine Learning algorithms implemented on Google Earth Engine

In this study, Synthetic Aperture Radar (SAR) and optical data are both considered for Earth surface classification. Specifically, the integration of Sentinel-1 (S-1) and Sentinel-2 (S-2) data is carried out through supervised Machine Learning (ML) algorithms implemented on the Google Earth Engine (GEE) platform for the classification of a particular region of interest. Achieved results demonstrate how in this case radar and optical remote detection provide complementary information, benefiting surface cover classification and generally leading to increased mapping accuracy. In addition, this paper works in the direction of proving the emerging role of GEE as an effective cloud-based tool for handling large amounts of satellite data.Comment: 4 pages, 7 figures, IEEE InGARSS conferenc

Multitemporal analysis in Google Earth Engine for detecting urban changes using optical data and machine learning algorithms

The aim of this work is to perform a multitemporal analysis using the Google Earth Engine (GEE) platform for the detection of changes in urban areas using optical data and specific machine learning (ML) algorithms. As a case study, Cairo City has been identified, in Egypt country, as one of the five most populous megacities of the last decade in the world. Classification and change detection analysis of the region of interest (ROI) have been carried out from July 2013 to July 2021. Results demonstrate the validity of the proposed method in identifying changed and unchanged urban areas over the selected period. Furthermore, this work aims to evidence the growing significance of GEE as an efficient cloud-based solution for managing large quantities of satellite data.Comment: 4 pages, 6 figures, 2023 InGARSS Conferenc

Vulnerability analysis of satellite-based synchronized smart grids monitoring systems

The large-scale deployment of wide-area monitoring systems could play a strategic role in supporting the evolution of traditional power systems toward smarter and self-healing grids. The correct operation of these synchronized monitoring systems requires a common and accurate timing reference usually provided by a satellite-based global positioning system. Although these satellites signals provide timing accuracy that easily exceeds the needs of the power industry, they are extremely vulnerable to radio frequency interference. Consequently, a comprehensive analysis aimed at identifying their potential vulnerabilities is of paramount importance for correct and safe wide-area monitoring system operation. Armed with such a vision, this article presents and discusses the results of an experimental analysis aimed at characterizing the vulnerability of global positioning system based wide-area monitoring systems to external interferences. The article outlines the potential strategies that could be adopted to protect global positioning system receivers from external cyber-attacks and proposes decentralized defense strategies based on self-organizing sensor networks aimed at assuring correct time synchronization in the presence of external attacks

Nonlinear Hydrodynamics of a Hard Sphere Fluid Near the Glass Transition

We conduct a numerical study of the dynamic behavior of a dense hard sphere fluid by deriving and integrating a set of Langevin equations. The statics of the system is described by a free energy functional of the Ramakrishnan-Yussouff form. We find that the system exhibits glassy behavior as evidenced through stretched exponential decay and two-stage relaxation of the density correlation function. The characteristic times grow with increasing density according to the Vogel-Fulcher law. The wavenumber dependence of the kinetics is extensively explored. The connection of our results with experiment, mode coupling theory, and molecular dynamics results is discussed.Comment: 34 Pages, Plain TeX, 12 PostScript Figures (not included, available on request

Ultrasonography of quadriceps femoris muscle and subcutaneous fat tissue and body composition by BIVA in chronic dialysis patients

Protein Energy Wasting (PEW) in hemodialysis (HD) patients is a multifactorial condition due to specific pathology-related pathogenetic mechanisms, leading to loss of skeletal muscle mass in HD patients. Computed Tomography and Magnetic Resonance Imaging still represent the gold standard techniques for body composition assessment. However, their widespread application in clinical practice is difficult and body composition evaluation in HD patients is mainly based on conventional anthropometric nutritional indexes and bioelectrical impedance vector analysis (BIVA). Little data is currently available on ultrasound (US)-based measurements of muscle mass and fat tissue in this clinical setting. The purpose of our study is to ascertain: (1) if there are differences between quadriceps rectus femoris muscle (QRFM) thickness and abdominal/thigh subcutaneous fat tissue (SFT) measured by US between HD patients and healthy subjects; (2) if there is any correlation between QRFM and abdominal/thigh SFT thickness by US, and BIVA/conventional nutritional indexes in HD patients. We enrolled 65 consecutive HD patients and 33 healthy subjects. Demographic and laboratory were collected. The malnutrition inflammation score (MIS) was calculated. Using B-mode US system, the QRFM and SFT thicknesses were measured at the level of three landmarks in both thighs (superior anterior iliac spine, upper pole of the patella, the midpoint of the tract included between the previous points). SFT was also measured at the level of the periumbilical point. The mono frequency (50 KHz) BIVA was conducted using bioelectrical measurements (Rz, resistance; Xc, reactance; adjusted for height, Rz/H and Xc/H; PA, phase angle). 58.5% were men and the mean age was 69 (SD 13.7) years. QRFM and thigh SFT thicknesses were reduced in HD patients as compared to healthy subjects (p < 0.01). Similarly, also BIVA parameters, expression of lean body mass, were lower (p < 0.001), except for Rz and Rz/H in HD patients. The average QRFM thickness of both thighs at top, mid, lower landmarks were positively correlated with PA and body cell mass (BCM) by BIVA, while negatively correlated with Rz/H (p < 0.05). Abdominal SFT was positively correlated with PA, BCM and basal metabolic rate (BMR) (p < 0.05). Our study shows that ultrasound QRFM and thigh SFT thicknesses were reduced in HD patients and that muscle ultrasound measurements were significantly correlated with BIVA parameters

Unsupervised Spike Sorting for Large-Scale, High-Density Multielectrode Arrays

We present a method for automated spike sorting for recordings with high-density, large-scale multielectrode arrays. Exploiting the dense sampling of single neurons by multiple electrodes, an efficient, low-dimensional representation of detected spikes consisting of estimated spatial spike locations and dominant spike shape features is exploited for fast and reliable clustering into single units. Millions of events can be sorted in minutes, and the method is parallelized and scales better than quadratically with the number of detected spikes. Performance is demonstrated using recordings with a 4,096-channel array and validated using anatomical imaging, optogenetic stimulation, and model-based quality control. A comparison with semi-automated, shape-based spike sorting exposes significant limitations of conventional methods. Our approach demonstrates that it is feasible to reliably isolate the activity of up to thousands of neurons and that dense, multi-channel probes substantially aid reliable spike sorting