Antenna 3D pattern measurement can be a tedious and time consuming task even for antennas with manageable sizes inside anechoic chambers. Performing onsite measurements by scanning the whole 4π [sr] solid angle around the antenna under test (AUT) is more complicated. In this paper, with the aim of minimum duration of flight, a test scenario using unmanned aerial vehicles (UAV) is proposed. A pr...

Compressive Sensing (CS) has demonstrated to be particularly adapt for dealing with the directions-of-arrival (DoAs) estimation of electromagnetic signals impinging on an array of sensors. Unlike deterministic CS methods, the Bayesian CS (BCS) allows to overcome some theoretical limitations of the CS, such to enable a reliable and versatile DoAs estimation tool able to work with different array...

In order to study the effect of R2O/Al2O3 (where R=Na or K), SiO2/Al2O3, Na2O/K2O and H2O/R2O molar ratios on the compressive strength (CS) of Metakaolin base geopolymers, more than forty data were gathered from literature. To increase the number of data, some experiments were also designed. The resulted data were utilized to train and test the three layer artificial neural network (ANN). Bayes...

In this paper, we investigate a Bayesian sparse reconstruction algorithm called compressive sensing via Bayesian support detection (CS-BSD). This algorithm is quite robust against measurement noise and achieves the performance of an minimum mean square error (MMSE) estimator that has support knowledge beyond a certain SNR thredhold. The key idea behind CS-BSD is that reconstruction takes a dete...

Objective: To estimate the pretest probability of Cushing’s syndrome (CS) diagnosis by a Bayesian approach using intuitive clinical judgment. Materials and methods: Physicians were requested, in seven endocrinology meetings, to answer three questions: “Based on your personal expertise, after obtaining clinical history and physical examination, without using laboratorial tests, what is your prob...

Introduction: J-coupling causes spectral splitting and complicated signal modulation that limit the detection of important brain metabolites, such as Glu, in proton spectroscopic imaging. While 2D spectroscopy, e.g. 2DJPRESS [1] and CTPRESS [2], has been demonstrated to successfully improve signal detection of coupled spins, it carries a penalty in scan time and reconstruction complexity. To co...

Compressive Sensing (CS) is presented in a Bayesian framework for realistic radar cases whose likelihood or priors are usually non-Gaussian. Its sparse-signal processing is modelbased and detection-driven, and also done numerically using Monte-Carlo methods. This approach aims for the stochastic description of sparse solutions, and the flexibility to use any prior information on signals or on d...

In this paper, we propose a sparsity model that allows the use of Compressive Sensing (CS) for the online recovery of large data sets in real Wireless Sensor Network (WSN) scenarios. We advocate the joint use of CS for the recovery and of Principal Component Analysis (PCA) to capture the spatial and temporal characteristics of real signals. The statistical characteristics of the signals are thu...

Traditional Compressive Sensing (CS) recovery techniques resorts a dictionary matrix to recover a signal. The success of recovery heavily relies on finding a dictionary matrix in which the signal representation is sparse. Achieving a sparse representation does not only depend on the dictionary matrix, but also depends on the data. It is a challenging issue to find an optimal dictionary to recov...