Effective autocorrelation-based spectrum sensing technique for cognitive radio network applications

Spectrum sensing based on detection techniques enables cognitive radio networks to detect vacant frequency bands. The spectrum sensing gives the opportunity to increase the radio spectrum channels re-utilization. However, the main challenge in spectrum sensing is the simplicity of the considered detection approach and the amount of prior information needed to make an accurate decision. This paper proposes a novel sensing technique based on the autocorrelation function. This novel approach is based on the speed of convergence to zero of all autocorrelation coefficients. This technique shows the highest probability of detection for the same probability of false alarm target at low signal-to-noise ratio (SNR) compared with many standard detection techniques. The proposed method has been implemented using GNU Radio software and SDR (software-defined radio) platforms. The experimental results show the effectiveness of the proposed method under real scenarios.     

Local maximum likelihood segmentation of echocardiographic images with Rayleigh distribution

In order to interpret ultrasound images, it is important to understand their formation and the properties that affect them, especially speckle noise. This image texture, or speckle, is a correlated and multiplicative noise that inherently occurs in all types of coherent imaging systems. Indeed, its statistics depend on the density and on the type of scatterers in the tissues. This paper presents a new method for echocardiographic images segmentation in a variational level set framework. A partial differential equation-based flow is designed locally in order to achieve a maximum likelihood segmentation of the region of interest. A Rayleigh probability distribution is considered to model the local B-mode ultrasound images intensities. In order to confront more the speckle noise and local changes of intensity, the proposed local region term is combined with a local phase-based geodesic active contours term. Comparison results on natural and simulated images show that the proposed model is robust to attenuations and captures well the low-contrast boundaries.     

Encapsulation of phase change materials with alginate modified by nanostructured sodium carbonate and silicate

The encapsulation of phase change materials (PCMs) as thermal energy storage materials is a big issue. PCM is usually encapsulated to avoid spillage, flammability and its reaction with the surrounding environment to improve its application. In the last decade, various methods have been employed and all kinds of microencapsulated PCM are produced. In this paper, we present a facile route to produce an encapsulated PCM with an organic and inorganic shell. The encapsulated phase change material (PCM) was prepared using a coaxial micro-fluidic system combined with an ionic cross-linking process. The alginate was used as the basic shell and a range of capsules was obtained by modifying the original shell using two inorganic components such as sodium carbonate and sodium silicate. Various samples, each with a different surrounding layer, were prepared by combining alginate calcium (Alg–Ca) as an organic shell with an inorganic component such as alginate calcium carbonate (Alg–CaCO3) and alginate calcium silicate (Alg–CaSiO3). In these experimental works, we have investigated the compatibility and the stability of capsules modified with the inorganic component. The scanning electron microscopy (SEM) technique and optical microscopy were utilized to study the capsule morphology. The chemical composition of the shell was evaluated by Fourier transform infrared spectroscopy (ATR-FTIR), thermogravimetry analysis and SEM coupled with the EDX analysis, and the capsule stability was estimated under an accelerated thermal cycling.     

Gradient doping of Cu(I) and Cu(II) in ZnO nanorod photoanode by electrochemical deposition for enhanced photocurrent generation

This study describes the synthesis and characterization of Cu-doped ZnO nanorods (NRs) by an electrochemical method in the presence of two different Cu precursor (Cu⁺² and Cu⁺) in order to improve photocurrent generation. Analyses of the resulting materials by X-ray diffraction (XRD), scanning electron microscopy (SEM), UV–Vis and electrochemical photocurrent (ECP) spectroscopy confirm the formation of well-aligned ZnO Würtzite nanostructures in the form of hexagonal rods. For both doping source with a concentration of up to 0.5%, the following changes were observed: a distortion of the ZnO morphology, an increase in transmittance to 96% for ZnO doped with Cu⁺², and a reduction of the energy gap from 3.36 eV to 3.06 and 3.02 eV for ZnO doped with Cu⁺² and Cu⁺, respectively. From photoelectrochemical tests, the photo-current density was improved up to 0.05 mA cm^-2 in the presence of Cu doping, which is twelve times superior to that of undoped ZnO nanorods, which means that the incorporation of Cu+2 or Cu + significantly improves the separation efficiency of photogenerated electron-hole pairs. These results can be considered promising for optoelectronic and photocatalysis applications.     

A new generalised α scale spaces quadrature filters

The α   scale spaces is a recent theory that open new possibilities of phase-based image processing. It is a parameterised class (α∈]0,1])$(\alpha \in ]0,1\left]\right)$ of linear scale space representations, which allows a continuous connection beyond the well-known Gaussian scale space (α=1$\alpha =1$). In this paper, we make use of this unified representation to derive new families of band pass quadrature filters, built from derivatives and difference of the α scale space generating kernels. This construction leads to generalised α kernel filters including the commonly known families derived from the Gaussian and the Poisson kernels. The properties of each family are first studied and then experiments on one- and two-dimensional signals are shown to exemplify how the suggested filters can be used for edge detection. This work is complemented by an experimental evaluation, which demonstrates that the new proposed filters are a good alternative to the commonly used Log-Gabor filter.     

Parallelised production of fine and calibrated emulsions by coupling flow-focusing technique and partial wetting phenomenon

The capacity of microfluidic technology to fabricate monodisperse emulsion droplets is well established. Parallelisation of droplet production is a prerequisite for using such an approach for making high-quality materials for either fundamental or industrial applications where product quantity matters. Here, we investigate the emulsification efficiency of parallelised drop generators based on a flow-focusing geometry when incorporating the role of partial wetting in order to make emulsion droplets with a diameter below 10 μm. Confinement intrinsically encountered in microsystems intensifies the role played by interfaces between liquids and solids. We thus take advantage of partial wetting to enhance the maximum confinement accessible due to liquid flow focusing. We compare the performances brought by partial wetting to more established routes such as step emulsification. We show that the step configuration and the partial wetting regime are both well suited for being parallelised and thus open the way to the production of fine and calibrated emulsions for further applications. Finally, this new route of emulsification that exploits partial wetting between the fluids and the channel walls opens possibilities to the formation of substantially smaller droplets, as required in many fields of application.     

Feature selection on probabilistic symbolic objects

In data analysis tasks, we are often confronted to very high dimensional data. Based on the purpose of a data analysis study, feature selection will find and select the relevant subset of features from the original features. Many feature selection algorithms have been proposed in classical data analysis, but very few in symbolic data analysis （SDA） which is an extension of the classical data analysis, since it uses rich objects instead to simple matrices. A symbolic object, compared to the data used in classical data analysis can describe not only individuals, but also most of the time a cluster of individuals. In this paper we present an unsupervised feature selection algorithm on probabilistic symbolic objects （PSOs）, with the purpose of discrimination. A PSO is a symbolic object that describes a cluster of individuals by modal variables using relative frequency distribution associated with each value. This paper presents new dissimilarity measures between PSOs, which are used as feature selection criteria, and explains how to reduce the complexity of the algorithm by using the discrimination matrix.