Blind Separation of Cyclostationary Sources Sharing Common Cyclic Frequencies Using Joint Diagonalization Algorithm

We propose a new method for blind source separation of cyclostationary sources, whose cyclic frequencies are unknown and may share one or more common cyclic frequencies. The suggested method exploits the cyclic correlation function of observation signals to compose a set of matrices which has a particular algebraic structure. The aforesaid matrices are automatically selected by proposing two new criteria. Then, they are jointly diagonalized so as to estimate the mixing matrix and retrieve the source signals as a consequence. The nonunitary joint diagonalization (NU-JD) is ensured by Broyden-Fletcher-Goldfarb-Shanno (BFGS) method which is the most commonly used update strategy for implementing a quasi-Newton technique. The efficiency of the method is illustrated by numerical simulations in digital communications context, which show good performances comparing to other stateof-the-art methods

AVPV neurons containing estrogen receptor-beta in adult male rats are influenced by soy isoflavones

BACKGROUND: Isoflavones, the most abundant phytoestrogens in soy foods, are structurally similar to 17beta-estradiol. It is known that 17beta-estradiol induces apoptosis in anteroventral periventricular nucleus (AVPV) in rat brain. Also, there is evidence that consumption of soy isoflavones reduces the volume of AVPV in male rats. Therefore, in this study, we examined the influence of dietary soy isoflavones on apoptosis in AVPV of 150 day-old male rats fed either a soy isoflavone-free diet (Phyto-free) or a soy isoflavone-rich diet (Phyto-600). RESULTS: The occurrence of apoptosis in AVPV was examined by TUNEL staining. The incidence of apoptosis was about 10 times higher in the Phyto-600 group (33.1 ± 1.7%) than in the Phyto-free group (3.6 ± 1.0%). Furthermore, these apoptotic cells were identified as neurons by dual immunofluorescent staining of GFAP and NeuN as markers of astrocytes and neurons, respectively. Then the dopaminergic neurons in AVPV were detected by immunohistochemistry staining of tyrosine hydroxylase (TH). No significant difference in the number of TH neurons was observed between the diet treatment groups. When estrogen receptor (ER) alpha and beta were examined by immunohistochemistry, we observed a 22% reduction of ERbeta-positive cell numbers in AVPV with consumption of soy isoflavones, whereas no significant change in ERalpha-positive cell numbers was detected. Furthermore, almost all the apoptotic cells were ERbeta-immunoreactive (ir), but not ERalpha-ir. Last, subcutaneous injections of equol (a major isoflavone metabolite) that accounts for approximately 70–90% of the total circulating plasma isoflavone levels did not alter the volume of AVPV in adult male rats. CONCLUSION: In summary, these findings provide direct evidence that consumption of soy isoflavones, but not the exposure to equol, influences the loss of ERbeta-containing neurons in male AVPV

a multi scalar approach to long term dynamics spatial relations and economic networks of roman secondary settlements in italy and the ombrone valley system southern tuscany towards a model

In Roman landscapes, the particular sites defined as secondary settlements (also known as vici/villages, minor centres, agglomerations secondaires and/or stationes/mansiones) have played an 'intermediary' role between the cities and other rural structures (villae/farms), linked to medium- and long-distance economic and commercial trajectories. The aim of this paper is to apply a multi-scalar approach to model their long-term spatial relationships and connectivity with the Mediterranean exchange network. On the macro-scale, we have analysed a sample of 219 reviewed sites to understand the diachronic trends and spatial dynamics of attraction/proximity to significant elements of the landscape such as towns, roads, rivers and coastline. The Ombrone Valley (Tuscany, Italy) represents a micro-scale case study of a complex system, in which the imported pottery (amphorae, African Red Slip ware, ingobbiata di rosso) found in the vicus/mansio of Santa Cristina in Caio, the Roman villa of La Befa and the town of Siena (Saena Iulia) provided diagnostic 'macroeconomic' perspectives. The results show how the secondary settlements occupied a nodal position in the Roman landscape in terms of resilience (long period of occupation until the Early Middle Ages) and spatial organization with a close relationship to natural and anthropic infrastructures and trade functions linked to Mediterranean routes

Robust Estimation of Balanced Simplicity-Accuracy Neural Networks-Based Models

International audienceNeural networks are powerful tools for black box system identification. However, their main drawback is the large number of parameters usually required to deal with complex systems. Classically, the model's parameters minimize a L2-norm-based criterion. However, when using strongly corrupted data, namely, outliers, the L2-norm-based estimation algorithms become ineffective. In order to deal with outliers and the model's complexity, the main contribution of this paper is to propose a robust system identification methodology providing neuromodels with a convenient balance between simplicity and accuracy. The estimation robustness is ensured by means of the Huberian function. Simplicity and accuracy are achieved by a dedicated neural network design based on a recurrent three-layer architecture and an efficient model order reduction procedure proposed in a previous work (Romero-Ugalde et al., 2013, “Neural Network Design and Model Reduction Approach for Black Box Nonlinear System Identification With Reduced Number of Parameters,” Neurocomputing, 101, pp. 170–180). Validation is done using real data, measured on a piezoelectric actuator, containing strong natural outliers in the output data due to its microdisplacements. Comparisons with others black box system identification methods, including a previous work (Corbier and Carmona, 2015, “Extension of the Tuning Constant in the Huber's Function for Robust Modeling of Piezoelectric Systems,” Int. J. Adapt. Control Signal Process., 29(8), pp. 1008–1023) where a pseudolinear model was used to identify the same piezoelectric system, show the relevance of the proposed robust estimation method leading balanced simplicity-accuracy neuromodel

Blind identification of FIR MIMO systems driven by cyclostationary inputs in wireless communications using joint block diagonalization

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Balanced simplicity–accuracy neural network model families for system identification

International audienceNonlinear system identification tends to pro- vide highly accurate models these last decades; however, the user remains interested in finding a good balance between high-accuracy models and moderate complexity. In this paper, four balanced accuracy–complexity identifi- cation model families are proposed. These models are derived, by selecting different combinations of activation functions in a dedicated neural network design presented in our previous work (Romero-Ugalde et al. in Neurocom- puting 101:170–180. doi:10.1016/j.neucom.2012.08.013, 2013). The neural network, based on a recurrent three-layer architecture, helps to reduce the number of parameters of the model after the training phase without any loss of estimation accuracy. Even if this reduction is achieved by a convenient choice of the activation functions and the initial conditions of the synaptic weights, it nevertheless leads to a wide range of models among the most encountered in the literature. To validate the proposed approach, three dif- ferent systems are identified: The first one corresponds to the unavoidable Wiener–Hammerstein system proposed in SYSID2009 as a benchmark; the second system is a flex- ible robot arm; and the third system corresponds to an acoustic duct