Publications by authors named "Ignacio Bosch"

Allopregnanolone (ALLO) is a known neurosteroid and a progesterone metabolite synthesized in the ovary, CNS, PNS, adrenals and placenta. Its role in the neuroendocrine control of ovarian physiology has been studied, but its in situ ovarian effects are still largely unknown. The aims of this work were to characterize the effects of intrabursal ALLO administration on different ovarian parameters, and the probable mechanism of action.

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Magnetic resonance (MR) imaging is the most sensitive clinical tool in the diagnosis and monitoring of multiple sclerosis (MS) alterations. Spinal cord evaluation has gained interest in this clinical scenario in recent years, but, unlike the brain, there is a more limited choice of algorithms to assist spinal cord segmentation. Our goal was to investigate and develop an automatic MR cervical cord segmentation method, enabling automated and seamless spinal cord atrophy assessment and setting the stage for the development of an aggregated algorithm for the extraction of lesion-related imaging biomarkers.

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Lamb waves have emerged as a valuable tool to examine long plate-like structures in a faster way compared to conventional bulk wave techniques, which make them attractive in non-destructive testing. However, they present a multimodal and dispersive nature, which hinders signal identification. Oblique incidence is one of the most known methods to generate and receive Lamb waves and it is applied in different experimental arrangements with different types of sensors.

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Aiming at the limitations of the existing Limited Feedback Interference Alignment algorithms, this paper proposes a direct codeword selection scheme that maximizes the lower-bound of the user rate and reduces the sum rate loss by integrating the Bit Allocation algorithm. The target signal is decoded using the maximum signal to interference plus noise ratio (MAX-SINR) algorithm. Moreover, low complexity and global searching mechanisms are deployed to select the optimized codewords from the generated sets of codewords that approach the ideal precoder.

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According to World Health Organization (WHO) estimations, one out of five adults worldwide will be obese by 2025. Worldwide obesity has doubled since 1980. In fact, more than 1.

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The main aim of smart cities is to achieve the sustainable use of resources. In order to make the correct use of resources, an accurate monitoring and management is needed. In some places, like underground aquifers, access for monitoring can be difficult, therefore the use of sensors can be a good solution.

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The first step to detect when a vineyard has any type of deficiency, pest or disease is to observe its stems, its grapes and/or its leaves. To place a sensor in each leaf of every vineyard is obviously not feasible in terms of cost and deployment. We should thus look for new methods to detect these symptoms precisely and economically.

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We consider in this paper the problem of automatic detection of ultrasonic echo pulses in a grain noise background. We start by assuming a reference model for grain noise: multivariate correlated Gaussian model having, in general, different variances under every hypothesis. We show that, even for this simple model, there is not practical optimum solution, except if the variances are equal under every hypothesis and the echo pulse satisfies a spectral constraint.

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