A spiking neural network for real-time Spanish vowel phonemes recognition

This paper explores neuromorphic approach capabilities applied to real-time speech processing. A spiking recognition neural network composed of three types of neurons is proposed. These neurons are based on an integrative and fire model and are capable of recognizing auditory frequency patterns, such as vowel phonemes; words are recognized as sequences of vowel phonemes. For demonstrating real-time operation, a complete spiking recognition neural network has been described in VHDL for detecting certain Spanish words, and it has been tested in a FPGA platform. This is a stand-alone and fully hardware system that allows to embed it in a mobile system. To stimulate the network, a spiking digital-filter-based cochlea has been implemented in VHDL. In the implementation, an Address Event Representation (AER) is used for transmitting information between neurons.Ministerio de Economía y Competitividad TEC2012-37868-C04-02/0

Linear nested Artin approximation theorem for algebraic power series

We give a new and elementary proof of the nested Artin approximation Theorem for linear equations with algebraic power series coefficients. Moreover, for any Noetherian local subring of the ring of formal power series, we clarify the relationship between this theorem and the problem of the com-mutation of two operations for ideals: the operation of replacing an ideal by its completion and the operation of replacing an ideal by one of its elimination ideals.Comment: Last version. To appear in Manuscripta Mat

On the computation of Bernstein–Sato ideals

In this paper we compare the approach of Brianc¸onand Maisonobe for computing Bernstein–Sato ideals—based on computations in a Poincar´e–Birkhoff–Witt algebra—with the readily available method of Oaku and Takayama. We show that it can deal with interesting examples that have proved intractable so far.Ministerio de Ciencia y Tecnología BFM-2001-3164Junta de Andalucía FQM-33

Explicit Comparison Theorems for D -modules

We prove in an explicit way a duality formula between two A2-modules Mlog and Mflog associated to a plane curve and we give an application of this duality to the comparison between Mflog and the A2-module of rational functions along the curve. We treat the analytic case as well

The Effectiveness of Advanced Practice Nurses with Respect to Complex Chronic Wounds in the Management of Venous Ulcers

This study aims to evaluate the effectiveness of advanced practice nurses with respect to complex chronic wounds (APN-CCWs) in the care of patients with venous ulcers. A multicentric, quasi-experimental pre-post study was conducted without a control group in the sanitary management areas where the APN-CCW program is being piloted. The intervention consisted of a mass training of clinical nurses from the participating districts on the proper management of injuries and the use of compression therapy. The data were collected through a specifically constructed questionnaire with questions regarding descriptive variables of injuries and their treatment. A total of 643 professionals responded (response rate of 89.1%), attending to a total population of 707,814 inhabitants. An increase in multilayer bandage use by 15.67%, an increase in elastic bandage use by 13.24%, and a significant decrease in the referral of patients to consultation with hospital specialists was achieved, from 21.08% to 12.34%. The number of patients referred to the APNs was 13.25%, which implied a resolution rate of 94.08% of their injuries. In conclusion, the coordination by the APN-CCWs in patients with venous ulcers was effective in improving the continuity of care, in the optimization of resources, and in their care role

Deep Neural Networks for the Recognition and Classification of Heart Murmurs Using Neuromorphic Auditory Sensors

Auscultation is one of the most used techniques for detecting cardiovascular diseases, which is one of the main causes of death in the world. Heart murmurs are the most common abnormal finding when a patient visits the physician for auscultation. These heart sounds can either be innocent, which are harmless, or abnormal, which may be a sign of a more serious heart condition. However, the accuracy rate of primary care physicians and expert cardiologists when auscultating is not good enough to avoid most of both type-I (healthy patients are sent for echocardiogram) and type-II (pathological patients are sent home without medication or treatment) errors made. In this paper, the authors present a novel convolutional neural network based tool for classifying between healthy people and pathological patients using a neuromorphic auditory sensor for FPGA that is able to decompose the audio into frequency bands in real time. For this purpose, different networks have been trained with the heart murmur information contained in heart sound recordings obtained from nine different heart sound databases sourced from multiple research groups. These samples are segmented and preprocessed using the neuromorphic auditory sensor to decompose their audio information into frequency bands and, after that, sonogram images with the same size are generated. These images have been used to train and test different convolutional neural network architectures. The best results have been obtained with a modified version of the AlexNet model, achieving 97% accuracy (specificity: 95.12%, sensitivity: 93.20%, PhysioNet/CinC Challenge 2016 score: 0.9416). This tool could aid cardiologists and primary care physicians in the auscultation process, improving the decision making task and reducing type-I and type-II errors.Ministerio de Economía y Competitividad TEC2016-77785-