A Reduced Complexity Acoustic-Based 3D DoA Estimation with Zero Cyclic Sum.

Accurate direction of arrival (DoA) estimation is paramount in various fields, from surveillance and security to spatial audio processing. This work introduces an innovative approach that refines the DoA estimation process and demonstrates its applicability in diverse and critical domains. We propose a two-stage method that capitalizes on the often-overlooked secondary peaks of the cross-correlation function by introducing a reduced complexity DoA estimation method.

Screening for active pulmonary tuberculosis: Development and applicability of artificial neural network models.

Tuberculosis (TB) remains a significant public health challenge, motivated by the diversity of healthcare epidemiological settings, as other factors. Cost-effective screening has substantial importance for TB control, demanding new diagnostic tools. This paper proposes a decision support tool (DST) for screening pulmonary TB (PTB) patients at a secondary clinic.

Applying an artificial neural network model for developing a severity score for patients with hereditary amyloid polyneuropathy.

Hereditary (familial) amyloid polyneuropathy (FAP) is a systemic disease that includes a sensorimotor polyneuropathy related to transthyretin (TTR) mutations. So far, a scale designed to classify the severity of this disease has not yet been validated. This work proposes the implementation of an artificial neural network (ANN) in order to develop a severity scale for monitoring the disease progression in FAP patients.

A screening system for smear-negative pulmonary tuberculosis using artificial neural networks.

Objectives: Molecular tests show low sensitivity for smear-negative pulmonary tuberculosis (PTB). A screening and risk assessment system for smear-negative PTB using artificial neural networks (ANNs) based on patient signs and symptoms is proposed.

Methods: The prognostic and risk assessment models exploit a multilayer perceptron (MLP) and inspired adaptive resonance theory (iART) network.

Independent component analysis-based method for electroencephalogram analysis during photic stimulation.

Background: Intermittent photic stimulation (IPS) leads to phase- and time-locked activities in the electroencephalogram (EEG). While the first are easily obtained by means of averaging techniques (the evoked response), this latter reflects changes in the ongoing EEG that are important in event-related synchronization/desynchronization (ERS/ERD) studies. Techniques have been proposed for assessing such changes but they only provide the spectral estimate of the time-locked activities.