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We describe optimal cue mapping (OCM), a potentially eal-time binaural signal processing method for segregating sound source in the presence of multiple interfering 3D ound sources. Spatial cues are extracted from a multisource inaural mixture and used to train artificial neural etworks (ANNs) to estimate the spectral energy fraction of wanted speech source in the mixture. Once trained, the NN outputs...
There is a relation, not always linear, between the blood pressure and the pulse duration, obtained from photoplethysmography (PPG) signal. In order to estimate the blood pressure from the PPG signal, in this paper the Artificial Neural Networks (ANNs) are used. Training data were extracted from the Multiparameter Intelligent Monitoring in Intensive Care waveform database for better representation...
Neural networks (NNs) have proven to be a very powerful tool both for one-dimensional (1D) and two-dimensional (2D) direction of arrival (DOA) estimation. By avoiding complex and time-consuming mathematical calculations, NNs estimate DOAs almost instantaneously. This feature makes them very convenient for real-time applications. Further, unlike the well known MUSIC algorithm, neural network-based...
PMSM drives are the upcoming drives nowadays as these have many advantages such as high efficiency, high speed, high torque to inertia ratio, high torque to current ratio etc., The estimation of PMSM drive parameters is an important consideration in their field. Many methods are available for this. However the optimal way to estimate the parameters is normally preferred making use of neural networks...
Estimation of systolic and diastolic pressures from the oscillometric waveform is a challenging task in noninvasive electronic blood pressure (BP) monitoring devices. Since the conventional oscillometric algorithms cannot model and extract the complex and nonlinear relationship that may exist between BP and oscillometric waveform, artificial neural networks (NNs) have been proposed as a possible alternative...
This paper presents neural estimators of the state variable for drive system with elastic joints. The main stages of the design methodology of neural estimators of the torsional torque and the load machine speed were presented. For the optimization of the structure of each neural networks the Optimal Brain Damage method was implemented. The signals estimated by neural networks were tested in the control...
One of most perspective techniques for sensing in ubiquitous computing systems is neural networks. In this paper we describe features of usage of neural networks in ubiquitous computing and its implementation for solving of some tasks in middleware ubiquitous computing system for smart environment.
As rotation speed is necessary for high-performance induction motor control, how to estimate the speed quickly and accurately is concerned by most scholars. On the analysis of theoretic invertibility of the induction motorpsilas mathematic model, a speed estimation based on neural networks inversion is proposed. The structure of multi-layer feed-forward neural network (MFNN) is trained by advanced...
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