Neural networks and the DOA-estimation framework
Application of Artificial Neural Networks for Efficient High-Resolution 2D DOA Estimation
... of neural networks and comparing their ...RBF neural network can be expressed using statistical parameters such as worst case error (WCE (%)), average case error (ACE (%)) and Pearson Product-Moment ...
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A Survey on Applications of Multi Layer Perceptron Neural Networks in DOA Estimation for Smart Antennas
... A typical basic neuron element is shown in Figure1 [31].The inputs are represented as X i , weights are denoted as W i . Symbol represents ‘sum’ or ‘linear combination’ while represents the ‘activation function’. The ...
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Hybrid Orthogonal Projection and Estimation (HOPE): A New Framework to Learn Neural Networks
... to neural networks (NNs) currently widely used in deep ...deep neural networks (DNNs), as opposed to learning shallow NNs to mimic a pre-trained deep neural network as in Ba and Caruana ...
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deepQuest: A Framework for Neural based Quality Estimation
... Quality Estimation (QE) methods has drastically improved recently with the introduction of neural approaches to the ...far neural approaches have only been designed for word and sentence-level ...a ...
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Speech intelligibility estimation via neural networks
... Speech intelligibility estimation via neural networks Stephen Knight Follow this and additional works at: http://scholarworks.rit.edu/theses This Thesis is brought to you for free and open access by ...
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Scene disparity estimation with convolutional neural networks
... disparity estimation with some focus on the real-time processing requirement that is critical for applications in robotics and autonomous ...disparity estimation the occlusion effects should be explicitly ...
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Wind speed estimation using neural networks
... propagation networks consists of at least three layers that input layer, hidden layer and the output layer [29, ...gives estimation of the expected production of wind ...artificial neural network ...
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On grid Adaptive Compressive Sensing Framework for Underdetermined DOA Estimation by Employing Singular Value Decomposition
... arrival estimation has become the most addressed hot-topic of the researchers, playing a key role in several applications such as RADAR, SONAR, Seismology, wireless communication ...subspace-based ...
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An Artificial Neural Network Approach to DOA Estimation and Switched Beamforming in Rectangular Array Based Smart Antennas
... (DoA) estimation methods (commonly MUltiple SIgnal Classification (MUSIC) and Estimation of Signal Parameters via Rotational Invariance Techniques (ESPRIT)) are generally computationally ...signal ...
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DOA estimation and tracking of ULAs with mutual coupling
... is employed for tracking the fast-varying subspace. Hence, the fast-varying DOAs can be estimated using the framework previously developed in the paper. The KFVM differs from the conventional Kalman filter in that ...
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Beamforming for Direction of Arrival (DOA) Estimation A Survey
... The concept of the spatial sparsity of sources can be exploited to accomplish source localization in arbitrary shaped sensor arrays for both narrowband and wideband signals by using a very small number of measurements, ...
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SimpleNets: Quality Estimation with Resource Light Neural Networks
... ral Networks for Text Simplification Quality As- sessment: the small amount of training data avail- ...Recurrent Neural Network would be able to reliably learn a qual- ity prediction model if it was ...
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Laplace Approximation for Uncertainty Estimation of Deep Neural Networks
... all networks exhibit a convex loss surface around the learned minimum and are roughly circular or elliptical in shape 1 , both desirable properties for a Gaussian ...Generally, networks with fewer ...
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SHEF NN: Translation Quality Estimation with Neural Networks
... shallow neural networks, deep neural networks can use more hidden layers and have been shown to perform ...deep neural networks with four hidden layers: a first layer for the ...
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Unsupervised Training of Deep Neural Networks for Motion Estimation
... motion estimation has evolved over time to sev- eral variations which improve the ...the estimation considerably [16]. The estimation further improves by integ- rating descriptor matching in the ...
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Head Pose Estimation Using Convolutional Neural Networks
... The approach proceeds in 3 stages. Firstly, a face detector roughly classifies the pose as frontal, left, or right profile. Then, the classifiers will train withAdaboost using Haar-like features; detect distinctive ...
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Battery State-of-Charge Estimation Using Neural Networks
... SoC estimation algorithm of the latter approach, where SoC resets at every point when the electric vehicle ...SoC estimation technique (such as coulomb-counting or EKF) ...SoC estimation strategy ...
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Effort Estimation of Software Development Projects with Neural Networks
... chapter, the author seeks to give a structured overview over the defining characteristic of a SDP and of what factors influence the effort of SDP’s, with an emphasis on software metrics. Next, the most common methods of ...
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Estimation of Credit Risk by Artificial Neural Networks Models
... artificial neural networks (ANN) are widely used for credit risk ...biological neural networks and allow to analyze complex dynamic non- linear ...
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Cellular neural networks for motion estimation and obstacle detection
... Since the calculation of correspondences between two im- ages is a computationally very expensive operation, it is es- sential to restrict the matching process only to those regions where correspondences can be ...
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