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Reference frame, disturbances are also considers in the process of integrations due to practical reasons, in the project a model reference adaptive control has been proposed to remove the disturbances in estimating the stator flux and to enhance the accuracy of the rotor angle estimation. Model reference adaptive control is a self-tuner, and in the project SVPWM has been used instead of using integrations for finding the rotor angle. By using SVPWM error can be reduced. The performance of the proposed sensor less method has been mainly assessed through simulation The rotor angle can be estimated from the stator flux in permanent magnet synchronous motor (PMSM). Although integrations are essential to estimate the stator flux from the voltages and currents in the stationary at low speed operations, senseless drive of PMSM is regarded as being extremely difficult without the signal injection. NOTE: Hear rotor angle estimation is done by MRAS (model reference adaptive control).

In this paper, we present a novel approach for Remaining Useful Life (RUL) estimation problem in prognostics using a proposed ‘sequential learning Meta-cognitive Regression Neural Network (McRNN) algorithm for function approximation’. The McRNN has two components, namely, a cognitive component and a meta-cognitive components. The cognitive component is an evolving single hidden layer Radial Basis Function (RBF) network with Gaussian activation functions. The meta-cognitive component present in McRNN helps to cognitive component in selecting proper samples to learn based on its current knowledge and evolve architecture automatically. The McRNN employs extended Kalman Filter (EKF) to find optimal network parameters in training. First, the performance of the proposed sequential learning McRNN algorithm has been evaluated using a set of benchmark function approximation problems and is compared with existing sequential learning algorithms. The performance results on these problems show the better performance of McRNN algorithm over the other algorithms. Next, the proposed McRNN algorithm has been applied to RUL estimation problem based on sensor data. For simulation studies, we have used Prognostics Health Management (PHM) 2008 Data Challenge data set and compared with the existing approaches based on state-of-the-art regression algorithms. The experimental results show that our proposed McRNN algorithm based approach can accurately estimate RUL of the system.

In this paper, we propose a multimodal multi-stream deep learning framework to tackle the egocentric activity recognition problem, using both the video and sensor data. First, we experiment and extend a multi-stream Convolutional Neural Network to learn the spatial and temporal features from egocentric videos. Second, we propose a multistream Long Short-Term Memory architecture to learn the features from multiple sensor streams (accelerometer, gyroscope, etc.). Third, we propose to use a two-level fusion technique and experiment different pooling techniques to compute the prediction results. Experimental results using a multimodal egocentric dataset show that our proposed method can achieve very encouraging performance, despite the constraint that the scale of the existing egocentric datasets is still quite limited.

In this paper, we present an approach for Alzheimer's Disease (AD) detection from magnetic resonance images using Meta-cognitive Radial Basis Function Network (McRBFN) classifier. We propose a Recursive Feature Elimination (RFE) approach with efficient classification method Projection based Learning-McRBFN (referred as PBL-McRBFN-RFE) to identify the most meaningful imaging biomarkers with a predictive power for AD detection in male persons. The study has been conducted using the well-known open access series of imaging studies data set. The performance results of the PBL-McRBFN-RFE classifier clearly indicates the better performance for AD detection. The proposed imaging biomarkers identification mechanism indicates that in male persons insula region may be responsible for the onset of AD.

In this paper, we present a novel approach for classification of Mild Cognitive Impairment (MCI) and normal subjects from Magnetic Resonance Images (MRI) using a proposed `sequential Projection Based Learning for Meta-cognitive q-Gaussian Radial Basis Function Network (PBL-McqRBFN)' classifier. The McqRBFN has two components, namely, a cognitive component and a meta-cognitive components. The cognitive component is a single hidden layer Radial Basis Function (RBF) network with a q-Gaussian activation function, that allows different RBF's in one network, like the Gaussian, the Inverse Multiquadratic, and the Cauchy functions, by changing a real q-parameter. The meta-cognitive component present in McqRBFN helps in selecting proper samples to learn based on its current knowledge and evolve architecture automatically. The McqRBFN employs a sequential Projection Based Learning (PBL) algorithm to reduce the computational effort used in training. For simulation studies, we have used MRI data from the Alzheimer's Disease Neuroimaging Initiative database. Voxel Based Morphometry (VBM) is used for feature extraction from MRI data and extracted VBM features are fed into the PBL-McqRBFN classifier. The experimental results show that our proposed PBL-McqRBFN classifier can accurately differentiate MCI and normal subjects.

In this paper, we propose a `Meta-cognitive Radial Basis Function Network (McRBFN)' and its `Projection Based Learning (PBL)' algorithm for classification problems. McRBFN emulates human-like meta-cognitive learning principles. As each sample is presented to the network, McRBFN uses the estimated class label, the maximum hinge error and class-wise significance to address the self-regulating principles of what-to-learn, when-to-learn and how-to-learn in a meta-cognitive framework. McRBFN addresses the what-to-learn by choosing samples to participate in the learning process, also deleting samples with information similar to that already learnt by the network. A few samples that satisfy neither of these criteria are pushed to the rear end of the training data stack to be used in future, thereby satisfying the when-to-learn. The how-to-learn component of meta-cognition is addressed by using the participating samples to either add a neuron or update the output weights. Initially, McRBFN begins with zero hidden neurons and adds required number of neurons to approximate the decision surface. When a neuron is added, its parameters are initialized based on the sample overlapping conditions. The output weights are updated using a PBL algorithm such that the network finds the minimum point of an energy function defined by the hinge-loss error. The use of human meta-cognitive principles ensures efficient learning. Moreover, as samples with similar information are deleted, overtraining is avoided. The PBL algorithm helps to reduce the computational effort used in training. The performance of the PBL-McRBFN classifier is evaluated using a set of benchmark classification problems from the UCI machine learning repository. The performance evaluation study on these problems clearly indicates the superior performance of PBL-McRBFN classifier over results reported in the literature.

In this paper, we present a novel approach with Voxel-Based Morphometry (VBM) detected features using a proposed `Projection Based Learning for Meta-cognitive Radial Basis Function Network (PBL-McRBFN)' classifier for the detection of Alzheimer's disease (AD) from Magnetic Resonance Imaging (MRI) scans. McRBFN emulates human-like meta-cognitive learning principles. As each sample is presented to the network, McRBFN uses the estimated class label, the maximum hinge error and class-wise significance to address the self-regulating principles of what-to-learn, when-to-learn and how-to-learn in a meta-cognitive framework. Initially, McRBFN begins with zero hidden neurons and adds required number of neurons to approximate the decision surface. When a neuron is added, its parameters are initialized based on the sample overlapping conditions. The output weights are updated using a PBL algorithm such that the network finds the minimum point of an energy function defined by the hinge-loss error. Moreover, as samples with similar information are deleted, over-training is avoided. The PBL algorithm helps to reduce the computational effort used in training. For simulation studies, we have used well-known open access series of imaging studies data set. The performance of the PBL-McRBFN classifier is evaluated on complete morphometric features set obtained from the VBM analysis and also on reduced features sets from Independent Component Analysis (ICA). The performance evaluation study clearly indicates the superior performance of PBL-McRBFN classifier over results reported in the literature.