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Three-phase unbalanced system can be represented mathematically in terms of positive, zero, and negative sequence components. These components are really useful to provide information regarding power quality disturbances in an unbalanced system. Thus effective estimation of sequence components using appropriate signal processing models helps to detect short-duration disturbances like sag, swell, etc. The use of fractional-order calculus-based signal processing models has given a new dimension to estimate parameters and track non-stationary power quality events. The input matrix and weight vector are generated using trigonometric expansion considering the voltages across all the three phases. The magnitude and phase of the sequence components can be estimated from the optimal weight vector which is recursively updated using the proposed algorithm. In this study, the strength of fractional-order calculus is exploited using least mean square/fourth (LMS/F) to estimate sequence components in an unbalanced three-phase power system. The proposed fractional LMS/F (FLMS/F)-based model is tested using signals generated from MATLAB 2020 and IEEE 1159 PQE database. All the comparisons of results are made using standard performance measures, and also the convergence analysis of the algorithm is presented. [ABSTRACT FROM AUTHOR]

Summary: Accurate estimation and tracking of power quality (PQ) disturbances requires efficient adaptive model‐based techniques, which should have elegant structures to be applicable in practical systems. Though extended Kalman filter (EKF) has been used as a popular estimator to track the time‐varying PQ events, the performance is limited due to higher‐order nonlinearity exists in system dynamics. Moreover, the computational complexity and sensitivity to the measurement noise affect the estimation accuracy and error convergence properties. If the underlying sparse behavior of adaptive filters can be exploited through norm penalty, then significant improvement can be achieved in terms of convergence speed, stability, and steady state excess mean square error performance. In this paper, a modification to EKF is proposed by using sparse model to estimate PQ disturbances with better estimation accuracy and faster convergence speed. A thorough comparison is presented in terms of simulation results by using a family of existing algorithms like least mean square, recursive least square, and EKF. Adaptive estimation model incorporates real and complex state space representations to represent discrete time PQ disturbances. The convergence of the proposed sparse model–based filter is also mathematically proved considering the mean square error performance. [ABSTRACT FROM AUTHOR]

Hybrid precoding is an emerging solution for millimeter wave massive MIMO system to achieve reduced complexity and enhanced spectral efficiency. Hybrid precoding fully exploits spatial information to achieve high channel gain which reduces excessive path loss. The research presented in this paper is aiming at the design of optimal precoder which is based on Logarithmic hyperbolic cosine cost function with ZA and RZA criterion and has the advantage of reduced RF Chains. The channel state information (CSI) is generated by using random values of angle of arrival (AoA) and angle of departure (AoD) information. This CSI information is crucial to generate the precoding matrices and the row wise elements of the digital precoding matrix are recursively updated by using hyperbolic cosine filtering algorithm. Proposed precoder achieves spectral efficiency of 13 bps/Hz with 20 dB SNR which is comparatively higher than the precoders designed using other state of art methods. The performance is tested by varying base station antennas, mobile station antennas and number of users. The achievable spectral efficiency with variation of antenna elements and users establish the superior performance of the precoder. Thus the design can be suitable for fifth and next generation wireless communication systems when higher data rate is the requirement in millimeter wave band. [ABSTRACT FROM AUTHOR]

Correct estimation of harmonics and sequence components is essential to monitor the overall performance of three phase power systems. Least mean square (LMS) based adaptive models have simpler structures to estimate the required parameters for a given power quality (PQ) event. However, these models are not very much effective in case of time-varying and short duration disturbances. Least mean fourth (LMF) is comparatively a better approach than LMS to deal with different PQ problems, but high computational complexity limits the filter performance in case of practical applications. LMS/fourth (LMS/F) filter is a compromise between LMS and LMF to improve the error convergence properties. The proposed model explained in this paper is based on the concept of LMS/F with Volterra expansions of input samples. The proposed Volterrra LMS/F filter is tested in case of distorted power signals to estimate harmonics, decaying dc, and sequence components in presence of white Gaussian noise. The detail comparison results are presented using MATLAB simulations to verify the performance of the adaptive model. [ABSTRACT FROM AUTHOR]

Breast cancer is a leading cause of mortality affecting women across the world. Early detection and diagnosis can decrease the mortality rate due to this cancer. Machine learning-based models are gaining popularity for biomedical applications due to the ability of nonlinear mapping between input and output patterns using supervised training phase. The research work in the paper is focused on the optimal adaptive threshold for mammogram mass segmentation, and detection in order to assist radiologist in accurate diagnosis Legendre neural network with single layer is used to develop the model, and the training is performed through Block Based Normalized Sign-Sign Least Mean Square (BBNSSLMS) algorithm. Legendre neural network expands the input vector using standard Legendre polynomial, and the recursive update principle is followed for the weight vector in higher dimension. The optimal threshold is indirectly used for proper segmentation of mammogram mass. The proposed segmentation method involves training phase with 30 images and testing phase by 151 images obtained from standard Mammogram Image Analysis Society (MIAS) database. The proposed model achieved a sensitivity of 95% and accuracy of 96% with false positives per image calculated as 1.19. • Threshold selection is carried out using single-layer Legendre NN with reduced computational complexity. BNSSLMS algorithm process the data samples block wise instead of sample by sample basis. Optimal threshold is generated according to the varying image properties which helps in correct segmentation and detection. • Due to sparse nature of the adaptive model, more numbers of weight coefficients are tending to zero which also helps in faster convergence. Mammogram Mass Detection Steps. [ABSTRACT FROM AUTHOR]

In the remote sensing community, HyperSpectral (HS) images (HSI) are becoming increasingly popular as the advancement of technology and the consequent reduction of cost make them financially more accessible. The reason for their success is the higher capability they can offer, with respect to multispectral data, to discriminate classes that are spectrally similar. A series of pre-processing steps such as geometric, radiometric and atmospheric corrections are carried out on raw data captured by HS sensors. The processed data is then passed to the data analyst, whose work generally relies on the assumption that the received HS data is 'clean,' as all possible corrections have already been implemented; however, corrections are hardly perfect and residual disturbances can still bias the quality of results. At this stage, however, all corrections based on ancillary information have already been made and the possibilities for 'exogenous' correction of data are exhausted. More could be possibly done by sourcing additional information from the data itself. In this paper, we propose a simple yet effective additional step for error suppression through energy scaling, termed 'Error Removal by Energy Scaling' (ERES). In classification problems, the absolute value of wavelength λ is often overlooked, except for, e.g. removal of strong absorption bands; yet the λ value can actually further support the classification process if their physical meaning is tapped. The proposed ERES method is indeed a non-linear scaling method, derived from physical phenomena linked with radiation extinction properties. In ERES, each band is associated with an energy level, that is inversely related to its own wavelength. The associated nonlinear energy information in HSI, neglected in most classification strategies, prevents optimal separation of class-specific spectral signatures, that are generated by the physics of wave-matter interaction. This is especially true for linear classifiers such as Support Vector Machines (SVM). Removing this physics-linked information makes data more suitable to be classified with physics-unaware classification strategies, typically used in down stream remotely sensed data processing. The relevance of the issue, and the benefits of ERES, are discussed and validated in this work over three different datasets, using accuracy improvements on the popular Spectral Angle Mapper and SVM classifiers as a means to gauge the effectiveness of the correction strategy. Results clearly reveal the positive impact of applying ERES to the data before proceeding to classification. [ABSTRACT FROM AUTHOR]

Classification is an important problem in a large variety of applications, which makes it an open-ended forum for researchers in various disciplines. In this paper, the proposed two approaches are mostly focused on Nearest Regularized Subspace (NRS) classifier. The proposed pair of variants are: (1) reducing the computational complexity of NRS classifier termed as Conditional Fast Nearest Regularized Subspace (CFNRS) classifier and (2) incorporation of dissimilarity feature termed as Conditional Dissimilarity-based Nearest Regularized Subspace (CDNRS) classifier. Regarding the first approach, the simple k-NN classifier result is used as a condition to evaluate NRS classifier. Regarding the second approach, an intra-feature dissimilarity measure is considered to create a pair of dictionary which contains a conditional binary matrix and a dissimilarity feature matrix. Each conditional binary matrix is a collection of distinct sub-spaces representing their respective classes. The incoming data is classified with the help of a dictionary and classification performance is validated over other state-of-art approaches. As Hyper-Spectral (HS) data have a strong spectral correlation in terms of spectral signatures, various such HS data are included for validation of our approach. The experimental study reveals the pros and cons of different classifiers along with the effectiveness of our proposed approach. The proposed conditional classifier is found to be able to compete with other, standard classifiers in terms of accuracy and computational complexity, mostly where features possess some sort of sequence information. The suitability of the proposed classifier is also verified for both binary and multi-class classification problems. Along with the experimental validation, the statistical significance of the proposed conditional measure is assessed over other standard state-of-art methods, and the proposed one outperformed the others. The experimental results reveal the importance of dissimilarity features for classification and also suggest to incorporate a simple classifier such as k-NN, prior to any other classifier. This is mainly meant to reduce the computational complexity of the selected classifier without compromising the accuracy. [ABSTRACT FROM AUTHOR]