Sky Imager-Based Forecast of Solar Irradiance Using Machine Learning

Ahead-of-time forecasting of the output power of power plants is essential for the stability of the electricity grid and ensuring uninterrupted service. However, forecasting renewable energy sources is difficult due to the chaotic behavior of natural energy sources. This paper presents a new approach to estimate short-term solar irradiance from sky images. The~proposed algorithm extracts features from sky images and use learning-based techniques to estimate the solar irradiance. The~performance of proposed machine learning (ML) algorithm is evaluated using two publicly available datasets of sky images. The~datasets contain over 350,000 images for an interval of 16 years, from 2004 to 2020, with the corresponding global horizontal irradiance (GHI) of each image as the ground truth. Compared to the state-of-the-art computationally heavy algorithms proposed in the literature, our approach achieves competitive results with much less computational complexity for both nowcasting and forecasting up to 4 h ahead of time.Comment: Published in MDPI Electronics Journa

FPGA Hardware Implementation of DOA Estimation Algorithm Employing LU Decomposition

In this paper, authors present their work on field-programmable gate array (FPGA) hardware implementation of proposed direction of arrival estimation algorithms employing LU factorization. Both L and U matrices were considered in computing the angle estimates. Hardware implementation was done on a Virtex-5 FPGA and its experimental verification was performed using National Instruments PXI platform which provides hardware modules for data acquisition, RF down-conversion, digitization, etc. A uniform linear array consisting of four antenna elements was deployed at the receiver. LabVIEW FPGA modules with high throughput math functions were used for implementing the proposed algorithms. MATLAB simulations of the proposed algorithms were also performed to validate the efficacy of the proposed algorithms prior to hardware implementation of the same. Both MATLAB simulation and experimental verification establish the superiority of the proposed methods over existing methods reported in the literature, such as QR decomposition-based implementations. FPGA compilation results report low resource usage and faster computation time compared with the QR-based hardware implementation. Performance comparison in terms of estimation accuracy, percentage resource utilization, and processing time is also presented for different data and matrix sizes

EFFICIENT ITERATIVE DECONVOLUTION OF NOISY WAVEFORMS.

A simplified but reliable design procedure is proposed for the deconvolution noise reduction compensators of S. M. Riad and Nahman-Guillaume. This procedure produces a compensator that yields close to optimum deconvolution results with the least possible iterations

A New MSE Approach for Combined Linear-Viterbi Equalizers

Combined linear-Viterbi equalization (CLVE) is a technique that employs a linear pre-filter in conjunction with the Viterbi algorithm (VA) to mitigate the effects of intersymbol interference. The aim of the linear pre-filter is to shape the original channel impulse response to some shorter desired impulse response (DIR) in order to reduce the complexity of the VA. In this paper, we presentanewMSEbased approach for optimizing CLVEs. This approach takes advantage of the recent modifications to the VA which are suitable for channels having coarsely located coefficients. Specifically, the new approach has the flexibilityinchoosing the positions and optimizing the values of nonzero coefficients of DIR. As a result, it includes the conventional MSE-based approaches as a special case. Simulation results have been presented to illustrate the performance of proposed method

Cumulants and genetic algorithm for parameters estimation of noncausal AR models

This paper introduces a new method to estimating the parameters of a noncausal AR model. This method is based on a new formulation that relates the unknown AR parameters to both second- and higher- order cumulants. A genetic algorithm has been used in this paper to solve for the unknown AR parameters by minimizing a nonlinear cost function that is defined in terms of model's output cumulants

A Nonfiducial PPG-Based Subject Authentication Approach Using the Statistical Features of DWT-Based Filtered Signals

Nowadays, there is a global change in lifestyle that is moving more toward the use of e-services and smart devices which necessitate the verification of user identity. Different organizations have put into place a range of technologies, hardware, and/or software to authenticate users using fingerprints, iris recognition, and so forth. However, cost and reliability are significant limitations to the use of such technologies. This study presents a nonfiducial PPG-based subject authentication system. In particular, the photoplethysmogram (PPG) signal is first filtered into four signals using the discrete wavelet transform (DWT) and then segmented into frames. Ten simple statistical features are extracted from the frame of each signal band to compose the feature vector. Augmenting the feature vector with the same features extracted from the 1st derivative of the corresponding signal is investigated, along with different fusion approaches. A support vector machine (SVM) classifier is then employed for the purpose of identity authentication. The proposed authentication system achieved an average authentication accuracy of 99.3% using a 15 sec frame length with the augmented multiband approach