Extensive Air Showers (EAS), initiated by Ultra-High-Energy Cosmic Rays (UHECRs), generate geo-synchrotron and geo-magnetic radiations and they are, also, the source of excess charge processes. In the frequency range of 10 to 100 MHz, coherent radiation is formed. Many experiments use the radio detection technique for studying EAS features. The Auger Engineering Radio Array (AERA), part of the Pierre Auger Observatory, uses hundreds of radio antennas working in the frequency range of 30 to 80 MHz to support the investigation of UHECRs together with the standard surface and fluorescence detectors. The AERA radio frequency range is significantly contaminated by the human-made and usually narrow-band Radio Frequency Interference (RFI), e. g., shortwave radio transmitters. The presence of RFIs in the detected signals increases the ratio of spurious triggers,in consequence, empty data in ate the databases. This study proposes replacing the currently used IIR-notch nonadaptive , filter by the delayed version of the wellknown LEAST MEAN SQUARES (LMS) algorithm, which offers crucial advantage adjustment. The current study implemented 32/64-stage Delay LEAST MEAN SQUARES (DLMS) , filters on cost-effective Cyclone®,IV and Cyclone®,V as non-canonical Finite Input Response (FIR) with a sufficient safety margin for a global clock being at LEAST 20% higher than 200 MHz, which equals the ADC sampling frequency.

The Mixed LEAST SQUARES Meshfree (MDLSM) method has shown its appropriate efficiency for solving Partial Differential Equations (PDEs) governing the engineering problems. The method is based on the minimizing the residual functional. The residual functional is defined as a summation of the weighted residuals on the governing PDEs and the boundaries. The Moving LEAST SQUARES (MLS) is usually applied in the MDLSM method for constructing the shape functions. Although the required consistency and compatibility for the approximation function is satisfied by the MLS, the method loss its appropriate efficiency when the nodal points cluster too much. In the current study, the mentioned drawback is overcome using the novel approximation function called Mapped Moving LEAST SQUARES (MMLS). In this approach, the cluster of closed nodal points maps to standard nodal distribution. Then the approximation function and its derivatives compute noting the some consideration. The efficiency of suggested MMLS for overcoming the drawback of MLS is evaluated by approximating the mathematical function. The obtained results show the ability of suggested MMLS method to solve the drawback. The suggested approximation function is applied in MDLSM method, and used for solving the Burgers equations. Obtained results approve the efficiency of suggested method.

Selective partial update (SPU) strategy in adaptive filter algorithms is used to reduce the computational complexity. In this paper we apply the SPU normalized LEAST MEAN SQUARES algorithm (SPU-NLMS) for distributed estimation problem in an incremental network. The distributed SPU-NLMS (dSPU-NLMS) has close convergence speed to dNLMS, low steady-state MEAN square error (MSE), and low computational complexity features. In addition, the MEAN-square performance analysis of dSPU-NLMS algorithm for each individual node is presented. The theoretical expressions for stability bounds, transient and steady-state performance analysis of dSPU-NLMS are introduced. The validity of the theoretical results and the good performance of dSPU-NLMS are demonstrated by several computer simulations.

Among the variety of GPS signal interference, spoofing is considered as the most dangerous intentional interference. If spoofing signal would have existed in the received signal GPS, wrong information reaches the receiver which causes problems in time and location computation of the receiver. Defense against spoofing includes the spoofing detection and reduction. In this paper, we use the properties of correlation function for spoofing detection. In order to mitigate spoofing, we apply the method of LEAST MEAN SQUARES (LMS) based on sign-data algorithm. This approach reduces effect of the spoofing interference in the received signal GPS and defends against interference of kind of delay spoofing. The proposed approach have been implemented on real dataset and in the acquisition stage from GPS receiver processing. The results show that the adaptive method based on sign-data algorithm decrease effectiveness of spoofing on average 89 percent. In addition to spoofing reduction, the Position Dilution of Precision (PDOP) parameter improves at all of results. PDOP parameter indicates the spatial position of identified satellites.The PDOP value mitigated from 47 to 2.66 on average.

This study compared the performance of the partial LEAST SQUARES-structural equation modelling (PLS-SEM) and the robust partial LEAST SQUARES-structural equation modelling (RPLS-SEM) methods through Winsorisation approach The inputs and the outputs used in this model were based on the electricity generation data, derived from the Al-Zawiya Steam Power Plant, Libya. Furthermore, the researchers compared the novel RPLS-SEM approach with the traditional PLS-SEM approach and noted that the novel RPLS-SEM method was more efficient compared to PLS-SEM.

In this paper, we propose a nonparametric rank-based alternative to the LEAST-SQUARES independent component analysis algorithm developed. The basic idea is to estimate the squared-loss mutual information, which used as the objective function of the algorithm, based on its copula density version. Therefore, no marginal densities have to be estimated. We provide empirical evaluation of the proposed algorithm through simulation and real data analysis. Since the proposed algorithm uses rank values rather than the actual values of the observations, it is extremely robust to the outliers and suffers less from the presence of noise than the other algorithms.

Determination of the diffusion coefficient on the base of solution of a linear inverse problem of the parameter estimation using the LEAST-square method is presented in this research. For this propose a set of temperature measurements at a single sensor location inside the heat conducting body was considered. The corresponding direct problem was then solved by the application of the heat fundamental solution.

Image magnification is one of the current issues of image processing in which keeping the quality and structure of images is the main concern. In image magnification, it is necessary to insert information in extra pixels. Adding information to an image should be compatible with the image structure with- out making artificial blocks. In this research, extra pixels are estimated using the surface of LEAST SQUARES, and all the pixels are reviewed according to the suggested edge-improving algorithm. The suggested ethod keeps the edges and minimizes the magnified image opacity and the artificial blocks. Numerical results are presented by using PSNR and SSIM fidelity measures and compared to some other methods. The average PSNR of the original image and image zooming is 32.79 which it shows that image zooming is very similar to the original image. Experimental results show that the proposed method has a better performance than others and provides good image quality.

Pedomodels have become a popular topic in soil science and environmental research. They are predictive functions of certain soil properties based on other easily or cheaply measured properties. The common method for fitting pedomodels is to use classical regression analysis, based on the assumptions of data crispness and deterministic relations among variables. In modeling natural systems such as soil system, in which the above assumptions are not held true, prediction is influential and we must therefore attempt to analyze the behavior and structure of such systems more realistically. In this paper we consider fuzzy LEAST SQUARES regression as a MEANs of fitting pedomodels. The theoretical and practical considerations are illustrated by developing some examples of real pedomodels.