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This text explores the practical realities that arise from the employment of geolocation for electronic warfare in real-world systems, including position of the target, errors in sensor position, orientation, or velocity, and the impact of repeated measurements over time. The problems solved in the book have direct relevance to accurately locating and tracking UAVs, planes, and ships. As a companion volume to the author's previous book Emitter Detection and Geolocation for Electronic Warfare (Artech House, 2019), this book goes in depth on real-world complications that include: working within and converting between different coordinate systems, incorporation of prior information about targets, sensor uncertainties, the use of multiple snapshots over time, and estimating the current position and velocity of moving targets. The e-book version described here includes several links to software and videos that can be downloaded from the publicly available Git repository. The book also includes all MATLAB code necessary to develop novel algorithms that allow comparisons to classical techniques and enable you to account for errors in timing, position, velocity, or orientation of the sensors. With its unique and updated coverage of detailed geolocation techniques and data, and easy linkable access to additional software and videos, this is a must-have book for engineers and electronic warfare practitioners who need the best information available on the development or employment of geolocation algorithms. It is also a useful teaching resource for faculty and students in engineering departments covering RF signal processing topics, as well as anyone interested in novel applications of SDR's and UAVs.

MATLAB By Example guides the reader through each step of writing MATLAB programs.The book assumes no previous programming experience on the part of the reader, and uses multiple examples in clear language to introduce concepts and practical tools.

Introduction. Cascaded H-bridge multilevel inverters (CHB-MLI) are becoming increasingly used in applications such as distribution systems, electrical traction systems, high voltage direct conversion systems, and many others. Despite the fact that multilevel inverters contain a large number of control switches, detecting a malfunction takes a significant amount of time. In the fault switch configurations diode included for freewheeling operation during open-fault condition. During short circuit fault conditions are carried out by the fuse, which can reveal the freewheeling current direction. The fault category can be identified independently and also failure of power switches harmed by the functioning and reliability of CHB-MLI. This paper investigates the effects and performance of open and short switching faults of multilevel inverters. Output voltage characteristics of 5 level MLI are frequently determined from distinctive switch faults with modulation index value of 0.85 is used during simulation analysis. In the simulation experiment for the modulation index value of 0.85, one second open and short circuit faults are created for the place of faulty switch. Fault is identified automatically by means of artificial neural network (ANN) technique using sinusoidal pulse width modulation based on distorted total harmonic distortion (THD) and managed by its own. The novelty of the proposed work consists of a fast Fourier transform (FFT) and ANN to identify faulty switch. Purpose. The proposed architecture is to identify faulty switch during open and short failures, which has to be reduced THD and make the system in reliable operation. Methods. The proposed topology is to be design and evaluate using MATLAB/Simulink platform. Results. Using the FFT and ANN approaches, the normal and faulty conditions of the MLI are explored, and the faulty switch is detected based on voltage changing patterns in the output. Practical value. The proposed topology has been very supportive for implementing non-conventional energy sources based multilevel inverter, which is connected to large demand in grid.

We present PeriFast/Dynamics, a compact and user-friendly MATLAB code for fast peridynamic (PD) simulations for deformation and fracture. PeriFast/Dynamics uses the fast convolution-based method (FCBM) for spatial discretization and an explicit time marching scheme to solve large-scale dynamic fracture problems. Different from existing PD solvers, PeriFast/Dynamics does not require neighbor search and storage, due to the use of the Fast-Fourier transform and its inverse to compute the integral operator. Run-times and memory allocation are independent of the number of neighbors inside the PD horizon, leading to faster computations and lower storage requirements. The governing equations and discretization method are briefly reviewed, the code structure explained, and individual modules described in detail. A demonstrative example on dynamic brittle fracture in 3D, with multiple crack branching events, is solved using three different constitutive models: a bond-based, an ordinary state-based, and a correspondence model. The small differences between results with the three different constitutive models are explained. Users are provided with a step-by-step description of the problem setup and execution of the code. PeriFast/Dynamics is a branch of the PeriFast suite of codes, and is available for download at the GitHub link provided in reference [ 1 ].

This paper proposes an algorithm for detecting weak pulses against a powerful pulse. Justification of the parameters of the developed algorithm was performed using computer modelling in the MATLAB. The thresholds for detecting weak pulses against a powerful unmodulated radio pulse were obtained depending on the signal-to-noise ratio (SNR) for different types of weighting windows, the number of fast Fourier transform (FFT) points and the probability of false detection for a weak pulse. It is shown that at the SNR of a relatively powerful unmodulated radio pulse of at least 30 dB, the required threshold value of weak pulse detection for the Hamming window is 7.5...8 dB lower than for the Hann window, and 27.5...29 dB lower than for a rectangular window. Increasing the number of FFT points from 256 to 1024 allows us to reduce the threshold value of weak pulse detection by 3...6 dB. Reducing the probability of false detection for a weak pulse from 10-3 to 10-7 requires increasing the threshold value for weak pulse detection by 1.2...5.2 dB.

Owing to the 4.0 industrial revolution condition monitoring maintenance is widely accepted as a useful approach to avoiding plant disturbances and shutdown. Recently, Motor Current Signature Analysis (MCSA) is widely reported as a condition monitoring technique in the detection and identification of individual and multiple Induction Motor (IM) faults. However, checking the fault detection and classification with deep learning models and its comparison among themselves or conventional approaches is rarely reported in the literature. Therefore, in this work, we present the detection and identification of induction motor faults with MCSA and three Deep Learning (DL) models namely MLP, LSTM, and 1D-CNN. Initially, we have developed the model of Squirrel Cage induction motor in MATLAB and simulated it for single phasing and stator winding faults (SWF) using Fast Fourier Transform (FFT), Short Time Fourier Transform (STFT), and Continuous Wavelet Transform (CWT) to detect and identify the healthy and unhealthy conditions with phase to ground, single phasing and in multiple fault conditions using Motor Current Signature Analysis. The faults impact on stator current is presented in the time and frequency domain (i.e., power spectrum). The simulation results show that the scalogram has shown good results in time-frequency analysis for fault and showing its impact on the energy of current during individual fault and multiple fault conditions. This is further investigated with three deep learning models (i.e., MLP, LSTM, and 1D-CNN) for checking the fault detection and identification (i.e., classification) improvement in a three-phase induction motor. By simulating the three-phase induction motor in various healthy and unhealthy conditions in MATLAB, we have collected current signature data in the time domain, labeled them accordingly and created the 50 thousand samples dataset for DL models. All the DL models are trained and validated with a suitable number of architecture layers. By simulation, the multiclass confusion matrix, precision, recall, and F1-score are obtained in several conditions. The result shows that the stator current signature of the motor can be used to detect individual and multiple faults. Moreover, deep learning models can efficiently classify the induction motor faults based on time-domain data of the stator current signature. In deep learning (DL) models, the LSTM has shown better accuracy among all other three models. These results show that employing deep learning in fault detection and identification of induction motors can be very useful in predictive maintenance to avoid shutdown and production cycle stoppage in the industry.

Aquatic vegetation plays an important role in natural water environments by interacting with the flow and generating turbulence that affects the air‐water and sediment‐water interfacial transfer. Regular and staggered arrays are often set as simplified layouts for vegetation canopy to study both mean flow and turbulence statistics in vegetated flows, which creates uniform spacing between vegetation elements, resulting in preferential flow paths within the array. Such preferential paths can produce local high velocity and strong turbulence, which do not necessarily happen in natural environments where vegetation is randomly distributed. How the randomness of the canopy affects interfacial processes by altering spatial turbulence distribution, which can potentially lead to different turbulence feedback on the interfacial transfer process, remains an open question. This study conducted a series of laboratory experiments in a race‐track flume using rigid cylinders as plant surrogates. Mean and turbulent flow statistics were characterized by horizontal‐ and vertical‐sliced PIV. Based on the measured flow characteristics under different stem diameters and array configurations, we propose a method to quantify the randomness of the vegetation array and update a sediment‐water‐air interfacial gas transfer model with the randomness parameter to improve its accuracy. The updated model agrees well with the dissolved oxygen experimental data from our study and data from existing literature at various scales. The study provides critical insight into water quality management in vegetated channels with improved dissolved oxygen predictions considering vegetation layout as part of the interfacial transfer model. Plain Language Summary Aquatic vegetation plays an important role in natural water environments by interacting with the flow and generating turbulence, which affects the interfacial gas transfer across the air‐water and sediment‐water interfaces. Researchers often used regular and staggered arrays as simplified vegetation layouts in laboratory experiments to study the flow hydrodynamics of vegetated channels. However, such regular vegetation pattern creates uniform spacing between stem elements, resulting in preferential flow paths within the canopy, which is unrealistic in nature where plants are randomly distributed. To understand the discrepancies between the idealized and the actual field cases, laboratory experiments were conducted in a race‐track flume using arrays of rigid cylinders as plant surrogates. We investigate the effects of randomly distributed vegetation on hydrodynamics and how spatial heterogeneity can alter turbulence feedback on interfacial transfer processes. A randomness index was proposed based on the measured flow characteristics under different stem diameters and array configurations to update previous interfacial transfer models. The study provides helpful insight into water quality management in vegetated channels, with improved dissolved oxygen predictions via a more accurate and universal interfacial transfer model with different vegetation distribution patterns. Key Points An index based on lateral flow variations is proposed to quantify the randomness in the distribution of vegetation elements in a canopy Layout of emergent vegetation does not affect surface gas transfer rates, as the average of the horizontal‐shear turbulence remains the same Contributions from coherent structures by flow‐stem‐bed interaction need to be corrected by the randomness index for sediment‐water transfer

We introduce PeriFast/Corrosion, a MATLAB code that uses the fast convolution-based method (FCBM) for peridynamic (PD) models of corrosion damage. The FCBM uses the convolutional structure of PD equations and employs the Fast Fourier transform (FFT) to achieve a computational complexity of O ( N l o g N ) . PeriFast/Corrosion has significantly lower memory allocation needs, O ( N ) , compared with, for example, the meshfree method with direct summation for PD models that requires O ( N 2 ) . The PD corrosion model and the fast convolution-based method are briefly reviewed, and the detailed structure of the code is presented. The code efficiently solves 3D uniform corrosion (example for copper) and pitting corrosion (example for stainless steel) problems with multiple growing and merging pits, set in a complicated shape sample. Discussions on possible immediate extensions of the code to other corrosion damage problems are provided. PeriFast/Corrosion is a branch of PeriFast codes and is freely available on GitHub [ 1 ].

Accurate knowledge of the neutron energy spectra is useful in basic research and applications. The overall procedure of measuring and unfolding the fast neutron energy spectra with BC501A liquid scintillation detector is described. The recoil proton spectrum of 241Am-Be neutrons was obtained experimentally. With the NRESP7 code, the response matrix of detector was simulated. Combining the recoil proton spectrum and response matrix, the unfolding of neutron spectra was performed by GRAVEL iterative algorithm. A MatLab program based on the GRAVEL method was developed. The continuous neutron spectrum of 241Am-Be source and monoenergetic neutron spectrum of D-T source have been unfolded successfully and are in good agreement with their standard reference spectra. The unfolded 241Am-Be spectrum are more accurate than the spectra unfolded by artificial neural networks in recent years.

In this article, a Fast Linear Elastic Eigenvalue Problem Solver (FLEEPS) is developed to calculate the band structures of three-dimensional (3D) isotropic phononic crystals (PnCs). In brief, FLEEPS solves in linear time complexity the smallest few eigenvalues and associated eigenvectors of the linear elastic eigenvalue problem originating from the finite difference discretization of the frequency-domain linear elastic wave equation. Notably, FLEEPS employs the weighted singular value decomposition based preconditioner to greatly improve the convergence rate of the conjugate gradient iteration, and uses the fast Fourier transform algorithm to accelerate this preconditioner times a vector, based on the structured decomposition of the dense unitary factor T of this preconditioner. Band structure calculations of several 3D isotropic PnCs are presented to showcase the capabilities of FLEEPS. The preliminary MATLAB implementation of FLEEPS is available at https://github.com/FAME-GPU/FLEEPS-MATLAB .