MATLAB Examples Research Articles

Recycling mode selection for the reverse supply chain of waste power batteries: an environmental responsibility perspective

ABSTRACT With the popularization of electric vehicles and the wide application of power batteries, the explosion and environmental pollution problems caused by power batteries are increasing day by day. Therefore, this study focuses on selecting optimal recycling modes for power battery reverse supply chains under environmental responsibility. Three models were analyzed: manufacturer-led decentralized decision-making, centralized decision-making, and consumer-led decision-making. Results from MATLAB examples indicate that manufacturer-led decentralization suits small enterprises reliant on local third-party recycling. Centralized decision-making is cost-effective for large manufacturers with advanced technology and brand recognition. Consumer-led approaches offer flexibility and adaptability, ideal for innovative firms. Meanwhile, the effective implementation of environmental responsibility not only promotes the active participation of consumers in recycling activities, but also increases the recycling amount of used batteries. While protecting the environment, enterprises also achieve profit maximization, reduce costs and increase efficiency.

Neurophysiological recordings from parietal areas of macaque brain during an instructed-delay reaching task

Facilitating data sharing in scientific research, especially in the domain of animal studies, holds immense value, particularly in mitigating distress and enhancing the efficiency of data collection. This study unveils a meticulously curated collection of neural activity data extracted from six electrophysiological datasets recorded from three parietal areas (V6A, PEc, PE) of two Macaca fascicularis during an instructed-delay foveated reaching task. This valuable resource is now accessible to the public, featuring spike timestamps, behavioural event timings and supplementary metadata, all presented alongside a comprehensive description of the encompassing structure. To enhance accessibility, data are stored as HDF5 files, a convenient format due to its flexible structure and the capability to attach diverse information to each hierarchical sub-level. To guarantee ready-to-use datasets, we also provide some MATLAB and Python code examples, enabling users to quickly familiarize themselves with the data structure.

Cover Image

Protein ScienceVolume 32, Issue 5 Cover ImageFree Access Cover Image First published: 17 April 2023 https://doi.org/10.1002/pro.4636AboutRelatedInformationPDFPDF ToolsRequest permissionExport citationAdd to favoritesTrack citation ShareShare Give accessShare full text accessClose modalShare full-text accessPlease review our Terms and Conditions of Use and check box below to share full-text version of article.I have read and accept the Wiley Online Library Terms and Conditions of UseShareable LinkUse the link below to share a full-text version of this article with your friends and colleagues. Learn more.Copy URL Graphical Abstract Crowders impact globular protein stability by their exclusion or inclusion from the vicinity of the protein. The degree of exclusion or inclusion is determined by the balance between hard and soft (chemical) interactions. The image depicts crowder and solvent molecules in the bulk and protein domains at equilibrium. Protein Science, 2023. https://doi.org/10.1002/pro.4573. Volume32, Issue5May 2023 RelatedInformation RecommendedImage SegmentationFundamentals of Digital Image Processing: A Practical Approach with Examples in Matlab, [1]Image SegmentationImage Processing: Principles and Applications, [1]Image SegmentationPractical Image and Video Processing Using MATLAB®, [1]Image Processing TechniquesJohn C. Russ, Encyclopedia of Imaging Science and Technology, [1]

Automatic identifying and counting blood cells in smear images by using single shot detector and Taguchi method

Researchers have tried to identify and count different blood cells in microscopic smear images by using deep learning methods of artificial intelligence to solve the highly time-consuming problem. The three types of blood cells are platelets, red blood cells, and white blood cells. This study used the Resnet50 network as a backbone network of the single shot detector (SSD) for automatically identifying and counting different blood cells and, meanwhile, proposed a systematic method to find a better combination of algorithm hyperparameters of the Resnet50 network for promoting accuracy for identifying and counting blood cells. The Resnet50 backbone network of the SSD with its optimized algorithm hyperparameters, which is called the Resnet50-SSD model, was developed to enhance the feature extraction ability for identifying and counting blood cells. Furthermore, the algorithm hyperparameters of Resnet50 backbone networks of the SSD were optimized by the Taguchi experimental method for promoting detection accuracy of the Resnet50-SSD model. The experimental result shows that the detection accuracy of the Resnet50-SSD model with 512 × 512 × 3 input images was better than that of the Resnet50-SSD model with 300 × 300 × 3 input images on the test set of blood cells images. Additionally, the detection accuracy of the Resnet50-SSD model using the combination of algorithm hyperparameters got by the Taguchi method was better than that of the Resnet50-SSD model using the combination of algorithm hyperparameters given by the Matlab example. In blood cell images acquired from the BCCD dataset, the proposed Resnet50-SSD model had higher accuracy in identifying and counting blood cells, especially white blood cells and red blood cells.

Approximate solution of Fredholm Integro Differential equation using Quadrature Formulas methods

There are two reasons for this research, the first which is the main was to clarify the use of a closed quadrature formulas included ( Trapizoidal , Simpson’s 1/3 rule and Simpson’s 3/8 rule )which are the most familiar formula of numerical integration ,to evaluating the integral part to find the approximate solution of the 2nd kind of FIDE’s of the 1st order and reducing it to linear system of (n) equation with n unknowns of the solution sample value y(ti) ,i=0,1,2,3,…,n .The other reason was to explain the differences between three Quadrature formulas in solving equation according to the specified period , has been clarified through examples. Finally, Acomparison was made between the three methods ,programs for methods were written in MATLAB language and examples with satisfactory results are given .

PyMPDATA v1: Numba-accelerated implementation of MPDATA with examples in Python, Julia and Matlab

Bartman et al., (2022). PyMPDATA v1: Numba-accelerated implementation of MPDATA with examples in Python, Julia and Matlab. Journal of Open Source Software, 7(77), 3896, https://doi.org/10.21105/joss.03896

Education

The Education section in this issue presents two contributions. The first paper, “$LU$ and $CR$ Elimination,” is written by Gilbert Strang and Cleve Moler. This is an expository paper which is helpful to instructors and students interested in linear algebra and matrix manipulations. The focus of the paper is the column-row factorization for any matrix $A$ of rank $r$. The matrix is represented as $A=CR$, where the matrix $C$ contains the first $r$ independent columns of $A$, and the matrix $R$ contains the nonzero rows of the reduced row echelon form of $A$. The authors explain how to obtain the factorization and contrast it with $LU$, $QR$ factorizations, the single-value decomposition, and another factorization, $A= CW^{-1}B$, which they call magic. In section 6, the authors point to an important timely application pertaining to randomized algorithms for a low-rank approximation of large sparse matrices. The paper is clearly and nicely written. It includes a discussion about relevant MATLAB functions, examples, and several references to randomized linear algebra literature. The second paper is “Newton's Method in Mixed Precision,” presented by C. T. Kelley. The paper discusses the impact of precision in calculations on the rate of convergence of Newton's method for solving a system of nonlinear equations $F(x)=0.$ The vector-function $F$ is differentiable and its Jacobian $F' (x)$ is nonsingular. It is assumed that the step $s$ in each iteration is determined by solving the linear equation $F' (x)s= - F(x)$ via Gaussian elimination with column pivoting. The paper starts with the classical result on the local convergence of the method under standard assumptions and then passes onto analyzing the effect of errors arising in calculation of the function and in approximation of the Jacobian. The author's derivations predict no significant difference in the convergence of the nonlinear iteration between a double-precision analytic Jacobian with double precision in the linear solver and a forward-difference approximate Jacobian with a single precision in the linear solver. An interesting part of the paper deals with the question of estimating the effect of the backward error in the solver. The author observes that the worst case estimates are too pessimistic and rarely seen in practice. Invoking recent techniques in probabilistic rounding analysis, he derives more realistic results. The theoretical statements are supplemented by a numerical illustration in section 3. An additional example demonstrates how the theory breaks down if the Jacobian is singular at the solution.

Classification of human electrocardiograms by multi-layer convolutional neural network and hyperparameter optimization

A multi-layer convolutional neural network (MCNN) with hyperparameter optimization (HyperMCNN) is proposed for classifying human electrocardiograms (ECGs). For performance tests of the HyperMCNN, ECG recordings for patients with cardiac arrhythmia (ARR), congestive heart failure (CHF), and normal sinus rhythm (NSR) were obtained from three PhysioNet databases: MIT-BIH Arrhythmia Database, BIDMC Congestive Heart Failure Database, and MIT-BIH Normal Sinus Rhythm Database, respectively. The MCNN hyperparameters in convolutional layers included number of filters, filter size, padding, and filter stride. The hyperparameters in max-pooling layers were pooling size and pooling stride. Gradient method was also a hyperparameter used to train the MCNN model. Uniform experimental design approach was used to optimize the hyperparameter combination for the MCNN. In performance tests, the resulting 16-layer CNN with an appropriate hyperparameter combination (16-layer HyperMCNN) was used to distinguish among ARR, CHF, and NSR. The experimental results showed that the average correct rate and standard deviation obtained by the 16-layer HyperMCNN were superior to those obtained by a 16-layer CNN with a hyperparameter combination given by Matlab examples. Furthermore, in terms of performance in distinguishing among ARR, CHF, and NSR, the 16-layer HyperMCNN was superior to the 25-layer AlexNet, which was the neural network that had the best image identification performance in the ImageNet Large Scale Visual Recognition Challenge in 2012.

Consensus based least squares estimation for single-integrator multi-agent systems with a time-varying reference state

This paper discusses consensus tracking of single-integrator multi-agent systems with a time varying reference state based on the least squares estimation theory to deal with the case of communications disturbance. Since none of the followers can communicate with the leader within the accepted time due to communications disturbance, least squares estimation is applied for online estimation of the time varying reference state and reducing the tracking errors occurred due to communications disturbance. A theoretical proof is provided under some assumptions while the necessary and sufficient conditions are demonstrated for which consensus is reached on the time-varying reference state in case of communications disturbance. Finally, a Matlab example is given to validate the effectiveness of the proposed approach.

Conic Fitting in Geometric Algebra Setting

We present an algorithm for a conic fitting based on a generalization of planar version of conformal geometric algebra to geometric algebra for conics (GAC). We introduce a novel normalization condition that follows naturally from this setting and which is invariant with respect to rotations and scaling. Finally, we provide a comparison to standard methods demonstrated on examples in MATLAB.

Book Reviews

Sometimes, people confuse artificial neural networks (ANNs) with real biological brains, at least metaphorically. The section starts with a featured review by Braden Brinkman on the book An Introduction to Modeling Neuronal Dynamics, by Christoph Börgers. This book deals with mathematical models for real biological neurons, and from the review one can conclude that they are much more interesting and also much more complicated than usual ANNs. Braden praises this book as “the latest sign of maturity for the field of mathematical neuroscience” and as “essential for training the next generation of neuroscientists.” The Book Reviews section also offers the other foundation of the usage of ANNs---the mathematical optimization concepts which are fundamental to the practical usage of ANNs. Tim Hoheisel reviews First-Order Methods in Optimization, by Amir Beck, which serves as a reference and includes many MATLAB examples. Like in the previous issue, we have again a review on another disease modeling book. It's the book Modelling Disease Ecology with Mathematics, by Robert Smith? (the question mark is part of the name), reviewed by Chris Bauch. Besides rigorous mathematics, this book contains valuable advice for humans who, facing a zombie apocalypse, must design strategies to counter zombie populations growing through a contagious process. We have five other insightful reviews on different topics. Here, I would like to put particular emphasis on the review of David Goluskin on Exploring ODEs, by Nick Trefethen, Ásgeir Birkisson, and Tobin Driscoll. David recommends this book as a “main text for a first course in ODEs.”

Biothermodynamics: Principles and Applications

The book offers an interesting insight into the application of these principles to inquiries involving biological and agricultural systems. Indeed, as the preface indicates, the text has been used for (primarily) mixed graduate students from mechanical engineering, chemical engineering, biomedical engineering, food engineering, medicine, genetics, and biotechnology. The text is comprised of five chapters and includes a CD that contains several MATLAB examples that are discussed in the text. With the first three chapters establishing an overview of thermodynamics and its application to both inorganic and organic systems, the stage is well set for the final two chapters, which are meant to be supplementary material for related projects based upon this text.

Signal-to-noise Ratio Study on Pipelined Fast Fourier Transform Processor

Fast Fourier transform (FFT) processor is a prevailing tool in converting signal in time domain to frequency domain. This paper provides signal-to-noise ratio (SNR) study on 16-point pipelined FFT processor implemented on field-programable gate array (FPGA). This processor can be used in vast digital signal applications such as wireless sensor network, digital video broadcasting and many more. These applications require accuracy in their data communication part, that is why SNR is an important analysis. SNR is a measure of signal strength relative to noise. The measurement is usually in decibles (dB). Previously, SNR studies have been carried out in software simulation, for example in Matlab. However, in this paper, pipelined FFT and SNR modules are developed in hardware form. SNR module is designed in Modelsim using Verilog code before implemented on FPGA board. The SNR module is connected directly to the output of the pipelined FFT module. Three different pipelined FFT with different architectures were studied. The result shows that SNR for radix-8 and R4SDC FFT architecture design are above 40dB, which represent a very excellent signal. SNR module on the FPGA and the SNR results of different pipelined FFT architecture can be consider as the novelty of this paper.

Correlation in the application of the triple-to-double coincidence ratio method with unequal photomultiplier tube efficiencies

Counting data from the triple-to-double coincidence ratio (TDCR) method are by their nature highly correlated. In the general case of unequal phototube detection efficiencies, the highly correlated nature of the data combined with the need to apply methods to solve a system of equations makes proper uncertainty analysis difficult. Removing the correlations from the data prior to analysis may assist in this endeavor. This paper describes the de-correlation of TDCR data by the application of a particular linear transform, the Mahalanobis transform. A programming example in MATLAB is given and a practical example of the use of this technique in the analysis of TDCR data is presented.

Introduction to Control Systems Design Using Matlab

Control systems theory is a wide area covering a range of artificial and physical phenomena. Control systems are systems that are designed to operate under strict specifications, to satisfy certain aims, like safety regulations in the industry, optimal production of goods, disturbance rejection in vehicles, smooth movement and placement of objects in warehousing, regulation of drug administration in medical operations, level control in chemical processes and many more. The present work provides an introduction to the fundamental principles of control system's analysis and design through the programming environment of Matlab and Simulink. Analysis of transfer function models is carried out though multiple examples in Matlab and Simulink, analyzing the dynamics of 1st and 2nd order systems, the role of the poles and zeros in the system's dynamic response, the effects of delay and the possibility to approximate higher order systems by lower order ones. In addition, examples are given from the fields of mechanical systems, medically induced anesthesia, neuroprosthetics and water level control, showcasing the use of controllers that satisfy certain design specifications.

Controller System Design Using the Coefficient Diagram Method

Coefficient diagram method is a controller design technique for linear time-invariant systems. This design procedure occurs into two different domains: an algebraic and a graphical. The former is closely paired to a conventional pole placement method and the latter consists on a diagram whose reading from the plotted curves leads to insights regarding closed-loop control system time response, stability and robustness. The controller structure has two degrees of freedom and the design process leads to both low overshoot closed-loop time response and good robustness performance regarding mismatches between the real system and the design model. This article presents an overview on this design method. In order to make more transparent the presented theoretical concepts, examples in Matlab ®code are provided. The included code illustrates both the algebraic and the graphical nature of the coefficient diagram design method.

Finite difference diagonalization to simulate nuclear magnetic resonance diffusion experiments in porous media

A finite difference approach for computing Laplacian eigenvalues and eigenvectors in discrete porous media is derived and used to approximately solve the Bloch–Torrey equations. Neumann, Dirichlet, and Robin boundary conditions are considered and applications to simulate nuclear magnetic resonance diffusion experiments are shown. The method is illustrated with MATLAB examples and computational tests in one and two dimensions and the extension to three dimensions is outlined. © 2015 Wiley Periodicals, Inc. Concepts Magn Reson Part A 44A: 160–180, 2015.

散焦图像的维纳滤波复原及振铃抑制

图像复原是数字图像处理的重要研究内容。基于频域的维纳滤波实现图像复原，并针对复原过程中伴随的振铃效应，采用扩展边缘的方法做了消除振铃预处理。然后进一步分析点扩散函数半径、图像梯度对振铃样式的影响，并由此建立边界振铃样式数据库R。并结合轮廓提取以及边缘膨胀法，得到振铃影响域内某点到轮廓的距离，从而调用对应库里的振铃样式做振铃修正。最后基于Matlab用简单轮廓图形验证该振铃抵消方法的有效性，从而将振铃幅度控制在±2之内，极大程度的恢复了原始高清图像。 Image restoration is a key research point of digital image processing. Wiener filter is used for basic image restoration, and boundary extension is a good method of ringing suppression preprocessing. Further analysis is made to find the relation between ringing pattern and precondition, including PSF radius and the gradient of outline. According to the relation, we build a 2D database matrix R to store the outline ringing pattern information. Combined with edge detection and image dilation, we can invoke the pattern database to suppress the ringing in a certain pre-condition and distance. Finally we verify the effectiveness of method with a simple outline example in Matlab, thus controlling the amplitude of ringing within ±2, and then the image information is restored as much as impossible.

Correlation: A Brief Introduction

An important concept in many applications is the correlation function between two signals. The correlation function is more of a measure of similarity between functions. In spite of the importance of the correlation function, it is not covered in most books on signals and systems and signal processing. The few books that cover it do not provide examples students can follow. This paper provides a simple introduction to the correlation function in continuous-time and discrete-time. It provides graphical examples and MATLAB examples.

Single-Bit Digital Frequency Synthesis via Dithered Nyquist-Rate Sinewave Quantization

Single-Bit Nyquist-rate quantization of sinewaves with random dithering is studied as a means for all-digital frequency synthesis. Quantizer's output spectrum is analytically derived and related to the cumulative distribution function of the random dither formed by independent and identically distributed random variables. Necessary and sufficient conditions for spurs-free output are derived. The noise floor level due to random dithering is derived analytically and the output dynamic range is defined. The trade-off between selective frequency-spurs presence and dynamic range improvement is studied. Several MATLAB examples illustrate the theory and its applications.