The Experts below are selected from a list of 383394 Experts worldwide ranked by ideXlab platform
Vikram Kapila - One of the best experts on this subject based on the ideXlab platform.
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Session 2220 Development of a MATLAB-Based Graphical User Interface Environment for PIC Microcontroller Projects
2014Co-Authors: Sang-hoon Lee, Vikram KapilaAbstract:Peripheral Interface Controllers (PICs) are inexpensive microcontroller units with built-in serial communication functionality. Similarly, MATLAB, a widely used technical computing software, allows serial communication with external devices. In addition, MATLAB provides graphical design tools such as Simulink and Dials and Gauges Blockset. This paper exploits the serial communication capability of PIC microcontrollers and the MATLAB software along with graphical design tools of MATLAB to create a MATLAB-based graphical user interface (GUI) environment for PIC microcontroller projects. Three examples are included to illustrate that the integration of low-cost PIC microcontrollers with the MATLAB-based GUI environment allows data acquisition, data processing, data visualization, and control
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development of a MATLAB based graphical user interface environment for pic microcontroller projects
Computers in Education Journal, 2005Co-Authors: Yanfang Li, Vikram KapilaAbstract:Peripheral Interface Controllers (PICs) are inexpensive microcontroller units with built-in serial communication functionality. Similarly, MATLAB, a widely used technical computing software, allows serial communication with external devices. In addition, MATLAB provides graphical design tools such as Simulink and Dials and Gauges Blockset. This paper exploits the serial communication capability of PIC microcontrollers and the MATLAB software along with graphical design tools of MATLAB to create a MATLAB-based graphical user interface (GUI) environment for PIC microcontroller projects. Three examples are included to illustrate that the integration of low-cost PIC microcontrollers with the MATLAB-based GUI environment allows data acquisition, data processing, data visualization, and control.
Ma Wenlai - One of the best experts on this subject based on the ideXlab platform.
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A Study on Automatic Control Principle Courseware Based on MATLAB
The International Institute for Science Technology and Education (IISTE), 2015Co-Authors: Zhu Shouxi, Ma WenlaiAbstract:The course of automatic control principle needs to draw a lot of curves, but actually it is difficult to achieve. In order to solve the problems that the diagrams of automatic control principle is difficult to draw and the knowledge is difficult to understand, this paper presents a kind of automatic control principle courseware based on VISUAL C++ and MATLAB hybrid programming. The hybrid programming methods of VISUAL C++ and MATLAB are discussed in this paper, then analyzes the concrete realization method of VISUAL C++ calling MATLAB engine. In order to get a friendly user interface, the courseware using VISUAL C++ to write GUI(Graphical User Interface) and related data processing utilizing the MATLAB control system toolbox. Through MATLAB engine, this courseware can easy to draw the Bode diagram, Nyquist curve, Root Locus diagram and so on. The courseware make full use of the advantages of VISUAL C++ and MATLAB, has a friendly GUI and basically achieved all functions of MATLAB, which are convenient for teaching. The courseware designed in this paper can help the student to study the principle of automatic control and can improve the study effect to a certain extent. Keywords: VISUAL C++, MATLAB engine, Hybrid programming, Coursewar
Shouxi Zhu - One of the best experts on this subject based on the ideXlab platform.
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A Study on Automatic Control Principle Courseware Based on
2016Co-Authors: Shouxi ZhuAbstract:The course of automatic control principle needs to draw a lot of curves, but actually it is difficult to achieve. In order to solve the problems that the diagrams of automatic control principle is difficult to draw and the knowledge is difficult to understand, this paper presents a kind of automatic control principle courseware based on VISUAL C++ and MATLAB hybrid programming. The hybrid programming methods of VISUAL C++ and MATLAB are discussed in this paper, then analyzes the concrete realization method of VISUAL C++ calling MATLAB engine. In order to get a friendly user interface, the courseware using VISUAL C++ to write GUI(Graphical User Interface) and related data processing utilizing the MATLAB control system toolbox. Through MATLAB engine, this courseware can easy to draw the Bode diagram, Nyquist curve, Root Locus diagram and so on. The courseware make full use of the advantages of VISUAL C++ and MATLAB, has a friendly GUI and basically achieved all functions of MATLAB, which are convenient for teaching. The courseware designed in this paper can help the student to study the principle of automatic control and can improve the study effect to a certain extent
Zhu Shouxi - One of the best experts on this subject based on the ideXlab platform.
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A Study on Automatic Control Principle Courseware Based on MATLAB
The International Institute for Science Technology and Education (IISTE), 2015Co-Authors: Zhu Shouxi, Ma WenlaiAbstract:The course of automatic control principle needs to draw a lot of curves, but actually it is difficult to achieve. In order to solve the problems that the diagrams of automatic control principle is difficult to draw and the knowledge is difficult to understand, this paper presents a kind of automatic control principle courseware based on VISUAL C++ and MATLAB hybrid programming. The hybrid programming methods of VISUAL C++ and MATLAB are discussed in this paper, then analyzes the concrete realization method of VISUAL C++ calling MATLAB engine. In order to get a friendly user interface, the courseware using VISUAL C++ to write GUI(Graphical User Interface) and related data processing utilizing the MATLAB control system toolbox. Through MATLAB engine, this courseware can easy to draw the Bode diagram, Nyquist curve, Root Locus diagram and so on. The courseware make full use of the advantages of VISUAL C++ and MATLAB, has a friendly GUI and basically achieved all functions of MATLAB, which are convenient for teaching. The courseware designed in this paper can help the student to study the principle of automatic control and can improve the study effect to a certain extent. Keywords: VISUAL C++, MATLAB engine, Hybrid programming, Coursewar
Angel Martinez - One of the best experts on this subject based on the ideXlab platform.
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exploratory data analysis with MATLAB
2004Co-Authors: Wendy Martinez, Angel MartinezAbstract:INTRODUCTION TO EXPLORATORY DATA ANALYSIS Introduction to Exploratory Data Analysis What Is Exploratory Data Analysis Overview of the Text A Few Words about Notation Data Sets Used in the Book Transforming Data EDA AS PATTERN DISCOVERY Dimensionality Reduction - Linear Methods Introduction Principal Component Analysis (PCA) Singular Value Decomposition (SVD) Nonnegative Matrix Factorization Factor Analysis Fisher's Linear Discriminant Intrinsic Dimensionality Dimensionality Reduction - Nonlinear Methods Multidimensional Scaling (MDS) Manifold Learning Artificial Neural Network Approaches Data Tours Grand Tour Interpolation Tours Projection Pursuit Projection Pursuit Indexes Independent Component Analysis Finding Clusters Introduction Hierarchical Methods Optimization Methods-k-Means Spectral Clustering Document Clustering Evaluating the Clusters Model-Based Clustering Overview of Model-Based Clustering Finite Mixtures Expectation-Maximization Algorithm Hierarchical Agglomerative Model-Based Clustering Model-Based Clustering MBC for Density Estimation and Discriminant Analysis Generating Random Variables from a Mixture Model Smoothing Scatterplots Introduction Loess Robust Loess Residuals and Diagnostics with Loess Smoothing Splines Choosing the Smoothing Parameter Bivariate Distribution Smooths Curve Fitting Toolbox GRAPHICAL METHODS FOR EDA Visualizing Clusters Dendrogram Treemaps Rectangle Plots ReClus Plots Data Image Distribution Shapes Histograms Boxplots Quantile Plots Bagplots Rangefinder Boxplot Multivariate Visualization Glyph Plots Scatterplots Dynamic Graphics Coplots Dot Charts Plotting Points as Curves Data Tours Revisited Biplots Appendix A: Proximity Measures Appendix B: Software Resources for EDA Appendix C: Description of Data Sets Appendix D: Introduction to MATLAB Appendix E: MATLAB Functions References Index Summary, Further Reading, and Exercises appear at the end of each chapter.
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computational statistics handbook with MATLAB
2001Co-Authors: Wendy Martinez, Angel MartinezAbstract:Prefaces Introduction What Is Computational Statistics? An Overview of the Book Probability Concepts Introduction Probability Conditional Probability and Independence Expectation Common Distributions Sampling Concepts Introduction Sampling Terminology and Concepts Sampling Distributions Parameter Estimation Empirical Distribution Function Generating Random Variables Introduction General Techniques for Generating Random Variables Generating Continuous Random Variables Generating Discrete Random Variables Exploratory Data Analysis Introduction Exploring Univariate Data Exploring Bivariate and Trivariate Data Exploring Multidimensional Data Finding Structure Introduction Projecting Data Principal Component Analysis Projection Pursuit EDA Independent Component Analysis Grand Tour Nonlinear Dimensionality Reduction Monte Carlo Methods for Inferential Statistics Introduction Classical Inferential Statistics Monte Carlo Methods for Inferential Statistics Bootstrap Methods Data Partitioning Introduction Cross-Validation Jackknife Better Bootstrap Confidence Intervals Jackknife-after-Bootstrap Probability Density Estimation Introduction Histograms Kernel Density Estimation Finite Mixtures Generating Random Variables Supervised Learning Introduction Bayes' Decision Theory Evaluating the Classifier Classification Trees Combining Classifiers Unsupervised Learning Introduction Measures of Distance Hierarchical Clustering K-Means Clustering Model-Based Clustering Assessing Cluster Results Parametric Models Introduction Spline Regression Models Logistic Regression Generalized Linear Models Nonparametric models Introduction Some Smoothing Methods Kernel Methods Smoothing Splines Nonparametric Regression-Other Details Regression Trees Additive Models Markov Chain Monte Carlo Methods Introduction Background Metropolis-Hastings Algorithms The Gibbs Sampler Convergence Monitoring Spatial Statistics Introduction Visualizing Spatial Point Processes Exploring First-Order and Second-Order Properties Modeling Spatial Point Processes Simulating Spatial Point Processes Appendix A: Introduction to MATLAB What Is MATLAB? Getting Help in MATLAB File and Workspace Management Punctuation in MATLAB Arithmetic Operators Data Constructs in MATLAB Script Files and Functions Control Flow Simple Plotting Contact Information Appendix B: Projection Pursuit Indexes Indexes MATLAB Source Code Appendix C: MATLAB Statistics Toolbox Appendix D: Computational Statistics Toolbox Appendix E: Exploratory Data Analysis Toolboxes Introduction EDA Toolbox EDA GUI Toolbox Appendix F: Data Sets Appendix G: NOTATION References INDEX MATLAB Code, Further Reading, and Exercises appear at the end of each chapter.
