| Description |
1 online resource |
| Contents |
Intro; Preface; Contents; Computer Vision; 1 Denoising of Ultrasound Medical Images Using the DM6437 High-Performance Digital Media Processor; Abstract; 1.1 Introduction; 1.2 Literature Review; 1.3 Methods; 1.3.1 Ultrasound Image Formation; 1.3.1.1 B-Mode; 1.3.1.2 Speckle Noise; 1.3.2 Despeckling Filters; 1.3.2.1 Median Filter; 1.3.2.2 Lee Filter; 1.3.2.3 Kuan Filter; 1.3.2.4 Frost Filter; 1.3.2.5 SRAD Filter; 1.3.3 Description of the TMS320DM6437 Digital Media Processor; 1.3.3.1 DSP Core Description; 1.3.3.2 Evaluation Module; 1.3.3.3 Memory Map; 1.3.4 Metrics; 1.4 Results |
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1.4.1 Experiments on Synthetic Data1.4.2 Experiments on Real Data; 1.5 Conclusions; References; 2 Morphological Neural Networks with Dendritic Processing for Pattern Classification; Abstract; 2.1 Introduction; 2.2 Basics on MNNDPs; 2.3 Training Algorithms; 2.3.1 Elimination and Merging Methods; 2.3.1.1 Elimination Method; 2.3.1.2 Merging Method; 2.3.2 Divide and Conquer Methods; 2.3.2.1 Divide and Conquer Method; 2.3.2.2 Linear Divide and Conquer Method; 2.3.3 Evolutionary-Based Methods; 2.3.4 Other Related Works; 2.4 Comparison; 2.4.1 Results with Synthetic Data; 2.4.2 Results with Real Data |
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2.5 Summary, Conclusions, and Present and Future ResearchAcknowledgements; References; 3 Mobile Augmented Reality Prototype for the Manufacturing of an All-Terrain Vehicle; Abstract; 3.1 Introduction; 3.2 All-Terrain Vehicles; 3.3 Literature Review; 3.4 Proposed Methodology; 3.4.1 Selection of Development Tools; 3.4.2 Selection and Design of 3D Models; 3.4.3 Markers Design; 3.4.4 Development of the MAR Application; 3.4.4.1 Welding Inspection; 3.4.4.2 Measuring Critical Dimensions; 3.4.4.3 Accessories Mounting; 3.4.5 GUI Design; 3.4.5.1 Scene Creation in Unity; 3.5 Experimental Results |
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3.5.1 Scope of Markers Detection3.5.2 Survey for Users; 3.5.3 Discussion; 3.6 Conclusions; References; 4 Feature Selection for Pattern Recognition: Upcoming Challenges; Abstract; 4.1 Introduction; 4.2 Theoretical Context and State of the Art; 4.2.1 Statistical-Based Methods for Feature Selection; 4.2.1.1 Statistical Measures; 4.2.1.2 Statistical-Based Methods: State of the Art; 4.2.2 Information Theory-Based Methods; 4.2.2.1 Basic Concepts; 4.2.2.2 Information Theory-Based Methods: State of the Art; 4.2.3 Similarity-Based Methods; 4.2.3.1 Similarity-Basic Concepts |
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4.2.3.2 Similarity-Based Methods: State of the Art4.2.4 Neural Networks-Based Feature Selection; 4.2.4.1 Basic Concepts; 4.2.4.2 Artificial Neural Networks Methods for Feature Selection: State of the Art; 4.2.5 Sparse Learning Methods for Feature Selection; 4.2.5.1 Introductory Concepts; 4.2.5.2 Sparse Learning-Based Methods: State of the Art; 4.3 Summary of Methods and Their Capability to Handle Chronologically Linked Data; 4.4 Upcoming Challenges; 4.4.1 Characteristics of Chronologically Linked Data; 4.4.2 Challenges for Feature Selection Algorithms; 4.5 Conclusions; References |
| Summary |
The field of mechatronics (which is the synergistic combination of precision mechanical engineering, electronic control and systems thinking in the design of products and manufacturing processes) is gaining much attention in industries and academics. It was detected that the topics of computer vision, control and robotics are imperative for the successful of mechatronics systems. This book includes several chapters which report successful study cases about computer vision, control and robotics. The readers will have the latest information related to mechatronics, that contains the details of implementation, and the description of the test scenarios |
| Notes |
Print version record |
| Subject |
Computer vision.
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Robotics.
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Control theory.
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Mechatronics.
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Robotics
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Automatic control engineering.
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COMPUTERS -- General.
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Computer vision
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Control theory
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Mechatronics
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Robotics
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| Form |
Electronic book
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| Author |
Villegas, Osslan Ois Vergara, editor
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Nandayapa, Manuel, editor
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Soto, Israel, editor
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| ISBN |
9783319777702 |
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331977770X |
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3319777696 |
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9783319777696 |
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