Soft Computing Techniques in Engineering Applications

1. Front Matter

   Pages i-vi

   PDF

2. Machine Vision Solutions in Automotive Industry

   • Pinnamaneni Bhanu Prasad, N. Radhakrishnan, S. Shankar Bharathi

   Pages 1-14

3. Kinect Quality Enhancement for Triangular Mesh Reconstruction with a Medical Image Application

   • A. Khongma, M. Ruchanurucks, T. Koanantakool, T. Phatrapornnant, Y. Koike, P. Rakprayoon

   Pages 15-32

4. A Matlab GUI Package for Comparing Data Clustering Algorithms

   • Anirban Mukhopadhyay, Sudip Poddar

   Pages 33-48

5. Multi Objective Line Symmetry Based Evolutionary Clustering Approach

   • Singh Vijendra, Sahoo Laxman

   Pages 49-58

6. An Efficient Method for Contrast Enhancement of Digital Mammographic Images

   • Sanjeev Kumar, Mahesh Chandra

   Pages 59-74

7. Simulation of Obstacle Detection and Speed Control for Autonomous Robotic Vehicle

   • Shaunak Agastya Vyas, Lovekumar D. Thakker, Amit Patwardhan

   Pages 75-97

8. A Review of Global Path Planning Algorithms for Planar Navigation of Autonomous Underwater Robots

   • Divya Konda, Keerthana Bhoopanam, Saravanakumar Subramanian

   Pages 99-113

9. Pseudo-Fractional Tap-Length Learning Based Applied Soft Computing for Structure Adaptation of LMS in High Noise Environment

   • Asutosh Kar, Mahesh Chandra

   Pages 115-129

10. Medical Image Analysis Using Soft Computing Techniques

    • D. Jude Hemanth, J. Anitha

    Pages 131-139

11. Selection of Robotic Grippers Under MCDM Environment: An Optimized Trade Off Technique

    • Anirudha Bhattacharjee, Bikash Bepari, Subhasis Bhaumik

    Pages 141-158

12. Numerical Study of Viscous Flow in the Hydraulic System of Electro Optical Tracking System

    • R. K. Dey, H. S. Panda, A. K. Biswas, B. K. Das

    Pages 159-181

13. Comparison of Edge Detection Algorithm for Part Identification in a Vision Guided Robotic Assembly System

    • Bunil Kumar Balabantaray, Bandita Das, Bibhuti Bhusan Biswal

    Pages 183-206

The Soft Computing techniques, which are based on the information processing of biological systems are now massively used in the area of pattern recognition, making prediction & planning, as well as acting on the environment. Ideally speaking, soft computing is not a subject of homogeneous concepts and techniques; rather, it is an amalgamation of distinct methods that confirms to its guiding principle. At present, the main aim of soft computing is to exploit the tolerance for imprecision and uncertainty to achieve tractability, robustness and low solutions cost. The principal constituents of soft computing techniques are probabilistic reasoning, fuzzy logic, neuro-computing, genetic algorithms, belief networks, chaotic systems, as well as learning theory. This book covers contributions from various authors to demonstrate the use of soft computing techniques in various applications of engineering.

• Computational Intelligence
• Soft Computing
• Vision Science

From the book reviews:

“This book is a collection of recent applications of soft computing in medical imaging, the automotive industry, and robotics. … the book can be used as reference textbook to give beginners an idea of how to apply soft computing algorithms to practical applications.” (Jindong Liu, Computing Reviews, June, 2014)

• Department of Computer Science and Engineering, SOA University, Bhubaneswar, India

  Srikanta Patnaik

• School of Computer Science and Technology, Soochow University, Suzhou, China

  Baojiang Zhong

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