"Coding Theory" ed. by Sudhakar Radhakrishnan, Muhammad Sarfraz

This book is intended to attract the attention of practitioners and researchers in academia and industry interested in challenging paradigms of coding theory and computer vision.

The chapters in this comprehensive reference explore the latest developments, methods, approaches, and applications of coding theory in a wide variety of fields and endeavours. This book is compiled with a view to provide researchers, academicians, and readers with an in-depth discussion of the latest advances in this field. It consists of twelve chapters from academicians, practitioners, and researchers from different disciplines of life. All the chapters are authored by various researchers around the world covering the field of coding theory and image and video processing. This book mainly focusses on researchers who can do quality research in the area of coding theory and image and video processing and related fields. Each chapter is an independent research study, which will motivate young researchers to think about. These twelve chapters are presented in three sections and will be an eye-opener for all who systematic researchers in these fields.


Contents
1.Generalized Low-Density Parity-Check Codes: Construction and Decoding Algorithms
2.Polynomials in Error Detection and Correction in Data Communication System
3.A Direct Construction of Intergroup Complementary Code Set for CDMA
4.Z2Z2-Linear and Z2Z2-Cyclic Codes
5.The Adaptive Coding Techniques for Dependable Medical Network Channel
6.Combined Crosstalk Avoidance Code with Error Control Code for Detection and Correction of Random and Burst Errors
7.Efficient Depth Estimation Using Sparse Stereo-Vision with Other Perception Techniques
8.Advances in Signal and Image Processing in Biomedical Applications
9.Phase-Stretch Adaptive Gradient-Field Extractor (PAGE)
10.Many-Core Algorithm of the Embedded Zerotree Wavelet Encoder
11.On the Application of Dictionary Learning to Image Compression
12.The DICOM Image Compression and Patient Data Integration using Run Length and Huffman Encoder