This book provides comprehensive coverage of fundamentals of database management system. It contains a detailed description on Relational Database Management System Concepts. There are a variety of solved examples and review questions with solutions. This book is for those who require a better understanding of relational data modeling, its purpose, its nature, and the standards used in creating relational data model.

Meant for students and practicing engineers, this book provides a clear, comprehensive and up-to-date introduction to Digital Image Processing in a pragmatic style. An illustrative approach, practical examples and MATLAB applications given in the book help in bringing the theory to life.

Digital signal processing deals with extraction of useful information from signals. Signal processing algorithms help observe, analyse and transform signals. The objective of this book is to develop signal processing algorithms using Python. Python is an interpreted, object-oriented high-level programming language widely used in various software development fields such as data science, machine learning, web development and more. Digital Signal Laboratory is playing an important role in realizing signal processing algorithms, utilizing different software solutions. The intention of this textbook is to implement signal processing algorithms using Python. Since Python is an open-source language, students, researchers, and faculty can install and work with it without spending money, reducing the financial burden on institutions. Each chapter in this book begins with prelab questions, a set of Python examples to illustrate the concepts, exercises to strengthen the understanding of the concepts, and objective questions to help students prepare for competitive examinations. This book serves as an undergraduate textbook, it can be used for individual study, and it can also be used as the textbook for related courses.

Digital image processing plays a crucial role in facilitating efficient storage, transmission, manipulation, and retrieval of images. Python, renowned for its open-source nature and strong community support, offers a user-friendly platform with extensive image processing capabilities through libraries such as computer vision and scikit-learn. This textbook serves as a practical guide to digital image processing using Python, presenting fundamental concepts, techniques, and algorithms with illustrative examples in Python. Each chapter begins with clear learning objectives and concludes with exercises and multiple-choice questions for self-assessment. Drawing from a diverse range of sources including research articles and books, the references provided at the end of each chapter encourage further exploration. Tailored for undergraduate and postgraduate students, research scholars, engineers, and faculty specializing in image processing, it assumes a foundational understanding of set theory, matrix algebra, probability, and random variables.