Wireless sensor networks (WSNs) have attracted high interest over the last few decades in the wireless and mobile computing research community. Applications of WSNs are numerous and growing, including indoor deployment scenarios in the home and office to outdoor deployment in an adversary's territory in a tactical background. However, due to their distributed nature and deployment in remote areas, these networks are vulnerable to numerous security threats that can adversely affect their performance. This problem is more critical if the network is deployed for some mission-critical applications, such as in a tactical battlefield. Random failure of nodes is also very likely in real-life deployment scenarios. Due to resource constraints in the sensor nodes, a traditional security mechanism with high overhead of computation and communication is not feasible in WSNs. Design and implementation of secure WSNs is, therefore, a particularly challenging task. This book covers a comprehensive discussion on state-of-the-art security technologies for WSNs. It identifies various possible attacks at different layers of the communication protocol stack in a typical WSN and presents their possible countermeasures. A brief discussion on the future direction of research in WSN security is also included.

This book highlights progress in terms of Virus Biology and Infection Detection, Prevention, and Control, along with Screening, Testing, and Detection Techniques, that will provide learners and researchers (from basic to advanced) with the most innovative computer-driven methodologies for the fight against COVID-19. In addition, this book also covers the Pre- and Post-Impact of the COVID-19 Pandemic Crisis that will definitely provide useful content for researchers to think broadly about the analytical areas affected by COVID-19. This ultimately shows different paths to the same destination to help understand the nature of the COVID-19 pandemic and how to avoid it in the future.

A TEXTBOOK ON PATTERN RECOGNITION presents both classical and current theoretical foundation of pattern recognition and practice of supervised, unsupervised and reinforces learning to build a complete background of professionals and engineering students. Every pattern of the image and design cycles has been discussed in detail through illustrative examples and applications. The very latest method are incorporated in this text book including k-mean clustering, k-nearest neighborhood methods etc. Coverage of Learning & Adaptation, Design Cycle of Pattern Recognition Algorithms have been presented in step by step in illustration of the same in problems. Probability distribution function with Cumulative & Uniform PDF methods Coverage of Optical Character recognition, face recognition, and speech recognition and image analysis. The information is clearly presented and illustrated by many examples and applications and is designed for use as a text for course in pattern recognition for both undergraduate and post graduate engineering students.