Bøger af Noureddine Melikechi

We use our eyes to see, but we don't all see the same things. Ever wonder why some people need glasses, what it looks like to be color blind, or what the difference is between being near-sighted and far-sighted? With kid-friendly explanations of vision, light, color, and eye anatomy, How Do You See the World? explores a variety of eye conditions and the helpful tools that people use to better see the world around them.

This is an interdisciplinary book that presents the applications of novel laser spectroscopy and imaging techniques for the detection of cancers recently developed by some of the world's most renown researchers. The book consists of three parts and a total of 16 chapters. Each chapter is written by leading experts who are actively seeking to develop novel spectroscopic and analytical methods for cancer detection and diagnosis.In Part I, the authors present fundamentals on optics, atoms and molecules, biophysics, cancer and machine learning. These chapters are intended for those who are not experts in the field but wish to learn about fundamentals' aspects of some of the key topics that are addressed in this book. Particular attention has been given to providing key references for those who wish to go further into the fundamental aspects of atoms and molecules, light-matter interaction, optical instrumentation, machine learning and cancer.In Part II, the authors present key applications of various laser spectroscopic methods in cancer diagnosis. They have provided recent progress in cancer diagnostics obtained by combining laser spectroscopy and machine learning for the analysis of the spectra acquired from biomedical tissues and biofluids.In Part III, the authors present chapters that discuss key developments in the applications of various laser imaging techniques for cancer detection.This is one of the few books that addresses cancer detection and diagnosis using laser spectroscopic and imaging tools with an eye on providing the reader the scientific tools, including machine learning ones.