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+\chapter*{Foreward}
+I am delighted to introduce the first book on Multimedia Data Mining.  When I came to know about this book project undertaken by two of the most active young researchers in the field, I was pleased that this book is coming in early stage of a field that will need it more than most fields do.  In most emerging research fields, a book can play a significant role in bringing some maturity to the field.  Research fields advance through research papers.  In research papers, however, only a limited perspective could be provided about the field, its application potential, and the techniques required and already developed in the field.  A book gives such a chance.  I liked the idea that there will be a book that will try to unify the field by bringing in disparate topics already available in several papers that are not easy to find and understand.  I was supportive of this book project even before I had seen any material on it.  The project was a brilliant and a bold idea by two active researchers.  Now that I have it on my screen, it appears to be even a better idea.  
+
+Multimedia started gaining recognition in 1990s as a field.  Processing, storage, communication, and capture and display technologies had advanced enough that researchers and technologists started building approaches to combine information in multiple types of signals such as audio, images, video, and  text.  Multimedia computing and communication techniques recognize correlated information in multiple sources as well as insufficiency of information in any individual source.    By properly selecting sources to provide complementary information, such systems aspire, much like human perception system, to create a holistic picture of a situation using only partial information from separate sources.
+
+Data mining is a direct outgrowth of progress in data storage and processing speeds.  When it became possible to store large volume of data and run different statistical computations to explore all possible and even unlikely correlations among data, the field of data mining was born.  Data mining allowed people to hypothesize relationships among data entities and explore support for those.  This field has been put to applications in many diverse domains and keeps getting more applications.  In fact many new fields are direct outgrowth of data mining and it is likely to become a powerful computational tool.\vadjust{\vfill\pagebreak}
+
+\thispagestyle{empty}
+

diff --git a/BookGPU/frontmatter/Preface.tex b/BookGPU/frontmatter/Preface.tex
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+\chapter*{Preface}
+
+Approximately 17 million people in the USA (6{\%} of the
+population) and 140 million people worldwide (this number is
+expected to rise to almost 300 million by the year 2025) suffer
+from \textit{diabetes mellitus}. Currently, there a few dozens of
+commercialised devices for detecting blood glucose levels [1].
+However, most of them are invasive. The development of a
+noninvasive method would considerably improve the quality of life
+for diabetic patients, facilitate their compliance for glucose
+monitoring, and reduce complications and mortality associated with
+this disease. Noninvasive and continuous monitoring of glucose
+concentration in blood and tissues is one of the most challenging
+and exciting applications of optics in medicine. The major
+difficulty in development and clinical application of optical
+noninvasive blood glucose sensors is associated with very low
+signal produced by glucose molecules. This results in low
+sensitivity and specificity of glucose monitoring by optical
+methods and needs a lot of efforts to overcome this difficulty.
+
+A wide range of optical technologies have been designed in
+attempts to develop robust noninvasive methods for glucose
+sensing. The methods include infrared absorption, near-infrared
+scattering, Raman, fluorescent, and thermal gradient
+spectroscopies, as well as polarimetric, polarization
+heterodyning, photonic crystal, optoacoustic, optothermal, and
+optical coherence tomography (OCT) techniques [1-31].
+
+For example, the polarimetric quantification of glucose is based
+on the phenomenon of optical rotatory dispersion, whereby a chiral
+molecule in an aqueous solution rotates the plane of linearly
+polarized light passing through the solution. The angle of
+rotation depends linearly on the concentration of the chiral
+species, the pathlength through the sample, and the molecule
+specific rotation. However, polarization sensitive optical
+technique makes it difficult to measure \textit{in vivo} glucose
+concentration in blood through the skin because of the strong
+light scattering which causes light depolarization. For this
+reason, the anterior chamber of the eye has been suggested as a
+sight well suited for polarimetric measurements, since scattering
+in the eye is generally very low compared to that in other
+tissues, and a high correlation exists between the glucose in the
+blood and in the aqueous humor. The high accuracy of anterior eye
+chamber measurements is also due to the low concentration of
+optically active aqueous proteins within the aqueous humor.
+
+On the other hand, the concept of noninvasive blood glucose
+sensing using the scattering properties of blood and tissues as an
+alternative to spectral absorption and polarization methods for
+monitoring of physiological glucose concentrations in diabetic
+patients has been under intensive discussion for the last decade.
+Many of the considered  effects, such as changing of the size,
+refractive index, packing, and aggregation of RBC under glucose
+variation, are important for glucose monitoring in diabetic
+patients. Indeed, at physiological concentrations of glucose,
+ranging from 40 to 400 mg/dl, the role of some of the effects may
+be modified, and some other effects, such as glucose penetration
+inside the RBC and the followed hemoglobin glycation, may be
+important [30-32].
+
+Noninvasive determination of glucose was attempted using light
+scattering of skin tissue components measured by a
+spatially-resolved diffuse reflectance or NIR fre\-quen\-cy-domain
+reflectance techniques. Both approaches are based on change in
+glucose concentration, which affects the refractive index mismatch
+between the interstitial fluid and tissue fibers, and hence
+reduces scattering coefficient. A glucose clamp experiment showed
+that reduced scattering coefficient measured in the visible range
+qualitatively tracked changes in blood glucose concentration for
+the volunteer with diabetes studied.
+
+
+