This compendium provides a detailed account of the lognormality principle characterizing the human motor behavior by summarizing a sound theoretical framework for modeling such a behavior, introducing the most recent algorithms for extracting the lognormal components of complex movements in 2, 2.5 and 3 dimensions. It also vividly reports the most advanced applications to handwriting analysis and recognition, signature and writer verification, gesture recognition and calligraphy generation, evaluation of motor skills, improvement/degradation with aging, handwriting learning, education and developmental deficits, prescreening of children with ADHD (Attention Development and Hyperactivity Disorder), monitoring of concussion recovery, diagnosis and monitoring of Alzheimer's and Parkinson's diseases and aging effects in speech and handwriting.The volume provides a unique and useful source of references on the lognormality principle, an update on the most recent advances and an outlook at the most promising future developments in e-Security, e-Learning and e-Health.

This book constitutes the proceedings of the 20th International Conference of the International Graphonomics Society, IGS 2021/2022. Originally planned to be celebrated in September 2021, due to the COVID-19 pandemic it was held in June 2022. The 36 full and 3 short papers presented at the conference were carefully reviewed and selected from 41 submissions and reviewed by a committee of 67 reviewers. Out of the 41 conferences, 26 papers were selected to be published in this proceedings. The program was comprised of 6 oral sessions on the following topics: historical documents, forensic handwriting examinations, handwriting learning and development, and motor control. One additional poster session included papers on all previous topics, and three special sessions included papers on neurodegenerative disorders.

This 3rd edition tackles the basic principle of deep learning as well as the application of combination of wavelet theory with deep learning to pattern recognition. Five new chapters related to the combination of wavelet theory and deep learning are added with many novel research results.The useful reference text will benefit academics, researchers, computer scientists, electronic engineers and graduate students in the field of pattern recognition, image analysis, machine learning and electrical and electronic engineering.

In recent years, libraries and archives all around the world have increased their efforts to digitize historical manuscripts. To integrate the manuscripts into digital libraries, pattern recognition and machine learning methods are needed to extract and index the contents of the scanned images.The unique compendium describes the outcome of the HisDoc research project, a pioneering attempt to study the whole processing chain of layout analysis, handwriting recognition, and retrieval of historical manuscripts. This description is complemented with an overview of other related research projects, in order to convey the current state of the art in the field and outline future trends.This must-have volume is a relevant reference work for librarians, archivists and computer scientists.

Mathematical Statistics with Applications in R, Second Edition, offers a modern calculus-based theoretical introduction to mathematical statistics and applications. The book covers many modern statistical computational and simulation concepts that are not covered in other texts, such as the Jackknife, bootstrap methods, the EM algorithms, and Markov chain Monte Carlo (MCMC) methods such as the Metropolis algorithm, Metropolis-Hastings algorithm and the Gibbs sampler. By combining the discussion on the theory of statistics with a wealth of real-world applications, the book helps students to approach statistical problem solving in a logical manner.This book provides a step-by-step procedure to solve real problems, making the topic more accessible. It includes goodness of fit methods to identify the probability distribution that characterizes the probabilistic behavior or a given set of data. Exercises as well as practical, real-world chapter projects are included, and each chapter has an optional section on using Minitab, SPSS and SAS commands. The text also boasts a wide array of coverage of ANOVA, nonparametric, MCMC, Bayesian and empirical methods; solutions to selected problems; data sets; and an image bank for students.Advanced undergraduate and graduate students taking a one or two semester mathematical statistics course will find this book extremely useful in their studies. - Step-by-step procedure to solve real problems, making the topic more accessible - Exercises blend theory and modern applications - Practical, real-world chapter projects - Provides an optional section in each chapter on using Minitab, SPSS and SAS commands - Wide array of coverage of ANOVA, Nonparametric, MCMC, Bayesian and empirical methods