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In this extended meditation, Jean Lave interweaves analysis of the process of apprenticeship among the Vai and Gola tailors of Liberia with reflections on the evolution of her research on those tailors in the late 1970s. In so doing, she provides both a detailed account of her apprenticeship in the art of sustained fieldwork and an insightful overview of thirty years of changes in the empirical and theoretical facets of ethnographic practice. Examining the issues she confronted in her own work, Lave shows how the critical questions raised by ethnographic research erode conventional assumptions, altering the direction of the work that follows. As ethnography takes on increasing significance to an ever widening field of thinkers on topics from education to ecology, this erudite but accessible book will be essential to anyone tackling the question of what it means to undertake critical and conceptually challenging fieldwork. Apprenticeship in Critical Ethnographic Practice explains how to seriously explore what it means to be human in a complex world-and why it is so important.

Vai (African people) --- Gola (African people) --- Tailors --- Ethnology --- Social life and customs. --- Apprentices. --- apprenticeship, vai, gola, tailors, liberia, fieldwork, anthropology, sociology, labor, africa, ethnology, nonfiction, academia, institutional arrangements, training, methodology, research, scholarship, learning transfer, development, education, skill building, monrovia, happy corner, artisan, activity, praxis, theory, communities of practice.

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Practical Guide for Biomedical Signals Analysis Using Machine Learning Techniques: A MATLAB Based Approach presents how machine learning and biomedical signal processing methods can be used in biomedical signal analysis. Different machine learning applications in biomedical signal analysis, including those for electrocardiogram, electroencephalogram and electromyogram are described in a practical and comprehensive way, helping readers with limited knowledge. Sections cover biomedical signals and machine learning techniques, biomedical signals, such as electroencephalogram (EEG), electromyogram (EMG) and electrocardiogram (ECG), different signal-processing techniques, signal de-noising, feature extraction and dimension reduction techniques, such as PCA, ICA, KPCA, MSPCA, entropy measures, and other statistical measures, and more. This book is a valuable source for bioinformaticians, medical doctors and other members of the biomedical field who need a cogent resource on the most recent and promising machine learning techniques for biomedical signals analysis.--

Biosensors. --- Artificial intelligence --- Signal processing --- Medical applications. --- Digital techniques. --- Signal Processing, Computer-Assisted. --- Machine Learning. --- Spectrum Analysis. --- Analysis, Spectrum --- Spectrometry --- Spectroscopy --- Transfer Learning --- Learning, Machine --- Learning, Transfer --- Digital Signal Processing --- Signal Interpretation, Computer-Assisted --- Signal Processing, Digital --- Computer-Assisted Signal Interpretation --- Computer-Assisted Signal Interpretations --- Computer-Assisted Signal Processing --- Interpretation, Computer-Assisted Signal --- Interpretations, Computer-Assisted Signal --- Signal Interpretation, Computer Assisted --- Signal Interpretations, Computer-Assisted --- Signal Processing, Computer Assisted --- Fetal Monitoring --- Monitoring, Physiologic --- Data Compression --- Digital signal processing --- Digital communications --- Digital electronics --- Medicine --- Biodetectors --- Biological detectors --- Biological sensors --- Biomedical detectors --- Biomedical sensors --- Detectors --- Medical instruments and apparatus --- Physiological apparatus --- Data processing