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1. |
Record Nr. |
UNINA9910154753103321 |
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Autore |
Birman Joan S. |
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Titolo |
Braids, Links, and Mapping Class Groups. (AM-82), Volume 82 / / Joan S. Birman |
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Pubbl/distr/stampa |
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Princeton, NJ : , : Princeton University Press, , [2016] |
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©1975 |
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ISBN |
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Descrizione fisica |
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1 online resource (241 pages) : illustrations |
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Collana |
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Annals of Mathematics Studies ; ; 238 |
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Disciplina |
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Soggetti |
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Braid theory |
Knot theory |
Representations of groups |
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Lingua di pubblicazione |
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Formato |
Materiale a stampa |
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Livello bibliografico |
Monografia |
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Note generali |
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Nota di bibliografia |
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Includes bibliographical references and index. |
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Nota di contenuto |
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Frontmatter -- PREFACE -- TABLE OF CONTENTS -- CHAPTER 1. BRAID GROUPS -- CHAPTER 2. BRAIDS AND LINKS -- CHAPTER 3. MAGNUS REPRESENTATIONS -- CHAPTER 4. MAPPING CLASS GROUPS -- CHAPTER 5. PLATS AND LINKS -- APPENDIX: RESEARCH PROBLEMS -- BIBLIOGRAPHY -- INDEX -- Backmatter |
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Sommario/riassunto |
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The central theme of this study is Artin's braid group and the many ways that the notion of a braid has proved to be important in low-dimensional topology.In Chapter 1 the author is concerned with the concept of a braid as a group of motions of points in a manifold. She studies structural and algebraic properties of the braid groups of two manifolds, and derives systems of defining relations for the braid groups of the plane and sphere. In Chapter 2 she focuses on the connections between the classical braid group and the classical knot problem. After reviewing basic results she proceeds to an exploration of some possible implications of the Garside and Markov theorems.Chapter 3 offers discussion of matrix representations of the free group and of subgroups of the automorphism group of the free group. These ideas come to a focus in the difficult open question of whether Burau's matrix representation of the braid group is faithful. Chapter 4 is a broad view of recent results on the connections between braid groups |
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and mapping class groups of surfaces. Chapter 5 contains a brief discussion of the theory of "plats." Research problems are included in an appendix. |
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2. |
Record Nr. |
UNINA9910557346003321 |
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Autore |
Kyamakya Kyandoghere |
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Titolo |
Emotion and Stress Recognition Related Sensors and Machine Learning Technologies |
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Pubbl/distr/stampa |
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Basel, Switzerland, : MDPI - Multidisciplinary Digital Publishing Institute, 2021 |
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Descrizione fisica |
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1 online resource (550 p.) |
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Soggetti |
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Technology: general issues |
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Lingua di pubblicazione |
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Formato |
Materiale a stampa |
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Livello bibliografico |
Monografia |
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Sommario/riassunto |
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This book includes impactful chapters which present scientific concepts, frameworks, architectures and ideas on sensing technologies and machine learning techniques. These are relevant in tackling the following challenges: (i) the field readiness and use of intrusive sensor systems and devices for capturing biosignals, including EEG sensor systems, ECG sensor systems and electrodermal activity sensor systems; (ii) the quality assessment and management of sensor data; (iii) data preprocessing, noise filtering and calibration concepts for biosignals; (iv) the field readiness and use of nonintrusive sensor technologies, including visual sensors, acoustic sensors, vibration sensors and piezoelectric sensors; (v) emotion recognition using mobile phones and smartwatches; (vi) body area sensor networks for emotion and stress studies; (vii) the use of experimental datasets in emotion recognition, including dataset generation principles and concepts, quality insurance and emotion elicitation material and concepts; (viii) machine learning techniques for robust emotion recognition, including graphical models, neural network methods, deep learning methods, |
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statistical learning and multivariate empirical mode decomposition; (ix) subject-independent emotion and stress recognition concepts and systems, including facial expression-based systems, speech-based systems, EEG-based systems, ECG-based systems, electrodermal activity-based systems, multimodal recognition systems and sensor fusion concepts and (x) emotion and stress estimation and forecasting from a nonlinear dynamical system perspective. |
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