Based on the authors' years of extensive experience, this is an authoritative overview of Wide Bandgap (WBG) device characterization. It provides essential tools to assist researchers, advanced students and practicing engineers in performing both static and dynamic characterization of WBG devices, particularly those based on using silicon carbide (SiC) and gallium nitride (GaN) power semiconductors. The book presents practical considerations for real applications, and includes examples of applying the described methodology.

This book discusses efforts to control the low-frequency vibration transmission of typical power equipment and pipeline systems of ships, exploring the use of active and passive hybrid vibration isolation and adjustable dynamic vibration absorption technologies. It also proposes an adaptive feed-forward control strategy and studies a distributed feed-forward control hardware system. In addition, the book presents a three-way dynamic vibration absorption theory used to design a pipeline-system adjustable dynamic vibration absorber, which offers a number of advantages, such as compact structure, easy assembly and disassembly, low power consumption, excellent vibration control effect and wide frequency band adjustable ability, etc. This book is a valuable resource for researchers and engineers in the fields of noise and vibration control, active control systems, active vibration isolation and adaptive dynamic vibration absorption.

This book critically examines the international, geopolitical, policy, institutional, and curricular challenges facing Canadian offshore school programs. Bringing together scholars and practitioners concerned with addressing the pedagogical, organizational, curriculum, and policy aspects of this transnational mode of schooling, it represents a ground-breaking exploration of K-12 offshore schools within the wider contexts of global geopolitics and forms of soft power.

The book examines the vulnerability that arises from having to manoeuvre political, social, geopolitical, and economic policy simultaneously in both the host and home-licencing countries. It delves into conflicts within the context of neoliberal economic agendas, neocolonial and geopolitical interests, and social class reproduction within host countries. The book is the first scholarly space that questions how international educational initiatives are affected by emerging global threats, such as the recent Covid pandemic. Additionally, it unpacks the question of citizenship and its intersections with social class, immigration, and sociocultural dynamics. It explores how these intersections forge new paths not only to mobility but also to new configurations of power and new spaces of politics and identity. With a range of reflexive, empirical, and theoretical contributions that cover every aspect of offshore schools, the book reassesses the trope of globalization dominated by Eurocentric perspectives. It decompartmentalizes diverse perspectives and insights on the internationalisation of schooling opportunities, and provides an overview of the challenges and possibilities open to offshore schools in different cultural contexts, making it the first comprehensive body of research on this type of schooling.

This book will be of great value to researchers, faculty, scholars, and postgraduate students working across international and comparative education. It will be particularly useful to those interested in the intersections betweeneducation and geopolitically situated forms of soft power.

Transductive Prostate Segmentation for CT Image Guided Radiotherapy.- Model-Driven Centerline Extraction for Severely Occluded Major Coronary Arteries.- MRI Confirmed Prostate Tissue Classification with Laplacian Eigenmaps of Ultrasound RF Spectra.- Hierarchical Ensemble of Multi-level Classifiers for Diagnosis of Alzheimer's Disease.- Dense Deformation Reconstruction via Sparse Coding.- Group Sparsity Constrained Automatic Brain Label Propagation.- Sparse Patch-Guided Deformation Estimation for Improved Image Registration.- Computer-Aided Detection of Aneurysms in 3D Time-of-Flight MRA Datasets.- Data Driven Constraints for the SVM.- Towards Improving the Accuracy of Sensorless Freehand 3D Ultrasound by Learning.- A Novel 3D Joint MGRF Framework for Precise Lung Segmentation.- Nonlinear Discriminant Graph Embeddings for Detecting White Matter Lesions in FLAIR MRI.- Use of Pattern-Information Analysis in Vision Science: A Pragmatic Examination.- Human Age Estimation with Surface-Based Features from MRI Images.- Biomedical Images Classification by Universal Nearest Neighbours Classifier Using Posterior Probability.- Simultaneous Registration and Segmentation by L1 Minimization.- On the Creation of Generic fMRI Feature Networks Using 3-D Moment Invariants.- Description and Classification of Confocal Endomicroscopic Images for the Automatic Diagnosis of Inflammatory Bowel Disease.- A Localized MKL Method for Brain Classification with Known Intra-class Variability.- Supervised Image Segmentation across Scanner Protocols: A Transfer Learning Approach.- Learning to Locate Cortical Bone in MRI.- Quality Classification of Microscopic Imagery with Weakly Supervised Learning.- Graph-Based Inter-subject Classification of Local fMRI Patterns.- Combining Multiple Image Segmentations by Maximizing Expert Agreement.- Cardiac LV and RV Segmentation Using Mutual Context Information.- Non-parametric Density Modeling and Outlier Detection in Medical Imaging Datasets.- Learning Correspondences in Knee MR Images from the Osteoarthritis Initiative.- Gradient Projection Learning for Parametric Nonrigid Registration.- Learning to Rank from Medical Imaging Data.- Integrating Statistical Shape Models into a Graph Cut Framework for Tooth Segmentation.- A Random Forest Based Approach for One Class Classification in Medical Imaging.- Finding Deformable Shapes by Correspondence-Free Instantiation and Registration of Statistical Shape Models.- Computer Aided Skin Lesion Diagnosis with Humans in the Loop. Model-Driven Centerline Extraction for Severely Occluded Major Coronary Arteries.- MRI Confirmed Prostate Tissue Classification with Laplacian Eigenmaps of Ultrasound RF Spectra.- Hierarchical Ensemble of Multi-level Classifiers for Diagnosis of Alzheimer's Disease.- Dense Deformation Reconstruction via Sparse Coding.- Group Sparsity Constrained Automatic Brain Label Propagation.- Sparse Patch-Guided Deformation Estimation for Improved Image Registration.- Computer-Aided Detection of Aneurysms in 3D Time-of-Flight MRA Datasets.- Data Driven Constraints for the SVM.- Towards Improving the Accuracy of Sensorless Freehand 3D Ultrasound by Learning.- A Novel 3D Joint MGRF Framework for Precise Lung Segmentation.- Nonlinear Discriminant Graph Embeddings for Detecting White Matter Lesions in FLAIR MRI.- Use of Pattern-Information Analysis in Vision Science: A Pragmatic Examination.- Human Age Estimation with Surface-Based Features from MRI Images.- Biomedical Images Classification by Universal Nearest Neighbours Classifier Using Posterior Probability.- Simultaneous Registration and Segmentation by L1 Minimization.- On the Creation of Generic fMRI Feature Networks Using 3-D Moment Invariants.- Description and Classification of Confocal Endomicroscopic Images for the Automatic Diagnosis of Inflammatory Bowel Disease.- A Localized MKL Method for Brain Classification with Known Intra-class Variability.- Supervised Image Segmentation across Scanner Protocols: A T

The first International Workshop on Machine Learning in Medical Imaging, MLMI 2010, was held at the China National Convention Center, Beijing, China on Sept- ber 20, 2010 in conjunction with the International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI) 2010. Machine learning plays an essential role in the medical imaging field, including image segmentation, image registration, computer-aided diagnosis, image fusion, ima- guided therapy, image annotation, and image database retrieval. With advances in me- cal imaging, new imaging modalities, and methodologies such as cone-beam/multi-slice CT, 3D Ultrasound, tomosynthesis, diffusion-weighted MRI, electrical impedance to- graphy, and diffuse optical tomography, new machine-learning algorithms/applications are demanded in the medical imaging field. Single-sample evidence provided by the patient's imaging data is often not sufficient to provide satisfactory performance; the- fore tasks in medical imaging require learning from examples to simulate a physician's prior knowledge of the data. The MLMI 2010 is the first workshop on this topic. The workshop focuses on major trends and challenges in this area, and works to identify new techniques and their use in medical imaging. Our goal is to help advance the scientific research within the broad field of medical imaging and machine learning. The range and level of submission for this year's meeting was of very high quality. Authors were asked to submit full-length papers for review. A total of 38 papers were submitted to the workshop in response to the call for papers.