Preface Acknowledgments 1. Introduction 2. Dispersion principles 3. Unbounded isotropic and anisotropic media 4. Reflection and refraction 5. Oblique incidence 6. Waves in plates 7. Surface and subsurface waves 8. Finite element method for guided wave mechanics 9. The semi-analytical finite element method (SAFE) 10. Guided waves in hollow cylinders 11. Circumferential guided waves 12. Guided waves in layered structures 13. Source influence on guided wave excitation 14. Horizontal shear 15. Guided waves in anisotropic media 16. Guided wave phased arrays in piping 17. Guided waves in viscoelastic media 18. Ultrasonic vibrations 19. Guided wave array transducers 20. Introduction to guided wave nonlinear methods 21. Guided wave imaging methods Appendix A: ultrasonic nondestructive testing principles, analysis and display technology Appendix B: basic formulas and concepts in the theory of elasticity Appendix C: physically based signal processing concepts for guided waves Appendix D: guided wave mode and frequency selection tips.

Ultrasonic Guided Waves in Solid Media

Acute myelogenous leukemia (AML) is a subtype of acute leukemia, which is characterized by the accumulation of myeloid blasts in the bone marrow. Careful microscopic examination of stained blood smear or bone marrow aspirate is still the most significant diagnostic methodology for initial AML screening and considered as the first step toward diagnosis. It is time-consuming and due to the elusive nature of the signs and symptoms of AML; wrong diagnosis may occur by pathologists. Therefore, the need for automation of leukemia detection has arisen. In this paper, an automatic technique for identification and detection of AML and its prevalent subtypes, i.e., M2-M5 is presented. At first, microscopic images are acquired from blood smears of patients with AML and normal cases. After applying image preprocessing, color segmentation strategy is applied for segmenting white blood cells from other blood components and then discriminative features, i.e., irregularity, nucleus-cytoplasm ratio, Hausdorff dimension, shape, color, and texture features are extracted from the entire nucleus in the whole images containing multiple nuclei. Images are classified to cancerous and noncancerous images by binary support vector machine (SVM) classifier with 10-fold cross validation technique. Classifier performance is evaluated by three parameters, i.e., sensitivity, specificity, and accuracy. Cancerous images are also classified into their prevalent subtypes by multi-SVM classifier. The results show that the proposed algorithm has achieved an acceptable performance for diagnosis of AML and its common subtypes. Therefore, it can be used as an assistant diagnostic tool for pathologists.

Automatic Recognition of Acute Myelogenous Leukemia in Blood Microscopic Images Using K-means Clustering and Support Vector Machine.

For the condensation of degrees of freedom in large structure problems, various methods have been proposed to approximate the lower eigenvalues that represent the global behavior of the structures. One of the dynamic condensation methods was introduced by Guyan [1]. This method involves elimination of the degrees of freedom which do not give any significant influence on the solution field. O’Callahan has improved the Guyana’s method by considering the first order approximation in the transformation formula of slave degrees of freedom [2]. Zhang has proposed the successive-level approximate reduction method [3]. While these techniques could greatly reduce computational effort by eliminating degrees of freedom, they suffer from nontrivial flaws. Because they ignore several higher order terms, results of these methods are highly dependent on the choice of master degrees of freedom. For this reason, several techniques have been proposed to select the master degrees of freedom effectively. The one of the most effective technique is the sequential elimination of the degrees of freedom of which the ratio of stiffness to mass in the diagonal terms of mass and stiffness matrices is highest. This method has been proposed by Shah and Raymund [4]. Shah method shows the effective selection of master degrees of freedom among others and the prediction of lower mode eigenvalue is very reliable. However, it takes considerable computing time in selecting the master degrees of freedom which is required.

Element-Based Node Selection Method for Reduction of Eigenvalue Problems

Computational modelling of string–body interaction for the violin family and simulation of wolf notes

Adaptive mode superposition and acceleration technique with application to frequency response function and its sensitivity

This article reviews the solution procedures used to extract eigenvalues and eigenvectors from linear dynamic systems using the finite element method. The main focus of this review article is on eigensolution techniques that provide only a partial eigensolution. Such eigensolvers extract only a small subset (normally the lowest) of the eigenvalues and eigenvectors present in the discretized system. They represent the most efficient approach to extracting eigenvalues and eigenvectors from large degree of freedom systems. The techniques covered include the subspace iteration method, the Lanczos method, the conjugate gradient method, the Ritz vector method, the substructure synthesis methods (including component mode synthesis), and the condensation techniques (including dynamic substructuring). Each subsection contains a variety of references that cover the current state of research as well as the origin of each technique. A brief critique of the eigensolution procedures used for solving small (less than 250 degree of freedom) eigenproblems is also included for completeness. The discussion is generally limited to eigensolution techniques for linear undamped eigenproblems of the form AΦ = CΦΛ, where A and C are frequently symmetric and positive definite. A brief review of eigensolvers for the nonsymmetric eigenproblem, such as that arising in damped structures, is included at the end. This review article contains 320 references.

Adrian F. Bertolini

Papers

Review of Eigensolution Procedures for Linear Dynamic Finite Element Analysis