Week of Events
IEEE SPS SBC Webinar: Data-driven Approaches for Improved Reconstruction and Segmentation in Medical Imaging.(By Dr. Raji Susan Mathew)
IEEE SPS SBC Webinar: Data-driven Approaches for Improved Reconstruction and Segmentation in Medical Imaging.(By Dr. Raji Susan Mathew)
This talk deals with two aspects of medical Imaging: image reconstruction and segmentation. The first part covers the image reconstruction for magnetic resonance imaging (MRI). Despite the capability of providing high-resolution images, the difficulties associated with lengthy acquisition time necessitate reconstruction of the final image from a limited number of k-space samples. The reconstruction problem falls under the broad class of ill-posed inverse problems, and regularization is necessary for obtaining stable and meaningful solutions. However, the accuracy of regularized output depends on the regularization parameter choice. The adaptive estimation of the regularization parameter from the data for sparsity-promoting methods will be discussed. The second part focuses on nerve segmentation in ultrasound images. The automated segmentation of the median nerve at the wrist and from wrist to elbow using different deep learning models along with the associated challenges will be discussed. Finally, the talk will conclude with a discussion on the implementation of the model in real-time. Speaker(s): Dr. Raji Susan Mathew, Virtual: https://events.vtools.ieee.org/m/377685
IEEE SPS SBC Webinar: Data-driven Approaches for Improved Reconstruction and Segmentation in Medical Imaging.(By Dr. Raji Susan Mathew)
IEEE SPS SBC Webinar: Data-driven Approaches for Improved Reconstruction and Segmentation in Medical Imaging.(By Dr. Raji Susan Mathew)
This talk deals with two aspects of medical Imaging: image reconstruction and segmentation. The first part covers the image reconstruction for magnetic resonance imaging (MRI). Despite the capability of providing high-resolution images, the difficulties associated with lengthy acquisition time necessitate reconstruction of the final image from a limited number of k-space samples. The reconstruction problem falls under the broad class of ill-posed inverse problems, and regularization is necessary for obtaining stable and meaningful solutions. However, the accuracy of regularized output depends on the regularization parameter choice. The adaptive estimation of the regularization parameter from the data for sparsity-promoting methods will be discussed. The second part focuses on nerve segmentation in ultrasound images. The automated segmentation of the median nerve at the wrist and from wrist to elbow using different deep learning models along with the associated challenges will be discussed. Finally, the talk will conclude with a discussion on the implementation of the model in real-time. Speaker(s): Dr. Raji Susan Mathew, Virtual: https://events.vtools.ieee.org/m/377686
Can perfectly electric conducting obstacles modify the permeability of metamaterial?
Can perfectly electric conducting obstacles modify the permeability of metamaterial?
An array of perfectly electric conducting (PEC) obstacles in a host dielectric medium modifies the constitutive parameters of the composite medium or metamaterial. Typically, for such a composite medium, the effective (homogenized) parameters are estimated using the Lorentz model. The Lorentz model indicates that inclusion of PEC obstacles in a host medium introduces both electric and magnetic polarizabilities that modify the permittivity and permeability tensors of the composite medium. On the other hand, the Floquet eigenmodal analysis shows that the PEC obstacles, while modifying the permittivity tensor, have no effect on the permeability tensor. Hence, as a corollary, Floquet model leads us to conclude that a negative permeability metamaterial using PEC obstacles is not possible to obtain. Interestingly, numerical results obtained from full wave analyses of metamaterial-slabs support the Floquet model. We will present the Lorentz and Floquet models and examine the source of inaccuracy in the Lorentz model. We will discuss the characteristic features of metamaterials made with non-resonant and resonant types of obstacles. Numerical results obtained from four independent models will be compared and discussed. In light of Maxwell’s equations, we will scrutinize the rationality of the claims of “negative-refraction” metamaterials. Co-sponsored by: IEEE AP-MTTS SBC IIT Kharagpur Speaker(s): Arun K. Bhattacharyya, Room: NKN Room, Bldg: E&ECE Department, Kharagpur, West Bengal, India, IIT Kharagpur, Kharagpur, West Bengal, India, 721302
IEEE SPS SBC Webinar: Navigating the Post-Ph.D. Landscape: Unlocking Opportunities for Success (By Dr. Ayan Mondal)
IEEE SPS SBC Webinar: Navigating the Post-Ph.D. Landscape: Unlocking Opportunities for Success (By Dr. Ayan Mondal)
Embarking on a journey towards a Ph.D. is a monumental achievement, but it's only the beginning of a remarkable career. This talk is designed to guide Ph.D. graduates through the myriad of opportunities that await them after earning their doctoral degree. In this engaging and informative presentation, we will explore the diverse avenues available to Ph.D. holders, from academia and industry to entrepreneurship and beyond. We will delve into strategies for effectively transitioning from academia to other sectors, leveraging unique skills and expertise, and crafting a fulfilling and impactful career path. Whether you're considering different career options, seeking guidance on post-Ph.D. job searches, or aiming to make the most of your doctoral experience, this talk will provide valuable insights, actionable advice, and inspiration to help you chart your course towards a successful and fulfilling post-Ph.D. future. Speaker(s): Dr. Ayan Mondal, Virtual: https://events.vtools.ieee.org/m/377691