Markus Zahn - IEEE Xplore Author Profile
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Markus Zahn

Also published under: M. Zahn

Publications
142
Citations
3,603
Publications by Year
19742014
Co-Authors:
Show All Co-Authors (150)
Image of author Markus Zahn

Markus Zahn

Also published under: M. Zahn

Affiliation

Department of Electrical Engineering and Computer Science
Massachusetts Institute of Technology
Cambridge, MA, USA

Publication Topics

Biography

Markus Zahn (S'68-M'71-SM'78-F'93) received the B.S.E.E., M.S.E.E., electrical engineer, and D.Sc. degrees in electrical engineering from the Massachusetts Institute of Technology (MIT), Cambridge, MA, in 1968, 1968, 1969, and 1970, respectively. He is the Thomas and Gerd Perkins Professor of Electrical Engineering at MIT since 2000 and is Director of the VI - A Internship Program since 1994, a cooperative work/study program with industry. His primary research areas are ferrohydrodynamics and electrohydrodynamics for microfluidic, biomedical and micro/nano electromechanical system (MEMSINEMS) applications, nanoparticle technology for improved high-voltage performance of electric power apparatus, Kerr electro-optic field and space charge map...
Publications
142
Citations
3,603
Publications by Year
19742014
Co-Authors:
Show All Co-Authors (150)
Author's Published Works

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Reconstruction and Classification of Brain Strokes Using Deep Learning-Based Microwave Imaging

Sayyed Saleh Sayyed Mousavi;Mohammad Saeed Majedi

IEEE Access
Year: 2025 | Volume: 13 | Journal Article |
Prompt identification of the type of brain stroke is a pivotal measure for medical practitioners in commencing therapeutic interventions for patients afflicted with stroke. In this paper, our purpose is to diagnose the type of stroke using high-quality images. This paper presents a novel methodology for image reconstruction using U-Net, followed by the classification of brain stroke type through a...Show More

Reconstruction and Classification of Brain Strokes Using Deep Learning-Based Microwave Imaging

Sayyed Saleh Sayyed Mousavi;Mohammad Saeed Majedi

Year: 2025 | Volume: 13 | Journal Article |

A Modified Microwave Imaging Method for Brain Stroke Detection

Sayyed Saleh Sayyed Mousavi;Mohammad Saeed Majedi

IEEE Transactions on Antennas and Propagation
Year: 2024 | Volume: 72, Issue: 1 | Journal Article |
Cited by: Papers (3)
In this article, a modified microwave imaging method for stroke diagnosis is proposed. This method is based on introducing a suitable initial guess for the distorted Born iterative method (DBIM), which leads to improving the quality of DBIM images. To this end, as a further novelty of the article, we investigate the dependence of the Born iterative method (BIM) and DBIM on the initial guess. We de...Show More

A Modified Microwave Imaging Method for Brain Stroke Detection

Sayyed Saleh Sayyed Mousavi;Mohammad Saeed Majedi

Year: 2024 | Volume: 72, Issue: 1 | Journal Article |

High Quality Brain Image Reconstruction Based on DBIM and U-Net

Sayyed Saleh Sayyed Mousavi;Mohammad Saeed Majedi

2023 30th National and 8th International Iranian Conference on Biomedical Engineering (ICBME)
Year: 2023 | Conference Paper |
Cited by: Papers (1)
Diagnosing the type of stroke through brain image reconstruction is a widely employed method that initiates the treatment of stroke patients. Microwave imaging has emerged as a recent technique for image reconstruction, yet it suffers from the drawback of low resolution. In this study, we propose a novel approach to enhancing the quality of reconstructed brain images by leveraging the power of the...Show More

High Quality Brain Image Reconstruction Based on DBIM and U-Net

Sayyed Saleh Sayyed Mousavi;Mohammad Saeed Majedi

Year: 2023 | Conference Paper |

A Quantitative Microwave Imaging Approach for Brain Stroke Classification Based on the Generalized Tikhonov Regularization

Sayyed Saleh Sayyed Mousavi;Mohammad Saeed Majedi

IEEE Access
Year: 2023 | Volume: 11 | Journal Article |
Cited by: Papers (5)
Early diagnosis of stroke type by imaging is one of the most important tasks for stroke patients. In this article, we propose a new approach to reconstruct the brain image with high accuracy and quality. In this approach, first we use the Born iterative method to reconstruct the brain image. Then by comparing this image with a set of MRI-based brain images, using structural similarity index measur...Show More

A Quantitative Microwave Imaging Approach for Brain Stroke Classification Based on the Generalized Tikhonov Regularization

Sayyed Saleh Sayyed Mousavi;Mohammad Saeed Majedi

Year: 2023 | Volume: 11 | Journal Article |

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