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Biomedical Engineering and Bioengineering Commons™
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- Air plasma (1)
- Atmospheric pressure (1)
- Block matching (1)
- Cell (1)
- Collaborative filtering (1)
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- Corrupted images (1)
- Cost functions (1)
- Costs (1)
- Denoising methods (1)
- Eukaryote (1)
- Evolutionary algorithms (1)
- Glow discharge (1)
- Image de-noising (1)
- Image denoising methods (1)
- Imaging systems (1)
- MRI image (1)
- Medical imaging (1)
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- Multiobjective optimization (1)
- Noise removal (1)
- Non-equilibrium (1)
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- Parameter optimization (1)
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- Pareto principle (1)
- Random noise (1)
- Remove noise (1)
- Shape optimization (1)
- Signal filtering and prediction (1)
Articles 1 - 2 of 2
Full-Text Articles in Biomedical Engineering and Bioengineering
Effects Of Non-Equilibrium Plasma On Eukaryotic Cells (Final Report: Grant Fa9550-06-1-0004), Mounir Laroussi, Fred C. Dobbs, Old Dominion University
Effects Of Non-Equilibrium Plasma On Eukaryotic Cells (Final Report: Grant Fa9550-06-1-0004), Mounir Laroussi, Fred C. Dobbs, Old Dominion University
Electrical & Computer Engineering Faculty Publications
This document is our final report describing the research activities carried out under AFOSR Grant FA9550-06-1-0004. First, descriptions of our cold plasma generation systems are presented. Two systems, developed with past and present AFOSR support, are available in our laboratory. The first is a pulsed device capable of emitting a cold plasma plume in room air. The second is an air plasma generator the core of which is a dielectric barrier discharge excited by a high AC voltage. Following these brief descriptions we first present the effects of an atmospheric pressure air plasma on four different types of eukaryotic microalgae. …
Parameter Optimization For Image Denoising Based On Block Matching And 3d Collaborative Filtering, Ramu Pedada, Emin Kugu, Jiang Li, Zhanfeng Yue, Yuzhong Shen, Josien P.W. Pluim (Ed.), Benoit M. Dawant (Ed.)
Parameter Optimization For Image Denoising Based On Block Matching And 3d Collaborative Filtering, Ramu Pedada, Emin Kugu, Jiang Li, Zhanfeng Yue, Yuzhong Shen, Josien P.W. Pluim (Ed.), Benoit M. Dawant (Ed.)
Electrical & Computer Engineering Faculty Publications
Clinical MRI images are generally corrupted by random noise during acquisition with blurred subtle structure features. Many denoising methods have been proposed to remove noise from corrupted images at the expense of distorted structure features. Therefore, there is always compromise between removing noise and preserving structure information for denoising methods. For a specific denoising method, it is crucial to tune it so that the best tradeoff can be obtained. In this paper, we define several cost functions to assess the quality of noise removal and that of structure information preserved in the denoised image. Strength Pareto Evolutionary Algorithm 2 (SPEA2) …