Materials Map

Discover the materials research landscape. Find experts, partners, networks.

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The Materials Map is an open tool for improving networking and interdisciplinary exchange within materials research. It enables cross-database search for cooperation and network partners and discovering of the research landscape.

The dashboard provides detailed information about the selected scientist, e.g. publications. The dashboard can be filtered and shows the relationship to co-authors in different diagrams. In addition, a link is provided to find contact information.

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Materials Map under construction

The Materials Map is still under development. In its current state, it is only based on one single data source and, thus, incomplete and contains duplicates. We are working on incorporating new open data sources like ORCID to improve the quality and the timeliness of our data. We will update Materials Map as soon as possible and kindly ask for your patience.

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in Cooperation with on an Cooperation-Score of 37%

Topics

Publications (2/2 displayed)

  • 2021Underwater Image Enhancement With Optimal Histogram Using Hybridized Particle Swarm and Dragonfly5citations
  • 2013Spatial Distribution of Full-Field Residual Stress and Its Correlation with Fracture Strength of Thin Silicon Waferscitations

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Chart of shared publication
Kumar, A.
1 / 94 shared
Danyluk, S.
1 / 1 shared
Melkote, S.
1 / 1 shared
Skenes, K.
1 / 1 shared
Yang, C.
1 / 15 shared
Chart of publication period
2021
2013

Co-Authors (by relevance)

  • Kumar, A.
  • Danyluk, S.
  • Melkote, S.
  • Skenes, K.
  • Yang, C.
OrganizationsLocationPeople

article

Underwater Image Enhancement With Optimal Histogram Using Hybridized Particle Swarm and Dragonfly

  • Prasath, R.
Abstract

<jats:title>Abstract</jats:title><jats:p>Typically, underwater image processing is mainly concerned with balancing the color change distortion or light scattering. Various researches have been processed under these issues. This proposed model incorporates two phases, namely, contrast correction and color correction. Moreover, two processes are involved within the contrast correction model, namely: (i) global contrast correction and (ii) local contrast correction. For the image enhancement, the main target is on the histogram evaluation, and therefore, the optimal selection of histogram limit is very essential. For this optimization purpose, a new hybrid algorithm is introduced namely, swarm updated Dragonfly Algorithm, which is the hybridization of Particle Swarm Optimization (PSO) and Dragonfly Algorithm (DA). Further, this paper mainly focused on Integrated Global and Local Contrast Correction (IGLCC). The proposed model is finally distinguished over the other conventional models like Contrast Limited Adaptive Histogram, IGLCC, dynamic stretching IGLCC-Genetic Algorithm, IGLCC-PSO, IGLCC- Firefly and IGLCC-Cuckoo Search, IGLCC-Distance-Oriented Cuckoo Search and DA, and the superiority of the proposed work is proved.</jats:p>

Topics
  • impedance spectroscopy
  • phase
  • light scattering