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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1.080 Topics available

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977 Locations available

693.932 PEOPLE
693.932 People People

693.932 People

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V., Anchitaalagammai J.

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in Cooperation with on an Cooperation-Score of 37%

Topics

Publications (1/1 displayed)

  • 2019Best Practicescitations

Places of action

Chart of shared publication
Padmadevi, S.
1 / 1 shared
Shantha Lakshmi Revathy, J.
1 / 1 shared
Kavitha, S.
1 / 3 shared
Murali, S.
1 / 3 shared
Chart of publication period
2019

Co-Authors (by relevance)

  • Padmadevi, S.
  • Shantha Lakshmi Revathy, J.
  • Kavitha, S.
  • Murali, S.
OrganizationsLocationPeople

booksection

Best Practices

  • V., Anchitaalagammai J.
  • Padmadevi, S.
  • Shantha Lakshmi Revathy, J.
  • Kavitha, S.
  • Murali, S.
Abstract

Internet of things (IoT) describes an emerging trend where a large number of embedded devices (things) are connected to the internet to participate in automating activities that create compounded value for the end consumers as well as for the enterprises. One of the greatest concerns in IoT is security, and how software engineers address it will play a deeper role. As devices interact with each other, businesses need to be able to securely handle the data deluge. With focused approach, it is possible to minimize the vulnerabilities and risks exposed to the devices and networks. Adopting security-induced software development lifecycle (SDL) is one of the major steps in identifying and minimizing the zero-day vulnerabilities and hence to secure the IoT applications and devices. This chapter focuses best practices for adopting security into the software development process with the help of two approaches: cryptographic and machine learning techniques to integrate secure coding and security testing ingrained as part of software development lifecycle.

Topics
  • impedance spectroscopy
  • machine learning