Materials Map

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

  • About
  • Privacy Policy
  • Legal Notice
  • Contact

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.

×

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.

To Graph

1.080 Topics available

To Map

977 Locations available

693.932 PEOPLE
693.932 People People

693.932 People

Show results for 693.932 people that are selected by your search filters.

←

Page 1 of 27758

→
←

Page 1 of 0

→
PeopleLocationsStatistics
Naji, M.
  • 2
  • 13
  • 3
  • 2025
Motta, Antonella
  • 8
  • 52
  • 159
  • 2025
Aletan, Dirar
  • 1
  • 1
  • 0
  • 2025
Mohamed, Tarek
  • 1
  • 7
  • 2
  • 2025
Ertürk, Emre
  • 2
  • 3
  • 0
  • 2025
Taccardi, Nicola
  • 9
  • 81
  • 75
  • 2025
Kononenko, Denys
  • 1
  • 8
  • 2
  • 2025
Petrov, R. H.Madrid
  • 46
  • 125
  • 1k
  • 2025
Alshaaer, MazenBrussels
  • 17
  • 31
  • 172
  • 2025
Bih, L.
  • 15
  • 44
  • 145
  • 2025
Casati, R.
  • 31
  • 86
  • 661
  • 2025
Muller, Hermance
  • 1
  • 11
  • 0
  • 2025
Kočí, JanPrague
  • 28
  • 34
  • 209
  • 2025
Šuljagić, Marija
  • 10
  • 33
  • 43
  • 2025
Kalteremidou, Kalliopi-ArtemiBrussels
  • 14
  • 22
  • 158
  • 2025
Azam, Siraj
  • 1
  • 3
  • 2
  • 2025
Ospanova, Alyiya
  • 1
  • 6
  • 0
  • 2025
Blanpain, Bart
  • 568
  • 653
  • 13k
  • 2025
Ali, M. A.
  • 7
  • 75
  • 187
  • 2025
Popa, V.
  • 5
  • 12
  • 45
  • 2025
Rančić, M.
  • 2
  • 13
  • 0
  • 2025
Ollier, Nadège
  • 28
  • 75
  • 239
  • 2025
Azevedo, Nuno Monteiro
  • 4
  • 8
  • 25
  • 2025
Landes, Michael
  • 1
  • 9
  • 2
  • 2025
Rignanese, Gian-Marco
  • 15
  • 98
  • 805
  • 2025

Gardner, Peter

  • Google
  • 3
  • 14
  • 26

University of Manchester

in Cooperation with on an Cooperation-Score of 37%

Topics

Publications (3/3 displayed)

  • 2021Study of low terahertz radar signal backscattering for surface identification24citations
  • 2018Signal Reduction by tree leaves in Low-THz Automotive Radar2citations
  • 2015Influence of Uncertainty in Dielectric Properties on the Design Performance of a Tunable Composite Right/Left Handed Leaky Wave Antennacitations

Places of action

Chart of shared publication
Bystrov, Alex
1 / 1 shared
Sabery, Sm
1 / 1 shared
Navarro-Cia, Miguel
1 / 2 shared
Gashinova, Marina
2 / 3 shared
Cherniakov, Mikhail
1 / 1 shared
Hoare, Edward
1 / 1 shared
Sabery, Shahrzad
1 / 1 shared
Norouzian, Fatemeh
1 / 1 shared
Jancar, B.
1 / 1 shared
Kechik, M. M. Awang
1 / 1 shared
Jackson, Timothy
1 / 12 shared
Gao, Xiang
1 / 3 shared
Belous, A.
1 / 3 shared
Ovchar, Oleg
1 / 1 shared
Chart of publication period
2021
2018
2015

Co-Authors (by relevance)

  • Bystrov, Alex
  • Sabery, Sm
  • Navarro-Cia, Miguel
  • Gashinova, Marina
  • Cherniakov, Mikhail
  • Hoare, Edward
  • Sabery, Shahrzad
  • Norouzian, Fatemeh
  • Jancar, B.
  • Kechik, M. M. Awang
  • Jackson, Timothy
  • Gao, Xiang
  • Belous, A.
  • Ovchar, Oleg
OrganizationsLocationPeople

article

Study of low terahertz radar signal backscattering for surface identification

  • Bystrov, Alex
  • Sabery, Sm
  • Navarro-Cia, Miguel
  • Gashinova, Marina
  • Gardner, Peter
Abstract

This study explores the scattering of signals within the mm and low Terahertz frequency range, represented by frequencies 79 GHz, 150 GHz, 300 GHz, and 670 GHz, from surfaces with different roughness, to demonstrate advantages of low THz radar for surface discrimination for automotive sensing. The responses of four test surfaces of different roughness were measured and their normalized radar cross sections were estimated as a function of grazing angle and polarization. The Fraunhofer criterion was used as a guideline for determining the type of backscattering (specular and diffuse). The proposed experimental technique provides high accuracy of backscattering coefficient measurement depending on the frequency of the signal, polarization, and grazing angle. An empirical scattering model was used to provide a reference. To compare theoretical and experimental results of the signal scattering on test surfaces, the permittivity of sandpaper has been measured using time-domain spectroscopy. It was shown that the empirical methods for diffuse radar signal scattering developed for lower radar frequencies can be extended for the low THz range with sufficient accuracy. The results obtained will provide reference information for creating remote surface identification systems for automotive use, which will be of particular advantage in surface classification, object classification, and path determination in autonomous automotive vehicle operation.

Topics
  • surface
  • spectroscopy