Proceedings of the 2018 8th International Conference on Applied Science, Engineering and Technology (ICASET 2018)

The Experiment Research on Giant Magnetoresistance Based on Data Analysis of SPSS

Authors
Shuhong Tang
Corresponding Author
Shuhong Tang
Available Online April 2018.
DOI
10.2991/icaset-18.2018.36How to use a DOI?
Keywords
Giant Magnetoresistance; the sensor of Giant Magnetoresistance; SPSS
Abstract

The basic principles of GMR is introduced and the characteristics of the sensor of GMR is studied by experiment. SPSS is put to use to do data analysis. The results indicate that there is the cubic relationship between the output voltage of the sensor and the external magnetic field and between the output voltage of the sensor and the angle between the sensitive axis of the sensor and the external magnetic field. What’s more, the linear relationship between the output voltage and working output voltage of the sensor is proved and the fitting curves of the models and the optimal model equation are acquired.

Copyright
© 2018, the Authors. Published by Atlantis Press.
Open Access
This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

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Volume Title
Proceedings of the 2018 8th International Conference on Applied Science, Engineering and Technology (ICASET 2018)
Series
Advances in Engineering Research
Publication Date
April 2018
ISBN
10.2991/icaset-18.2018.36
ISSN
2352-5401
DOI
10.2991/icaset-18.2018.36How to use a DOI?
Copyright
© 2018, the Authors. Published by Atlantis Press.
Open Access
This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

Cite this article

TY  - CONF
AU  - Shuhong Tang
PY  - 2018/04
DA  - 2018/04
TI  - The Experiment Research on Giant Magnetoresistance Based on Data Analysis of SPSS
BT  - Proceedings of the 2018 8th International Conference on Applied Science, Engineering and Technology (ICASET 2018)
PB  - Atlantis Press
SP  - 174
EP  - 180
SN  - 2352-5401
UR  - https://doi.org/10.2991/icaset-18.2018.36
DO  - 10.2991/icaset-18.2018.36
ID  - Tang2018/04
ER  -