Low power neuromorphic hardware based multi-modal authentication system

Verma, Shridu; Bhatia, Narayani; Singh, Salam Thoi Thoi

doi:10.1109/icecs.2017.8292017

2017 24th IEEE International Conference on Electronics, Circuits and Systems (ICECS) 2017

DOI: 10.1109/icecs.2017.8292017

|View full text |Cite

Low power neuromorphic hardware based multi-modal authentication system

Shridu Verma¹,

Narayani Bhatia²,

Salam Thoi Thoi Singh³

Help me understand this report

Search citation statements

Order By: Relevance

Paper Sections

Select...

Citation Types

Supporting

Mentioning

Contrasting

Year Published

2018

2019

Publication Types

Select...

Other3

Relationship

Self Cite0

Independent3

Authors

Journals

Cited by 3 publications

References 6 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

Efficient Low-Power Material Analysis using Neuromorphic Hardware: A spectral case study

Bhatia¹

2018

2018 International Joint Conference on Neural Networks (IJCNN)

View full text Add to dashboard Cite

scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.

Contact Info

customersupport@researchsolutions.com

10624 S. Eastern Ave., Ste. A-614

Henderson, NV 89052, USA

This site is protected by reCAPTCHA and the Google Privacy Policy and Terms of Service apply.

Blog Terms and Conditions API Terms Privacy Policy Contact Cookie Preferences Do Not Sell or Share My Personal Information

Made with 💙 for researchers

Part of the Research Solutions Family.

Low power neuromorphic hardware based multi-modal authentication system

Cited by 3 publications

References 6 publications

Efficient Low-Power Material Analysis using Neuromorphic Hardware: A spectral case study

Efficient Low-Power Material Analysis using Neuromorphic Hardware: A spectral case study

Hyperspectral Image Classification for Remote Sensing Using Low-Power Neuromorphic Hardware

Design of an Optimized CMOS ELM Accelerator

Contact Info

Product

Resources

About