Home > Computer Science > Real-time Computing > Volume-1 > Issue-6 > Real-Time Data Representation Control in Convolution Neural Networks Based Indoor Wi-Fi Localization for Internet of Things

Real-Time Data Representation Control in Convolution Neural Networks Based Indoor Wi-Fi Localization for Internet of Things

Call for Papers

Volume-4 | Issue-5

Last date : 27-Aug-2020

Best International Journal
Open Access | Peer Reviewed | Best International Journal | Indexing & IF | 24*7 Support | Dedicated Qualified Team | Rapid Publication Process | International Editor, Reviewer Board | Attractive User Interface with Easy Navigation

Journal Type : Open Access

Processing Charges : 700/- INR Only OR 25 USD (for foreign users)

Paper Publish : Within 2-4 Days after submitting

Submit Paper Online

For Author

IJTSRD Publication

Research Area


Real-Time Data Representation Control in Convolution Neural Networks Based Indoor Wi-Fi Localization for Internet of Things


Dr. P. Srimanchari | Dr. G. Anandharaj

https://doi.org/10.31142/ijtsrd4694



Dr. P. Srimanchari | Dr. G. Anandharaj "Real-Time Data Representation Control in Convolution Neural Networks Based Indoor Wi-Fi Localization for Internet of Things" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-1 | Issue-6, October 2017, pp.1043-1050, URL: https://www.ijtsrd.com/papers/ijtsrd4694.pdf

The prediction of simultaneous limb motions is a highly desirable feature for the control of artificial limbs. In this work, we investigate different classification strategies for individual and simultaneous movements based on pattern recognition of myoelectric signals. Our results suggest that any classifier can be potentially employed in the prediction of simultaneous movements if arranged in a distributed topology. Compressive sensing has been successfully used for optimized operations in wireless sensor networks. However, raw data collected by sensors may be neither originally sparse nor easily transformed into a sparse data representation. This work addresses the problem of transforming source data collected by sensor nodes into a sparse representation with a few nonzero elements. Our contributions that address three major issues include: 1) an effective method that extracts population sparsity of the data, 2) a sparsity ratio guarantee scheme, and 3) a customized learning algorithm of the sparsifying dictionary. We introduce an unsupervised neural network to extract an intrinsic sparse coding of the data. As the underlying semiconductor technologies are getting less and less reliable, the probability that some components of computing devices fail also increases, preventing designers from realizing the full potential benefits of on-chip exascale integration derived from near atomic scale feature dimensions. As the quest for performance confronts permanent and transient faults, device variation, and thermal issues, major breakthroughs in computing efficiency are expected to benefit from unconventional and new models of computation, such as brain inspired computing. The challenge is then high-performance and energy-efficient, but also fault-tolerant computing solutions.

data representation, device variation, wireless sensor networks


IJTSRD4694
Volume-1 | Issue-6, October 2017
1043-1050
IJTSRD | www.ijtsrd.com | E-ISSN 2456-6470
Copyright © 2019 by author(s) and International Journal of Trend in Scientific Research and Development Journal. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (CC BY 4.0) (http://creativecommons.org/licenses/by/4.0)

International Journal of Trend in Scientific Research and Development - IJTSRD having online ISSN 2456-6470. IJTSRD is a leading Open Access, Peer-Reviewed International Journal which provides rapid publication of your research articles and aims to promote the theory and practice along with knowledge sharing between researchers, developers, engineers, students, and practitioners working in and around the world in many areas like Sciences, Technology, Innovation, Engineering, Agriculture, Management and many more and it is recommended by all Universities, review articles and short communications in all subjects. IJTSRD running an International Journal who are proving quality publication of peer reviewed and refereed international journals from diverse fields that emphasizes new research, development and their applications. IJTSRD provides an online access to exchange your research work, technical notes & surveying results among professionals throughout the world in e-journals. IJTSRD is a fastest growing and dynamic professional organization. The aim of this organization is to provide access not only to world class research resources, but through its professionals aim to bring in a significant transformation in the real of open access journals and online publishing.

Thomson Reuters
Google Scholer
Academia.edu

ResearchBib
Scribd.com
archive

PdfSR
issuu
Slideshare

WorldJournalAlerts
Twitter
Linkedin