scholarly journals Overcoming Security Vulnerabilities in Deep Learning--based Indoor Localization Frameworks on Mobile Devices

2020 ◽  
Vol 18 (6) ◽  
pp. 1-24
Author(s):  
Saideep Tiku ◽  
Sudeep Pasricha
Keyword(s):  
Deep Learning ◽  
Mobile Devices ◽  
Indoor Localization ◽  
Security Vulnerabilities

scholarly journals Comparison of CNN Applications for RSSI-based Fingerprint Indoor Localization

Electronics ◽  
2019 ◽  
Vol 8 (9) ◽  
pp. 989 ◽  
Author(s):  
Sinha ◽  
Hwang
Keyword(s):  
Neural Networks ◽  
Deep Learning ◽  
Mobile Devices ◽  
Convolutional Neural Networks ◽  
Indoor Localization ◽  
Signal Strength ◽  
Test Accuracy ◽  
Computational Power ◽  
Location Error ◽  
Received Signal Strength Indicator

The intelligent use of deep learning (DL) techniques can assist in overcoming noise and uncertainty during fingerprinting-based localization. With the rise in the available computational power on mobile devices, it is now possible to employ DL techniques, such as convolutional neural networks (CNNs), for smartphones. In this paper, we introduce a CNN model based on received signal strength indicator (RSSI) fingerprint datasets and compare it with different CNN application models, such as AlexNet, ResNet, ZFNet, Inception v3, and MobileNet v2, for indoor localization. The experimental results show that the proposed CNN model can achieve a test accuracy of 94.45% and an average location error as low as 1.44 m. Therefore, our CNN model outperforms conventional CNN applications for RSSI-based indoor positioning.

scholarly journals Design of facial recognition system implemented in an unmanned aerial vehicle for citizen security in Latin America

2019 ◽  
Vol 27 ◽  
pp. 04002
Author(s):  
Diego Herrera ◽  
Hiroki Imamura
Keyword(s):  
Neural Network ◽  
Latin America ◽  
Deep Learning ◽  
Mobile Devices ◽  
Unmanned Aerial Vehicle ◽  
Facial Recognition ◽  
Recognition System ◽  
Criminal Records ◽  
Aerial Vehicle ◽  
Facial Recognition System

In the new technological era, facial recognition has become a central issue for a great number of engineers. Currently, there are a great number of techniques for facial recognition, but in this research, we focus on the use of deep learning. The problems with current facial recognition convection systems are that they are developed in non-mobile devices. This research intends to develop a Facial Recognition System implemented in an unmanned aerial vehicle of the quadcopter type. While it is true, there are quadcopters capable of detecting faces and/or shapes and following them, but most are for fun and entertainment. This research focuses on the facial recognition of people with criminal records, for which a neural network is trained. The Caffe framework is used for the training of a convolutional neural network. The system is developed on the NVIDIA Jetson TX2 motherboard. The design and construction of the quadcopter are done from scratch because we need the UAV for adapt to our requirements. This research aims to reduce violence and crime in Latin America.

scholarly journals Wheat Stripe Rust Grading by Deep Learning With Attention Mechanism and Images From Mobile Devices

2020 ◽  
Vol 11 ◽  
Author(s):  
Zhiwen Mi ◽  
Xudong Zhang ◽  
Jinya Su ◽  
Dejun Han ◽  
Baofeng Su
Keyword(s):  
Deep Learning ◽  
Mobile Devices ◽  
Stripe Rust ◽  
Attention Mechanism ◽  
Wheat Stripe Rust

scholarly journals SDR Based Indoor Beacon Localization Using 3D Probabilistic Multipath Exploitation and Deep Learning

Electronics ◽  
2019 ◽  
Vol 8 (11) ◽  
pp. 1323 ◽  
Author(s):  
Donald L. Hall ◽  
Ram M. Narayanan ◽  
David M. Jenkins
Keyword(s):  
Deep Learning ◽  
Software Defined Radio ◽  
High Performance ◽  
Nearest Neighbor ◽  
Indoor Localization ◽  
Signal Propagation ◽  
Multipath Channel ◽  
K Nearest Neighbor ◽  
Positioning Systems ◽  
Channel Characteristics

Wireless indoor positioning systems (IPS) are ever-growing as traditional global positioning systems (GPS) are ineffective due to non-line-of-sight (NLoS) signal propagation. In this paper, we present a novel approach to learning three-dimensional (3D) multipath channel characteristics in a probabilistic manner for providing high performance indoor localization of wireless beacons. The proposed system employs a single triad dipole vector sensor (TDVS) for polarization diversity, a deep learning model deemed the denoising autoencoder to extract unique fingerprints from 3D multipath channel information, and a probabilistic k-nearest-neighbor (PkNN) to exploit the 3D multipath characteristics. The proposed system is the first to exploit 3D multipath channel characteristics for indoor wireless beacon localization via vector sensing methodologies, a software defined radio (SDR) platform, and multipath channel estimation.

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