DeepLocate: Smartphone Based Indoor Localization with a Deep Neural Network Ensemble Classifier

A quickly growing location-based services area has led to increased demand for indoor positioning and localization. Undoubtedly, Wi-Fi fingerprint-based localization is one of the promising indoor localization techniques, yet the variation of received signal strength is a major problem for accurate localization. Magnetic field-based localization has emerged as a new player and proved a potential indoor localization technology. However, one of its major limitations is degradation in localization accuracy when various smartphones are used. The localization performance is different from various smartphones even with the same localization technique. This research leverages the use of a deep neural network-based ensemble classifier to perform indoor localization with heterogeneous devices. The chief aim is to devise an approach that can achieve a similar localization accuracy using various smartphones. Features extracted from magnetic data of Galaxy S8 are fed into neural networks (NNs) for training. The experiments are performed with Galaxy S8, LG G6, LG G7, and Galaxy A8 smartphones to investigate the impact of device dependence on localization accuracy. Results demonstrate that NNs can play a significant role in mitigating the impact of device heterogeneity and increasing indoor localization accuracy. The proposed approach is able to achieve a localization accuracy of 2.64 m at 50% on four different devices. The mean error is 2.23 m, 2.52 m, 2.59 m, and 2.78 m for Galaxy S8, LG G6, LG G7, and Galaxy A8, respectively. Experiments on a publicly available magnetic dataset of Sony Xperia M2 using the proposed approach show a mean error of 2.84 m with a standard deviation of 2.24 m, while the error at 50% is 2.33 m. Furthermore, the impact of devices on various attitudes on the localization accuracy is investigated.

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Deep neural network ensemble for on-the-fly quality control-driven segmentation of cardiac MRI T1 mapping

Medical Image Analysis ◽

10.1016/j.media.2021.102029 ◽

2021 ◽

Vol 71 ◽

pp. 102029

Author(s):

Evan Hann ◽

Iulia A. Popescu ◽

Qiang Zhang ◽

Ricardo A. Gonzales ◽

Ahmet Barutçu ◽

...

Keyword(s):

Neural Network ◽

Quality Control ◽

Cardiac Mri ◽

Deep Neural Network ◽

T1 Mapping ◽

Neural Network Ensemble

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Validating the impact of accounting disclosures on stock market: A deep neural network approach

Technological Forecasting and Social Change ◽

10.1016/j.techfore.2021.120903 ◽

2021 ◽

Vol 170 ◽

pp. 120903

Author(s):

Prajwal Eachempati ◽

Praveen Ranjan Srivastava ◽

Ajay Kumar ◽

Kim Hua Tan ◽

Shivam Gupta

Keyword(s):

Neural Network ◽

Stock Market ◽

Deep Neural Network ◽

Network Approach ◽

Neural Network Approach ◽

Accounting Disclosures ◽

The Impact

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An Indoor Localization System Using Residual Learning with Channel State Information

Entropy ◽

10.3390/e23050574 ◽

2021 ◽

Vol 23 (5) ◽

pp. 574

Author(s):

Chendong Xu ◽

Weigang Wang ◽

Yunwei Zhang ◽

Jie Qin ◽

Shujuan Yu ◽

...

Keyword(s):

Channel State Information ◽

Indoor Environment ◽

Indoor Localization ◽

Location Based Services ◽

Channel State ◽

State Information ◽

Localization Accuracy ◽

Localization System ◽

Increasing Demand ◽

Degradation Problem

With the increasing demand of location-based services, neural network (NN)-based intelligent indoor localization has attracted great interest due to its high localization accuracy. However, deep NNs are usually affected by degradation and gradient vanishing. To fill this gap, we propose a novel indoor localization system, including denoising NN and residual network (ResNet), to predict the location of moving object by the channel state information (CSI). In the ResNet, to prevent overfitting, we replace all the residual blocks by the stochastic residual blocks. Specially, we explore the long-range stochastic shortcut connection (LRSSC) to solve the degradation problem and gradient vanishing. To obtain a large receptive field without losing information, we leverage the dilated convolution at the rear of the ResNet. Experimental results are presented to confirm that our system outperforms state-of-the-art methods in a representative indoor environment.

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Adaptive Deep Neural Network Ensemble for Inference-as-a-Service on Edge Computing Platforms

10.1109/mass52906.2021.00013 ◽

2021 ◽

Author(s):

Yang Bai ◽

Lixing Chen ◽

Letian Zhang ◽

Mohamed Abdel-Mottaleb ◽

Jie Xu

Keyword(s):

Neural Network ◽

Deep Neural Network ◽

Edge Computing ◽

Neural Network Ensemble ◽

Computing Platforms

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A Novel Passive Indoor Localization Method by Fusion CSI Amplitude and Phase Information

Sensors ◽

10.3390/s19040875 ◽

2019 ◽

Vol 19 (4) ◽

pp. 875 ◽

Cited By ~ 10

Author(s):

Xiaochao Dang ◽

Xiong Si ◽

Zhanjun Hao ◽

Yaning Huang

Keyword(s):

Indoor Localization ◽

Rapid Development ◽

Location Based Services ◽

Signal Interference ◽

Indoor Environments ◽

Phase Information ◽

Localization Accuracy ◽

Localization Method ◽

Indoor Location ◽

Conference Room

With the rapid development of wireless network technology, wireless passive indoor localization has become an increasingly important technique that is widely used in indoor location-based services. Channel state information (CSI) can provide more detailed and specific subcarrier information, which has gained the attention of researchers and has become an emphasis in indoor localization technology. However, existing research has generally adopted amplitude information for eigenvalue calculations. There are few research studies that have used phase information from CSI signals for localization purposes. To eliminate the signal interference existing in indoor environments, we present a passive human indoor localization method named FapFi, which fuses CSI amplitude and phase information to fully utilize richer signal characteristics to find location. In the offline stage, we filter out redundant values and outliers in the CSI amplitude information and then process the CSI phase information. A fusion method is utilized to store the processed amplitude and phase information as a fingerprint database. The experimental data from two typical laboratory and conference room environments were gathered and analyzed. The extensive experimental results demonstrate that the proposed algorithm is more efficient than other algorithms in data processing and achieves decimeter-level localization accuracy.

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Genetic Algorithm Based Optimization of Deep Neural Network Ensemble for Personal Identification in Pedestrians Behaviors

2019 International Conference on Data Mining Workshops (ICDMW) ◽

10.1109/icdmw.2019.00054 ◽

2019 ◽

Author(s):

Xuanang Feng ◽

Jianing Zhao ◽

Eisuke Kita

Keyword(s):

Neural Network ◽

Genetic Algorithm ◽

Deep Neural Network ◽

Personal Identification ◽

Neural Network Ensemble

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An Algorithm for Accurate and Robust Indoor Localization Based on Nonlinear Programming

Electronics ◽

10.3390/electronics9010065 ◽

2020 ◽

Vol 9 (1) ◽

pp. 65 ◽

Cited By ~ 5

Author(s):

Stefania Monica ◽

Federico Bergenti

Keyword(s):

Nonlinear Programming ◽

Mobile Devices ◽

Optimization Problem ◽

Indoor Localization ◽

Wide Band ◽

Location Based Services ◽

Experimental Results ◽

Indoor Environments ◽

Anchor Nodes ◽

The Impact

The study of techniques to estimate the position of mobile devices with a high level of accuracy and robustness is essential to provide advanced location based services in indoor environments. An algorithm to enable mobile devices to estimate their positions in known indoor environments is proposed in this paper under the assumption that fixed anchor nodes are available at known locations. The proposed algorithm is specifically designed to be executed on the mobile device whose position is under investigation, and it allows the device to estimate its position within the environment by actively measuring distance estimates from the anchor nodes. In order to reduce the impact of the errors caused by the arrangement of the anchor nodes in the environment, the proposed algorithm first transforms the localization problem into an optimization problem, and then, it solves the derived optimization problem using techniques inspired by nonlinear programming. Experimental results obtained using ultra-wide band signaling are presented to assess the performance of the algorithm and to compare it with reference alternatives. The presented experimental results confirm that the proposed algorithm provides an increased level of accuracy and robustness with respect to two reference alternatives, regardless of the position of the anchor nodes.

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Modelling the functional dependency between root and shoot compartments to predict the impact of the environment on the architecture of the whole plant. Methodology for model fitting on simulated data using Deep Learning techniques

in silico Plants ◽

10.1093/insilicoplants/diab036 ◽

2021 ◽

Author(s):

Abel Louis Masson ◽

Yves Caraglio ◽

Eric Nicolini ◽

Philippe Borianne ◽

Jean-Francois Barczi

Keyword(s):

Neural Network ◽

Climate Change ◽

Model Fitting ◽

Simulated Data ◽

Structural Plasticity ◽

Tree Roots ◽

Mean Error ◽

Whole Plant ◽

Growing Conditions ◽

The Impact

Abstract Tree structural and biomass growth studies mainly focus on the shoot compartment. Tree roots usually have to be taken apart due to the difficulties involved in measuring and observing this compartment, particularly root growth. In the context of climate change, the study of tree structural plasticity has become crucial and both shoot and root systems need to be considered simultaneously as they play a joint role in adapting traits to climate change (water availability for roots and light or carbon availability for shoots). We developed a botanically accurate whole-plant model and its simulator (RoCoCau) with a linkable external module (TOY) to represent shoot and root compartment dependencies and hence tree structural plasticity in different air and soil environments. This paper describes a new deep neural network calibration trained on simulated datasets computed from a set of more than 360 000 random TOY parameter values and random climate values. These datasets were used for training and for validation. For this purpose, we chose Voxnet, a convolutional neural network designed to classify 3D objects represented as a voxelized scene. We recommend further improvements for Voxnet inputs, outputs, and training. We were able to teach the network to predict the value of environment data well (mean error < 2%), and to predict the value of TOY parameters for plants under water stress conditions (mean error < 5% for all parameters), and for any environmental growing conditions (mean error < 20%).

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Neurophysiology Based on Deep Neural Network under Artificial Prosthesis Vision

Journal of Physics Conference Series ◽

10.1088/1742-6596/2074/1/012083 ◽

2021 ◽

Vol 2074 (1) ◽

pp. 012083

Author(s):

Xiangli Lin

Keyword(s):

Neural Network ◽

Image Processing ◽

Image Recognition ◽

Deep Neural Network ◽

Recognition Rate ◽

Visual Prosthesis ◽

Processing Strategies ◽

Recognition Ability ◽

Reduction Methods ◽

The Impact

Abstract With the vigorous development of electronic technology and computer technology, as well as the continuous advancement of research in the fields of neurophysiology, bionics and medicine, the artificial visual prosthesis has brought hope to the blind to restore their vision. Artificial optical prosthesis research has confirmed that prosthetic vision can restore part of the visual function of patients with non-congenital blindness, but the mechanism of early prosthetic image processing still needs to be clarified through neurophysiological research. The purpose of this article is to study neurophysiology based on deep neural networks under simulated prosthetic vision. This article uses neurophysiological experiments and mathematical statistical methods to study the vision of simulated prostheses, and test and improve the image processing strategies used to simulate the visual design of prostheses. In this paper, based on the low-pixel image recognition of the simulating irregular phantom view point array, the deep neural network is used in the image processing strategy of prosthetic vision, and the effect of the image processing method on object image recognition is evaluated by the recognition rate. The experimental results show that the recognition rate of the two low-pixel segmentation and low-pixel background reduction methods proposed by the deep neural network under simulated prosthetic vision is about 70%, which can significantly increase the impact of object recognition, thereby improving the overall recognition ability of visual guidance.

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