scholarly journals Artificial Neural Network Approach to Mobile Location Estimation in GSM Network

2017 ◽  
Vol 63 (1) ◽  
pp. 39-44 ◽  
Author(s):  
Longinus S. Ezema ◽  
Cosmas I. Ani
Keyword(s):  
Neural Network ◽  
Network Architecture ◽  
Performance Metrics ◽  
Federal Communication Commission ◽  
Location Estimation ◽  
Location Based Services ◽  
Estimation Accuracy ◽  
Neural Network Approach ◽  
Mobile Location ◽  
Gsm Network

AbstractThe increase in utilisation of mobile location-based services for commercial, safety and security purposes among others are the key drivers for improving location estimation accuracy to better serve those purposes. This paper proposes the application of Levenberg Marquardt training algorithm on new robust multilayered perceptron neural network architecture for mobile positioning fitting for the urban area in the considered GSM network using received signal strength (RSS). The key performance metrics such as accuracy, cost, reliability and coverage are the major points considered in this paper. The technique was evaluated using real data from field measurement and the results obtained proved the proposed model provides a practical positioning that meet Federal Communication Commission (FCC) accuracy requirement.

scholarly journals Neural Network for WGDOP Approximation and Mobile Location

2013 ◽  
Vol 2013 ◽  
pp. 1-11 ◽  
Author(s):  
Chien-Sheng Chen ◽  
Jium-Ming Lin ◽  
Chin-Tan Lee
Keyword(s):  
Neural Network ◽  
Communication Systems ◽  
Learning Algorithm ◽  
Location Estimation ◽  
Estimation Accuracy ◽  
Neural Network Learning ◽  
Mobile Location ◽  
Positioning Systems ◽  
Geometric Dilution Of Precision ◽  
Location Methods

This paper considers location methods that are applicable in global positioning systems (GPS), wireless sensor networks (WSN), and cellular communication systems. The approach is to employ the resilient backpropagation (Rprop), an artificial neural network learning algorithm, to compute weighted geometric dilution of precision (WGDOP), which represents the geometric effect on the relationship between measurement error and positioning error. The original four kinds of input-output mapping based on BPNN for GDOP calculation are extended to WGDOP based on Rprop. In addition, we propose two novel Rprop–based architectures to approximate WGDOP. To further reduce the complexity of our approach, the first is to select the serving BS and then combines it with three other measurements to estimate MS location. As such, the number of subsets is reduced greatly without compromising the location estimation accuracy. We further employed another Rprop that takes the higher precision MS locations of the first several minimum WGDOPs as the inputs into consideration to determine the final MS location estimation. This method can not only eliminate the poor geometry effects but also significantly improve the location accuracy.

An artificial neural network approach to discrete-event simulation

1995 ◽  
Vol 9 (1) ◽  
pp. 37-49 ◽  
Author(s):  
Ian Flood ◽  
Kenneth Worley
Keyword(s):  
Neural Network ◽  
Discrete Event Simulation ◽  
Network Architecture ◽  
Discrete Event ◽  
Network Approach ◽  
Neural Network Approach ◽  
Event Simulation ◽  
The Neural Network ◽  
Component Processes ◽  
And Performance

AbstractThis paper proposes and evaluates a neural network-based method for simulating manufacturing processes that exhibit both noncontinuous and stochastic behavior processes more conventionally modeled, using discrete-event simulation algorithms. The incentive for developing the technique is its potential for rapid execution of a simulation through parallel processing, and facilitation of the development and improvement of models particularly where there is limited theory describing the dependence between component processes. A brief introduction is provided to a radial-Gaussian neural network architecture and training process, the system adopted for the work presented in this paper. A description of the basic approach proposed for applying this technology to simulation is then described. This involves the use of a modularized neural network approach to model construction and the prediction of the occurrence of events using information retained from several previous states of the simulation. A class of earth-moving systems, comprising a push-dozer and a fleet of scrapers, is used as the basis for assessing the viability and performance of the proposed approach. A series of experiments show the neural network to be capable of both capturing the characteristic behavior and making an accurate prediction of production rates of scraper-based earth-moving systems. The paper concludes with an indication of some areas for further development and evaluation of the technique.

scholarly journals Infrastructure-Free Indoor Pedestrian Tracking with Smartphone Acoustic-Based Enhancement

Sensors ◽  
2019 ◽  
Vol 19 (11) ◽  
pp. 2458 ◽  
Author(s):  
Chao Liu ◽  
Sining Jiang ◽  
Shuo Zhao ◽  
Zhongwen Guo
Keyword(s):  
Doppler Effect ◽  
Location Estimation ◽  
Location Based Services ◽  
Estimation Accuracy ◽  
Measurement Unit ◽  
Average Error ◽  
Pedestrian Tracking ◽  
Tracking Accuracy ◽  
Acoustic Frequency ◽  
Indoor Pedestrian Tracking

Indoor pedestrian tracking has been identified as a key technology for indoor location-based services such as emergency locating, advertising, and gaming. However, existing smartphone-based approaches to pedestrian tracking in indoor environments have various limitations including a high cost of infrastructure constructing, labor-intensive fingerprint collection, and a vulnerability to moving obstacles. Moreover, our empirical study reveals that the accuracy of indoor locations estimated by a smartphone Inertial Measurement Unit (IMU) decreases severely when the pedestrian is arbitrarily wandering with an unstable speed. To improve the indoor tracking performance by enhancing the location estimation accuracy, we exploit smartphone-based acoustic techniques and propose an infrastructure-free indoor pedestrian tracking approach, called iIPT. The novelty of iIPT lies in the pedestrian speed reliability metric, which characterizes the reliability of the pedestrian speed provided by the smartphone IMU, and in a speed enhancing method, where we adjust a relatively less reliable pedestrian speed to the more reliable speed of a passing by “enhancer” based on the acoustic Doppler effect. iIPT thus changes the encountered pedestrians from an“obstacle” into an “enhancer.” Extensive real-world experiments in indoor scenarios have been conducted to verify the feasibility of realizing the acoustic Doppler effect between smartphones and to identify the applicable acoustic frequency range and transmission distance while reducing battery consumption. The experiment results demonstrate that iIPT can largely improve the tracking accuracy and decrease the average error compared with a conventional IMU-based method.

scholarly journals A Novel Indoor Positioning System Using Kernel Local Discriminant Analysis in Internet-of-Things

2018 ◽  
Vol 2018 ◽  
pp. 1-9 ◽  
Author(s):  
Sajida Imran ◽  
Young-Bae Ko
Keyword(s):  
Feature Extraction ◽  
Discriminant Analysis ◽  
Indoor Positioning ◽  
Location Estimation ◽  
Location Based Services ◽  
Estimation Methods ◽  
Estimation Accuracy ◽  
Positioning System ◽  
Local Fisher Discriminant Analysis ◽  
Indoor Positioning System

WLAN based localization is a key technique of location-based services (LBS) indoors. However, the indoor environment is complex; received signal strength (RSS) is highly uncertain, multimodal, and nonlinear. The traditional location estimation methods fail to provide fair estimation accuracy under the said environment. We proposed a novel indoor positioning system that considers the nonlinear discriminative feature extraction of RSS using kernel local Fisher discriminant analysis (KLFDA). KLFDA extracts location features in a well-preserved kernelized space. In the new kernel featured space, nonlinear RSS features are characterized effectively. Along with handling of nonlinearity, KLFDA also copes well with the multimodality in the RSS data. By performing KLFDA, the discriminating information contained in RSS is reorganized and maximally extracted. Prior to feature extraction, we performed outlier detection on RSS data to remove any anomalies present in the data. Experimental results show that the proposed approach obtains higher positioning accuracy by extracting maximal discriminate location features and discarding outlying information present in the RSS data.

scholarly journals A Multi-Task Pipeline with Specialized Streams for Classification and Segmentation of Infection Manifestations in COVID-19 Scans

2020 ◽  
Author(s):  
Ahmad Al-Kabbany ◽  
Shimaa El-bana ◽  
Maha Sharkas
Keyword(s):  
Neural Network ◽  
Network Architecture ◽  
Performance Metrics ◽  
Recent Literature ◽  
Ct Scans ◽  
Computational Time ◽  
Neural Network Models ◽  
Research Attention ◽  
X Ray ◽  
Deep Model

We are concerned with the challenge of coronavirus disease (COVID-19) detection in chest<br>X-ray and Computed Tomography (CT) scans, and the classification and segmentation of related<br>infection manifestations. Even though it is arguably not an established diagnostic tool, using machine<br>learning-based analysis of COVID-19 medical scans has shown the potential to provide a preliminary<br>digital second opinion. This can help in managing the current pandemic, and thus has been attracting<br>significant research attention. In this research, we propose a multi-task pipeline that takes advantage<br>of the growing advances in deep neural network models. In the first stage, we fine-tuned an<br>Inception-v3 deep model for COVID-19 recognition using multi-modal learning, i.e., using X-ray and<br>CT scans. In addition to outperforming other deep models on the same task in the recent literature,<br>with an attained accuracy of 99.4%, we also present comparative analysis for multi-modal learning<br>against learning from X-ray scans alone. The second and the third stages of the proposed pipeline<br>complement one another in dealing with different types of infection manifestations. The former<br>features a convolutional neural network architecture for recognizing three types of manifestations,<br>while the latter transfers learning from another knowledge domain, namely, pulmonary nodule<br>segmentation in CT scans, to produce binary masks for segmenting the regions corresponding to<br>these manifestations. Our proposed pipeline also features specialized streams in which multiple deep<br>models are trained separately to segment specific types of infection manifestations, and we show the<br>significant impact that this framework has on various performance metrics. We evaluate the<br>proposed models on widely adopted datasets, and we demonstrate an increase of approximately 4%<br>and 7% for dice coefficient and mean intersection-over-union (mIoU), respectively, while achieving<br>60% reduction in computational time, compared to the recent literature.<br> <br>

scholarly journals VIAE-Net: An End-to-End Altitude Estimation through Monocular Vision and Inertial Feature Fusion Neural Networks for UAV Autonomous Landing

Sensors ◽  
2021 ◽  
Vol 21 (18) ◽  
pp. 6302
Author(s):  
Xupei Zhang ◽  
Zhanzhuang He ◽  
Zhong Ma ◽  
Peng Jun ◽  
Kun Yang
Keyword(s):  
Neural Network ◽  
Network Architecture ◽  
Feature Fusion ◽  
Estimation Error ◽  
Estimation Method ◽  
Signal Interference ◽  
Estimation Accuracy ◽  
Measurement Unit ◽  
End To End ◽  
Altitude Estimation

Altitude estimation is one of the fundamental tasks of unmanned aerial vehicle (UAV) automatic navigation, where it aims to accurately and robustly estimate the relative altitude between the UAV and specific areas. However, most methods rely on auxiliary signal reception or expensive equipment, which are not always available, or applicable owing to signal interference, cost or power-consuming limitations in real application scenarios. In addition, fixed-wing UAVs have more complex kinematic models than vertical take-off and landing UAVs. Therefore, an altitude estimation method which can be robustly applied in a GPS denied environment for fixed-wing UAVs must be considered. In this paper, we present a method for high-precision altitude estimation that combines the vision information from a monocular camera and poses information from the inertial measurement unit (IMU) through a novel end-to-end deep neural network architecture. Our method has numerous advantages over existing approaches. First, we utilize the visual-inertial information and physics-based reasoning to build an ideal altitude model that provides general applicability and data efficiency for neural network learning. A further advantage is that we have designed a novel feature fusion module to simplify the tedious manual calibration and synchronization of the camera and IMU, which are required for the standard visual or visual-inertial methods to obtain the data association for altitude estimation modeling. Finally, the proposed method was evaluated, and validated using real flight data obtained during a fixed-wing UAV landing phase. The results show the average estimation error of our method is less than 3% of the actual altitude, which vastly improves the altitude estimation accuracy compared to other visual and visual-inertial based methods.

scholarly journals Early Detection of Alzheimer’s Disease using Convolutional Neural Network Architecture

2021 ◽  
Vol 8 (2) ◽  
pp. 48-57
Author(s):  
Deepthi Kamath ◽  
Misba Firdose Fathima ◽  
Monica K. P ◽  
Kusuma Mohanchandra
Keyword(s):  
Neural Network ◽  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Convolutional Neural Network ◽  
Network Architecture ◽  
Brain Mri ◽  
Memory Loss ◽  
Neural Network Approach ◽  
The Neural Network ◽  
Mri Scans

Alzheimer's disease is an extremely popular cause of dementia which leads to memory loss, problem-solving and other thinking abilities that are severe enough to interfere with daily life. Detection of Alzheimer’s at a prior stage is crucial as it can prevent significant damage to the patient’s brain. In this paper, a method to detect Alzheimer’s  Disease from Brain MRI images is proposed. The proposed approach extracts shape features and texture of the Hippocampus region from the MRI scans and a Neural Network is used as a Multi-Class Classifier for detection of AD. The proposed approach is implemented and it gives better accuracy as compared to conventional approaches. In this paper, Convolutional Neural Network is the Neural Network approach used for the detection of AD at a prodromal stage.

A Hybrid Connectionist/ Substitution Approach for Data Encryption

2020 ◽  
pp. 23-42
Author(s):  
Raed Abu Zitar ◽  
Muhammed Jassem Al-Muhammed
Keyword(s):  
Neural Network ◽  
Hybrid Model ◽  
Network Architecture ◽  
Real Life ◽  
Data Encryption ◽  
Substitution Method ◽  
Neural Network Approach ◽  
The Neural Network ◽  
Encryption Method ◽  
Symbolic Approach

The authors believe that the hybridization of two different approaches results in more complex encryption outcomes. The proposed method combines a symbolic approach, which is a table substitution method, with another paradigm that models real-life neurons (connectionist approach). This hybrid model is compact, nonlinear, and parallel. The neural network approach focuses on generating keys (weights) based on a feedforward neural network architecture that works as a mirror. The weights are used as an input for the substitution method. The hybrid model is verified and validated as a successful encryption method.

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