scholarly journals Influence of Noise-Limited Censored Path Loss on Model Fitting and Path Loss-Based Positioning

Sensors ◽  
2021 ◽  
Vol 21 (3) ◽  
pp. 987
Author(s):  
Aki Karttunen ◽  
Mikko Valkama ◽  
Jukka Talvitie
Keyword(s):  
Model Fitting ◽  
Path Loss ◽  
Threshold Level ◽  
Training Data ◽  
Link Distance ◽  
Strength Based ◽  
Radio Systems ◽  
Two Phases ◽  
Wireless Standards ◽  
Influence Of Noise

Positioning is considered one of the key features in various novel industry verticals in future radio systems. Since path loss (PL) or received signal strength-based measurements are widely available in the majority of wireless standards, PL-based positioning has an important role among positioning technologies. Conventionally, PL-based positioning has two phases—fitting a PL model to training data and positioning based on the link distance estimates. However, in both phases, the maximum measurable PL is limited by measurement noise. Such immeasurable samples are called censored PL data and such noisy data are commonly neglected in both the model fitting and in the positioning phase. In the case of censored PL, the loss is known to be above a known threshold level and that information can be used in model fitting and in the positioning phase. In this paper, we examine and propose how to use censored PL data in PL model-based positioning. Additionally, we demonstrate with several simulations the potential of the proposed approach for considerable improvements in positioning accuracy (23–57%) and improved robustness against PL model fitting errors.

Spatial Opportunity for Cognitive Radio Systems with Heterogeneous Path Loss Conditions

2007 ◽  
Author(s):  
Kentaro Nishimori ◽  
Rocco Di Taranto ◽  
Hiroyuki Yomo ◽  
Petar Popovski ◽  
Yasushi Takatori ◽  
...  
Keyword(s):  
Cognitive Radio ◽  
Path Loss ◽  
Cognitive Radio Systems ◽  
Radio Systems

Analysis of Two Phases Queue With Vacations and Breakdowns Under T-Policy

2019 ◽  
pp. 13-31
Author(s):  
Khalid Alnowibet ◽  
Lotfi Tadj
Keyword(s):  
Markov Chain ◽  
Steady State ◽  
Performance Measures ◽  
Threshold Level ◽  
System Size ◽  
State System ◽  
Markov Chain Approach ◽  
Optimal Value ◽  
Two Phases ◽  
Random Breakdowns

The service system considered in this chapter is characterized by an unreliable server. Random breakdowns occur on the server and the repair may not be immediate. The authors assume the possibility that the server may take a vacation at the end of a given service completion. The server resumes operation according to T-policy to check if enough customers have arrived while he was away. The actual service of any arrival takes place in two consecutive phases. Both service phases are independent of each other. A Markov chain approach is used to obtain the steady state system size probabilities and different performance measures. The optimal value of the threshold level is obtained analytically.

scholarly journals Sistem Klasifikasi Kendaraan Berbasis Pengolahan Citra Digital dengan Metode Multilayer Perceptron

2017 ◽  
Vol 7 (2) ◽  
pp. 139 ◽  
Author(s):  
Muhammad Irfan ◽  
Bakhtiar Alldino Ardi Sumbodo ◽  
Ika Candradewi
Keyword(s):  
Multilayer Perceptron ◽  
Traffic Monitoring ◽  
Training Data ◽  
Vehicle Classification ◽  
Test Form ◽  
Average Value ◽  
Toll Road ◽  
Haar Cascade ◽  
Two Phases ◽  
Video Sensors

The evolution of video sensors and hardware can be used for developing traffic monitoring system vision based.  It can provide information about vehicle passing by utilizing the camera, so that monitoring can be done automatically. It is needed for the processing systems to provide some information regarding traffic conditions. One such approach is to utilize digital image processing.This research consisted of two phases image processing, namely the process of detection and classification. The process of detection using Haar Cascade Classifier with the training data image form the vehicle and data test form the image state of toll road drawn at random. While, Multilayer Perceptron classification process uses by utilizing the result of the detection process. Vehicle classification is divided into three types, namely car, bus and truck. Then the classification parameters were evaluated by accuracy. The test results vehicle detection indicate the value of accuracy is 92.67. Meanwhile, the classification process is done with phase trial and error to evaluate the parameters that have been determined.  Results of the study show the classification system has an average value of the accuracy is  87.60%.

scholarly journals Effect of Membership Functions and Data Size on the Performance of ANFIS-Based Model for Predicting Path Losses in the VHF and UHF Bands

2021 ◽  
Author(s):  
Surajudeen-Bakinde N. T. ◽  
◽  
Nasir Faruk ◽  
Abubakar Abdulkarim ◽  
Abdulkarim A. Oloyede ◽  
...  
Keyword(s):  
Path Loss ◽  
Cellular Systems ◽  
Training Data ◽  
Base Stations ◽  
Membership Functions ◽  
Anfis Model ◽  
System Parameters ◽  
Loss Prediction ◽  
Path Loss Prediction ◽  
And Training

This paper investigates the effect of number and shape of membership function (MF), and training data size on the performance of ANFIS model for predicting path losses in the VHF and UHF bands in built-up environments. Path loss propagation measurements were conducted in four cities of Nigeria over the cellular and broadcasting frequencies. A total of 17 broadcast transmission and cellular base stations were utilized for this study. From the results obtained, it can be concluded for the broadcasting bands that the generalized bell MF shows better performance with an average RMSE of 3.00 dB across all the routes, followed by gaussian, Pi, trapezoid and triangular MFs in that other with average RMSE values of 3.09 dB, 3.11 dB, 3.16 dB and 3.23 dB respectively. For the cellular systems, Triangular MF outperformed other MFs with the lowest average RMSE. The generalized bell MF was found to be suited for WCDMA band while triangular MF is most suited for GSM band. Furthermore, it can also be concluded that the higher the number of membership functions, the lower the RMSE, whereas, a decrease in the data size leads to a reduction in the RMSE values. The findings of this study would help researchers and network planners to make a more informed decision on choosing appropriate system parameters when modeling ANFIS models for path loss prediction.

scholarly journals A Machine Learning Approach to Fitting Prescription for Hearing Aids

Electronics ◽  
2019 ◽  
Vol 8 (7) ◽  
pp. 736
Author(s):  
Mondol ◽  
Lee
Keyword(s):  
Hearing Loss ◽  
Hearing Aids ◽  
Real Life ◽  
Training Data ◽  
Prescription Database ◽  
Data Set ◽  
Promising Tool ◽  
Proposed Model ◽  
Machine Learning Approach ◽  
Two Phases

A successful Hearing-Aid Fitting (HAF) is more than just selecting an appropriate HearingAid (HA) device for a patient with Hearing Loss (HL). The initial fitting is given by the prescriptionbased on user’s hearing loss; however, it is often necessary for the audiologist to readjust someparameters to satisfy the user demands. Therefore, in this paper, we concentrated on a new applicationof Neural Network (NN) combined with a Transfer Learning (TL) strategy to develop a fittingalgorithm with the prescription database for hearing loss and readjusted gain to minimize the gapbetween fitting satisfaction. As prior information, we generated the data set from two popularhearing-aid fitting software, then fed the training data to our proposed model, and verified theperformance of the architecture. Pondering real life circumstances, where numerous fitting recordsmay not always be accessible, we first investigated the number of minimum fitting records requiredfor possible sufficient training. After that, we evaluated the performance of the proposed algorithmin two phases: (a) NN with refined hyper parameter showed enhanced performance in compareto state-of-the-art DNN approach, and (b) the TL approach boosted the performance of the NNalgorithm in a broad way. Altogether, our model provides a pragmatic and promising tool for HAF.

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