Random sampling algorithm in RFID indoor location system

2006 ◽  
Author(s):  
Bao Xu ◽  
Wang Gang
Keyword(s):  
Random Sampling ◽  
Sampling Algorithm ◽  
Indoor Location ◽  
Location System

An indoor location system based on neural network and genetic algorithm

2015 ◽  
Vol 19 (3/4) ◽  
pp. 204 ◽  
Author(s):  
R.C. Chen ◽  
S.W. Huang ◽  
Y.C. Lin ◽  
Q.F. Zhao
Keyword(s):  
Neural Network ◽  
Genetic Algorithm ◽  
Indoor Location ◽  
Location System

Active RFID Indoor Location System Based on RSSI Ranging Correction

2013 ◽  
Vol 677 ◽  
pp. 449-454 ◽  
Author(s):  
Qing Bin Meng ◽  
Lan Ju ◽  
Jie Jin ◽  
Wei Xiang Li
Keyword(s):  
High Precision ◽  
Environmental Parameters ◽  
Statistical Calculation ◽  
Average Deviation ◽  
Active Rfid ◽  
Indoor Location ◽  
Location System ◽  
Statistical Experiments ◽  
Location Algorithm

For indoor location, an active RFID indoor location system is designed. The system is designed and implemented by using a RSSI-based ranging technology location algorithm. In this article, the author amended the RSSI ranging equation, proposed and implemented a way to extract and estimate the environmental parameters of specific application scenarios, and improved the accuracy of the RSSI ranging. After many statistical experiments, the results show that the system’s deviation can reach 10cm on the diagonal of the region, and about 30cm on the edge. Through statistical calculation, the average deviation of system position is about 10.6cm, which is a good location system of high precision. When implemented for different applications, this system has the advantages of simplicity and adaptability.

Evaluation of an RSS-based indoor location system

2011 ◽  
Vol 167 (1) ◽  
pp. 110-116 ◽  
Author(s):  
Yuri Álvarez López ◽  
Mª Elena de Cos Gómez ◽  
José Lorenzo Álvarez ◽  
Fernando Las-Heras Andrés
Keyword(s):  
Indoor Location ◽  
Location System

scholarly journals In search of an optimum sampling algorithm for prediction of soil properties from infrared spectra

PeerJ ◽  
2018 ◽  
Vol 6 ◽  
pp. e5722 ◽  
Author(s):  
Wartini Ng ◽  
Budiman Minasny ◽  
Brendan Malone ◽  
Patrick Filippi
Keyword(s):  
Soil Properties ◽  
Sample Size ◽  
Random Sampling ◽  
Regression Models ◽  
Sampling Method ◽  
Partial Least Square ◽  
Least Square ◽  
Sampling Algorithm ◽  
Calibration Sample ◽  
Sampling Algorithms

Background The use of visible-near infrared (vis-NIR) spectroscopy for rapid soil characterisation has gained a lot of interest in recent times. Soil spectra absorbance from the visible-infrared range can be calibrated using regression models to predict a set of soil properties. The accuracy of these regression models relies heavily on the calibration set. The optimum sample size and the overall sample representativeness of the dataset could further improve the model performance. However, there is no guideline on which sampling method should be used under different size of datasets. Methods Here, we show different sampling algorithms performed differently under different data size and different regression models (Cubist regression tree and Partial Least Square Regression (PLSR)). We analysed the effect of three sampling algorithms: Kennard-Stone (KS), conditioned Latin Hypercube Sampling (cLHS) and k-means clustering (KM) against random sampling on the prediction of up to five different soil properties (sand, clay, carbon content, cation exchange capacity and pH) on three datasets. These datasets have different coverages: a European continental dataset (LUCAS, n = 5,639), a regional dataset from Australia (Geeves, n = 379), and a local dataset from New South Wales, Australia (Hillston, n = 384). Calibration sample sizes ranging from 50 to 3,000 were derived and tested for the continental dataset; and from 50 to 200 samples for the regional and local datasets. Results Overall, the PLSR gives a better prediction in comparison to the Cubist model for the prediction of various soil properties. It is also less prone to the choice of sampling algorithm. The KM algorithm is more representative in the larger dataset up to a certain calibration sample size. The KS algorithm appears to be more efficient (as compared to random sampling) in small datasets; however, the prediction performance varied a lot between soil properties. The cLHS sampling algorithm is the most robust sampling method for multiple soil properties regardless of the sample size. Discussion Our results suggested that the optimum calibration sample size relied on how much generalization the model had to create. The use of the sampling algorithm is beneficial for larger datasets than smaller datasets where only small improvements can be made. KM is suitable for large datasets, KS is efficient in small datasets but results can be variable, while cLHS is less affected by sample size.

Tracking Using a Random Sampling Algorithm *

1993 ◽  
Vol 26 (2) ◽  
pp. 361-364
Author(s):  
J.A. O'Sullivan ◽  
M.L. Miller ◽  
A. Srivastava ◽  
D.L. Snyder
Keyword(s):  
Random Sampling ◽  
Sampling Algorithm
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