scholarly journals Adaptive Residual Weighted K-Nearest Neighbor Fingerprint Positioning Algorithm Based on Visible Light Communication

Sensors ◽  
2020 ◽  
Vol 20 (16) ◽  
pp. 4432 ◽  
Author(s):  
Shiwu Xu ◽  
Chih-Cheng Chen ◽  
Yi Wu ◽  
Xufang Wang ◽  
Fen Wei
Keyword(s):  
Visible Light ◽  
Nearest Neighbor ◽  
Signal To Noise Ratio ◽  
Visible Light Communication ◽  
Least Square ◽  
Manhattan Distance ◽  
Positioning Error ◽  
K Nearest Neighbor ◽  
Positioning Algorithm ◽  
Self Adaptive

The weighted K-nearest neighbor (WKNN) algorithm is a commonly used fingerprint positioning, the difficulty of which lies in how to optimize the value of K to obtain the minimum positioning error. In this paper, we propose an adaptive residual weighted K-nearest neighbor (ARWKNN) fingerprint positioning algorithm based on visible light communication. Firstly, the target matches the fingerprints according to the received signal strength indication (RSSI) vector. Secondly, K is a dynamic value according to the matched RSSI residual. Simulation results show the ARWKNN algorithm presents a reduced average positioning error when compared with random forest (81.82%), extreme learning machine (83.93%), artificial neural network (86.06%), grid-independent least square (60.15%), self-adaptive WKNN (43.84%), WKNN (47.81%), and KNN (73.36%). These results were obtained when the signal-to-noise ratio was set to 20 dB, and Manhattan distance was used in a two-dimensional (2-D) space. The ARWKNN algorithm based on Clark distance and minimum maximum distance metrics produces the minimum average positioning error in 2-D and 3-D, respectively. Compared with self-adaptive WKNN (SAWKNN), WKNN and KNN algorithms, the ARWKNN algorithm achieves a significant reduction in the average positioning error while maintaining similar algorithm complexity.

scholarly journals DBSCAN and TD Integrated Wi-Fi Positioning Algorithm

2022 ◽  
Vol 14 (2) ◽  
pp. 297
Author(s):  
Jingxue Bi ◽  
Hongji Cao ◽  
Yunjia Wang ◽  
Guoqiang Zheng ◽  
Keqiang Liu ◽  
...  
Keyword(s):  
High Resolution ◽  
Nearest Neighbor ◽  
Reference Points ◽  
Manhattan Distance ◽  
Support Vector ◽  
Indoor Environments ◽  
K Nearest Neighbor ◽  
Positioning Accuracy ◽  
Positioning Algorithm ◽  
Accuracy And Stability

A density-based spatial clustering of applications with noise (DBSCAN) and three distances (TD) integrated Wi-Fi positioning algorithm was proposed, aiming to enhance the positioning accuracy and stability of fingerprinting by the dynamic selection of signal-domain distance to obtain reliable nearest reference points (RPs). Two stages were included in this algorithm. One was the offline stage, where the offline fingerprint database was constructed and the other was the online positioning stage. Three distances (Euclidean distance, Manhattan distance, and cosine distance), DBSCAN, and high-resolution distance selection principle were combined to obtain more reliable nearest RPs and optimal signal-domain distance in the online stage. Fused distance, the fusion of position-domain and signal-domain distances, was applied for DBSCAN to generate the clustering results, considering both the spatial structure and signal strength of RPs. Based on the principle that the higher resolution the distance, the more clusters will be obtained, the high-resolution distance was used to compute positioning results. The weighted K-nearest neighbor (WKNN) considering signal-domain distance selection was used to estimate positions. Two scenarios were selected as test areas; a complex-layout room (Scenario A) for post-graduates and a typical large indoor environment (Scenario B) covering 3200 m2. In both Scenarios A and B, compared with support vector machine (SVM), Gaussian process regression (GPR) and rank algorithms, the improvement rates of positioning accuracy and stability of the proposed algorithm were up to 60.44 and 60.93%, respectively. Experimental results show that the proposed algorithm has a better positioning performance in complex and large indoor environments.

scholarly journals Optimization of deep learning features for age-invariant face recognition

2020 ◽  
Vol 10 (2) ◽  
pp. 1833
Author(s):  
Amal A. Moustafa ◽  
Ahmed Elnakib ◽  
Nihal F. F. Areed
Keyword(s):  
Deep Learning ◽  
Face Recognition ◽  
Nearest Neighbor ◽  
Recognition Rate ◽  
Manhattan Distance ◽  
Distance Metrics ◽  
K Nearest Neighbor ◽  
Face Images ◽  
Knn Classifier ◽  
Deep Learning Features

This paper presents a methodology for Age-Invariant Face Recognition (AIFR), based on the optimization of deep learning features. The proposed method extracts deep learning features using transfer deep learning, extracted from the unprocessed face images. To optimize the extracted features, a Genetic Algorithm (GA) procedure is designed in order to select the most relevant features to the problem of identifying a person based on his/her facial images over different ages. For classification, K-Nearest Neighbor (KNN) classifiers with different distance metrics are investigated, i.e., Correlation, Euclidian, Cosine, and Manhattan distance metrics. Experimental results using a Manhattan distance KNN classifier achieves the best Rank-1 recognition rate of 86.2% and 96% on the standard FGNET and MORPH datasets, respectively. Compared to the state-of-the-art methods, our proposed method needs no preprocessing stages. In addition, the experiments show its privilege over other related methods.

Optical SNR estimation using machine learning

2021 ◽  
Vol 0 (0) ◽  
Author(s):  
Palash Rai ◽  
Rahul Kaushik
Keyword(s):  
Machine Learning ◽  
Signal Analysis ◽  
Nearest Neighbor ◽  
Signal To Noise Ratio ◽  
Optical Signal ◽  
Machine Learning Algorithms ◽  
K Nearest Neighbor ◽  
Eye Diagram ◽  
Test Instrument ◽  
Data Signal

Abstract A technique for the estimation of an optical signal-to-noise ratio (OSNR) using machine learning algorithms has been proposed. The algorithms are trained with parameters derived from eye-diagram via simulation in 10 Gb/s On-Off Keying (OOK) nonreturn-to-zero (NRZ) data signal. The performance of different machine learning (ML) techniques namely, multiple linear regression, random forest, and K-nearest neighbor (K-NN) for OSNR estimation in terms of mean square error and R-squared value has been compared. The proposed methods may be useful for intelligent signal analysis in a test instrument and to monitor optical performance.

scholarly journals A Full-Duplex LED-to-LED Visible Light Communication System

Electronics ◽  
2020 ◽  
Vol 9 (10) ◽  
pp. 1713
Author(s):  
Hyunwoo Jung ◽  
Sung-Man Kim
Keyword(s):  
Visible Light ◽  
Bit Error Rate ◽  
Error Rate ◽  
Signal To Noise Ratio ◽  
Light Emitting Diode ◽  
Visible Light Communication ◽  
Full Duplex ◽  
Signal To Noise ◽  
Light Emitting ◽  
Half Duplex

We experimentally demonstrated full-duplex light-emitting diode (LED)-to-LED visible light communication (VLC) using LEDs as the transmitter and receiver. Firstly, we investigated the performance dependency on the wavelengths of the LED transmitter and receiver by measuring the rise time and signal-to-noise ratio (SNR). Through the investigation, we were able to choose the optimal LED color set for LED-to-LED VLC using Shannon’s channel capacity law. The bit error rate (BER) results of full-duplex and half-duplex LED-to-LED VLC systems with the optimal LED sets are shown to compare the performance. Furthermore, we discuss major distortions and signal losses in the full-duplex LED-to-LED VLC system.

scholarly journals Polarization Differential Visible Light Communication: Theory and Experimental Evaluation

Sensors ◽  
2020 ◽  
Vol 20 (19) ◽  
pp. 5661
Author(s):  
Jorik De Bruycker ◽  
Willem Raes ◽  
Stanislav Zvánovec ◽  
Nobby Stevens
Keyword(s):  
Visible Light ◽  
Communication Theory ◽  
Rotation Angle ◽  
Signal To Noise Ratio ◽  
Visible Light Communication ◽  
Signal Quality ◽  
Polarization Direction ◽  
Signal To Noise ◽  
Research Attention ◽  
Analytical Expression

Visible Light Communication (VLC) has received substantial research attention in the last decade. The vast majority of VLC focuses on the modulation of the transmitted light intensity. In this work, however, the intensity is kept constant while the polarization direction is deployed as a carrier of information. Demodulation is realized by using a differential receiver pair equipped with mutually orthogonal polarizers. An analytical expression to evaluate the Signal-to-Noise Ratio (SNR) as a function of the rotation angle of the receiver is derived. It is demonstrated that the signal quality can deteriorate heavily with receiver orientation when using a single differential receiver pair. A way to overcome this drawback using two receiver pairs is described. The analytical expression is experimentally verified through measurements with two different receiver setups. This work demonstrates the potential of polarization-based modulation in the field of VLC, where receiver rotation robustness has been achieved by means of a dedicated quadrant photodiode receiver.

scholarly journals WSN based Improved Bayesian Algorithm Combined with Enhanced Least-Squares Algorithm for Target Localizing and Tracking

2020 ◽  
Vol 2 (2) ◽  
pp. 59-67
Author(s):  
Dr. Wang Haoxiang ◽  
Dr. Smys S.
Keyword(s):  
Kalman Filter ◽  
Expectation Maximization ◽  
Nearest Neighbor ◽  
Joint Probability ◽  
Target Prediction ◽  
Least Square ◽  
K Nearest Neighbor ◽  
Bayesian Algorithm ◽  
Individual Measurement ◽  
Localization And Tracking

For wireless sensor network (WSN), localization and tracking of targets are implemented extensively by means of traditional tracking algorithms like classical least-square (CLS) algorithm, extended Kalman filter (EKF) and the Bayesian algorithm. For the purpose of tracking and moving target localization of WSN, this paper proposes an improved Bayesian algorithm that combines the principles of least-square algorithm. For forming a matrix of range joint probability and using target predictive location of obtaining a sub-range probability set, an improved Bayesian algorithm is implemented. During the dormant state of the WSN testbed, an automatic update of the range joint probability matrix occurs. Further, the range probability matrix is used for the calculation and normalization of the weight of every individual measurement. Lastly, based on the weighted least-square algorithm, calculation of the target prediction position and its correction value is performed. The accuracy of positioning of the proposed algorithm is improved when compared to variational Bayes expectation maximization (VBEM), dual-factor enhanced VBAKF (EVBAKF), variational Bayesian adaptive Kalman filtering (VBAKF), the fingerprint Kalman filter (FKF), the position Kalman filter (PKF), the weighted K-nearest neighbor (WKNN) and the EKF algorithms with the values of 0.5%, 7%, 14%, 19%, 33% and 35% respectively. Along with this, when compared to Bayesian algorithm, the computation burden is reduced by the proposed algorithm by a factor of over 80%.

scholarly journals TOF-Based Fast Self-Positioning Algorithm for UWB Mobile Base Stations

Sensors ◽  
2021 ◽  
Vol 21 (19) ◽  
pp. 6359
Author(s):  
Yuxiang Han ◽  
Xiaoming Zhang ◽  
Zhengxi Lai ◽  
Yuchen Geng
Keyword(s):  
Local Coordinate System ◽  
Base Station ◽  
Least Square ◽  
Base Stations ◽  
Ultra Wideband ◽  
Positioning Error ◽  
Position Measurement ◽  
Propagation Law ◽  
Positioning Algorithm ◽  
Mobile Base

To solve the problem of heavy workload and high cost when acquiring the position of Ultra-Wideband (UWB) mobile base stations in sports fields, a fast self-positioning algorithm for UWB mobile base stations algorithm based on Time of Flight (TOF) is proposed. First, according to the layout of the base stations in the sports field, the local coordinate system is determined, and an equation based on the ranging information between the base stations is established; the Least Square method is used to calculate the coordinates of each base station, and the Newton Iteration method is used to converge the positioning results. Then the origin and propagation law of positioning error, as well as the method of reducing the positioning error are analyzed. The simulation data and experimental results show that the average positioning accuracy of the mobile base station is within 0.05 m, which meets the expected accuracy of the base station position measurement. Compared with traditional manual measurement methods, base station self-positioning can effectively save deployment time and reduce workload.

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