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 A Preliminary Performance Evaluation of K-means, KNN and EM Unsupervised Machine Learning Methods for Network Flow Classification

2016 ◽  
Vol 6 (2) ◽  
pp. 778
Author(s):  
Alhamza Alalousi ◽  
Rozmie Razif ◽  
Mosleh AbuAlhaj ◽  
Mohammed Anbar ◽  
Shahrul Nizam
Keyword(s):  
Machine Learning ◽  
Expectation Maximization ◽  
Classification Accuracy ◽  
Network Flow ◽  
Processing Time ◽  
Nearest Neighbor ◽  
K Nearest Neighbor ◽  
Popular Method ◽  
Flow Classification ◽  
Machine Learning Methods

Unsupervised leaning is a popular method for classify unlabeled dataset i.e. without prior knowledge about data class. Many of unsupervised learning are used to inspect and classify network flow. This paper presents in-deep study for three unsupervised classifiers, namely: K-means, K-nearest neighbor and Expectation maximization. The methodologies and how it’s employed to classify network flow are elaborated in details. The three classifiers are evaluated using three significant metrics, which are classification accuracy, classification speed and memory consuming. The K-nearest neighbor introduce better results for accuracy and memory; while K-means announce lowest processing time.

scholarly journals An RSSD-Based Fingerprint Positioning Method for Detection of an Unknown Radio Transmitter Using WLS and Factor Graph

2018 ◽  
Vol 2018 ◽  
pp. 1-11
Author(s):  
Liyang Zhang ◽  
Taihang Du ◽  
Chundong Jiang
Keyword(s):  
Nearest Neighbor ◽  
Access Point ◽  
Reference Points ◽  
Least Square ◽  
Factor Graph ◽  
K Nearest Neighbor ◽  
Positioning Accuracy ◽  
Radio Transmitter ◽  
Error Weight ◽  
The Impact

Realizing accurate detection of an unknown radio transmitter (URT) has become a challenge problem due to its unknown parameter information. A method based on received signal strength difference (RSSD) fingerprint positioning technique and using factor graph (FG) has been successfully developed to achieve the localization of an URT. However, the RSSD-based FG model is not accurate enough to express the relationship between the RSSD and the corresponding location coordinates since the RSSD variances of reference points are different in practice. This paper proposes an enhanced RSSD-based FG algorithm using weighted least square (WLS) to effectively reduce the impact of RSSD measurement variance difference on positioning accuracy. By the use of stochastic RSSD errors between the measured value and the estimated value of the selected reference points, we utilize the error weight matrix to establish a new WLSFG model. Then, the positioning process of proposed RSSD-WLSFG algorithm is derived with the sum-product principle. In addition, the paper also explores the effects of different access point (AP) numbers and grid distances on positioning accuracy. The simulation experiment results show that the proposed algorithm can obtain the best positioning performance compared with the conventional RSSD-based K nearest neighbor (RSSD-KNN) and RSSD-FG algorithms in the case of different AP numbers and grid distances.

Continuous wide spectrum odor sensing for electronic nose system

Sensor Review ◽  
2018 ◽  
Vol 38 (2) ◽  
pp. 223-230
Author(s):  
Wenli Zhang ◽  
Fengchun Tian ◽  
An Song ◽  
Zhenzhen Zhao ◽  
Youwen Hu ◽  
...  
Keyword(s):  
Support Vector Machine ◽  
Nearest Neighbor ◽  
Wide Spectrum ◽  
Least Square ◽  
Support Vector ◽  
Detection Accuracy ◽  
K Nearest Neighbor ◽  
Sensing Element ◽  
Content Type ◽  
System Errors

Purpose This paper aims to propose an odor sensing system based on wide spectrum for e-nose, based on comprehensive analysis on the merits and drawbacks of current e-nose. Design/methodology/approach The wide spectral light is used as the sensing medium in the e-nose system based on continuous wide spectrum (CWS) odor sensing, and the sensing response of each sensing element is the change of light intensity distribution. Findings Experimental results not only verify the feasibility and effectiveness of the proposed system but also show the effectiveness of least square support vector machine (LSSVM) in eliminating system errors. Practical implications Theoretical model of the system was constructed, and experimental tests were carried out by using NO2 and SO2. System errors in the test data were eliminated using the LSSVM, and the preprocessed data were classified by euclidean distance to centroids (EDC), k-nearest neighbor (KNN), support vector machine (SVM), LSSVM, respectively. Originality/value The system not only has the advantages of current e-nose but also realizes expansion of sensing array by means of light source and the spectrometer with their wide spectrum, high resolution characteristics which improve the detection accuracy and realize real-time detection.

A Preliminary Performance Evaluation of K-means, KNN and EM Unsupervised Machine Learning Methods for Network Flow Classification

2016 ◽  
Vol 6 (2) ◽  
pp. 778 ◽  
Author(s):  
Alhamza Alalousi ◽  
Rozmie Razif ◽  
Mosleh AbuAlhaj ◽  
Mohammed Anbar ◽  
Shahrul Nizam
Keyword(s):  
Machine Learning ◽  
Expectation Maximization ◽  
Classification Accuracy ◽  
Network Flow ◽  
Processing Time ◽  
Nearest Neighbor ◽  
K Nearest Neighbor ◽  
Popular Method ◽  
Flow Classification ◽  
Machine Learning Methods

Unsupervised leaning is a popular method for classify unlabeled dataset i.e. without prior knowledge about data class. Many of unsupervised learning are used to inspect and classify network flow. This paper presents in-deep study for three unsupervised classifiers, namely: K-means, K-nearest neighbor and Expectation maximization. The methodologies and how it’s employed to classify network flow are elaborated in details. The three classifiers are evaluated using three significant metrics, which are classification accuracy, classification speed and memory consuming. The K-nearest neighbor introduce better results for accuracy and memory; while K-means announce lowest processing time.

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 Comparison of Approaches to Photometric Redshift Estimation of Quasars

2015 ◽  
Vol 11 (S319) ◽  
pp. 146-146
Author(s):  
Yang Tu ◽  
Yan-Xia Zhang ◽  
Yong-Heng Zhao ◽  
Hai-Jun Tian
Keyword(s):  
Gradient Descent ◽  
Nearest Neighbor ◽  
Partial Least Square ◽  
Least Square ◽  
Partial Least Square Regression ◽  
Stochastic Gradient Descent ◽  
K Nearest Neighbor ◽  
Photometric Redshift ◽  
K Nearest Neighbor Algorithm ◽  
Selection Operator

AbstractWe probe many kinds of approaches used for photometric redshift estimation of quasars, including KNN (K-nearest neighbor algorithm), Lasso (Least Absolute Shrinkage and Selection Operator), PLS (Partial Least Square regression), ridge regression, SGD (Stochastic Gradient Descent) and Extra-Trees.

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