An Indoor Localization of WiFi Based on Support Vector Machines

2014 ◽  
Vol 926-930 ◽  
pp. 2438-2441 ◽  
Author(s):  
Feng Yu ◽  
Ming Hua Jiang ◽  
Jing Liang ◽  
Xiao Qin ◽  
Ming Hu ◽  
...  
Keyword(s):  
Support Vector Machines ◽  
Basis Function ◽  
Indoor Localization ◽  
Signal Strength ◽  
Kernel Functions ◽  
Support Vector ◽  
Localization Accuracy ◽  
Svm Algorithm ◽  
Vector Machines ◽  
Wifi Signal

The recent growing interest for indoor localization-based services has created a need for more accurate and real-time indoor localization solutions. Indoor localization based on existing WiFi signal strength is becoming increasingly prevalent and ubiquity. In this paper, we utilize the information of the signal strength received from the surrounding access points (APs) to determine the user localization. The propose algorithm based on support vector machines (SVM) algorithm, and comparing with three kernel functions, radial basis function (RBF) performs best of all. Experimental results indicate that the proposed algorithm leads to improvement on localization accuracy.

scholarly journals Efficient Technique for Facial Image Recognition With Support Vector Machines in 2D Images With Cross-validation in Matlab

2020 ◽  
Vol 15 ◽  
Keyword(s):  
Support Vector Machines ◽  
Cross Validation ◽  
Facial Recognition ◽  
Kernel Functions ◽  
Efficient Technique ◽  
Support Vector ◽  
Facial Image ◽  
Vector Machines ◽  
Validation Technique ◽  
2D Images

This article presented in the context of 2D global facial recognition, using Gabor Wavelet's feature extraction algorithms, and facial recognition Support Vector Machines (SVM), the latter incorporating the kernel functions: linear, cubic and Gaussian. The models generated by these kernels were validated by the cross validation technique through the Matlab application. The objective is to observe the results of facial recognition in each case. An efficient technique is proposed that includes the mentioned algorithms for a database of 2D images. The technique has been processed in its training and testing phases, for the facial image databases FERET [1] and MUCT [2], and the models generated by the technique allowed to perform the tests, whose results achieved a facial recognition of individuals over 96%.

Classification in GIS Using Support Vector Machines

2010 ◽  
pp. 106-112 ◽  
Author(s):  
Alina Lazar ◽  
Bradley A. Shellito
Keyword(s):  
Support Vector Machines ◽  
Urban Development ◽  
Rural Areas ◽  
Urban Areas ◽  
Kernel Functions ◽  
Support Vector ◽  
Future Trends ◽  
Error Signal ◽  
Vector Machines

Support Vector Machines (SVM) are powerful tools for classification of data. This article describes the functionality of SVM including their design and operation. SVM have been shown to provide high classification accuracies and have good generalization capabilities. SVM can classify linearly separable data as well as nonlinearly separable data through the use of the kernel function. The advantages of using SVM are discussed along with the standard types of kernel functions. Furthermore, the effectiveness of applying SVM to large, spatial datasets derived from Geographic Information Systems (GIS) is also described. Future trends and applications are also discussed – the described extracted dataset contains seven independent variables related to urban development plus a class label which denotes the urban areas versus the rural areas. This large dataset, with over a million instances really proves the generalization capabilities of the SVM methods. Also, the spatial property allows experts to analyze the error signal.

scholarly journals Automatic Detection of Dynamic and Static Activities of the Older Adults Using a Wearable Sensor and Support Vector Machines

Sci ◽  
2020 ◽  
Vol 2 (3) ◽  
pp. 62
Author(s):  
Jian Zhang ◽  
Rahul Soangra ◽  
Thurmon E. Lockhart
Keyword(s):  
Older Adults ◽  
Support Vector Machines ◽  
Daily Life ◽  
Human Motion ◽  
Function Parameter ◽  
Support Vector ◽  
Activities Of Daily Life ◽  
Svm Algorithm ◽  
Vector Machines ◽  
Two Parameters

Although Support Vector Machines (SVM) are widely used for classifying human motion patterns, their application in the automatic recognition of dynamic and static activities of daily life in the healthy older adults is limited. Using a body mounted wireless inertial measurement unit (IMU), this paper explores the use of SVM approach for classifying dynamic (walking) and static (sitting, standing and lying) activities of the older adults. Specifically, data formatting and feature extraction methods associated with IMU signals are discussed. To evaluate the performance of the SVM algorithm, the effects of two parameters involved in SVM algorithm—the soft margin constant C and the kernel function parameter γ—are investigated. The changes associated with adding white-noise and pink-noise on these two parameters along with adding different sources of movement variations (i.e., localized muscle fatigue and mixed activities) are further discussed. The results indicate that the SVM algorithm is capable of keeping high overall accuracy by adjusting the two parameters for dynamic as well as static activities, and may be applied as a tool for automatically identifying dynamic and static activities of daily life in the older adults.

scholarly journals Automatic Detection of Dynamic and Static Activities of the Older Adults Using a Wearable Sensor and Support Vector Machines

Sci ◽  
2020 ◽  
Vol 2 (3) ◽  
pp. 60
Author(s):  
Jian Zhang ◽  
Rahul Soangra ◽  
Thurmon E. Lockhart
Keyword(s):  
Older Adults ◽  
Support Vector Machines ◽  
Daily Life ◽  
Human Motion ◽  
Function Parameter ◽  
Support Vector ◽  
Activities Of Daily Life ◽  
Svm Algorithm ◽  
Vector Machines ◽  
Two Parameters

Although Support Vector Machines (SVM) are widely used for classifying human motion patterns, their application in the automatic recognition of dynamic and static activities of daily life in the healthy older adults is limited. Using a body mounted wireless inertial measurement unit (IMU), this paper explores the use of SVM approach for classifying dynamic (walking) and static (sitting, standing and lying) activities of the older adults. Specifically, data formatting and feature extraction methods associated with IMU signals are discussed. To evaluate the performance of the SVM algorithm, the effects of two parameters involved in SVM algorithm—the soft margin constant C and the kernel function parameter γ—are investigated. The changes associated with adding white-noise and pink-noise on these two parameters along with adding different sources of movement variations (i.e., localized muscle fatigue and mixed activities) are further discussed. The results indicate that the SVM algorithm is capable of keeping high overall accuracy by adjusting the two parameters for dynamic as well as static activities, and may be applied as a tool for automatically identifying dynamic and static activities of daily life in the older adults.

Outdoor Navigating Scene Labeling Using Importance Factor Based I-RELIEF and Feature Weighted Support Vector Machines

2014 ◽  
Vol 511-512 ◽  
pp. 467-474
Author(s):  
Jun Tu ◽  
Cheng Liang Liu ◽  
Zhong Hua Miao
Keyword(s):  
Support Vector Machines ◽  
Image Data ◽  
Kernel Functions ◽  
Support Vector ◽  
Terrain Classification ◽  
Relief Algorithm ◽  
Novel Approach ◽  
Scene Labeling ◽  
Vector Machines ◽  
Importance Factor

Feature selection plays an important role in terrain classification for outdoor robot navigation. For terrain classification, the image data usually have a large number of feature dimensions. The better selection of features usually results in higher labeling accuracy. In this work, a novel approach for terrain perception using Importance Factor based I-Relief algorithm and Feature Weighted Support Vector Machines (IFIR-FWSVM) is put forward. Firstly, the weight of each feature for classification is computed by using Importance Factor based I-Relief algorithm (IFIR) and the irrelevant features are eliminated. Then the weighted features are used to compute the kernel functions of SVM and trained the classifier. Finally, the trained SVM is employed to predict the terrain label in the far-field regions. Experimental results based on DARPA datasets show that the proposed method IFIR-FWSVM is superior over traditional SVM.

Image Segmentation Based on Least Squares Support Vector Machines

2014 ◽  
Vol 644-650 ◽  
pp. 4314-4318
Author(s):  
Xin You Wang ◽  
Ya Li Ning ◽  
Xi Ping He
Keyword(s):  
Image Segmentation ◽  
Support Vector Machines ◽  
Least Squares ◽  
Color Image ◽  
Pso Algorithm ◽  
Kernel Functions ◽  
Color Image Segmentation ◽  
Support Vector ◽  
Standard Data ◽  
Vector Machines

In order to solve the problem of the conventional methods operated directly in the image, difficult to obtain good results because they are poor in high dimension performance. In this paper, a new method was proposed, which use the Least Squares Support Vector Machines in image segmentation. Furthermore, the parameters of kernel functions are also be optimized by Particle Swarm Optimization (PSO) algorithm. The practical application in various of standard data sets and color image segmentation experiment. The results show that, LS-SVM can use a variety of features in image, the experiments have achieved good results of image segmentation, and the time needed for segmentation is greatly reduced than standard SVM.

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