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LANDMARC with Improved k-nearest algorithm for RFID Location System 2

LANDMARC with Improved <i>k</i>-Nearest Algorithm for RFID L...

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2nd IEEE International Conference on Computer and Communications (ICCC)

作者： Liu, Xing Wen, Meng Qin, Guangcheng Liu, Rong PLA Univ Sci & Technol Inst Commun Nanjing Jiangsu Peoples R China Inst China Elect Syst Engn Corp Beijing Peoples R China

ISBN: (纸本)9781467390262

The RFID (Radio Frequency Identification) localization technology has become one of the most developed technologies owing to its low cost, full-fledged application and flexible deployment. However, current RFID localization system cannot achieve the task of precise and highly accurate object localization due to the limitations of localization algorithm. Traditionally, the localization algorithm cannot make the localization more accuracy. The demand to improve the localization precision is still growing dramatically. LANDMARC system is a typical case that uses virtual tag to improve the overall accuracy of locating moving objects. However, this overall accuracy is fluctuates due to the multipath effect and the various indoor environment. This research work attempts to improve localization precision by using the sensing information network coordination and the log-distance path loss model. From the results, this approach can deliver a localization precision.

关键词： RFID LANDMARC localization k-nearest algorithm

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Comparative Study of Various Machine Learning Techniques for Parkinson Disease Detection Based on Handwriting 1

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4th Congress on Intelligent Systems (CIS)

作者： Singh, Gurpreet Sharma, Sukesha Panjab Univ UIET Chandigarh India

ISBN: (数字)9789819990405

ISBN: (纸本)9789819990399;9789819990405

The most prevalent neurodegenerative condition that substantially impairs elderly people's motor capabilities is Parkinson's disease (PD). Even today, in less developed regions of the world, the diagnosis and monitoring of PD remain an expensive and difficult process. This is comparative study of methods for predicting Parkinson's disease using hand-drawn spiral pictures using computer vision and machine learning approaches. In this paper, five machine learning algorithms are used, which are Decision Tree, k-nearest Neighbors, Support Vector Classifier, Logistic Regression, and Random Forest. Along with this, HOG feature descriptor is used for the extraction of the features from the spiral images. In this work, the kNN machine learning algorithm performed with the best accuracy of 90%. Similarly, the Random Forest is the second best algorithm with accuracy 83.3%.

关键词： Parkinson's disease Machine learning Spiral images Hog algorithm k-nearest algorithm Logistic regression

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ADVANCED LANDMARC WITH ADAPTIVE k-nearest algorithm FOR RFID LOCATION SYSTEM

ADVANCED LANDMARC WITH ADAPTIVE k-NEAREST ALGORITHM FOR RFID...

引用

2010 2nd IEEE International Conference on Network Infrasturcture & Digital Content(2010年第二届IEEE网络基础设施与数字内容国际会议 IC-NIDC 2010)

作者： kyuwon Han Sung Ho Cho Division of Electrical and Computer Engineering Hanyang University Seoul Korea

This paper proposed advanced LANDMARC with adaptive k-nearest neighbor algorithm for RFID location system. LANDMARC was based on Received Signal Strength (RSS). It sometimes showed bad performance for fluctuation. Therefore, adaptive k-nearest neighbor algorithm was introduced for improving accuracy and was applied to original LANDMARC. In our simulation, we showed that the proposed method can achieve a better performance for location sensing.

关键词： RFID LANDMARC Location Sensing k-nearest algorithm

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