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A Fast and Precise Indoor Localization Algorithm Based on an Online Sequential Extreme Learning Machine

Han Zou, Xiaoxuan Lu, Hao Jiang, Lihua Xie
2015 Sensors  
In this paper, we propose an indoor localization algorithm based on an online sequential extreme learning machine (OS-ELM) to address the above problems accordingly.  ...  Meanwhile, its online sequential learning ability enables the proposed localization algorithm to adapt in a timely manner to environmental dynamics.  ...  Author Contributions Han Zou proposed the OS-ELM-based localization algorithm and expanded the conference version to a journal version.  ... 
doi:10.3390/s150101804 pmid:25599427 pmcid:PMC4327104 fatcat:34ufrv52hzdcpamfmfrxvxfnie

A Hybrid Model Based on Constraint OSELM, Adaptive Weighted SRC and KNN for Large-Scale Indoor Localization

Hengyi Gan, Mohd Haris Bin Md Khir, Gunawan Witjaksono Bin Djaswadi, Nordin Ramli
2019 IEEE Access  
In this paper, a novel hybrid model based on the constraint online sequential extreme learning machine (COSELM) classifier with adaptive weighted sparse representation classification (WSRC) and k nearest  ...  INDEX TERMS Constraint online sequential extreme learning machine, k nearest neighbor, weighted sparse representation classification, WiFi-based IPS.  ...  Moreover, online sequential extreme learning machine (OSELM) [22] was developed and applied for the indoor localization in [23] to address the problems accordingly by using the traditional batch learning  ... 
doi:10.1109/access.2018.2890111 fatcat:dfvnbdwi5bcgpc5iipgkdd2p5y

Mobile Robot Indoor Positioning System Based on K-ELM

Haixia Wang, Junliang Li, Wei Cui, Xiao Lu, Zhiguo Zhang, Chunyang Sheng, Qingde Liu
2019 Journal of Sensors  
We apply the Kernel extreme learning machine (K-ELM) algorithm as our positioning algorithm after comparing four different algorithms in simulation experiments.  ...  Real-world indoor localization experiments are conducted, and the results demonstrate that the proposed system can not only improve positioning accuracy but also greatly reduce the installation efforts  ...  The extreme learning machine (ELM) is an algorithm for solving single hidden layer feedforward networks (SLFNs) [23, 24] .  ... 
doi:10.1155/2019/7547648 fatcat:bz2qfpx5wnhpldazymgu33plbu

Review of Indoor Positioning: Radio Wave Technology

Tan Kim Geok, Khaing Zar Aung, Moe Sandar Aung, Min Thu Soe, Azlan Abdaziz, Chia Pao Liew, Ferdous Hossain, Chih P. Tso, Wong Hin Yong
2020 Applied Sciences  
Moreover, advanced algorithms, machine learning, and valuable algorithms have given rise to effective ways in determining indoor locations.  ...  This paper presents a comprehensive review on the positioning algorithms for indoors, based on advances reported in radio wave, infrared, visible light, sound, and magnetic field technologies.  ...  . 6 Performance based on machine learning algorithms and extreme learning machine (ELM).  ... 
doi:10.3390/app11010279 fatcat:le6p5l7gebftndyiirslmgdx5a

A Survey of Machine Learning for Indoor Positioning

Ahasanun Nessa, Bhagawat Adhikari, Fatima Hussain, Xavier Fernando
2020 IEEE Access  
To improve the positioning accuracy authors in [126] proposed a fusion location framework where an Extreme Learning Machine (ELM) regression algorithm is used to predict the position based on motion  ...  Reinforcement learning is another promising ML technique, that can achieve fast network control based on defined learned policies.  ... 
doi:10.1109/access.2020.3039271 fatcat:htzgf2mwp5gmjbx3cczg5rl7ru

Performance Evaluation of Online Machine Learning Models Based on Cyclic Dynamic and Feature-Adaptive Time Series

Ahmed Salih AL-KHALEEFA, Rosilah HASSAN, Mohd Riduan AHMAD, Faizan QAMAR, Zheng WEN, Azana Hafizah MOHD AMAN, Keping YU
2021 IEICE transactions on information and systems  
To address this challenge, we evaluate and analyze four widely used online machine learning models: Online Sequential Extreme Learning Machine (OSELM), Feature Adaptive OSELM (FA-OSELM), Knowledge Preserving  ...  One most important challenge is the non-steady performance of various machine learning models during online learning and operation.  ...  Online sequential extreme learning machine (OSELM) as a famous NN of shallow type is prone to huge knowledge loss whenever the NN changes.  ... 
doi:10.1587/transinf.2020bdp0002 fatcat:bnixgzpbojekdhcacbzvh4le6y

A Survey of Recent Indoor Localization Scenarios and Methodologies

Tian Yang, Adnane Cabani, Houcine Chafouk
2021 Sensors  
The key localization techniques like RSSI-based fingerprinting technique are presented using supervised machine learning methods, namely SVM (support vector machine), KNN (K nearest neighbors) and NN (  ...  The trilateration is known as a classic theoretical model of geometric-based indoor localization, with uniform RSSI data that can be transferred directly into distance ranges.  ...  The program is cofinanced by the Normandy Region and the European Union. Europe is committed in Normandy with the European Regional Development Fund (ERDF).  ... 
doi:10.3390/s21238086 pmid:34884090 fatcat:juacgglap5f5jaezn3w2lrur3u

Online Learning-based WIFI Radio Map Updating Considering High-dynamic Environmental Factors

Xiaoguang Niu, Zejun Zhang, Ankang Wang, Jingbin Liu, Shubo Liu
2019 IEEE Access  
Indoor localization has been recognized as a promising research around the world, and fingerprint-based localization method which leverages WIFI Received Signal Strength (RSS) has been extensively studied  ...  In this work, we propose OWUH, an Online Learning-based WIFI Radio Map Updating service considering influences of high-dynamic factors.  ...  Specifically, we employ improved OS-ELM (online sequential extreme learning machine) as our basic training framework, which has an advantage of fast training and considering data credibility.  ... 
doi:10.1109/access.2019.2933583 fatcat:tzunx6mw4fgslnpjhxlu5jasmi

Sensors for Indoor Mapping and Navigation

Kourosh Khoshelham, Sisi Zlatanova
2016 Sensors  
A fast and precise indoor localization algorithm based on an online sequential extreme learning machine. Sensors 2015, 15, 1804-1824. [CrossRef] [PubMed] 28.  ...  In [46] a simultaneous localization and mapping (SLAM) approach based on data captured by a 2D laser scanner and a monocular camera is introduced.  ... 
doi:10.3390/s16050655 pmid:27171079 pmcid:PMC4883346 fatcat:uxkb45tktfeoxfkcrf6b6omh3y

An Ensemble Filter for Indoor Positioning in a Retail Store Using Bluetooth Low Energy Beacons

Vasilis Stavrou, Cleopatra Bardaki, Dimitris Papakyriakopoulos, Katerina Pramatari
2019 Sensors  
This paper has developed and deployed a Bluetooth Low Energy (BLE) beacon-based indoor positioning system in a two-floor retail store.  ...  The more accurate the consumer localization, the more accurate and rich insights on the customers' shopping behavior.  ...  techniques, such as decision trees [23] , unsupervised labelling on sequential data [43] , unsupervised clustering for multi-floor environments [9] and online sequential extreme learning [44, 45]  ... 
doi:10.3390/s19204550 fatcat:6wkyek6wsrhgborokt67f65rti

Paper titles

2020 2020 IEEE International Conference on Consumer Electronics - Taiwan (ICCE-Taiwan)  
System Indoor Localization using Machine Learning and Beacons Indoor Navigation System for Evacuation Route in case of Fire by using Environment and Location Data Indoor Positioning Method for Smart Mobile  ...  for Image Dehazing Applications SOH Estimation of Battery Pack Composed on Reused Li-Ion Cells based on Adaptive ANN Machine Learning Algorithm Soiling Detection for Photovoltaic Modules Based on an Intelligent  ... 
doi:10.1109/icce-taiwan49838.2020.9258179 fatcat:2eheaztzhncixhbvp7nrbzml4m

Deep Learning in Mobile and Wireless Networking: A Survey [article]

Chaoyun Zhang, Paul Patras, Hamed Haddadi
2019 arXiv   pre-print
One potential solution is to resort to advanced machine learning techniques to help managing the rise in data volumes and algorithm-driven applications.  ...  Subsequently, we provide an encyclopedic review of mobile and wireless networking research based on deep learning, which we categorize by different domains.  ...  [311] Indoor fingerprinting CSI RBM First deep learning driven indoor localization based on CSI Wang et al.  ... 
arXiv:1803.04311v3 fatcat:awuvyviarvbr5kd5ilqndpfsde

Mobile big data analysis with machine learning [article]

Jiyang Xie, Zeyu Song, Yupeng Li, Zhanyu Ma
2020 arXiv   pre-print
This paper investigates to identify the requirement and the development of machine learning-based mobile big data analysis through discussing the insights of challenges in the mobile big data (MBD).  ...  Three typical applications of MBD analysis, namely wireless channel modeling, human online and offline behavior analysis, and speech recognition in the internet of vehicles, are introduced respectively  ...  [98] proposed an online SVM learning algorithm to deal with the classification problem for sequentially provided input data.  ... 
arXiv:1808.00803v2 fatcat:42l62ikc2rhd3bzuao25hhrwgm

Deep Learning in Mobile and Wireless Networking: A Survey

Chaoyun Zhang, Paul Patras, Hamed Haddadi
2019 IEEE Communications Surveys and Tutorials  
One potential solution is to resort to advanced machine learning techniques, in order to help manage the rise in data volumes and algorithm-driven applications.  ...  Subsequently, we provide an encyclopedic review of mobile and wireless networking research based on deep learning, which we categorize by different domains.  ...  [314] Indoor fingerprinting CSI Device-based RBM First deep learning driven indoor localization based on CSI Wang et al.  ... 
doi:10.1109/comst.2019.2904897 fatcat:xmmrndjbsfdetpa5ef5e3v4xda

Application of Machine Learning in Wireless Networks: Key Techniques and Open Issues [article]

Yaohua Sun, Mugen Peng, Yangcheng Zhou, Yuzhe Huang, Shiwen Mao
2019 arXiv   pre-print
As a key technique for enabling artificial intelligence, machine learning (ML) is capable of solving complex problems without explicit programming.  ...  in the network layer, and localization in the application layer.  ...  RNN is suitable for processing time series to learn features in time domain, while the advantage of extreme learning machine lies in good generalization performance at an extremely fast learning speed  ... 
arXiv:1809.08707v2 fatcat:6tnzliwthfehrpuxpmm45hs4vq
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