Filters








2,484 Hits in 6.6 sec

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  
Moreover, the localization error is approximately 2 m, while for the random forest, it is 2.5 m.  ...  To the best of the authors' knowledge, this is the first work concerning indoor localization of consumers in a real retail store.  ...  On the contrary, for the random forest classifier, in 80% of the cases the localization error is approximately 2.5 m.  ... 
doi:10.3390/s19204550 fatcat:6wkyek6wsrhgborokt67f65rti

Indoor Position Detection Using Smartwatch and Beacons

Sittichai Sukreep, Chakarida Nukoolkit, Pornchai Mongkolnam
2020 Sensors and materials  
Various useful screens with easy-to-understand visualizations are provided for monitoring subject behaviors and time spent in certain areas, giving a summary of indoor positioning.  ...  Data mining techniques were applied to classify indoor positioning zones. A noise reduction process combining two data smoothing techniques was incorporated.  ...  Anthony French for English proofreading and all the volunteers involved in our data collection.  ... 
doi:10.18494/sam.2020.2386 fatcat:oevrjrrhc5fhrhlbewldab3sj4

Visible light communication-based positioning for indoor environments using supervised learning

Mehmet C. Ilter, Alexis A. Dowhuszko, Kiran K. Vangapattu, Jyri Hamalainen, Risto Wichman
2020 GLOBECOM 2020 - 2020 IEEE Global Communications Conference  
Then, the CSI of the VLC receiver is used to train a Random Forest classifier, which will predict the position of the object during the assessment phase.  ...  paper studies a novel way to estimate the position of an object in an indoor environment, using the Channel State Information (CSI) that a Visible Light Communication (VLC) system collects to maintain  ...  RANDOM FOREST ALGORITHM Random Forest [17] is a supervised learning algorithm in which a collective learning process is carried out in a group of decision trees.  ... 
doi:10.1109/globecom42002.2020.9322577 fatcat:yfq2vfds3ba33pdu7urvpr5ncq

Towards an Indoor Location System using Indoor-light as Information Source

Carlos E. Galván-Tejada, Jorge I. Galván-Tejada, Selene Ramírez-Rosales, F. J. Martinez-Ruiz, S. Paulizeth Maldonado-Luján
2015 Research in Computing Science  
In this paper, we present an alternative approach based on the use of indoor lighting variations, feature extraction of the signal and a deterministic selection to get a model in order to estimate localization  ...  of an individual in indoor environments.  ...  using well-known Random Forest Classifier [3] .  ... 
doi:10.13053/rcs-91-1-1 fatcat:l4pdeynd55eexlhnu44toryo2a

Mixed-Mode Wireless Indoor Positioning System Using Proximity Detection and Database Correlation

Piotr Korbel, Piotr Wawrzyniak, Piotr Skulimowski, Paweł Poryzała
2014 Proceedings of the 2014 Federated Conference on Computer Science and Information Systems  
The paper presents a prototype mixed-mode wireless indoor positioning and navigation system.  ...  The radio nodes of the network can operate in two power modes providing basis for both rough and precise user positioning.  ...  The experiments conducted in a large office building resulted in average positioning error not exceeding 2.32 meters when Random Forest classifier was used with combined proximity sensing and database  ... 
doi:10.15439/2014f328 dblp:conf/fedcsis/KorbelWSP14 fatcat:3zsrbyc5ejg2bdo4f2g3u4i3bu

Multimodal Approaches for Indoor Localization for Ambient Assisted Living in Smart Homes

Nirmalya Thakur, Chia Y. Han
2021 Information  
This work makes multiple scientific contributions to the field of Indoor Localization for Ambient Assisted Living in Smart Homes.  ...  indoor location in a specific 'activity-based zone' during Activities of Daily Living.  ...  We did not develop the RapidMiner "process" by using the Random Forest approach in this section as we had already developed the same in Figure 12 and discussed its performance characteristics in terms  ... 
doi:10.3390/info12030114 fatcat:ankfyi77inhqxg22fra3rcgzxq

Random Forest and Interpolation Techniques for Fingerprint-based Indoor Positioning System in Un-ideal Environment

Dwi Joko Suroso, Alvin S. H. Rudianto, Muhammad Arifin, Singgih Hawibowo
2021 International Journal of Computing and Digital Systems  
We conducted a measurement campaign in an unideal indoor environment to see how far our proposed method can still handle the fluctuated values of RSSI.  ...  However, in almost all scenarios, the Random Forest can better perform MED in terms of decreasing the maximum estimated error.  ...  Received Signal Strength Indicator (RSSI) In a simple indoor localization/IPS, we often find the straightforward use of RSSI.  ... 
doi:10.12785/ijcds/100166 fatcat:25p4wdsavnbyxowgcdqi2rqppu

Received Signal Strength Fingerprinting-Based Indoor Location Estimation Employing Machine Learning

Ladislav Polak, Stanislav Rozum, Martin Slanina, Tomas Bravenec, Tomas Fryza, Aggelos Pikrakis
2021 Sensors  
The fingerprinting technique is a popular approach to reveal location of persons, instruments or devices in an indoor environment.  ...  In our scenario, the most promising machine learning technique being the Random Forest with classification accuracy over 99%.  ...  Introduction Indoor localization, as defined in [1] , is a process that is employed to obtain a user or device location in an indoor environment.  ... 
doi:10.3390/s21134605 fatcat:2tnufajpqrdgblcqemdty3pj24

A Generalized Model for Indoor Location Estimation Using Environmental Sound from Human Activity Recognition

Carlos Galván-Tejada, F. López-Monteagudo, O. Alonso-González, Jorge Galván-Tejada, José Celaya-Padilla, Hamurabi Gamboa-Rosales, Rafael Magallanes-Quintanar, Laura Zanella-Calzada
2018 ISPRS International Journal of Geo-Information  
A feature extraction approach to the sound signal is performed to feed a random forest algorithm in order to generate a model to estimate the location of the user.  ...  Experimental results show that developing an indoor location system (ILS) that uses contextual information from human activities (identified with data provided from the environmental sound) can achieve  ...  activity that is done in a certain room in an indoor environment.  ... 
doi:10.3390/ijgi7030081 fatcat:jky5x337gfdnlafih7jdhl4dbq

A Survey of Machine Learning for Indoor Positioning

Ahasanun Nessa, Bhagawat Adhikari, Fatima Hussain, Xavier Fernando
2020 IEEE Access  
Supervised Learning: Random Forest Random forest algorithm is a collection of number of Decision Trees and each tree in the forest gives a classification.  ...  In this case Random Forest can be used to eliminate the over-fitting problem.  ... 
doi:10.1109/access.2020.3039271 fatcat:htzgf2mwp5gmjbx3cczg5rl7ru

Towards Indoor Transportation Mode Detection Using Mobile Sensing [chapter]

Thor Siiger Prentow, Henrik Blunck, Mikkel Baun Kjærgaard, Allan Stisen
2015 Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering  
used indoors, especially in large building complexes.  ...  In addition, we explore methods for transportation mode detection we deem suitable in indoor settings, and we perform an extensive real-world evaluation of (combinations of) such methods at a large hospital  ...  We see that random forests seem to be superior to support vector machines and K-nearest neighbour classifiers in this setup, while all are superior to the C4.5 classifiers.  ... 
doi:10.1007/978-3-319-29003-4_15 fatcat:vwxy44dm45djzcz243gkomot4i

Visible light communication-based monitoring for indoor environments using unsupervised learning [article]

Mehmet C. Ilter, Alexis A. Dowhuszko, Jyri Hämäläinen, Risto Wichman
2021 arXiv   pre-print
This way, different clusters can be created on the collected CSI data, which could be then mapped into relevant events to-be-monitored in the indoor environments, such as the presence of a new object in  ...  When compared to supervised learning algorithms, the proposed approach does not need to add tags in the training data, simplifying notably the implementation of the machine learning classifier.  ...  For example, the authors of [7] , [8] presented an object identification and object localization algorithm, respectively, using a Random Forest Classifier that was trained using the Channel State Information  ... 
arXiv:2101.10838v1 fatcat:4jkwwknncjcexngb2k3ni54yca

A Model to Support Fluid Transitions between Environments for Mobile Augmented Reality Applications

Tiago Davi Oliveira de Araújo, Carlos Gustavo Resque dos Santos, Rodrigo Santos do Amor Divino Lima, Bianchi Serique Meiguins
2019 Sensors  
To assess the model, we developed a MAR application and conducted a navigation test with volunteers to validate transitions between outdoor and indoor environments, followed by a short interview.  ...  The transition between environments is hard because there are currently no localization techniques that work well in any place: sensor-based applications can be harmed by obstacles that hamper sensor communication  ...  In all three cases, the Random Forest classifier performed better, even with a low number of random trees (20) , thus we chose this classifier in the implementation of the application prototype.  ... 
doi:10.3390/s19194254 fatcat:skt4bbj6ljc2jbjxhcbanfxxta

MapSense

Mohamed Abdelaal, Suriya Sekar, Frank Dürr, Kurt Rothermel, Susanne Becker, Dieter Fritsch
2020 ACM Transactions on Internet of Things  
To model the indoor environments in a proper manner, novel data acquisition concepts and data modeling algorithms have been devised to meet the requirements of indoor spatial applications.  ...  Indoor environments de facto differ from outdoor spaces in two aspects: spaces are smaller and there are many structural objects such as walls, doors, and furniture.  ...  Moreover, ray tracing and random forest methods are used for detecting and classifying other objects, e.g. doors, tables, and chairs. (4) We define a formal grammar through which, in a second step, the  ... 
doi:10.1145/3379342 dblp:journals/tiot/AbdelaalSDRBF20 fatcat:yovxy2thebd3ncmsg5n4xpsyee

Hierarchical Multi-Building And Multi-Floor Indoor Localization Based On Recurrent Neural Networks [article]

Abdalla Elmokhtar Ahmed Elesawi, Kyeong Soo Kim
2021 arXiv   pre-print
In such an environment, indoor localization becomes one of the essential services, and the indoor localization systems to be deployed should be scalable enough to cover the expected expansion of those  ...  In this paper, we propose hierarchical multi-building and multi-floor indoor localization based on a recurrent neural network (RNN) using Wi-Fi fingerprinting, eliminating the need of complicated data  ...  ACKNOWLEDGMENT This work was supported in part by Postgraduate Research Scholarships (under Grant PGRS1912001) and Key Program Special Fund (under Grant KSF-E-25) of Xi'an Jiaotong-Liverpool University  ... 
arXiv:2112.12478v1 fatcat:a6r3ujnrmbd75mmaihxvurisle
« Previous Showing results 1 — 15 out of 2,484 results