Literatura científica selecionada sobre o tema "Received signal power (RSS)"
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Artigos de revistas sobre o assunto "Received signal power (RSS)"
Xu, Yihuai, Xin Hu, Yimao Sun, Yanbing Yang, Lei Zhang, Xiong Deng e Liangyin Chen. "High-Accuracy Height-Independent 3D VLP Based on Received Signal Strength Ratio". Sensors 22, n.º 19 (21 de setembro de 2022): 7165. http://dx.doi.org/10.3390/s22197165.
Texto completo da fonteRaes, Willem, Nicolas Knudde, Jorik De Bruycker, Tom Dhaene e Nobby Stevens. "Experimental Evaluation of Machine Learning Methods for Robust Received Signal Strength-Based Visible Light Positioning". Sensors 20, n.º 21 (27 de outubro de 2020): 6109. http://dx.doi.org/10.3390/s20216109.
Texto completo da fonteVeselý, Jiří, Petr Hubáček e Jana Olivová. "The Power Gain Difference Method Analysis". Sensors 20, n.º 11 (26 de maio de 2020): 3018. http://dx.doi.org/10.3390/s20113018.
Texto completo da fonteMartínez-Ciro, Roger Alexander, Francisco Eugenio López-Giraldo, José Martín Luna-Rivera e Atziry Magaly Ramírez-Aguilera. "An Indoor Visible Light Positioning System for Multi-Cell Networks". Photonics 9, n.º 3 (1 de março de 2022): 146. http://dx.doi.org/10.3390/photonics9030146.
Texto completo da fonteAhmad, A., P. Claudio, A. Alizadeh Naeini e G. Sohn. "WI-FI RSS FINGERPRINTING FOR INDOOR LOCALIZATION USING AUGMENTED REALITY". ISPRS Annals of Photogrammetry, Remote Sensing and Spatial Information Sciences V-4-2020 (3 de agosto de 2020): 57–64. http://dx.doi.org/10.5194/isprs-annals-v-4-2020-57-2020.
Texto completo da fonteMuttair, Karrar Shakir, Mahmood Farhan Mosleh e Oras Ahmed Shareef. "Optimal transmitter location using multi-scale algorithm based on real measurement for outdoor communication". IAES International Journal of Artificial Intelligence (IJ-AI) 11, n.º 4 (1 de dezembro de 2022): 1384. http://dx.doi.org/10.11591/ijai.v11.i4.pp1384-1394.
Texto completo da fonteSergi, Simone, Fabrizio Pancaldi e Giorgio M. Vitetta. "Cluster-Based Ranging for Accurate Localization in Wireless Sensor Networks". International Journal of Navigation and Observation 2010 (29 de julho de 2010): 1–11. http://dx.doi.org/10.1155/2010/460860.
Texto completo da fonteRzymowski, Mateusz, Krzysztof Nyka e Lukasz Kulas. "Direction of Arrival Estimation Based on Received Signal Strength Using Two-Row Electronically Steerable Parasitic Array Radiator Antenna". Sensors 22, n.º 5 (5 de março de 2022): 2034. http://dx.doi.org/10.3390/s22052034.
Texto completo da fonteBi, Jingxue, Yunjia Wang, Xin Li, Hongxia Qi, Hongji Cao e Shenglei Xu. "An Adaptive Weighted KNN Positioning Method Based on Omnidirectional Fingerprint Database and Twice Affinity Propagation Clustering". Sensors 18, n.º 8 (1 de agosto de 2018): 2502. http://dx.doi.org/10.3390/s18082502.
Texto completo da fontePerihanoglu, G. M., e H. Karaman. "SPATIAL PREDICTION OF RECEIVED SIGNAL STRENGTH FOR CELLULAR COMMUNICATION USING SUPPORT VECTOR MACHINE AND K-NEAREST NEIGHBOURS REGRESSION". International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLVIII-4/W9-2024 (8 de março de 2024): 291–97. http://dx.doi.org/10.5194/isprs-archives-xlviii-4-w9-2024-291-2024.
Texto completo da fonteTeses / dissertações sobre o assunto "Received signal power (RSS)"
Obeidat, Huthaifa A. N. "Investigation of Indoor Propagation Algorithms for Localization Purposes: Simulation and Measurements of Indoor Propagation Algorithms for Localization Applications using Wall Correction Factors, Local Mean Power Estimation and Ray Tracing Validations". Thesis, University of Bradford, 2018. http://hdl.handle.net/10454/17385.
Texto completo da fonteLiu, Siyang. "Efficient machine learning techniques for indoor localization in wireless communication systems". Electronic Thesis or Diss., université Paris-Saclay, 2022. http://www.theses.fr/2022UPAST188.
Texto completo da fonteWith rapid development of Internet of Things (IoT), the need of indoor location-based services such as asset management, navigation and tracking has also grown overtime. For indoor localization, navigation satellite systems such as GPS has limited usage since a direct line-of-sight to satellites is unavailable.Various solutions have been proposed for indoor localization such as trilateration, triangulation, dead reckoning, but their performance is limited by indoor channel conditions, such as shadowing and multipath fading. By exploiting the mapping between wireless signal feature measurements and positions, fingerprinting based methods have shown the potential to provide good localization performance with sufficient data. However, indoor localization still faces challenges like scalability, cost and complexity, privacy, etc.The focus of this thesis is to improve efficiency of indoor localization using machine learning techniques. We divide the localization process into two phases: offline radio mapping phase and online localization phase. During the offline phase, we introduce dataset analysis as an intermediate step between dataset creation and localization. We propose two numerical dataset quality indicators which can provide feedback to improve the radio map. Moreover, feature extraction and dataset processing using machine learning tools are integrated to improve efficiency by reducing the data size and computation complexity while improving localization performance. We propose a k-means based radio mapping method which can reduce the number of fingerprints by over % without losing useful information in the radio map or degrading localization performance. By exploring the hierarchical nature of large datasets, we propose a hierarchical feature extraction method which can further reduce localization complexity without compromising localization performance.For the online localization phase, we explore both traditional machine learning and deep learning. We first introduce several traditional machine learning methods and compare the localization performance on public datasets. We aim to improve localization performance of traditional methods.To cope with privacy and complexity issue, we introduce federated learning framework for indoor localization problem. In this framework, the clients share only their local models to the central server instead of the fingerprinting data. We first compare the performance with federated and centralized learning. Then, we further study the impact on different client numbers and local data size. To reduce communication cost during the training process, we evaluate different measures including client selection, gradient accumulation and model compression. An efficient compression method is proposed to compress local models which can reduce the uplink communication cost by 91.5% without compromising localization performance. At last, we consider a limit on uplink capacity and evaluate different compression strategies
Zegeye, Wondimu K., e Seifemichael B. Amsalu. "Minimum Euclidean Distance Algorithm for Indoor WiFi Received Signal Strength (RSS) Fingerprinting". International Foundation for Telemetering, 2016. http://hdl.handle.net/10150/624190.
Texto completo da fonteLi, Kejiong. "Indoor and outdoor location estimation in large areas using received signal strength". Thesis, Queen Mary, University of London, 2013. http://qmro.qmul.ac.uk/xmlui/handle/123456789/8537.
Texto completo da fonteGalbraith, Andrew. "Multilateration in Direct ShortRange Communications Networks : Utilising Basic Safety Messages and Received Signal Strength Ranging". Thesis, Linnéuniversitetet, Institutionen för datavetenskap och medieteknik (DM), 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:lnu:diva-97682.
Texto completo da fonteTadokoro, Yukihiro, Hiraku Okada, Takaya Yamazato e Masaaki Katayama. "The Optimum Received Signal-Power Distribution for CDMA Packet Communication Systems Employing Successive Interference Cancellation". IEEE, 2004. http://hdl.handle.net/2237/7763.
Texto completo da fonteSundberg, Simon. "Localization of eNodeBs with a Large Set of Measurements from Train Routers". Thesis, Karlstads universitet, Institutionen för matematik och datavetenskap (from 2013), 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:kau:diva-75456.
Texto completo da fonteAhmed, Rana R. "Performance Modelling and Analysis of a New CoMP-based Handover Scheme for Next Generation Wireless Networks. Performance Modelling and Analysis for the Design and Development of a New Handover Scheme for Cell Edge Users in Next Generation Wireless Networks (NGWNs) Based on the Coordinated Multi-Point (CoMP) Joint Transmission (JT) Technique". Thesis, University of Bradford, 2017. http://hdl.handle.net/10454/16785.
Texto completo da fonteZang, Yuzhang. "UWB Motion and Micro-Gesture Detection -Applications to interactive electronic gaming and remote sensing". Digital WPI, 2016. https://digitalcommons.wpi.edu/etd-theses/1241.
Texto completo da fonteFekih, Hassen Wiem. "A ubiquitous navigation service on smartphones". Thesis, Lyon, 2020. http://www.theses.fr/2020LYSEI006.
Texto completo da fontePedestrian navigation is a growing research field, which aims at developing services and applications that ensure the continuous positioning and navigation of people inside and outside covered areas (e.g. buildings). In this thesis, we propose a ubiquitous pedestrian navigation service based on user preferences and the most suitable efficient available positioning technology (e.g. WiFi, GNSS). Our main objective is to estimate continuously the position of a pedestrian carrying a smartphone equipped with a variety of technologies and sensors. First, we propose a novel positioning technology selection algorithm, called UCOSA for the complete ubiquitous navigation service in indoor and outdoor environments. UCOSA algorithm starts by inferring the need of a handover between the available positioning technologies on the overlapped coverage areas using fuzzy logic technique. If a handover process is required, a score is calculated for each captured Radio Frequency (RF) positioning technology. The score function consists of two parts: the first part represents the user preferences weights computed based on the Analytic Hierarchy Process (AHP). Whereas, the second part provides the user requirements (normalized values). UCOSA algorithm also integrates the Pedestrian Dead Reckoning (PDR) positioning technique through the navigation process to enhance the estimation of the smartphone's position. Second, we focus on the RSS fingerprinting positioning technique as it is the most widely used technique, which principle is to return the smartphone's position by comparing the real time recorded RSS values with the radiomap (i.e. a database of previous stored RSS values). Most of radiomap are organized in a grid, formed or Reference Point (RP): we propose a new design of radiomap which complements the grid with other RPs located at the center of gravity of each grid square. Third, we address the challenge of constructing a graph for a multi-floor building. We propose an algorithm that starts by creating the horizontal graph of each floor, separately, and then, adds vertical links between the different floors. Finally, we implement a novel algorithm, called SIONA that calculates and displays in a continuous manner the pathway between two distinct points being located indoor or outdoor. We conduct several real experiments inside the campus of the University of Passau in Germany to evaluate the performance of the proposed algorithms. They yield promising results in terms of continuity and accuracy (around 1.8 m indoor) of navigation service
Capítulos de livros sobre o assunto "Received signal power (RSS)"
Arthi, R., Digvijay Singh Rawat, Abhiviraj Pillai, Yash Nair e S. S. Kausik. "Analysis of Indoor Localization Algorithm for WiFi Using Received Signal Strength". In Advances in Power Systems and Energy Management, 423–31. Singapore: Springer Singapore, 2021. http://dx.doi.org/10.1007/978-981-15-7504-4_40.
Texto completo da fonteLei, Qianqian, Erhu Zhao, Min Lin e Yin Shi. "A Low Power Received Signal Strength Indicator for Short Distance Receiver". In Lecture Notes in Electrical Engineering, 755–63. Cham: Springer International Publishing, 2013. http://dx.doi.org/10.1007/978-3-319-01273-5_84.
Texto completo da fonteHan, Mingzhi, e Yongyi Mao. "An Indoor Floor Location Method Based on Minimum Received Signal Strength (RSS) Dynamic Compensation and Multi Label Classification". In Advances in Natural Computation, Fuzzy Systems and Knowledge Discovery, 584–91. Cham: Springer International Publishing, 2023. http://dx.doi.org/10.1007/978-3-031-20738-9_67.
Texto completo da fonteSharma, Prachi, e Rajendra Kumar Dwivedi. "Detection of High Transmission Power Based Wormhole Attack Using Received Signal Strength Indicator (RSSI)". In Communications in Computer and Information Science, 142–52. Singapore: Springer Singapore, 2018. http://dx.doi.org/10.1007/978-981-13-2372-0_13.
Texto completo da fonteElmokashfi, Ahmed, e Amund Kvalbein. "Chapter 5 Using Bluetooth for contact tracing". In Simula SpringerBriefs on Computing, 81–98. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-05466-2_5.
Texto completo da fonteFuada, Syifaul, Mariella Särestöniemi, Marcos Katz, Simone Soderi e Matti Hämäläinen. "Experimental Study of In-Body Devices Misalignment Impact on Light-Based In-Body Communications". In Communications in Computer and Information Science, 451–66. Cham: Springer Nature Switzerland, 2024. http://dx.doi.org/10.1007/978-3-031-59091-7_30.
Texto completo da fontePatwari, Neal, e Piyush Agrawal. "Calibration and Measurement of Signal Strength for Sensor Localization". In Localization Algorithms and Strategies for Wireless Sensor Networks, 122–45. IGI Global, 2009. http://dx.doi.org/10.4018/978-1-60566-396-8.ch005.
Texto completo da fonteFang, Shih-Hau. "Robustness in Fingerprinting-Based Indoor Positioning Systems". In Advances in Wireless Technologies and Telecommunication, 88–141. IGI Global, 2018. http://dx.doi.org/10.4018/978-1-5225-3528-7.ch003.
Texto completo da fonteBri, Diana, Jaime Lloret, Carlos Turro e Miguel Garcia. "Measuring Specific Absorption Rate by using Standard Communications Equipment". In Advances in Healthcare Information Systems and Administration, 81–111. IGI Global, 2012. http://dx.doi.org/10.4018/978-1-4666-0888-7.ch004.
Texto completo da fonteShahra, Essa Qasem, Tarek Rahil Sheltami e Elhadi M. Shakshuki. "A Comparative Study of Range-Free and Range-Based Localization Protocols for Wireless Sensor Network". In Sensor Technology, 1522–37. IGI Global, 2020. http://dx.doi.org/10.4018/978-1-7998-2454-1.ch071.
Texto completo da fonteTrabalhos de conferências sobre o assunto "Received signal power (RSS)"
Kompostiotis, Dimitris, Dimitris Vordonis e Vassilis Paliouras. "Received Power Maximization with Practical Phase-Dependent Amplitude Response in RIS-Aided OFDM Wireless Communications". In ICASSP 2023 - 2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). IEEE, 2023. http://dx.doi.org/10.1109/icassp49357.2023.10095408.
Texto completo da fonteNilas, Phongchai, e Burin Baitoei. "Indoor Positioning System Based on Received Signal Strength: RSS". In 3rd Annual International Conference on Advanced Topics in Artificial Intelligence. Global Science Technology Forum, 2012. http://dx.doi.org/10.5176/2251-2179_atai12.19.
Texto completo da fonteChandra, K. Ramesh, M. V. Pathi Amudalapalli, N. V. Satyanarayana e Prudhvi Raj Budumuru. "Received Signal Strength (RSS) Based Channel Modelling, Localization and Tracking". In 2021 2nd International Conference on Advances in Computing, Communication, Embedded and Secure Systems (ACCESS). IEEE, 2021. http://dx.doi.org/10.1109/access51619.2021.9563331.
Texto completo da fonteMailaender, Laurence. "Geolocation Bounds for Received Signal Strength (RSS) in Correlated Shadow Fading". In 2011 IEEE Vehicular Technology Conference (VTC Fall). IEEE, 2011. http://dx.doi.org/10.1109/vetecf.2011.6092847.
Texto completo da fonteMailaender, Laurence. "On the CRLB scaling law for Received Signal Strength (RSS) geolocation". In 2011 45th Annual Conference on Information Sciences and Systems (CISS). IEEE, 2011. http://dx.doi.org/10.1109/ciss.2011.5766210.
Texto completo da fonteSuwadi, Mike Yuliana e Wirawan. "Polynomial Tope (PT) Key Group Generation Based Received Signal Strength (RSS)". In 2021 4th International Seminar on Research of Information Technology and Intelligent Systems (ISRITI). IEEE, 2021. http://dx.doi.org/10.1109/isriti54043.2021.9702835.
Texto completo da fontePajovic, Milutin, Philip Orlik, Toshiaki Koike-Akino, Kyeong Jin Kim, Hideto Aikawa e Toshinori Hori. "An Unsupervised Indoor Localization Method Based on Received Signal Strength (RSS) Measurements". In GLOBECOM 2015 - 2015 IEEE Global Communications Conference. IEEE, 2014. http://dx.doi.org/10.1109/glocom.2014.7417708.
Texto completo da fonteHashim, M. S. M., M. Azlan Shah Shahrol Aman, Loke Kah Wai, Teh Jia Yap e M. Juhairi Aziz Safar. "Indoor localization approach based on received signal strength (RSS) and trilateration technique". In INTERNATIONAL CONFERENCE ON MATHEMATICS, ENGINEERING AND INDUSTRIAL APPLICATIONS 2016 (ICoMEIA2016): Proceedings of the 2nd International Conference on Mathematics, Engineering and Industrial Applications 2016. Author(s), 2016. http://dx.doi.org/10.1063/1.4965148.
Texto completo da fontePajovic, Milutin, Philip Orlik, Toshiaki Koike-Akino, Kyeong Jin Kim, Hideto Aikawa e Toshinori Hori. "An Unsupervised Indoor Localization Method Based on Received Signal Strength (RSS) Measurements". In GLOBECOM 2015 - 2015 IEEE Global Communications Conference. IEEE, 2015. http://dx.doi.org/10.1109/glocom.2015.7417708.
Texto completo da fonteWang, Sichun, Robert Inkol e Brad R. Jackson. "Relationship between the maximum likelihood emitter location estimators based on received signal strength (RSS) and received signal strength difference (RSSD)". In 2012 26th Biennial Symposium on Communications (QBSC). IEEE, 2012. http://dx.doi.org/10.1109/qbsc.2012.6221353.
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