scholarly journals Evaluation of Different Signal Propagation Models for a Mixed Indoor-Outdoor Scenario Using Empirical Data

2015 ◽  
pp. 3-14 ◽  
Author(s):  
Oleksandr Artemenko ◽  
Adarsh Harishchandra Nayak ◽  
Sanjeeth Baptist Menezes ◽  
Andreas Mitschele-Thiel
Keyword(s):  
Empirical Data ◽  
Signal Propagation ◽  
Propagation Models

scholarly journals Evaluation of different signal propagation models for a mixed indoor-outdoor scenario using empirical data

2016 ◽  
Vol 2 (7) ◽  
pp. 151519
Author(s):  
Oleksandr Artemenko ◽  
Alina Rubina ◽  
Adarsh Harishchandra Nayak ◽  
Sanjeeth Baptist Menezes ◽  
Andreas Mitschele-Thiel
Keyword(s):  
Empirical Data ◽  
Signal Propagation ◽  
Propagation Models

scholarly journals CEnsLoc: Infrastructure-Less Indoor Localization Methodology Using GMM Clustering-Based Classification Ensembles

2018 ◽  
Vol 2018 ◽  
pp. 1-11 ◽  
Author(s):  
Beenish Ayesha Akram ◽  
Ali Hammad Akbar ◽  
Ki-Hyung Kim
Keyword(s):  
Machine Learning ◽  
Indoor Localization ◽  
Principal Component ◽  
Nearest Neighbors ◽  
Signal Propagation ◽  
Location Based Services ◽  
Radio Propagation ◽  
Propagation Models ◽  
Related Information ◽  
Real Time Applications

Indoor localization has continued to garner interest over the last decade or so, due to the fact that its realization remains a challenge. Fingerprinting-based systems are exciting because these embody signal propagation-related information intrinsically as compared to radio propagation models. Wi-Fi (an RF technology) is best suited for indoor localization because it is so widely deployed that literally, no additional infrastructure is required. Since location-based services depend on the fingerprints acquired through the underlying technology, smart mechanisms such as machine learning are increasingly being incorporated to extract intelligible information. We propose CEnsLoc, a new easy to train-and-deploy Wi-Fi localization methodology established on GMM clustering and Random Forest Ensembles (RFEs). Principal component analysis was applied for dimension reduction of raw data. Conducted experimentation demonstrates that it provides 97% accuracy for room prediction. However, artificial neural networks, k-nearest neighbors, K∗, FURIA, and DeepLearning4J-based localization solutions provided mean 85%, 91%, 90%, 92%, and 73% accuracy on our collected real-world dataset, respectively. It delivers high room-level accuracy with negligible response time, making it viable and befitted for real-time applications.

NONLINEARITY IN CICADA SOUND SIGNALS

2013 ◽  
Vol 21 (01) ◽  
pp. 1350004 ◽  
Author(s):  
KOSSI EDOH ◽  
DERKE HUGHES ◽  
RICHARD KATZ
Keyword(s):  
Partial Differential Equations ◽  
Dynamical Systems ◽  
Acoustic Signal ◽  
Volterra Equation ◽  
Burgers Equation ◽  
Signal Propagation ◽  
Acoustic Signals ◽  
Propagation Models ◽  
Nonlinear Characteristics ◽  
Nonlinear Acoustic

The nonlinearity of acoustic signals produced by male cicadas and their propagation in the atmosphere using the theory of dynamical systems and partial differential equations are explored in this paper. Previous research using a Volterra equation has shown that the signal data from the vibrations of cicada tymbals and that from the recordings of the acoustic signals about 5 inches away from the cicada exhibit some nonlinear characteristics. The experimental results shown in this paper confirm the nonlinearity of the signals farther from the cicada. A number of nonlinear acoustic signal propagation models are discussed — among them the Burgers' equation which has been implemented and whose results are quite promising.

Carbon Nanotubes From Electrodynamics to Signal Propagation Models

2017 ◽  
pp. 1-24 ◽  
Author(s):  
Antonio Maffucci ◽  
Sergey A. Maksimenko ◽  
Giovanni Miano ◽  
G.Ya. Slepyan
Keyword(s):  
Carbon Nanotubes ◽  
Signal Propagation ◽  
Propagation Models
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