Nicholas Corrêa, Julio Cesar Marques de Lima, T. Russomano, M. A. D. Santos
Introduction: Skateboarding is one of the most popular cultures in Brazil, with more than 8.5 million skateboarders. Nowadays, the discipline of street skating has gained recognition among other more classical sports and awaits its debut at the Tokyo 2020 Summer Olympic Games. This study aimed to explore the state-of-the-art for inertial measurement unit (IMU) use in skateboarding trick detection, and to develop new classification methods using supervised machine learning and artificial neural networks (ANN). Methods: State-of-the-art knowledge regarding motion detection in skateboarding was used to generate 543 artificial acceleration signals through signal modeling, corresponding to 181 flat ground tricks divided into five classes (NOLLIE, NSHOV, FLIP, SHOV, OLLIE). The classifier consisted of a multilayer feed-forward neural network created with three layers and a supervised learning algorithm (backpropagation). Results: The use of ANNs trained specifically for each measured axis of acceleration resulted in error percentages inferior to 0.05%, with a computational efficiency that makes real-time application possible. Conclusion: Machine learning can be a useful technique for classifying skateboarding flat ground tricks, assuming that the classifiers are properly constructed and trained, and the acceleration signals are preprocessed correctly.
{"title":"Development of a skateboarding trick classifier using accelerometry and machine learning","authors":"Nicholas Corrêa, Julio Cesar Marques de Lima, T. Russomano, M. A. D. Santos","doi":"10.1590/2446-4740.04717","DOIUrl":"https://doi.org/10.1590/2446-4740.04717","url":null,"abstract":"Introduction: Skateboarding is one of the most popular cultures in Brazil, with more than 8.5 million skateboarders. Nowadays, the discipline of street skating has gained recognition among other more classical sports and awaits its debut at the Tokyo 2020 Summer Olympic Games. This study aimed to explore the state-of-the-art for inertial measurement unit (IMU) use in skateboarding trick detection, and to develop new classification methods using supervised machine learning and artificial neural networks (ANN). Methods: State-of-the-art knowledge regarding motion detection in skateboarding was used to generate 543 artificial acceleration signals through signal modeling, corresponding to 181 flat ground tricks divided into five classes (NOLLIE, NSHOV, FLIP, SHOV, OLLIE). The classifier consisted of a multilayer feed-forward neural network created with three layers and a supervised learning algorithm (backpropagation). Results: The use of ANNs trained specifically for each measured axis of acceleration resulted in error percentages inferior to 0.05%, with a computational efficiency that makes real-time application possible. Conclusion: Machine learning can be a useful technique for classifying skateboarding flat ground tricks, assuming that the classifiers are properly constructed and trained, and the acceleration signals are preprocessed correctly.","PeriodicalId":8487,"journal":{"name":"arXiv: Signal Processing","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2017-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"77145887","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2017-09-12DOI: 10.15406/jabb.2017.03.00086
M. K. Azad, Amirtahà Taebi, Joseph H Mansy, H. Mansy
Background: Pulmonary auscultation is a common tool for diagnosing various respiratory diseases. Previous studies have documented many details of pulmonary sounds in humans. However, information on sound generation and pressure loss inside animal airways is scarce. Since the morphology of animal airways can be significantly different from human, the characteristics of pulmonary sounds and pressure loss inside animal airways can be different. Objective: The objective of this study is to investigate the sound and static pressure loss measured at the trachea of a miniature pig airway tree model based on the geometric details extracted from physical measurements. Methods: In the current study, static pressure loss and sound generation measured in the trachea was documented at different flow rates of a miniature pig airway tree. Results: Results showed that the static pressure and the amplitude of the recorded sound at the trachea increased as the flow rate increased. The dominant frequency was found to be around 1840-1870 Hz for flow rates of 0.2-0.55 lit/s. Conclusion: The results suggested that the dominant frequency of the measured sounds remained similar for flow rates from 0.20 to 0.55 lit/s. Further investigation is needed to study sound generation under different inlet flow and pulsatile flow conditions.
{"title":"Pressure Loss and Sound Generated In a Miniature Pig Airway Tree Model","authors":"M. K. Azad, Amirtahà Taebi, Joseph H Mansy, H. Mansy","doi":"10.15406/jabb.2017.03.00086","DOIUrl":"https://doi.org/10.15406/jabb.2017.03.00086","url":null,"abstract":"Background: Pulmonary auscultation is a common tool for diagnosing various respiratory diseases. Previous studies have documented many details of pulmonary sounds in humans. However, information on sound generation and pressure loss inside animal airways is scarce. Since the morphology of animal airways can be significantly different from human, the characteristics of pulmonary sounds and pressure loss inside animal airways can be different. Objective: The objective of this study is to investigate the sound and static pressure loss measured at the trachea of a miniature pig airway tree model based on the geometric details extracted from physical measurements. Methods: In the current study, static pressure loss and sound generation measured in the trachea was documented at different flow rates of a miniature pig airway tree. Results: Results showed that the static pressure and the amplitude of the recorded sound at the trachea increased as the flow rate increased. The dominant frequency was found to be around 1840-1870 Hz for flow rates of 0.2-0.55 lit/s. Conclusion: The results suggested that the dominant frequency of the measured sounds remained similar for flow rates from 0.20 to 0.55 lit/s. Further investigation is needed to study sound generation under different inlet flow and pulsatile flow conditions.","PeriodicalId":8487,"journal":{"name":"arXiv: Signal Processing","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2017-09-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"84089898","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
L-band Digital Aeronautical Communication System type-1 (L-DACS1) is an emerging standard that aims at enhancing air traffic management (ATM) by transitioning the traditional analog aeronautical communication systems to the superior and highly efficient digital domain. L-DACS1 employs modern and efficient orthogonal frequency division multiplexing (OFDM) modulation technique to achieve more efficient and higher data rate in comparison to the existing aeronautical communication systems. However, the performance of OFDM systems is very sensitive to synchronization errors. L-DACS1 transmission is in the L-band aeronautical channels that suffer from large interference and large Doppler shifts, which makes the synchronization for L-DACS more challenging. This paper proposes a novel computationally efficient synchronization method for L-DACS1 systems that offers robust performance. Through simulation, the proposed method is shown to provide accurate symbol timing offset (STO) estimation as well as fractional carrier frequency offset (CFO) estimation in a range of aeronautical channels. In particular, it can yield excellent synchronization performance in the face of a large carrier frequency offset.
{"title":"An Efficient Data-aided Synchronization in L-DACS1 for Aeronautical Communications","authors":"T. Pham, A. P. Vinod, A. Madhukumar","doi":"10.1145/3089871.3089892","DOIUrl":"https://doi.org/10.1145/3089871.3089892","url":null,"abstract":"L-band Digital Aeronautical Communication System type-1 (L-DACS1) is an emerging standard that aims at enhancing air traffic management (ATM) by transitioning the traditional analog aeronautical communication systems to the superior and highly efficient digital domain. L-DACS1 employs modern and efficient orthogonal frequency division multiplexing (OFDM) modulation technique to achieve more efficient and higher data rate in comparison to the existing aeronautical communication systems. However, the performance of OFDM systems is very sensitive to synchronization errors. L-DACS1 transmission is in the L-band aeronautical channels that suffer from large interference and large Doppler shifts, which makes the synchronization for L-DACS more challenging. This paper proposes a novel computationally efficient synchronization method for L-DACS1 systems that offers robust performance. Through simulation, the proposed method is shown to provide accurate symbol timing offset (STO) estimation as well as fractional carrier frequency offset (CFO) estimation in a range of aeronautical channels. In particular, it can yield excellent synchronization performance in the face of a large carrier frequency offset.","PeriodicalId":8487,"journal":{"name":"arXiv: Signal Processing","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2017-05-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"90240658","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
J. Sidorenko, N. Scherer-Negenborn, Michael Arens, E. Michaelsen
In the field of localization the linear least square solution is frequently used. This solution is compared to nonlinear solvers more effected by noise, but able to provide a position estimation without the knowledge of any starting condition. The linear least square solution is able to minimize Gaussian noise by solving an overdetermined equation with the MoorePenrose pseudoinverse. Unfortunately this solution fails if it comes to non Gaussian noise. This publication presents a direct solution which is able to use prefiltered data for the LPM (RNL) equation. The used input for the linear position estimation will not be the raw data but over the time filtered data, for this reason this solution will be called direct solution. It will be shown that the presented symmetrical direct solution is superior to non symmetrical direct solution and especially to the not prefiltered linear least square solution.
{"title":"Improved linear direct solution for asynchronous radio network localization (RNL)","authors":"J. Sidorenko, N. Scherer-Negenborn, Michael Arens, E. Michaelsen","doi":"10.33012/2017.15036","DOIUrl":"https://doi.org/10.33012/2017.15036","url":null,"abstract":"In the field of localization the linear least square solution is frequently used. This solution is compared to nonlinear solvers more effected by noise, but able to provide a position estimation without the knowledge of any starting condition. The linear least square solution is able to minimize Gaussian noise by solving an overdetermined equation with the MoorePenrose pseudoinverse. Unfortunately this solution fails if it comes to non Gaussian noise. This publication presents a direct solution which is able to use prefiltered data for the LPM (RNL) equation. The used input for the linear position estimation will not be the raw data but over the time filtered data, for this reason this solution will be called direct solution. It will be shown that the presented symmetrical direct solution is superior to non symmetrical direct solution and especially to the not prefiltered linear least square solution.","PeriodicalId":8487,"journal":{"name":"arXiv: Signal Processing","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2017-05-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"75271112","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
This paper presents a new method for detecting and classifying a predefined set of hand gestures using a single transmitter and a single receiver utilizing a linearly frequency modulated ultrasonic signal. Gestures are identified based on estimated range and received signal strength (RSS) of reflected signal from the hand. Support Vector Machine (SVM) was used for gesture detection and classification. The system was tested using experimental setup and achieved an average accuracy of 88%.
{"title":"Hand Gesture Recognition Using Ultrasonic Waves","authors":"M. Alsharif, M. Saad, T. Al-Naffouri","doi":"10.25781/KAUST-6S4B0","DOIUrl":"https://doi.org/10.25781/KAUST-6S4B0","url":null,"abstract":"This paper presents a new method for detecting and classifying a predefined set of hand gestures using a single transmitter and a single receiver utilizing a linearly frequency modulated ultrasonic signal. Gestures are identified based on estimated range and received signal strength (RSS) of reflected signal from the hand. Support Vector Machine (SVM) was used for gesture detection and classification. The system was tested using experimental setup and achieved an average accuracy of 88%.","PeriodicalId":8487,"journal":{"name":"arXiv: Signal Processing","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2017-03-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"91072905","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2016-11-02DOI: 10.1364/ACPC.2016.AS1B.3
A. Dochhan, Nicklas Eiselt, Jinlong Wei, H. Griesser, M. Eiselt, J. Olmos, I. Monroy, J. Elbers
We review three solutions for low-cost data center interconnects with a target reach of up to 80 km. Directly detected DMT, PAM-4 and multi-band CAP are promising modulation schemes, enabling 400 Gbit/s by combining eight channels of 56 Gbit/s.
{"title":"Practical Solutions for 400 Gbit/s Data Center Transmission","authors":"A. Dochhan, Nicklas Eiselt, Jinlong Wei, H. Griesser, M. Eiselt, J. Olmos, I. Monroy, J. Elbers","doi":"10.1364/ACPC.2016.AS1B.3","DOIUrl":"https://doi.org/10.1364/ACPC.2016.AS1B.3","url":null,"abstract":"We review three solutions for low-cost data center interconnects with a target reach of up to 80 km. Directly detected DMT, PAM-4 and multi-band CAP are promising modulation schemes, enabling 400 Gbit/s by combining eight channels of 56 Gbit/s.","PeriodicalId":8487,"journal":{"name":"arXiv: Signal Processing","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2016-11-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"81872632","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Free Space Optics systems (FSO) is one of the evolving wireless technologies. FSO is the only technology with highest data rates in wireless mode of operation but it suffers from bad weather conditions. In this work, analysis is carried out on FSO system having certain parameters constant using different modulation formats (i.e. RZ, NRZ, MDRZ, MODB and CSRZ). Impact of data rate, link range, input power and attenuation factor has been computed. Weather conditions are supposed to be nearly clear and suitable for FSO communication while taking attenuation factor up to 10dB/Km. Q-factor, received signal power and BER is calculated in all scenarios for obtaining an estimate of system performance. Results have shown that NRZ & RZ formats are in the lead until now with highest Q values.
{"title":"Parametric Investigation Of Different Modulation Techniques On Free Space Optical Systems","authors":"Nauman Hameed, Tayyab Mehmood, A. Qasim","doi":"10.1109/FIT.2014.11","DOIUrl":"https://doi.org/10.1109/FIT.2014.11","url":null,"abstract":"Free Space Optics systems (FSO) is one of the evolving wireless technologies. FSO is the only technology with highest data rates in wireless mode of operation but it suffers from bad weather conditions. In this work, analysis is carried out on FSO system having certain parameters constant using different modulation formats (i.e. RZ, NRZ, MDRZ, MODB and CSRZ). Impact of data rate, link range, input power and attenuation factor has been computed. Weather conditions are supposed to be nearly clear and suitable for FSO communication while taking attenuation factor up to 10dB/Km. Q-factor, received signal power and BER is calculated in all scenarios for obtaining an estimate of system performance. Results have shown that NRZ & RZ formats are in the lead until now with highest Q values.","PeriodicalId":8487,"journal":{"name":"arXiv: Signal Processing","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2014-12-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"88591464","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
京公网安备 11010802042870号
京ICP备2023020795号-1
扫码关注我们
求助内容：
应助结果提醒方式：