scholarly journals An EMD-Based Algorithm for Emboli Detection in Echo Doppler Audio Signals

Electronics ◽  
2019 ◽  
Vol 8 (8) ◽  
pp. 824 ◽  
Author(s):  
Paola Pierleoni ◽  
Lorenzo Palma ◽  
Alberto Belli ◽  
Massimo Pieri ◽  
Lorenzo Maurizi ◽  
...  
Keyword(s):  
Real Time ◽  
Vascular System ◽  
Decompression Sickness ◽  
Time Algorithm ◽  
Detection Algorithm ◽  
Risk Level ◽  
Health State ◽  
Post Surgery ◽  
Mode Decomposition ◽  
Emboli Detection

Divers’ health state after underwater activity can be assessed after the immersion using precordial echo Doppler examination. An audio analysis of the acquired signals is performed by specialist doctors to detect circulating gas bubbles in the vascular system and to evaluate the decompression sickness risk. Since on-site medical assistance cannot always be guaranteed, we propose a system for automatic emboli detection using a custom portable device connected to the echo Doppler instrument. The empirical mode decomposition method is used to develop a real-time algorithm able to automatically detect embolic events and, consequently, assess the decompression sickness risk according to the Spencer’s scale. The proposed algorithm has been tested according to an experimental protocol approved by the Divers Alert Network. It involved 30 volunteer divers and produced 37 echo Doppler files useful for the algorithm’s performances evaluation. The results obtained by the proposed emboli detection algorithm (83% sensitivity and 76% specificity) make the system particularly suitable for real-time evaluation of the decompression sickness risk level. Furthermore, the system could also be used in continuous monitoring of hospitalized patients with embolic risks such as post surgery ones.

scholarly journals Structure and performance of a real-time algorithm to detect tsunami or tsunami-like alert conditions based on sea-level records analysis

2011 ◽  
Vol 11 (5) ◽  
pp. 1499-1521 ◽  
Author(s):  
L. Bressan ◽  
S. Tinti
Keyword(s):  
Real Time ◽  
Sea Level ◽  
Tide Gauge ◽  
Sampling Rate ◽  
High Amplitude ◽  
Time Algorithm ◽  
Detection Algorithm ◽  
Detection Methods ◽  
Tsunami Waves ◽  
Single Station

Abstract. The goal of this paper is to present an original real-time algorithm devised for detection of tsunami or tsunami-like waves we call TEDA (Tsunami Early Detection Algorithm), and to introduce a methodology to evaluate its performance. TEDA works on the sea level records of a single station and implements two distinct modules running concurrently: one to assess the presence of tsunami waves ("tsunami detection") and the other to identify high-amplitude long waves ("secure detection"). Both detection methods are based on continuously updated time functions depending on a number of parameters that can be varied according to the application. In order to select the most adequate parameter setting for a given station, a methodology to evaluate TEDA performance has been devised, that is based on a number of indicators and that is simple to use. In this paper an example of TEDA application is given by using data from a tide gauge located at the Adak Island in Alaska, USA, that resulted in being quite suitable since it recorded several tsunamis in the last years using the sampling rate of 1 min.

scholarly journals An Improved Multipath Mitigation Method and Its Application in Real-Time Bridge Deformation Monitoring

2021 ◽  
Vol 13 (12) ◽  
pp. 2259
Author(s):  
Ruicheng Zhang ◽  
Chengfa Gao ◽  
Qing Zhao ◽  
Zihan Peng ◽  
Rui Shang
Keyword(s):  
Real Time ◽  
Sampling Rate ◽  
Threshold Value ◽  
Deformation Monitoring ◽  
Success Rates ◽  
Multipath Mitigation ◽  
Mode Decomposition ◽  
Observation Noise ◽  
Original Observation ◽  
Better Than

A multipath is a major error source in bridge deformation monitoring and the key to achieving millimeter-level monitoring. Although the traditional MHM (multipath hemispherical map) algorithm can be applied to multipath mitigation in real-time scenarios, accuracy needs to be further improved due to the influence of observation noise and the multipath differences between different satellites. Aiming at the insufficiency of MHM in dealing with the adverse impact of observation noise, we proposed the MHM_V model, based on Variational Mode Decomposition (VMD) and the MHM algorithm. Utilizing the VMD algorithm to extract the multipath from single-difference (SD) residuals, and according to the principle of the closest elevation and azimuth, the original observation of carrier phase in the few days following the implementation are corrected to mitigate the influence of the multipath. The MHM_V model proposed in this paper is verified and compared with the traditional MHM algorithm by using the observed data of the Forth Road Bridge with a seven day and 10 s sampling rate. The results show that the correlation coefficient of the multipath on two adjacent days was increased by about 10% after residual denoising with the VMD algorithm; the standard deviations of residual error in the L1/L2 frequencies were improved by 37.8% and 40.7%, respectively, which were better than the scores of 26.1% and 31.0% for the MHM algorithm. Taking a ratio equal to three as the threshold value, the fixed success rates of ambiguity were 88.0% without multipath mitigation and 99.4% after mitigating the multipath with MHM_V. The MHM_V algorithm can effectively improve the success rate, reliability, and convergence rate of ambiguity resolution in a bridge multipath environment and perform better than the MHM algorithm.

scholarly journals Real-time 2D–3D door detection and state classification on a low-power device

2021 ◽  
Vol 3 (5) ◽  
Author(s):  
João Gaspar Ramôa ◽  
Vasco Lopes ◽  
Luís A. Alexandre ◽  
S. Mogo
Keyword(s):  
Low Power ◽  
Real Time ◽  
Object Classification ◽  
Semantic Segmentation ◽  
Detection Algorithm ◽  
Power Device ◽  
Indoor Environments ◽  
State Classification ◽  
Segmentation Algorithms ◽  
Indoor Spaces

AbstractIn this paper, we propose three methods for door state classification with the goal to improve robot navigation in indoor spaces. These methods were also developed to be used in other areas and applications since they are not limited to door detection as other related works are. Our methods work offline, in low-powered computers as the Jetson Nano, in real-time with the ability to differentiate between open, closed and semi-open doors. We use the 3D object classification, PointNet, real-time semantic segmentation algorithms such as, FastFCN, FC-HarDNet, SegNet and BiSeNet, the object detection algorithm, DetectNet and 2D object classification networks, AlexNet and GoogleNet. We built a 3D and RGB door dataset with images from several indoor environments using a 3D Realsense camera D435. This dataset is freely available online. All methods are analysed taking into account their accuracy and the speed of the algorithm in a low powered computer. We conclude that it is possible to have a door classification algorithm running in real-time on a low-power device.

A Distributed Real-Time Algorithm for Preference-Based Agreement

2011 ◽  
Vol 44 (1) ◽  
pp. 8933-8938
Author(s):  
Daniel Zelazo ◽  
Mathias Bürger ◽  
Frank Allgöwer
Keyword(s):  
Real Time ◽  
Time Algorithm

scholarly journals Real-Time Algorithm for Versatile Displacement Sensors Based on Self-Mixing Interferometry

2016 ◽  
Vol 16 (1) ◽  
pp. 195-202 ◽  
Author(s):  
Antonio Luna Arriaga ◽  
Francis Bony ◽  
Thierry Bosch
Keyword(s):  
Real Time ◽  
Time Algorithm ◽  
Displacement Sensors
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