Advances in PD measuring system techniques: Parallel synchronous detection, time domain and frequency domain processing for improved sensitivity

2008 ◽  
Author(s):  
S. M. Markalous ◽  
T. Strehl ◽  
E. Lemke
Keyword(s):  
Frequency Domain ◽  
Time Domain ◽  
Measuring System ◽  
Detection Time ◽  
Synchronous Detection ◽  
Frequency Domain Processing

scholarly journals Contactless Monitoring of Breathing Patterns and Respiratory Rate at the Pit of the Neck: A Single Camera Approach

2018 ◽  
Vol 2018 ◽  
pp. 1-13 ◽  
Author(s):  
Carlo Massaroni ◽  
Daniel Simões Lopes ◽  
Daniela Lo Presti ◽  
Emiliano Schena ◽  
Sergio Silvestri
Keyword(s):  
Respiratory Rate ◽  
Frequency Domain ◽  
Time Domain ◽  
Breathing Pattern ◽  
Vital Signs ◽  
Image Resolution ◽  
Measuring System ◽  
Respiratory Pattern ◽  
Breathing Patterns ◽  
Vital Signs Monitoring

Vital signs monitoring is pivotal not only in clinical settings but also in home environments. Remote monitoring devices, systems, and services are emerging as tracking vital signs must be performed on a daily basis. Different types of sensors can be used to monitor breathing patterns and respiratory rate. However, the latter remains the least measured vital sign in several scenarios due to the intrusiveness of most adopted sensors. In this paper, we propose an inexpensive, off-the-shelf, and contactless measuring system for respiration signals taking as region of interest the pit of the neck. The system analyses video recorded by a single RGB camera and extracts the respiratory pattern from intensity variations of reflected light at the level of the collar bones and above the sternum. Breath-by-breath respiratory rate is then estimated from the processed breathing pattern. In addition, the effect of image resolution on monitoring breathing patterns and respiratory rate has been investigated. The proposed system was tested on twelve healthy volunteers (males and females) during quiet breathing at different sensor resolution (i.e., HD 720, PAL, WVGA, VGA, SVGA, and NTSC). Signals collected with the proposed system have been compared against a reference signal in both the frequency domain and time domain. By using the HD 720 resolution, frequency domain analysis showed perfect agreement between average breathing frequency values gathered by the proposed measuring system and reference instrument. An average mean absolute error (MAE) of 0.55 breaths/min was assessed in breath-by-breath monitoring in the time domain, while Bland-Altman showed a bias of −0.03 ± 1.78 breaths/min. Even in the case of lower camera resolution setting (i.e., NTSC), the system demonstrated good performances (MAE of 1.53 breaths/min, bias of −0.06 ± 2.08 breaths/min) for contactless monitoring of both breathing pattern and breath-by-breath respiratory rate over time.

The Application of Adaptive Canceling in Chaos Identification

2003 ◽  
Author(s):  
Shuyong Liu ◽  
Shijian Zhu ◽  
Zhenming Liu ◽  
Weiian Qian
Keyword(s):  
Frequency Domain ◽  
Time Domain ◽  
Chaotic Signal ◽  
Lms Algorithm ◽  
Measuring System ◽  
Adaptive Technique ◽  
Time Domain Response ◽  
The Time Domain

The time domain response of a system is inevitably contaminated by noise arising from the environment as well as the measuring system itself. So an effective method is needed to reduce the noise components. The characteristic of the chaotic signal and noise in the frequency domain is analyzed. An adaptive canceling system based on the LMS algorithm is applied to process the contaminated signal. The simulation result shows that the adaptive technique can meet the goal.

JOINT INTERPRETATION OF AIRBORNE ELECTROMAGNETIC DATA IN THE TIME DOMAIN AND FREQUENCY DOMAIN

2018 ◽  
Vol 12 (7-8) ◽  
pp. 76-83
Author(s):  
E. V. KARSHAKOV ◽  
J. MOILANEN
Keyword(s):  
Fourier Transform ◽  
Frequency Domain ◽  
Time Domain ◽  
Frequency Interval ◽  
Single Spectrum ◽  
Simultaneous Inversion ◽  
Homogeneous Half Space ◽  
Time Domain Data ◽  
The Time Domain

Тhe advantage of combine processing of frequency domain and time domain data provided by the EQUATOR system is discussed. The heliborne complex has a towed transmitter, and, raised above it on the same cable a towed receiver. The excitation signal contains both pulsed and harmonic components. In fact, there are two independent transmitters operate in the system: one of them is a normal pulsed domain transmitter, with a half-sinusoidal pulse and a small "cut" on the falling edge, and the other one is a classical frequency domain transmitter at several specially selected frequencies. The received signal is first processed to a direct Fourier transform with high Q-factor detection at all significant frequencies. After that, in the spectral region, operations of converting the spectra of two sounding signals to a single spectrum of an ideal transmitter are performed. Than we do an inverse Fourier transform and return to the time domain. The detection of spectral components is done at a frequency band of several Hz, the receiver has the ability to perfectly suppress all sorts of extra-band noise. The detection bandwidth is several dozen times less the frequency interval between the harmonics, it turns out thatto achieve the same measurement quality of ground response without using out-of-band suppression you need several dozen times higher moment of airborne transmitting system. The data obtained from the model of a homogeneous half-space, a two-layered model, and a model of a horizontally layered medium is considered. A time-domain data makes it easier to detect a conductor in a relative insulator at greater depths. The data in the frequency domain gives more detailed information about subsurface. These conclusions are illustrated by the example of processing the survey data of the Republic of Rwanda in 2017. The simultaneous inversion of data in frequency domain and time domain can significantly improve the quality of interpretation.

Time‐domain and frequency‐domain analysis of acoustic scattering by spheres

1994 ◽  
Vol 95 (5) ◽  
pp. 2478-2487 ◽  
Author(s):  
Peter D. Thorne ◽  
Terry J. Brudner ◽  
Kendall R. Waters
Keyword(s):  
Frequency Domain ◽  
Time Domain ◽  
Acoustic Scattering ◽  
Domain Analysis ◽  
Frequency Domain Analysis
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