Fourier domain local narrow-band signal extraction algorithm and its application to real-time infrared gas detection

Xie, Qun; Li, Jianping; Gao, Xinliang; Jia, Jian

doi:10.1016/j.snb.2010.02.003

Cited by 9 publications

(4 citation statements)

References 10 publications

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“…The pressure sensor is a piezoresistive silicon sensor in which the piezoresistors are arranged in the Wheatstone bridge configuration to achieve higher voltage sensitivity and low temperature sensitivity [28] . Since the signals which are acquired from the sensor may be too weak and noisy, a band-pass filter amplifying circuit is designed to intensify pressure signals and filter the noise [17] .…”

Section: Methodsmentioning

confidence: 99%

Effectively Measuring Respiratory Flow With Portable Pressure Data Using Back Propagation Neural Network

Fan

Yang

Zhang

et al. 2018

IEEE J. Transl. Eng. Health Med.

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Continuous respiratory monitoring is an important tool for clinical monitoring. The most widely used flow measure device is nasal cannulae connected to a pressure transducer. However, most of these devices are not easy to carry and continue working in uncontrolled environments which is also a problem. For portable breathing equipment, due to the volume limit, the pressure signals acquired by using the airway tube may be too weak and contain some noise, leading to huge errors in respiratory flow measures. In this paper, a cost-effective portable pressure sensor-based respiratory measure device is designed. This device has a new airway tube design, which enables the pressure drop efficiently after the air flowing through the airway tube. Also, a new back propagation (BP) neural network-based algorithm is proposed to stabilize the device calibration and remove pressure signal noise. For improving the reliability and accuracy of proposed respiratory device, a through experimental evaluation and a case study of the proposed BP neural network algorithm have been carried out. The results show that giving proper parameters setting, the proposed BP neural network algorithm is capable of efficiently improving the reliability of newly designed respiratory device.

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Section: Methodsmentioning

confidence: 99%

Effectively Measuring Respiratory Flow With Portable Pressure Data Using Back Propagation Neural Network

Fan

Yang

Zhang

et al. 2018

IEEE J. Transl. Eng. Health Med.

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“…For the dual-laser OA-ICOS sensor, it is highly desirable to be able to perform real-time concentration measurements with high precision, while the main factors limiting measurement precision are the presence of system noises. These noises from practical measurements enable researchers to use various digital processing methods [25][26][27][28] to estimate the gas concentration. The exponential moving average (EMA) algorithm [29,30], as an effective digital filtering method, can filter these noises and increase the precision of the OA-ICOS sensor.…”

Section: Performance Optimization Using Exponential Moving Averagementioning

confidence: 99%

A Dual-Laser Sensor Based on Off-Axis Integrated Cavity Output Spectroscopy and Time-Division Multiplexing Method

Wang

Shao

Chen

et al. 2020

Sensors

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In this article, a compact dual-laser sensor based on an off-axis integrated-cavity output spectroscopy and time-division multiplexing method is reported. A complete dual-channel optical structure is developed and integrated on an optical cavity, which allows two distributed feedback (DFB) lasers operating at wavelengths of 1603 nm and 1651 nm to measure the concentration of CO2 and CH4, simultaneously. Performances of the dual-laser sensor are experimentally evaluated by using standard air (with a mixture of CO2 and CH4). The limit of detection (LoD) is 0.271 ppm and 1.743 ppb at a 20 s for CO2 and CH4, respectively, and the noise equivalent absorption sensitivities are 2.68 × 10−10 cm−1 Hz−1/2 and 3.88 × 10−10 cm−1 Hz−1/2, respectively. Together with a commercial instrument, the dual-laser sensor is used to measure CO2 and CH4 concentration over 120 h and verify the regular operation of the sensor for the detection of ambient air. Furthermore, a first-order exponential moving average algorithm is implemented as an effective digital filtering method to estimate the gas concentration.

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“…However, weak signal is vulnerable to interference by circuit. In order to improve the stability of the output signal, we adopt low frequency electric modulation techniques [ 13 , 14 ]. The data obtained by multiple experiments have proven that the modulation parameters T = 180 ms and D = 50% and they were adopted in the system to eliminate the fluctuations, as shown in Figure 4 [ 15 ].…”

Section: System Hardwarementioning

confidence: 99%

A Real-Time Monitoring System of Industry Carbon Monoxide Based on Wireless Sensor Networks

Yang

Zhou

et al. 2015

Sensors

108

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Carbon monoxide (CO) burns or explodes at over-standard concentration. Hence, in this paper, a Wifi-based, real-time monitoring of a CO system is proposed for application in the construction industry, in which a sensor measuring node is designed by low-frequency modulation method to acquire CO concentration reliably, and a digital filtering method is adopted for noise filtering. According to the triangulation, the Wifi network is constructed to transmit information and determine the position of nodes. The measured data are displayed on a computer or smart phone by a graphical interface. The experiment shows that the monitoring system obtains excellent accuracy and stability in long-term continuous monitoring.

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Fourier domain local narrow-band signal extraction algorithm and its application to real-time infrared gas detection

Cited by 9 publications

References 10 publications

Effectively Measuring Respiratory Flow With Portable Pressure Data Using Back Propagation Neural Network

Effectively Measuring Respiratory Flow With Portable Pressure Data Using Back Propagation Neural Network

A Dual-Laser Sensor Based on Off-Axis Integrated Cavity Output Spectroscopy and Time-Division Multiplexing Method

A Real-Time Monitoring System of Industry Carbon Monoxide Based on Wireless Sensor Networks

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