Application of Fiber-Optical Gas Sensor Network in Gas Detection Based on Digital Signal Processing Algorithm

2017 ◽  
Vol 12 (7) ◽  
pp. 697-700
Author(s):  
Guannan Li
Keyword(s):  
Signal Processing ◽  
Digital Signal Processing ◽  
Sensor Network ◽  
Gas Sensor ◽  
Digital Signal ◽  
Gas Detection ◽  
Signal Processing Algorithm ◽  
Fiber Optical ◽  
Optical Gas Sensor ◽  
Digital Signal Processing Algorithm

scholarly journals Dynamic Digital Signal Processing Algorithm for Vital Signs Extraction in Continuous-Wave Radars

2021 ◽  
Vol 13 (20) ◽  
pp. 4079
Author(s):  
Carolina Gouveia ◽  
Daniel Albuquerque ◽  
José Vieira ◽  
Pedro Pinho
Keyword(s):  
Signal Processing ◽  
Digital Signal Processing ◽  
Continuous Wave ◽  
Vital Signs ◽  
Digital Signal ◽  
Processing Algorithm ◽  
Signal Processing Algorithm ◽  
Radar Systems ◽  
Fitting Method ◽  
Digital Signal Processing Algorithm

Radar systems have been widely explored as a monitoring tool able to assess the subject’s vital signs remotely. However, their implementation in real application scenarios is not straightforward. Received signals encompass parasitic reflections that occur in the monitoring environment. Generally, those parasitic components, often treated as a complex DC (CDC) offsets, must be removed in order to correctly extract the bio-signals information. Fitting methods can be used, but their implementation were revealed to be challenging when bio-signals are weak or when these parasitic reflections arise from non-static targets, changing the CDC offset properties over time. In this work, we propose a dynamic digital signal processing algorithm to extract the vital signs from radar systems. This algorithm includes a novel arc fitting method to estimate the CDC offsets on the received signal. The method revealed being robust to weaker signals, presenting a success rate of 95%, irrespective of the considered monitoring conditions. Furthermore, the proposed algorithm is able to adapt to slow changes in the propagation environment.

scholarly journals Analysis of speech waveform quantization methods

2008 ◽  
Vol 18 (1) ◽  
pp. 19-22
Author(s):  
Predrag Tadic ◽  
Zeljko Djurovic ◽  
Branko Kovacevic
Keyword(s):  
Signal Processing ◽  
Probability Density Function ◽  
Digital Signal Processing ◽  
Speech Signal ◽  
Digital Signal ◽  
Quantization Noise ◽  
Signal Processing Algorithm ◽  
Memory Space ◽  
Pdf Estimation ◽  
Digital Signal Processing Algorithm

Digitalization, consisting of sampling and quantization, is the first step in any digital signal processing algorithm. In most cases, the quantization is uniform. However, having knowledge of certain stochastic attributes of the signal (namely, the probability density function, or pdf), quantization can be made more efficient, in the sense of achieving a greater signal to quantization noise ratio. This means that narrower channel bandwidths are required for transmitting a signal of the same quality. Alternatively, if signal storage is of interest, rather than transmission, considerable savings in memory space can be made. This paper presents several available methods for speech signal pdf estimation, and quantizer optimization in the sense of minimizing the quantization error power.

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