|کد مقاله||کد نشریه||سال انتشار||مقاله انگلیسی||ترجمه فارسی||نسخه تمام متن|
|4977365||1367710||2018||8 صفحه PDF||سفارش دهید||دانلود کنید|
- The proposed SRNN method is a combination of stochastic resonance and neural network, which effectively detects extremely weak NQR signals.
- The SRNN method can detect a variety of NQR signals which have similar NQR parameters, which shows SRNNs good commonality, as well as robustness to the possible time-variation of NQR signal properties in real life settings.
- The SRNN method also has good performance in the presence of interference.
- We anticipate that the proposed SRNN method can be applicable to other problems of detecting weak signals under a similar framework.
Nuclear Quadrupole Resonance (NQR) signal detection is widely used for searching related substances of interest, such as explosives, petroleum, drugs, etc. NQR responses from these substances are usually very weak compared to background noise. Moreover, in some applications such as landmine detection, NQR responses decay with time quickly, and the required scanning times are usually prohibitively long. This paper presents a novel approach which can detect NQR signals of very low SNRs in such scenarios, by combining a stochastic resonance framework and neural network theory. Firstly, the approach relies on the design of a stochastic resonance (SR) system which can transform the original data into a nonlinear waveform with special SR features. Secondly, a (feedforward) robust neural network is trained to discern this nonlinear waveform accurately, in order to identify the NQR signal. Our results demonstrate that the neural network approach outer-performs traditional signal processing detection and estimation methods. Moreover, this stochastic resonance neural network approach (SRNN) can be designed to detect a variety of NQR signals which have similar NQR parameters. The SRNN approach can also be effective in cases where both noise and radio frequency interference are strong relative to the NQR response.
Journal: Signal Processing - Volume 142, January 2018, Pages 96-103