In recent years, optical fiber sensing technology has been greatly developed and widely used in temperature, strain, harmonic vibration testing, especially acoustic detection based on optical fiber sensor, widely used in hydrophones, optical fiber microphones and other real-time detection and early warning systems. However, there is no good solution to the traceability problem of sensors based on optical fiber principle. In this paper, the traceability principle and process of traditional acoustic sensors are introduced in detail. Based on this opportunity, a traceability system which can be applied to optical fiber acoustic sensors is proposed. The system consists of a complete anechoic laboratory, an acoustic analyzer, an acoustic calibrator, a power amplifier, a piston generator, a sound source and microphone. The system has been successfully used to calibrate and trace the acoustic sensitivity and other parameters of the fiber acoustic sensor. The experimental results show that the scheme is effective. This paper has a certain significance for the development of optical fiber technology.
At present, the verification method of hand-held laser range finder has characteristics of high labor intensity, low working efficiency and low measuring accuracy. To solve these problems, an automatic hand-held laser range finder verification system is built in this paper. This system contains a 50m marble calibration platform, a measuring trolley with visual measuring module, and a multi-degree of freedom holder of range finder. It can automatically identify the observed readings of rangefinder through optical character recognition (OCR) technology, compare with the standard ranging value obtained by the trolley, and then evaluate error of indication. Thus achieving the purpose of calibrating the rangefinder. All of the communication between PC and trolley is realized by WiFi. Experiments show this device has comprehensive functions, high level of automation, practical application value and broad market prospects.
In order to maintain the normal running of economy in China, anti-counterfeiting detection of paper currency has
been an important technology in the coinage company and the bank, but the detection using spectrum for Chinese
paper currency anti-counterfeiting has not been applied in China. A real-time detection method, with broad
spectrum including ultraviolet and infrared wavelengths, is proposed in this paper, which achieves the purpose of
anti-counterfeiting by using anti-fake properties of paper currency's coating surface, through different lights
stimulation the full spectrum light irradiation on currency surface, with its reflection spectrum detected by
spectrometer. The proposed method has such advantages as high technology, high detection precision and easy to
identify, and has been applied to a practical system, which satisfies the real-time requirement.
Based on the unique characteristic, the paper currency numbers can be put into record and the automatic identification
equipment for paper currency numbers is supplied to currency circulation market in order to provide convenience for
financial sectors to trace the fiduciary circulation socially and provide effective supervision on paper currency.
Simultaneously it is favorable for identifying forged notes, blacklisting the forged notes numbers and solving the major
social problems, such as armor cash carrier robbery, money laundering. For the purpose of recognizing the paper
currency numbers, a recognition algorithm based on neural network is presented in the paper. Number lines in original
paper currency images can be draw out through image processing, such as image de-noising, skew correction,
segmentation, and image normalization. According to the different characteristics between digits and letters in serial
number, two kinds of classifiers are designed. With the characteristics of associative memory, optimization-compute and
rapid convergence, the Discrete Hopfield Neural Network (DHNN) is utilized to recognize the letters; with the
characteristics of simple structure, quick learning and global optimum, the Radial-Basis Function Neural Network
(RBFNN) is adopted to identify the digits. Then the final recognition results are obtained by combining the two kinds of
recognition results in regular sequence. Through the simulation tests, it is confirmed by simulation results that the
recognition algorithm of combination of two kinds of recognition methods has such advantages as high recognition rate
and faster recognition simultaneously, which is worthy of broad application prospect.
Access to the requested content is limited to institutions that have purchased or subscribe to SPIE eBooks.
You are receiving this notice because your organization may not have SPIE eBooks access.*
*Shibboleth/Open Athens users─please
sign in
to access your institution's subscriptions.
To obtain this item, you may purchase the complete book in print or electronic format on
SPIE.org.
INSTITUTIONAL Select your institution to access the SPIE Digital Library.
PERSONAL Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.