Laser imaging techniques have advantages for EMI (Electro Magnetic Interference) immunity and abundant image
information. This contribution describes the research activity on the scannerless laser imaging detection technique using
direct detection aimed at laser fuze applications. The technique using a pulsed laser to illuminate the target and a focal
plane array can be used as a receiver. The range information is achieved by means of a direct time of light measurement.
Information about the reflectivity of the target is gathered by recording the amplitude of the received pulse. In this paper
a high-repetition-frequency, narrow pulse semiconductor laser floodlight emitting system is designed; corresponding
optics is used to generate the homogenously illuminated FOI (field of illumination). The echo of laser is collected by
receiving optical system fed to focal plane array. Some experiments were done with the emitting and receiving systems
that had been designed. Experiments show the validity and rationality of this method. The scannerless structure is robust
and provides instantaneous snapshot-type imaging. Avoiding any moving mechanical parts, scannerless laser imaging
system have distinct characteristics such as small, compact, high frame rate, wide field of view and high reliability. It is
an optimal approach to realize laser imaging fuze.
Infrared imaging fuze is invulnerable to the electromagnetic interference, and it has the ability to recognize the local
image of the target. At present, the infrared imaging fuze technology has become one of the key technologies which
perform the target detection and the ignition of the warhead in the complex tactical environment. According to the
scanning mechanism of the infrared imaging fuze, based on the analysis of features of the infrared image of tank target,
this paper presents a feature extraction method based on knowledge to recognize infrared gray image. The geometric
features and gray level features are extracted. The geometric features include the corner features and angular features.
The corners of the image are extracted through the SUSAN corner detection principle,the angular feature is extracted by
Freeman chain code. The hot-zone gray feature is extracted by the template matching and image binarization principle.
In order to realize real-time recognition, this paper uses FPGA technology to achieve recognition circuit. The
experiments show that the recognition method has a certain anti-interference ability.
In order to diagnose the compressors' fault on line, an intelligent checking method is presented in the paper. A vibration
sensor was put on the compressors that should be detected. The vibration signals obtained by the sensor contain a great
deal information, which reflects the compressors' qualities and their type of faults.
It has been proved that, the vibration signals obtained from compressors with different faults have different time domain
features and frequency domain features. We extract those features, and then get a feature vector which is sent to an
intelligent information processor.
In order to improve the generalization and robustness of the processor, we adopt a fuzzy clustering radial basis function
(RBF) neural networks as the information processor. A method of fuzzy C-means clustering based on minimized mean
square error rule is used to determine the RBF layer, and the shape factors of RBF neurons are determined by the grades
The experimental results show that, fuzzy clustering RBF neural networks neural networks have powerful ability of
pattern recognition, and the faults diagnosis method is feasible to diagnose the fault of the revolving machinery.