<sec>Active illumination is a crucial technology for active imaging, active tracking and aiming system. But the atmosphere turbulence distributed over the entire path causes the intensity to fluctuate, which reduces the illumination uniformity seriously. Therefore, it is desirable to find ways to reduce the intensity fluctuation and improve the uniformity of active illumination. It has been revealed that one can improve illumination uniformity by using multi-beam laser illuminator. Another effective approach is partially coherent beam illumination.</sec><sec>In this paper, a novel method is suggested to improve the illumination uniformity. Phase disturbance is induced by a ladder-like phase modulator (LPM) and the transmitting field of narrow spectrum laser is confused, and thus the atmosphere turbulence will be compensated and the illumination uniformity will be improved. The physical models of narrow spectrum laser phase modulation and atmosphere propagation are deduced, and the expression of the facular distribution is obtained. The active-illumination experimental setup with a laser propagation distance of 1.8 km through horizontal atmosphere is established. Based on the facular distribution of illumination laser at 1.8 km, the uniformity and stabilization are achieved. The experimental results indicate that the illumination uniformity and stabilization are both improved. The spatial and central time scintillation indexes are improved from 0.73 to 0.33 and from 0.38 to 0.14, respectively.</sec>
Oil spills lead to catastrophic problems. In most oil spill cases, the spatial and temporal intractability of the detriment cannot be neglected, and problems related to economic, social and environmental factors constantly appear for a long time. Remote sensing has been widely used as a powerful means to conduct oil spill detection. Optical polarization remote sensing, thriving in recent years, shows a novel potential for oil spill detection. This paper provides a demonstration of the use of open-source POLDER/PARASOL polarization time-series data to detect oil spill. The Deepwater Horizon oil spill, one of the largest oil spill disasters, is utilized to explore the potential of optical polarization remote sensing for oil spill detection. A total of 24 feature combinations are organized to quantitatively study the positive effect of adding polarization information and the appropriate way to describe polarization characteristics. Random forest classifier models are trained with different combinations, and the results are assessed by 10-fold cross-validation. The improvement from adding polarization characteristics is remarkable ((average) accuracy: +0.51%; recall: +2.83%; precision: +3.49%; F1 score: +3.01%, (maximum) accuracy: +0.80%; recall: +5.09%; precision: +6.92%; F1 score: +4.72%), and coupling between the degree of polarization and the phase angle of polarization provides the best description of polarization information. This study confirms the potential of optical polarization remote sensing for oil spill detection, and some detailed problems related to model establishment and polarization feature characterization are discussed for the further application of polarization information.
The contents of a study and developments in Intelligent Transportation Systems (ITS) in China and the concept of Geographic Information Systems (GIS) are summarized in this paper, and then the fabricating procedure of digitalization of GIS and the designing principle of GIS database are expatiated upon at the same time. Finally the application of GIS technologies are particularly analyzed in ITS. The study and development of GIS technologies in ITS can enhance contact with roads, vehicles, drivers and managers with one another. The automation of transportation management and the intelligence of vehicular driving will be realized, thus traffic congestion can be effectively solved and road traffic faculties can be improved. Therefore the efficiency of road transportation and travelling safety will be ensured.
At present, pattern recognition technology has been widely used in the fields of objects, faces, fingerprints, military target recognition, etc. However, pattern recognition method still has obvious shortcomings when applied to the above fields. It is currently restricted to the use of image information for identification. When the image features of the research object are highly similar, the accuracy of pattern recognition is low and cannot meet the actual application requirements. For example, in the case of mixed true and false targets, it is difficult to obtain satisfactory recognition results using only image information. Aiming at the above problems, a pattern recognition method integrating image information and spectral information is proposed in this paper. Firstly, the image recognition model based on the convolutional neural network model is built to identify object categories based on the semantic features of objects and obtain preliminary recognition results. Then, on the basis of the preliminary recognition results, the measured spectral data of the object (spectrum range 400-1 000 nm, spectral resolution 2 nm) is used to perform true and fake identification of the object based on the back propagation (BP) neural network model. The principle of true and false recognition is that the true and false targets are different in material, causing significant difference in their hyperspectral information. Finally, recognition results are obtained. In order to verify the accuracy of the proposed method, true and fake apples and grapes are used as experimental subjects and the result is that:The recognition accuracy obtained by using only image information is 38.50%, and the recognition accuracy obtained by using only spectral information is 63.00%, however, the recognition accuracy obtained by the method proposed in this paper is 95.00%. Compared with the existing pattern recognition method without spectral information participation, the pattern recognition method using image information and spectral information proposed in this paper improves the pattern recognition accuracy under the mixed condition of true and false targets, and can be widely applied to object recognition, face recognition, fingerprint recognition, military target recognition and other fields.
In the field of laser applications, it needs to know the optical characters of sea's atmosphere. By referring, measuring and theoretically studying, the optical characters of atmosphere of China's sea are studied herein. These optical characters are mainly about Cn2, and visibility, and so on. And the differences between sea and earth are compared with their optical characters. Then, some basic and important conclusions are made.
In this paper the authors analyzed the data collection of seabed terrain, the influence factors on measurement precision and the data computation in time or frequency domain. In order to estimate the noise embedded in the received data, the structure and algorithm of characteristic matching filter based on the entropy concept is developed and discussed in detail. It is shown that the noise data can be removed effectively and the high precision seabed terrain be simulated by the filtering method.
Generally, nearly every step in the manufacture of integrated silicon devices will introduce the problem of residual stresses, which will cause crack in the active devices structure. In particular, crack often happens in the wafer saw, die attach, die bond and moulding process. Some crack figure can easily be fixed such as wafer saw induced from chip out at the edge of die. But many dies which fail die crack can't be explained exactly. In this paper, we will gives some FA cases to explain many confusing crack which is induced in die attach was highly suspected.
'/%3e%3cuse xlink:href='%23a' transform='rotate(23.036 .093 25.536)'/%3e%3cuse xlink:href='%23a' transform='rotate(45.87 1.273 16.18)'/%3e%3cuse xlink:href='%23a' transform='rotate(69.945 .996 12.078)'/%3e%3cuse xlink:href='%23a' transform='rotate(20.66 -19.689 31.932)'/%3e%3c/svg%3e)