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Infrared scene simulation has extensive application value in military and civil fields. According to the specific experiment environment, this paper simulation of an infrared scene and according to the experimental data produced infrared texture library. First of all, through the 3Dmax modeling software to establish the target infrared radiation model, and calculate the relevant radiation data of it. Then analyze the radiation characteristics of the relevant materials, the establishment of infrared texture library for each material. And finally, use OGRE engine image rendering technology and GPU programmable pipeline technique to simulate a complex infrared scene close to the real.

Edge detection that is an important means to realize image segmentation has important application significance in image processing, industrial detection, artificial intelligence and the target recognition field. As the demand for real-time and rapidity in image processing, the embedded image processing technology has been widely applied. But the realization of real-time edge detection for image requires a large amount of data processing, limited system resources of embedded system is the main reason of the embedded image processing technology development. In order to shorten time embedded systems edge detection processing large amounts of data, based on adaptive threshold Canny algorithm, this paper as the FPGA data processing DSP chips and made a FPGA + DSP hardware architecture, effectively improve the system real-time, get a good edge detection results.

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<p>El Niño is a large-scale ocean-atmospheric coupling phenomenon in the Pacific. The interaction among marine and atmospheric variables over the tropical Pacific modulate the evolution of El Niño. The latest research shows that machine learning and neural network (NN) have appeared as effective tools to achieve meaningful information from multiple marine and atmospheric parameters. In this paper, we aim to predict the El Niño index more accurately and increase the forecast efficiency of El Niño events. Here, we propose an approach combining a neural network technique with long short-term memory (LSTM) neural network to forecast El Niño phenomenon. The attributes of model are resulted from physical explanation which are tested with the experiments and observations. The neural network represents the connection among multiple variables and machine learning creates models to identify the El Niño events. The preliminary experimental results exhibit that training NN-LSTM model on network metrics time series dataset provides great potential for predicting El Niño phenomenon at lag times of up to more than 6 months.  </p>