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The key issue of the purpose of detecting target vehicle speed based on video is how to obtain the accurate and efficient real-time information of moving targets using the video sequence in unstructured environment. Vehicle speed detection based on visual perception mechanism is a kind of new method. Visual mechanism inspired the method, first of all it should learn the over-complete characterization of primary visual cortex cells from nature images. Then it compared with the dynamic threshold to identify the moving target based on over-complete visual calculation model according to neuron response coefficient characterization and the difference of neuronal response coefficient between background images and goal images. Next, it achieved target tracking through the iterative method. Compared with the traditional method it got the displacement of the vehicle according to the matching method of characteristics in different video sequences on the basis of target tracking. Finally it could realize speed detection by this algorithm. Experimental results show that this algorithm can test the vehicle speed of different types in the actual road scene under real-time conditions and reach more than 96.55% accuracy. The algorithm improves the vehicle speed detection in accuracy and robustness compared with the traditional method. [ABSTRACT FROM AUTHOR]

About online BCI based on multi-class motor imagery, how to handle high-speed EEG data streams is a diificulty for the realizing of online awareness recognition, and the key is high-speed computing and prediction under complicated conditions. This paper took thread concurrency as the entry point of high-speed computing firstly, it decomposed the task of EEG signal analysis into more thread subtasks, and solved the coordination problem brought by thread concurrency with buffer management policies; then, for the complicated change of high-speed EEG data streams s it adopted adaptive one-sided fuzzy inference to predict the telescopic change of data streams ; lastly, against the disorders of intermediate result due to thread concurrency, it designed a method of mutual exclusion and synchronization with semaphore to recombine the intermediate data blocks orderly. Numerous experiments with multiple subjects show that the average delay time of a single Trial decreases obviously. Therefore, thread concurrency and fuzzy inference can solve the problem of high-speed computing and prediction in online BCI, and improve the information transmiision rates. [ABSTRACT FROM AUTHOR]