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2073 results about "Single frame" patented technology

Pilot signals for synchronization and/or channel estimation

InactiveUS6987746B1Optimal autocorrelation resultEliminate and prevent sidelobesSynchronisation arrangementTime-division multiplexCode division multiple accessCorrelation function
The frame words of the preferred embodiment are especially suitable for frame synchronization and/or channel estimation. By adding the autocorrelation and cross-correlation functions of frame words, double maximum values equal in magnitude and opposite polarity at zero and middle shifts are obtained. This property can be used to slot-by-slot, double-check frame synchronization timing, single frame synchronization and/or channel estimation and allows reduction of the synchronization search time. Further, the present invention allows a simpler construction of a correlator circuit for a receiver. A frame synchronization apparatus and method using an optimal pilot pattern is used in a wide band code division multiple Access (W-CDMA) next generation mobile communication system. This method includes the steps of storing column sequences demodulated and inputted by slots, in a frame unit, in detecting frame synchronization for upward and downward link channels; converting the stored column sequences according to a pattern characteristic related to each sequence by using the pattern characteristic obtained from the relation between the column sequences; adding the converted column sequences by slots; and performing a correlation process of the added result to a previously designated code column.
Owner:LG ELECTRONICS INC

Urban rail transit panoramic monitoring video fault detection method based on depth learning

The invention provides an urban rail transit panoramic monitoring video fault detection method based on depth learning. The method comprises a data set construction process, a model training generation process and an image classification recognition process. The data set construction process processes a definition abnormity video, a colour cast abnormity video and a normal video in an urban rail transit panoramic monitoring video. A training set and a test set are classified. The model training generation process comprises model training and model test. The model training is to train a fault video image recognition model based on a convolution neural network. The convolutional neural network comprises a plurality of convolution layers and a plurality of full connection layers. The model test is to calculate the test accuracy. If expectation is not fulfilled, the fault video image recognition model is optimized. The image classification recognition process comprises the steps that a single-frame image to be recognized is input into the model, and the fault video image recognition model outputs an image classification result to complete the fault image detection of the urban rail transit panoramic monitoring video.
Owner:HUAZHONG NORMAL UNIV +1
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