Abstract: A method for recognizing face expressions is provided. The method includes: recognizing expression categories of expressions in a plurality of face images and obtaining recognition results between each expression category and another expression category; obtaining similarities between each expression category and another expression category according to the recognition results; classifying the expression categories into a plurality of expression groups according to the similarities; training a first recognition model to classify the expressions in the face images into the expression groups; and training a second recognition model for each of the expression groups to classify the face images in each of the expression group into one of the expression categories.

Abstract: A method for scheduling encoding of streaming data by a scheduling apparatus includes: when there is at least one idle hardware encoder and there is no full GOF in any one of the at least one streaming buffer, but there is at least one non-full GOF in any one of the at least one streaming buffer, then, calculating a reception rate of each of the at least one non-full GOF, and starting encoding already-received frames of the one of the at least one non-full GOF with a highest reception rate using one of the at least one idle hardware encoder; and if there is at least one idle hardware encoder and there is at least one full GOF in any one of the at least one streaming buffer, encoding one of the at least one full GOF using one of the at least one idle hardware encoder.

Abstract: A method for recognizing face expressions is provided. The method includes: recognizing expression categories of expressions in a plurality of face images and obtaining recognition results between each expression category and another expression category; obtaining similarities between each expression category and another expression category according to the recognition results; classifying the expression categories into a plurality of expression groups according to the similarities; training a first recognition model to classify the expressions in the face images into the expression groups; and training a second recognition model for each of the expression groups to classify the face images in each of the expression group into one of the expression categories.

Abstract: A character recognition method is provided, including the steps of: recognizing a character string, which includes one or more characters, in a region of interest (ROI) using a first convolutional neural network (CNN) model to generate a recognized character string; and comparing the recognized character string with a plurality of character strings in a character database to find a character string corresponding to the recognized character string, wherein each of the character strings includes one or more characters; wherein if the character string corresponding to the recognized character string is found, the recognized character string is used as a character recognition result, otherwise, using a second CNN model to correct the recognized string and generate a new recognized character string as the character recognition result.

Abstract: A character recognition method is provided, including the steps of: recognizing a character string, which includes one or more characters, in a region of interest (ROI) using a first convolutional neural network (CNN) model to generate a recognized character string; and comparing the recognized character string with a plurality of character strings in a character database to find a character string corresponding to the recognized character string, wherein each of the character strings includes one or more characters; wherein if the character string corresponding to the recognized character string is found, the recognized character string is used as a character recognition result, otherwise, using a second CNN model to correct the recognized string and generate a new recognized character string as the character recognition result.

Abstract: A method for scheduling encoding of streaming data by a scheduling apparatus includes: when there is at least one idle hardware encoder and there is no full GOF in any one of the at least one streaming buffer, but there is at least one non-full GOF in any one of the at least one streaming buffer, then, calculating a reception rate of each of the at least one non-full GOF, and starting encoding already-received frames of the one of the at least one non-full GOF with a highest reception rate using one of the at least one idle hardware encoder; and if there is at least one idle hardware encoder and there is at least one full GOF in any one of the at least one streaming buffer, encoding one of the at least one full GOF using one of the at least one idle hardware encoder.

Abstract: A data processing apparatus and a data processing method thereof are provided. The data processing apparatus includes a register and a processor electrically connected to the register. The register is stored with a plurality of data. The plurality of data each includes a first sub-datum and a second sub-datum. The plurality of first sub-data corresponds to a first column and the plurality of second sub-data corresponds to a second column. The processor compresses the first sub-data by a first compression algorithm according to a first characteristic of the plurality of first sub-data and compresses the second sub-data by a second compression algorithm according to a second characteristic of the plurality of second sub-data.

Abstract: A data processing apparatus and a data processing method thereof are provided. The data processing apparatus includes a register and a processor electrically connected to the register. The register is stored with a plurality of data. The plurality of data each includes a first sub-datum and a second sub-datum. The plurality of first sub-data corresponds to a first column and the plurality of second sub-data corresponds to a second column. The processor compresses the first sub-data by a first compression algorithm according to a first characteristic of the plurality of first sub-data and compresses the second sub-data by a second compression algorithm according to a second characteristic of the plurality of second sub-data.