Abstract: An apparatus and a method for outputting image data, and an electronic device are provided. The apparatus includes a first parameter setter, an image signal processor, and an encoder, wherein the first parameter setter sets a first configuration parameter for the image signal processor based on a complexity associated with an input image frame, wherein the image signal processor performs an image signal processing on at least one image frame to generate processed image data based on the first configuration parameter, wherein the encoder performs encoding processing on the processed image data to generate encoded image data. The apparatus and the method for outputting image data, and the electronic device of the present disclosure adjust parameters of the image signal processor and the encoder according to the complexity associated with an input image frame, thereby effectively improving the overall performance of image processing, encoding and compression.

Abstract: A user equipment (UE) management method system and device, the method including obtaining, by a base station that stores a management list and that manages access to the base station by the UE, a UE type of the UE. The management list includes a UE type, a feature, and a home list. Each UE type corresponds to at least one feature. Each feature corresponding to each UE type corresponds to one home list. The home list indicates whether to allow UE of a type corresponding to the home list to execute a feature corresponding to the home list. The method further includes searching, by the base station according to the obtained UE type of the UE, the management list for a found feature and a found home list, and determining, according to the found home list, whether to allow the UE to execute the found feature.

Abstract: A gesture recognition system comprises: a signal collection terminal, configured to collect and preprocess gesture data of a gesture object; a local recognition device, configured to extract features from the gesture data received from the signal collection terminal, form a multi-dimensional feature vector based on extracted features, establish local gesture models based on the multi-dimensional feature vector and perform local gesture recognition according to the local gesture models; and a cloud server, configured to receive the multi-dimensional feature vector from the local recognition device when there is a network connection between the local recognition device and the cloud server, establish cloud gesture models based on the received multi-dimensional feature vector and perform cloud gesture recognition according to the cloud gesture models. A gesture recognition method is also disclosed.

Abstract: A user equipment (UE) management method system and device, the method including obtaining, by a base station that stores a management list and that manages access to the base station by the UE, a UE type of the UE. The management list includes a UE type, a feature, and a home list. Each UE type corresponds to at least one feature. Each feature corresponding to each UE type corresponds to one home list. The home list indicates whether to allow UE of a type corresponding to the home list to execute a feature corresponding to the home list. The method further includes searching, by the base station according to the obtained UE type of the UE, the management list for a found feature and a found home list, and determining, according to the found home list, whether to allow the UE to execute the found feature.

Abstract: A virtual inputting device comprises: a signal collection unit including a bioelectrical sensor for collecting bioelectrical signals and an acceleration sensor for collecting acceleration signals; a signal preprocessing unit for performing preprocessing for the bioelectrical signals and the acceleration signals collected by the signal collection unit; a signal segmentation unit for performing segmentation processing for the preprocessed bioelectrical signals and acceleration signals so as to obtain a plurality of gesture segments; a feature extracting unit for extracting feature values from the bioelectrical signals and the acceleration signals for respective gesture segments; a feature combination unit for combining feature values extracted from the feature extracting unit to form a combined feature vector; a gesture recognition unit for performing gesture recognition based on the combined feature vector; and a character mapping unit for obtaining characters corresponding to the recognized gesture according

Abstract: A virtual inputting device comprises: a signal collection unit including a bioelectrical sensor for collecting bioelectrical signals and an acceleration sensor for collecting acceleration signals; a signal preprocessing unit for performing preprocessing for the bioelectrical signals and the acceleration signals collected by the signal collection unit; a signal segmentation unit for performing segmentation processing for the preprocessed bioelectrical signals and acceleration signals so as to obtain a plurality of gesture segments; a feature extracting unit for extracting feature values from the bioelectrical signals and the acceleration signals for respective gesture segments; a feature combination unit for combining feature values extracted from the feature extracting unit to form a combined feature vector; a gesture recognition unit for performing gesture recognition based on the combined feature vector; and a character mapping unit for obtaining characters corresponding to the recognized gesture according

Abstract: A gesture recognition system comprises: a signal collection terminal, configured to collect and preprocess gesture data of a gesture object; a local recognition device, configured to extract features from the gesture data received from the signal collection terminal, form a multi-dimensional feature vector based on extracted features, establish local gesture models based on the multi-dimensional feature vector and perform local gesture recognition according to the local gesture models; and a cloud server, configured to receive the multi-dimensional feature vector from the local recognition device when there is a network connection between the local recognition device and the cloud server, establish cloud gesture models based on the received multi-dimensional feature vector and perform cloud gesture recognition according to the cloud gesture models. A gesture recognition method is also disclosed.