Abstract: An equalizer includes a first feed-forward stage that provides a measure of low-frequency ISI and a second feed-forward stage that includes a cascade of stages each making an ISI estimate. The ISI estimate from each stage is further equalized by application of the measures of low-frequency ISI from the first feed-forward stage and fed to the next in the cascade of stages. The ISI estimates from the stages thus become progressively more accurate. The number of stages applied to a given signal can be optimized to achieve a suitably low bit-error rate. Power is saved by disabling stages which are not required to meet that goal.

Abstract: An equalizer includes a first feed-forward stage that provides a measure of low-frequency ISI and a second feed-forward stage that includes a cascade of stages each making an ISI estimate . The ISI estimate from each stage is further equalized by application of the measures of low-frequency ISI from the first feed-forward stage and fed to the next in the cascade of stages. The ISI estimates from the stages thus become progressively more accurate. The number of stages applied to a given signal can be optimized to achieve a suitably low bit-error rate. Power is saved by disabling stages which are not required to meet that goal.

Abstract: An equalizer includes a first feed-forward stage that provides a measure of low-frequency IS I and a second feed-forward stage that includes a cascade of stages each making an IS I estimate. The IS I estimate from each stage is further equalized by application of the measures of low-frequency IS I from the first feed-forward stage and fed to the next in the cascade of stages. The IS I estimate from the stages thus become progressively more accurate. The number of stages applied to a given signal can be optimized to achieve a suitably low bit-error rate. Power is saved by disabling stages which are not required to meet that goal.

Abstract: An equalizer includes a first feed-forward stage that provides a measure of low-frequency IS I and a second feed-forward stage that includes a cascade of stages each making an IS I estimate. The IS I estimate from each stage is further equalized by application of the measures of low-frequency IS I from the first feed-forward stage and fed to the next in the cascade of stages. The IS I estimate from the stages thus become progressively more accurate. The number of stages applied to a given signal can be optimized to achieve a suitably low bit-error rate. Power is saved by disabling stages which are not required to meet that goal.

Abstract: Embodiments of the specification provide a product object publishing method and a system. The method includes: obtaining an image of a product object; sending the image to a server; receiving a plurality of types of product attribute information obtained by image recognition on the image of the product object performed by an image recognizer set; displaying the plurality of types of product attribute information for selection by a user; generating structured information of the product object according to selection of the product attribute information; and publishing the product object, wherein the publishing comprises publishing the image and the structured information of the product object. Accuracy and efficiency of product object publishing can be improved.

Abstract: This invention is related to a network for generating 3D shape, including an image feature network, an initial ellipsoid mesh, and a cascaded mesh deformation network. The image feature network is a Visual Geometry Group Net (VGGN) containing five successive convolutional layer groups, and four pooling layers sandwiched by the five convolutional layer groups; and the cascaded mesh deformation network is a graph-based convolution network (GCN) containing three successive deformation blocks, and two graph unpooling layers sandwiched by the three successive deformation blocks. This invention is also related to a system and a method thereof.

Abstract: This invention is related to a network for generating 3D shape, including an image feature network, an initial ellipsoid mesh, and a cascaded mesh deformation network. The image feature network is a Visual Geometry Group Net (VGGN) containing five successive convolutional layer groups, and four pooling layers sandwiched by the five convolutional layer groups; and the cascaded mesh deformation network is a graph-based convolution network (GCN) containing three successive deformation blocks, and two graph unpooling layers sandwiched by the three successive deformation blocks. This invention is also related to a system and a method thereof.

Abstract: This invention is related to a network for generating 3D shape, including an image feature network, an initial ellipsoid mesh, and a cascaded mesh deformation network. The image feature network is a Visual Geometry Group Net (VGGN) containing five successive convolutional layer groups, and four pooling layers sandwiched by the five convolutional layer groups; and the cascaded mesh deformation network is a graph-based convolution network (GCN) containing three successive deformation blocks, and two graph unpooling layers sandwiched by the three successive deformation blocks. This invention is also related to a system and a method thereof.

Abstract: This invention is related to a network for generating 3D shape, including an image feature network, an initial ellipsoid mesh, and a cascaded mesh deformation network. The image feature network is a Visual Geometry Group Net (VGGN) containing five successive convolutional layer groups, and four pooling layers sandwiched by the five convolutional layer groups; and the cascaded mesh deformation network is a graph-based convolution network (GCN) containing three successive deformation blocks, and two graph unpooling layers sandwiched by the three successive deformation blocks. This invention is also related to a system and a method thereof.