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36results about How to "Robust handling" patented technology

Scalable wireless messaging system

A method for operating a wireless messaging system, the messaging system being configured to comprise a plurality of mobile wireless clients (1, 1′, 1″), a core messaging system (7) and at least two gateway processes or gateways (5, 5′, 5″) acting as proxies on behalf of the clients (1, 1′, 1″), the method comprising, in order for a client (1) to establish a connection with one of the gateways (5, 5′, 5″), a two phase connect procedure with the steps of,
    • the client (1) maintaining a list of access points, each access point defining an address at one of the gateways (5, 5′, 5″);
    • the client (1) sending, over a wireless bearer (4, 4′, 4″), a phase one connection request to one of the access points;
    • the gateway (5, 5′, 5″) that is addressed by the phase one connection request, sending, in response to the phase one connection request, over a wireless bearer (4, 4′, 4″), a list of connect points to the client (1), each connect point defining an address at one of the gateways (5, 5′, 5″);
    • the client (1) sending, over a wireless bearer (4, 4′, 4″), a phase two connection request to at least one of the connect points; and
    • the gateway (5, 5′, 5″) that is addressed by the phase two connection request responding to the phase two connection request, and the client (1) establishing a connection with the gateway (5, 5′, 5″) specified by said connect point.
Owner:TAIWAN SEMICON MFG CO LTD

Image processing device and method, data processing device and method, program, and recording medium

An eigenprojection matrix (#14) is generated from a learning image group (#10), in which high-quality images and low-quality images are paired up, by a projection operation (#12) using a locality relationship, and a projection nuclear tensor (#16) that defines the correspondence relationship between the low-quality image and an intermediate eigenspace and the correspondence relationship between the high-quality image and the intermediate engenspace is created. A first sub nuclear tensor is created (#24) by first setting from the projection nuclear tensor, and a coefficient vector in the intermediate eigenspace is calculated by projecting (#30) an inputted low-quality image (#20) using the eigenprojection matrix and the first sub nuclear tensor. A high-quality image (#36) is obtained by projecting (#34) the coefficient vector using a second sub nuclear tensor (#26) created by second setting from the projection nuclear tensor, and the eigenprojection matrix. Consequently, highly accurate and highly robust image conversion that enables the relaxation of the input condition of an image that is a conversion source can be implemented, and also the reduction of the processing load, the increase of the processing speed, and the reduction of the required amount of memory can be achieved.
Owner:FUJIFILM CORP

Dense correspondence prediction method based on non-rigid point cloud

ActiveCN112750198AImprove correspondence accuracyRobust handlingImage enhancementImage analysisPoint cloudAlgorithm
The invention discloses a dense correspondence prediction method based on non-rigid point clouds. The method comprises the following steps: respectively extracting geometric features of a three-dimensional template and point clouds by using a graph convolutional neural network and a plurality of set abstraction layers; utilizing a global regression network to deduce global displacement according to the associated global features of the template and the point cloud; utilizing a local feature embedding technology, introducing an attention mechanism, and fusing local depth features of the point cloud and geometric features of the graph; utilizing a local regression network to predict displacement increment; utilizing a weak supervision fine tuning method to process the real point cloud, and unifying the real point cloud and the two-stage regression network in a complete framework. The local geometric features of the point cloud are fully utilized, the attention strategy is adopted to improve the corresponding precision, the weak supervision fine tuning method is adopted to robustly process the real point cloud, and the conditions that the prediction model is unreasonably distorted and is obviously inconsistent with the input shape due to the lack of training data are effectively improved.
Owner:NANJING UNIV OF SCI & TECH

Scalable wireless messaging system

A method for operating a wireless messaging system, the messaging system being configured to comprise a plurality of mobile wireless clients (1, 1′, 1″), a core messaging system (7) and at least two gateway processes or gateways (5, 5′, 5″) acting as proxies on behalf of the clients (1, 1′, 1″), the method comprising, in order for a client (1) to establish a connection with one of the gateways (5, 5′, 5″), a two phase connect procedure with the steps of,the client (1) maintaining a list of access points, each access point defining an address at one of the gateways (5, 5′, 5″);the client (1) sending, over a wireless bearer (4, 4′, 4″), a phase one connection request to one of the access points;the gateway (5, 5′, 5″) that is addressed by the phase one connection request, sending, in response to the phase one connection request, over a wireless bearer (4, 4′, 4″), a list of connect points to the client (1), each connect point defining an address at one of the gateways (5, 5′, 5″);the client (1) sending, over a wireless bearer (4, 4′, 4″), a phase two connection request to at least one of the connect points; andthe gateway (5, 5′, 5″) that is addressed by the phase two connection request responding to the phase two connection request, and the client (1) establishing a connection with the gateway (5, 5′, 5″) specified by said connect point.
Owner:TAIWAN SEMICON MFG CO LTD

Grabbing device and working method thereof for earthwork engineering construction

The invention discloses a grabbing device for earthwork engineering construction and a working method thereof. The grabbing device includes a frame bottom plate and a mounting plate. The two mounting plates are rectangular parallelepiped and arranged symmetrically. The frame bottom plate is symmetrically arranged on both sides of the bottom of the mounting plate. A reinforced connection mechanism is provided above the mounting plate; the piston rod of the cylindrical cylinder block is connected with a rotating plate passing through the waist-shaped hole through a hinge, and a through hole is opened at the bottom of the rotating plate, and the through hole is interference fit with a rotating shaft. Both ends are fixedly connected with a grabbing mechanism; the grabbing mechanism includes a melon-grabbing swing arm, a melon-grabbing connecting plate, and a melon-grabbing swing arm. The melon-grabbing connecting plate is welded, and the melon-grabbing connecting plate is in the shape of a rectangular plate, and the inner side of the melon-grabbing connecting plate is threadedly connected with several parallel-arranged melons. Working methods include sampling operations and grabbing operations. The grasping device of the present invention has high flexibility, convenient operation, high safety and the function of sampling.
Owner:湖北地龙鸿业建设集团有限公司

Image processing device and method, data processing device and method, program, and recording medium

Generate an intrinsic projection matrix (#14) from a learning image population (#10) comprising high-quality images and low-quality image pairs by using projection computation (#12) of local relations to create a projection core tensor (#16), which Projection kernel tensors are used to define the correspondence between low-quality images and intermediate eigenspaces and the correspondence between high-quality images and intermediate eigenspaces. Create (#24) the first sub-kernel tensor from the projection kernel tensor based on the first setting, and project (#30) the input low-quality image (#20) based on the intrinsic projection matrix and the first sub-kernel tensor, to compute the coefficient vector in the intermediate eigenspace. The coefficient vector is projected (#34) based on the second sub-kernel tensor created from the projected kernel tensor by the second setting (#26) and based on the intrinsic projection matrix to obtain a high-quality image (#36). This can realize highly accurate and highly robust (robust) image conversion, which can ease the input condition of an image as a conversion source, and can reduce processing load, can speed up processing, and can suppress required memory size.
Owner:FUJIFILM CORP
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