Abstract: This application relates to an image correction model training method including obtaining a training image, and performing random data distortion on the training image to obtain a distorted image and distortion parameter information; inputting the distorted image into an initial image correction model to predict a correction parameter, to obtain initial correction parameter information, and performing image correction on the distorted image to obtain an initial corrected image; calculating a loss between the initial correction parameter information and correction parameter information corresponding to the distortion parameter information, to obtain parameter loss information, and calculating a loss between the training image and the initial corrected image to obtain image loss information; updating the initial image correction model to obtain an updated image correction model; and performing training and obtaining the updated image correction model iteratively, until a target image correction model is obtaine

Abstract: Systems and methods for selecting one or more characteristics for a netlist. A graph data structure is generated, the components of the netlist and connections therebetween being represented by nodes and edges, respectively, in the graph data structure, wherein the edges between the nodes indicate types of the connections between the components. The graph data structure is processed to select one or more characteristics for the components.

Abstract: Systems and methods for computer-assisted design of an inductor are described. Target specifications for an inductor are received. An inductor design is generated segment-by-segment using a reinforcement learning agent to generate segment parameters for each added segment. The reinforcement learning agent implements a policy that is learned using a reward computed based on performance of the generated inductor design relative to the target specifications. The generated inductor design is outputted as a candidate inductor design after determining that the generated inductor design satisfies a predefined performance threshold.

Abstract: A biological toxicity test method for evaluating an ecological safety of an advanced oxidation process comprising the following steps: (1) collecting (preparing) a waste water to be determined; (2) collecting the waste water and a tap water after the advanced oxidation process treatment; (3) subjecting Koi (Cyprinus carpio haematopterus) to the water after treatment for exposure to poison; (4) Determining an anti-oxidation enzyme activity of a liver of the Koi after exposure; (5) Data analyzing. By comparing the changes of liver enzyme activities in different water, the present method evaluates the toxicity changes of micro-pollutant containing water before and after treatment, which fills in the gap of the ecological risk assessment for advanced oxidation technology.

Abstract: A transmitter driver is disclosed. The transmitter driver includes first and second voltage mode drivers and a secondary data path. The secondary data path is connected in parallel with the first and second voltage mode drivers. A high frequency booster provides a high frequency path for boost current from the current source, to enable a high frequency voltage boost at the output ports.

Abstract: A transmitter driver is disclosed. The transmitter driver includes first and second voltage mode drivers and a secondary data path. The secondary data path is connected in parallel with the first and second voltage mode drivers. A high frequency booster provides a high frequency path for boost current from the current source, to enable a high frequency voltage boost at the output ports.