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04-28

Teach you to apply linear filters on real image data

Convolutional neural networks usually learn useful features from training data. The features learned by the first convolutional layer are often the basic elements of some training data depending on the task. For example, in image data, the learned features can reflect edges and spots. In the subsequent network layer, these learned features can represent more abstract and advanced features.
Teach you to apply linear filters on real image data
04-28

5G infrastructure speeds up, can ceramic filter capacity supply meet the peak demand?

Following the request of the Ministry of Industry and Information Technology on February 22 to resume the 5G construction, the Politburo meeting on March 4 again emphasized the acceleration of the construction progress of projects under construction and promoted the rapid development of 5G networks.
5G infrastructure speeds up, can ceramic filter capacity supply meet the peak demand?
04-28

Filter insertion loss characteristics, network impedance determination

From the above figure, we see that the differential mode insertion loss of the filter has a great relationship with the network impedance of the filter. 99% of filter manufacturers only provide curves A and B, but not C and D. The selection of filters based on manufacturer curves A and B alone does not guarantee that the test will pass.
Filter insertion loss characteristics, network impedance determination
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