Abstract: A dry granular ceramic tile from a wet slurry spraying process and a preparation method thereof, comprises: applying an overglaze on a green body, applying a pattern by ink-jet printing, applying a dry granular glaze by bell-shaped spraying, and sintering to obtain ceramic tiles. The dry granular glaze contains: by mass percentage, dry granular frit A: 15%, dry granular frit B: 12% to 15%, dry granular frit C: 13% to 17%. The softening temperature of the dry granular frit A is 1135° C. to 1175° C., 980° C. to 1050° C. for the dry granular frit B, and 1020° C. to 1127° C. for the dry granular frit C. The dry granular frits used in the present invention adopts a combination of dry granular frits with three different melting points, and using such a matching method, it is convenient for the effective adjustment of the brick shape and the firing temperature during production.

Abstract: The present application provides a high-wear-resistance far-infrared ceramic polished glazed tile and preparation method therefor. The preparation method includes application of far-infrared overglaze, ink-jet printing, application of transparent far-infrared polished glaze and application of abrasion-resistant far-infrared polished glaze in sequence on a body, firing, and polishing. By adopting the far-infrared overglaze, the transparent far-infrared polished glaze and the abrasion-resistant far-infrared polished glaze in combination, the polished glaze tile can have a far-infrared function, high transparency, and high abrasion resistance.

Abstract: The present invention relates to a millimeter-wave beam tracking method in a microwave and millimeter wave heterogeneous network scenario, including: establishing a millimeter-wave beam tracking problem in the microwave and millimeter wave heterogeneous network scenario; for the millimeter-wave beam tracking problem, constructing a deep neural network (DNN) fusion model that performs millimeter-wave beam tracking by using time sequence microwave channel information and user location information; pre-training the DNN fusion model by using training data; configuring a pre-trained beam tracking deep learning model in a base station controller of an actual access network, performing millimeter-wave beam tracking according to actually input microwave channel information and user location information, and selecting a millimeter-wave optimum beam; and collecting the input information to continuously train and update the DNN fusion model.

Abstract: A Wi-Fi camera includes an accelerometer and detects mechanical shock events. An electronic processor is communicatively coupled to the accelerometer and produces a record of the shock events detected by the accelerometer. The electronic processor performs shock monitoring while the camera is in low power and sleep modes. A system-on-chip performs shock monitoring while the camera is in full operation, standby and idle modes.

Abstract: A Wi-Fi camera includes an accelerometer and detects mechanical shock events. An electronic processor is communicatively coupled to the accelerometer and produces a record of the shock events detected by the accelerometer. The electronic processor performs shock monitoring while the camera is in low power and sleep modes. A system-on-chip performs shock monitoring while the camera is in full operation, standby and idle modes.

Abstract: A WiFi camera includes an accelerometer and detects mechanical shock events. An electronic processor is communicatively coupled to the accelerometer and produces a record of the shock events detected by the accelerometer. The electronic processor performs shock monitoring while the camera is in low power and sleep modes. A system-on-chip performs shock monitoring while the camera is in full operation, standby and idle modes.