Abstract: Aspects described herein include devices and methods for smart ultra wideband transmissions. In one aspect, an apparatus includes pulse generation circuitry configured to output a plurality of transmission (TX) pulse samples at a selected signal sample rate, where each pulse sample of the plurality of TX pulse samples comprises a value associated with a pulse amplitude at a corresponding sample time The apparatus includes a plurality of power amplifier (PA) cells, with each PA cell of the plurality of PA cells comprising a corresponding current source and associated gates, and where the associated gates of a PA cell are selectable to configure an on state and an off state. Logic circuitry of the apparatus is configured to set the on state or the off state for each PA cell.

Abstract: Aspects described herein include devices and methods for smart ultra wideband transmissions. In one aspect, an apparatus includes pulse generation circuitry configured to output a plurality of transmission (TX) pulse samples at a selected signal sample rate, where each pulse sample of the plurality of TX pulse samples comprises a value associated with a pulse amplitude at a corresponding sample time The apparatus includes a plurality of power amplifier (PA) cells, with each PA cell of the plurality of PA cells comprising a corresponding current source and associated gates, and where the associated gates of a PA cell are selectable to configure an on state and an off state. Logic circuitry of the apparatus is configured to set the on state or the off state for each PA cell.

Abstract: An aspect of the disclosure relates to a transmitter including a phase lock loop (PLL) configured to generate a digital-to-analog (DAC) sampling signal and a local oscillator (LO) signal; a digital-to-analog (DAC) converter configured to convert a transmit digital signal into a transmit analog signal based on the DAC sampling signal; and a mixer configured to frequency upconvert the transmit analog signal based on the LO signal.

Abstract: The present disclosure provides a method of positioning vertebra in a CT image, an apparatus, a computer device, and a computer readable storage medium. The method includes: pre-processing vertebra CT image data; inputting the pre-processed vertebra CT image data into a pre-trained neural network to obtain regression results of heat maps of key points corresponding to the pre-processed vertebra CT image data; regressing of 3D heat maps corresponding to the positions of the key points of the vertebra mass center based on the regression results of the heat maps of the key points and the pre-processed vertebra CT image data; serving 3D heat maps corresponding to the positions of the key points of the vertebra mass center as labels, and networked regressing 3D heat map information to position the vertebra. Effects caused by scanning machine difference and scanning noise are avoided, and the vertebra with complex forms is accurately positioned.

Abstract: An apparatus includes a low-noise amplifier having an input and an output, a first switch coupled between the input of the low-noise amplifier and the output of the low-noise amplifier, and a transformer including a first inductor and a second inductor, wherein the first inductor is coupled to the output of the low-noise amplifier. The apparatus also includes a power amplifier having an input and an output, and a switching circuit coupled between the output of the power amplifier and the second inductor.

Abstract: The present disclosure provides a method of positioning vertebra in a CT image, an apparatus, a computer device, and a computer readable storage medium. The method includes: pre-processing vertebra CT image data; inputting the pre-processed vertebra CT image data into a pre-trained neural network to obtain regression results of heat maps of key points corresponding to the pre-processed vertebra CT image data; regressing of 3D heat maps corresponding to the positions of the key points of the vertebra mass center based on the regression results of the heat maps of the key points and the pre-processed vertebra CT image data; serving 3D heat maps corresponding to the positions of the key points of the vertebra mass center as labels, and networked regressing 3D heat map information to position the vertebra. Effects caused by scanning machine difference and scanning noise are avoided, and the vertebra with complex forms is accurately positioned.

Abstract: The present disclosure relates to an artificial intelligence field using a neural network, and publishes a method for confirming a cup-disc ratio based on a neural network, an apparatus, a computer device, and a computer readable storage medium. The method includes: obtaining a retinal image, and detecting an optical disc region in the retinal image to obtain the optical disc region; inputting the optical disc region into a pre-trained neural network to obtain a prediction cup-disc ratio and segment images of an optical cup and an optical disc; computing a cup-disc ratio based on the segment images of the optical cup and the optical disc; and confirming a practical cup-disc ratio based on the prediction cup-disc ratio and the computed cup-disc ratio.