Abstract: The present invention provides a method of angiography for cerebrovascular obliteration includes: using a classifier to obtain a 2D medical image using from a plurality of Multiphase CTA images; using a gray-scale conversion to obtain a N*M pixels grayscale image; filtering the grayscale image not being meet a condition of grayscale threshold and performing an image binarization to obtain a binarized image; confirming at least one vascular region and performing an image skeletonization; filtering according to a vascular image features of a vascular region to obtain a vascular-enhanced image; using a fracture analysis to obtain an analysis report related to a plurality of quantifying parameters of vascular characteristics; wherein the quantifying parameters of vascular characteristics comprises a quantitative value of fractal dimension (FD), vessel density (VD), skeleton density (SD) and vascular diameter index (VDI).

Abstract: A custom gesture collection and recognition system having a machine learning accelerator includes a transmission unit, a first reception chain, a second reception chain, a customized gesture collection engine and a machine learning accelerator. The transmission unit transmits a transmission signal to detect a gesture. The first reception chain receives a first signal and generates first feature map data corresponding to the first signal. The second reception chain receives a second signal and generates second feature map data corresponding to the second signal. The first signal and the second signal are generated by the gesture reflecting the transmission signal. The customized gesture collection engine generates gesture data according to at least the first feature map data and the second feature map data. The machine learning accelerator performs machine learning with the gesture data. The accuracy and correctness of gesture recognition may be improved by means of machine learning.

Abstract: A radar signal processing system with a self-interference cancelling function includes an analog front end (AFE) processor, an analog to digital converter (ADC), an adaptive interference canceller (AIC), and a digital to analog converter (DAC). The AFE processor receives an original input signal and generates an analog input signal. The ADC converts the analog input signal to a digital input signal. The AIC generates a digital interference signal digital interference signal by performing an adaptive interference cancellation process according to the digital input signal. The DAC converts the digital interference signal to an analog interference signal. Finally, the analog interference signal is fed back to the AFE and cancelled from the original input signal in the AFE processor while performing the front end process, reducing the interference of the static interference from the leaking of a close-by transmitter during the front end process.

Abstract: An earphone device having gesture recognition functions includes a gesture recognition element, a signal transmission unit, and a voice output element. The gesture recognition element includes a transmission unit, a reception chain and a processing unit. The transmission unit transmits a transmission signal to detect the gesture. The reception chain receives a gesture signal to generate a feature map data. The processing unit is coupled to the reception chain for receiving the feature map data and utilizes an identification algorithm to recognize gesture according to the feature map data to generate a gesture controlling signal. The signal transmission unit receives and transmits the gesture controlling signal to an electronic device. The processing unit receives a controlling action generated by the electronic device according to the gesture controlling signal via the signal transmission unit.

Abstract: A range Doppler angle detection method executed by a range Doppler angle detection device includes steps of: receiving a first sensing signal and a second sensing signal; performing 1D Fast Fourier Transform (FFT) and 2D FFT to the first sensing signal for calculating one first 2D FFT map; performing the 1D FFT and the 2D FFT to the second sensing signal for calculating one second 2D FFT map; picking up one column of the first 2D FFT map and one column of the second 2D FFT map according to a given Doppler index; performing the 3D FFT to the picked column of the first 2D FFT map and the picked column of the second 2D FFT map for calculating a range Doppler angle. Therefore, a computation loading of the gesture recognition function can be reduced.

Abstract: A generic gesture detecting method executed by a generic gesture detecting device includes steps of: receiving a current sensing signal from a sensing unit; generating a current image according to the current sensing signal; determining whether the current image is similar with a stored image stored in a memory unit; when the current image is similar with the stored image, detecting the current image and the stored image to be a gesture signal; when the current image is different from the stored image, storing the current image into the memory unit, and returning to the step of receiving a current sensing signal. Since the generic gesture detecting device can automatically detect the gesture signal, the user may not need to enable a detecting time period before implementing a command motion. Therefore, the user can make the command motion without enabling the detecting time period, and the convenience can be increased.

Abstract: A custom gesture collection and recognition system having a machine learning accelerator includes a transmission unit, a first reception chain, a second reception chain, a customized gesture collection engine and a machine learning accelerator. The transmission unit transmits a transmission signal to detect a gesture. The first reception chain receives a first signal and generates first feature map data corresponding to the first signal. The second reception chain receives a second signal and generates second feature map data corresponding to the second signal. The first signal and the second signal are generated by the gesture reflecting the transmission signal. The customized gesture collection engine generates gesture data according to at least the first feature map data and the second feature map data. The machine learning accelerator performs machine learning with the gesture data. The accuracy and correctness of gesture recognition may be improved by means of machine learning.

Abstract: A range Doppler angle detection method executed by a range Doppler angle detection device includes steps of: receiving a first sensing signal and a second sensing signal; performing 1D Fast Fourier Transform (FFT) and 2D FFT to the first sensing signal for calculating one first 2D FFT map; performing the 1D FFT and the 2D FFT to the second sensing signal for calculating one second 2D FFT map; picking up one column of the first 2D FFT map and one column of the second 2D FFT map according to a given Doppler index; performing the 3D FFT to the picked column of the first 2D FFT map and the picked column of the second 2D FFT map for calculating a range Doppler angle. Therefore, a computation loading of the gesture recognition function can be reduced.

Abstract: The invention discloses an oscillator, including a voltage switching circuit, a voltage adjustment circuit and a frequency generation circuit. The voltage switching circuit receives an output voltage signal whereby the output voltage signal switches a first input voltage signal to a first voltage level signal and switches a second input voltage signal to a second voltage level signal. The voltage adjustment circuit receives the first voltage level signal and the second voltage level signal, whereby the first voltage level signal and the second voltage level signal generate the first adjustment voltage signal and the second adjustment voltage signal. The frequency generation circuit is connected to the voltage adjustment circuit, and receives the first adjustment voltage signal and the second adjustment voltage signal to generate the first output frequency signal and the second output frequency signal according to the first adjustment voltage signal and the second adjustment voltage signal.

Abstract: A performing device of an impulse-like gesture recognition system executes an impulse-like gesture recognition method. A performing procedure of the impulse-like gesture recognition method includes steps of: receiving a sensing signal from a sensing unit; determining a prediction with at least one impulse-like label according to the sensing frames by a deep learning-based model; and classifying at least one gesture event according to the prediction. The gesture event is classified to determine the motion of the user. Since the at least one impulse-like label is used to label at least one detection score of the deep learning-based model, the detection score is non-decreasing, reaction time of the at least one gesture event for an incoming gesture is fast, rapid consecutive gestures are easily decomposed, and an expensive post-processing is not needed.

Abstract: A styrene-isoprene-styrene triblock copolymer, a method for manufacturing the same, a composition, and an adhesive composition using the same are provided. The styrene-isoprene-styrene triblock copolymer includes a first polystyrene block, a polyisoprene block, and a second polystyrene block. Based on 100 wt % of the styrene-isoprene-styrene triblock copolymer, the content of the first polystyrene block and the second polystyrene block is 30-50 wt %, and the content of the polyisoprene block is 70-50 wt %. The weight average molecular weight (Mw) of the styrene-isoprene-styrene triblock copolymer is 40,000-75,000.

Abstract: A self-start-up control circuit adaptable to an oscillation circuit includes a state circuit that generates a reset signal according to a level of a control voltage for a voltage-controlled oscillator (VCO) of the oscillation circuit; and a start-up circuit that starts up the VCO by generating an enable signal according to the reset signal.

Abstract: An antenna packaging structure includes a top substrate, an antenna chip, a bottom substrate, and a plurality of antenna transmission lines. The top substrate includes an antenna. The bottom substrate includes a circuit. The antenna chip is mounted on the bottom substrate, and is electrically connected to the antenna of the top substrate through the circuit of the bottom substrate and the antenna transmission lines. Since the antenna chip is directly electrically connected to a processor through the circuit, a plurality of communication lines do not need to be electrically connected between the antenna and the circuit. Further, since the antenna transmission lines just need to transmit a simple antenna signal between the antenna and the antenna chip, an amount of the antenna transmission lines is smaller than an amount of communication lines. Moreover, the total size of the antenna packaging structure is reduced.

Abstract: An anti-EMI antenna includes a first substrate layer, a grounding layer, a first circuit layer, a second substrate layer, a second circuit layer, a third substrate layer, a third circuit layer, a fourth substrate layer, and a fourth circuit layer. The grounding layer is mounted on a bottom surface of the first substrate layer, and includes a grounding circuit. The grounding circuit fully covers the bottom surface of the first substrate layer. Since the grounding circuit fully covers the bottom surface of the first substrate layer, the antenna radiation circuit can prevent the EMI from a bottom side of the anti-EMI antenna. Therefore, electromagnetic waves can be mostly isolated to prevent noise caused by the EMI.

Abstract: An antenna packaging structure includes a top substrate, an antenna chip, a bottom substrate, and a plurality of antenna transmission lines. The top substrate includes an antenna. The bottom substrate includes a circuit. The antenna chip is mounted on the bottom substrate, and is electrically connected to the antenna of the top substrate through the circuit of the bottom substrate and the antenna transmission lines. Since the antenna chip is directly electrically connected to a processor through the circuit, a plurality of communication lines do not need to be electrically connected between the antenna and the circuit. Further, since the antenna transmission lines just need to transmit a simple antenna signal between the antenna and the antenna chip, an amount of the antenna transmission lines is smaller than an amount of communication lines. Moreover, the total size of the antenna packaging structure is reduced.

Abstract: An anti-EMI antenna includes a first substrate layer, a grounding layer, a first circuit layer, a second substrate layer, a second circuit layer, a third substrate layer, a third circuit layer, a fourth substrate layer, and a fourth circuit layer. The grounding layer is mounted on a bottom surface of the first substrate layer, and includes a grounding circuit. The grounding circuit fully covers the bottom surface of the first substrate layer. Since the grounding circuit fully covers the bottom surface of the first substrate layer, the antenna radiation circuit can prevent the EMI from a bottom side of the anti-EMI antenna. Therefore, electromagnetic waves can be mostly isolated to prevent noise caused by the EMI.

Abstract: A gesture recognition system includes a transmission unit, a first reception chain, a second reception chain, a customized gesture collection engine and a machine learning accelerator. The transmission unit is used to transmit a transmission signal to detect a gesture. The first reception chain is used to receive a first signal and generate first feature map data corresponding to the first signal. The second reception chain is used to receive a second signal and generate second feature map data corresponding to the second signal. The first signal and the second signal are generated by the gesture reflecting the transmission signal. The customized gesture collection engine is used to generate gesture data according to at least the first feature map data and the second feature map data. The machine learning accelerator is used to perform machine learning with the gesture data.

Abstract: A method of thermal management in a computing device using a management controller is provided. The method includes obtaining, via a virtual management controller, monitoring information of a thermally sensitive component untethered to the management controller. The monitoring information can include temperature information of the thermally sensitive component. The method also includes transmitting, via the virtual management controller, the monitoring information to the management controller via a system interface of the management controller. Finally, the method includes adjusting, via the management controller, operation of a thermal management component of the computing device tethered to the management controller.

Abstract: The present disclosure provides a system and method for retrieving temperature information of GPUs of a server system via PCIe topology, and using a baseboard management controller (BMC) to control fan speed(s) of cooling fans based at least upon the temperatures of the GPUs. In some implementations, the management controller can determine a PCIe topology of the server system via an operating system (OS), and get the BusID of each GPU or GPU card of the server system. Based upon the BusID, the management controller can retrieve temperature information of a corresponding GPU from a GPU library (e.g., NVIDIA Management Library [NVML]), and further control fan speed of cooling fan(s) based at least upon the temperature information.

Abstract: A low voltage inverter-based amplifier includes a first inverter-based amplification module, a second inverter-based amplification module, an inverter-based feedforward module, and an inverter-based common mode detector. The first inverter-based amplification module receives an input signal. The second inverter-based amplification module receives the input signal through the inverter-based feedforward module, and receives a first output signal from the first inverter-based amplification module. The inverter-based common mode detector receives an amplified signal from the second inverter-based amplification module, and outputs a feedback signal to the second inverter-based amplification module. Since the first and the second inverter-based amplification modules are both inverter-based, the supply voltage of the low voltage inverter-based amplifier is provided to supply one PMOS and one NMOS for normal operation.