Abstract: A temporal sequence alignment method includes the following steps: receiving gesture training data and gesture sample data; wherein the gesture training data includes multiple training frames and multiple training soft labels, and the gesture sample data includes multiple sample frames and multiple sample soft labels; compressing the training frames to generate a compressed training frame; compressing the sample frames to generate a compressed sample frame; calculating an alignment model of the compressed training frame and the compressed sample frame; aligning the sample soft labels to multiple aligned soft labels according to the alignment model; generating an aligned training data according to the gesture sample data and the aligned soft labels. The present invention uses the aforementioned steps to calibrate the sample soft labels of the gesture sample data, allowing a gesture recognition system to minimize time discrepancy for recognizing a gesture.

Abstract: A time division duplexed (TDD) frequency modulation continuous wave (FMCW) radar system includes P transmitter circuit chains and M receiver circuit chains. The P transmitter circuit chains are used to transmit a plurality of FMCW signals. A pth transmitter circuit chain is coupled to a single pole Op throw (SPQPT) radio frequency (RF) switch, the SPOT RF switch is coupled to Op antennas, Qp and P are positive integers, and p is a positive integer not larger than P. The M receiver circuit chains are used to receive a plurality of reflected FMCW signals. An mth receiver circuit chain is coupled to a single pole Nm throw (SPNmT) radio frequency (RF) switch, the SPNmT RF switch is coupled to Nm antennas, Nm and M are positive integers, and m is a positive integer not larger than M.

Abstract: In a radar sensor, a transmitting antenna is configured to radiate a transmitted RF signal, a receiving antenna is configured to receive a reflected RF signal from a target, and a frontend circuit is configured to calculate the distance between the target and the radar sensor by measuring the frequency shift between the transmitted RF signal and the reflected RF signal. The frontend circuit includes a crystal-less signal synthesizer configured to generate the transmitted RF signal without using a crystal, and a mixer configured to provide an IF-band signal associated with the frequency shift between the transmitted RF signal and the reflected RF signal by mixing the reflected RF signal and the transmitted RF signal.

Abstract: A wireless earphone with radar capabilities includes a first earphone having a first detection unit and a container. When the first earphone is placed inside the container, a first detection wave generated by the first detection unit passes through the container. The first detection wave is reflected from a human body as a first reflected wave, and when the first detection unit receives the first reflected wave through the container, the first detection unit generates a first detection result data and sends the first detection result data to an external device. The container containing the first earphone allows a user to hold the container with the first earphone within, and since the first earphone continues to detect movements outside of the container, the container is transformed into a multifunctional controller or detector for adapting to new application modes initiated by the external device.

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 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: 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 loop antenna which has a signal input/output wire, a first conductor, an upper conductor, an upper conductor, a second conductor, a first lower conductor, and a second lower conductor sequentially connected. The loop antenna further has a first grounding via, and a lower end of the first grounding via is connected to a first grounding layer, and an upper end of the first grounding via is disposed between and connecting the first lower conductor and the second lower conductor, wherein a first end of the second lower conductor is connected to the upper end of the first grounding via, and a second end of the second lower conductor is connected to the first conductor. A second grounding layer and the combination of the signal input/output wire, the first lower conductor, and the second lower conductor are disposed on the same layer disconnectedly.

Abstract: A loop antenna which has a signal input/output wire, a first conductor, an upper conductor, an upper conductor, a second conductor, a first lower conductor, and a second lower conductor sequentially connected. The loop antenna further has a first grounding via, and a lower end of the first grounding via is connected to a first grounding layer, and an upper end of the first grounding via is disposed between and connecting the first lower conductor and the second lower conductor, wherein a first end of the second lower conductor is connected to the upper end of the first grounding via, and a second end of the second lower conductor is connected to the first conductor. A second grounding layer and the combination of the signal input/output wire, the first lower conductor, and the second lower conductor are disposed on the same layer disconnectedly.

Abstract: A method of customizing a hand gesture provides a touch screen, a computing unit connected with the touch screen, and a hand gesture database connected with the computing unit, and the method includes the following steps: recording a hand gesture trajectory data input of an input hand gesture on the touch screen; converting the hand gesture trajectory data input into a 2D trajectory graph of the input hand gesture; the computing unit sequentially reads a 2D hand gesture reference graph from the hand gesture database, and correspondingly generates a 2D hand gesture reference graph set for the 2D hand gesture reference graph read, and then the computing unit compares the similarity between the 2D trajectory graph of the input hand gesture and each reference graph in the 2D hand gesture reference graph set to determine whether the input hand gesture is already in the hand gesture database.

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 temporal sequence alignment method includes the following steps: receiving gesture training data and gesture sample data; wherein the gesture training data includes multiple training frames and multiple training soft labels, and the gesture sample data includes multiple sample frames and multiple sample soft labels; compressing the training frames to generate a compressed training frame; compressing the sample frames to generate a compressed sample frame; calculating an alignment model of the compressed training frame and the compressed sample frame; aligning the sample soft labels to multiple aligned soft labels according to the alignment model; generating an aligned training data according to the gesture sample data and the aligned soft labels. The present invention uses the aforementioned steps to calibrate the sample soft labels of the gesture sample data, allowing a gesture recognition system to minimize time discrepancy for recognizing a gesture.

Abstract: A pressure sensing element has an elastic porous substrate, an electrode, an upper protective layer, and a lower protective layer. The elastic porous substrate is provided with a piezoelectric layer on a surface of the elastic porous substrate. The electrode is formed on at least one of a top and a bottom of the elastic porous substrate. The upper protective layer and a lower protective layer are provided respectively above and below the elastic porous substrate. The elastic porous substrate has multiple holes arranged in regular and repetitive patterns including gyroidal structures, lattice structures or schwarz structures.

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 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.