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 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 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: 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: 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 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 gesture recognition system executes a gesture recognition method which includes the following steps: receiving a sensing signal; selecting one of the sensing frames from the sensing signal; generating a sensing map by applying 2D FFT to the selected sensing frame; selecting a cell having a largest amplitude in the sensing map; calculating the velocity of the cell and setting the velocity of the selected sensing frame to be the velocity of the cell; labeling the selected sensing frame as a valid sensing frame if the velocity of the selected sensing frame exceeds a threshold value, otherwise labeling the selected sensing frame as an invalid sensing frame; using all of the sensing maps of the valid sensing frames in the sensing signal as the input data for the neural network of the gesture recognition system and accordingly performing gesture recognition and gesture event classification.

Abstract: A gesture recognition system executes a gesture recognition method. The gesture recognition method includes steps of: receiving a training signal; selecting one of the sensing frames of the sensing signal; generating a sensing map; selecting a cell having the max-amplitude; determining a frame amplitude, a frame phase, and a frame range of the selected one of the sensing frames; setting the frame amplitudes, the frame phases, and the frame ranges of all of the sensing frames to input data of a neural network to classify a gesture event. The present invention just uses a few data to be the input data of the neural network. Therefore, the neural network may not require high computational complexity, the gesture recognition system may decrease the calculation load of the processing unit, and the gesture recognition function may not influence a normal operation of a smart device.

Abstract: A performing device of a gesture recognition system for reducing a false alarm rate executes a performing procedure of a gesture recognition method for reducing the false alarm rate. The gesture recognition system includes two neural networks. A first recognition neural network is used to classify a gesture event, and a first noise neural network is used to determine whether the sensing signal is the noise. Since the first noise neural network can determine whether the sensing signal is the noise, the gesture event may not be executed when the sensing signal is the noise. Therefore, the false alarm rate may be reduced.

Abstract: A gesture recognition system using siamese neural network executes a gesture recognition method. The gesture recognition method includes steps of: receiving a first training signal to calculate a first feature; receiving a second training signal to calculate a second feature; determining a distance between the first feature and the second feature in a feature space; adjusting the distance between the first feature and the second feature in feature space according to a predetermined parameter. Two neural networks are used to generate the first feature and the second feature, and determine the distance between the first feature and the second feature in the feature space for training the neural networks. Therefore, the gesture recognition system does not need a big amount of data to train one neural network for classifying a sensing signal. A user may easily define a new personalized gesture.