Systems and methods for digital synthesis of an output signal using a frequency generated from a resonator and computing amplitude values that take into account temperature variations and resonant frequency variations resulting from manufacturing variability are described. A direct frequency synthesizer architecture is leveraged on a high Q resonator, such as a film bulk acoustic resonator (FBAR), a spectral multiband resonator (SMR), and a contour mode resonator (CMR) and is used to generate pristine signals.

A local oscillator device includes an oscillator module including a first inductive element and a capacitive element coupled in parallel with the inductive element. A frequency divider is coupled to the oscillator module for delivering a local oscillator signal. The local oscillator device includes an autotransformer including the first inductive element and two second inductive elements respectively coupled to the terminals of the first inductive element and to two output terminals of the autotransformer, the output terminals being further coupled to input terminals of the frequency divider.

A method for timing aperture synthesis arrays comprising the steps of: (a) coupling a plurality of independent crystal oscillators, each of the plurality of independent crystal oscillators having a unique output frequency; (b) digitally synchronizing the plurality of independent crystal oscillators in phase; (c) combining the unique output frequencies; and (d) obtaining a stable digital reference signal for timing at least one remote radio device of the aperture synthesis array.

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.

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.

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.

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.

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.

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.

A performing device of a gesture recognition system executes a performing procedure of a gesture recognition method. The performing procedure includes steps of: receiving a sensing signal; selecting one of sensing frames of the sensing signal; determining a soft label of the selected sensing frame; classifying a gesture event when the soft label of the selected sensing frame is approved. The gesture event is classified to determine the motion of the user. Therefore, the gesture recognition system does not need a predetermined time period to recognize the motion of the user. The time period for recognizing the motion of the user can be dynamical. A total time period for classifying a plurality of motions can be decreased, and the performance of the gesture recognition system can be improved.