Kaikutek Inc.

Guan-Sian Wu

Chun-Hsuan Kuo

Mike Chun-Hung Wang

Chieh Wu

Wen-Jyi Hwang

Fang Li

Chih-Wei Chen

Yu Feng Wu

Wen-Sheng Cheng

Ling Ya Huang

Wen-Yen Chou

Yu-Feng Wu

Chen-Lun Lin

Mike Chun Hung Wang

Chin-Wei Kuo

Ming Wei Kung

Khoi Duc Le

Wen-Yen Chang

Yu-Lin Chao

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.

Impulse-like gesture recognition method, and impulse-like gesture recognition system

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.

Generic gesture detecting method and generic gesture detecting device

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. The electronic device generates a voice message according to the controlling action and broadcasts the voice message.

Earphone device having gesture recognition functions

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.

Range doppler angle detection method and range doppler angle detection device

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.

Oscillator

A method for compressing an initial weight matrix includes generating a first weight matrix and a second weight matrix according to the initial weight matrix where the initial weight matrix is a Kronecker product of a transposed matrix of the second weight matrix and the first weight matrix; optimizing the first and second weight matrixes to generate an optimized first weight matrix and an optimized second weight matrix; generating a processed data matrix according to an initial data matrix where the initial data matrix is vectorization of the processed data matrix; multiplying the processed data matrix by the optimized first weight matrix to generate a first product; multiplying the optimized second weight matrix by the first product to generate a second product; and vectorizing the second product. The initial weight matrix requires a larger memory space than a combined memory space of the first and second weight matrixes.

Method for compressing initial weight matrix capable of reducing memory space

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Taichung, Taiwan

Guan-Sian Wu