4results about How to "Simplify the scale" patented technology

The present application relates to the field of signal detection, and particularly to a non-cooperative unmanned aerial vehicle burst frequency point detection method and device based on a Seg-Yolo model. The Seg-Yolo model proposed by the present application does not require prior sequences of non-cooperative unmanned aerial vehicle signals at all, and does not require prediction of specific sequences, and can directly realize continuous, discontinuous, overlapping and non-overlapping signal frequency point detection in a time-frequency graph. Through preprocessing, image noise reduction and the Seg-Yolo model, the present application reduces the image volume that needs to be processed and simplifies the size of the deep learning network, can accurately lock the center frequency of each frequency point on the basis of reducing the training time and improving the training efficiency, and improves the accuracy and reliability of signal extraction based on complex frequency hopping signals.

The invention discloses an addressing ultrasonic sensor array variable aperture excitation impedance matching method and circuit, and belongs to the technical field of ultrasonic detection. The invention provides a voltage gain compensation method based on an excitation sound field energy attenuation principle according to array element impedance change during addressing array variable aperture excitation and excitation sound field energy distribution difference. According to the method, the waveform parameters of the excitation signals can be automatically adjusted, the energy distribution of the excitation sound field is optimized, and the consistency of the ultrasonic excitation signals is improved, so that the optimal excitation of the array elements of the addressing ultrasonic sensor array under different excitation apertures is realized. According to the invention, distribution characteristics of excitation circuit impedance under different apertures are given, and a variable aperture array element impedance matching circuit is designed. The circuit system comprises an adjustable high-voltage circuit, a driving circuit, a sharp pulse excitation circuit and an array element impedance matching circuit. According to the invention, the addressing ultrasonic sensor array can achieve the optimal impedance matching effect in different aperture array element excitation modes.

The embodiment of the invention provides an ultra-low noise tile-type phased array plane based on a silicon-based chip, and the phased array plane comprises an antenna part PCB, and the antenna part PCB comprises an array antenna, an LNA chip, a board-level filter, a BFIC chip based on a silicon-based technology, and a power division network which are connected in sequence; wherein the array antenna is provided with a patch antenna, and a feed point of the patch antenna is directly connected with the LNA chip through a plated-through hole; the LNA chip is connected with the board-level filter through a plated-through hole, the board-level filter is connected with the BFIC chip through a plated-through hole, and the BFIC chip is connected with the power division network through a plated-through hole; the LNA chip is integrated with a dynamic tuning matching circuit, and dynamic impedance matching can be carried out according to the working condition of the patch antenna. Therefore, the tile-type phased array plane provided by the embodiment of the invention realizes the consideration of high integration level, low noise, low loss, dynamic matching and interference resistance, and overcomes the defects of the traditional tile-type phased array plane in the aspects of integration level, noise, loss, matching flexibility, filtering and the like.

The present application belongs to the technical field of image processing, and in particular to a multi-scale attention weak light image enhancement method and system. The present application comprises the following steps: step 1, constructing a network model: constructing a multi-scale attention weak light image enhancement network model, which comprises a decomposition network, a reflection image restoration network, and an illumination image enhancement network; step 2, preparing a data set: using the LOL-v1 data set to divide the training set and the test set and to perform preprocessing; using the LOL-v2 data set to fine-tune the model; step 3, training the network model: inputting the LOL-v1 data into the network for training until the preset training number or the loss function reaches the standard step; step 4, fine-tuning the model: using the second weak light image data set to further train and fine-tune the network model; and step 5, saving the model: solidifying and saving the final model parameters to realize the conversion output of weak light images into high-quality images. The present application designs an efficient multi-scale attention enhancement network for improving the performance of image features. This method can significantly improve the quality and clarity of the image, making the illumination and reflection details more distinct.