Abstract: Disclosed is a method for fast automatic calibration of a phased array based on a residual neural network. A phase setting matrix is set and an amplitude and a phase of a array far-field complex signal are measured with a network analyzer to obtain an amplitude and phase vector of the array far-field complex signal. A real part, an imaginary part, and a magnitude of the far-field measured complex signal value are separated and normalized, and mapped to RGB three-channel image data. Datasets are automatically generated according to a preset amplitude-phase error range by a simulation software, the datasets are proportionally divided into a training set and a test set to be input into the residual neural network for training to obtain a calibration model. Measured data is input into the calibration model for automatic estimation of the amplitude-phase error of the phased array.

Abstract: Disclosed is a method for fast automatic calibration of a phased array based on a residual neural network. A phase setting matrix is set and an amplitude and a phase of a array far-field complex signal are measured with a network analyzer to obtain an amplitude and phase vector of the array far-field complex signal. A real part, an imaginary part, and a magnitude of the far-field measured complex signal value are separated and normalized, and mapped to RGB three-channel image data. Datasets are automatically generated according to a preset amplitude-phase error range by a simulation software, the datasets are proportionally divided into a training set and a test set to be input into the residual neural network for training to obtain a calibration model. Measured data is input into the calibration model for automatic estimation of the amplitude-phase error of the phased array.

Abstract: Embodiments of the present disclosure provide a method for phased array calibration based on CNN-LSTM using power measurement, comprising: establishing a phased array calibration signal model, and utilizing a program to conveniently obtain a large amount of data for training a neural network without the need for actual measurements; converting and preprocessing the generated data, and saving as a training dataset in the form of feature data and labels; establishing a CNN-LSTM network, and inputting the training data with labels into the CNN-LSTM network for training until the CNN-LSTM network converges to obtain the final calibration model; measuring the phased array to be measured to obtain feature data, obtaining a calibration result of the phased array by inputting the feature data into the calibration model obtained from the training.

Abstract: An inductor current reconstruction circuit of a power converter can include: a switching current sampling circuit configured to acquire at least one of a current flowing through a main power transistor and a current flowing through a rectifier transistor to generate a switching current sampling signal; an inductor current generating circuit configured to generate a reconstruction signal representing an inductor current in one complete switching cycle; and where the reconstruction signal comprises the switching current sampling signal and a current analog signal generated according to the switching current sampling signal and an inductor voltage signal representing a voltage across an inductor in the power converter.

Abstract: Disclosed are a perovskite solar cell and a fabrication method thereof. The perovskite solar cell includes an electron transport layer, a perovskite layer, and a hole transport layer, where the hole transport layer includes an inorganic substance serving as a hole transport material, and an organic layer including a carbazole derivative is formed on the hole transport layer. A combination of a carbazole derivative and an inorganic material serving as a hole transport layer can allow the preparation of a perovskite solar cell with a high conversion efficiency.

Abstract: A method for target detection based on a visible camera, an infrared camera, and a LiDAR is provided. The method designates visible light images, infrared images, and LiDAR point clouds, which are synchronously acquired, as inputs, and generates an input pseudo-point cloud using visible light images and infrared images, to realize alignment of multimodal information in a three-dimensional space and fusion feature extraction. Then the method adopts a cascade strategy to output more accurate target detection results step by step. In the present disclosure, different characteristics of multi-sensors are complemented, which improves and extends traditional target detection algorithms, improves the accuracy and robustness of target detection, and realizes multi-category target detection in a road scene.

Abstract: A driving circuit for driving a piezoelectric load, can include: an energy-storage capacitor; a first power stage circuit configured to convert an input voltage into a first voltage, and to store the first voltage in the energy-storage capacitor; a second power stage circuit configured to receive the first voltage to charge the piezoelectric load during a first operation interval of an operation period, such that a power supply voltage signal provided to the piezoelectric load in the first operation interval corresponds to a reference voltage in a first interval; and a discharging circuit configured to discharge the piezoelectric load during a second operation interval of the operation period, such that the power supply voltage signal in the second operation interval corresponds to the reference voltage in a second interval.

Abstract: A driving circuit and a driving method are provided. The driving circuit includes an energy-storage capacitor, a charging circuit and a discharging circuit. The energy-storage capacitor is coupled between a piezoelectric load and the charging circuit. Operation states of the charging circuit and the discharging circuit are controlled, so that the charging circuit charges the energy-storage capacitor during a first operation interval of an operation period, to adjust a power supply voltage signal provided to the piezoelectric load to change with a reference voltage in a first interval, and at least the piezoelectric load discharges electricity through the discharging circuit during a second operation interval of the operation period, to adjust the power supply voltage signal to change with the reference voltage in a second interval. The driving circuit according to the present disclosure requires a few switches, thereby facilitating circuit integration.

Abstract: The present disclosure provides a voltage controlled oscillator for flipped and complementary low noise. The voltage controlled oscillator includes a first resonant cavity, a second resonant cavity, and an associated circuit. The associated circuit is configured to connect the first resonant cavity and the second resonant cavity in series and couple the first resonant cavity and the second resonant cavity. A resonant frequency of the first resonant cavity and a resonant frequency of the second resonant cavity satisfy a first preset condition.

Abstract: The embodiment of the present disclosure provides a deep neural network (DNN)-based multi-target constant false alarm rate (CFAR) detection method. The method includes: obtaining target values to be measured based on radar IF (IF) signals to be detected, the target values to be measured including a measured frequency value and a measured intensity value of the radar IF signals; obtaining peak sequences based on the target values to be measured; generating a target detection result by processing the peak sequences based on a DNN detector, the DNN detector being a machine learning model; generating approximated maximum likelihood estimation (AMLE) of a scale parameter based on an approximated maximum likelihood estimator; generating a false alarm adjustment threshold based on a preset false alarm rate and the AMLE; and generating a constant false alarm detection result by processing the target detection result based on the false alarm adjustment threshold.

Abstract: A driving circuit and a driving method are provided. The driving circuit includes a charging circuit and a discharging circuit. The charging circuit is configured to receive an input voltage to charge the piezoelectric load. The piezoelectric load discharges electricity through the discharging circuit. Operation states of the charging circuit and the discharging circuit are controlled. During a first operation interval of an operation period, the charging circuit charges the piezoelectric load so that a power supply voltage signal provided to the piezoelectric load corresponds to the reference voltage in a first interval. During a second operation interval of the operation period, the piezoelectric load discharges electricity through the discharging circuit so that the power supply voltage signal corresponds to the reference voltage in a second interval. The driving circuit according to the present disclosure requires a few switches, thereby facilitating circuit integration.

Abstract: A driving circuit and a driving method are provided. The driving circuit includes a power stage circuit and a full-bridge circuit. The power stage circuit is configured to receive an input voltage, and generate an output voltage at an output port of the power stage circuit. The full-bridge circuit is coupled to the output port of the power stage circuit and is configured to perform charging and discharging on a piezoelectric load. An operating mode of the full-bridge circuit is controlled, so that a supply voltage signal for driving the piezoelectric load during a first operating interval of an operating cycle corresponds to a reference voltage in a first interval, and the supply voltage signal during a second operating interval of the operating cycle corresponds to the reference voltage in a second interval. The driving circuit has a small volume, which is conducive to circuit integration.

Abstract: The embodiment of the present disclosure provides a deep neural network (DNN)-based multi-target constant false alarm rate (CFAR) detection method. The method includes: obtaining target values to be measured based on radar IF (IF) signals to be detected, the target values to be measured including a measured frequency value and a measured intensity value of the radar IF signals; obtaining peak sequences based on the target values to be measured; generating a target detection result by processing the peak sequences based on a DNN detector, the DNN detector being a machine learning model; generating approximated maximum likelihood estimation (AMLE) of a scale parameter based on an approximated maximum likelihood estimator; generating a false alarm adjustment threshold based on a preset false alarm rate and the AMLE; and generating a constant false alarm detection result by processing the target detection result based on the false alarm adjustment threshold.

Abstract: A method of driving a switching element in a switching circuit, where the switching element comprises a plurality of power transistors coupled in parallel, can include: determining a state of a load in the switching circuit; decreasing a driving voltage of at least one power transistor in order to reduce driving loss of the switching element when a load is in a first load state; and maintaining driving voltages of the plurality of power transistors at a first threshold when the load is in a second load state.

Abstract: The present disclosure provides a voltage controlled oscillator for flipped and complementary low noise. The voltage controlled oscillator includes a first resonant cavity, a second resonant cavity, and an associated circuit. The associated circuit is configured to connect the first resonant cavity and the second resonant cavity in series and couple the first resonant cavity and the second resonant cavity. A resonant frequency of the first resonant cavity and a resonant frequency of the second resonant cavity satisfy a first preset condition.

Abstract: The present disclosure discloses an adaptive satellite-aiming method for a low-orbit mobile satellite communication network. The method first receives signals through an array antenna, estimates an offset angle between an array antenna and a satellite beam using a MUSIC-based angle estimation method; then, based on the estimated offset angle, tracks a satellite direction using the adaptive coupling model and achieves tracking and alignment of an antenna direction based on feature parameters extracted during a tracking process; finally, based on the obtained feature parameters, adjusts parameters in the MUSIC-based angle estimation method, and repeats the above steps, to achieve real-time estimation of the offset angle and real-time tracking and alignment of the antenna direction.

Abstract: A dark-field optical system may include a rotational objective lens assembly with a dark-field objective lens to collect light from a sample within a collection numerical aperture, where the dark-field objective lens includes an entrance aperture and an exit aperture at symmetrically-opposed azimuth angles with respect to an optical axis, a rotational bearing to allow rotation of at least a part of the dark-field objective lens including the entrance aperture and the exit aperture around the optical axis, and a rotational driver to control a rotational angle of the entrance aperture. The system may also include a multi-angle illumination sub-system to illuminate the sample with an illumination beam through the entrance aperture at two or more illumination azimuth angles, where an azimuth angle of the illumination beam on the sample is selectable by rotating the objective lens to any of the two or more illumination azimuth angles.

Abstract: Methods and systems for detecting and classifying defects based on the phase of dark field scattering from a sample are described herein. In some embodiments, throughput is increased by detecting and classifying defects with the same optical system. In one aspect, a defect is classified based on the measured relative phase of scattered light collected from at least two spatially distinct locations in the collection pupil. The phase difference, if any, between the light transmitted through any two spatially distinct locations at the pupil plane is determined from the positions of the interference fringes in the imaging plane. The measured phase difference is indicative of the material composition of the measured sample. In another aspect, an inspection system includes a programmable pupil aperture device configured to sample the pupil at different, programmable locations in the collection pupil.

Abstract: The present disclosure discloses an on-chip synchronous self-repairing system based on a low-frequency reference signal. The system adopts a dual-input PLL stellate coupled structure or a dual-input PLL butterfly-shaped coupled structure, and delay of the whole loop is made to be an integral multiple of the reference signal by synchronizing the transmitted reference signal with the received reference signal, so as to ensure synchronization of local oscillation signal of each IC chip. The transmission wire based on an adjustable left-handed material is used as a delay wire to connect the dual-input PLL, thereby achieving low loss and reducing the physical distance of the delay wire. The system has the advantages of small area, low loss, strong adaptability and strict synchronization in various environments.