2423 results about "Bandpass filtering" patented technology

The invention provides an RSVP type unbalanced electroencephalogram signal classification method based on a self-adaptive channel mixed attention mechanism and decoupling learning, and aims to improve the feature extraction and classification performance of RSVP electroencephalogram signals. Firstly, collected RSVP electroencephalogram signals are preprocessed, including common average reference, band-pass filtering and standardization processing, and a training set and a test set are divided; then, multi-scale time and space features are extracted through a parallel convolution layer and a space convolution layer, and the features of different levels are fused through an interpolation method; the training process is decoupled into representation learning and classifier learning, the representation learning uses a triple loss function based on difficult sample sampling, and the classifier learning uses the optimized Focal Loss to perform feature learning and classifier optimization. A self-adaptive channel mixed attention mechanism is introduced, and the flexibility and robustness of the model are enhanced through dynamic weighting and multi-level feature fusion. According to the method, the problem of class imbalance in the RSVP task is solved.

The invention relates to the field of heart rate monitoring, and discloses a millimeter wave radar breath and heart rate synchronous monitoring method and system, and the method comprises the steps: transmitting a linear frequency modulation continuous wave signal according to a millimeter wave radar, and collecting original echo data reflected by a target region; baseband signal demodulation and phase information extraction are carried out on the original echo data to obtain original phase time sequence data, and the original phase time sequence data comprise thoracic cavity micro-motion features; and according to a Butterworth band-pass filter, preprocessing the original phase time sequence data through human body physiological signal frequency band characteristics to obtain a breathing frequency band signal and a heart rate frequency band signal. According to the method, the apnea event triggering threshold value and the arrhythmia early warning index are updated in real time through Kalman filtering and extended Kalman filtering, so that the monitoring system can dynamically adjust the health parameters, which means that the monitoring system can be optimized in real time and the abnormal health event can be accurately responded in different physiological states.

The invention provides an HPLC (High Performance Liquid Chromatography) and micropower wireless dual-mode communication method, which belongs to the technical field of multiplexing communication, is used for realizing cooperative transmission of power line carrier communication and micropower wireless communication, and comprises a modulation end method and a demodulation end method. According to the modulation end, firstly, HPLC and micropower wireless signals are collected, and band-pass filtering and spectrum sensing are carried out, so that frequency bands which do not interfere with each other are obtained. Then, a unified time reference is established, transmission delay is calculated, and delay compensation is carried out; and then, modulating and transmitting by adopting an orthogonal frequency division multiplexing technology. And at a receiving end, main and standby channels are selected according to the signal strength, and demodulation and data recovery are performed by adopting an adaptive modulation and demodulation technology. According to the scheme, the advantages of HPLC and micropower wireless are fully utilized, fusion of two communication technologies is achieved, and the technical problem that effective cooperation is difficult to achieve due to the fact that existing HPLC and micropower wireless communication often adopt independent communication carriers and transmission protocols is solved.

The invention relates to the technical field of radar sensing, and discloses a sensing method and system based on millimeter radar waves. The method comprises the steps that millimeter wave radar receiving signals are subjected to low-noise amplification, frequency bands are screened through a band-pass filter and then mixed with local oscillation signals to obtain distance and speed information, after analog-to-digital conversion digitization, features are extracted to distinguish a human body target in a closed / open space, and finally multi-scene adaptability analysis is executed to generate human body position state information. According to the invention, through construction of a signal processing link, from signal amplification, filtering, frequency mixing and digital processing to target identification and multi-scene adaptability analysis, comprehensive perception of a human body target is realized, a static human body and a non-human body interference source can be effectively distinguished, a processing strategy is automatically adjusted according to different space environment characteristics, and the processing efficiency is improved. And the accuracy and reliability of human body perception are greatly improved.

The invention belongs to the technical field of intelligent health monitoring, and particularly relates to an FMCW radar-based night respiration and heartbeat monitoring signal processing method, which comprises the following steps of S1, acquiring a chest vibration displacement signal of a human body by using an FMCW radar, and completing the acquisition of a radar signal; according to the recovery separation method based on the two-parameter LMS filter, monitoring and target positioning of multiple life signals can be achieved by the FMCW radar under the condition of low signal-to-noise ratio, and compared with a band-pass filtering (BPF) method and a wavelet transform (WT) method, more accurate breathing and heartbeat signals can be obtained by the method based on the two-parameter LMS filter, and the method has the advantages of being simple in structure, convenient to operate and high in practicability. Particularly, when the breathing signal has multiple harmonic waves and the frequency changes, the method is more accurate compared with other methods, breathing and heartbeat signals can be rapidly converged by setting a proper step length, and small steady-state deviation is kept when the signals are received next time.

The invention relates to the technical field of information transmission, in particular to a multi-band processing system for electronic communication, which comprises a signal separation module, a frequency deviation compensation module, an amplitude correction module, a phase correction module and a dynamic feedback adjustment module. According to the invention, a plurality of frequency bands are extracted to execute band-pass filtering to separate signal components, short-time power spectral density is calculated to describe energy distribution, normalization coefficient operation is carried out to enhance frequency domain balance, high-energy component screening is carried out to reduce noise interference, signal effectiveness is improved, and frequency offset compensation calculates a change rate based on adjacent phase data and eliminates abnormal fluctuation. A time domain coefficient is adjusted to correct a frequency offset error, amplitude correction depends on short-time Fourier transform to screen the maximum amplitude to optimize signal consistency, phase adjustment fits a group delay error curve to optimize compensation parameters, dynamic feedback optimizes a strategy according to a signal-to-noise ratio to enhance compensation precision, and signal transmission quality and stability are improved.

The invention relates to the technical field of inverter control, and discloses an inverter output power control method and device, equipment and a storage medium. The method comprises the following steps: performing power frequency fundamental wave extraction and multi-band band-pass filtering on acquired voltage and current sampling data; performing phase compensation, first coordinate transformation and positive and negative sequence decoupling operation on the filtered fundamental wave principal component data; on the basis of a synchronous phase angle and a power grid fundamental wave frequency obtained by positive and negative sequence decoupling operation, carrying out second coordinate transformation, moving average and harmonic power delay difference operation on three-phase current and voltage data of an alpha-beta coordinate system; performing multi-level power calculation, power capacity distribution, error characteristic evaluation and decoupling operation on the inversion fundamental component and the inversion harmonic component subjected to the difference operation; and performing output signal modulation on the inversion power control quantity of the decoupling operation based on the fundamental wave synchronization phase angle, and generating a power control result. According to the invention, the control performance of the inverter on the output power in actual operation is improved.

The invention discloses an emotion recognition method based on electroencephalogram (EEG) and expression data, which combines fusion analysis of electroencephalogram (EEG) signals and facial expression data, and provides an innovative solution for the complexity and accuracy of emotion recognition. The method comprises the following specific implementation steps: 1, finding an optimal frame in an expression video by using multi-instance learning, extracting global visual features by adopting Swin Transform, and realizing dimension reduction and balance representation of cross-modal information through a cross attention mechanism; 2, preprocessing the electroencephalogram signal by using band-pass filtering and a fast ICA algorithm, and extracting effective features by using PSD; and 3, designing a fusion module based on Transform, combining the characteristics of the two modes, and giving full play to the complementary advantage of the modes. Experimental results show that 94.85% of classification accuracy is achieved in four types of emotion classification tasks, and compared with an existing method, the accuracy is remarkably improved.

The invention relates to the technical field of data transmission, and discloses an ultrahigh frequency RFID system data transmission method, device and equipment and a storage medium, and the method comprises the steps: carrying out the band-pass filtering and orthogonal demodulation of a multi-tag backscattering signal received by an RFID reader, and obtaining a tag feature data sequence; performing multi-dimensional feature analysis and time sequence correlation calculation to obtain an independent label signal frame; boundary identification is carried out, and a label data frame starting point is determined; carrying out continuous sampling analysis and label signal conflict identification, and outputting a conflict position sequence; performing matrix interleaving transformation and position rearrangement on the data marked by the conflict position sequence to generate an anti-conflict data packet; the label access scheduling queue is constructed based on the transmission time sequence and the priority attribute of the anti-collision data packet, the multi-label concurrent transmission time sequence is output, and the data transmission performance of the ultrahigh-frequency RFID system is improved through multi-stage signal processing and an intelligent scheduling mechanism.

The invention discloses a rotating component fault early warning method and device based on multi-modal data fusion. The method and device are used for solving the technical problem that an existing rotating component fault early warning method is poor in early warning effect. The method comprises the following steps: acquiring multi-modal signal data, and performing preprocessing according to the multi-modal signal data by adopting a band-pass filtering algorithm to generate a multi-modal feature vector set; performing feature fusion on the multi-modal feature vector set by adopting a recurrent neural network based on an attention mechanism, and outputting a target health index; based on a preset optimization search algorithm, adopting an initial hidden Markov model to generate a target hidden state sequence according to the target health index; and adopting a preset nonlinear Wiener degradation model to generate a rotating assembly fault early warning result according to the target hidden state sequence and the multi-mode signal data.

According to the cooling tower early fault early warning method based on vibration state monitoring, vibration signals and working condition labels of key parts of the cooling tower are synchronously collected through multiple channels, and data quality is improved through preprocessing operation such as band-pass filtering and normalization; time-frequency features are extracted in a multi-scale mode through self-adaptive variational mode decomposition and wavelet packet transformation, signal complexity is quantized through energy entropy, and weak fault detection capacity is enhanced; the obtained features are input into a deep belief network after being subjected to principal component analysis dimensionality reduction, and automatic classification and recognition of the equipment operation state are achieved; dynamic early warning grade adaptation is carried out according to an identification result in combination with a working condition label, the environmental adaptability and stability of early warning are effectively improved, the method further has the functions of early warning sample recording and periodic model iterative optimization, and the fault identification precision and robustness in a complex noise environment are remarkably improved.

The invention discloses a defect identification system of an ultrasonic flaw detector, and relates to the technical field of nondestructive testing, the system comprises a signal acquisition and preprocessing module, a defect feature identification module, a physical modeling analysis module, a life prediction and evaluation module and an intelligent decision visualization module; the signal acquisition and preprocessing module adopts a multi-frequency-point phased array transducer array, obtains an original signal through low-noise amplification, band-pass filtering and analog-to-digital conversion, and outputs a time domain signal matrix through adaptive noise reduction and gain compensation processing; the multi-frequency-point phased array transducer array and the advanced signal processing technology are integrated, the defect recognition precision and efficiency are remarkably improved, the system obtains high-quality original signals through low-noise amplification, band-pass filtering and analog-to-digital conversion technologies at first, then the high-quality original signals are subjected to self-adaptive noise reduction and gain compensation processing, and the defect recognition accuracy is improved. The background noise interference is effectively eliminated, and the purity of the signal is ensured.

The invention relates to a double-end traveling wave distance measurement method and device suitable for a power distribution network and a storage medium, and the method comprises the steps: firstly, carrying out the preprocessing of a fault phase current traveling wave through the normalization and rough characteristic frequency band zero-phase band-pass filtering of a double-end current traveling wave; using spline wavelet transform to calibrate a proper modulus maximum point as initial estimation of a wave head starting point; further estimating a wave head on the double-end time domain original traveling wave waveform by using a threshold method, and obtaining an accurate wave head starting point through double-end amplitude interpolation correction; and finally, calculating the position of a fault point according to a double-end traveling wave distance measurement principle. According to the method, a fault phase current waveform is preprocessed into a current traveling wave in a rough characteristic frequency band through a zero-phase filtering means, interference outside the characteristic frequency band is eliminated, a wave head break point serves as a wave head starting point, the break point is estimated step by step through a wavelet modulus maximum value and a signal amplitude threshold value method, and the fault phase current waveform is obtained. And more accurate approximation of the break point is obtained through double-end amplitude interpolation, so that the range finding precision exceeding the sampling rate limit is achieved.

A method and apparatus for mitigation of GNSS-signal interference using an adaptive notch filter (ANF) operates based on signals received from one or more satellites of a Global Navigation Satellite System (GNSS) such as GPS, GLONASS, etc. In one embodiment, an apparatus comprises a notch filter having a tunable zero frequency of a transfer function receives an input signal and generates an output signal. A bandpass filter coupled to the output of the notch filter receives the output signal. An adaptive block is coupled to the bandpass filter and adjusts the notch filter parameters in order to achieve the minimization of a specific cost function.

The invention belongs to the field of power system stability analysis, and discloses a response data and model fused subsynchronous oscillation parameter identification method for a wind power plant grid-connected system. Firstly, the current of a grid-connected line is monitored, the energy is calculated, and an early warning is given out when the energy is continuously increased along with time. Then, self-adaptive VMD decomposition is carried out on the early warning signal, and the system state is preliminarily determined; then, proper band-pass filter parameters are set according to the decomposition result, and filtering operation is carried out on the signals. And solving a complete SSO current component from the filtering signal according to a derived mathematical relationship between related electrical quantities and parameters of the series compensation power transmission line, and further obtaining an oscillation frequency and a damping factor. And finally, carrying out a simulation test based on series compensation capacitor grid-connected data of the direct-driven wind power plant. The oscillation identification method provided by the invention has strong noise immunity and robustness, and can realize accurate and rapid identification of the dominant SSO mode.

The invention provides a large-model cross-modal collaborative understanding method and device. The fusion efficiency and the understanding capability of multi-modal information can be improved. The large-model cross-modal collaborative understanding method comprises the following steps: preprocessing visual data, language data and sound data to obtain preprocessed visual data, language data and sound data; wherein the preprocessing comprises the steps of performing adaptive size adjustment and normalization on visual data, performing word segmentation and dynamic truncation on language data, and performing band-pass filtering and spectral noise reduction on sound data; extracting visual features, language features and sound features based on the preprocessed visual data, language data and sound data; on the basis of an adaptive mapping network and a mixed granularity cross-modal attention mechanism, performing feature alignment on the visual features, the language features and the sound features to obtain aligned feature vectors; and based on a dynamic routing architecture, fusing the aligned feature vectors to generate a unified multi-modal representation.

The invention relates to the technical field of electrical variable measurement, in particular to a rolling mill speed reducer monitoring and diagnosing system based on multi-source information fusion, which comprises an electrical parameter signal acquisition module for acquiring continuous readings of three-phase voltage and three-phase current of a rolling mill driving motor and synchronizing the readings. According to the method, the three-phase voltage and current data of the rolling mill driving motor are synchronously acquired, and the amplitude and phase are calibrated, so that a more accurate calibration electrical measurement set is obtained, and error propagation in a signal acquisition stage is effectively avoided; using the calibrated electrical data to directly calculate instantaneous input electric power, deducting motor loss to obtain an accurate motor shaft power sequence, and then using a Fourier transform method to extract key harmonic characteristics in a shaft power spectrum; and meanwhile, band-pass filtering and Hilbert transform analysis are carried out on the motor stator current, so that a clearer and more recognizable spectrum kurtosis value is obtained, and the judgment capability of current impact and periodic characteristics is enhanced.

The invention discloses an electromagnetic field observation data-based sea wave characteristic analysis method, which comprises the following steps of: processing electromagnetic field data by adopting a time-frequency spectrum analysis method, identifying distribution characteristics of electromagnetic field signals caused by sea waves at different time and frequencies through time-frequency decomposition, and extracting periods and time-varying characteristics of the sea waves; preprocessing the electromagnetic field data by using dominant frequency analysis and band-pass filtering technologies to obtain sea wave induced magnetic field time domain data; time domain and frequency domain induction electromagnetic field polarization direction analysis technologies are combined, and the movement direction of sea waves and the change characteristics of the sea waves along with time are inferred by measuring the change of the electromagnetic field polarization direction; the quantitative relation between the sea wave induction electromagnetic field intensity and the sea wave height is established, and the sea wave height change of the place is effectively estimated through the sea wave induction electromagnetic field intensity. By comprehensively analyzing the electromagnetic field data, the sea wave period, the time-varying characteristics, the motion direction and the sea wave height are evaluated, and a new way is provided for marine environment monitoring.

The invention discloses a body feeling evaluation method based on electroencephalogram-electromyographic signal fusion and dynamic interaction modeling. The body feeling evaluation method comprises the steps that EEG signals and EMG signals in the lower limb movement process of a subject are synchronously collected; carrying out band-pass filtering, artifact removal and wavelet transform processing on the acquired signals, extracting multi-channel time-frequency features, and forming a preprocessing feature matrix; fusing the time-frequency features of the EEG signal and the EMG signal, constructing a multi-modal feature set, and compressing feature dimensions by adopting a sparse coding method; inputting the compressed feature sequence into a neural network model combining a long short-term memory network and an attention mechanism, and carrying out dynamic interaction modeling; and an interaction index sequence is generated based on model output, and an interaction matrix is constructed through a sliding window and Gaussian kernel smoothing processing, so that visualization of brain-muscle interaction strength and dynamic quantification of a proprioceptive function are realized. The invention further provides a system for implementing the method. The method is high in objectivity, high in feature extraction precision and excellent in dynamic modeling capability.

The invention discloses a motor imagery electroencephalogram recognition method based on a domain adaptation network, and the method comprises the steps: collecting multi-channel motor imagery electroencephalogram signals EEG of a subject, carrying out the band-pass filtering, obtaining a plurality of EEG signals of different frequency bands, taking the EEG signals of the target subject as target domain data, and taking the rest as source domain data. Secondly, on the basis of the source domain data and the target domain data, source domain features and target domain features are obtained through a multi-view convolution Transform feature extractor composed of a parallel space-time convolution module and a Transform module in sequence; and finally, performing adversarial domain adaptation by using the source domain features and the target domain features to obtain a model capable of adapting to target domain data, and identifying a motor imagery category corresponding to the electroencephalogram signal. According to the method, source domain data distribution can be guided to be transferred to a target domain, and the classification accuracy of cross-subject motor imagery electroencephalogram signals is improved.

The invention belongs to the technical field of electrical safety, and particularly discloses an insulation resistance intelligent detection system and method for an energy storage converter, and the system is based on a bridge balance structure, integrates a temperature and humidity sensor and a voltage sensor, and collects environment parameters and bridge unbalance signals in real time. A digital potentiometer and a resistor array are driven through a PID algorithm to dynamically adjust bridge balance, and environmental interference is eliminated in combination with a temperature and humidity compensation model; and carrying out intelligent analysis on the preprocessed multi-dimensional data by utilizing a band-pass filtering and detection model, and outputting a high-precision insulation resistance value. The method achieves full-automatic detection, is high in precision, is quick in response, is suitable for complex working conditions, and remarkably improves the reliability of the insulation performance evaluation of the energy storage converter.

The invention discloses a vibration displacement measurement method and system based on multi-source data fusion, and belongs to the technical field of vibration measurement. The vibration displacement measurement method based on multi-source data fusion comprises the following steps: acquiring motion sequence images of an elastic tube and a rigid tube through a high-speed camera, and acquiring a vibration acceleration signal of the elastic tube through an acceleration sensor; displacement tracks, obtained by the high-speed camera, of the elastic tube and the rigid tube are extracted, and a flow-induced vibration displacement time travel curve of the elastic tube is separated; performing frequency domain analysis on the displacement track data, and determining frequency bands of the rigid tube and the elastic tube; carrying out band-pass filtering on signals of the acceleration sensor based on a frequency band of flow-induced vibration, and compensating an error of an integral trend term of the acceleration sensor by utilizing time domain displacement data of the rigid tube; and carrying out secondary integration on the filtered acceleration sensor signal, and outputting corrected displacement information. By adopting the vibration displacement measurement method and measurement system based on multi-source data fusion, the problem that effective unification of measurement precision and engineering applicability cannot be realized by an existing vibration measurement method can be solved.

The invention discloses a method for estimating the transmittance of a time-varying channel based on a frequency division multiplexing pilot frequency, and aims to solve the problem that the existing method for estimating the transmittance by adopting average transmittance does not have the capabilities of timely feedback and dynamic adjustment. The method comprises the following steps of: measuring a regular position component and a regular momentum component of a received signal by receiving a quantum signal and a pilot signal of frequency division multiplexing in a local local oscillator continuous variable quantum key distribution system generated by a transmitting end in real time; compensating phase noise caused by frequency offset and channel jitter due to different center frequencies of two independent lasers at a receiving end and a transmitting end by using a frequency offset compensation and phase recovery algorithm, and performing band-pass filtering to obtain a recovered pilot signal; and calculating the power ratio of the obtained pilot signal to the pre-calibrated pilot signal at each moment to obtain the time-varying free space transmittance. The performance change of the system in different time periods can be accurately reflected, the communication performance of the CVQKD system can be optimized, the success rate of key transmission can be improved, and the adaptability and robustness of the system can be improved.

The invention discloses a vibrating wire sensor fault diagnosis and repair method, system and device, and a storage medium. The method comprises the steps of collecting an output signal of a vibrating wire sensor; amplitude values of all the collected output signals of the vibrating wire sensor are amplified, and a band-pass filter circuit is used for filtering; inputting the amplified and filtered data into a pre-constructed fault diagnosis and recognition model, and performing feature extraction and classification so as to recognize the vibrating wire sensor with the fault and the fault type of the vibrating wire sensor; according to the fault type, different modes are selected to repair the vibrating wire sensor with the fault; the diagnosis result and the working state of the fault diagnosis and recognition model and the real frequency of the repaired vibrating wire sensor are displayed; and storing all the data for subsequent analysis and iterative updating of a fault diagnosis and recognition model. According to the invention, fault diagnosis and online real-time monitoring can be accurately and effectively carried out on the vibrating wire sensor, and the vibrating wire sensor with faults can be rapidly repaired.

The invention relates to the technical field of quantum sensing and ocean detection, and discloses an ocean magnetic field sensor based on an NV color center and an ocean magnetic field measuring method.The sensor comprises a cylindrical pressure-resistant cabin, an optical platform arranged in the pressure-resistant cabin and a main control panel; a laser, a beam shaping unit, a dichroscope, a beam shaping lens group, an objective lens, a diamond NV color center chip, a plano-convex lens I, a band-pass filter and a photoelectric detector are mounted on the optical platform; two microstrip loop antennas are mounted on the outer side of the diamond NV color center chip; an FPGA + ARM dual-core processor, a digital lock-in amplifier and two microwave signal generators are installed on the main control board, and the microwave signal generators are connected with the microstrip loop antenna and achieve electromagnetic coupling with the diamond NV color center chip. The invention has the advantages of high sensitivity, good long-term stability and strong anti-interference capability, and provides a new generation of quantum sensing solution for applications such as ocean geomagnetic monitoring and underwater target identification.

The invention relates to the technical field of power system power distribution network fault detection, in particular to an arc fault diagnosis and analysis method based on an artificial intelligence algorithm, and the method comprises the four steps: synchronous data collection and topological excitation, deployment of terminals at transformer area nodes, injection of characteristic current, and synchronous collection of response waveforms; signal preprocessing: separating a key frequency band through double-digital band-pass filtering, and calculating energy ratio and other enhanced fault features; performing multi-source feature fusion and intelligent diagnosis, constructing a vector containing statistical features, time domain distortion features and topology identification, and inputting a gradient boosting decision tree and deep neural network hybrid model to obtain fault confidence and type; and based on fault positioning and verification of topology, scheduling multi-node cooperative monitoring, and determining a fault point in combination with a topological relation. The method improves the data reliability and diagnosis precision, achieves the precise positioning of a fault, and guarantees the safe operation of a power distribution network.

The invention belongs to the technical field of aquaculture and food engineering, and relates to a fish anesthesia recovery activity prediction method based on acoustic signal time-frequency domain feature fusion. The method comprises the following steps of: acquiring acoustic respiratory vibration signals in real time in a fish anesthesia recovery process, performing 0.1-50Hz band-pass filtering, denoising and other pretreatment on the acoustic respiratory vibration signals, and extracting ZCR and Gammatone characteristic spectrums; meanwhile, fish swimming data are collected by adopting a YOLOv11 visual tracking algorithm, and activity grading standards are established by calculating activity indexes; zCR and Gammatone maps are used as double-branch input data of a deep learning model, corresponding activity grading data are used as labels, and a multi-category classification prediction model is constructed. According to the method, the anesthesia recovery state of the fish can be accurately predicted, and excessive stress and death loss in the transportation process are avoided; a non-invasive and intelligent detection means is provided for activity monitoring in the aquatic product transportation process, and the market application prospect is wide.

Disclosed in the present invention is a photometric stereo-based device for rapidly detecting surface features of an object, capable of rapidly acquiring a plurality of images meeting the requirements of a photometric stereo algorithm. The device comprises a light emitting assembly, an acquisition assembly located above the light emitting assembly, and a data processing module; the acquisition assembly comprises a light path system and a camera group; the light path system comprises: a beam splitting prism group used for splitting reflected light from the surface of an object into a plurality of beams of light and respectively outputting the plurality of beams of light in different preset directions to form images to be processed, and a plurality of bandpass filters correspondingly arranged on the output sides of the plurality of beams of light used for forming said images, wherein the central wavelengths of the bandpass filters are respectively configured to be different wavebands of the reflected light from the surface of the object; the camera group comprises a plurality of cameras which are arranged behind the bandpass filters in a one-to-one correspondence mode and acquire filtered light beams; and the data processing module receives image information sent by the cameras, and performs compensation and photometric stereo reconstruction on the image information, so as to obtain surface features of the object. Further disclosed is a method implemented on the basis of the device.

One or more systems, devices, methods of use and / or methods of fabrication provided herein relate to a device that can facilitate qubit measurement with isolation imposed between a quantum processor and a respective qubit measurement circuit and / or which respective qubit measurement circuit can have a small footprint, such as within a respective cryogenic chamber of a quantum system. According to one embodiment, a device comprises an isolator circuit having a bandpass filter configuration coupled between a pair of ports and the bandpass filter configuration comprising two or more poles. Two or more shunt resonators can be realized as the two or more poles, wherein the two or more shunt resonators can comprise DC SQUIDs and can be coupled together with one or more admittance inverters. A non-reciprocal signal transmission can be generated between the two ports by RF pumping the DC SQUIDs.

The invention provides a multiplexer and a detector. The multiplexer comprises a dielectric substrate, a connecting structure and a plurality of band-pass filters with different working frequencies. A conductive film layer is arranged on the plane of one side of the dielectric substrate and comprises a signal line and a grounding plate located on the side edge of the signal line. The connecting structure is arranged on the conductive film layer and comprises a main branch and a plurality of branches, and the main branch is connected with the branches and serves as an input port of the multiplexer. And the plurality of band-pass filters with different working frequencies are connected with the signal line and the grounding plate. Each band-pass filter comprises a first port and a second port, the first port is connected with the corresponding branch, and the second port serves as an output port of the multiplexer. The signal line, the grounding plate, the connecting structure and the band-pass filter can be located on the same side plane of the dielectric substrate, metal loss and radiation loss are reduced, and integration with other components of the detector is facilitated.