408 results about "Motion artifacts" patented technology

The invention provides an artificial intelligence-based ultrasonic image data classification method and system, and the method comprises the steps: firstly obtaining a real-time ultrasonic scanning signal sequence containing the time sequence change characteristics of a tissue elastic parameter and a hemodynamic parameter, carrying out the noise suppression and motion artifact compensation processing, generating a standardized ultrasonic image sequence, and marking the coordinates of an anatomical boundary; then performing multi-scale anatomical structure decomposition on the ultrasonic image to obtain a local feature map set of different organization levels, inputting the local feature map set into a cascade deep classification network, and realizing cross-frame feature fusion and dynamic weight adjustment through spatial-temporal feature alignment and a multi-granularity attention distribution module to obtain a spatial-temporal feature fusion model; and the abnormal region classification probability distribution and the spatial topological relation graph are output, finally, a multi-modal diagnosis report is generated according to the abnormal region classification probability distribution and the spatial topological relation graph, an interactive three-dimensional visual interface containing risk level labels and treatment suggestions is generated after the multi-modal diagnosis report is compared with historical cases, and ultrasonic image classification accuracy and diagnosis efficiency are improved.

The invention discloses a time-space alignment fusion processing method and system for multi-source physiological signals, and relates to the technical field of data processing.The method comprises the steps that the multi-source physiological signals in the limb movement state are synchronously collected, and a heterogeneous time sequence data set is obtained; performing space-time alignment processing on the heterogeneous time sequence data set to generate a synchronous physiological signal matrix; performing signal quality evaluation based on the synchronous physiological signal matrix, constructing a weighted decision tree model, performing confidence fusion on the synchronous physiological signal matrix according to the weighted decision tree model, and generating multi-parameter joint monitoring data; and performing motion artifact suppression processing on the multi-parameter joint monitoring data, outputting a physiological parameter index set, and transmitting the physiological parameter index set to a first-aid equipment monitoring terminal. Therefore, the technical effects of eliminating signal distortion, improving monitoring data quality and ensuring first-aid monitoring precision are achieved.

The invention discloses an exoskeleton motion signal peak valley intelligent detection method and system, and relates to the technical field of computer assistance. The method is used for solving the problems of control delay and high misjudgment rate caused by large motion signal noise interference and inaccurate processing in an exoskeleton system. The method comprises the steps of firstly, suppressing motion artifact noise and generating a high-signal-to-noise-ratio preprocessing signal through multi-modal signal collaborative noise reduction and dynamic energy entropy segmentation, secondly, constructing a parallel convolution attention network to extract multi-modal features, and extracting peak and valley candidate points in combination with dynamic weight fusion and multi-scale differential detection; a peak valley point set is optimized based on a variable structure density sensing clustering algorithm and density gradient analysis, artifact interference is eliminated, finally, a multi-rule confidence model is constructed by fusing time sequence prediction of a bidirectional gating circulation unit and biomechanical correlation, and a threshold value is dynamically adjusted to trigger an exoskeleton joint assistance instruction. Closed-loop processing from signal acquisition to real-time control is realized, and peak valley detection precision and response speed are remarkably improved.

The invention discloses a multi-mode pet health monitoring and motion artifact elimination method and system based on a millimeter wave radar, and relates to the technical field of pet health monitoring, and the method comprises the following steps: S001, building a unified time baseline and an energy fingerprint auditing surface, constructing an energy distribution model from a chest to a tail, and taking the model as a reference for artifact evolution, identifying a signal spectrum coupling trend in a time-frequency domain; and S002, based on the energy distribution model, performing causal playback on continuous time sequence signals acquired by the millimeter-wave radar, extracting a pseudo-motion energy nucleus caused by tail or limb movement, and calibrating a phase anchor point and a space observation area of a respiratory signal. According to the method, multi-source physiological data are fused, pure respiratory signals are extracted through energy distribution modeling, causal playback, phase anchor point calibration and three-dimensional resampling, risk assessment is achieved based on physiological credibility tensor, and the accuracy and anti-interference capacity of health monitoring are improved by combining phase conjugate traction and a space-time regulation strategy.

The invention relates to a plastic film defect detection device in the field of detection equipment, which is provided with a fixed clamping assembly and a movable clamping assembly to tension intermittently conveyed plastic films section by section instead of a floating roller tensioning mode adopted by a traditional film detection device, so that film vibration and motion artifacts during continuous conveying and shooting of the films are reduced, and the detection efficiency is improved. The shot plastic film has better flatness, the quality of the shot image is improved, and the accuracy of defect detection is further improved; meanwhile, the two ends of the to-be-detected film are clamped and fixed through the fixed clamping assembly and the movable clamping assembly, the probability that a tearing opening is expanded to a non-detection part of the film after the film is tensioned and torn is reduced, and the output of plastic film waste during detection is reduced; in addition, through the cutting assembly and the secondary feeding mechanism, cutting of the unqualified part of the film and secondary feeding of the part to be detected are achieved.

The invention relates to the technical field of intelligent wearable devices, and discloses a multi-modal positioning and health monitoring combined method and device, and the method comprises the steps: obtaining first multi-modal sensor data of an intelligent wearable device, and carrying out the two-domain signal representation separation, and obtaining a physiological signal representation vector and a motion artifact representation vector; performing comparison loss optimization of motion posture modulation to obtain a first physiological signal representation and a first motion artifact representation; performing feature enhancement to obtain a second physiological signal representation and a second motion artifact representation; performing position-sensitive contrast characterization joint optimization and layered contrast characteristic distillation to obtain a lightweight model, processing data of the second multi-mode sensor through the lightweight model, and outputting a pure physiological signal and a target positioning result. The problem that the performance of a traditional attention mechanism is reduced at a motion conversion point is solved; and the adaptive capacity to a complex motion scene is obviously enhanced.

The invention discloses an electroencephalogram signal artifact removal method and an acquisition system. The method comprises the following steps: synchronously acquiring sensor data of a testee and each lead electroencephalogram signal by utilizing a unified trigger signal; performing timestamp alignment verification on the two types of synchronous data; a motion state quantitative index is determined, and if the motion state quantitative index does not exceed a preset motion threshold value, the collected electroencephalogram signals are output; if yes, an exception processing flow is triggered and comprises the steps that self-adaptive filtering and nonlinear compensation are sequentially executed on the electroencephalogram signals, static electroencephalogram data of the testee serve as reference in the self-adaptive filtering process, an NLMS filter with the step length dynamically adjusted is adopted, a motion artifact linear part and base noise are restrained, a self-adaptive filter is constructed based on the second-order Volterra series in the nonlinear compensation process, and the motion artifact linear part and the base noise are restrained; a kernel coefficient is optimized through an RLS algorithm, and nonlinear distortion is processed; and outputting the electroencephalogram signal after the exception processing. The method is suitable for non-linear and non-stationary motion artifact signals, signal synchronism and quality can be improved, and processing efficiency is considered.

The invention discloses a space-time speckle projection three-dimensional imaging method based on multi-frame optical flow alignment. Firstly, a projector based on DLP is used for projecting a space-time speckle pattern to a measured scene, and a binocular camera synchronously collects a three-dimensional space-time speckle image. The calibration parameters of the binocular camera are used to carry out stereo correction on an acquired original speckle image, and a parallax image is generated frame by frame in combination with a coarse-to-fine single-frame speckle matching strategy. And estimating a two-dimensional inter-frame displacement field between continuous disparity maps by using an optical flow method by taking an intermediate frame disparity map as a reference, compensating motion artifacts in a space-time speckle image, and ensuring strict space-time registration of a dynamic target. And based on the speckle image after motion correction, a speckle matching strategy is expanded to a time-space domain, and high-precision multi-frame three-dimensional measurement of a complex dynamic scene is realized. The method is suitable for performing rapid and high-precision three-dimensional modeling on a moving target in an unstructured environment, and can perform accurate three-dimensional measurement on a high-speed dynamic target undergoing any translation or rotation motion.

The invention discloses a digital imaging method of an ear-nose-throat examination endoscope, and relates to the technical field of medical treatment, and the method comprises the following steps: synchronously collecting a white light image, a photoacoustic signal, a stimulated Raman spectrum, pressure sensor data and a physiological signal of a target orifice through a multi-physical field probe; constructing a four-dimensional tensor bound with the anatomical features; based on a vocal cord vibration fundamental frequency harmonic characteristic optimization graph convolutional network, dynamically constructing an adjacent matrix and coupling cross-modal characteristics to generate a submucosal lesion enhanced image; a deformable convolutional network constrained by vocal cord biomechanics is adopted, and the shape of a convolution kernel is dynamically adjusted according to the relation between real-time strain and elastic modulus, so that motion artifacts caused by swallowing actions are inhibited; generating a curvature-driven asymmetric convolution kernel based on ear canal spiral geometry, and executing super-resolution reconstruction in combination with confrontation training of fractal constraint; and fusing the white light image gradient and the pressure gradient field, and outputting a three-dimensional lesion contour consistent with the anatomical structure.

The invention discloses an electroencephalogram signal processing method and system based on motion artifact prediction. The method comprises the following steps: acquiring experimental electroencephalogram data when a testee executes a motion imagination task; discrete wavelet transform is carried out on experimental electroencephalogram data, and signals are decomposed into low-frequency components and high-frequency components through a low-pass filter and a high-pass filter; inputting the low-frequency component into a pre-constructed and trained ARIMA model, and predicting to obtain a linear artifact; inputting the high-frequency component into a pre-constructed and trained XGBoost regression model, and predicting to obtain a nonlinear artifact; combining the linear artifacts and the nonlinear artifacts to generate a complete artifact prediction signal; real electroencephalogram signals are separated through difference value calculation of the experimental electroencephalogram data and the artifact prediction signals. According to the method, the time sequence change of the motion artifacts is predicted by utilizing the ARIMA model, and the nonlinear artifact features are captured in combination with the XGBoost regression model, so that the motion artifacts can be effectively removed, and purer electroencephalogram signals can be recovered.

The invention provides a textile cloth defect real-time detection method and system based on multi-modal feature fusion, and the method comprises the steps: collecting visual image data, infrared thermal imaging data and ultrasonic acoustic data of textile cloth through a multi-sensor array, and forming multi-modal input; performing time sequence alignment and noise filtering preprocessing on the multi-modal data to eliminate motion artifacts and environmental interference; a parallel feature extraction module is used for extracting texture features from the visual data, extracting temperature distribution features from the thermal imaging data and extracting acoustic impedance features from the acoustic data. By deeply fusing complementary information of three modes of vision, thermal imaging and ultrasonic wave, the detection capability is improved, the vision mode captures surface texture details, the thermal imaging mode reveals thermodynamic anomalies related to friction and materials, the ultrasonic wave mode perceives subcutaneous structure defects, hidden flaws which cannot be recognized by a single mode can be found, and the detection efficiency is improved. Therefore, the omission ratio is greatly reduced, and flaw types are distinguished more accurately.

The invention relates to the field of real-time wireless monitoring, in particular to a multi-parameter real-time wireless monitoring integrated device and method, and the device comprises a multi-sensor data collection module, a multi-modal fusion processing module, an anomaly detection analysis module and a multi-stage alarm module. The multi-sensor data acquisition module eliminates motion artifacts by using a generative adversarial network and acquires various physiological signals; the multi-modal fusion processing module realizes space-time alignment and correlation modeling by means of linear interpolation and a graph convolution network to generate a joint feature vector; the anomaly detection and analysis module presets a causal relationship based on a causal graph, optimizes a model through a weight matrix, and judges a root cause through anti-fact reasoning; the multi-level alarm module distinguishes alarm levels according to the risk scoring model and processes the alarm levels; the device and the method solve the problems of low precision, poor collaboration and the like of a traditional monitoring technology, realize multi-parameter accurate monitoring, intelligent analysis and efficient alarm, and can be widely applied to scenes such as remote medical monitoring and the like.

The invention relates to a mechanism and data combined dynamic PET-CT pharmacokinetic fusion modeling method, and belongs to the technical field of pharmacokinetic modeling, and the method comprises the steps: obtaining original image data of a historical case, and constructing a training data set according to the original image data; constructing a dynamic pharmacokinetic model based on a mechanism model and a data-driven network, and completing the training of the dynamic pharmacokinetic model according to the training data set; collecting actual image data of the current subject in a period of time; and inputting the actual image data into a trained dynamic pharmacokinetic model to obtain a prediction result. According to the method, the mechanism model and the data driving network are fused, the complete pharmacokinetic process can be accurately predicted only through image data in a short time in the early stage, the problems that a traditional model depends on long-time scanning and is easily interfered by motion artifacts and the like are solved, the parameter analysis efficiency and accuracy are greatly improved, and the method is suitable for clinical rapid diagnosis.

An OCTA image motion artifact and noise parallel removal method based on self-supervised learning comprises the steps of constructing an OCTA image data set, learning a mapping relation between a low-quality image and a high-quality image by using a deep learning method, and further constructing an OCTA image motion artifact and noise parallel removal model. And inputting a low-quality image into the model to realize OCTA image motion artifact removal and noise reduction. According to the method, the OCTA image data set is constructed through an image processing and calculation method, the complexity of data set construction is reduced, and the diversity and representativeness of the data set are enhanced. Model construction is a self-supervision mode, and the processing capacity of the model on complex image features is enhanced by combining a window, a channel attention mechanism and a multi-scale noise reduction module. A composite loss function is constructed by using wavelet transform and a mean square error loss function, and the recognition and removal capability of the model on artifacts and noise is optimized.

The invention discloses a strip steel outlet detection method and related equipment, and relates to the technical field of industrial automatic detection, and the method comprises the steps: obtaining image data of a target detection point in a strip steel outlet area; target detection is conducted on the image data based on a strip steel state detection model, the abnormal state of the strip steel is determined, and the abnormal state comprises the steel stacking state, the steel clamping state or the strip steel arching state; performing motion artifact compensation verification on the abnormal state based on the roller way vibration amplitude data to obtain a verification result; and generating a control instruction based on the verification result and the abnormal state to adjust the operation action of the production line. According to the method, manual monitoring is replaced by automatic detection, the omission ratio and the misjudgment rate are reduced, response and interlocking control of the abnormal state are achieved, non-planned shutdown of a production line is effectively avoided, and continuity and high efficiency of the production process are guaranteed.

The invention discloses a motion artifact elimination method and system based on PPG signals, and relates to the technical field of electronic digital data processing, in particular to a high-accuracy and high-robustness artifact elimination method oriented to the PPG signals in wearable equipment. Firstly, artifact detection is carried out through a multi-base learner fusion model based on meta-learning, and efficient identification of different individual motion artifacts is realized; afterwards, for the detected artifact signals, the system introduces an AVAE model to carry out artifact removal, through multi-loss function joint optimization, high-quality PPG signals with time sequence details reserved are recovered, an effective solution is provided for high-quality perception and robustness health monitoring of the PPG signals in the wearable device in a complex dynamic environment, and the method has the advantages of being high in accuracy and high in robustness. Good popularization and application prospects are realized.

The invention discloses a patch position correction method and system, relates to the technical field of biomedical engineering and digital health, and adopts a hardware-level time synchronization and motion-artifact coupling modeling technology to effectively solve the problems of time delay drift and motion interference of signal acquisition in a dynamic environment and remarkably improve the basic quality of electrocardiosignals. Secondly, by establishing a body surface reference coordinate system and a pose compensation parameter generation algorithm, the micro-displacement state of the patch can be accurately recognized, and visual position adjustment guidance is generated, so that the patch is always kept at the optimal measurement position, and the phenomenon of signal attenuation caused by poor contact is fundamentally reduced; a signal quality multi-dimensional evaluation system adapts to quality discrimination requirements in different motion states through an intelligent weighted fusion mechanism, and combines an adaptive filtering and deep learning enhancement technology, so that the morphological integrity of a QRS waveform is kept, various interference components are effectively inhibited, and the system can still keep excellent electrocardiosignals in a strenuous motion state.

The invention relates to the technical field of health management, in particular to a teenager scoliosis intelligent monitoring system based on multi-modal sensing. Comprising a multi-modal data acquisition unit; the data preprocessing unit is used for performing standardization processing on the multi-source original data output by the multi-modal data acquisition unit, eliminating environmental interference and motion artifacts based on an adaptive Kalman filtering algorithm, and extracting quantifiable physical characteristic parameters; an intelligent analysis unit; and an early warning execution unit. According to the invention, the optical image sensor and the inertial sensor are combined through the multi-modal data acquisition unit, and accurate matching calibration of static morphological parameters and dynamic motion data is realized through a space-time association algorithm of the data association module, so that the problem of insufficient complementarity caused by lack of space-time association of multi-modal data in the prior art is solved; a complementarity data set containing static and dynamic features can be formed.

The invention relates to the technical field of image processing and biological signal detection, discloses a facial physiological detection method and system based on signal quality driven ROI selection, and aims to solve the problem of signal degradation of a traditional fixed geometric ROI in a complex scene. The method comprises the following steps: collecting a user face video stream through a camera and preprocessing the user face video stream; detecting a face bounding box and dividing the face bounding box into a plurality of sub-regions; calculating the signal-to-noise ratio, the periodic intensity and the motion artifact interference degree of each sub-region; screening an optimal sub-region according to a weighted fusion formula to generate a dynamic ROI mask; extracting a pure rPPG signal from the dynamic ROI mask coverage area; detrending and band-pass filtering are carried out on the rPPG signals, and physiological parameters such as the heart rate and the blood oxygen saturation degree are extracted. The system comprises a face video acquisition module, a face region positioning and segmentation module, a signal quality evaluation module, a dynamic ROI selection module, an rPPG signal extraction module and a physiological parameter estimation module. According to the technical scheme, signal degradation caused by local shielding, illumination abrupt change or attitude offset can be effectively avoided, the signal-to-noise ratio and the stability of the rPPG signal are remarkably improved, and the universality and the robustness of the method are enhanced.

The invention discloses a multi-modal imaging system and method for a small animal living body. The system comprises a multi-modal fusion imaging module which is responsible for multi-modal image acquisition and primary processing; the real-time dynamic monitoring module is used for capturing bioluminescence / fluorescence signals in real time through a photon counting detector, receiving optical, nuclear medicine and magnetic resonance data, correcting motion artifacts based on a multi-scale space-time registration engine and adjusting scanning parameters through a real-time pharmacokinetic-physiological feedback mechanism; the low-radiation and biological compatible module is used for automatically optimizing the dosage of a tracer agent according to the weight of the animal, the scanning part and historical data by using a dosage prediction model; and the multi-modal data fusion module is used for performing non-rigid registration of multi-modal images based on a Transform cross-modal registration network, constructing a multi-species pharmacokinetic knowledge graph by utilizing multi-species metabolism chip data, integrating mouse, dog, primate and humanized liver chip data, and mining a cross-species metabolism rule through a graph neural network.

The invention relates to a method for identifying a hypoventilation type, computer equipment and a storage medium. The method comprises the following steps: acquiring breathing state data of a target user; determining breathing characteristic information according to the breathing state data; the breathing characteristic information comprises a breathing peak change rate, a breathing pressure change rate and a breathing flow ratio; and according to the breathing peak change rate, the breathing pressure change rate and the breathing flow ratio, the hypoventilation type of the target user is determined. By means of the collected breathing state data, whether a hypoventilation event occurs or not can be judged, the specific hypoventilation type can be determined in combination with the characteristics of the hypoventilation type, only the breathing-related data is collected, the obtained data is not affected by motion artifacts and changes of the elastic inertia of the pipeline, and the accuracy of hypoventilation is improved. The accuracy of the data for determining the hypoventilation type is improved, so that the accuracy of identifying the hypoventilation type is improved.

The invention relates to electroencephalogram signal acquisition, in particular to an electroencephalogram signal acquisition and monitoring system, which comprises a mobile terminal, a signal acquisition device, an EEG (electroencephalogram) signal, an fNIRS hemodynamic signal and IMU (inertial measurement unit) motion data, a physical information neural network method of a self-adaptive sampling strategy is adopted to identify a BCG vascular pulse pseudo-film segment in an EEG signal by using an fNIRS hemodynamic signal, and then a multiple linear regression model of motion artifacts is established based on the fNIRS hemodynamic signal and IMU motion data so as to identify a motion pseudo-film segment in the EEG signal. Performing interpolation restoration on the detected pseudo-film segments to eliminate artifacts in the EEG electroencephalogram signals, and drawing and displaying an electroencephalogram in real time according to the EEG electroencephalogram signals after the artifacts are eliminated; according to the technical scheme provided by the invention, the defect that BCG vascular pulsation artifacts and motion artifacts in the EEG signals are difficult to effectively eliminate can be effectively overcome.

The invention discloses a liver cancer focus segmentation method and system based on dynamic feature fusion, and relates to the technical field of liver cancer diagnosis assistance, and the method comprises the steps of multi-source data collection, feature extraction, dynamic fusion, segmentation output and result verification. A 4D image, a multi-posture liver image and a synchronous physiological signal are obtained through the multi-source data acquisition module, features are extracted through the static and dynamic feature extraction module, weights are distributed through the dynamic fusion module, and then processing is conducted through the segmentation output and result verification module. Through cooperative processing of the multi-source data acquisition module, the feature extraction module and the like, a real focus boundary and a motion artifact can be accurately distinguished, and the missing detection condition of a tiny focus caused by fuzzy boundary is effectively reduced; meanwhile, the omission ratio caused by the atypical single posture feature is greatly reduced, the identification accuracy is improved by capturing the stability difference of benign and malignant lesions, finally precise segmentation of the liver cancer focus is achieved, and a reliable basis is provided for clinical diagnosis and treatment decision making.

The invention relates to the technical field of image recognition, in particular to a medical image intelligent evaluation system based on image recognition. The system comprises an image registration module, an image segmentation module, a preliminary fusion module, an image evaluation module, an optimization feedback module and an image output module. According to the method, the CT image and the MRI image are subjected to image registration, spatial alignment is ensured, then the region of interest is segmented and fused, namely, the skeleton contour in the CT image is superposed on the MRI image, and due to the fact that motion artifacts generated by movement of a patient in the scanning process possibly exist in the original CT image, the skeleton contour in the CT image is fused with the motion artifacts in the MRI image. If the skeleton contour does not exist in the MRI image, the overlapping degree and the blank degree of the skeleton contour and the anatomical structure edge of the MRI image are analyzed, and an optimized registration parameter or segmentation parameter is fed back, so that when the segmentation network is trained, the segmentation precision under the conditions of artifacts and low contrast is improved, spectrum and texture information of the two images is reserved to the maximum extent, and the fusion effect is guaranteed.

The invention provides an intelligent motion evaluation and damage early warning method and system based on multi-source biological signal fusion, and is applied to the technical field of medical data processing. The method comprises the following steps: acquiring a multi-modal biological signal acquired based on a multi-source sensing node; carrying out self-adaptive preprocessing on the multi-modal biological signals, eliminating motion artifacts by adopting an improved spectral subtraction formula, realizing multi-rate synchronization through a Lagrange kernel, triggering node vibration by virtue of signal quality evaluation to prompt electrode reattachment, and generating preprocessed signal features; and constructing cross-modal fusion features by using multi-scale cavity convolution and the like, and outputting results such as a motion mode and the like. A target depth model is trained in combination with signal optimization and data enhancement and the like, a motion pattern classification result, joint load estimation data and a damage risk scoring result are processed based on the target depth model, damage early warning information is generated, and accurate and dynamic motion evaluation and damage early warning are achieved.

The invention discloses a scanning structured light microscopic imaging method and system, and the method comprises the steps: generating a vortex structured light field with spiral phase distribution according to the three-dimensional morphology characteristics of a target sample, synchronously collecting a reflection signal and a fluorescence signal, and obtaining a multi-mode excitation light field data set; performing multi-modal signal fusion processing according to the multi-modal excitation light field data set to generate a multi-dimensional fusion feature map; and according to the multi-dimensional fusion feature map, performing three-dimensional image reconstruction processing by adopting a physical model constrained compressed sensing reconstruction algorithm and combining prior topological information of the sample, and inhibiting motion artifacts and optical diffraction noise through a dynamic sparse base optimization technology to obtain an artifact-inhibited high-resolution microscopic image. According to the embodiment of the invention, motion artifacts and optical diffraction noise can be effectively inhibited, and high-resolution microscopic images can be obtained.

The invention provides a breast lump ultrasonic image classification method and system based on deep learning, relates to the technical field of ultrasonic image classification, realizes integrated processing of various ultrasonic image types through a unified feature extraction model, improves the diagnosis efficiency, and improves the diagnosis accuracy. Meanwhile, the influence of uneven probe pressure, equipment noise interference and motion artifacts on the imaging quality is quantified based on an image quality score generated based on the physical basic characteristics, and the knowledge enhancement intensity is dynamically adjusted according to the influence, so that the pathological sensitive characteristics are accurately compensated under the clamping of a medical knowledge graph; a hierarchical decision-making mechanism is established through a two-stage cascade classification network, subtype deep analysis is started for malignant lesions on the premise that rapid diagnosis is ensured, and the recognition rate is improved; and a two-factor dynamic calibration system is formed by combining model cognitive factors based on decision logic self-consistency to provide support for efficient and accurate classification of breast lump ultrasonic images.

The invention discloses a millimeter wave radar health monitoring method and system based on wearable equipment, and relates to the technical field of non-contact physiological signal monitoring, the specific steps are as follows: low-frequency life signals of a monitored object are collected based on frequency modulation continuous waves emitted by a radar system, and the low-frequency life signals are time sequence signals of multiple channels; performing wavelet packet decomposition and adaptive filtering on the low-frequency life signal to obtain a filtered signal; and inputting the filtered signal into a trained feature extraction and separation model, carrying out feature extraction and evaluation, and outputting a heartbeat signal and a respiration signal. According to the method, a strong anti-interference preprocessing process is constructed by fusing wavelet packet dynamic decomposition and a sub-band energy entropy threshold method and combining an NLMS adaptive filtering technology, motion artifacts and environmental noise can be effectively stripped, the signal-to-noise ratio of original millimeter wave radar signals is increased, and the anti-interference performance of the millimeter wave radar signals is improved. And a solid foundation is laid for subsequent accurate extraction of weak vital sign signals (respiration and heart rate).

The invention provides an injection molding machine man-machine cooperation intelligent control system comprising an acquisition device used for acquiring state data of an operator; the signal preprocessing module is used for eliminating motion artifacts in the state data by adopting an improved multivariate empirical mode decomposition algorithm; the cognitive state decoding module is used for inputting a concentration time sequence of state data and outputting cognitive state parameters of an operator based on a space-time diagram convolutional network model; the digital twin synchronization engine is used for realizing millisecond-level alignment of injection molding machine data and state data through an OPC UA protocol; and the collaborative optimization engine is used for dynamically adjusting the injection molding process of the injection molding machine and the complexity of a human-computer interface based on a depth deterministic strategy gradient algorithm. By integrating a functional operator state monitoring technology, a digital twinborn model and a dynamic optimization algorithm, real-time perception of an operator cognitive state and intelligent regulation and control of injection molding process parameters are realized.

The invention discloses a medical CT (Computed Tomography) communication method for high-precision synchronization of multi-source data and a CT system. Wherein the rotary control unit and the fixed control unit are connected with each other through a private wireless link, take an FPGA / MCU platform as a core, and realize microsecond-level synchronization by utilizing a unified clock source, a hardware timestamp and round-trip delay correction; during scanning, the fixing unit returns multi-source data such as bed codes, electrocardio and high-voltage parameters in real time, and the rotating unit fits and triggers instantaneous bed information based on cached data and sends the instantaneous bed information to the image reconstruction unit after the instantaneous bed information is strictly aligned with detector data. According to the scheme, traditional high-voltage cables and drag chains are replaced, hidden dangers of insulation aging, mechanical abrasion and data interruption are eliminated, and the fault rate is remarkably reduced; meanwhile, motion artifacts are inhibited, and the image definition and real-time performance are improved. Protocol multiplexing and dynamic bandwidth allocation further reduce delay and bit error rate, and support seamless expansion of subsequent modules.