5 results about "Adaptive hardware" patented technology

The application provides an intelligent detection system of an RGB camera combined with a deep learning algorithm, comprising a defect feature adaptive engine, a hardware component, an algorithm component and a feedback component; the defect feature adaptive engine serves as a core control unit, links the hardware component, the algorithm component and the feedback component to realize full-link closed-loop detection; the defect feature adaptive engine performs the following operations: identifying scene working conditions and core working condition pain points, extracting the subdivided features of target defects in the scene and matching the feature label library, dynamically calling the adaptive hardware configuration, algorithm combination and preprocessing strategy, optimizing the detection parameters in real time, allocating the computing power resources according to the defect risk level and triggering the corresponding early warning, realizing the intelligent identification, positioning, quantification and grading early warning of defects of equipment such as pressure pipelines and pressure vessels, and through the implementation of the above system, the problems of poor adaptability, insufficient precision and unreasonable resource allocation of the existing system are solved, and the industrial detection is realized in a scene-based, refined and efficient manner.

The present application relates to the technical fields of intelligent wearable device and health monitoring, in particular to a health detection control method of intelligent wearable watch, comprising: multi-source data acquisition step: synchronously acquiring physiological indexes and situation indexes, and acquiring apparent physiological data and situation characteristic data in real time through a sensor network; situation semantic analysis step: based on situation characteristic data analysis, calculating situation metabolic equivalent expected value through a motion dynamics model; compensation evaluation solving step: extracting heart rate variability and recovery rate characteristics, calculating the difference between actual metabolic level and situation metabolic equivalent expected value to obtain compensation deviation degree, and comprehensively generating compensation exhaustion index; adaptive hardware control step: dynamically generating control instruction sequence based on the numerical interval of compensation exhaustion index, and adjusting the working state of the sensor network; the present application significantly reduces the overall power consumption of the system, eliminates invalid false alarms, and improves the monitoring specificity and robustness in complex application environments.

The application discloses a kind of based on radio frequency and communication circuit calibration system, the present application relates to wireless communication transmission technical field, calibration system includes following module: environment perception module, intelligent algorithm module, adaptive hardware module and interactive verification module, the advantages of the present application are: reinforcement learning agent and cross-domain transfer learning model collaborative optimization calibration parameter, combine multi-modal environment perception data dynamic adjustment radio frequency circuit center frequency and impedance matching value, programmable hypersurface array is based on electromagnetic property reconstruction and realizes millisecond level beamforming, FPGA real-time loading optimization parameter, calibration cycle is greatly shortened, AR interactive interface is simplified artificial operation by high-precision gesture mapping and high-frequency calibration technology, overall calibration efficiency is improved by more than 40%, bit error rate is reduced to extremely low level, meet the real-time demand of 5G / 6G high-frequency communication, break through the efficiency bottleneck of traditional artificial debugging.

The application discloses a cross-patient seizure detection method based on a graph-enhanced pulse state space model, first, the brain electrical signal data of a patient is acquired and an electrode space topology graph is constructed; then, a pulse state space model is used to extract the time sequence features of physiological channels, and a pulse graph convolution network is used to perform message aggregation between adjacent nodes in a discrete pulse domain across time steps; finally, feature aggregation is performed through a global average pooling operation at a graph level, and a two-class prediction probability is output through a fully connected classifier. The application follows the design logic of "first time and then space", the pulse state space model models the potential brain state dynamics of the time sequence change, and realizes stable long context feature processing through the cyclic updating mode of the adaptive hardware; the pulse graph neural network completes the information transmission and fusion between channels based on the electrode correlation graph, can capture the spatial dependence relationship which is crucial for the representation of seizures, reduces the calculation energy consumption, and improves the overall performance.

This invention discloses an adaptive, hardware-friendly hybrid quantization method based on deep neural networks. By analyzing the sensitivity of different network layers to quantization errors, it adaptively selects the optimal quantization strategy for each layer, thereby reducing the model's storage and computational resource consumption while maximizing model accuracy. The method includes: obtaining a full-precision deep neural network model to be quantized; extracting the weight parameters and activation parameters of each network layer; performing statistical analysis on the weight parameters and activation parameters of each network layer to construct an evaluation index for measuring quantization error; performing quantization sensitivity analysis on different network layers based on the evaluation index; adaptively determining the quantization method corresponding to each network layer based on the quantization sensitivity analysis results; performing hybrid quantization processing on the weight parameters and activation parameters according to the quantization method to obtain a hybrid precision quantization model; and completing the deep neural network inference computation based on the quantized weight parameters and activation parameters.