83 results about "Signal perception" patented technology

The invention relates to the technical field of wifi (Wireless Fidelity) wireless positioning, in particular to a reverse synchronous perception-based wifi wireless positioning method and a reverse synchronous perception-based wifi wireless positioning system, which realize precise positioning. The positioning method comprises two steps, i.e. a training step and a positioning step, wherein the training step is only required to be implemented for one time; after the training step is finished, the positioning step can be implemented for any times. The positioning system comprises a synchronous controller, at least three wifi signal perception devices and a positioned mobile terminal. The method is different from the traditional method for positioning the mobile terminal by sensing a fixed wifi signal source of each position, by the positioned mobile terminal in that the positioned mobile terminal serves as the wifi signal source, and the mobile terminal is positioned through synchronous perception on the signal source of the positioned mobile terminal by a plurality of wifi signal perception devices with fixed positions.

The invention provides an AR-oriented navigation type interactive paradigm system. The system comprises an input and perception module, a fusion module and an application expression module; the inputand sensing module obtains hand skeleton depth information through kinect to obtain a gesture depth map, and gesture recognition is completed; t experimental keywords are classified through voice keyword recognition and extraction, andthe similarity probability is obtained through similarity calculation to complete voice recognition; the fusion module classifies user intentions by calculating signal perception of different states in different modes according to gesture recognition, voice recognition and force feedback expression, determines multi-mode intentions and sets an intention expert knowledge base; and the expression module is used for carrying out voice navigation and visual presentation on the virtual chemical experiment and carrying out perceptual force feedback expression. Gesture and voice multi-modes are fused by adopting a natural interaction technology, and a natural interaction mode is explored, so that the user load is reduced, and different user experiences are brought.

The invention relates to a falling protection system and a protection method. The protection system comprises a sign perception device fixed on the low back of a user, wherein the sign perception device comprises a signal perception and positioning unit, a safety controller, a 4G communication circuit and a perception device bluetooth circuit; the safety controller and the sign perception device are in power supply connection; the sign perception and positioning unit, the 4G communication circuit and the perception device bluetooth circuit are respectively connected with the safety controller; an air bag helmet is worn round the neck of the user, and two air bag kneecaps are enclosed on knees of the user. According to the protection system and the protection method, the air bag helmet and the air bag kneecaps are started before the user falls down, the impact on the critical part of the user in the falling process can be reduced, and the injury to the user in the falling process can be effectively reduced.

The invention relates to an intelligent monitoring system for an operation and maintenance state of power equipment. The system comprises a signal sensing device, a data acquisition device and an information operation diagnosis device which are connected in sequence, the signal sensing device comprises distributed optical fiber sensors which are distributed and installed on power equipment to be tested, and the information operation diagnosis device stores programs for executing the following steps that data reading trigger signals are sent out at regular time; and based on the collected data,prediction processing is performed by using the constructed prediction model based on the linear autoregression method, thereby realizing data flow optimization and obtaining final sampling data of the to-be-tested power equipment, and the monitoring result of the to-be-monitored power equipment is obtained based on the final sampling data. Compared with the prior art, the intelligent monitoringsystem has the advantages of reducing the defect occurrence rate of an optical fiber detection system, improving the working efficiency, ensuring safe and efficient operation of a power grid and the like.

The invention provides a video semantic communication method based on hierarchical knowledge expression, and mainly aims to solve the problems of incomplete semantic extraction, insufficient semantic representation capability and redundant semantic description in the prior art. According to the implementation scheme, the method comprises the following steps: constructing a hierarchical knowledge base consisting of a multi-level signal sensing network, a semantic abstract network, a semantic reconstruction network and a video reconstruction network; collecting a to-be-transmitted video signal; extracting structured semantic features of the video signals based on a signal sensing network and a semantic abstract network in the hierarchical knowledge base, and transmitting the structured semantic features through an ultra-narrowband channel; and reconstructing a video signal by using a semantic reconstruction network and a signal reconstruction network in the hierarchical knowledge base according to the structured semantic features. According to the method, semantic features of different scales are mined, and the semantics are structurally represented by using the structured data, so that the integrity of semantic extraction is improved, the semantic representation capability and the communication bandwidth utilization rate are improved, and the method can be applied to online conferences, human-computer interaction and intelligent Internet of Things.

The embodiment of the invention discloses a test method, device and equipment and a storage medium, and relates to the technical field of intelligent transportation. The specific implementation schemecomprises the steps: receiving V2X traffic signal road side information transmitted by road side equipment through a vehicle-mounted unit in the unmanned vehicle; determining traffic signal sensing information according to the map data in the unmanned vehicle and/or the environmental data sensed by the unmanned vehicle; and verifying a V2X traffic signal transmission function according to a comparison result between the V2X traffic signal road side message and the traffic signal sensing information. According to the embodiment of the invention, the unmanned vehicle technology and the V2X technology are combined, the test data form a closed loop based on the lane-level high-precision map and environment perception functions of the unmanned vehicle, the V2X traffic signal transmission function is automatically tested, the manual participation is greatly reduced and the test efficiency and accuracy are improved.

Disclosed is a signal perception method and a device, comprising first measuring the energy of the received signal, wherein the adjacent symbol pair of the signal is complementary; then determining the authorization user when the channel is in the working state if the energy of the received signal exceeds the scheduled threshold value, else showing that no authorization user is present in the working state. Due to no corresponding silence period in the implementing process in the embodiment of the invention, the interruption of the communication operation can be avoided, meanwhile, no channelestimation is performed in advance, thereby effectively simplifying the processing procedure of the perception authorization user.

The invention discloses a parallel nesting and autonomous preferential classifier for intelligent artificial limb brain-myoelectricity fusion perception. A multi-convolution neural network classifieris constructed, time span characteristic changes of electroencephalogram, myoelectricity and brain-myoelectricity fusion characteristics are considered, brain-myoelectricity weight indexes are constructed by measuring brain and myoelectricity activity degrees and characteristic levels, and the brain-myoelectricity weight indexes participate in construction and training of the classifier, so that the classifier can autonomously and intelligently adapt to a time span brain-myoelectricity fusion signal perception recognition task, and autonomous decision of optimal classification is realized. Compared with a traditional classifier which is simple in signal source and does not have an intelligent preferential function, the classifier has more excellent signal analysis performance and self-adaptive capability, and is suitable for equipment with time span model updating requirements such as rehabilitation artificial limbs.

The invention relates to the technical field of interference resistance of wireless communication equipment, and discloses an ultra-short wave radio station interference protection device which comprises an interference sampling antenna array, a radio frequency switch module, a radio frequency signal conditioning module, an adjustable radio frequency agility module and a digital signal processing module. A non-cooperative interference cancellation algorithm module, a signal intelligent sensing algorithm module and a display control algorithm module are arranged in the digital signal processing module. According to the interference protection device for an ultra-short wave radio station, the difference in the aspects of transmission channels and waveform characteristics of communication signals and interference is utilized, cancellation algorithm parameters are configured through signal perception andsignal quality of feedback, loops of signal perception, interference cancellation and signal quality evaluation feedback are formed, and non-cooperative interference signals are adaptively separated and cancelled.

The invention discloses an AE network intelligent sensor system, which aims at the distributed remote dynamic real-time monitoring of equipment and is composed of several main parts of an acoustic emission sensor, an embedded system, mobile agent cloud computing and the like, i.e. an acoustic emission sensor module, a signal acquisition module, a microprocessor module and a data transmission module are integrated into a sensor network node, and real-time cooperative processing is carried out on sound emission signals by utilizing a cloud computing technology to realize real-time monitoring onthe state of equipment or a key structure body, and the sound emission sensor adopts a 50KHz-1MHz broadband piezoelectric acoustic emission sensor for signal perception of the system. The acoustic emission network intelligent sensor system has the advantages that the design scheme based on cloud computing solves the problem that data management and distributed remote testing are difficult to carryout in a centralized mode in traditional acoustic emission detection. The strong computing capability of cloud computing can be used for quickly processing the acoustic emission signals, the real-time requirement of field detection is met, and the detection cost is undoubtedly reduced for distributed remote tests popularized in a large area.

The invention relates to a wearable passive fetus sound monitoring system and device based on a piezoelectric technology. The wearable passive fetus sound monitoring system comprises a passive signal perception module, a processing module, a communication module and a display module, wherein the passive signal perception module perceives each vital sign signal of a fetus and a pregnant woman, the perceived vital sign signals are processed in real time by virtue of the processing module, the display module receives and displays a curve of each vital sign signal of the fetus and the pregnant woman, the passive signal perception module is connected with the processing module, and the processing module is connected with the display module by virtue of the communication module. Vital signs of the pregnant woman and the fetus are monitored in real time in a 24-hour way, vital sign signal fluctuation curves of the fetus and the pregnant woman are compared and analyzed, and a multifunctional alarm is carried out during abnormality; meanwhile, the pregnant woman can consult a big data platform online doctor by virtue of one key if having any questions or problems; the abnormality can be beneficially and timely found, and measures can be timely taken; and the sensitivity is high, the measured data is precise, a passive mode is safe and nonradiative, and convenient use and good experience feeling are realized.

The invention discloses a nonlinearity indeterminacy time-lag system robustness control cloud network perceptual signal identification method. The method comprises the steps of controlling perceptual signal conversion through defining cloud theory nonlinearity indeterminacy time-lag system robustness H8, determining a decomposed type cloud network controlled perceptual signal algorithm of cloud network controlled perceptual signal cloud theory nonlinearity indeterminacy time-lag system robustness H8 controlled perceptual signal conversion, and conducting perceptual signal time-frequency distribution predication on the basis of general signals of the cloud network controlled perceptual signal of the cloud theory nonlinearity indeterminacy time-lag system robustness H8 controlled perceptual signal conversion. The method effectively realizes collection of identified cloud network controlled perceptual signals and general signal sensing time-frequency distribution predication of cloud network controlled perceptual signals, well solves the problem that short time nonlinearity indeterminacy time-lag system robustness H8 controlled perceptual signal conversion in an existing method can not simultaneously give consideration to time resolution ratio and the cloud network controlled frequency perceptual signal resolution ratio.

The invention belongs to the technical field of inertial signal perception and recognition and Parkinson's disease intelligent medical treatment, particularly relates to a wearable device for quantifying and recognizing dyskinesia of a Parkinson's disease patient, and aims to solve the problem of lack of a reliable and non-invasive wearable device for accurately quantifying and recognizing dyskinesia of the Parkinson's disease patient. The method comprises the steps that a wrist inertia measurement unit and an ankle inertia measurement unit which are worn on different sides of a measured object and the body of a healthy person are used for collecting a wrist movement signal and an ankle movement signal and extracting the characteristics of the signals; and the extracted features are sent to an output processing terminal, and the disease degree of the tested object is obtained through a preset Parkinson's disease patient dyskinesia quantification and identification method and then is output. The device is simple in structure, easy to operate, proper in size, convenient to wear and low in power consumption, and the judgment result is accurate and reliable in combination with movementsignals of the wrist and the ankle.

The invention discloses an ultra-short wave threat signal sensing method based on support vector data description. The method mainly comprises a signal acquisition module, a time-frequency analysis module, a data dimension reduction module, a support vector data description model training module, an online threat sensing module and the like. The method mainly comprises the following steps: acquiring our signals in a space by using a radio frequency board card; performing time-frequency analysis on the baseband complex signal according to a short-time Fourier transform theory to generate a time-frequency spectrogram, and constructing a our signal time-frequency spectrogram database; converting the time-frequency spectrogram into a low-dimensional feature vector according to an image preprocessing technology and a principal component analysis theory; and training a support vector data description model to construct a minimum hypersphere capable of including all target samples, determining our signal by the samples falling into the hypersphere, and determining the samples located outside the hypersphere as potential threat signals. According to the method, the support vector data description model of the non-threat signals is constructed only by utilizing a signal database of our party, so that the problem of opening sets of the threat signals is effectively solved.

The invention belongs to the technical field of computer virtual reality simulation, and particularly relates to a large-scale scene infrared imaging real-time simulation method for an airborne enhanced synthetic vision system. The invention relates to a forward-looking infrared sensor which is typically configured for a modern airplane. The invention discloses a physical model based on infrared radiation. Establishing signal perception simulation model, under the action of corresponding attributes described by a virtual environment, detection signals are output in real time through a virtualreality simulation engine, a flight instrument is superposed, the infrared video imaging working process of an enhanced synthetic vision system of an airplane in the flight process and image degradation caused by an airborne severe imaging environment are simulated, and real-time simulation of a large-scale scene is achieved. According to the method, the comprehensive effect of target infrared radiation is simulated by comprehensively utilizing factors such as infrared band radiation characteristics, geometric characteristics of the target and atmospheric effect, so that detail information isnot distorted, artificial traces such as manual mapping in a traditional algorithm are avoided, and the method has a relatively good calculation effect and simulation precision.

An image signal processing method, which is an image signal processing method for performing nonlinear correction on an image signal input to a display device (103), characterized in that: according to the brightness of a place where the display device (103) is installed, the nonlinear Modified properties. According to this method, even if the display luminance of the display device (103) is uniformly increased due to the influence of ambient light, the characteristic of brightness perceived by human beings with respect to an image signal can be made linear.

The invention discloses a topology recognition method and system for a heterogeneous wireless network. The method comprises the steps that a home-network device acquires time domain energy signals of data transmission among all other-network devices; the home-network device separates the time domain energy signals into an independent signal frame sequence and obtains signal frame feature information; the home-network device carries out cluster analysis according to the signal frame feature information so as to obtain an other-network neighbor list which represents the corresponding relation between all signal frames and the other-network devices; signal frame alignment and other-network device aggregation are realized according to the other-network neighbor list; the transfer relation among the signal frames is concluded according to the results of signal frame alignment and other-network device aggregation so as to obtain local topology information of the other-network devices, and all the local topology information of the other-network devices is converged to a home-network gateway, so as to reconstruct the global topological relations of other networks in a monitored range of a home network. According to the method, the recognition on topologies of heterogeneous other networks is formed through signal perception and analysis and is used for carrying out local (or global) communication coordinating and scheduling on the home-network device, and the use efficiency of spatial frequency spectra is increased.

The invention relates to a distributed radio monitoring network. The distributed radio monitoring network comprises a miniaturization receiver module cluster, a management station, a client terminal and a network connecting device; the miniaturization receiver module cluster is used for signal perception, the management station is used for network management, and the client terminal is used for executing monitoring tasks. The monitoring network is based on a TCP/IP protocol family to achieve communication, the client terminal and receivers transmit commands and data according to a TCP protocol, and the management station searches for the receivers according to a UDP protocol and multicast. The distributed monitoring network is adopted, the receivers can be closer to a signal transmission source in geographic position, and therefore the signal transmission possibility before signals are received is decreased; a monitoring device does not need high performance any more, and cost is reduced.

The invention provides a flexible sensor for sensing multi-modal muscle movement signals, and belongs to the field of sensor devices. The structure of the sensor is formed by laminating and connectingmultiple layers of films with the same shape, and the multiple layers of films at least comprise a first insulating layer, an electromyographic signal sensing layer, a second insulating layer, a muscle deformation signal sensing layer, a third insulating layer, an electromyographic signal sensing layer and a sensor protection layer in sequence from bottom to top. Multi-modal signals generated atthe same position of the surface during muscle movement can be measured at the same time, and the multi-modal signals comprise electromyographic signals, electromyographic sound signals and muscle deformation signals. The flexible sensor has the advantages of being easy to wear, homologous in measurement signal and the like. The deviation of a split type sensor in homologous signal measurement canbe effectively reduced, the measurement error caused by abnormal deformation due to the fact that muscles are pressed by a hard material or a rigid body structure is reduced, and the comfort of a wearer in the human-computer interaction process is improved.

The invention discloses an intelligent gear with a state perception function. A gear body and balance weight holes are included; a transmission state monitoring device and a clump weight are arrangedin the balance weight holes; the transmission state monitoring device comprises a shell, a sensor circuit board and a power source unit; the sensor circuit board is provided with a conditioning circuit unit, a control unit, a clock unit, a storage unit and a wireless unit I; and the signal inlet end of the conditioning circuit unit is connected with an acceleration sensor unit, a strain sensor unit and a temperature sensor unit. According to the intelligent gear with the state perception function, a micro sensor is directly installed at the position, needing to be monitored, of a mechanical transmission device, the signal perception strength is large and reliability is high; due to the embedded design, the sensing system is small in volume and high in integrated degree, the intelligent gear is directly installed in a gear box, the complicated mechanical environment in a box body can be overcome, and influences by the installing space are reduced; and the beneficial effects of microminiaturization, low cost, low power consumption, high efficiency and high precision are achieved.

The invention provides a signal processing method and device. The method is applied to wearable equipment, wherein the wearable equipment is provided with a biosensor and a body movement detection device group; the method comprises the following steps: acquiring human physiological signals collected by the biosensor and dividing the human physiological signals into a plurality of signal frames, wherein each signal frame corresponds to a time range; for each signal frame, determining whether a wearing user is in a body movement state or not according to body movement signals collected by the body movement detection device group in the time range corresponding to the signal frame; and if not, storing the signal frames in a preset cache. Body movement is sensed through the body movement signals collected by the body movement detection device group, and the human physiological signals collected by the biosensor are filtered, so that the filtered human physiological signals all are signalsof the wearing user in a resting state, the stability and reliability of medical health monitoring and diagnosis of the wearable equipment are improved, and the false alarm rate is reduced.