4533 results about "Identification technology" patented technology

The present invention relates to a digital asset transaction platform, comprising an asset entry device and a distributed accounting network. An asset is validated by the asset entry device. When compliance is achieved, asset project information is generated and is input to the asset transaction platform, wherein the asset project information includes a hash value of a digital asset certificate of the asset. The distributed accounting network comprises a plurality of accounting nodes, which are connected in a point-to-point manner and each store a transaction data information general ledger which is synchronized with each other and is based on a block chain database. The asset transaction platform supports mutual transaction of at least two different types of assets. The asset transaction platform provided by the invention adopts the block chain technology and the digital identity identification technology of assets and realizes de-centralization and trusted quick transaction of various assets.

The invention discloses a method for personalized television voice wake-up by voiceprint and voice identification, particularly a method for performing identity confirmation on a television user through voiceprint identification and controlling a television to perform personalized voice wake-up through confirmed identity and a voice identification result of user voice, and relates to voiceprint identification and voice identification technologies. A composition system comprises a voice control system (1), an information storage unit (2) and a television main controller (3) which are connected through electric signals. The method has the characteristics of short training time, very high voiceprint and voice identification speed and high identification rate. Voiceprint and voice identification can be finished by only offline training and testing, identification results do not need to be sent to a cloud server, use is convenient, and the safety of family information is guaranteed. The method also can be applied to user-personalized automatic voice channel change of the television, can be transplanted to a common high-speed DSP (digital signal processor) or chip for operation, and can be widely applied to the related fields of smart homes.

The invention provides a detection method and a device for a hidden process based on a virtual machine monitor, wherein the method comprises the steps of obtaining a user mode, a kernel mode and progress information in the virtual machine monitor; comparing the process information in the user mode with the process information in the kernel mode to obtain the hidden process in the user mode; comparing the process information in the kernel mode with the process information in the virtual machine monitor to obtain the hidden process in the kernel mode. The device comprises an acquision module, a first comparison module and a second comparison module. The scheme provided by the invention achieves multi-view hidden process detection and identification technology and provides good security for the virtual machine.

A biometric access control and time and attendance system comprises an integrated network including one or more remote access devices in electronic communication with a computer database. Each remote access device comprises a silicon chip based system and preferably includes a biometric input device, a liquid crystal display (LCD), computer processing capabilities based on embedded system architecture with configurable system-on-chip (CSOC) technology, and an electrical output for controlling a door lock or the like. The use of CSOC architecture in lieu of conventional personal computer technology (e.g. mother boards, hard drives, video controllers and the like) allows for a more compact and cost efficient design. A plurality of remote access devices is configured for communication with a primary computer database wherein data corresponding to biometric samples for all authorized users is stored. In an embodiment wherein the biometric input devices comprise fingerprint scanners, the devices are configured to facilitate fingerprint identification by incorporating an auto-targeting capability that enables the user to simply place his or her finger on the fingerprint scanner whereafter the system adjusts the scanned image by automatically shifting the scanned image data to a properly targeted position thereby enabling the system compare the scanned print to the biometric samples in the system's data storage memory. Auto-targeting capability eliminates the requirement for manual targeting present in systems of the background art thereby improving system performance and minimizing reliance on human interaction. The present invention contemplates the use of auto-targeting with other biometric systems, such as facial recognition and/or retinal scanning systems, or any other biometric identification technology.

The invention relates to a movement human abnormal behavior identification method based on template matching, which mainly comprises the steps of: video image acquisition and behavior characteristic extraction. The movement human abnormal behavior identification method is a mode identification technology based on statistical learning of samples. The movement of a human is analyzed and comprehended by using a computer vision technology, the behavior identification is directly carried out based on geometric calculation of a movement region and recording and alarming are carried out; the Gaussian filtering denoising and the neighborhood denoising are combined for realizing the denoising, thereby improving the independent analysis property and the intelligent monitoring capacity of an intelligent monitoring system, achieving higher identification accuracy for abnormal behaviors, effectively removing the complex background and the noise of a vision acquired image, and improving the efficiency and the robustness of the detection algorithm. The invention has simple modeling, simple algorithm and accurate detection, can be widely applied to occasions of banks, museums and the like, and is also helpful to improve the safety monitoring level of public occasions.

Wireless personal area network (Zigbee, Bluetooth, UWB) and wireless identification technologies (Near Field Communication (NFC), Radio Frequency Identification (RFID)) are implemented in particular client server functions and communications. Connected with an Authentication Server, a wireless HUB authenticates user identification and provides the user with access to secure data communication with a wireless terminal such as a cellular phone or a PDA. A Location Server provides user locations via methods such as RSSI, TDOA, and GPS and sends location information to a Center Control Server and the Authentication Server. With location information, the Center Control Server initiates and optimizes secure information processes and coordinates the functions of servers and user terminals.

The embodiment of the invention provides a man-machine interaction system, which comprises wearing equipment and a mobile terminal, wherein the wearing equipment is used for receiving and recording voice information of users and sending the voice information to the mobile terminal after the communication is built with the mobile terminal, the mobile terminal is used for carrying out voice identification on the voice information for obtaining instructions of users. The embodiment of the invention also provides a man-machine interaction method and a man-machine interaction device. The man-machine interaction system provided by the embodiment of the invention has the advantages that the voice information of users through the wearing equipment is firstly received and recorded, in addition, after the communication is built with the mobile terminal, the voice information is sent to the mobile terminal, and then, the mobile terminal identifies the voice information through the voice identification technology for obtaining the instructions of the users, so the users do not need to take out the mobile terminal for carrying out any operation, the voice identification technology can be applied, the man-machine interaction intelligentization is realized, convenience is provided for the users, and the experience of the users is improved.

An automated identification, weight measurement and data reporting system that allows users to have their weight measured at a remote location and sent electronically to a data center. The invention can use biometric identification technology to confirm the identity of the user at the time of the measurement of the user's weight. The user's body weight may be measured via an electronic scale connected to the biometric identification device. The measurement data is then sent via a network (such as the Internet) to a central database. This secure weight measurement data is then used to offer incentives to individuals to lose weight. The more weight an individual loses, the larger the earned incentives. The invention also includes programs to measure health functions and to provide incentives to improve their health including body fat reduction, quitting smoking, reducing their blood pressure, or other health function.

An in vivo face detection method based on a convolutional neural network relates to the machine learning and mode identification field, and is provided to face a cheat problem in the face identification. A conventional face identification technology is very easy to attack, the attackers frequently copy the faces of the legal users by the methods, such as the photographs, the videos, the 3D models, etc., if a face identification system can not distinguish the real faces and the fake faces effectively, the invaders can pass the face identification system very easily by the fake identities. Based on the problem, the present invention provides the in vivo face detection method based on the convolutional neural network. The convolutional neural network in the method provided by the present invention is realized based on a cuda_convnet framework, the network structure comprises four convolutional layers of two max-pooling layers, one full connection layer and one soft_max layer, and the soft_max layer comprises two nerve cells used for predicting the probability distribution of the real and fake faces.

The invention belongs to the radiation source signal identification technology field and particularly relates to a radar radiation source signal identification method based on the one-dimensional convolutional neural network. The method comprises steps that (1), a radar radiation source signal data set is generated; (2), data pre-processing is carried out; (3), the convolutional neural network is constructed; (4), parameters are set, and the convolutional neural network is trained; (5), prediction classification is carried out; (6), precision calculation is carried out; and (7), the result is outputted. The method is advantaged in that 1, a network structure of the convolutional neural network is utilized to carry out characteristic extraction of signals, and a manual characteristic design process in a traditional algorithm can be avoided; 2, signals with a signal to noise ratio lower than -10dB can be accurately identified; and 3, the method can be simply realized.

The invention relates to a multiple-template matching identity recognition method based on an ECG (Electrocardiogram) under an electrocardiogram abnormality state, and belongs to the technical field of biological characteristic identity recognition. The ECG data of a user to be recognized is compared with the data of a registered user in a template library to obtain an identity recognition result. The key technology of the method comprises the following steps: carrying out electrocardiosignal preprocessing for eliminating noise interference; carrying out electrocardiosignal decomposition to separate an electrocardiogram waveform of each period; carrying out standardized processing for independently achieving standardization on time and amplitude scales; carrying out characteristic extraction: in the step, characteristics are extracted by wavelet transform, and clustering analysis is carried out by an ISODATA (Iterative Self-organizing Data Analysis Techniques Algorithm) so as to construct an ECC template library; and carrying out correlation analysis: in the step, correlation between ECG test data and each template is calculated, an optimal matching template is selected, and finally, an identity recognition result is obtained. The multiple-template matching identity recognition method provided by the invention utilizes the intrinsic electrocardiosignal of a human body to recognize an identity, and the ECG data under the abnormality state is considered.

In one aspect, the present disclosure is directed toward a fastener tightening system. The system has a tightening tool configured to apply a torque to a fastener. Additionally, the system has a fastener data storage device located on the fastener and configured to store data related to the associated fastener. The system further has at least one component data storage device located on a component receiving the fastener to store data related to a tightening process. A data sensor is also included and is configured to sense data stored on the fastener data storage device and the at least one component data storage device. The system also has a controller configured to regulate operation of the tightening tool based on the sensed data and the sensed parameter.

Systems and methods are provided for monitoring transmissions of media content (such as audio and audiovisual content) in order to obtain independent and objective data regarding the use of specific media content recordings or works within the transmissions. Processing and reporting of such data is provided in various ways to serve a variety of business needs. Methods for employing content identification technology to efficiently and automatically obtain reliable, accurate, and precise monitoring data are also disclosed. Various information products and services based on such monitoring systems are proposed.

The invention discloses a traffic sign deep learning mode identification method. The method includes the following steps: pretreatment is performed on a test sample and, a training sample of a trafficsign image, a residual deep learning network based on the multiscale characteristic weight operation fusion of a convolution nerve network is designed, the characteristic is automatically extracted by the network in order to eliminate the artificial trace mainly through training, and identification is performed on the traffic sign test sample by using a deep color characteristic trained classifier. The image characteristic weight operation combining with the multiscale convolution fusion network is applied to the traffic mode identification technology, the training efficiency of the network is substantially improved, and the problem that the accuracy and the real-time performance are not ideal, the network structure is complex, the training time is long, the stability and the robustness are bad encountered in the traffic sign identification method can be effectively solved. The image recognition accuracy of the trained network in 43 kinds of traffic signs reaches 97%.

The invention discloses an advertisement injecting effect evaluation and intelligent push decision method based on face identification and big data analysis. The advertisement injecting effect evaluation and intelligent push decision method comprises a face identification technology is employed to obtain face feature data of the audience group, and the data is introduced into an advertisement injecting effect evaluation module as parameters, and is used for quantifying the advertisement injecting effect; furthermore, historical data is stored in a database, a data analysis system can carry out further analysis, the analysis result can be provided for the advertiser as an injecting decision reference, and can also be provided for the advertising agency who can carry out reasonable bidding and charging on the advertising position. At the same time, an FAEM matching algorithm is employed to match the advertisement played at present with audience face feature data at present, and advertisement push is carried out in dependence on the matching result. According to the invention, accurate evaluation of the advertisement injecting effect can be given out scientifically and objectively, and advertisements can be injected selectively and intelligently in dependence on audiences of the advertisements.

The invention discloses a biometrics-based device for detecting identity of people and identification, which comprises an omni-directional vision sensor used for acquiring the facial image of an ID card holder, an ID card reader used for reading the photograph information in a bulit-in chip of the second-generation ID card and a microprocessor used for comparing whether the facial photograph imagestored in the built-in chip of the second-genration ID card and the facial images acquired by the omni-directional vision sensor are consistent or not, wherein the microprocessor comprises a data module used for reading photograph image in the ID card, a first facial detecting module, a first facial feature extraction module, a panoramicpicture reading module, a second facial detecting module, animage expanding module, a second facial feature extraction module, an identity detecting module for the biological feature of people and the ID card and a decision-layer voting and processing module.The device adopts biometrics identification technology, can greatly improve the efficiency for validating the ID card, and reduce the working strength and error rate of office personnels.

The invention provides a physical education teaching auxiliary system based on a motion identification technology and an implementation method of the physical education teaching auxiliary system. The system is applicable to a personal computer (PC) or an embedded host. The system comprises a movement data collection module, a movement data acquisition module, an identification and training module and a virtual teaching environment module. The device is characterized in that: a micro-inertia measurement unit and an inertia parameter extraction unit are arranged in the movement data collection module; and a movement information resolving unit transmits data which is output by the inertia parameter extraction unit to a multi-sensor data fusion unit for resolving. The movement situation of a target is reflected comprehensively in an inertia tracking mode and an optical tracking mode, so a tracking range is effectively expanded, measurement accuracy is improved, and the problems that the integral information of the target cannot be acquired in the inertia tracking mode, complicated movement identification cannot be performed and sensitivity is poor are solved. A brand new teaching mode is provided for physical education teaching, and a physical education teaching method is digitalized, multi-media and scientifically standardized.

The invention disclose a bar code image identification method, belonging to the field of computer information automatic identification technology, the method comprises the following steps: S1. carrying out gradation to a colored image which is captured by an Android mobile phone camera, wherein the colored image is a bar code image; S2. conducting median filtering processing to a grayed image; S3. conducting binaryzation processing to the image which carries out the median filtering processing; S4. conducting bar code positioning to the image which carried out the binaryzation processing; S5. conducting geometric distortion correction to the positioned image; and S6. decoding the corrected image, so as to obtain a code word of the image. In the invention, the precision of the bar code identification is improved and the complexity is reduced.

The invention discloses a rolling bearing fault identification method under variable working conditions based on ATT-CNN, and relates to a rolling bearing fault identification technology. The problemthat the generalization ability of an existing rolling bearing fault recognition method under variable working conditions is limited to a certain extent for a complex classification problem is solved.The method comprises the following steps: firstly, mapping vibration data to a nonlinear space domain through a convolutional neural network (CNN), and adaptively extracting rolling bearing fault characteristics under variable working conditions by utilizing the characteristic that the CNN has invariance on micro displacement, scaling and other distortion forms of an input signal; Secondly, an attention mechanism (ATT) thought is put forward to be fused into a CNN structure, and the sensitivity of bearing vibration characteristics under variable working conditions is further improved; And meanwhile, more abundant and diverse training samples are obtained through a data enhancement method, so that the network can be learned more fully, and the robustness is improved. The proposed fault diagnosis model based on the attention mechanism CNN (ATT-CNN) can realize multi-state recognition and classification of the rolling bearing under variable working conditions, and compared with other methods, higher accuracy can be obtained.