37256results about How to "Improve detection efficiency" patented technology

A body scanning system includes a CT transmitter and a PET configured to radiate along a significant portion of the body and a plurality of sensors (202, 204) configured to detect photons along the same portion of the body. In order to facilitate the efficient collection of photons and to process the data on a real time basis, the body scanning system includes a new data processing pipeline that includes a sequentially implemented parallel processor (212) that is operable to create images in real time not withstanding the significant amounts of data generated by the CT and PET radiating devices.

The disclosed electrochemical screening and early diagnostic device for malignant tumor comprises: an immune detection chip (1) connected to a time-resolution multichannel potentiostat (2) that connects to a data process and display system (3) by an interface. Wherein, the chip (1) comprises eight working electrodes fixed different antigen films and Ag wire (a), Ag/AgCl reference electrode (b), a carbon-pair electrode (c), and an insulation film (d). This invention needs low cost, has intelligence and fit to fast detection.

The invention describes radiation cameras consisting of interlaced scintillation fiber arrays and a fiber readout method using pixellated photo-detector cameras. Several fabrication methods of the fiber arrays are described. The use of such Radiation Cameras in Medical Imaging systems is also described.

A method for the high power generation and detection of terahertz radiation is presented. It comprises of an optical waveguide with a core, and a mostly hollow cladding or terahertz wave transparent material surrounding the core. The cladding region is a terahertz waveguide. A pump light source is coupled to the core to promote nonlinear optical process, such as Raman scattering, in the core which in turn leads to terahertz radiation being emanated or received through fiber cladding.

The invention relates to a rapid target detection method based on a convolutional neural network, and relates to the computer vision technology. The rapid target detection method comprises the following steps: training convolutional neural network parameters by utilizing a training set; solving the problem of max-pooling losing feature by using an expander graph and generating a discriminative complete feature graph; regarding the full-connection weight of the convolutional neural network as a linear classifier, and estimating the generalization error of the linear classifier on the discriminative complete feature by using a probable approximately correct learning framework; estimating the required number of the linear classifiers according to the generalization error and the expected generalization error threshold value; and finally, completing the target detection on the discriminative complete feature graph by using the linear classifiers on the basis of a smooth window. The detection efficiency and the target detection precision are obviously improved.

A body scanning system includes a CT transmitter and a PET configured to radiate along a significant portion of the body and a plurality of sensors configured to detect photons along the same portion of the body. In order to facilitate the efficient collection of photons and to process the data on a real time basis, the body scanning system includes a new data processing pipeline that includes a sequentially implemented parallel processor that is operable to create images in real time notwithstanding the significant amounts of data generated by the CT and PET radiating devices.

The invention provides a security detection method and system of an Android application program, aiming to solve the problems of an antivirus method of the existing Android platform that the scanning speed is slow and the false positive ratio is high. The method comprises the following steps of: scanning an Android installation package and extracting appointed characteristic information from the Android installation package; finding characteristic records which are matched with the appointed single characteristic information or a combination thereof from a pre-arranged security identification base, wherein the security identification base comprises the characteristic records and security grades corresponding to the characteristic records, and each characteristic record comprises single characteristic information or the combination of the characteristic information; and respectively displaying the security grade corresponding to the found characteristic records in a security detection result of the Android installation package. The security detection method and system of the Android application program disclosed by the invention have the advantages of fast scanning speed and high searching and killing accuracy.

A welding visual detection method based on a convolutional neural network includes the following steps that firstly, at the training stage, a training sample is input into the convolutional neural network, and the connection weight and offset value of the convolutional neural network are obtained; secondly, at the testing state, a welding picture is read in and preprocessed with the digital picture processing technology, and a region of interest is extracted and then subjected to picture size normalization processing to serve as input of the convolutional neural network. The invention further provides a welding visual detection device based on the convolutional neural network. The welding visual detection device based on the convolutional neural network comprises a crawling mechanism, a power transmission mechanism, visual detection equipment and a weld defect detection and analysis system. By means of the welding visual detection method and device based on the convolutional neural network, the automaton and intelligence level is improved, the detection precision is effectively improved, and the detection speed is effectively increased.

The invention relates to voice detection technology in an automatic caption generating system, in particular to a self-adaptive voice endpoint detection method. The method comprises the following steps: dividing an audio sampling sequence into frames with fixed lengths, and forming a frame sequence; extracting three audio characteristic parameters comprising short-time energy, short-time zero-crossing rate and short-time information entropy aiming at data of each frame; calculating short-time energy frequency values of the data of each frame according to the audio characteristic parameters, and forming a short-time energy frequency value sequence; analyzing the short-time energy frequency value sequence from the data of the first frame, and seeking for a pair of voice starting point and ending point; analyzing background noise, and if the background noise is changed, recalculating the audio characteristic parameters of the background noise, and updating the short-time energy frequency value sequence; and repeating the processes till the detection is finished. The method can carry out voice endpoint detection for the continuous voice under the condition that the background noise is changed frequently so as to improve the voice endpoint detection efficiency under a complex noise background.

The invention provides a security detection method and system of an Android application program, solving the problems of low scanning speed and high false alarm rate of the traditional virus killing method of an Android platform. The security detection method comprises the steps of: scanning an Android installation package, extracting appointed characteristic information from the Android installation package; uploading the appointed characteristic information to a server, searching a characteristic record matched with appointed single characteristic information or combination from a security identification base preset in the server; and receiving a security detection result returned by the server aiming at the Android installation package, and displaying on a client end user interface, wherein the security detection result includes a security level corresponding to the characteristic record searched by the server. The client end detection is combined with the server detection, thus the security detection method is high in scanning speed and high in killing accuracy rate.

The embodiment of the invention provides a neural network training and construction method and device for object detection, and an object detection method and device based on a neural network. The neural network training and construction method for object detection comprises the steps: inputting a training image comprising a training object into the neural network, so as to obtain a prediction boundary frame of the training object; obtaining a first loss function according to the ratio of the intersection area and union area of the prediction boundary frame and a real boundary frame, wherein the real boundary frame is the boundary frame of the training object marked in the training image in advance; and adjusting the parameters of the neural network at least through the first loss function, so as to carry out the training of the neural network. According to the invention, the first loss function is employed for the regression of a target boundary frame into an integrated unit, and the object detection precision of the neural network is remarkably improved. Moreover, the training and detection efficiency of the neural network can be effectively improved through two branch structures of the neural network.

The invention discloses a text line extraction method and device. The method comprises the following steps of: obtaining a sample; detecting Characters in a document image; forming each candidate character box containing characters; and aggregating the candidate character boxes into one or more target text areas, the target text areas comprising at least one candidate character box, characters inthe at least one candidate character box belonging to at least one text line of the document image, and finally extracting each text line in the target text area. Visibly, the candidate character boxes are aggregated; aggregating the candidate character boxes of the document image into a target text area; According to the text line extraction method and the text line extraction device, each text line is extracted from the target text area, and various rules do not need to be set according to priori knowledge such as colors and sizes to define which candidate character boxes can be combined into the text line, so that the text line extraction method not only improves the accuracy of the extraction result of the text line, but also improves the detection efficiency.

The invention provides a detector for binary-code buffer-zone overflow bugs, and a detection method thereof. The detector comprises six functional modules, namely a debugger module, an input point positioning module, a disassembling module, a symbolic execution module, a loop operation analysis module and an intelligent FUZZ test module. Differing from static symbolic execution, the detection method dynamically loads programs through the self-developed debugger module to synchronously performing symbolic execution and single-step practical execution, and then applies a model to solve each difficulty in binary code analysis so as to test path coverage and detect overflow bugs in accordance with all executable programs of PE format under a Windows platform. The detection can also provide propagation paths of input data and other important information related to bug formation cause while positioning the bugs, only analyzes the selves of the executable programs, needs no source codes, is unrelated to the species of source codes and development tools, and is extensive in applicable occasions, so the detection method has good prospects for popularization and application.

The invention relates to the field of the car electronic and artificial intelligent field, and particularly discloses an active-mode intelligent safety monitoring instrument for a car and a monitoring method thereof. The active-mode intelligent safety monitoring instrument for the car comprises an embedded system (comprising a microprocessor, a monitoring program and relevant algorithm, which are cured inside a chip), a GPS module, a GSM module, an SIM card reader, an alarming device, a CCD camera, a multi-axis acceleration sensor, a power supply system (a lithium battery, a solar battery and a power supply adapter interface), an integrated circuit board, a shell (comprising a functional plate and a battery cover), an antitheft remote controller, a buckling-type rubbery-prevention remote controller and a power supply adapter. The instrument utilizes the GPS to realize the positioning, displacement bulgur prevention, speed measurement and timing, utilizes GSM to realize the auxiliary positioning and the SMS automatic alarming, utilizes the CCD combination mode to identify and monitor the fatigue driving, robbery and accident state and utilizes the acceleration sensor to detect the accident state. The instrument can realize the functions such as mobile positioning, over-speed alarming, positioning antitheft automatic alarming, positioning robbery-prevention alarming, fatigue driving prevention and warning, traffic accident automatic positioning, alarming and help, and the like.

The invention discloses an IC automatic detecting braiding method and an automatic pallet type detecting braiding machine, comprising a pallet-transport device, a braiding packing device, a waste materials collecting device. A PC control system and an electrical control system as well as a pallet-separate device are arranged on the front end of the pallet transport device and an empty pallet-collect device is arranged on the back end. The braiding lead direction of the braiding packing device is vertical to the pallet-transport direction of the pallet-transport device. A face side scan device and a back side device for detecting IC as well as a catch-and-put device for catching IC from the pallet are orderly arranged in the transportation range of the pallet-transport device. The catch-and-put device is electrically connected with the PC control system and the electrical control system and is a classification catch-and-put device which can classify the ICs according to the detecting results of the face side scan device and the back side device and then send the eligible ICs to the braiding packing device and the unqualified ICs to the waste materials collecting device. The braiding machine has the advantages of good accuracy and reliability for scanning and detecting, rapid speed for braiding and packing, low composite cost and high production efficiency.

The invention discloses a back bonding platform for a superchip. The back bonding platform comprises a substrate, a chip peeling and overturning unit, an X-Y direction movement unit, a multi-degree of freedom bonding head and a mounting platform unit, wherein the chip peeling and overturning unit is used for respectively peeling and overturning a chip on a wafer disk and sending the chip to a to-be-picked position; the multi-degree of freedom bonding head is arranged on a supporting guide rail of the X-Y direction movement unit in a cantilever mode, and has functions of automatically levelling and aligning; the mounting platform unit is used for adsorbing the substrate and matched with the bonding head, so that the chip and the substrate are mutually positioned. In addition, in order to ensure the high-precision movement or matching of units, a plurality of vision positioning systems are configured in the back bonding platform. The back bonding platform can reach micrometer-level alignment accuracy, has the parallel adjusting accuracy of higher than 0.01 degree, and has the advantages of compact structure, convenience for operation and the like, so that the back bonding platform is particularly suitable for back bonding of the superchip.

The invention provides a testing machine used for testing the appearance of a printing circuit board, comprising a frame which is provided with a transmission mechanism; the front end of the transmission mechanism is provided with an upper plate mechanism; the transmission mechanism is internally provided with two scanning mechanisms; a panel turnover mechanism is arranged between the two scanning mechanisms; the tail part of the transmission mechanism is butt-jointed with a sorting mechanism; and all mechanisms are controlled by control host machines cooperatively. The testing machine adopts a whole-line digital camera photographing technique to detect the defects on the printing circuit such as foreign matter, exposed copper, supplemented oil, scraping, poor gold-plating, character error, non-uniform green oil, non-uniform welding disc, non-uniform depth of channel, residual copper, omitted printing, developing and the like, automatically feeds and sorts the nondefective and waster, improves the simpleness and reliability of the operation, can observe the defects of the board in real-time by adopting a powerful image processing and analysis software system to provide a plurality of image processing and analysis systems, has quick detection speed, high detection efficiency, safe and reliable equipment, simple operation, is convenient and easy to be learned and meets the man-machine interaction.

The invention discloses a lane line predicating and early warning method suitable for a city road. According to the method, firstly, down sampling is performed on a collected road image, a detection region of interest is set and grayed, filtering enhancement is performed on the region of interest, edge detection is performed through a Canny operator, binarization is performed on the image after edge detection, then the image is mapped on a Hough plane, and lane lines are extracted through improved Hough conversion; then, parameters of the lane line are predicated by the adoption of a classical Kalman filtering algorithm, a region of interest is established, and the lane lines in the region of interest are fitted through a least square method to obtain the optimal predication value; finally, road surface information and current position information of automobiles are combined to establish a lane line departure warning model based on transverse distance, and therefore departure warning is achieved. The recognition method for the lane lines is good in real-time performance and robustness, according to the lane line departure warning model, calculation is easy, and warning is accurate.

The embodiment of the invention discloses a detection method and device for objects in images. The detection method and device are used for improving real timeliness of object detection. The method comprises the steps of inputting an image to be detected into a convolutional neural network which is previously trained, wherein the convolutional neural network is used for determining feature vectors corresponding to all candidate areas in the image; for the feature vectors corresponding to all the candidate areas, determining types of the objects contained in the candidate areas when the maximum values of type parameters in the feature vectors are larger than a set threshold value; determining position information of the objects contained in the candidate areas according to position parameters in the feature vectors. According to the detection method disclosed by the embodiment of the invention, the convolutional neutral network is utilized to determine the candidate areas in detection and calculate the feature vectors corresponding to all the candidate areas; thus, the type and the position of each object can be recognized, repeated operation is avoided, and real timeliness of detection is improved.

The invention discloses a multi-vision defect detecting equipment and a multi-vision defect detecting method for a large-size LCD glass substrate in a production line. For the equipment, a linear scanning imaging detection system is adopted for acquiring the high-definition gray image of an LCD glass substrate to be detected, and the equipment is simple in structure and convenient to operate; according to the method disclosed by the invention, the acquired image is processed, a kmeans clustering method is adopted to carry out defect existence judgment on the processed image of the LCD glass substrate, and by making the defect regions, the defect category is judged by a classification method of a support vector machine (SVM), and the defect quantity is counted. Processed image and detection results are transmitted to an office in real time by a filed bus control system for operators on duty to check, and the field production parameters are transmitted, so that remote monitoring of the system is realized.