241results about How to "Improve detection rate" patented technology

The present invention provides an adaptive, intelligent transform based Analog to Information Converter (AIC) for wideband signals by directly converting an analog signal to information (e.g., features, decisions). This direct conversion is achieved by (i) capturing most of the information of a wideband signal via hardware/software implemented mathematical transformations, (ii) effectively removing unwanted signals such as jammer and interfere from the input signal, and (iii) using novel algorithms for highly accurate decision making and feature extraction (e.g., high probability of detection with low probability of false alarm). The jump in the improvement over today's state-of-the-art is in terms of effective and optimum signal information extraction at high-speed.

This invention relates to a selecting method for up-line random access leading sequences, in which, the lead includes one or multiple ZC root sequences, if a base station needs a ZC root sequence, then the base station with wider covering sphere returns the sequence index with smaller CM value, if the station needs multiple ZC root sequences, then terminals far away from the base station selects the sequence index with smaller CM value or its corresponding expansion sequence.

The invention relates to a high spectral image target detection method based on high order statistics, which comprises the following steps: (1) reading data of a high spectral image in the environment of MATLAB R2008b by a computer; (2) preprocessing (i.e. de-equalization and whitening) the data by the computer; (3) constructing high order statistics for minimizing output data under the constraint that the spectral gain of the detection filter for the target is 1, and solving the optimal weight vector of the detection filter; and (4) setting an appropriate threshold, and acquiring the detection result image. The invention overcomes the defects in the prior art, and has good detection effect by fully utilizing the high order statistics of the data. In particular, the invention can enhance the detection probability on the premise of low false alarm rate. Thus, the invention has high practical value and wide application prospects in the technical field of target detection of high spectral remote sensing images.

Belonging to the technical field of system safety, the invention discloses an ROP attack detection method and an ROP attack detection system based on a virtual machine. The method comprises: 1) running a to-be-protected operating system in a virtual domain environment; 2) positioning a set target process, and acquiring the process information of the target process; 3) monitoring the running of the process in the system, and marking the stack of the current target process as read only when a context switches to the target process; 4) intercepting the write operation occurred after a page error is caused by marking a writable stack memory region as read only, and marking the corresponding stack page as writable; 5) positioning the next place with stack checking need in the implementation of the current target process, and setting a breakpoint; and 6) intercepting the breakpoint and detecting whether the ROP attack exists, stopping the current target process if the ROP attack is detected, continuing to running the target process if the ROP attack is not detected, and marking the stack of the target process as read only. The method has the advantages of a high detection rate, a low rate of false alarm, etc.

The invention provides a traffic sign detection method in a natural scene. The method comprises the steps that a detection image shot in the natural scene is acquired; luminance information of the detection image is subjected to statistical analysis, different luminance areas are divided according to grade luminance threshold values, pixel ratios of different luminance areas are calculated respectively, and the image is divided into a dark scene, a bright scene, a backlighting scene and a normal scene according to all the pixel ratios and scene classification threshold values; gamma parameter values are selected according to scene classification results, and an adaptive Gamma enhancement algorithm is adopted to perform image enhancement processing on a classification image; a partitioning algorithm is selected to perform image color partitioning according to different scenes in an RGB color space to obtain suspected target areas; a grayscale image obtained after color partitioning is subjected to binarization processing to obtain the suspected target areas after binarization; and the suspected target areas are screened through a feature screener to position traffic sign regions. Through the method, robustness and real-time performance of sign detection can be both considered.

The invention relates to an in vivo detection method based on relative attributes. The in vivo detection method comprises the following steps: 1) detecting a human face position on each frame of image in an input video; 2) obtaining human face key points; 3) obtaining an eye or mouth area according to the obtained key points; 4) judging whether the rule of attribute change values of the areas multiple continuous frames of images obtained in the step 3) meets the change rule of a true human face, if so, judging the human face as a true human face, and if not, judging the human face as a false human face. Compared with the prior art, the in vivo detection method provided by invention has the advantages of high detection accuracy, high speed, etc.

The invention discloses a detection device and a detection method for series arc faults. The detection device comprises a continuous current collection module, which is used for continuously collecting current at a fixed sampling rate through an A/D (Analog to Digital) converter, a signal preprocessing module, which is used for carrying out moving average processing on collected data, an arc fault processing and analyzing module, which is used for computing a rate of change of current, a sum of previous N harmonics and a harmonic time domain waveform effective value, identifying an arc according to the three variables and giving a corresponding command, a sound-light alarm module, a drive module and a tripping control module, which are used for deciding whether sound-light alarm is carried out and a circuit is disconnected according to the command of the arc fault processing and analyzing module, and a communication module, which is communicated with the exterior through an RS485 interface. According to the detection device and the detection method, a time domain and a frequency domain are together utilized to judge whether the arc fault occurs, and through analyzing and testing, the method has the advantages of simplicity in operation, easiness in implementation, low cost and misjudgment rate and high detection probability.

The invention relates to a pedestrian detecting method based on improved HOG feature and PCA (Principal Component Analysis). The method comprises the following steps: extracting a sample feature by using a feature extracting algorithm for HOG feature cascaded PCA in a concentrated area of gradient information of a pedestrian in a sample image; training a SVM classifier by utilizing the feature extracted from the sample; and utilizing a feature extracting method to extract a feature vector for a detected sample and utilizing the trained and obtained SVM classifier to detect the pedestrian. The invention provides a pedestrian detecting method based on improved HOG feature and PCA, with the capabilities of efficiently reducing the training speed and reducing the false detecting rate and missing report rate.

A method for uplink synchronization of a first transceiver and a second transceiver in a multi-user cellular communication system having communication resources divided into communication channels. The method includes the following steps. A first signature sequence is transmitted from the second transceiver to the first transceiver, where the signature sequence is selected from a first set of signature sequences. In the first transceiver, the received signal is correlated with at least one signature sequence from a second set of signature sequences to estimate the time of arrival of the signature sequence to synchronize transmission between the second transceiver and the first transceiver. In transmitting the first signature sequence from the second transceiver to the first transceiver, the first signature sequence constitutes at least part of a zero-correlation zone sequence.

A high resolution, low power marine radar for use in applications such as the newly mandated barge/river radars that are to be used in very confined spaces such as canals. An example radar system includes frequency-modulated/continuous-wave (FM/CW) radar that uses very low transmitter power (a fraction of a watt) and has an exceptionally short sensing range of a few feet or even inches if needed.

Certain embodiments of the present invention provide a system and method for automatically synchronizing multiple images. Multiple image sets of a single object are converted into a one dimensional data stream used to synchronize multiple image sets. A common reference point is located in the image sets. Various landmarks of the object are also located in the image sets. Corresponding landmarks among the image sets are noted. Distances from landmarks to the reference point and distances between landmarks are determined. Locations of the landmarks in relation to each other and in relation to the reference point are used to locate landmarks in another image set that correspond to landmarks in a selected image set. A first location in an image set may be identified using a first indicator. A second indicator corresponding to the location of the first indicator may then be determined in another image set.

A communication system, such as a wireless CDMA system, detects markers with fewer errors by having field units transmit the markers at different power levels (e.g., 9 dB for one marker and 11 dB for another marker). The difference in power levels of the markers allows the base station to identify the request markers using alternative criteria with a low probability of error, where the alternative criteria may include comparing the markers to respective energy level thresholds, monitoring occupancy of time slots, occupancy of mutually exclusive code channels, or combinations thereof. For example, in one particular embodiment, a request marker, which is generally a high priority marker, is transmitted with higher power, which improves the probability of detection and reduces the probability of false detection of the request marker.

A network anomaly recognition detection method and a device thereof provided by the invention use a distributed framework to process a large amount of real-time network data to improve the data processing speed and enrich the relationship between feature values between common feature values, and add, when detecting, the connection of network traffic in time to enable effective improvement of the detection rate of network anomalies, therefore, the specific types of abnormal traffic of the network may be identified by simply dividing the traffic into normal and abnormal traffic.

The invention discloses an application multidimensional privacy leak detection method and system for iOS platform; privacy leak detection is provided for iOS applications from the three dimensions, static analysis, dynamic analysis and data analysis, wherein the static analysis incudes the steps of application decrypting, app storage path locating, disassembling, and disassembly source code sensitive API (application program interface) analysis; the dynamic analysis includes the step of allowing Hook-sensitive API function to acquire a log record by running an application, and acquire a sensitive API calling sequence through log analysis; the data analysis incudes the steps of capturing application network data packets, and analyzing data packet protocol, address and content. The method and system of the invention can provide comprehensive detection for data privacy leaks of iOS applications to obtain complete privacy leak event data.

The invention discloses a hyperspectral abnormal object detection method based on structure sparse representation and internal cluster filtering, aiming at addressing the technical problem of low object detection effciency of current hyperspectral abnormal object detection methods. The technical solution involves: after selecting an initial background pixel, using the dictionary learning method which is based on principal component analysis to study a background dictionary which obtains rebustness, in the course of sparse vector resolution and image reconstruction, introducing re-weighted laplacian prior to increase the solution precision of sparse vector, computing the errors betwen an original image and a reconstructed image to obtain a sparse representation error, using the internal cluster filtering to represent space spectrum characteristics of hyperspectral data, obtaining the internal cluster error by computing the error between a to-be-tested pixel and other pixel linear representation result, and finally combining the sparse representation error and the linear weighting of the internal cluster error and implementing precise extraction of an abnormal object. According to the invention, the method increases 10-15% of detection rate with the proviso of a constant false alarm rate compared with prior art.

The invention discloses a method for detecting human body parts by performing parallel statistical learning based on three-dimensional depth image information. Specific to the problems of complex deformation, difficulty to describe, and the like, of human body parts (head, hands and feet), a novel feature, namely universal feature, for reflecting diversity of the human body parts, is constructed. A parallel statistical learning method is utilized to select effective and sufficient novel features and form a parallel cascaded classifier, so as to perform real-time efficient detection on the human body parts.

The invention provides a deception jamming detection method based on multiple antennas, aiming to solve the problems about detection and recognition of deception jamming for a satellite navigation system. The deception jamming detection method includes utilizing observed quality carrier phase capable of being provided by a receiver itself and adopting a multi-antenna direction finding process to achieve estimation of direction of arrival of satellite navigation signals or deception jamming signals, comparing the estimation result with ephemeris messages solved from the receiver so as to achieve detection and recognition of deception jamming. The deception jamming detection method based on multiple antennas utilizes the observed quality information provided from the receiver of the existing satellite navigation system sufficiently, has the advantages of simple structure, low complexity and wide application, and is a practical deception jamming detection and recognition algorithm for the field of satellite navigation deception monitoring.

False positive detection of discrimination targets within images is reduced, while detection processes are accelerated. A partial image generating means generates a plurality of partial images by scanning a subwindow over an entire image. A candidate classifier judges whether each of the partial images represent a face (discrimination target), and candidate images that possibly represent faces are detected. A discrimination target discriminating means judges whether each of the candidate images represents a face. The candidate classifier has performed learning, employing reference sample images and in-plane rotated sample images.

The invention relates to an in-vivo detection method based on attitude information. The method comprises the following steps: (1) detecting a face position on each frame image in an input video; (2) acquiring facial key points and a face shape formed by the key points; (3) performing normalization processing on the face shape based on a 3D face model, and acquiring a rotating angle of the face shape relative to the 3D face model; (4) connecting rotating angles of multiple continuous frame images to form a curve, judging whether the curve meets the set requirement, if so, executing a step (5), otherwise, judging that the face is a false face, and exiting; and (5) judging whether the area of a face color area in a frame image with the largest rotating angle is larger than K, if so, judging that the face is a real face, otherwise, the face is a false face. Compared with the prior art, the method disclosed by the invention has the advantages of low false positive rate, high speed and the like.

The invention relates to a burst traffic detection method based on dynamic threshold. By means of the method, the burst traffic in a high-speed network environment can be effectively detected on line in real time, and the problem that a fixed threshold method cannot well adapt to the dynamically changing network environment is solved. According to the technical scheme, a threshold is dynamically adjusted according to the source IP entropy value of the normal historical traffic, a sliding window mechanism is introduced, and burst traffic is detected by comparing whether the difference value between the current detection value and the average entropy value of a sliding window exceeds the dynamic threshold; the method comprises steps as follows: generation of an initial sliding window, generation of a reference value, generation of the current detection value, abnormality judgment of the current detection value, processing of normal traffic and processing of burst traffic. The detection method is rapid, simple, high in detection rate and low in false rate, the network safety is effectively guaranteed, and the detection method has high practicability and huge economic and social benefits.

The invention discloses a safety belt detecting method based on Hough transform, relating to the field of intelligent transportation. In the safety belt detecting method based on the Hough transform, two parallel straight lines on the edge of a safety belt are detected through the Hough transform of cumulative probability, and whether a driver and passengers wear the safety belts is determined by a geometrical way of shoulder and waist diagonal crossing of the safety belts. The safety belt detecting method based on the Hough transform comprises the following steps: extracting the edge of the safety belt; and performing straight line detection based on the Hough transform. Compared with other detection method, the safety belt detecting method based on the Hough transform has the advantages as follows: the detection rate of not wearing the safety belt is improved; for a motor vehicle picture with nonuniform illumination, the detection rate of not wearing the safety belt is improved; a detection scheme based on the Hough transform is provided for safety belt detection in an intelligent transportation system; the real-time performance is high; and the intelligent transportation system is easier to install and easy to maintain.

A hardware Trojan horse detection method based on a chip temperature field effect is disclosed. Based on the chip temperature field effect, a chip to be detected is firstly subjected to grid partition, then a test vector is inputted into the chip to be detected, the temperature in each grid of the chip during operation is measured to obtain a heat map of the chip, and the heat map is used for a neural network to identify Trojan horse information of the heat map. In addition, heat map sets of chips to which a Trojan horse is inputted and chips to which no Trojan horse is inputted are utilized to train the neural network, and then the trained neural network is utilized to detect the chip to be detected so as to determine whether a Trojan horse is inputted to the chip to be detected. The method has characteristics of a high detection rate and easy implementation, and can ensure correct detection of a hardware Trojan horse.

The invention provides a universal steganalysis method for JPEG (Joint Photographic Experts Group) images. The universal steganalysis method comprises a training process and a prediction process, wherein the training process comprises the following steps: extracting the features of all images in a training sample set, and training the obtained features by a classifier to obtain a model; and the prediction process comprises the following steps: extracting the features of to-be-predicted images in the same way, and under the condition of the same classifier, predicting by the model well trained in the training process to obtain a final result. The universal steganalysis method is characterized in that all the images in the training sample set and the to-be-predicted images are used as original images for extracting the features; and extraction is realized in the following way: global calibration processing is carried out on the original images to obtain globally calibrated images, the multi-direction differential Markov probability matrixes of the original images and the globally calibrated images are respectively calculated, and differential processing is carried out on calculated results to obtain features. The detection rate of the JPEG stego images is further increased by the method provided by the invention.

The invention relates to an Adaboost classifier on-line learning method and an Adaboost classifier on-line learning system. The Adaboost classifier on-line learning method comprises steps that: object detection is carried out by a strong classifier acquired through employing off-line training, and an object detection result is acquired; object detection is acquired by employing a background model to acquire a motion object; the object detection result acquired by the strong classifier is compared with the motion object acquired through detection by employing the background model to acquire an error classifier object; the error classifier object is taken as an on-line training sample to carry out on-line training to acquire a strong classifier after updating. According to the Adaboost classifier on-line learning method and the Adaboost classifier on-line learning system, the object detection result acquired by the off-line classifier is compared with the motion object acquired through detection by employing the background model to acquire the error classifier object, the error classifier object is taken as the on-line training sample to acquire the strong classifier after updating. Generalization performance of the object detection classifier is effectively improved, so the object detection classifier can adapt to a monitoring scene during operation, a detection ratio is improved, and a rate of false alarm is reduced.

The invention discloses a method for automatically judging a dress code through a surveillance video, and is mainly used for judging whether the dressing of the head of an operator is normative or not. The method comprises the following steps that: through a camera, collecting the head characteristics of the operator on line, and generating an image; and through a deep learning training model, extracting image characteristics, judging whether the dressing of the head of the operator is normative or not, and if the dressing of the head of the operator is not normative, outputting alarm information. By use of a deep learning method, features are automatically extracted, the extracted features exhibit better robustness so as to greatly improve a detection result, working efficiency is improved, supervision cost is saved, a supervision dimension is expanded, and supervision accuracy is improved.

The invention discloses an SAR image ship target detection method, and implementation steps thereof include: determining an SAR ship image to be input and carrying out clutter suppression; then usingan improved SLIC superpixel generation algorithm to segment the SAR ship image into super-pixel blocks, and then calculating self-information values of each superpixel block and setting a threshold value T1 to picking out candidate superpixel blocks; calculating expanding neighborhood weighted information entropy in four directions of candidate superpixel patches, and setting an expanding neighborhood weighted information entropy growth rate T2 to remove false alarm candidate superpixel patches; and performing Harris corner detection on a detection result, and setting the number T3 of cornersto further filter out the false alarm patches, so as to obtain a final SAR image ship target detection result. The SAR image ship target detection method provided by the invention fully utilizes the combination of superpixel segmentation, information theory detection and Harris corner detection to realize SAR image ship target detection, and the obtained detection result shows that the method provided by the invention has the advantages of high correct detection rate, low false alarm rate and omission rate, and low detection distortion rate.

The invention aims to research and construct a network security detection system focused on an Internet protocol version 6 (IPv6) network environment. By the network security detection system, a new problem caused by a new IPv6 characteristic to an intrusion detection system can be solved, the detection capacity of an IPv6 attack in a network and the network security prewarning capacity can be greatly improved and a good network environment can be provided to a user. According to the scheme, a data acquisition module, a preprocessing module and a detection module are used for detecting an intrusion; and an initializing module and a response module are used for supplying auxiliary support to the system. By a preprocessing plugin and an active detection method, the detection concept and the detection measure of the intrusion detection system are expanded, and the detection efficiency of the intrusion detection system is also improved.

Methods and systems for non-intrusively detecting the existence of fissile materials in a container via the measurement of energetic prompt neutrons are disclosed. The methods and systems use the unique nature of the prompt neutron energy spectrum from photo-fission arising from the emission of neutrons from almost fully accelerated fragments to unambiguously identify fissile material. The angular distribution of the prompt neutrons from photo-fission and the energy distribution correlated to neutron angle relative to the photon beam are used to distinguish odd-even from even-even nuclei undergoing photo-fission. The independence of the neutron yield curve (yield as a function of electron beam energy or photon energy) on neutron energy also is also used to distinguish photo-fission from other processes such as (γ, n). Different beam geometries are used to detect localized samples of fissile material and also fissile materials dispersed as small fragments or thin sheets over broad regions. These signals from photo-fission are unique and allow the detection of any material in the actinide region of the nuclear periodic table.

There is provided a radio base station apparatus which is capable of increasing the probability of detection of a first path even if a radio transmission path fluctuates due to fading or the like, and of downsizing and reducing the cost of mobile radio terminal apparatus. SIR estimators 13-1 through 13-N calculate reception SIRs for respective subcarriers from output signals from pilot demodulators 12-1 through 12-N. SIR averager 14 averages the SIRs for the respective subcarriers to calculate an SIR for the overall signal. Based on the calculated SIR, threshold value setting unit 15 sets a threshold value for a path search. First path detector 19 conducts a path search based on the threshold set by threshold value setting unit 15. Arrival time calculator 21 calculates an arrival time based on a first path detected by first path detector 19.

The invention discloses a detection method for radar multi-target Hough transform target-by-target elimination, and belongs to the field of tracking processing before radar detection. The conventional tracking processing method before radar multi-target detection has the defects of high operational complexity, great mutual influence between flight paths, simple setting of a second threshold and the like. Therefore, each parameter unit is detected and judged one by one by adopting Hough transform target-by-target elimination technology. By utilizing the capacity of the Hough transform of simultaneously forming peak values through multiple targets and adopting a target-by-target elimination strategy, the technology greatly reduces the mutual influence between target flight paths; meanwhile,different second thresholds are set to detect different parameter units, the technology does not need prior information in a target number and multi-hypothesis test and has obviously lower operational complexity; meanwhile, target motion information, flight path feasibility and other factors are considered in the process of the setting of the second thresholds, the false-alarm probability is reduced and the detection probability of a detector is improved.