88 results about "Threshold estimation" patented technology

The present invention provides a method and apparatus for detecting and preventing a plurality of denial of service (DOS) and distributed denial of service (DDOS) attacks. The apparatus includes classifiers for parsing packets; meters storing statistics for the classified packets and detecting flood thresholds; an Ager for maintaining timeouts; a decision multiplexer for multiplexing inputs from various meters and determines whether to allow or deny the packet; and a threshold estimation means for estimating thresholds based on past data from meters, baselines, trends and seasonality. The apparatus includes a PCI interface through which a host can interact, learn continuously and set thresholds in a continuous and adaptive manner so as to prevent rate based DOS and DDOS attacks. The apparatus includes a mechanism to track culprit sources at layer 2 and layer 3 through a multiplicative increment method.

Approaches for estimating capture thresholds for alternate pacing vectors of multi-electrode pacing devices are described. Capture thresholds of at least one initial pacing vector is measured. The impedance of the initial pacing vector and at least one alternate pacing vector is measured. The initial and alternate pacing vectors have an electrode in common. The common electrode has the same polarity in both the initial and the alternate pacing vectors. The capture threshold for the alternate pacing vector may be estimated based on the measured capture threshold of the initial pacing vector, the measured the impedance of the initial pacing vector, and the measured impedance of the alternate pacing vector.

Approaches for estimating capture thresholds for alternate pacing vectors of multi-electrode pacing devices are described. Capture thresholds of at least one initial pacing vector is measured. The impedance of the initial pacing vector and at least one alternate pacing vector is measured. The initial and alternate pacing vectors have an electrode in common. The common electrode has the same polarity in both the initial and the alternate pacing vectors. The capture threshold for the alternate pacing vector may be estimated based on the measured capture threshold of the initial pacing vector, the measured the impedance of the initial pacing vector, and the measured impedance of the alternate pacing vector.

The invention discloses an MCMC sampling and threshold low-rank approximation-based image de-noising method. According to the method, firstly, during the denoising process, an image block is generated through the MONTE-CARLO sampling process. Secondly, based on a plurality of statistical features in a histogram, a similarity judging function that meets the condition of the Markov chain can be obtained. Thirdly, the singular value decomposition on all kinds of image block clusters is conducted and the self-adaptive threshold estimation for singular values is conducted according to the prior information corresponding to an image. Fourthly, on the basis of a decomposed low-rank structure, the image reconstruction is conducted according to the low-rank approximation algorithm, so that the de-noising purpose is realized. According to the invention, the characteristics of the similar non-local geometric structure information of images and the better treatment of high-dimensional data based on a low-rank matrix are fully utilized. Meanwhile, the defect in the prior art that the conventional non-local mean-value traversing search method is high in block-selecting complexity can be overcome. The block-selecting robustness is therefore improved. moreover, to a certain extent, the algorithm operating period is shortened.

A bit flip count is determined for each bin in a plurality of bins, including by: (1) performing a first read on a group of solid state storage cells at a first threshold that corresponds to a lower bound for a given bin and (2) performing a second read on the group of solid state storage cells at a second threshold that corresponds to an upper bound for the given bin. A minimum is determined using the bit flip counts corresponding to the plurality of bins and the minimum is used to estimate an optimal threshold.

The invention discloses a method for predicting the remaining life of a multi-source data driven aero-turbofan engine. According to the method, the remaining life of the engine is predicted by completely utilizing monitoring data acquired by an engine sensor. The method comprises four steps of estimating multi-source monitoring data fusion and failure threshold values; modeling the degradation process of the engine and estimating parameters; describing the remaining life of the engine; and predicting the remaining life. Compared with the prior art, the method has the following advantages: multi-source monitoring data is fused on the basis of common main component analysis and Euclidean distance to extract the health index and the failure threshold value representing the operating state of the engine and solve the problem that monitoring data information in the traditional prediction method is utilized incompletely; the Wiener process with nonlinear drift is established to represent the aero-engine degradation process with uncertain degradation rate, and real-time on-line life prediction is conducted on the basis of the aero-engine remaining life distribution; and data support is provided for on-condition maintenance, the time on wing of the engine is increased, major accidents are avoided, and engineering application value is achieved.

The present invention discloses a method for estimating noise bottom and signal component threshold, and device thereof. The method according to the invention comprises the following steps: measuring the measuring point and the sub-measuring point thereof according to a preset measuring time interval to obtain the signal channel data of the measuring point and the sub-measuring point thereof; eliminating the largest and smallest signal channel data accounting a certain percentage in the signal channel data of the measuring pint and the sub-measuring point thereof; and estimating the noise bottom according to the residual signal channel data and estimating the threshold between the effective signal component and the noise component according to the noise bottom. The method according to the invention can make that the noise bottom and signal component obtained through calculation are more precise. The validity and reliability for subsequently extracting the characteristic parameter of the signal channel can be increased.

The invention discloses a GBAS integrity warning threshold estimation method based on a Gaussian expansion method. The method comprises the following steps: calculating integrity sample data accordingto integrity monitoring criteria; then, dividing parameter intervals according to the satellite elevation angle, and calculating mean value and standard deviation of the sample data in each interval;carrying out normalization on the sample data; then, drawing a distribution histogram of the normalized data; and finally, determining the optimal expansion factor through the Gaussian expansion method and generating a warning threshold value. The method meets requirements of a ground-based augmentation system for false alarm rate, and meanwhile, the integrity warning threshold determined throughthe Gaussian expansion method based on system actual data accords with the actual characteristics of the system more.

A method for determining an optimal threshold of a nonvolatile memory device, the method including: reading a page from a nonvolatile memory device with a default threshold and attempting to hard decode the page using the default threshold; reading the page two more times with a predetermined offset voltage when the hard decoding fails and attempting to soft decode the page using the default threshold; approximating an empirical distribution of successfully decoded bits with a Gaussian distribution for each level; finding an intersection of the Gaussian distributions; and setting the intersection as a new reading threshold and reading the page again with the new reading threshold.

The invention discloses a grid compensation-based mono-phase full-coverage remote sensing data retrieval method. The method comprises the following steps of: firstly segmenting a specific area into small grids according to a five-layer fifteen-level segmentation standard; respectively retrieving image data which respectively covers each grid from a mass remote sensing data set; carrying out weight sorting on the retrieved image data and calculating positions of the grids projected to an image overview; calculating a cloud cover percentage of the image overview in a projection range by adoption of a rapid overview cloud cover threshold estimation method; if the cloud cover percentage is in a threshold range expected by users, retaining remote sensing images covering the grids; and finally circularly retrieving cloud-free mono-phase full-coverage data of all the grids.

The invention relates to a de-noising method for ultrasonic testing of a high-voltage bushing lead of a transformer, comprising the following steps: performing wavelet decomposition on an ultrasonic echo signal reflected by the high-voltage bushing lead of the transformer, and performing signal analysis from a detail component and an approximate component; introducing an adaptive threshold methodbased on SURE unbiased estimation, adopting a gradient descent method by the adaptive wavelet threshold estimation, taking the gradient function deltalambda as an objective function, corresponding theminimum value of the gradient function deltalambda to the optimal threshold, introducing the Sigmoid function as a threshold function at the same time, and combining the above variables to obtain a new gradient function deltalambda; combining the grey wolf optimization algorithm with the adaptive threshold method, and optimizing the objective function by using the gray wolf algorithm; de-noisingeach detail wavelet component by using the gray wolf optimization adaptive optimal threshold, and performing wavelet inverse transform signal reconstruction on the de-noised detail component and approximate component, thereby obtaining a de-noised ultrasonic echo signal. Compared with the prior art, the invention has the advantages of improving the accuracy of ultrasonic detection and the like.

The invention relates to an inter-domain access control method based on dynamic self regulation. The problems of too rough access control granularity, difficulty in change of mapping relation, lack of evolution semantic and the like in the current inter-domain interoperation scene are solved. In order to solve the problems of too rough access control granularity, difficulty in change of mapping relation, lack of evolution semantic and the like in most inter-domain interoperation policy integrating schemes, a DRG (Directional Role Diagram) is created to model RBAC (Role-Based policies Access Control), evolution semantic between nodes is established by using an attribute constrain space, a mapping rule regulating mechanism is realized through related threshold estimation of constrain satisfactory factors, safety estimation factors and the like, and a complete evaluation implementation scheme is presented. By the scheme, 'an initial domain role identifier acquired through a management way' and 'a collaboration domain role identifier acquired through a mapping way' are differentiated clearly, so that the traditional security constraint of attribute level violation on a mapping rule is broken through.

The invention discloses a method and system for training a spectrum sensing model, and a spectrum sensing method and system. The spectrum sensing method comprises the following steps of acquiring a to-be-detected radio signal in real time; obtaining a frequency spectrum sampling covariance matrix of the radio signal to be detected; converting the frequency spectrum sampling covariance matrix of the to-be-detected radio signal into th egrey-scale map information of the to-be-detected radio signal; and determining a spectrum sensing result corresponding to the to-be-detected radio signal according to the grey-scale map information of the to-be-detected radio signal. According to the present invention, by extracting the characteristics from the sampling covariance matrix of the received radiosignal by using the convolutional neural network model, the detection performance deterioration of an energy detection method under a low signal-to-noise ratio and the prior information of a signal required by a cyclic stationary feature detection method are avoided, and by using the convolutional neural network for learning the characteristics of the covariance matrix, the complexity of matrix operation during characteristic extraction and the errors during threshold estimation are avoided, and finally, the requirements of real-time detection and real-time blind detection are met.

The invention discloses a transmitting antenna number blind estimation method applicable to an MIMO (Multiple Input Multiple Output) system, and mainly solves the problem of low accuracy of identifying a number of transmitting antennas of the MIMO system in a time-variant fast fading channel environment and under the condition of a low signal-to-noise ratio. The method comprises the steps of: (1) constructing a received sample matrix by utilizing a received signal of a non-cooperative receiving machine, and implementing data acquisition of the non-cooperative receiving machine; (2) constructing a covariance matrix by utilizing the received signal sample matrix; (3) carrying out feature value decomposition on the covariance matrix; (4) setting a bilateral detection threshold, and initializing a counting variable; (5) calculating a second-order moment; (6) calculating a feature value judgment threshold; and (7) according to a feature value and a threshold, carrying out judgment to obtain the number of the transmitting antennas of the MIMO system. According to the transmitting antenna number blind estimation method disclosed by the invention, a correct identification rate in a fast fading channel under the condition of small sample received data and under the condition of the low signal-to-noise ratio is ensured, and applicability in military communication resistance and in the time-variant fast fading channel is ensured.

A client and server system that performs a reinforcement learning-based encryption and decryption method according to an aspect of the present invention may include: a key management module configured to manage an encryption key required in performing an encryption and a decryption of data; a secret sharing module configured to perform a secret sharing of a threshold value for a lifetime and availability of the data; and a threshold estimation module configured to perform an estimation of the threshold value; and can improve the availability and security of data to satisfy user demands in a self-destructing environment for privacy protection.

The invention relates to an image coding method based on a JND model. After the DCT conversion of an image is completed, a module formed by quantization, inverse quantization reconstruction, JND threshold value estimation and an enhanced quantizer further increases a quantifying step size, removes visual redundancy, reduces the compression code rate in the condition of maintaining visual quality, and improves code efficiency. The method can accurately track the quality of compressed images according to the needs of a user on compression code rate, and provide accurate JND threshold values to design quantized enhanced values. In addition, a modified JND threshold estimation method enables extra bit expense not to be necessarily added to a coding end, and the code stream generated by a designed encoder is maintained to be compatible with a JPEG coding standard.

The invention relates to a method for constructing an adaptive threshold estimation signal source number in a white noise background, belonging to the technical field of array signal processing. Basedon the idea of an adaptive threshold, a basis for threshold setting and a reference formula suitable for threshold setting in the condition of a small number of sensors are presented, and the accuracy of signal source number estimation at a low signal-to-noise ratio can be effectively improved. A result is obtained through a simulation test, and the accuracy of the signal source number estimationin a low signal-to-noise ratio environment can be improved by using an adaptive threshold, which proves that the research of the adaptive threshold has certain research prospects and can be applied to an actual environment effectively.

The invention provides a three-level automatic gain control device. The three-level automatic gain control device comprises a variable gain amplifier, an analog / digital converter and a feedback circuit, wherein the feedback circuit comprises a lookup table module, a threshold estimation module, a threshold error module, a gain step updating module, a gain counter and a digital / analog converter; the gain step updating module is used for comparing a threshold error with a three-level threshold value to obtain the gain step of a next update period; when the threshold error is smaller, the gain step is smaller; and the gain step is obtained through arithmetic shift. According to the three-level automatic gain control device, the three-level threshold value is adopted; processing at three levels is performed according to the threshold error to obtain the gain step of the next update period; and rapid adjustment and stable adjustment of a gain are realized. A reference threshold value is set to be 33 percent of the update period, so that optimal signal-to-noise ratio loss can be reached, and the device is suitable for processing data with a low signal-to-noise ratio. The device is suitable for all analog / digital converters with 2 bits and over. The invention further provides a three-level automatic gain control method.

The invention discloses an SAR image water area automatic detection method based on a recursive OTSU algorithm. The method is characterized by obtaining SAR image power data, and carrying out coherent speckle filtering on the SAR image power data to obtain SAR image power data obtained after coherent speckle filtering; carrying out normalization on the SAR image power data obtained after coherent speckle filtering to obtain normalized SAR image data; carrying out graying processing on the normalized SAR image data to obtain an SAR gray image; carrying out threshold estimation on the SAR gray image to determine an optimum threshold value of the SAR gray image; constructing a matrix same with the SAR image power data and recording the matrix as a mark matrix; according to the optimum threshold value of the SAR gray image, obtaining a binary mark matrix corresponding to the SAR image power data; and finally, carrying out morphological processing on the binary mark matrix corresponding to the SAR image power data to obtain a water area detection mark graph of the SAR image power data.

The invention discloses a two-stage image denoising method based on an adaptive singular value threshold, which performs denoising estimation twice, and comprises the following steps of: dividing an original image to be denoised into a plurality of image blocks with the same size, and grouping similar image blocks through a block matching method to form a similar group matrix; converting a denoising problem into a low-rank approximation problem, and estimating similar image blocks of each group by using an adaptive singular value threshold; aggregating the estimated image blocks into an initial denoised image; in order to improve the first denoising result, adopting a two-stage strategy with a back projection step to further suppress noise residual errors, and generating a new noise image;and repeating the steps of the previous first stage to generate final denoising estimation. According to the method, more edge details which are easily blurred can be retained subjectively, the problem that a denoising result is too smooth is solved, the visual effect of the image is improved, and the calculation cost is reduced through fewer iterations.

The invention provides an incremental motion structure restoration optimization method based on an image sequence, which relates to the field of three-dimensional reconstruction. Includes acquiring images of various angles of the object to be reconstructed; acquiring the relationship of feature points and descriptors between the two images; filtering outliers of feature points; eliminating heterogeneity; forming an initial point cloud model; rich point cloud model; the final point cloud model is obtained. The point cloud model is optimized. In this method, the adaptive threshold estimation method is used in the initial image pair selection process, so that the value of the abnormal feature points can be automatically adjusted and filtered, and the constraint is introduced to further filter, so as to obtain more accurate initial image pairs, ensure the robust initial reconstruction, and improve the overall quality of three-dimensional reconstruction; EPNP method is used in incremental addition iteration to improve the efficiency and precision of iteration, so as to improve the performance of this method. Through BA method, the precision of model optimization is further improved, andthe problem of error accumulation in incremental iteration is solved.

An optimal read threshold estimation method includes determining a flip difference corresponding to an optimal step size Δopt, estimating a first slope m1 at a first read point and a second slope m2 at a second read point, and obtaining an optimal read threshold (XLPopt) as the intersection of a first line with the first slope m1 and a second line with the second slope m2.

The invention relates to an MOS-used adaptive body bias dynamic threshold generator, which particularly relates to an electronic device. The electronic device comprises a transistor with a body and a body bias circuit, wherein the body bias circuit comprises a threshold estimation circuit for estimating a threshold voltage of the transistor and a comparison circuit for comparing the threshold voltage of the transistor and a reference threshold voltage and thus generating a comparison signal. A bias adjustment circuit generates a body bias voltage for biasing the body of the transistor according to the comparison signal, and the body bias voltage is a voltage for adjusting the threshold voltage to the reference threshold voltage when being applied to the body of the transistor.

The invention provides an incoherent underwater acoustic communication method and an incoherent underwater acoustic communication system based on actual channel fading statistics. The method comprisesthe following steps that: a transmitting end performs irregular code encoding, interleaving and accumulation on information bits to be transmitted, performs multi-carrier on-off keying modulation onthe accumulated encoding bits, converts a modulated signal into an analog signal and transmits the analog signal to a receiving end; the receiving end performs on-off keying demodulation, log-likelihood ratio bias processing, ACC internal code interleaving decoding, de-interleaving operation and irregular code iterative decoding and iterative threshold estimation calculation on the received analogsignal, and outputs decoded information bits to complete incoherent underwater acoustic communication; and meanwhile, channel fading characteristics are counted, irregular code degree optimization iscarried out, and the optimized degree distribution is fed back to a transmitting end through a control channel for irregular code coding of next communication. The incoherent underwater acoustic communication method and the incoherent underwater acoustic communication system based on actual channel fading statistics have the advantages of high spectrum utilization rate and good robustness.

An optimal read threshold estimation method includes determining a flip difference corresponding to an optimal step size Δopt, estimating a first slope m1 at a first read point and a second slope m2 at a second read point, and obtaining an optimal read threshold (XLPopt) as the intersection of a first line with the first slope m1 and a second line with the second slope m2.

The invention provides an undistorted boundary continuation scheme for wavelet online denoising. The method comprises the steps: S1, acquiring a signal segment xn, carrying out distortionless boundarycontinuation processing on the signal segment to obtain M + N + L data, wherein M is the number of historical data used for distortionless left continuation, L is the number of future data used for distortionless right continuation, and N is the number of data to be denoised; s2, performing lifting wavelet j-layer decomposition on the N pieces of to-be-denoised data to obtain an approximation coefficient sj and detail coefficients {dj,..., d2, d1}; s3, calculating a threshold value Tj of each layer of the lifting wavelet; s4, performing threshold quantification on the detail coefficient {dj,..., d2, d1} of each layer to obtain a detail coefficient estimated value; and S5, carrying out wavelet reconstruction by using the approximation coefficient sj and the detail coefficient estimated value after threshold quantization to obtain denoised reconstructed signal S6 output data. According to the method, the working time sequence of online denoising is designed, a distortionless continuation scheme is provided for the serious boundary interference problem during online denoising, and boundary interference is eliminated; and a fundamental wavelet, a decomposition layer number and a threshold estimation value adopted by an algorithm denoising algorithm are determined according to the actual characteristics of the magnetic flux leakage detection signal. A traditional threshold functionis improved, the denoising performance is further improved, and a better denoising effect is achieved.

A method for determining an optimal threshold of a nonvolatile memory device, the method including: reading a page from a nonvolatile memory device with a default threshold and attempting to hard decode the page using the default threshold; reading the page two more times with a predetermined offset voltage when the hard decoding fails and attempting to soft decode the page using the default threshold; approximating an empirical distribution of successfully decoded bits with a Gaussian distribution for each level; finding an intersection of the Gaussian distributions; and setting the intersection as a new reading threshold and reading the page again with the new reading threshold.

The invention discloses a video denoising method and belongs to the technical field of video processing. The video denoising method comprises performing separation of three color channels of R (red), G (green) and B (blue) on an source video; leading every color channel to be subjected to hard threshold grouping, collaborative filtering and aggregating in a four-dimensional space to obtain a hard threshold estimation video; leading the hard threshold estimation video to be subjected to Wiener filtering grouping, collaborative filtering and aggregating in the four-dimensional space to obtain denoised videos; and aggregating the denoised videos of the three color channels into an RGB three-dimensional video. According to the video denoising method, spatiotemporal correlation of spatiotemporal blocks of a video sequence is fully taken advantages of, spatial domain linear transformation and filtering transformation are performed in the four-dimensional space, noises are eliminated, meanwhile, fine structures in the video is well kept, and the spatiotemporal redundancy of the video sequence can be fully eliminated.

The invention provides a non-cooperative signal detection method based on a frequency spectrum, which aims at overcoming the defects of the existing non-cooperative signal detection technology, performs frequency spectrum detection on a multi-signal coexisting non-cooperative received signal by utilizing global and local adaptive threshold estimation of an intercepted signal frequency spectrum, and estimates to obtain an effective signal contained in the non-cooperative received signal. According to the method, a dual-threshold signal detection algorithm based on global and local frequency domain characteristics is proposed for the first time, the accuracy of signal detection is effectively improved, the problems of false alarm and missing detection of traditional non-cooperative signal detection are solved, narrow-band signals in non-cooperative signals can be effectively detected, and the problem that the narrow-band signals are mistakenly eliminated during filtering processing is avoided; and the method can effectively cope with a real electromagnetic environment in which large and small signals coexist and ground noise energy has large fluctuation, effectively improves the problem of signal detection misjudgment of a traditional single-threshold and double-threshold detection algorithm in a scene in which the large and small signals coexist, and is high in environmental applicability and low in complexity.

The embodiment of the invention discloses a masking threshold estimation method and device, electronic equipment and a storage medium. The method comprises the following steps of: acquiring an amplitude spectrum of a noisy voice signal, and acquiring an amplitude spectrum of a noise signal in the noisy voice signal; determining the voice characteristic spectrum deviation of the noisy voice signal according to the amplitude spectrum of the noisy voice signal and the amplitude spectrum of the noise signal, and determining the voice characteristic flatness according to the amplitude spectrum of the noisy voice signal; determining pure tone coefficients of different frequency bands in the noisy voice signal according to the voice characteristic spectrum deviation and the voice characteristic flatness; determining an intermediate masking threshold according to the power spectrum of the noisy voice signal, the amplitude spectrum of the noisy voice signal and the pure tone coefficient; and determining a target masking threshold according to a comparison result of a predetermined absolute masking threshold and the intermediate masking threshold. According to the masking threshold estimation method and device, the electronic equipment and the storage medium of the embodiment of the invention, the accuracy of masking threshold estimation can be improved, the noise suppression result can be effectively enhanced, and the voice recognition effect is improved.