326results about How to "Few samples" patented technology

The invention discloses a sparse sampling and signal compressive sensing reconstruction method. The method comprises: establishing a signal sampling interval of each time, sampling point number, and the number of points recovering, establishing random sparse sampling lower than a Nyquist sampling theorem value; and designing a measurement matrix by random sampling timing sequence values, designing a transformation matrix of a sparse expression domain of signals, determining a compressive sensing matrix, and separated compressive sensing optimizing signal reconstruction in a nonlinear manner. The method is based on rationality of objective world rules, and makes full use of signal sparsity, uses transformation space to describe the signals, and establishes theoretical framework of new signal description and processing, so under the condition that information is not lost is ensured, signals are sampled by speed much lower than required speed of a Shannon's sampling theorem. Simultaneously, signals can be recovered completely, that is, sampling of signals is converted into sampling of information. The invention provides a whole set of complete method. The method can be used in one-dimensional and multidimensional signals, and can process audio frequency, videos, nuclear magnetic resonance, and other signals.

The invention relates to a system for forecasting Qunzhi radar sea clutters. The system comprises radar, a database and an upper computer. The radar, the database and the upper computer are sequentially and mutually connected; the radar is used for illuminating a detecting sea area and storing radar sea clutter data in the database; the upper computer comprises a data pre-processing module, a robustness forecasting model building module, a Qunzhi optimizing module, a sea clutter forecasting module, a discriminative model updating module and a result display module. The invention also provides a method for forecasting the Qunzhi radar sea clutters. The system and the method for forecasting the Qunzhi radar sea clutters have the advantages of quick response and high intelligence and can obtain optimal forecasting results.

A separation module operates to fractionate or separate an analyte into fractions according to pI, i.e., pI bands, utilizing capillary isoelectric focusing (“CIEF”) within a first microchannel. The fractions are stacked to form plugs, the number of which is determined by a number of parallel second microchannels integrally connected to the first microchannel, into which the fractions are directed according to the buffer characteristics found in each of the individual microchannels. Within the microchannels the plugs are separated into proteins according to a different chemical property, i.e., “m/z,” utilizing capillary electrophoresis (“CE”).

The invention discloses a synthetic aperture radar imaging method based on L<1/2> regularization and relates to a radar two-dimension imaging technology. The synthetic aperture radar imaging method includes the steps of a, establishing a synthetic aperture radar imaging model based on the L<1/2> regularization according to a synthetic aperture radar observation model; and b, using an iteration half threshold value algorithm to reconstruct an observation scene backscattering coefficient. Compared with traditional synthetic aperture radar imaging methods, the synthetic aperture radar imaging method is capable of reducing a sampling quantity required by the correct reconstruction of an objective scene and achieving effective imaging of synthetic aperture radar data.

The invention discloses a human face identification method and apparatus, and belongs to the field of human face identification. The method comprises: performing feature extraction on a to-be-identified human face image by using a plurality of pre-trained convolutional neural networks to obtain a plurality of sub-feature vectors of the to-be-identified human face image, wherein the sub-feature vectors of the to-be-identified human face image are same in number of dimensions; normalizing the sub-feature vectors of the to-be-identified human face image; performing addition on the normalized sub-feature vectors of the to-be-identified human face image, and multiplying the sum of the normalized sub-feature vectors by a coefficient to obtain a union feature vector of the to-be-identified human face image; and performing human face identification by using the union feature vector of the to-be-identified human face image or/and the sub-feature vectors of the to-be-identified human face image. According to the human face identification method and apparatus, the training time of the convolutional neural networks is shortened, the over-fitting of the convolutional neural networks is avoided, and the operation is simple and convenient; and identification modes are more diversified and the accuracy is higher.

The invention discloses an intelligent identification method and system for a hand-written table. The method comprises the following steps: after carrying out preprocessing on a hand-written table image obtained by shooting, carrying out differential treatment and hough transformation in sequence to obtain the hand-written table image obtained after angle correction; carrying out segmentation on the hand-written table image obtained after angle correction to obtain a plurality of rectangular regions comprising hand-written portions; and carrying out analysis and identification on the contents in the plurality of rectangular regions based on characteristics or a classifier. Through the method and system disclosed in the invention, the hand-written table can be identified quickly and accurately.

A method and apparatus for reducing scan time, eddy currents and image factors in dynamic magnetic resonance (MR) imaging associated with at least a portion a k-space. The method includes scanning at least a portion of the k-space with an Echo-Planar Imaging (EPI) pulse sequence technique, acquiring a randomly under-sampled k-space; and reconstructing the under-sampled k-space utilizing a constrained reconstruction technique. A dynamic image is constructed of the at least a portion of the k-space based on EPI and the randomly undersampled k-space techniques to each segment of the EPI pulse sequence technique.

This invention provides a filter system which may be implemented with less hardware and software resources than traditional filters. In addition, the filter system structure reduces the complexities typically associated with filter design by permitting direct specification of the filter frequency response. Thus, the filter system may adaptively change the filter frequency response on the fly without incurring excessive time or computational costs. The filter system may provide a filtered signal output to any subsequent processing system, such as a voice recognition system or audio reproduction system.

The invention discloses a data aggregation method based on compressed sensing in a wireless sensor network. The method includes: uniform clustering of the sensor network is performed, a node with the most residual energy is selected as a cluster head node, member nodes independently select whether to participate in sampling with the probability ptx, and the cluster head node always participate in sampling; then sampling nodes obtain original signals f and obtain sparse representations x thereof through transform of sparse transform bases, x are projected in a measuring matrix phi, sparse measuring signals y are obtained and sent to the cluster head node, and the cluster head node merges the collected measuring signals to a signal Y by employing vectorization operators and sends the signal Y to a fusion center; and finally the fusion center performs reconstruction on the signals one by one by employing an adaptive weight GPSR algorithm and recovers the sparse representations X thereof. According to the method, the characteristics of noise-containing signals, large data bulk, and high timeliness requirement of the wireless sensor network are completely achieved, the adaptive weight GPSR algorithm does not need to know the signal sparsity in advance, and all high-dimensional signals can be accurately reconstructed in a short period.

The invention provides a method for predicting the dirt change trend of a large condenser. The method comprises the steps as follows: 1) a support vector machine prediction structure is established, and two subnets are respectively used for predicting the change trends of soft dirt and hard dirt; 2) the product of the current working condition, the historical dirt degree, the cleaning period and the like of the condenser and proportional divisor is used as the input of a support vector machine, the support vector machine is used for obtaining the prediction values of the soft dirt and the hard dirt, and then the sum of the soft dirt and the hard dirt is multiplied with the proportional divisor to be used as a finial dirt prediction result. The proportional divisor can be adjusted by an online learning algorithm to be fit for different conditions. The invention can overcome the difficulty that the dirt is difficult to be described by an exact mathematic model, and the invention is applicable to prediction under different working conditions, is applicable to the prediction of condenser sets with different capacities, can satisfy the prediction of different time spans and has self learning capacity, thereby realizing the exact prediction of the dirt change trend of the condenser.

The invention relates to a method for constructing and identifying molecular marker fingerprint chromatogram of Dendrobium huoshanense and similar species thereof, which solves the problems that a method of identifying medicinal plants in the prior art has high cost, complicated procedures and long time consumption. The method comprises the steps of: 1, the collection of Dendrobium huoshanense samples; 2 the extraction and purification of DNA of genomes of the samples; 3. ISSR-PCR amplification; 4, agarose gel electrophoresis; 4, the construction of ISSR molecular marker fingerprint chromatogram of the samples to be tested; and 5, the identification of Dendrobium huoshanense germplasm by utilizing the constructed ISSR molecular marker fingerprint chromatogram. The method has the advantages that the method saves the time and the cost, and can obtain an accurate and reliable identification chromatogram through the extraction to DNA of Dendrobium plants, the ISSR-PCR amplification, and the agarose gel electrophoresis; the method has the advantages of simplicity, convenience, quickness, good repetitiveness, and high resolution on the identification of raw materials which are easy to confuse in appearance; and the method can identify in a seedling stage, which has important effect on ensuring the accuracy and stability of base resources of medicinal materials.

The invention belongs to the field of biotechnology and clinical molecular diagnosis and in particular relates to a fluorescent genotyping detection kit and a detection method for eight human K-ras gene mutations. According to the method, an MGB (minor groove binder) probe technology is combined with a mismatch ARMS (amplification refractory mutation system) technology, an MGB probe complementary with a DNA (deoxyribonucleic acid) plus strand and an ARMS downstream primer complementary with a DNA minus strand are designed at allelic gene loci, a mismatch locus is introduced in the ARMS downstream primer, the MGB probe is overlapped with the ARMS primer by not more than 5 bp, and the eight human K-ras gene mutations are subjected to genotyping detection by four reaction pipes based on multiple fluorescent PCR (polymerase chain reaction). The method has the advantages of good specificity, high sensitivity, simplicity and quickness for operation, accuracy for genotyping, simplicity for result interpretation and the like, and can be used for detecting the clinical K-ras gene mutations and helping doctors screen the crowed with effective anti-EGFR (epidermal growth factor receptor) treatment.

The invention discloses a determination method of an amount of phthalic acid esters. The determination method comprises the following steps: carrying out alkaline hydrolysis on samples in order; acidifying to converse all phthalic acid esters in samples into phthalic acid; extracting by ethyl acetate; drying out and redissolving with methanol; filtering by organic filter membrane to obtain sampleliquids; determining phthalic acid content in the sample liquids by high performance liquid chromatography; carrying out conversion according to a phthalic acid standard substance peak area- mass concentration standard curve and a linear regression equation to calculate out the amount of phthalic acid esters in the samples. The determination method of the amount of phthalic acid esters has advantages of simple pre-treatment, rapidness and high sensitivity, is beneficial for large-scale sample determination and suitable for determination of the amount of phthalic acid ester in oil food, like edible oil, seasoning sauce for instant noodle and convenient rice noodle, and urine biological specimen.

The invention discloses a signal angle-of-arrival high-precision estimation method under high sampling 1 bit quantification conditions. The method includes the steps: firstly, generating a snapshot signal received by a uniform array comprising M array elements and performing 1 bit quantification sampling on the signal; secondly, building a sparse signal representation model taking a rho norm as a sparse constraint item and combining an insensitive epsilon-SVR (support vector regression) model, and performing sparse representation in original signal information by the aid of a non-convex optimization model; finally, resolving the non-convex optimization model by an ADMM (alternative direction multiplier method) to obtain a sparse representation coefficient for determining the arriving direction of the signal. According to the method, the arriving direction of the signal can be estimated in a high-precision manner without calculating an autocorrelation matrix and without reference to coherence of information sources when sampling quantity is small (such as single-snapshot), and signal arriving direction estimation based on the 1 bit quantification sampling conditions is more easily and rapidly implemented in engineering.

The invention discloses a kit for quickly detecting nitrite in edible bird's nest, which comprises a box body and reagents, wherein a sponge bracket is arranged in the box body and provided with holes and grooves; a brown reagent bottle filled with Griess solution A ( i.e. detecting solution A), a brown reagent bottle filled with Griess solution B (i.e. detecting solution B), and a brown reagent bottle filled with protein precipitant (i.e. detecting solution C) are mounted and inserted into the holes in the sponge bracket; a glass sample cup, a dropper, a clear glass color comparison tube, and a clear glass nitrite enriching column are placed in the grooves in the sponge bracket; and a colorimetric card well sealed by a black plastic bag is placed in the box body. The kit can be applied to food detection, especially to the content detection of nitrite in the edible bird's nest. The kit disclosed by the invention and the application of the kit in edible bird's nest detection have the characteristics of less sample amount, low detection limit, wide detection range, simplicity in pretreatment of the sample, and the like.

The invention provides a kit and method for detecting the mutation of an EGFR gene and particularly discloses a method, a primer and a probe for detecting the mutation of the EGFR gene, a kit including prime probe mixing liquid, a primer and a probe for detecting the mutation of a C797S locus, a primer and a probe for detecting the mutation of a T790M locus, a primer and a probe for detecting themutation of a 19del locus and a primer and a probe for detecting the mutation of a L858 locus. According to the method provided by the invention, 0.1% mutation rate can be detected based on a digitalPCR platform; and the method has the advantages that the optimization process is simple, much detected mutation types can be detected, the absolute quantification is realized, the sensitivity is high,samples are easily acquired, and the like.

The invention discloses a positive model and inverse model-based quantitative spectrometric analysis and calibration method of a multi-component gas. The method comprises the following steps of: establishing a positive model by taking concentrations of a target gas and an interference gas as input and a spectral line value as output; generating an additional sample by the positive model, wherein the additional sample is obtained by performing calculation by an interpolation method; extracting characteristic variable from a sample set consisting of the manufactured samples and the additional sample so as to form a new sample set; and finally establishing an analysis model of each target gas by a neural network modeling method by taking the characteristic variable as input and the concentration of the target gas as output. The method greatly reduces the number of samples required by the adoption of the conventional black box calibration method during the quantitative spectrometric analysis of the multi-component gas, so that the spectral analysis can be independently applied to the fields related to the quantitative analysis of the multi-component gas, such as gas logging, mine safety, environmental protection, fault diagnosis, product quality detection and the like.

The invention provides an intelligent apparatus that has the advantages of simple operation, short measurement time and high sensitivity and that can measure the indoor gas formaldehyde concentration accurately. The intelligent indoor gas formaldehyde concentration measuring apparatus comprises: an air sampling unit, a color developing unit, a liquor relief unit, a color comparison unit and a control unit. The formaldehyde concentration measuring apparatus integrates air sampling, formaldehyde absorption, liquor relief, color development reaction, color development reaction temperature control and color comparison pool together, adopts a low power consumption single-chip machine to control the implementation of all procedures, so that the whole test process can be completed by pressing one button after the apparatus is powered on, no manual operation is needed; a folding multi-optical path is introduced into the formaldehyde concentration measuring apparatus so as to increase the sensitivity of the measurement, reduce the air sampling amount and further shorten the test period.

The invention provides a method for measuring the content of vitamin E in food with ultra-performance convergence chromatography. The method specially comprises steps as follows: performing vitamin E extraction pretreatment on the food to obtain an extraction solution; separating and detecting the content of eight vitamin E isomers in the extraction solution respectively with an ultra-performance convergence chromatographic instrument to obtain respective mass concentration, wherein the detection conditions are as follows: the mobile phase is a mixed solution of supercritical carbon dioxide and methyl alcohol/isopropyl alcohol (1:1), the gradient elution ratio of the supercritical carbon dioxide is 90%-100% within 0-2.0 min, 75%-85% within 2.0-6.0 min and 90%-100% within 6.0-6.2 min, and the detection wavelength is 294 nm; calculating the content through a formula. The content of the eight vitamin E isomers in the food can be quickly separated and quantitatively detected respectively within 7 min with the method, and the method has the advantages of high detection speed, high separation degree, high sensitivity, good reproducibility and the like.