36 results about "Band ratio" patented technology

A wavelet-based band difference-sum ratio method reduces the computation cost of classification and feature extraction (identification) tasks. A Generalized Difference Feature Index (GDFI), computed using wavelets such as Daubechies wavelets, is employed in a method to automatically generate a large sequence of generalized band ratio images. In select embodiments of the present invention, judicious data mining of the large set of GDFI bands produces a small subset of GDFI bands suitable to identify specific Terrain Category/Classification (TERCAT) features. Other wavelets, such as Vaidyanathan, Coiflet, Beylkin, and Symmlet and the like may be employed in select embodiments. The classification and feature extraction (identification) performance of the band ratio method of the present invention is comparable to that obtained with the same or similar data sets using much more sophisticated methods such as discriminants, neural net classification, endmember Gibbs-based partitioning, and genetic algorithms.

Disclosed herein is a method and apparatus for monitoring, identifying and determining the breathing cycle of an individual from processed acoustic signal waveform data. The breathing sounds of an individual are recorded using a microphone and digitized such that the breathing sounds may be plotted. The data is segmented and transformed to form a plurality of segments representative of a frequency spectrum. The frequency spectrum data is transformed so as to produce magnitude bins and the sum of lower magnitude bins and the sum of higher magnitude bins are determined in a sampling of segments. A Bands Ratio is determined from the sum of lower magnitude bins and the sum of higher magnitude bins in the sampling of segments. A first bands ratio is then determined within a given segment and compared to the mean bands ratio. If the first bands ratio of the given segment is greater than the mean bands ratio by at least a predetermined multiplier, the given segment is labeled as inspiration. If the first bands ratio of the given segment is less than the mean bands ratio by at least a predetermined multiplier, the given segment is labeled as expiration.

The invention discloses a practical atmospheric correction method for remote sensing images, which comprises three steps: a first step, inverting the optical thickness of atmospheric aerosol from MODIS data by means of a 6S model lookup table based on a dense vegetation algorithm; a second step, calculating total content of atmospheric vapor by using an MODIS data band ratio method, and then calculating the optical thickness of the atmospheric vapor; and a third step, combining the inverted optical thickness of the atmospheric aerosol, the optical thickness of the atmospheric vapor and a DOS model to invert the earth surface reflectance. The calculated earth surface reflectance has high precision; the method has strong practicability, and is applicable to various land satellite images; and the MODIS data can be acquired freely every day; therefore, the method can be used as a service atmospheric correction method for the remote sensing images.

The invention discloses a permeable band ratio factor-based water depth inversion method, which comprises the following steps of: 1, performing radiant calibration on a remote sensing image; 2, performing atmospheric correction on the remote sensing image subjected to the radiant calibration; 3, performing image enhancement on the remote sensing image subjected to the atmospheric correction; 4, performing amphibious separation on the remote sensing image subjected to the image enhancement; 5, performing geometric correction and data processing on the remote sensing image subjected to the amphibious separation; and 6, establishing a neural network water depth inversion model. Compared with a neural network model using single permeable band as input, the permeable band ratio factor-based neural network inversion model has low standard deviation and large related coefficients. The model is not affected by the seawater type and the undersea sediment, and simultaneously has the function ofsolving nonlinear mapping and good promotion capability.

The present invention relates to a method of calculating a Topography Adjusted Vegetation Index (TAVI) based on a band ratio model and a solar altitude angle. The method includes the folio wing steps: obtaining the apparent reflectance data of a remote sensing image through image preprocessing, analyzing the quality of the image and numerical distribution, calculating a Shadow Vegetation Index (SVI), and constructing a TAVI combinational algorithm:

calculating an adjustment factor f (Δ) with the solar altitude angle, and finally obtaining anti-topographic effect TAVI vegetation information. The TAVI in the present invention is composed of two band ratio submodels RVI and SVI, the denominators of both of which are red band data of a remote sensing image, and the adjustment factor f(Δ), which is calculated by a solar altitude angle as a parameter with a sensor factor applied, has great physical significance. The TAVI calculation method does not need digital elevation model (DEM) data and remote sensing image classification when not depending on ground survey data,, and ensures that the interference of the topographic effects with the vegetation information can be effectively eliminated by the TAVI, thereby avoiding the problem of reduced inversion accuracy of ground vegetation information due to different registration accuracy of a remote sensing image and DEM data.

The invention discloses a band ratio method based maize embryo segmentation method in a high-spectral reflection image. The high-spectral image of a maize seed is collected with the embryo surface of the seed upwards; multiple pixels and spectrums thereof are extracted from the endosperm and embryo and normalized; the spectral ratio of two random wave bands is calculated and serves as the band ratio, and the optimal band ratio is selected; a band subset is selected, variance of all pixels in the band subset is calculated, a seed binary image is obtained via threshold segmentation, and a background removed high-spectral image of the maize seed is obtained via mask operation; and a gray scale image of the background removed high-spectral image of the maize seed under the optimal band ratio is calculated via the high-spectral image, the gray scale image is segmented in a region growing method to obtain an embryo binary image, the embryo binary image is processed to obtain a seed embryo binary image, and the high-spectral image of the maize seed embryo is obtained via mask operation. The method can be used to segment the maize seed embryo in the visible near-infrared high-spectral image effectively, the segmentation effect is good, and the practicality is higher.

The invention discloses a method for identifying early-stage disease spots of sclerotinia sclerotiorum and botrytis of rape. The method comprises steps as follows: (1), a high-spectrum image of a to-be-detected leaf of the rape is taken, and the pixel of a local disease spot of the high-spectrum image is picked; (2), a band ratio operation value and a band differential operation value of the pixel at the band of 681.95 nm-748.23 nm are calculated and obtained; (3), a feature vector composed of a spectrum value of the pixel at the band of 555.29 nm, the band ratio operation value and the band differential operation value is input into a trained least square support vector machine model, and the type of the disease spot is judged according to an output result. A band with a characteristic difference is obtained from the high-spectrum image information with the early-stage disease spots of sclerotinia sclerotiorum and botrytis through analysis, the early-stage disease spots of sclerotinia sclerotiorum and botrytis are accurately classified by combination of a characteristic wavelength, the band ratio operation value, the band differential operation value and a least square support vector machine, and the identification accuracy is higher.

The invention provides a microstrip and substrate integrated waveguide-based dual-frequency band-pass filter and a design method thereof. The filter comprises two coupled gaps positioned in a signal input end and a signal output end respectively, a phase step impedance resonator-based three-order low-pass filter, a 1/4 wavelength phase step impedance resonator-based two-order band-pass filter, a section of embedded open circuit stub, three air filled and connected circular cavities, a plurality of metalized via holes distributed in the edges of the circular cavities, a three-layer dielectric substrate, a layer of shared metal ground and a layer of bottom surface metal cover plate. The low-pass filter can realize the frequency response characteristic complementary with the band-pass filterby adjusting lengths of high and low impedances, thereby realizing the microwave band-pass filter based on an ultra-wide stop band; and then by combination with a substrate integrated waveguide circular cavity-based millimeter wave band-pass filter with low-frequency cut-off characteristics, the dual-frequency band-pass filter with ultra-high transmission band ratio is further realized.

The invention discloses a dual-band dual-polarized electromagnetic band gap structure, belonging to the field of wireless communication. The electromagnetic band gap structure comprises a medium substrate, a metal floor arranged on the back of the medium substrate, a variant 'Jerusalem' cross-type cell arranged on the front of the medium substrate, and four T-type cells. The dual-band dual-polarized electromagnetic band gap structure can be widely used in the field of wireless communication, can be used for designing dual-band dual-polarized antennas to improve the performance of the antennas.The dual-band dual-polarized electromagnetic band gap structure has both two 'in-phase reflection phase' bands and two 'surface wave suppression' bands, has a wider band ratio adjustment range and iscompact.

The invention discloses a method for performing rock classification based on spectral features. The method comprises: step one, carrying out spectrum analysis on carbonate rocks by using a band ratio method; step two, establishing a training zone by combining a geological map and spectral features and classifying shale and marble based on a supervised classification method; step four, establishing a spectrum library by using spectrums of rock samples and supplementing spectral features of rocks in an implementing area by combining a USGS spectral library; step four, carrying out spectrum analysis and calculation and classifying sandstones and granite; and step five, carrying out supervised classification by using a minimum distance classification method to realize classification of other rocks in the remaining area. Therefore, a problem of low geological map interpretation by using a remote sensing image in a difficult remote sensing interpretation area, especially a karst area is solved.

The light is filtered by a two- or three-band optical device integrated with two- and three-band mixing method so that only the light in the wavelength range of the pass bands can pass through this optical device and reach the optical sensor for the opto-electronic sensing application, or human eyes for the visual instrument application, or the surface of target object for the illumination application. The color difference and/or chromaticness difference can be enhanced by this method. And the target identification or object classification can be implemented by this method in terms of color attributes. With this method, two- and/or three-band ratio criteria widely used in remote sensing and machine vision applications can be calculated in terms of color attributes, and two- and/or three-band multi-spectral imaging can be acquired so that the complicated and expensive two and three band multi-spectral imaging system can be replaced by this kind of sensing device.

The invention discloses a water depth inversion method based on satellite-borne single-photon laser active and passive remote sensing fusion. The method comprises the following steps: firstly, reading an ICESat-2ATL03 original data set; then extracting shallow water body water depth data by adopting a DBSCAN method; then selecting a Sentinel remote sensing image with less cloud in the region, and carrying out atmospheric correction, space cutting and land cloud layer masking on the Sentinel remote sensing image; substituting the water depth data detected in the ICESat-2 data into a band ratio empirical model for regression training to obtain parameters, and finally substituting the parameters into the Sentinel remote sensing image to invert a water depth map of the region. According to the invention, no in-situ sounding data is used as a control point, water depth measurement can be carried out in a region where airborne water depth measurement is not easy, and good measurement precision is maintained.

The invention provides a target freshness detection method and system. The method comprises the steps of obtaining an optimal band ratio feature of a hyperspectral image of a to-be-detected target and a texture feature of an optimal band ratio image; and according to the optimal band ratio feature and the texture feature, determining the new degree of the to-be-detected target. The system executes the method. According to the method, the texture feature of the image with the optimal band ratio is obtained by using the optimal band ratio of the hyperspectral image of the target to be detected, and the rapid nondestructive detection of the freshness of the target to be detected, such as crop seeds, is realized based on the optimal band ratio feature and the texture feature. And the development difficulty and cost of rapid nondestructive testing equipment are greatly reduced.

A receiver and a sampling mixer capable of optimizing the sampling rate of an output signal in accordance with the band ratio of a modulation band to the RF frequency of a received signal. A sampling mixer comprises a history capacitor (6) that integrates input signals current-converted for a continuous time interval; rotation capacitors (7-14) that repeat the integration and discharging of the input signals; a digital control (104) that controls the integration intervals of the rotation capacitors (7-14); and a control part (105) that controls the discharging of the rotation capacitors (7-14). In the sampling mixer, the number of ones of the rotation capacitors (7-14) that are simultaneously connected to a buffer capacitor (15) is changed in accordance with the band ratio of the modulation band to the RF frequency. In this way, the decimation ratio is made variable, whereby the quantized noise during the A/D conversion can be reduced.

The invention relates to the field of temperature identification and classification of pyroelectric detection, in particular to a temperature identification and classification method based on a dual-band pyroelectric device. According to the dual-band characteristic output by a dual-band pyroelectric device, the characteristic that different temperatures of a target black body can map different dual-band ratios is utilized, the dual-band ratios serve as the main characteristic of a signal, and part of characteristics of a time domain and a frequency domain are additionally added to serve as a characteristic set of the signal, so that extraction of the main characteristic of the signal is completed; and the method is combined with machine learning, so that a good temperature identification and classification effect is achieved. The method is easy to implement, can achieve a good classification effect, effectively solves the problem that an existing temperature recognition and classification method is limited by respective categories and is low in universality, can be applied to other fields, and provides a new thought for development of other fields.