12 results about "Vibrational spectrum" patented technology

This disclosure relates to automated systems and methods for predicting the expression of one or more biomarkers in a biological sample. In some embodiments, the sample is a sample having an unknown fixed state or a sample subjected to a fixed duration of unknown duration. In some embodiments, the predicted expression is a quantitative estimate of the positive percentage of one or more biomarkers. In other embodiments, the predicted expression is a quantitative estimate of the staining intensity of one or more biomarkers. In some embodiments, the system and method utilize a trained biomarker expression estimation engine that has been trained with multiple training samples, wherein the trained biomarker expression estimation engine is adapted to derive biomarker expression features from the sample. In some embodiments, the trained biomarker expression estimation engine includes a machine learning algorithm based on a projection to a latent structural regression model. In some embodiments, the trained biomarker expression estimation engine includes a neural network.

This invention relates to the fields of aerospace remote sensing and satellite overall design technology, and particularly to a method for analyzing spectral aliasing caused by flywheel micro-vibration in hyperspectral imagers. The method involves acquiring flywheel operating parameters and satellite structural dynamics parameters, calculating the time series of line-of-sight jitter displacement caused by flywheel micro-vibration, acquiring a standard hyperspectral data cube of the target scene, performing a dynamic spectral sampling process using the line-of-sight jitter displacement time series, continuously integrating the data cube by simulating the instantaneous line-of-sight motion trajectory of the imager, and outputting a hyperspectral image affected by micro-vibration. The method also involves extracting the spectral curves of the pixels to be evaluated and the ideal spectral curve, and calculating the spectral aliasing error index. This invention quantifies the impact of micro-vibration on spectral dimensions by constructing a complete analytical chain of flywheel disturbance, structural transmission, and dynamic sampling, solving the problem of the lack of quantitative analysis of spectral aliasing in existing technologies, and significantly improving the reliability of hyperspectral remote sensing data in agricultural crop classification and growth monitoring.

The present invention improves charge / discharge characteristics. This secondary battery comprises a positive electrode, a negative electrode, and an electrolyte solution. The positive electrode is provided with a positive electrode current collector containing aluminum. The electrolyte solution contains an electrolyte and a solvent. The electrolyte contains a bis(fluorosulfonyl) imide salt. The solvent contains at least one compound selected from a first group consisting of ethylene carbonate, propylene carbonate, fluoroethylene carbonate, dimethyl carbonate, and γ-butyrolactone. The intrinsic molar ratio of the solvent to lithium ions, calculated from the vibrational spectrum of the electrolyte, is greater than 0 and not greater than 1.76.

In certain examples, methods and apparatuses are directed to small molecules (e.g., drug-like molecules) binding to a target protein in live cells. In a specific example, a method includes: processing a laser beam, from a quantum cascade laser (QCL), carrying light in a wavelength range that overlaps a mid-IR range; using optics, in response to the laser beam, to process the light in a set of related beams along respective paths, including a reference path and a sample path, respectively towards a reference target and a sample target that includes living cells: and detecting, via a vibrational spectroscopy detector, small molecules binding to a target protein in live cells. The detection is carried out by collecting incident light, relative to the reference target and from the sample target, carried along respective incident paths and by discerning (e.g., measuring) vibrational probe frequency shifts in the sample target.

To improve charge and discharge characteristics. [Solution] The secondary battery comprises a positive electrode, a negative electrode, and an electrolyte. The positive electrode comprises a positive electrode current collector containing aluminum and a positive electrode active material layer provided on the positive electrode current collector. The positive electrode active material layer contains a lithium-containing compound. The electrolyte comprises an electrolyte, a solvent, and an additive. The electrolyte contains a bis(fluorosulfonyl)imide salt. The solvent contains at least one from the first group consisting of ethylene carbonate, propylene carbonate, fluoroethylene carbonate, dimethyl carbonate, and gamma butyrolactone. The additive contains at least one from lithium fluorophosphate, sulfinyl compounds, and dinitrile compounds. The intrinsic molar ratio of the solvent to lithium ions, calculated from the vibrational spectroscopy spectrum of the electrolyte, is greater than 0 and 1.78 or less.

This invention discloses a non-destructive mango separation intelligent agricultural harvesting device and method. The harvesting device includes a walking mechanism, a lifting mechanism, a horizontal sliding mechanism, a robotic arm, an energy-induced end effector, and a flexible buffer collection mechanism. The energy-induced end effector includes a flexible fruit clamping component, an end recognition camera, a micro-vibration spectral generation and analysis module, and a multi-frequency resonant energy-induced module. The micro-vibration spectral generation and analysis module is used to perform non-contact detection of the fruit stalk, obtain the spectral response of the fruit stalk cells, and compare it with the in vitro difficulty fingerprint spectrum to determine whether the optimal harvesting period has been reached. If so, a decision command containing the inherent resonant frequency and phase characteristics of the fruit stalk is output. The multi-frequency resonant energy-induced module emits a resonant wave with the same frequency but opposite phase as the inherent vibration frequency of the fruit stalk, and uses the phase cancellation interference principle to instantaneously weaken the cell binding force at the molecular level to achieve non-destructive separation.

The application provides a DFT calculation model optimization method for vibration-electron interaction in X-type double-spiralene configuration, and belongs to the technical field of quantum chemistry multi-scale calculation; first, experimental data of X-type double-spiralene material is obtained through spectral technology, and microscopic detailed data is obtained through quantum chemistry DFT calculation, so that the energy barrier, vibration spectrum, electron cloud density and molecular orbital energy level data of the X-type double-spiralene material are obtained. For the molecular aggregate state, the periodic DFT and molecular mechanics MM coupling method is used, the active area is described by DFT, and the weak interaction area is processed by MM, and the periodic characteristics of the energy band structure are calculated based on the mixed precision calculation framework. The multi-scale coupled DFT algorithm is used to correlate the vibration-electron interaction at the atomic scale with the energy band structure, non-radiative transition and other mesoscopic characteristics of the molecule, and the macroscopic photoelectric characteristics of the material are output, so that the calculation closed loop from the micro to the macro is completed.

Disclosed are systems and methods for measuring vibrational spectra of living cells and conducting comparative precision noise reduction (CPNR). A system according to some embodiments can include at least one photon source configured to generate a first photon beam that passes through living objects in medium and a second photon beam that passes through a reference medium. Detectors can be configured to produce first and second analog signals representative of the vibrational spectra of the objects and the noise spectra of the reference medium. At least one analog-to-digital converter can be configured to convert the first and second analog signals into first and second digital representations. At least one processor can be configured to define one or more reference events from the second digital representation, identify one or more very similar matching events from the first digital representation, and remove matching events from the first digital representation data.

The invention belongs to the technical field of serum lipid detection, and provides a Raman spectrum technology-based serum lipid detection method, which comprises the following steps: serum sample collection, Raman spectrum collection, and detection identification. The construction process of the serum lipid detection model comprises the steps of sample set setting, VAP processing, spectral data acquisition, interval screening, characteristic peak identification, interference factor elimination and supervised learning training. By directly capturing the characteristic vibration spectrum of lipid molecules, high-sensitivity and non-destructive detection is realized, rapid identification and relative quantification of various lipid components can be completed, and the molecular structure and dynamic change information of the lipid can be synchronously analyzed, so that the information dimension and the detection efficiency are remarkably improved; through interval screening, characteristic peak identification and interference factor elimination, the operation is simplified, the interference is reduced, and the information dimension is improved.

The invention discloses a vibration spectrum analysis method based on convolutional neural network feature extraction, and relates to the technical field of spectrum analysis. Comprising the following steps: collecting and preprocessing vibration spectrum data to obtain standardized and enhanced vibration spectrum data; based on the standardized and enhanced vibration spectrum data, designing and training a convolutional neural network to obtain a model for spectral feature extraction; based on the trained model, calculating a gradient weighted class activation mapping thermodynamic diagram to realize feature visualization; the interpolation thermodynamic diagram is matched with the size of the original spectrum, contribution analysis is carried out on spectral characteristic peaks, and key peak positioning of the original spectrum is achieved. According to the method, the spectrum difference and the key absorption peak can be automatically identified, the uncertainty caused by manual comparison and subjective judgment is remarkably reduced, and an efficient, objective and standardized spectrum analysis process is realized.

The invention relates to a cardiovascular disease classification system and method of a multi-source vibration spectrum fusion network based on common-private features and application. A cardiovascular disease classification system of a multi-source vibration spectrum fusion network based on common-private features comprises a data collection module for collecting Raman spectrum data and infrared spectrum data of serum; the data preprocessing module is used for preprocessing the Raman spectrum data and the infrared spectrum data; the multi-scale private feature extractor module is used for extracting multi-scale private features of the preprocessed Raman spectrum data and infrared spectrum data; the dynamic sharing characteristic generation module is used for extracting dynamic sharing characteristics of the preprocessed Raman spectrum data and infrared spectrum data; the bidirectional attention fusion module is used for performing deep interaction and adaptive fusion on the multi-scale private features; and the classifier module is used for splicing and classifying the fused multi-scale private features and the dynamic sharing features. According to the technical scheme, the classification performance is good.

This invention relates to the field of spectroscopic analysis technology, specifically to a method for molecular vibration and hydrogen bond network analysis. This method involves acquiring terahertz absorption spectra of samples at different temperatures; acquiring Raman scattering spectra of samples; constructing a unit cell model of the target peptide and calculating its theoretical vibrational spectrum; obtaining the evolution of frequency offset parameters and full width at half maximum (FWHM) parameters with temperature, and performing mathematical fitting; comparing the terahertz absorption and Raman scattering spectra with the theoretical vibrational spectrum to identify the molecular vibrational modes corresponding to each characteristic absorption and scattering peak; performing mode decomposition on the calculated multiple infrared active vibrational modes to quantitatively distinguish the contribution ratios of intermolecular translation, intermolecular rotation, and intramolecular vibration in each vibrational mode; and analyzing the relative changes in bond length and bond angle of intermolecular hydrogen bonds within the target peptide unit cell. This invention systematically analyzes the vibrational behavior of peptide molecules, especially KLVFF peptides.