374 results about "Color mapping" patented technology

The invention relates to the technical field of image processing, in particular to an image acquisition and analysis method and system, and provides the following scheme: obtaining a visible light and near-infrared multispectral image, generating a spectral difference image, and performing weighted fusion to obtain a first image; segmenting a target region based on the fused saliency map, and calculating a pixel reflectance ratio; solving a color mapping matrix according to the reflectance ratio, and carrying out color correction on the target region to obtain a standardized feature image; and extracting characteristic parameters such as spectrums, colors and textures, inputting the characteristic parameters to a multi-branch convolutional neural network, fusing the characteristic parameters through an attention mechanism, and outputting a state classification result and a quantitative index. The cross-spectral imaging difference can be adaptively compensated, and the fusion precision and the analysis stability are improved.

The invention discloses a video image color correction method and system based on artificial intelligence, and relates to the technical field of color correction, and the method comprises the steps: collecting video data, carrying out the image size normalization and denoising processing, and obtaining the preprocessed video frame sequence data; and extracting reflectivity data and illumination data by using a Retinex-Net deep learning model, calculating an illumination adjustment coefficient, carrying out pixel correction, and carrying out color temperature correction on the pixel data of the video frame subjected to pixel correction according to the illumination adjustment coefficient. According to the method, the preliminary color mapping matrix is generated through the color histogram matching method, the color distribution of the image can be optimized, the image is enabled to better conform to the color features of the target reference frame, the color temperature adjustment is performed on the image according to the illumination adjustment coefficient and the illumination threshold, the cold and warm tones of the video are enabled to be accurately adjusted, and the image quality is improved. Through successively carrying out pixel correction, color correction and local area correction, a stable color adjustment frame is provided, so that the video styles are consistent.

The invention relates to the technical field of medical cosmetology and computer vision, in particular to a 3D skin measurement method based on high-precision measurement of a face concave-convex degree region, and the method comprises the following steps: arranging a structured light camera to collect a face RGB image and depth data, and carrying out the filtering and denoising to generate a smooth point cloud; detecting an RGB image based on FaceMesh to obtain face key points, and constructing a multi-region segmentation algorithm; segmenting an RGB local area and converting the RGB local area into point cloud data; iDW interpolation is carried out to generate a dense point cloud, and a two-dimensional contour line is extracted through a Marking Square algorithm; and mapping the color of the matched color card to an image area to generate a contour map. According to the method, the face recognition model is constructed through the FaceMesh architecture, accurate positioning of multiple face types is adapted, two-dimensional contour mapping is adopted to three-dimensional contour mapping, data are intercepted, local processing is combined, the operation load is reduced by 60%, and efficiency is remarkably improved.

The invention discloses an industrial image change anomaly detection method and system based on artificial intelligence, and relates to the technical field of image recognition, and the method comprises the steps: collecting a dual-light-source industrial image, employing frequency domain saliency to guide fusion, and carrying out visual enhancement processing through color mapping and edge enhancement; inputting the enhanced image into a CNN convolutional network to generate a multi-scale feature map, extracting a multi-scale high-pass residual image through two-dimensional fast Fourier transform and a high-pass filtering template, and splicing all scales and coding to generate a token sequence through local attention guide fusion; constructing a self-induction visual model, and performing feature reconstruction on the token sequence to generate a reconstructed feature map; and calculating and reconstructing an error scoring graph by adopting double error indexes, sampling to obtain an abnormal smooth graph, and segmenting an abnormal region based on the abnormal smooth graph. And finally, a dual anomaly detection system of image-level judgment and region-level identification is constructed.

The invention relates to the technical field of tire production, in particular to a method for identifying abnormal wear of a tire through color difference of an automobile tire tread, which comprises the following steps: determining scanning parameters; correcting scanning parameters; obtaining a point cloud picture; quantitative parameters are selected, and a wear state model is trained based on historical detection data; establishing a mapping relation; according to the detection data of the to-be-detected automobile tire and the wear state model, obtaining a quantization parameter value of the automobile tire; generating a quantization parameter point cloud picture; verifying whether the quantization parameter value is accurate or not according to the spectrogram of the reflected light and the quantization parameter point cloud atlas; and generating a visual quantization parameter point cloud picture. According to the method, scanning parameters are dynamically corrected according to the actual perimeter to ensure the accuracy of point cloud data, automatic calculation of quantization parameters is realized in combination with a machine learning model, and finally, abstract wear data are converted into a visual image through color mapping, so that the wear condition is directly obtained by observing colors, and the accuracy of the wear condition is improved. And the detection efficiency of abnormal wear of the tire is improved.

The invention relates to a photovoltaic monocrystalline silicon material resistivity uniformity evaluation method, and belongs to the technical field of photovoltaic material detection. The method comprises the following steps: evaluating all-form resistivity and doping uniformity of a photovoltaic single crystal silicon rod, a single crystal silicon wafer and a single crystal silicon square wafer; carrying out standardized point distribution on the monocrystalline silicon wafer by using a concentric circular ring and a small circle, and calculating in-plane uniformity by using a triple integral derivation equation; the monocrystalline silicon square pieces are distributed in the form of grid small squares, and in-plane uniformity is calculated by means of a triple integral derivation equation; and for the axial direction of the silicon rod, the uniformity is quantified by utilizing range and variance models through the resistivity of the head, middle and tail wafers. Meanwhile, a continuous grid is generated through interpolation, and a 3D color mapping curved surface diagram with plane projection is drawn to visually present distribution. According to axial and in-plane uniformity results, silicon wafers are classified and graded, the doping uniformity of monocrystalline silicon is accurately reflected, and a support is provided for quality control, process optimization and silicon wafer sorting of photovoltaic monocrystalline silicon.

The invention relates to the technical field of medical education, in particular to a virtual patient skin care teaching system with enhanced pathological features and a method thereof, the system comprises pathological feature generation, parameter control, physical modeling, intelligent evaluation and a multi-mode feedback module, and can edit and simulate various skin pathological features in real time, such as pressure sores and diabetic feet, so that the system can be applied to the field of medical education. The visual display of the lesion degree is realized through a five-segment color mapping system, the system also simulates percolate reflection and cuticle spalling, the sense of reality is enhanced, the intelligent assessment module provides risk assessment and healing prediction according to physiological and morphological parameters, the multi-modal feedback module integrates visual sense, tactile sense and user behavior feedback, the interactive experience is improved, and the system is suitable for popularization and application. And the adaptive optimization module dynamically adjusts system parameters according to user feedback and learning effects, and provides personalized teaching. Compared with a traditional method, the method is higher in parameter control precision, supports personalized teaching, and is of great significance to medical education.

The invention discloses an intelligent perception analysis system for the real-time participation degree and cognitive state of a mathematics classroom. The intelligent perception analysis system comprises a multi-source data acquisition module, a fusion analysis engine module, a real-time feedback module and an offline optimization module. The method has the beneficial effects that a dynamic participation index is innovatively designed, and an attention attenuation resetting mechanism is introduced; the real-time feedback module generates a cognitive thermodynamic diagram of HSL color mapping to position group obstacle points, and triggers self-adaptive question pushing; and the off-line optimization module dynamically updates the edge weight of the knowledge graph through error co-occurrence analysis, and early warns a cognitive confusion relationship without textbook association. According to the method, the limitation of single-mode perception is broken through, the problem solving step quality visual diagnosis and the cross-cycle cognitive impairment prediction are realized, a teaching closed loop of'multi-source perception-hierarchical quantification-real-time intervention-knowledge evolution 'is formed, and the mathematical classroom cognitive state analysis precision and the teaching intervention timeliness are remarkably improved.

The invention discloses a zero sample object instance segmentation system and method in a robot environment, and belongs to the technical field of robot visual perception, and the method comprises the steps: firstly carrying out the Vibriis color mapping preprocessing of a depth image, and then generating an initial object mask candidate through an SAM (Segment Anything Model) model; meanwhile, a pre-trained Vision Transform is used as a feature description model of a scene to process the image, an attention map of the last layer is extracted, and a feature weighting mechanism is constructed based on the information entropy of the attention map; a similarity matrix of the image blocks and the background blocks is calculated to remove non-object masks, a K-Medoids clustering algorithm is adopted for each remaining object mask candidate to obtain representative sampling points, and finally the points serve as prompt information to be input into an SAM model to obtain an accurate object instance segmentation result. According to the method, the zero sample generalization ability of the visual basic model is fully utilized, accurate segmentation of the unseen object can be realized without additional training, and the method has good universality and practical value.

The invention provides a fatigue test method, system and equipment based on texture change of carbon fibers and a medium, and belongs to the technical field of material tests.The method comprises the steps that a carbon fiber composite material is selected as a test sample; applying a fatigue load to the test sample according to a designed test condition, and collecting a surface image of the test sample; the method comprises the following steps of: preprocessing an acquired image, clustering according to the gray level and texture similarity of pixels to obtain a plurality of clustering subareas, and marking different clustering areas by using different colors to complete color mapping; extracting texture feature parameters from the image after color mapping, and performing PCA dimension reduction to obtain key texture feature parameters; and comparing the key texture feature parameters of the image under different test conditions, identifying texture feature changes, and completing fatigue performance evaluation. According to the method, image processing and texture feature analysis are combined, so that non-destructive testing of the CFRP fatigue performance is realized, and the testing accuracy and efficiency are improved.

A three-dimensional temperature gradient field visualization method comprises the following steps: after analyzing a part model and storing a point-surface relationship and temperature information, calculating a data point temperature gradient and carrying out point cloud, grid and entity model visualization; after creating temperature field color mapping and calculating a temperature field contour line, representing a temperature gradient field based on a point icon, representing a temperature gradient field based on a streamline and representing a temperature gradient field based on texture; through design of a data visualization core algorithm and model building, visualization of three-dimensional data of various vector fields for an unstructured grid model is realized.

The invention provides an ultra-high-definition long-distance full-color imaging method under extremely low illumination and a module thereof, and relates to the technical field of image processing, and the method comprises the steps: carrying out the short-exposure shooting of the same scene, synchronously obtaining a plurality of visible light frames and a plurality of infrared frames, and carrying out the image registration; performing brightness enhancement on the plurality of corresponding visible light frames based on the plurality of infrared frames, and synthesizing the plurality of visible light frames after brightness enhancement by using an adaptive weighted fusion algorithm to generate a long exposure equivalent single-frame image; performing noise reduction processing on the long exposure equivalent single-frame image and the plurality of infrared frames; inputting the long-exposure equivalent single-frame image after noise reduction processing and the plurality of infrared frames into a pre-trained color derivation model for color reconstruction, and generating a color information matrix for constructing a full-color image; and based on the color information matrix, performing color mapping and coloring on the long-exposure equivalent single-frame image to generate a final full-color image.

The invention relates to the technical field of computer graphics and deep learning, in particular to a simulation bait automatic coloring method and system based on a 3D model. The method comprises the following steps: acquiring an uncolored 3D model and a reference image, and generating standard data through analysis verification, curvature grid division and image compliance detection; performing color conversion, texture enhancement and multi-scale downsampling on the compliant image to construct an image pyramid; performing multi-level feature extraction and adversarial training optimization based on a pre-trained convolutional neural network and a generative adversarial network, and generating an enhanced color texture map; performing UV expansion, color mapping and normal mapping fusion in combination with the model topology, and constructing an intermediate model with physical rendering attributes; and batch color consistency verification is realized through color histogram comparison, adaptive threshold segmentation and iteration parameter adjustment, and a standard model group is generated. According to the invention, efficient and highly realistic automatic coloring of the simulated bait is realized, and color consistency and rendering quality in batch production are guaranteed.

The invention discloses a method for optimizing the texture quality of a generative 3D model based on a texture expansion graph, and relates to the field of computer vision. Rendering the 3D model to obtain a multi-modal image set under multiple view angles; obtaining a training data set and training a multi-view generation model; generating an RGB image set by using a multi-view generation model; high-quality RGB images under the six visual angles are back-projected to the surface of the 3D model, colors of 3D points are mapped to a UV space through UV mapping, and a multi-visual-angle texture map and a mask map of the UV space under the six visual angles are obtained; fusing the multi-view texture maps to obtain a rough texture map; and optimizing the rough texture map through the image restoration model to obtain a high-quality complete texture expansion image. Through the multi-view generation model and the UV space image restoration technology, the texture quality of the generation type 3D model is optimized, and the problems of artifacts and distortion caused by the fact that the image content is inconsistent with the three-dimensional model geometry when high-quality multi-views are generated based on images are effectively solved.

Aspects of hair simulation, hair classification, and networks therefor including aspects to train such networks are provided. A classifier model alleviate the impact of human bias where the modeling of the real label distribution and annotators' biases are separated by incorporating annotator confusion matrices into a baseline model. The model was trained using a consistency-based semi-supervised learning framework. With the use of only 1000 labeled data, the final classifier model achieved a classification accuracy that was 20% higher than a human professional annotator. The model is useful as a color classifier to train generative models for hair color translation. There is provided a generative model for hair simulation (e.g. via VTO) that is guided during training by a hair classifier model. Further provided is a color mapping network to process an input image and target hair color for the generative model to define the hair simulation.

The invention belongs to the technical field of building single or multi-view-angle to 3D modeling reconstruction, and provides a 3D reconstruction method based on a real estate building cluster point cloud to MESH neural network. Comprising the steps of original point cloud data acquisition, noise removal, grid filtering, normal vector optimization, density uniformity processing, continuous multilayer 3D convolutional neural network processing, color mapping, texture mapping, color consistency optimization and texture distortion optimization. Through uniform sampling, noise removal and grid filtering, the point cloud data volume is reduced, and the quality of the point cloud data is improved; through the continuous multi-layer 3D convolutional neural network, the local geometric pattern of the building is automatically learned from the data and the features of the building are extracted layer by layer, so that the precision of the three-dimensional grid model is improved; through color consistency optimization and texture distortion optimization, the color and texture mapping effect is optimized, and refined reconstruction of the building is realized.

The invention discloses an automatic texture color mapping method for large-scale point cloud data. The automatic texture color mapping method comprises the following steps: firstly, realizing geometric alignment of geographic texture data and point cloud data; then realizing release of intra-core buffer by means of a mass point cloud space segmentation and retrieval management technology, and segmenting the mass point cloud into a plurality of regional point cloud blocks; carrying out filtering processing on the regional point cloud blocks to filter out discrete flying spots; secondly, considering correction constraints of different texture source information to realize seamless generation of geographic texture data; secondly, efficient point cloud preliminary color assignment is achieved by means of the spatial relation between geometric mapping and local aggregation of regional point cloud blocks and geographic texture data; secondly, eliminating the texture information (color) of the wrong coloring point cloud by using the self-shielding judgment result of the point cloud; and finally, realizing spatial merging of regional point cloud block data sets by using an efficient out-of-core file merging technology. According to the method, the dependence on hardware equipment (such as a large memory and a high graphics card) is avoided, and the block processing of massive point clouds is efficiently realized.

Embodiments of the present disclosure provide techniques for colorizing visual content using artificial intelligence models. An example method generally includes receiving an image and an input prompt specifying a colorization to apply to the image. Based on an encoded version of the image and a textual description of the image input into a machine learning model, one or more color maps associated with the specified colorization to apply to the image are generated. A colorized version of the image is generated by a generative artificial intelligence model based on combining a grayscale version of the image and the one or more color maps, and the colorized version of the image is output.

The invention discloses a digital twinning-based hydrodynamic model display method, system and device and a storage medium, and the method comprises the steps: obtaining a simulation result of a hydrodynamic model from local storage or network data service, and storing the simulation result in a data structure of a digital twinning system; a water body three-dimensional model is generated through the grid Shp file, vertex flow field and water level data are calculated based on a model simulation result, and after normalization, the vertex flow field and water level data are mapped to a color channel and vertex height of the water body three-dimensional model; a water surface dynamic effect is enhanced by adopting a water surface ripple normal mapping and flow velocity color mapping, and PostProcessVolume is created to simulate interface refraction and underwater visual effects of the water surface and air. The three-dimensional flow field and water level change of the hydrodynamic model can be displayed, visualization of underwater simulation results is supported, and the sense of reality of the flow effect is improved; and in combination with network data service and a local file access mode, the data loading and updating speed is increased, and the method is suitable for various data access environments.

The invention provides a digital pathological image high-color accurate splicing method and device based on feature point matching. The method comprises the following steps: obtaining a target splicing region in a target splicing image and a to-be-spliced region in a to-be-spliced image based on a scanning sequence of digital pathological images; performing affine transformation on the to-be-spliced region by using the affine transformation matrix to obtain an initial correction region; constructing a global color mapping function, and mapping the initial correction image by using the global color mapping function to obtain a color correction image; and splicing the color correction image and the target splicing image based on the scanning sequence of the pathological images to obtain a digital pathological image splicing result. According to the scheme, only the target splicing area and the to-be-spliced area are subjected to feature point matching, the cumulative distribution function of the effective feature points is calculated according to the RGB channels, and the global color mapping function is constructed and optimized, so that accurate color correction is realized, and the visual integrity and consistency of the spliced image are improved.

The invention discloses a fluorescent microscopic image intelligent enhancement method and device and a readable storage medium, and belongs to the field of medical image processing. The method aims at solving the problems that multi-channel fusion of fluorescence images is difficult, pseudo-color mapping is not accurate, RGB processing is not uniform, and parameters depend on experience. The method comprises the following steps: acquiring a multi-channel image and separating; performing pseudo-color mapping and synthesis based on wavelength physical characteristics to obtain a synthesized image; obtaining R, G and B three-channel target histogram features of the standard library; carrying out R, G and B three-channel independent histogram specification on the synthesized image; by utilizing a genetic algorithm, taking similarity with a target histogram as a target, and adaptively optimizing independent Gamma parameters of three channels R, G and B; and performing independent Gamma correction by using the optimal parameter to obtain a final enhanced image. According to the method, physical accurate mapping, standardized correction and parameter self-adaption are realized, and the image quality is improved.

The invention discloses a bearing fault diagnosis method based on wavelet time-frequency coding and a convolution visual converter. The method comprises the following steps: collecting vibration signals of a bearing under different working conditions; performing continuous wavelet transform on the one-dimensional vibration signal to generate a two-dimensional time-frequency diagram with high time-frequency resolution; carrying out coding and image enhancement on the time-frequency graph through an enhanced color mapping mode; inputting the time-frequency graph into a convolution visual converter model, and realizing local and global combined extraction of vibration features through convolution projection, a self-attention mechanism and a multi-stage feature fusion structure; the features are classified to identify a state of health or type of fault of the bearing. According to the method, the diagnosis precision and robustness in a complex working condition and high-noise environment are effectively improved, and the method is suitable for state monitoring and early fault early warning of various types of rotary mechanical equipment.

Embodiments of the present disclosure relate to classifying the color of objects in digital images. This disclosure relates to a color classification system that accurately classifies objects in digital images based on color. Specifically, in one or more embodiments, the color classification system utilizes a multidimensional color space and one or more color maps to match objects to colors. In practice, the color classification system can utilize one or more color similarity regions generated in the multidimensional color space to accurately and efficiently detect the color of an object.

The invention provides a tree damage range intelligent identification method and system based on a satellite remote sensing image, and the method comprises the steps: determining a disaster cut image based on a satellite remote sensing image of a disaster area; fusing different spectral band data with the disaster cut image by adopting a multi-spectral band fusion technology to obtain a fused image; on the basis of the fused image and in combination with multiple indexes, a color mapping technology and a clustering algorithm are adopted for combined comparative analysis, and a post-disaster damage area preliminary screening image is obtained; inputting the post-disaster damage area preliminary screening image into a pre-constructed refined segmentation model to obtain a post-disaster tree damage detection result; performing spatial overlay analysis on the post-disaster tree damage detection result and power grid geographic data to obtain a risk identification result of a tree damage range; wherein the refined segmentation model comprises an encoder part adopting a visual architecture based on a shift window mechanism. The damage condition of trees around the power transmission line can be rapidly obtained after a disaster occurs, and the line patrol time is shortened.

The invention provides a civil aircraft safety perception method and system based on virtual flight, and relates to the technical field of aircraft safety perception. A dynamic and hierarchical three-dimensional wind shear field model is constructed through space-time alignment and fusion of multi-source meteorological data; deep coupling of wind disturbance and aircraft dynamic response is realized based on a small disturbance theory and a flight dynamics model; continuous and dynamic quantification of risks is realized by combining and analyzing flight parameters and a safety threshold value dynamically adjusted based on wind disturbance intensity and utilizing multi-parameter risk level weighted integration; according to the method, the risk coefficient is coupled with the three-dimensional terrain and the virtual flight trajectory, hierarchical color mapping is adopted to carry out visual space visualization, and a complete closed loop from environment modeling to risk early warning is formed, so that the identification accuracy and timeliness of the flight risk in the wind shear environment are greatly improved, and the scientificity and practicability of security situation awareness are enhanced.

The invention provides a PRPD atlas processing method and related equipment, and the method comprises the steps: obtaining an original atlas corresponding to an atlas processing instruction in response to the atlas processing instruction; performing content extraction on the original atlas to obtain atlas content in the original atlas; adjusting the size of the map content of the original map to obtain a standard size map; background information in the standard size map is eliminated, and a target map is obtained; rGB data of all target pixel points in the target map are converted into gray values, a pseudo-color map is generated according to the gray values obtained through conversion by means of a preset pseudo-color mapping formula, and the target pixel points are pixel points not smaller than a preset gray threshold value. By applying the method provided by the embodiment of the invention, the background information in the atlas can be efficiently eliminated.

The invention discloses a finite element dynamic layering visualization method based on digital twinning. The finite element dynamic layering visualization method comprises the steps that a finite element analysis result is obtained; a Mesh grid is generated through fitting according to the geometric data; constructing an index of each vertex in the Mesh grid; storing the numerical data into an image file, and establishing a mapping relationship between a pixel index of the image file and the vertex index; taking the image file as a texture and binding the image file to a Mesh grid; in the vertex shader, sampling and determining numerical data corresponding to the vertex according to the vertex index, and transmitting the numerical data to the fragment shader; and in the fragment shader, outputting the color of the Mesh grid by using a preset color mapping table. According to the method, a finite element calculation result is rebuilt through a twinning method, the finite element is lightened, cross-platform application of the finite element is achieved, the finite element calculation result is visually displayed in real time, engineers are helped to judge entity world changes more accurately, potential problems are found in time, prevention measures are taken, and the real-time real-time visualization display of the finite element calculation result is achieved. Therefore, safety and stability of buildings and infrastructures are guaranteed.

The invention relates to the field of image processing, in particular to an adrenal metastatic tumor image feature visualization method and system. The method comprises the following steps: collecting an adrenal metastatic tumor PET-CT image, carrying out spatial deviation elimination and image fusion optimization, and constructing a cross-modal standard fusion image; performing adrenal gland network segmentation and three-dimensional voxel reconstruction on the cross-modal standard fusion image, and constructing a three-dimensional adrenal gland voxel rendering model; extracting a metastatic tumor area sub-model based on the three-dimensional adrenal voxel rendering model, performing multi-time-point metabolic information change calculation, and constructing a metastatic tumor dynamic metabolic change curve; and performing internal metabolic activity distribution characteristic and heterogeneity color mapping according to the metastatic tumor dynamic metabolic change curve, and constructing an internal metabolic activity distribution diagram. According to the invention, accurate image presentation is provided, so that the morphology, metabolism and growth conditions of metastatic tumors are more intuitive and clearer.

The invention relates to a method and system for assisting clinical psychotherapy through traditional Chinese medicine music therapy. The method comprises the following steps: extracting five-tone frequency, rhythm and timbre characteristics of music, dynamically converting audio data into a dynamic image frame sequence in combination with a color mapping rule, and realizing multi-sensory real-time matching of the music and visual elements by utilizing a time synchronization algorithm and collaborative intensity calculation. And finally, dynamic collaborative stimulation is output through a streaming media transmission technology. Therefore, on the basis of retaining the kernel of the five-tone regulation theory in the traditional Chinese medicine, the problems of audio-visual splitting and difficulty in quantitative implementation of the theory in the traditional method are solved, and the multi-modal synergistic effect and clinical suitability of the music therapy in emotion and cognitive regulation are remarkably improved.

The invention discloses a signal modulation identification method and device based on deep learning, and relates to the technical field of signal modulation identification, and the method comprises the steps: obtaining a to-be-identified modulation signal; removing noise from the modulation signal to be identified by using a Gaussian filter, and carrying out short-time Fourier transform on the modulation signal to be identified after noise removal to obtain a gray scale time-frequency graph; performing normalization processing on the gray scale time-frequency graph, and converting the normalized gray scale time-frequency graph into a color time-frequency graph by adopting a color mapping function; processing the color time-frequency graph by adopting a trained deep learning network to obtain a classification result of the modulation signal to be identified; wherein the trained deep learning network is obtained by training the initial deep learning network by taking data of a preset category as a training data set. According to the invention, the identification accuracy of the modulation mode in a low signal-to-noise ratio environment can be improved.