color mapping
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2021 ◽  
pp. 095679762110345
Author(s):  
Guido Marco Cicchini ◽  
Giovanni Anobile ◽  
Eleonora Chelli ◽  
Roberto Arrighi ◽  
David C. Burr

Mapping number to space is natural and spontaneous but often nonveridical, showing a clear compressive nonlinearity that is thought to reflect intrinsic logarithmic encoding of numerical values. We asked 78 adult participants to map dot arrays onto a number line across nine trials. Combining participant data, we confirmed that on the first trial, mapping was heavily compressed along the number line, but it became more linear across trials. Responses were well described by logarithmic compression but also by a parameter-free Bayesian model of central tendency, which quantitatively predicted the relationship between nonlinearity and number acuity. To experimentally test the Bayesian hypothesis, we asked 90 new participants to complete a color-line task in which they mapped noise-perturbed color patches to a “color line.” When there was more noise at the high end of the color line, the mapping was logarithmic, but it became exponential with noise at the low end. We conclude that the nonlinearity of both number and color mapping reflects contextual Bayesian inference processes rather than intrinsic logarithmic encoding.


2021 ◽  
Vol 2021 ◽  
pp. 1-12
Author(s):  
Bo Cheng ◽  
Xiaomei Hu ◽  
Zhiqiang Liu ◽  
Xiuliang Gong

Propulsive force and exhaust fluid temperature are important indicators in the performance of an engine. An investigation of the effects of propellant composition, plane flight conditions, and engine operating environment on rocket thrust and the range of smoke plume temperature can provide references in the design of engine mechanics at the optimization of propellant composition, in monitoring of target identification and in the evolving of stealth of stealth technology. In order to understand the characteristics of the engine tail flame, a visual simulation of the engine tail flame was carried out by combining the engine operating conditions with the tail flame conditions. Based on the advantages of the bicubic spline interpolation algorithm and the Kriging interpolation algorithm, this paper proposes a hybrid interpolation algorithm, which performs color mapping and three-dimensional space separation in the engine plume data set and model, and visualizes the engine and engine plume. The simulation realizes real-time monitoring of the functions of various engine components through characteristic colors. The research results show that the hybrid interpolation method can effectively visualize the engine exhaust flame. The simulated plume has a relatively obvious temperature peak at 0.7 m, and the temperature of the plume flow field is significantly higher than that of the frozen plume flow field by about 200 ~1000 K. This shows that the algorithm in this paper helps to visualize the expression of engine tail flame information.


2021 ◽  
Author(s):  
Carlos Martinez ◽  
Ewa Nieminsky

Abstract Research on optical modes, such as orbital, temporal, or parity, bring much attention, for these new degrees of freedom allow larger quantum communication alphabets. Each lab usually adapts the Bloch or Poincare sphere to their experiment or light mode. This takes an extra effort and time and produces a plethora of spheres and notations. Yet, we miss a common framework or convention valid among diverse physical-modes. We aim to unite in one representation the best points from many different spheres. Such common-sphere could also help to compare distant experiments, for an intuitive understanding of quantum optical states. We built a common representation by mathematically aligning the Hilbert space and a three dimensional color space. We define a unique color for each one of the three Poincare axes and positive Pauli vectors. Beyond three primary colors and states, our equations associate each Hilbert state to a specific tonality, among the infinite combinations in Color space. These maths achieve a new ability to unequivocally represent any quantum state by its precise combination of colors. Thus, with these equations, quantum states ‘yellow’ or ‘magenta’ are not mere names, rather each one denotes an exact superposition in Hilbert space. To handle disparities between SO3 vs. SU2 space operations, we propose a darkness bit and a Hermite-inspired shape. A simulation of HG modes let us align distinct shapes to quantum optical states. Three examples of applications show our color sphere in practice. First, we apply the Hilbert-Color mapping in Polarization. Then, the same color-space is shown in Orbital Angular Momentum. We also represent location paths in this color-mapping. The simulations and practical comparisons let us refine the proposed color sphere convention. For higher-order and path-to-industry, any sphere section serves as color constellation diagram. One color-space sphere served as common ground to represent coexisting concepts among diverse physical areas. The introduced change diagrams are visual tools to communicate setups and operators. The examples showed a unique notation matches many physical processes. The resulting diagram of superposition of spatially separated optical paths is coherent with a Plate on Polarization or cylinder lens on OAM Hermite Gaussian modes. A unique change diagram describes the three examples. The meaning persist despite the physical implementation. Found also how this color space let us grasp visually some meaning. Thus, the amount of blue in a state representation indicated the degree of its phase shift. Overall, we presented math and visual tools to display and compare experiments. We showed examples in different physical modes, all linked to a unique color sphere.


2021 ◽  
Vol 8 ◽  
Author(s):  
Hongtao Kang ◽  
Die Luo ◽  
Weihua Feng ◽  
Shaoqun Zeng ◽  
Tingwei Quan ◽  
...  

Stain normalization often refers to transferring the color distribution to the target image and has been widely used in biomedical image analysis. The conventional stain normalization usually achieves through a pixel-by-pixel color mapping model, which depends on one reference image, and it is hard to achieve accurately the style transformation between image datasets. In principle, this difficulty can be well-solved by deep learning-based methods, whereas, its complicated structure results in low computational efficiency and artifacts in the style transformation, which has restricted the practical application. Here, we use distillation learning to reduce the complexity of deep learning methods and a fast and robust network called StainNet to learn the color mapping between the source image and the target image. StainNet can learn the color mapping relationship from a whole dataset and adjust the color value in a pixel-to-pixel manner. The pixel-to-pixel manner restricts the network size and avoids artifacts in the style transformation. The results on the cytopathology and histopathology datasets show that StainNet can achieve comparable performance to the deep learning-based methods. Computation results demonstrate StainNet is more than 40 times faster than StainGAN and can normalize a 100,000 × 100,000 whole slide image in 40 s.


2021 ◽  
Vol 9 ◽  
Author(s):  
Zhilong Li ◽  
Jian Zuo ◽  
Yuanmeng Zhao ◽  
Zhongde Han ◽  
Zhihao Xu ◽  
...  

When terahertz imaging technology is used for the nondestructive testing of composite materials, the signal is often affected by the experimental environment and internal noise of the system, as well as the absorption and scattering effect of the tested materials. The obtained image has degradation phenomena such as low contrast, poor resolution of small targets and blurred details. In order to improve the image quality, this paper proposes a novel method for the enhancement of composite materials’ terahertz image by using unsharp masking and guided filtering technology. The method includes the processing steps of hard threshold shrinkage denoising based on discrete wavelet transform, amplitude imaging, unsharp masking, guided filtering, contrast stretching, and pseudo-color mapping. In this paper, these steps are reasonably combined and optimized to obtain the final resulting image. To verify the effectiveness of the proposed method, a 150–220 GHz high frequency terahertz frequency modulated radar imaging system was used to image three commonly used sandwich structure composites, and the enhancement processing were carried out. The resulting images with significantly enhanced contrast, detail resolution and edge information were obtained, and the prefabricated defects were all detected; Five objective evaluation indexes including standard deviation, mean gradient, information entropy, energy gradient and local contrast were used to compare and analyze the processing results of different image enhancement methods. The subjective and objective evaluation results showed that the proposed method can effectively suppress the noise in terahertz detection signals, enhance the ability of defect detection and positioning, and improve the accuracy of detection. The proposed method in this paper is expected to play a positive role in improving the practicability of terahertz imaging detection technology and expanding its application fields.


2021 ◽  
Vol 42 (Supplement_1) ◽  
Author(s):  
M Shimizu ◽  
H Miyazaki ◽  
S Cho ◽  
Y Misu ◽  
R Tateishi ◽  
...  

Abstract Background Several patients with persistent atrial fibrillation (per-AF) suffer from recurrence after pulmonary vein isolation (PVI). Various methods to predict the recurrence were tried, but deep learning on 12-leads electrocardiography (ECG) after PVI was not studied. Purpose To elucidate diagnostic performance of deep learning on 12-leads ECG after PVI in patients with per-AF Methods We enrolled consecutive 109 patients with per-AF who underwent PVI (68.8±10.0 years, 83 males) excluding failure cases. We defined recurrence in 3–12 months after PVI. From the ECG just after PVI, five beats of each lead were sampled separately. Deep learning (convolutional neural network on bitmap ECG image) was performed by transfer learning of Inception-Resnet-V2 model. Gradient weighted class activation color mapping (GradCam) was performed to detect convolutional importance in the lead. Results Thirty-six patients showed recurrence in the period. Lead II (accuracy 0.701), aVR (0.690) were the top 2 leads of prediction, which showed larger accuracy than statistical accuracies of Non PV foci = SVC (accuracy = 0.541) and left atrial diameter >50mm (0.596). In lead II, GradCam spotlighted strong convolution of latter half of P wave in recurrent case, and former half of P wave and T wave in no-recurrent case. Conclusions Deep learning on ECG was a powerful tool to predict recurrence of per-AF after PVI. FUNDunding Acknowledgement Type of funding sources: None. Results of deep learning Results of GradCam


2021 ◽  
Vol 10 (18) ◽  
pp. 4044
Author(s):  
Dominika Ślósarz ◽  
Elżbieta Poniewierka ◽  
Katarzyna Neubauer ◽  
Radosław Kempiński

Inflammatory bowel disease (IBD) is a chronic condition affecting primarily the gastrointestinal tract and characterized by growing incidence worldwide. Complex diagnostic process of IBD as well as evaluation of disease activity and intestinal complications that are crucial for the therapeutic decisions, require repetitive, invasive, expensive, time-consuming and poorly tolerated tests. In contrast to endoscopy and computed tomography, ultrasound elastography (UE) is non-invasive, non-radiating and non-contrasting dependent tool which might be utilized in IBD patients for the assessment of the intestinal changes. Therefore, we performed the systematic review to evaluate the possible application of the ultrasound elastography for assessment of the intestinal changes in IBD. After the search of three databases: PubMed, World of Knowledge and Scopus, we identified 12 papers which were included in the final analysis. The majority of the studies were focused on the evaluation of the symptomatic ileal/ileocolonic strictures in Crohn’s disease patients that required surgical resection. Only one study concerned ulcerative colitis. The authors evaluated different UE techniques: strain elastography (SE), acoustic radiation force impulse (ARFI) and shear wave elastography (SWE). Results were expressed with semi-quantitative color mapping and strain measurement. Histological scores of inflammation and fibrosis in Crohn’s disease were used as a reference test in the majority of studies. Ultrasound elastography seems to be a promising novel imaging technique supporting evaluation of the intestinal strictures in Crohn’s disease patients in respect to fibrosis detection as well as differentiation between fibrosis and inflammation. However, further research is needed to establish the position of ultrasound elastography in IBD management.


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