visualization algorithms
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2022 ◽  
Author(s):  
Armen Gharibans ◽  
Tommy Hayes ◽  
Daniel Carson ◽  
Stefan Calder ◽  
Chris Varghese ◽  
...  

Abstract Disorders of gastric function are highly prevalent, but diagnosis often remains symptom-based and inconclusive. Body surface gastric mapping is an emerging diagnostic solution, but current approaches lack scalability and are cumbersome and clinically impractical. We present a novel scalable system for non-invasively mapping gastric electrophysiology in high-resolution (HR) at the body-surface. The system comprises a custom-designed flexible HR sensor array and portable data-logger synchronized to an App, with automated analysis and visualization algorithms. The novel system underwent performance testing then validation in 24 healthy subjects. In all subjects, gastric electrophysiology and meal responses were successfully captured and mapped non-invasively (mean frequency 2.9 ± 0.3 cycles per minute; peak amplitude at mean 60 m postprandially with return to baseline in <4 h). Spatiotemporal mapping showed regular and consistent wave activity of mean direction 182.7°±73 (74.7% antegrade, 7.8% retrograde, 17.5% indeterminate). The presented system is a new diagnostic tool for assessing gastric function that is scalable, validated, and ready for clinical applications, offering several biomarkers that are new to gastroenterology practice.


2021 ◽  
Vol 2021 ◽  
pp. 1-9
Author(s):  
Xianhua Huang

The study focused on the intelligent algorithms-based segmentation of computed tomography (CT) images of patients with cardiovascular diseases (CVD) and the realization of visualization algorithms. The first step was to design a method for precise segmentation under the cylinder model based on the coronary body data of the coarse segmentation, and then the principles of different visualization algorithms were discussed. The results showed that the precise segmentation method can effectively eliminate most of the branches and calcified lesions; curved planar reformation (CPR) and straightened CPR can display the entire blood vessel on one image; and spherical CPR can display the complete coronary artery tree on an image, so that a problem with a certain blood vessel can be quickly found. In conclusion, the precise segmentation of CT images of CVD and visualization algorithm based on the cylinder model have clinical significance in the diagnosis of CVD.


2021 ◽  
Vol 11 (5) ◽  
pp. 2199
Author(s):  
Christian Schorr ◽  
Payman Goodarzi ◽  
Fei Chen ◽  
Tim Dahmen

Trust in artificial intelligence (AI) predictions is a crucial point for a widespread acceptance of new technologies, especially in sensitive areas like autonomous driving. The need for tools explaining AI for deep learning of images is thus eminent. Our proposed toolbox Neuroscope addresses this demand by offering state-of-the-art visualization algorithms for image classification and newly adapted methods for semantic segmentation of convolutional neural nets (CNNs). With its easy to use graphical user interface (GUI), it provides visualization on all layers of a CNN. Due to its open model-view-controller architecture, networks generated and trained with Keras and PyTorch are processable, with an interface allowing extension to additional frameworks. We demonstrate the explanation abilities provided by Neuroscope using the example of traffic scene analysis.


Author(s):  
Vasiliki G. Vrana ◽  
Dimitrios A. Kydros ◽  
Evangelos C. Kehris ◽  
Anastasios-Ioannis T. Theocharidis ◽  
George I. Kavavasilis

Pictures speak louder than words. In this fast-moving world where people hardly have time to read anything, photo-sharing sites become more and more popular. Instagram is being used by millions of people and has created a “sharing ecosystem” that also encourages curation, expression, and produces feedback. Museums are moving quickly to integrate Instagram into their marketing strategies, provide information, engage with audience and connect to other museums Instagram accounts. Taking into consideration that people may not see museum accounts in the same way that the other museum accounts do, the article first describes accounts' performance of the top, most visited museums worldwide and next investigates their interconnection. The analysis uses techniques from social network analysis, including visualization algorithms and calculations of well-established metrics. The research reveals the most important modes of the network by calculating the appropriate centrality metrics and shows that the network formed by the museum Instagram accounts is a scale–free small world network.


2021 ◽  
Vol 102 ◽  
pp. 01007
Author(s):  
Nhi Nguyen Van ◽  
Son Luu Xuan ◽  
Iurii Lezhenin ◽  
Natalia Bogach ◽  
Evgeny Pyshkin

In tonal languages, tones are associated with both and phonological and lexical domains. Accurate tone articulation is required in order to convey the correct meaning. Learning tones at both word and phrase levels is often challenging for L2 learners with non-tonal language background, because of possible subtle difference between the close tones. In this paper, we discuss an adoption of StudyIntonation CAPT tools to the case of Vietnamese language being a good example of register tonal language with a complex system of tones comprising such features as tone pitch, its length, contour melody, intensity and phonation. The particular focus of this contribution is to assess the adoption of StudyIntonation course toolkit and its pitch processing and visualization algorithms in order to evaluate how the combined use of audio and visual perception mechanisms supported by StudyIntonation may help learners to improve the accuracy of their pronunciation and intonation with respect to tonal languages.


2020 ◽  
Vol 5 (2) ◽  
pp. 8-24
Author(s):  
Iurii Svoyski ◽  
◽  
Gennadii Khlopachev ◽  
Ekaterina Romanenko ◽  
Mariia Polkovnikova ◽  
...  

One of the plots of the portable art forms of Eastern Europe is abstract, geometric, symbolic images that cannot be deciphered directly. Most of the currently known such geometric images from the archaeological sites of the Upper Paleolithic and Mesolithic of Eastern Europe were applied to finished bone products — tools, weapons, household and non-utilitarian items, as well as bones of various animals without traces of processing using various cutting techniques. The bone is well preserved in the cultural layers of the sites of the late Pleistocene — early Holocene of the Russian Plain, which makes objects from this material an important source for the study of geometric images. However, despite the richness of the source base, the problem of classification and systematization of geometric images in the art of small forms remains poorly developed. The purpose of the article is to consider the issues of practical application of three-dimensional 3D modeling in the study of art objects of small forms of the Upper Paleolithic and Mesolithic. The authors describe the practice of photographing such objects and the peculiarities of the lighting schemes and camera positioning, developed taking into account the specifics of the geometry and material of the documented objects. The minimal technical requirements for the resolution of the models have been determined, which provide the possibility of using visualization algorithms to study fine engravings on bone and stone. The practical application of mathematical visualization algorithms both directly on polygonal models and on height maps built on their basis is described. A method for visualizing and systematizing research results and providing remote access to them using modern web technologies is proposed.


2020 ◽  
Vol 12 (23) ◽  
pp. 3848
Author(s):  
Rongxin Tang ◽  
Hualin Liu ◽  
Jingbo Wei

The visualization of near infrared hyperspectral images is valuable for quick view and information survey, whereas methods using band selection or dimension reduction fail to produce good colors as reasonable as corresponding multispectral images. In this paper, an end-to-end neural network of hyperspectral visualization is proposed, based on the convolutional neural networks, to transform a hyperspectral image of hundreds of near infrared bands to a three-band image. Supervised learning is used to train the network where multispectral images are targeted to reconstruct naturally looking images. Each pair of the training images shares the same geographic location and similar moments. The generative adversarial framework is used with an adversarial network to improve the training of the generating network. In the experimental procedure, the proposed method is tested for the near infrared bands of EO-1 Hyperion images with LandSat-8 images as the benchmark, which is compared with five state-of-the-art visualization algorithms. The experimental results show that the proposed method performs better in producing naturally looking details and colors for near infrared hyperspectral images.


2020 ◽  
pp. 1-5
Author(s):  
Usman Khan ◽  
Usman Khan ◽  
AmanUllah Yasin ◽  
Imran Shafi ◽  
Muhammad Abid

In this work GPU implementation of classic 3D visualization algorithms namely Marching Cubes and Raycasting has been carried for cervical vertebra using VTK libraries. A proposed framework has been introduced for efficient and duly calibrated 3D reconstruction using Dicom Affine transform and Python Mayavi framework to address the limitation of benchmark visualization techniques i.e. lack of calibration, surface reconstruction artifacts and latency.


Author(s):  
Thaís Scheneider ◽  
Robson Lemos

The learning analytics in serious games, corresponds to a subject in increasing demand in the educational field. In this context, there is a need to study how data visualizations found in the literature are adopted in learning analytics in serious games. This paper presents a Systematic Literature Review (SLR) on how the evolution of studies associated with the use of learning analytics interactive dashboards in serious games is processed, seeking to investigate the characteristics of using dashboards for viewing educational data. A bibliometric analysis was carried out in which 75 relevant studies were selected from the Scopus, Web of Science, and IEEExplore databases. From the data analysis, it was observed that in the current literature there is a reduced number of studies containing the main actors in the learning process, as follows: teachers/instructors, students/participants, game developers/designers, and managers/researchers. In the vast majority of investigated studies, data visualization algorithms are used, where the main focus takes into account only actors, such as teachers/instructors and students/participants.


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