Proceedings of the 2021 ACM Southeast Conference最新文献_第2页

Web-based 3D visualization system for anatomy online instruction 基于web的三维可视化解剖学在线教学系统

Proceedings of the 2021 ACM Southeast Conference Pub Date : 2021-04-15 DOI: 10.1145/3409334.3452080

E. Maclean, Felix G. Hamza-Lup, April Garrity, C. Keck, Michelle Smith

{"title":"Web-based 3D visualization system for anatomy online instruction","authors":"E. Maclean, Felix G. Hamza-Lup, April Garrity, C. Keck, Michelle Smith","doi":"10.1145/3409334.3452080","DOIUrl":"https://doi.org/10.1145/3409334.3452080","url":null,"abstract":"Problem-based instruction is an active learning instructional practice that requires students to use rational and critical thinking skills to generate reasonable solutions to problem-based scenarios. For complex medical conditions such as stroke, degenerative diseases, and traumatic brain injury, students must have a strong command of neuroanatomy and physiology. While virtual and synthetic dissection simulation tools alleviate the need for procuring and maintaining costly resources, like cadavers, these tools are costly, inaccessible to students online, and inadequate in the teaching of practical knowledge needed to solve real-life clinical problems. In the wake of the Covid-19 pandemic, many courses have switched to an online format surprising students and faculty. The web-based visualization repository presented is intended to provide medical students with a comprehensive, web-based visual and problem-based learning tool to assist their learning of anatomical and neurophysiological concepts as applied to various medical disorders. The application can be used for online learning, as well as for in-person learning.","PeriodicalId":148741,"journal":{"name":"Proceedings of the 2021 ACM Southeast Conference","volume":"32 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-04-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128354310","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Using cross-examples in viola-jones algorithm for thermal face detection 利用交叉样例viola-jones算法进行热人脸检测

Proceedings of the 2021 ACM Southeast Conference Pub Date : 2021-04-15 DOI: 10.1145/3409334.3452083

H. Tran, Chunhua Dong, M. Naghedolfeizi, Xiangyan Zeng

引用次数: 1

Epileptic seizures classification in EEG using PCA based genetic algorithm through machine learning 基于机器学习的PCA遗传算法在脑电图中的癫痫发作分类

Proceedings of the 2021 ACM Southeast Conference Pub Date : 2021-04-15 DOI: 10.1145/3409334.3452065

Md Khurram Monir Rabby, A. Islam, S. Belkasim, M. Bikdash

引用次数: 9

A study on students' views toward K-12 computer science teaching career 学生对中小学计算机科学教学生涯的看法研究

Proceedings of the 2021 ACM Southeast Conference Pub Date : 2021-04-15 DOI: 10.1145/3409334.3452075

D. Lo, B. Lawler

引用次数: 0

Characterizing networking performance and interrupt overhead of container overlay networks 表征容器覆盖网络的网络性能和中断开销

Proceedings of the 2021 ACM Southeast Conference Pub Date : 2021-04-15 DOI: 10.1145/3409334.3452040

Kun Suo, Yong Shi, Ahyoung Lee, S. Baidya

引用次数: 3

Teaching a computer forensics course 教授计算机取证课程

Proceedings of the 2021 ACM Southeast Conference Pub Date : 2021-04-15 DOI: 10.1145/3409334.3452069

F. Ali

引用次数: 0

A survey of wireless network simulation and/or emulation software for use in higher education 高等教育用无线网络仿真及/或仿真软件综述

Proceedings of the 2021 ACM Southeast Conference Pub Date : 2021-04-15 DOI: 10.1145/3409334.3452066

Theodore A. Richards V, Eric Gamess, David Thornton

引用次数: 1

Detection of local structures in images using local entropy information 利用局部熵信息检测图像中的局部结构

Proceedings of the 2021 ACM Southeast Conference Pub Date : 2021-04-15 DOI: 10.1145/3409334.3452061

Torumoy Ghoshal, Yixin Chen

引用次数: 0

A three layer spatial-spectral hyperspectral image classification model using guided median filters 基于引导中值滤波的三层空间光谱高光谱图像分类模型

Proceedings of the 2021 ACM Southeast Conference Pub Date : 2021-04-15 DOI: 10.1145/3409334.3452045

S. Dinç, Luis Alberto Cueva Parra

{"title":"A three layer spatial-spectral hyperspectral image classification model using guided median filters","authors":"S. Dinç, Luis Alberto Cueva Parra","doi":"10.1145/3409334.3452045","DOIUrl":"https://doi.org/10.1145/3409334.3452045","url":null,"abstract":"Hyperspectral images (HSI) contain rich spectral information from a large portion of the electromagnetic spectrum. Using these images, it is possible to make pixel-level classification as each pixel holds hundreds of features. In this paper, we propose an efficient, three-layer hyperspectral image classification model by utilizing spectral/spatial features. The first layer of the system includes two classifiers that work in parallel. These classifiers generate probability scores that form the \"new feature set\" of the original dataset. The second layer is an ensemble classifier that combines the new features to generate the initial region classification. The third layer introduces a novel approach for enhancing the initial region classification's accuracy from the second layer by utilizing the spatial characteristics of the dataset. A new proximity-based 2D edge preserving order-statistic filtering called Guided Median Filter (GMF) is introduced with weights assigned to each neighboring pixel. Experimental results show that the proposed system improves our previously published results and reaches over 96% overall accuracy on Indian Pines dataset by exceeding some well-known traditional classifiers. Moreover, our GMF based system produced comparable results with the state-of-the-art neural network based methodologies without complex training stage and lack of interpretability of classification model.","PeriodicalId":148741,"journal":{"name":"Proceedings of the 2021 ACM Southeast Conference","volume":"34 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-04-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116598099","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 3

Detecting fabric density and weft distortion in woven fabrics using the discrete fourier transform 利用离散傅里叶变换检测机织物的织物密度和纬纱畸变

Proceedings of the 2021 ACM Southeast Conference Pub Date : 2021-04-15 DOI: 10.1145/3409334.3452049

Bach Le, David Troendle, Byunghyun Jang

{"title":"Detecting fabric density and weft distortion in woven fabrics using the discrete fourier transform","authors":"Bach Le, David Troendle, Byunghyun Jang","doi":"10.1145/3409334.3452049","DOIUrl":"https://doi.org/10.1145/3409334.3452049","url":null,"abstract":"Fabric density and distortion offer important information on fabric attributes and quality during the manufacturing process. However, most current procedures require human effort, which is often inefficient, time-consuming, and imprecise. In this paper, we propose to use an automatic method using the 2D Fast Fourier Transform (2D-FFT) to count the number of yarns and determine the angle rotation of weft yarns in fabric images. First, we explain the mathematical background of Fourier Transform and 2D-FFT. Then, we use a customized and optimized software package to apply a 2D-FFT to extract image magnitude, phase, and power spectrum. We apply the inverse 2D Fast Fourier Transform (2D-iFFT) on selected frequencies corresponding to periodic structures - basic weave patterns - to reconstruct the original image and extract warp and weft yarns separately. Finally, we use a local adaptive threshold process to convert reconstructed images into binary images for the counting and calculating process. For the weft rotation, we apply a mathematical calculation on the frequency domain to collect the angular distribution and then figure out the major rotation of weft yarns. Our experiments show that the proposed method is highly accurate and capable of inspecting different patterns of fabric. We also observe that the processing time of our proposal method is practical and time-efficient.","PeriodicalId":148741,"journal":{"name":"Proceedings of the 2021 ACM Southeast Conference","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-04-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122928874","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 1