2018 IEEE International Conference on Computational Intelligence and Virtual Environments for Measurement Systems and Applications (CIVEMSA)最新文献_第2页

Virtual Reality and Tracking the Mating Behavior of Fruit Flies: a Machine Learning Approach 虚拟现实和跟踪果蝇的交配行为:一种机器学习方法

2018 IEEE International Conference on Computational Intelligence and Virtual Environments for Measurement Systems and Applications (CIVEMSA) Pub Date : 2018-06-01 DOI: 10.1109/CIVEMSA.2018.8439989

M. Mozaffari, Shuangyue Wen, Won-Sook Lee

{"title":"Virtual Reality and Tracking the Mating Behavior of Fruit Flies: a Machine Learning Approach","authors":"M. Mozaffari, Shuangyue Wen, Won-Sook Lee","doi":"10.1109/CIVEMSA.2018.8439989","DOIUrl":"https://doi.org/10.1109/CIVEMSA.2018.8439989","url":null,"abstract":"Study of social behaviors of Drosophila melanogaster, i.e., fruit flies, is used to understand certain behaviors of human. Automation of capturing and real-time analysis of fruit fly social movements helps quantitative study of those behavior which in turns can be applied for studying human behavior. To achieve this, we present Virtual Reality environment setting to stimulate fruit flies behavior and tracking of their motions automatically in real-time. As an experiment in a real application, we selected study of mating behavior of fruit flies. Male fruit flies tend to extend their mating duration when exposed to rivals as published in previous biology studies. We designed a Virtual Reality environment where synthetic male fruit flies are virtually simulated to stimulate a male fruit fly to study the effect of rivals. Bezier curve fitting and Gaussian random distribution are utilized for movement simulation. A machine learning approaches (logistic regression) employed to track, detect, and classify fruit flies mating behavior. We performed connected component labeling as an operator for tracking and classification of mating and non-mating status for comparison purposes. The machine learning based approach shows superior result in terms of speed and accuracy.","PeriodicalId":305399,"journal":{"name":"2018 IEEE International Conference on Computational Intelligence and Virtual Environments for Measurement Systems and Applications (CIVEMSA)","volume":"13 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128021404","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

Pictorial Visualization of EMR Summary Interface and Medical Information Extraction of Clinical Notes EMR摘要界面的图形化可视化与临床记录的医学信息提取

2018 IEEE International Conference on Computational Intelligence and Virtual Environments for Measurement Systems and Applications (CIVEMSA) Pub Date : 2018-06-01 DOI: 10.1109/CIVEMSA.2018.8439958

W. Ruan, Naveenkumar Appasani, Katherine Kim, Joseph Vincelli, Hyun Kim, Won-sook Lee

{"title":"Pictorial Visualization of EMR Summary Interface and Medical Information Extraction of Clinical Notes","authors":"W. Ruan, Naveenkumar Appasani, Katherine Kim, Joseph Vincelli, Hyun Kim, Won-sook Lee","doi":"10.1109/CIVEMSA.2018.8439958","DOIUrl":"https://doi.org/10.1109/CIVEMSA.2018.8439958","url":null,"abstract":"Current Electronic Medical Records (EMR) systems contain large amounts of texts and various tables, to show numerous health data. This type of presentation limits people from promptly determining medical conditions or quickly finding desired information given the large volume of texts that needs to be read. We aim to tackle this as information visualization and extraction problems by creation of easy and intuitive user interfaces for visualizing medical information. We present both a novel graphical interface for visualizing a summary of medical information and an information extraction system that is able to extract and visualize the patient’s medical information from structured clinical notes. The graphical interface allows spatial-position based representations of medical information on human body images (front and back views) and temporal-time based representation of it through interconnected time axes. Medical histories are classified into several event categories and 6 physiological systems to enable efficient browsing of selected information. To extract visual tags from a given clinical note, we use natural language processing. We employ Metamap of 2014AA knowledge source for medical information extraction. We trained 1294 English clinical notes with a Time-Entity Detection model by Apache Open NLP to abstract the time expressions. Extracted location of illness is assigned into one of 6 physiological systems is displayed in spatial interface while the related data are denoted on a horizontal timeline of temporal interface.","PeriodicalId":305399,"journal":{"name":"2018 IEEE International Conference on Computational Intelligence and Virtual Environments for Measurement Systems and Applications (CIVEMSA)","volume":"90 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126835965","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: 13

CIVEMSA 2018 Author Index CIVEMSA 2018作者索引

2018 IEEE International Conference on Computational Intelligence and Virtual Environments for Measurement Systems and Applications (CIVEMSA) Pub Date : 2018-06-01 DOI: 10.1109/civemsa.2018.8439963

引用次数: 0

Sliding Mode Controller and Hierarchical Perturbation Compensator in a UAV Quadrotor 无人机四旋翼滑模控制器与层次摄动补偿器

2018 IEEE International Conference on Computational Intelligence and Virtual Environments for Measurement Systems and Applications (CIVEMSA) Pub Date : 2018-06-01 DOI: 10.1109/CIVEMSA.2018.8440003

W. Alqaisi, B. Brahmi, J. Ghommam, M. Saad, V. Nerguizian

引用次数: 3

Performance Analysis of Granular versus Traditional Neural Network Classifiers: Preliminary Results 粒状神经网络分类器与传统神经网络分类器的性能分析：初步结果

2018 IEEE International Conference on Computational Intelligence and Virtual Environments for Measurement Systems and Applications (CIVEMSA) Pub Date : 2018-06-01 DOI: 10.1109/CIVEMSA.2018.8439971

Gerardo Felix, G. Nápoles, R. Falcon, Rafael Bello, K. Vanhoof

引用次数: 4

A Virtual Tactile Sensor with Adjustable Precision and Size for Object Recognition 用于物体识别的可调节精度和尺寸的虚拟触觉传感器

2018 IEEE International Conference on Computational Intelligence and Virtual Environments for Measurement Systems and Applications (CIVEMSA) Pub Date : 2018-06-01 DOI: 10.1109/CIVEMSA.2018.8439966

Ghazal Rouhafzay, A. Crétu

引用次数: 2

Cognitive Ability Evaluation using Virtual Reality and Eye Tracking 利用虚拟现实和眼动仪进行认知能力评估

2018 IEEE International Conference on Computational Intelligence and Virtual Environments for Measurement Systems and Applications (CIVEMSA) Pub Date : 2018-06-01 DOI: 10.1109/CIVEMSA.2018.8439999

Julius Pettersson, Anton Albo, J. Eriksson, Patrik Larsson, Kertsin Watson Falkman, P. Falkman

引用次数: 16

UWB TDOA/TOA measurement system with wireless time synchronization and simultaneous tag and anchor positioning UWB TDOA/TOA测量系统，具有无线时间同步和同步标签和锚定位

2018 IEEE International Conference on Computational Intelligence and Virtual Environments for Measurement Systems and Applications (CIVEMSA) Pub Date : 2018-06-01 DOI: 10.1109/CIVEMSA.2018.8439949

B. Choi, Koangkyun La, Sangrok Lee

引用次数: 11

A Novel Reduced-Layer Deep Learning System via Pixel Rearrangement for Object Detection in Multispectral Imagery 一种新的基于像素重排的多光谱图像目标检测的减层深度学习系统

2018 IEEE International Conference on Computational Intelligence and Virtual Environments for Measurement Systems and Applications (CIVEMSA) Pub Date : 2018-06-01 DOI: 10.1109/CIVEMSA.2018.8439982

Anusha K. Vishwanathan, D. Megherbi

{"title":"A Novel Reduced-Layer Deep Learning System via Pixel Rearrangement for Object Detection in Multispectral Imagery","authors":"Anusha K. Vishwanathan, D. Megherbi","doi":"10.1109/CIVEMSA.2018.8439982","DOIUrl":"https://doi.org/10.1109/CIVEMSA.2018.8439982","url":null,"abstract":"In this paper, we study the problem of object detection on multispectral images. We present a generalized “Fully” Convolutional Neural Network (FCNN)-based deep learning system with a novel Pixel Rearrangement Technique, with reduced computational complexity and improved prediction accuracy than its state-of-the-art counterparts. In particular, we (a) define a key strategy based on spectral signatures to select a set of highly informative multispectral bands for the system; (b) for the first time, introduce a pixel rearrangement technique that efficiently utilizes pixels from the network's feature maps that results into accurate pixelwise prediction images; (c) propose dual stage global and adaptive thresholding methodologies that transform the pixelwise prediction images to binary. We evaluate the proposed system for automatic airborne building detection using the SpaceNet dataset. We use the three NVIDIA GeForce GTX 1060 GPUs at CMINDS Research Center and Tensorflow deep learning framework to implement the proposed system. Our findings show an improvement in the performance by 0.3% in comparison to the top winning submission of the national SpaceNet Building Challenge II, that took place in April 2017, but with an additional 43% reduction in the number of FCNN layers. Finally, we also present a comparison chart with various existing approaches to highlight the proposed reduced computational complexity system.","PeriodicalId":305399,"journal":{"name":"2018 IEEE International Conference on Computational Intelligence and Virtual Environments for Measurement Systems and Applications (CIVEMSA)","volume":"29 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133154124","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

The study of driver's reaction for traffic information on actual driving and DS using fNIRS 利用近红外光谱研究驾驶员在实际驾驶和自动驾驶中对交通信息的反应

2018 IEEE International Conference on Computational Intelligence and Virtual Environments for Measurement Systems and Applications (CIVEMSA) Pub Date : 2018-06-01 DOI: 10.1109/CIVEMSA.2018.8440002

Kouji Yamamoto, Hideki Takahashi, T. Sugimachi, Y. Suda

引用次数: 3