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Proceedings of the 2002 IEEE Engineering in Medicine and Biology 24th Annual Conference and the 2002 Fall Meeting of the Biomedical Engineering Society (BMES / EMBS)

作者： Song, Mingzhou Haralick, Robert M. Sheehan, Florence H. Johnson, Richard K. Department of Computer Science Queens College City University of New York Flushing NY 11367 United States Doctoral Program in Computer Sci. Graduate Center City University of New York New York NY 10016 United States Department of Medicine University of Washington Seattle WA 98195 United States Quantigraphics Inc. P.O. Box 329 Mercer Island WA 98040 United States

We describe an integrated Bayesian solution to find a left ventricle model, including both epicardium and endocardium surfaces, from freehand 3-D echocardiographic images. The observed images and prior shape knowledge are combined to make the most consistent inference about unknown surface models using the maximum à posteriori rule. Typical model-based computer vision techniques divide the overall problem into two separate low- and high-level subproblems. Unlike previous approaches, our approach unifies these two levels through a pixel class prediction mechanism. A putative surface model is generated from a catalog of 86 representative surface models. For each observed pixel, its appearance probability profile from different classes is first computed. Then the class prediction probability profile is also computed, based only on the putative surface model. An optimal surface model has the best overall match between these two profiles for all the pixels. The probability models are obtained off-line by the expectation maximization algorithm from 20 training studies. Quantitative experimental results on 25 test studies show the advantage of the integrated approach.

关键词： Echocardiography

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The development on obstacle avoidance design for a humanoid robot based on four ultrasonic sensors for the learning behavior and performance

The development on obstacle avoidance design for a humanoid ...

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IEEE International Conference on Industrial Engineering and Engineering Management, IEEM2010

作者： Li, Ya-Ju Chou, Wei-Chung Chen, Chen-Yuan Shih, Bih-Yaw Chen, Lien-Tung Chung, Pei-Yin Department of Computer Science National Pingtung University of Education No. 4-18 Ming Shen Rd. Pingtung 90003 Taiwan Doctoral Program in Management National Kaohsiung First University of Science and Technology Kaohsiung Taiwan Department of Management Information System Yung-Ta Institute of Technology and Commerce Pingtung 90003 Taiwan Department of Information Management Meiho University 23 Pingguang Rd. Neipu Pingtung Taiwan

ISBN: (纸本)9781424485031

A behavioral strategy designed for a humanoid robot for the purpose of obstacle avoidance based on four ultrasonic sensors for the learning behavior and performance is proposed and implemented with an autonomous humanoid robot. A mechanical structure with 4 degrees of freedom is designed so that a small-size humanoid robot named ARSR is able to accomplish three types of walking motion. One experiment is presented to illustrate ow the the proposed bipedal structure lets the ARSR move forward, turn left and turn right. Four ultrasonic sensors are mounted on the ARSR to obtain environmental information and detect obstacles. Based on the information obtained from these sensors, a decision tree method is proposed to decide upon one behavior from three possible types of movement: walk forward, turn left and turn right. An experiment is carried out to show how the robot can autonomously avoid obstacles to effectively arrive at its destination. Based on the mechanic design and development, we will get a good agreement for the learning behavior and performance. ©2010 IEEE.

关键词： Anthropomorphic robots

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A Study on Text Feature Selection Using Ant Colony and Grey Wolf Optimization

A Study on Text Feature Selection Using Ant Colony and Grey ...

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International Conference on Informatics and Computing (ICIC)

作者： Joan Angelina Widians Retantyo Wardoyo Sri Hartati Doctoral Program dept. of Computer Science and Electronics Universitas Gadjah Mada Dept. of Informatics Faculty of Engineering Universitas Mulawarman Samarinda Indonesia Dept. of Computer Science and Electronics Faculty of Mathematics and Natural Sciences Universitas Gadjah Mada Yogyakarta Indonesia

ISBN: (纸本)9798350345728

Text classification (TC) is widely used for organizing digital documents. The issues in TC are numerous characteristics and high-element dimensions. Many pattern classification issues require feature selection (FS), which is pertinent. FS removes unneeded and redundant data from the dataset. The Ant Colony Optimization (ACO) and Grey Wolf Optimizer (GWO) for FS are the main topics of our thorough assessment of the literature on the Swarm Intelligence (SI) algorithm. Furthermore, it illustrates how the hybrid SI technique is used in FS across various sectors. The hybrid SI technique uses applicable data from various FS methods to find feature subsets with smaller sizes and better classification performance than those found by regular FS algorithms.

关键词： Ant colony optimization Text categorization Pattern classification Filtering algorithms Feature extraction Classification algorithms Particle swarm optimization

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Minimal Non-simple Sets in 4-Dimensional Binary Images with (8,80)-Adjacency

Lecture Notes in Computer Science (including subseries Lectu...

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Lecture Notes in computer science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) 2004年 3322卷 318-333页

作者： Yung Kong, T. Gau, Chyi-Jou Department of Computer Science Queens College City University of New York 65-30 Kissena Boulevard Flushing NY 11367 United States Doctoral Program in Computer Science Graduate School and University Center City University of New York 365 Fifth Avenue New York NY 10016 United States

We first give a definition of simple sets of 1's in 4D binary images that is consistent with (8,80)-adjacency-i.e., the use of 8adjacency to define connectedness of sets of 1's and 80-adjacency to define connectedness of sets of 0's. Using this definition, it is shown that in any 4D binary image every minimal non-simple set of 1's must be isometric to one of eight sets, the largest of which has just four elements. Our result provides the basis for a fairly general method of verifying that proposed 4D parallel thinning algorithms preserve topology in our (8,80) sense. This work complements the authors' earlier work on 4D minimal nonsimple sets, which essentially used (80,8)-adjacency-80-adjacency on 1's and 8-adjacency on 0's. © Springer-Verlag 2004.

关键词： Binary images

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An Improvement Data Hiding Scheme Based on Formula Fully Exploiting Modification Directions and Pixel Value Differencing Method

An Improvement Data Hiding Scheme Based on Formula Fully Exp...

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Asia Joint Conference on Information Security (Asia JCIS)

作者： Wen-Chung Kuo Jyun-Jia Li Chun-Cheng Wang Lih-Chyau Wuu Yu-Chih Huang Department of Computer Science and Information Engineering National Yunlin University of Science & Technology Taiwan R.O.C. Graduate School of Engineering Science and Technology Doctoral Program National Yunlin University of Science & Technology Taiwan R.O.C.

ISBN: (纸本)9781509022861

Recently, a modified data hiding scheme based on pixel value differencing and improving exploiting modification directions is proposed by Shen and Huang. There are two major contributions in this scheme. One is to enhance the embedding rate and good embedding capacity and the other is an optimization problem used to solve the overflow/ underflow problem. In fact, they propose the similar brute force method to solve overflow/underflow problem in their proposed scheme. To overcome the overflow/underflow problem exactly, we will propose the close form to solve this problem in this paper. Then, an improvement data hiding scheme based on FFEMD (Formula Fully Exploiting Modification Directions) and pixel value differencing method is proposed. According to our analysis, it is not only to keep the advantages of Shen-Huang scheme but also to use the close form to solve the overflow/ underflow problem.

关键词： Integrated circuits Cryptography Partitioning algorithms Streaming media Electronic mail Distortion

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A Preliminary Study of Decision Support Model of Photovoltaic for Village Area in South of Sumatera

A Preliminary Study of Decision Support Model of Photovoltai...

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International Conference on Informatics, Multimedia, Cyber and Information System (ICIMCIS)

作者： Feby Ardianto Ermatita Ermatita Armin Sofijan Vina Ayumi Handrie Noprisson Mariana Purba Doctoral Program in Engineering Science Universitas Sriwijaya Palembang Indonesia Engineering Faculty Universitas Muhammadiyah Palembang Palembang Indonesia Faculty of Computer Science Universitas Sriwijaya Palembang Indonesia Engineering Faculty Universitas Sriwijaya South Sumatera Indonesia Faculty of Computer Science Universitas Mercu Buana Jakarta Indonesia Faculty of Computer Science Universitas Sjahyakirti Palembang Indonesia

ISBN: (纸本)9781665473286

Indonesia has a very high potential for solar energy irradiation, including in 66 countries in the sunbelt area. This can be alternative for power generation system that converts energy sources from solar radiation into electricity through solar cells or photovoltaic cells. This research goal is to develop an initial decision support model for the photovoltaic village area in South Sumatra, Indonesia. This research was conducted six months in Pandan Arang Village, Kandis District, Ogan Ilir Regency, South Sumatra. As the result, the proposed model is consisted of four sub-system, including photovoltaic system, data logger, database management and decision support system. Based on the results of experiments on solar panels with the parameters of current, voltage and light intensity, the highest value of light intensity was 1989.1 W/m2, while the voltage and current of 15.49V and current of 14.91 A were displayed through a 16x2 LCD display which served as information display in real time. The expected result in this study is an alternative fault detection PV system in the form of normal conditions, shadows, and overloads.

关键词： Decision support systems Databases Photovoltaic cells Fault detection Voltage Real-time systems Liquid crystal displays

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Seafloor Imaging Maps by Incorporating Side Scan Sonar Technology on Hybrid Autonomous Underwater Glider

Seafloor Imaging Maps by Incorporating Side Scan Sonar Techn...

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International Seminar on Research of Information Technology and Intelligent Systems (ISRITI)

作者： Ardian Budi Kusuma Atmaja Egi Muhammad Idris Hidayat Bambang Riyanto Trilaksono Simon Siregar Natsir Habibullah School of Electrical Engineering and Informatics Institut Teknologi Bandung Bandung Indonesia Department Computer Engineering Faculty of Applied Science Doctoral Program of Electrical Engineering Institut Teknologi Bandung Telkom University

This research aims to construct a two-dimensional image to represent an underwater geometry map with a Side Scan Sonar (SSS) mounted on a Hybrid Autonomous Underwater Glider (HAUG). Building the underwater map has two stages of the process. The first stage is preprocessing which includes Time-varying gains (TVG), slant range correction, and ground range correction. The second is HAUG navigation, the movement of HAUG orientation to the angle and distance of SSS readings and plotting the geometry of the grid map. With these two stages, a two-dimensional grid map will be formed. Meanwhile, the 3D plot utilizes slant range and ground range correction to obtain the surface height of the bottom of the pool. From the experiments conducted to conduct mapping in the pool, two-dimensional and three-dimensional visual forms of the pool floor and pool walls were produced. The results of the estimated measurement of the width of the pool obtained 14.05 meters and the height of the pool 3.199 meters with an SSS beam angle of 58 degrees.

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Comparison of Machine Learning Text Classification for Intent Sentiment Analysis

Comparison of Machine Learning Text Classification for Inten...

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International Conference on Information Technology, Information Systems and Electrical Engineering (ICITISEE)

作者： Abulwafa Muhammad Sarjon Defit Gunadi Widi Nur Cahyo Department of Informatics Technology Faculty of Computer Science Universitas Putra Indonesia YPTK Padang Padang Indonesia Doctoral Program in Information Technology Universitas Putra Indonesia YPTK Padang Padang Indonesia

Tourist destination reviews on Google Maps have become a valuable point of reference for visitors seeking enjoyable spots to visit. Additionally, users can gain insight into the reasons for writing reviews. Text classification is used to determine the motivation behind these reviews. The aim of this study is to compare machine learning (ML) text classification techniques for identifying the intent behind sentiments expressed as complaints (0), suggestions (1), opinions (2), statements(3), and awards (4). The ML techniques assessed are Multinomial Naive Bayes (MNB), Support Vector Machine (SVM), and K-Nearest Neighbor (KNN). To achieve this, tourist destination review data sets, comprising 738 reviews, were collected using web scraping from the Google Maps website. Preprocessing stages, including case-folding, tokenisation, filtering and stemming, are executed prior to the data being prepped for feature extraction with TF-IDF. The employed classification models perform well with KNN achieving 1.00 accuracy on a 90:10 data split, and SVM obtaining 0.90 on the same data split. Although, the Multinomial Naive Bayes algorithm with 0.70 accuracy on a 90:10 data split is classified as fair. Comparison of methods was conducted by means of a Receiver Operating Characteristic (ROC) diagram. The testing accuracy for the SVM method was found to be 95%, MNB at 91%, and KNN at 84%.

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GNOME and LBM model evaluation on ocean oil spill far-field impacts to highly sensitive areas

arXiv

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arXiv 2021年

作者： Zhang, Zhanyang Schäfer, Tobias Kress, Michael E. Doctoral Program in Computer Science The Graduate Center City University of New York 365 5th Avenue New YorkNY10016 United States Doctoral Program in Physics The Graduate Center City University of New York 365 5th Avenue New YorkNY10016 United States

In case of an ocean oil spill, there are certain areas, e.g. shrimp farms, which are highly sensitive to small amounts of oil pollution while they are geographically far from the spill. We investigate the Lattice Boltzmann Method (LBM) and GNOME, a tool developed and used by NOAA, in terms of far-field impacts to sensitive areas. We present our simulation results of both models in limited scale (a sub area of Gulf of Mexico) under the same oil spill condition using real ocean current data from the Unified Wave Interface-Coupled Model (UWIN-CM). Our study shows that the kinetic theory based LBM model outperforms the stochastic particle based GNOME model in accuracy and computation time due to their fundamental difference in representation of oil pollution advection and diffusion mechanisms. We propose LBM as a viable alternative to the Lagrangian particle calculation component of GNOME in modeling the far-field impacts to highly sensitive *** Codes 37N10, 65M75, 76R50 Copyright © 2021, The Authors. All rights reserved.

关键词： Kinetic theory

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Theory-guided randomized neural networks for decoding medication-taking behavior

Theory-guided randomized neural networks for decoding medica...

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2021 IEEE EMBS International Conference on Biomedical and Health Informatics, BHI 2021

作者： Kaur, Navreet Gonzales, Manuel Alcaraz, Cristian Garcia Barnes, Laura E. Wells, Kristen J. Gong, Jiaqi Department of Engineering Systems and Environment University of Virginia VA22904 United States SDSU UC San Diego Joint Doctoral Program in Clinical Psychology San DiegoCA92120 United States Department of Psychology San Diego State University San DiegoCA92182 United States Department of Computer Science The University of Alabama TuscaloosaAL35487 United States

ISBN: (纸本)9781665403580

Long-term endocrine therapy (e.g. Tamoxifen, aromatase inhibitors) is crucial to prevent breast cancer recurrence, yet rates of adherence to these medications are low. To develop, evaluate, and sustain future interventions, individual-level modeling can be used to understand breast cancer survivors' behavioral mechanisms of medication-taking. This paper presents interdisciplinary research, wherein a model employing randomized neural networks was developed to predict breast cancer survivors' daily medication-taking behavior based on their survey data over three time periods (baseline, 4 months, 8 months). The neural network structure was guided by random utility theory developed in psychology and behavioral economics. Comparative analysis indicates that the proposed model outperforms existing computational models in terms of prediction accuracy under conditions of randomness. © 2021 IEEE

关键词： Diseases

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