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IEEE International Conference on Robotics and Automation (ICRA)

作者： Zetao Chen Adam Jacobson Uğur M. Erdem Michael E. Hasselmo Michael Milford School of Electrical Engineering Computer Science at the Queensland University of Technology Center for Memory and Brain and Graduate Program for Neuroscience at Boston University

This paper presents a novel place recognition algorithm inspired by the recent discovery of overlapping and multi-scale spatial maps in the rodent brain. We mimic this hierarchical framework by training arrays of Support Vector Machines to recognize places at multiple spatial scales. Place match hypotheses are then cross-validated across all spatial scales, a process which combines the spatial specificity of the finest spatial map with the consensus provided by broader mapping scales. Experiments on three real-world datasets including a large robotics benchmark demonstrate that mapping over multiple scales uniformly improves place recognition performance over a single scale approach without sacrificing localization accuracy. We present analysis that illustrates how matching over multiple scales leads to better place recognition performance and discuss several promising areas for future investigation.

关键词： Training Support vector machines Feature extraction Rodents Image segmentation Robot sensing systems

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Highly selective hydrogen sensing of Pt-loaded WO3 synthesized by hydrothermal/impregnation methods

Highly selective hydrogen sensing of Pt-loaded WO3 synthesiz...

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作者： Samerjai, Thanittha Liewhiran, Chaikarn Wisitsoraat, Anurat Tuantranont, Adisorn Khanta, Chanitpa Phanichphant, Sukon Nanoscience and Nanotechnology Program Faculty of Graduate School Chiang Mai University Chiang Mai 50200 Thailand Department of Physics and Materials Science Faculty of Science Chiang Mai University Chiang Mai 50200 Thailand Nanoelectronics and MEMS Laboratory National Electronics and Computer Technology Center KlongLuang Pathumthani 12120 Thailand Materials Science Research Center Faculty of Science Chiang Mai University Chiang Mai 50200 Thailand

Unloaded and 0.25-1.0 wt% Pt-loaded WO3 nanoparticles were synthesized by hydrothermal method using sodium tungstate dihydrate and sodium chloride as precursors in an acidic condition and impregnated using platinum acetylacetonate. Pt-loaded WO3 films on an Al2O 3 substrate with interdigitated Au electrodes were prepared by spin-coating technique. The response of WO3 sensors with different Pt-loading concentrations was tested towards 0.01-1.0 vol% of H2 in air as a function of operating temperature (200-350 °C). The 1.0 wt% Pt-loaded WO3 sensing film showed the highest response of ∼2.16 × 104 to 1.0 vol% H2 at 250 °C. Therefore, an operating temperature of 250 °C was optimal for H2 detection. The responses of 1.0 wt% Pt-loaded WO3 sensing film to other flammable gases, including C2H5OH, C2H4 and CO, were considerably less, demonstrating Pt-loaded WO3 sensing film to be highly selective to H2. © 2014, Hydrogen Energy Publications, LLC. Published by Elsevier Ltd. All rights reserved.

关键词： Tungsten compounds

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Proceedings of the 15th Annual UT-KBRIN Bioinformatics Summit 2016 : Cadiz, KY, USA. 8-10 April 2016

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BMC bioinformatics 2016年第SUPPL 10期17卷 297页

作者： Eric C. Rouchka Julia H. Chariker Benjamin J. Harrison Juw Won Park Xueyuan Cao Stanley Pounds Susana Raimondi James Downing Raul Ribeiro Jeffery Rubnitz Jatinder Lamba Bernie J. Daigle Deborah Burgess Stephanie Gehrlich John C. Carmen Nicholas Johnson Chandrakanth Emani Kalpani De Silva Michael P. Heaton Theodore S. Kalbfleisch Teeradache Viangteeravat Rahul Mudunuri Oluwaseun Ajayi Fatih Şen Eunice Y. Huang Mohammad Mohebbi Luaire Florian Douglas J. Jackson John F. Naber AKM Sabbir Sally R. Ellingson Yuping Lu Charles A Phillips Michael A. Langston Rahul K. Sevakula Raghuveer Thirukovalluru Nishchal K. Verma Yan Cui Mohammed Sayed Jing Wang Qi Liu Yu Shyr Xiaofei Zhang Naresh Prodduturi Gavin R. Oliver Diane Grill Jie Na Jeanette Eckel-Passow Eric W. Klee Michael M. Goodin Mark Farman Harrison Inocencio Chanyong Jang Jerzy W. Jaromczyk Neil Moore Kelly Sovacool Leon Dent Mike Izban Sammed Mandape Shruti Sakhare Siddharth Pratap Dana Marshall M Scotty DePriest James N. MacLeod Hanady Adam Ethan Blandford Joel Campbell Joshua Castlen Brittany Dixon Ginger Gilbert Aaron Hall Philip Kreisle Jessica Lasher Bethany Oakes Allison Speer Maximilian Valentine Naga Satya V. Rao Nagisetty Rony Jose Robert Rooney David Hains Department of Computer Engineering and Computer Science University of Louisville Duthie Center for Engineering Louisville USA Kentucky Biomedical Research Infrastructure (KBRIN) Bioinformatics Core Louisville USA Department of Psychological and Brain Sciences University of Louisville Louisville USA Department of Anatomical Sciences and Neurobiology University of Louisville Louisville USA Department of Biostatistics St. Jude Children’s Research Hospital Memphis USA Department of Pathology St. Jude Children’s Research Hospital Memphis USA Department of Oncology St. Jude Children’s Research Hospital Memphis USA Department of Pharmacotherapy and Translational Research College of Pharmacy University of Florida Gainesville USA Department of Biological Sciences University of Memphis Memphis USA Department of Computer Science University of Memphis Memphis USA Department of Computer Science Northern Kentucky University Highland Heights USA Department of Biological Sciences Northern Kentucky University Highland Heights USA Department of Biology Western Kentucky University Bowling Green USA Interdisciplinary Studies Program: Specialization in Bioinformatics University of Louisville Louisville USA United States Department of Agriculture Agricultural Research Service United States Meat Animal Research Center Clay Center USA Department of Biochemistry and Molecular Genetics School of Medicine University of Louisville Louisville USA Biomedical Informatics Core Children’s Foundation Research Institute Memphis USA Department of Pediatrics University of Tennessee Health Science Center Memphis USA Department of Electrical and Computer Engineering University of Louisville Louisville USA Department of Computer Science University of Kentucky Lexington USA Division of Biomedical Informatics College of Medicine University of Kentucky Lexington USA Department of Electrical Engineering and Computer Science University of Tennessee Knoxville USA Department of Electrical E

I1 Proceedings of the Fifteenth Annual UT- KBRIN Bioinformatics Summit 2016 Eric C. Rouchka, Julia H. Chariker, Benjamin J. Harrison, Juw Won Park P1 CC-PROMISE: Projection onto the Most Interesting Statistical Evidence (PROMISE) with Canonical Correlation to integrate gene expression and methylation data with multiple pharmacologic and clinical endpoints Xueyuan Cao, Stanley Pounds, Susana Raimondi, James Downing, Raul Ribeiro, Jeffery Rubnitz, Jatinder Lamba P2 Integration of microRNA-mRNA interaction networks with gene expression data to increase experimental power Bernie J Daigle, Jr. P3 Designing and writing software for in silico subtractive hybridization of large eukaryotic genomes Deborah Burgess, Stephanie Gehrlich, John C Carmen P4 Tracking the molecular evolution of Pax gene Nicholas Johnson; Chandrakanth Emani P5 Identifying genetic differences in thermally dimorphic and state specific fungi using in silico genomic comparison Stephanie Gehrlich, Deborah Burgess, John C Carmen P6 Identification of conserved genomic regions and variation therein amongst Cetartiodactyla species using next generation sequencing Kalpani De Silva, Michael P Heaton, Theodore S Kalbfleisch P7 Mining physiological data to identify patients with similar medical events and phenotypes Teeradache Viangteeravat, Rahul Mudunuri, Oluwaseun Ajayi, Fatih Şen, Eunice Y Huang P8 Smart brief for home health monitoring Mohammad Mohebbi, Luaire Florian, Douglas J Jackson, John F Naber P9 Side-effect term matching for computational adverse drug reaction predictions AKM Sabbir, Sally R Ellingson P10 Enrichment vs robustness: A comparison of transcriptomic data clustering metrics Yuping Lu, Charles A Phillips, Michael A Langston P11 Deep neural networks for transcriptome-based cancer classification Rahul K Sevakula, Raghuveer Thirukovalluru, Nishchal K. Verma, Yan Cui P12 Motif discovery using K-means clustering Mohammed Sayed, Juw Won Park P13 Large scale discovery of active enhancers from nasce

关键词： True Negative Differential Expression UCSC Genome Browser Laboratory Information Management System Biomedical Informatics

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Robust Handwriting Extraction and Lecture Video Summarization

Robust Handwriting Extraction and Lecture Video Summarizatio...

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International Conference on Intelligent Information Hiding and Multimedia Signal Processing (IIHMSP)

作者： Fu Hao Yeh Greg C. Lee Ying Ju Chen Chien Hsing Liao Program of Information Technology Fooyin University Kaohsiung Taiwan Department of Computer Science and Information Engineering Nation Taiwan Normal University Taipei Taiwan

Video tools developed today, teachers can record lecture videos and upload these lecture videos to e-learning system themselves. However, some students may only do not understand some fragments but they have to waste unnecessary time download entire video, and therefore video scene segmentation is relative importance. In addition, in traditional teaching model, students must listen and transcribe content on blackboard, when the lecture's write speed is too fast, students are very difficult to pay attention in class and easy to transcription errors. If we can record the lecture video and automatically extract content on the blackboard, students not only enable index videos easily, but also not easily transcribe the wrong notes. Hence, this paper presents an intelligent assistance system for lecture videos. We use K-mean Segmentation to extract blackboard area and then only update this area to avoid lecturer's body cover the content on blackboard. Then we use adaptive threshold to extract chalk and design a method to reduce noise. After that we detecting the lecture videos split timing with statistics chalk pixel count and analysis the variation. Finally, we further interception the most complete content as lecture note to facilitate the students to search for video clips.

关键词： Image segmentation Image color analysis Indexes Educational institutions Electronic learning Noise Semantics

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Production of Hydrogen and Methane from Banana Peel by Two Phase Anaerobic Fermentation

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Energy Procedia 2014年 50卷 702-710页

作者： Chananchida Nathoa Ubonrat Sirisukpoca Nipon Pisutpaisal Department of Agro-Industrial Food and Environmental Technology Faculty of Applied Science King Mongkut's University of Technology North Bangkok Bangkok 10800 Thailand Program of Computer Faculty of Science and Technology Nakhon Pathom Rajabhat University Nakhon Pathom 73000 Thailand The Biosensor and Bioelectronics Technology Centre King Mongkut's University of Technology Northn Bangkok 10800 Thailand

A two phase anaerobic digestion of banana peel is an attractive process for producing hydrogen and methane. Comparative performance and total energy recovery between two phase process (sequential hydrogen and methane fermentation) and one phase process (methane fermentation) in were evaluated in batch reactor under mesophilic incubation at varying ratios of feedstock to microbial inoculum (F/M) ranging from 2.5 – 10. F/M ratios influence biogas yield, production rate, and potential. Best performance of one phase methane fermentation was observed at F/M of 5.0. At this condition, methane yield, production rate, and potential were 251.3 mL g -1 VS, 2.05 mL h -1 , and 352.9 mL, respectively. Hydrogen and methane yields of 209.9 and 284.1 mL g -1 VS mL g -1 VS were achieved at F/M of 5.0 in two stage process. Acetic acid is the main volatile fatty acids (VFAs) produced in the hydrogen fermentation stage. Little amount of VFAs were accumulated in methane fermentation in both stage processes. Total energy recovery in two stage process is higher than that in one stage by 81%. This work demonstrated two phase achieved a better performance than one phase fermentation of banana peel.

关键词： Hydorogen methane banana peel fermentation two phase process

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Development of Rapid Chemical Oxygen Demand Analysis Using Ozone as Oxidizing Agent

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Energy Procedia 2014年 50卷 711-718页

作者： Nipon Pisutpaisal Ubonrat Sirisukpoca Department of Agro-Industrial Food and Environment Technology Faculty of Applied Science King Mongkut's University of Technology North Bangkok 10800 Thailand The Biosensor and Bioelectronics Technology Centre King Mongkut's University of Technology North Bangkok 10800 Thailand Program of Computer Faculty of Science and Technology Nakhon Pathom Rajabhat University Nakhon Pathom 73000 Thailand

A new and environmental friendly method for the rapid determination of Chemical Oxygen Demand (COD) as a measure of organic pollution was developed. Ozone was used as an oxidizing agent in the current method. Experiment was setup in 500 mL glass bottle containing 390 mL distilled water and 10 mL wastewater sample at 31 °C. The extent of dissolved ozone reacted with the wastewater sample was monitored through an ozone sensor. Reaction time of 60 seconds achieved good linear correlation between the extent of ozone degradation and the wastewater concentrations. The results showed that the rate and extent of ozone degradation are linearly proportional to the initial COD concentrations in range of 0-80 mg/L for the glucose- containing distilled water and canteen wastewater.

关键词： COD chemical oxygen demand ozone water quality analysis

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A multi-layer architecture proposal for conducting trains employing CBR

A multi-layer architecture proposal for conducting trains em...

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International Conference on computer Supported Cooperative Work in Design

作者： André Pinz Borges Osmar Betazzi Dordal Denise Maria Vecino Sato Bráulio Coelho Ávila Fabrício Enembreck Edson Emílio Scalabrin Richardson Ribeiro Graduate Program in Computer Science Pontificia Universidade Católica do Paraná Curitiba Brazil Graduate Program in Computer Science Pontifícia Universidade Católica do Paraná Curitiba Brazil Department of Informatics (DAINF) Federal University of Technology Paraná Pato Branco Brazil

This paper presents a planning approach using Case-Based Reasoning (CBR) modeled as a Subsumption Architecture to generate plans for driving trains. The main idea of a planning strategy is to generate a sequence of actions for an agent, which can use these actions to change its environment. CBR allows using prior experiences for new task assignments. In the proposed ap-proach, each previous experience (if not applicable) is adjusted us-ing one or more adaptation methods like substitutive and genetic algorithm. Our interest is to create a flexible architecture for an agent and apply it to simulate train conductions. We expect that the plans generated by this approach generate better results com-pared to another studies already developed for the area mainly considering fuel consumption and travel time.

关键词： Acceleration Force Fuels Rails Context Rail transportation Equations

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The design of naming features in App Inventor 2

The design of naming features in App Inventor 2

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IEEE Symposium on Visual Languages and Human Centric Computing (VL/HCC)

作者： Franklyn Turbak David Wolber Paul Medlock-Walton Computer Science Department Wellesley College Wellesley Massachusetts USA Computer Science Department University of San Francisco San Francisco California USA Scheller Teacher Education Program Massachusetts Institute of Technology Cambridge Massachusetts USA

Blocks languages, in which programs are constructed by connecting blocks resembling puzzle pieces, are increasingly used to introduce novices to programming. MIT App Inventor 2 has a blocks language for specifying the behavior of mobile apps. Its naming features (involving event and procedure parameters, global and local variables, and names for procedures, components, and component properties) were designed to address problems with names in other blocks languages, including its predecessor, MIT App Inventor Classic. We discuss the design of these features, and evaluate them with respect to cognitive dimensions and fundamental computer science naming concepts.

关键词： Gettering Sockets Visualization Educational institutions Assembly Context Plugs

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Calibrationless parallel imaging reconstruction based on structured low-rank matrix completion

Calibrationless parallel imaging reconstruction based on str...

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作者： Shin, Peter J. Larson, Peder E. Z. Ohliger, Michael A. Elad, Michael Pauly, John M. Vigneron, Daniel B. Lustig, Michael Department of Radiology and Biomedical Imaging University of California at San Francisco San FranciscoCA United States UC Berkeley-UCSF Graduate Program in Bioengineering CA United States Department of Electrical Engineering and Computer Science University of California at Berkeley BerkeleyCA United States Department of Computer Science Technion'Israel Institute of Technology Haifa Israel Magnetic Resonance Systems Research Laboratory Department of Electrical Engineering Stanford University StanfordCA United States

Purpose: A calibrationless parallel imaging reconstruction method, termed simultaneous autocalibrating and k-space estimation (SAKE), is presented. It is a data-driven, coil-by-coil reconstruction method that does not require a separate calibration step for estimating coil sensitivity information. Methods: In SAKE, an undersampled, multichannel dataset is structured into a single data matrix. The reconstruction is then formulated as a structured low-rank matrix completion problem. An iterative solution that implements a projection-ontosets algorithm with singular value thresholding is described. Results: Reconstruction results are demonstrated for retrospectively and prospectively undersampled, multichannel Cartesian data having no calibration signals. Additionally, non-Cartesian data reconstruction is presented. Finally, improved image quality is demonstrated by combining SAKE with wavelet-based compressed sensing. Conclusion: Because estimation of coil sensitivity information is not needed, the proposed method could potentially benefit MR applications where acquiring accurate calibration data is limiting or not possible at all. © 2013 Wiley Periodicals, Inc.

关键词： Compressed sensing

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Publisher Correction: Re-analysis of public genetic data reveals a rare X-chromosomal variant associated with type 2 diabetes

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Nature communications 2018年第1期9卷 2162页

作者： Sílvia Bonàs-Guarch Marta Guindo-Martínez Irene Miguel-Escalada Niels Grarup David Sebastian Elias Rodriguez-Fos Friman Sánchez Mercè Planas-Fèlix Paula Cortes-Sánchez Santi González Pascal Timshel Tune H Pers Claire C Morgan Ignasi Moran Goutham Atla Juan R González Montserrat Puiggros Jonathan Martí Ehm A Andersson Carlos Díaz Rosa M Badia Miriam Udler Aaron Leong Varindepal Kaur Jason Flannick Torben Jørgensen Allan Linneberg Marit E Jørgensen Daniel R Witte Cramer Christensen Ivan Brandslund Emil V Appel Robert A Scott Jian'an Luan Claudia Langenberg Nicholas J Wareham Oluf Pedersen Antonio Zorzano Jose C Florez Torben Hansen Jorge Ferrer Josep Maria Mercader David Torrents Barcelona Supercomputing Center (BSC) Joint BSC-CRG-IRB Research Program in Computational Biology 08034 Barcelona Spain. Genomic Programming of Beta-cells Laboratory Institut d'Investigacions August Pi i Sunyer (IDIBAPS) 08036 Barcelona Spain. Instituto de Salud Carlos III Centro de Investigación Biomédica en Red de Diabetes y Enfermedades Metabólicas Asociadas (CIBERDEM) 28029 Madrid Spain. Section of Epigenomics and Disease Department of Medicine Imperial College London London W12 0NN UK. The Novo Nordisk Foundation Center for Basic Metabolic Research Section for Metabolic Genetics Faculty of Health and Medical Sciences University of Copenhagen 2100 Copenhagen Denmark. Institute for Research in Biomedicine (IRB Barcelona) The Barcelona Institute of Science and Technology Baldiri Reixac 10-12 08028 Barcelona Spain. Departament de Bioquímica i Biomedicina Molecular Facultat de Biologia Universitat de Barcelona 08028 Barcelona Spain. Computer Sciences Department Barcelona Supercomputing Center (BSC-CNS) 08034 Barcelona Spain. Department of Epidemiology Research Statens Serum Institut 2300 Copenhagen Denmark. Division of Endocrinology and Center for Basic and Translational Obesity Research Boston Children's Hospital Boston MA 02116 USA. Medical and Population Genetics Program Broad Institute of MIT and Harvard Cambridge MA 02142 USA. ISGlobal Centre for Research in Environmental Epidemiology (CREAL) 08003 Barcelona Spain. CIBER Epidemiología y Salud Pública (CIBERESP) 28029 Madrid Spain. Universitat Pompeu Fabra (UPF) 08003 Barcelona Spain. Artificial Intelligence Research Institute (IIIA) Spanish Council for Scientific Research (CSIC) 28006 Madrid Spain. Programs in Metabolism and Medical and Population Genetics Broad Institute of Harvard and MIT Cambridge MA 02142 USA. Diabetes Unit and Center for Genomic Medicine Massachusetts General Hospital Boston MA 02114 USA. Division of General Internal Medicine Massachusetts General Hospital Bost

In the originally published version of this Article, the affiliation details for Santi González, Jian'an Luan and Claudia Langenberg were inadvertently omitted. Santi González should have been affiliated with 'Barcelona Supercomputing Center (BSC), Joint BSC-CRG-IRB Research program in Computational Biology, 08034 Barcelona, Spain', and Jian'an Luan and Claudia Langenberg should have been affiliated with 'MRC Epidemiology Unit, University of Cambridge School of Clinical Medicine, Cambridge Biomedical Campus, Cambridge CB2 0QQ, UK'. Furthermore, the abstract contained an error in the SNP ID for the rare variant in chromosome Xq23, which was incorrectly given as rs146662057 and should have been rs146662075. These errors have now been corrected in both the PDF and HTML versions of the Article.

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