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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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Optimized hybrid verification of embedded software

Optimized hybrid verification of embedded software

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Latin American Test Workshop, LATW

作者： Jörg Behrend Alexander Gruenhage Douglas Schroeder Djones Lettnin Jürgen Ruf Thomas Kropf Wolfgang Rosenstiel Department of Computer Engineering University of Tübingen Tübingen Germany Graduate Program in Computer Science (PPGCC) Federal University of Santa Catarina Florianópolis SC Brazil

The verification of embedded software has become an important subject over the last years. However, neither standalone verification approaches, like simulation-based or formal verification, nor state-of-the-art hybrid/semiformal verification approaches are able to verify large and complex embedded software with hardware dependencies. This work presents an optimized scalable hybrid verification approach for the verification of embedded software with hardware dependencies using a mixed bottom-up/top-down algorithm with optimized static parameter assignment (SPA). These algorithms and methodologies like SPA and counterexample guided simulation are used to combine simulation-based and formal verification in a new way. SPA offers a way to interact between dynamic and static verification approaches based on an automated ranking heuristic of possible function parameters according to the impact on the model size. Furthermore, SPA inserts initialization code for specific function parameters into the source code under test and supports model building and optimization algorithms to reduce the state space. We have successfully applied this optimized hybrid verification methodology to an embedded software application: Motorola's Powerstone Benchmark suite. The results show that our approach scales better than stand-alone software model checkers to reach deep state spaces.

关键词： Embedded software Switches Benchmark testing Hardware Monitoring Software algorithms

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HMAX image processing pipeline with coupled oscillator acceleration

HMAX image processing pipeline with coupled oscillator accel...

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IEEE Workshop on Signal Processing Systems (SIPS)

作者： Brandon B. Jennings Reggie Barnett Chet Gnegy John A. Carpenter Yan Fang Donald M. Chiarulli Steven P. Levitan Computer Engineering Graduate Program The University of Pittsburgh Pittsburgh PA 15260 University of Pittsburgh Pittsburgh PA US Computer Science Department The University of Pittsburgh Pittsburgh PA 15260

In this paper we report on the performance of a coupled oscillator based implementation of the HMAX image-processing pipeline. Within this pipeline we have used coupled oscillator arrays to replace traditional Boolean logic with a Degree-of-Match (DoM) function that measures the L2 distance squared between two vectors in an n-dimensional space. We show that this operation can be used in three stages of the pipeline: 1) as a substitute for convolution in filtering operations, 2) as a computational kernel for pattern matching, and 3) as a distance function in a nearest neighbor classification algorithm. In this study, we have modeled the performance of the latter two and report our recognition results over a test set from the Neo Vision2 image database.

关键词： Oscillators Training Pipelines Libraries Computational modeling Synchronization Support vector machine classification

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Comparative study of leaf image recognition with a novel learning-based approach

Comparative study of leaf image recognition with a novel lea...

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science and Information Conference (SAI)

作者： Jou-Ken Hsiao Li-Wei Kang Ching-Long Chang Chih-Yang Lin Department of Computer Science and Information Engineering National Yunlin University of Science and Technology Yunlin Taiwan Graduate School of Engineering Science and Technology-Doctoral Program National Yunlin University of Science and Technology Yunlin Taiwan Department of Computer Science and Information Engineering Asia University Taichung Taiwan

Automatic plant identification via computer vision techniques has been greatly important for a number of professionals, such as environmental protectors, land managers, and foresters. In this paper, we conduct a comparative study on leaf image recognition and propose a novel learning-based leaf image recognition technique via sparse representation (or sparse coding) for automatic plant identification. In our learning-based method, in order to model leaf images, we learn an overcomplete dictionary for sparsely representing the training images of each leaf species. Each dictionary is learned using a set of descriptors extracted from the training images in such a way that each descriptor is represented by linear combination of a small number of dictionary atoms. Moreover, we also implement a general bag-of-words (BoW) model-based recognition system for leaf images, used for comparison. We experimentally compare the two approaches and show unique characteristics of our sparse coding-based framework. As a result, efficient leaf recognition can be achieved on public leaf image dataset based on the two evaluated methods, where the proposed sparse coding-based framework can perform better.

关键词： Image recognition Dictionaries Training Feature extraction Shape Vectors Image coding

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M2M-enabled real-time Trip Planner

M2M-enabled real-time Trip Planner

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International Conference on Parallel and Distributed Systems (ICPADS)

作者： Eduardo Cerritos Fuchun Joseph Lin EECS International Graduate Program National Chiao Tung University Hsinchu Taiwan Department of Computer Science National Chiao Tung University Hsinchu Taiwan

ISBN: (纸本)9781479976164

Uncertainty is a key factor that prevents a commuter from using public transportation system. More and more transportation agencies are incorporating real-time Trip Planners to empower commuters with opportune information. However, such systems require continuous status updates from the vehicles and involves expensive communication cost. In this paper we propose an architecture that takes advantage of Machine-to-Machine Communication concepts and provides a degree of intelligence to the vehicles, to alleviate unnecessary communication between the vehicles and the Trip Planner.

关键词： Ground penetrating radar Containers Transportation Monitoring

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Corrigendum to "Target-specific near-IR induced drug release and photothermal therapy with accumulated Au/Ag hollow nanoshells on pulmonary cancer cell membranes" [Biomaterials 45 (2015) 81-92]

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Biomaterials 2015年 65卷 124-125页

作者： Mi Suk Noh Somin Lee Homan Kang Jin-Kyoung Yang Hyunmi Lee Doyk Hwang Jong Woo Lee Sinyoung Jeong Yoonjeong Jang Bong-Hyun Jun Dae Hong Jeong Seong Keun Kim Yoon-Sik Lee Myung-Haing Cho Laboratory of Toxicology College of Veterinary Medicine Seoul National University Seoul 151-742 Republic of Korea Program in Nano Science and Technology Graduate School of Convergence Science and Technology Seoul National University Seoul 151-742 Republic of Korea Medical Supplies Evaluation Center Korea Testing Certification Gunpo 435-862 Republic of Korea. Graduate Group of Tumor Biology Seoul National University Seoul 151-742 Republic of Korea. Interdisciplinary Program in Nano-Science and Technology Seoul National University Seoul 151-742 Republic of Korea School of Electrical Engineering and Computer Science Seoul National University Seoul 151-742 Republic of Korea. School of Chemical and Biological Engineering Seoul National University Seoul 151-742 Republic of Korea. Department of Biophysics and Chemical Biology and Department of Chemistry Seoul National University Seoul 151-742 Republic of Korea. Department of Chemistry Education Seoul National University Seoul 151-742 Republic of Korea. Laboratory of Toxicology College of Veterinary Medicine Seoul National University Seoul 151-742 Republic of Korea. Department of Bioscience and Biotechnology Konkuk University Seoul 143-701 Republic of Korea. Department of Chemistry Education Seoul National University Seoul 151-742 Republic of Korea. School of Chemical and Biological Engineering Seoul National University Seoul 151-742 Republic of Korea. Electronic address: yslee@snu.ac.kr. Graduate Group of Tumor Biology Seoul National University Seoul 151-742 Republic of Korea Advanced Institute of Convergence Technology Seoul National University Suwon 443-270 Republic of Korea. Electronic address: mchotox@snu.ac.kr.

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Integrating human context and occlusion reasoning to improve handheld object tracking

Integrating human context and occlusion reasoning to improve...

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IEEE International Conference on Image Processing

作者： Daniel Parks Laurent Itti Neuroscience Graduate Program University of Southern California Los Angeles CA USA Department of Computer Science University of Southern California Los Angeles CA USA

Tracking an unknown number of various objects involving occlusion and multiple entry and exit points automatically is a challenging problem. Here we integrate spatial knowledge of human-object interactions into a high performing tracker to show that human context can further improve both detection and tracking. We use the DARPA Mind's Eye Action Recognition Dataset, which is comprised of street level scenes with humans interacting with handheld objects, to show this improvement. We find that human context can greatly reduce the number of false positive detections at the expense of increasing false negatives over a large test set (>230k frames). To minimize this, we add occlusion reasoning, where object detections are hallucinated when a human detection overlaps an object detection. These components together result in an average F 1 improvement of 107% per object category and a 69% reduction in track latency.

关键词： Context Detectors Cognition Object detection Proposals Object recognition computer vision

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Learning sparse representation for leaf image recognition

Learning sparse representation for leaf image recognition

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IEEE International Conference on Consumer Electronics-Taiwan (ICCE-TW)

作者： Jou-Ken Hsiao Li-Wei Kang Ching-Long Chang Chao-Yung Hsu Chia-Yen Chen Department of Computer Science and Information Engineering National Yunlin University of Science and Technology Yunlin Taiwan Graduate School of Engineering Science and Technology-Doctoral Program National Yunlin University of Science and Technology Yunlin Taiwan Institute of Information Science Taipei Taiwan Department of Computer Science and Information Engineering National University of Kaohsiung Taiwan

Automatic plant identification via computer vision techniques has been greatly important for a number of professionals, such as environmental protectors, land managers, and foresters. In this paper, a novel leaf image recognition technique via sparse representation is proposed for automatic plant identification. In order to model leaf images, we learn an overcomplete dictionary for sparsely representing the training images of each leaf species. Each dictionary is learned using a set of descriptors extracted from the training images in such a way that each descriptor is represented by linear combination of a small number of dictionary atoms. For each test leaf image, we calculate the correlation between the image and each learned dictionary of leaf species to achieve the identification of the leaf image. As a result, efficient leaf recognition can be achieved on public leaf dataset based on the proposed framework leading to a more compact and richer representation of leaf images compared to traditional clustering approaches. Moreover, our method is also adapted to newly added leaf species without retraining classifiers and suitable to be highly parallelized as well as integrated with any leaf image descriptors/features.

关键词： Image recognition Dictionaries Training Feature extraction Shape Educational institutions computer vision

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Sequence data and association statistics from 12,940 type 2 diabetes cases and controls

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Scientific data 2017年第1期4卷 170179页

作者： Jason Flannick Christian Fuchsberger Anubha Mahajan Tanya M Teslovich Vineeta Agarwala Kyle J Gaulton Lizz Caulkins Ryan Koesterer Clement Ma Loukas Moutsianas Davis J McCarthy Manuel A Rivas John R B Perry Xueling Sim Thomas W Blackwell Neil R Robertson N William Rayner Pablo Cingolani Adam E Locke Juan Fernandez Tajes Heather M Highland Josee Dupuis Peter S Chines Cecilia M Lindgren Christopher Hartl Anne U Jackson Han Chen Jeroen R Huyghe Martijn van de Bunt Richard D Pearson Ashish Kumar Martina Müller-Nurasyid Niels Grarup Heather M Stringham Eric R Gamazon Jaehoon Lee Yuhui Chen Robert A Scott Jennifer E Below Peng Chen Jinyan Huang Min Jin Go Michael L Stitzel Dorota Pasko Stephen C J Parker Tibor V Varga Todd Green Nicola L Beer Aaron G Day-Williams Teresa Ferreira Tasha Fingerlin Momoko Horikoshi Cheng Hu Iksoo Huh Mohammad Kamran Ikram Bong-Jo Kim Yongkang Kim Young Jin Kim Min-Seok Kwon Juyoung Lee Selyeong Lee Keng-Han Lin Taylor J Maxwell Yoshihiko Nagai Xu Wang Ryan P Welch Joon Yoon Weihua Zhang Nir Barzilai Benjamin F Voight Bok-Ghee Han Christopher P Jenkinson Teemu Kuulasmaa Johanna Kuusisto Alisa Manning Maggie C Y Ng Nicholette D Palmer Beverley Balkau Alena Stančáková Hanna E Abboud Heiner Boeing Vilmantas Giedraitis Dorairaj Prabhakaran Omri Gottesman James Scott Jason Carey Phoenix Kwan George Grant Joshua D Smith Benjamin M Neale Shaun Purcell Adam S Butterworth Joanna M M Howson Heung Man Lee Yingchang Lu Soo-Heon Kwak Wei Zhao John Danesh Vincent K L Lam Kyong Soo Park Danish Saleheen Wing Yee So Claudia H T Tam Uzma Afzal David Aguilar Rector Arya Tin Aung Edmund Chan Carmen Navarro Ching-Yu Cheng Domenico Palli Adolfo Correa Joanne E Curran Dennis Rybin Vidya S Farook Sharon P Fowler Barry I Freedman Michael Griswold Daniel Esten Hale Pamela J Hicks Chiea-Chuen Khor Satish Kumar Benjamin Lehne Dorothée Thuillier Wei Yen Lim Jianjun Liu Marie Loh Solomon K Musani Sobha Puppala William R Scott Loïc Yengo Sian-Tsung Tan Herman A Taylor Farook Thameem Gregory Wilson Tien Yin Wong Pål Rasmu Department of Molecular Biology Massachusetts General Hospital Boston Massachusetts USA. Program in Medical and Population Genetics Broad Institute Cambridge Massachusetts USA. Department of Biostatistics and Center for Statistical Genetics University of Michigan Ann Arbor Michigan USA. Wellcome Trust Centre for Human Genetics Nuffield Department of Medicine University of Oxford Oxford UK. Harvard-MIT Division of Health Sciences and Technology Massachusetts Institute of Technology Cambridge Massachusetts USA. Department of Statistics University of Oxford Oxford UK. Genetics of Complex Traits University of Exeter Medical School University of Exeter Exeter UK. MRC Epidemiology Unit Institute of Metabolic Science University of Cambridge Cambridge UK. Department of Twin Research and Genetic Epidemiology King's College London London UK. Oxford Centre for Diabetes Endocrinology and Metabolism Radcliffe Department of Medicine University of Oxford Oxford UK. Department of Human Genetics Wellcome Trust Sanger Institute Hinxton Cambridgeshire UK. School of Computer Science McGill University Montreal Quebec Canada. McGill University and Génome Québec Innovation Centre Montreal Quebec Canada. Human Genetics Center The University of Texas Graduate School of Biomedical Sciences at Houston The University of Texas Health Science Center at Houston Houston Texas USA. Department of Biostatistics Boston University School of Public Health Boston Massachusetts USA. National Heart Lung and Blood Institute's Framingham Heart Study Framingham Massachusetts USA. Medical Genomics and Metabolic Genetics Branch National Human Genome Research Institute National Institutes of Health Bethesda Maryland USA. Department of Biostatistics Harvard School of Public Health Boston Massachusetts USA. Chronic Disease Epidemiology Swiss Tropical and Public Health Institute University of Basel Basel Switzerland. Institute of Genetic Epidemiology Helmholtz Zentrum München German R

To investigate the genetic basis of type 2 diabetes (T2D) to high resolution, the GoT2D and T2D-GENES consortia catalogued variation from whole-genome sequencing of 2,657 European individuals and exome sequencing of 12,940 individuals of multiple ancestries. Over 27M SNPs, indels, and structural variants were identified, including 99% of low-frequency (minor allele frequency [MAF] 0.1-5%) non-coding variants in the whole-genome sequenced individuals and 99.7% of low-frequency coding variants in the whole-exome sequenced individuals. Each variant was tested for association with T2D in the sequenced individuals, and, to increase power, most were tested in larger numbers of individuals (>80% of low-frequency coding variants in ~82 K Europeans via the exome chip, and ~90% of low-frequency non-coding variants in ~44 K Europeans via genotype imputation). The variants, genotypes, and association statistics from these analyses provide the largest reference to date of human genetic information relevant to T2D, for use in activities such as T2D-focused genotype imputation, functional characterization of variants or genes, and other novel analyses to detect associations between sequence variation and T2D.

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Transgenetic Algorithms for the Multi-objective Quadratic Assignment Problem

Transgenetic Algorithms for the Multi-objective Quadratic As...

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Brazilian Conference on Intelligent Systems (BRACIS)

作者： Carolina Paula de Almeida Richard Aderbal Gonçalves Elizabeth Ferreira Goldbarg Marco Cesar Goldbarg Myriam Regattieri Delgado Computer Science Department-UNICENTRO Guarapuava PR Brazil Federal University of Rio Grande do Norte Natal RN Brazil Graduate Program in Electrical and Computer Engineering Federal University of Parana Curitiba PR Brazil

The multi-objective Quadratic Assignment Problem (mQAP) is a hard optimization problem with many real-world applications, such as in hospital layouts. The main purposes of this paper are: (1) the investigation of hybrid algorithms combining Transgenetic Algorithms and Evolutionary Multi-objective Optimization (EMO) frameworks to deal with mQAP and (2) to compare the ability of EMO algorithms based on Pareto dominance with those based on decomposition to deal with the mQAP. Transgenetic Algorithms (TAs) are evolutionary algorithms based on cooperation as the main evolutionary strategy. Two hybrid algorithms are proposed to deal with the mQAP: NSTA (TA + NSGA-II) and MOTA/D (TA + MOEA/D). To analyze the performance of the proposed algorithms, non-parametric statistical tests and multi-objective quality indicators are used. The proposed algorithms are compared with NSGA-II and MOEA/D in 126 instances of the mQAP. The results demonstrate the superiority of decomposition and transgenetic based algorithms, particularly in MOTA/D.

关键词： Biological cells Vectors Sociology Statistics Sorting Optimization Algorithm design and analysis

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