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作者： Jeong, Intae Kwon, Jooseong Kim, Changsoon Park, Young June Program in Nano Science and Technology Graduate School of Convergence Science and Technology Seoul National University Seoul 151-742 Korea Republic of Department of Electrical and Computer Engineering School of Engineering Seoul National University Seoul 151-747 Korea Republic of Advanced Institutes of Convergence Technology Suwon Gyeonggi 443-270 Korea Republic of

Heavily-doped strained germanium (Ge) can emit light efficiently thanks to its pseudo direct band gap characteristic. This makes Ge a good candidate for on-chip monolithic light sources in silicon (Si) photonics systems. We propose fin-shaped Ge-Si heterojunction lightemitting diode (LED) with metal gates, which can enhance light emission by coupling with surface plasmon resonant modes and modulate light emission from the LED. We verify these two aspects through numerical analysis and device simulations. We develop the method to find the optimal device structure and specific device dimensions to maximize the spontaneous emission rate enhancement. Also we find that the LED can be modulated by a gate voltage bias. © 2014 Optical Society of America.

关键词： Surface plasmons

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Multi-scale bio-inspired place recognition

Multi-scale bio-inspired place recognition

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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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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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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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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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Erratum to: The real cost of sequencing: scaling computation to keep pace with data generation

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Genome biology 2016年第1期17卷 78页

作者： Paul Muir Shantao Li Shaoke Lou Daifeng Wang Daniel J Spakowicz Leonidas Salichos Jing Zhang George M Weinstock Farren Isaacs Joel Rozowsky Mark Gerstein Department of Molecular Cellular and Developmental Biology Yale University New Haven CT 06520 USA. Systems Biology Institute Yale University West Haven CT 06516 USA. Integrated Graduate Program in Physical and Engineering Biology Yale University New Haven CT 06520 USA. Program in Computational Biology and Bioinformatics Yale University New Haven CT 06520 USA. Department of Molecular Biophysics and Biochemistry Yale University New Haven CT 06520 USA. The Jackson Laboratory for Genomic Medicine Farmington CT 06032 USA. Program in Computational Biology and Bioinformatics Yale University New Haven CT 06520 USA. mark@***. Department of Molecular Biophysics and Biochemistry Yale University New Haven CT 06520 USA. mark@***. Department of Computer Science Yale University New Haven CT 06520 USA. mark@***.

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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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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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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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