Showing 13 open source projects for "cite"

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  • 1
    Eventer

    Eventer

    Rapid, unbiased, reproducible analysis of synaptic events

    ...The software is coded in MATLAB, but has been compiled as standalone applications for Windows, Mac and Linux. Please visit the official Eventer website for more info https://eventerneuro.netlify.app/ While the paper is in preparation, please cite as; Winchester, G., Liu, S., Steele, O.G., Aziz, W. and Penn, A.C. (2020) Eventer. Software for the detection of spontaneous synaptic events measured by electrophysiology or imaging. http://doi.org/10.5281/zenodo.3991676
    Downloads: 2 This Week
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  • 2
    DeepImageTranslator

    DeepImageTranslator

    DeepImageTranslator: a deep-learning utility for image translation

    Created by: Run Zhou Ye, En Zhou Ye, and En Hui Ye DeepImageTranslator: a free, user-friendly tool for image translation using deep-learning and its applications in CT image analysis Citation: Please cite this software as: Ye RZ, Noll C, Richard G, Lepage M, Turcotte ÉE, Carpentier AC. DeepImageTranslator: a free, user-friendly graphical interface for image translation using deep-learning and its applications in 3D CT image analysis. SLAS technology. 2022 Feb 1;27(1):76-84. https://doi.org/10.1016/j.slast.2021.10.014
    Downloads: 2 This Week
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  • 3
    Phenalysis

    Phenalysis

    Analyze agronomic plant research plots in aerial orthomosaic images.

    A graphical user interface to import, analyze and export plots from orthomosaic images of agronomic trials. Please cite the following reference in your work if you use Phenalysis: Khan Z and Miklavcic SJ (2019) An Automatic Field Plot Extraction Method From Aerial Orthomosaic Images. Front. Plant Sci. 10:683. doi: https://doi.org/10.3389/fpls.2019.00683 This tool is being developed through the sponsorship of the Australian Research Council's Industrial Transformation Research Hub on Wheat in a Hot and Dry Climate. ...
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  • 4

    SPAWNN

    SPatial Analysis With self-organizing Neural Networks

    The SPAWNN toolkit is an innovative toolkit for spatial analysis with self-organizing neural networks which is particularily useful for spatial analysis, visualization and geographical data mining. To run the toolkit, simply download and execute (double-click) the jar-file. Please cite: - Hagenauer, J., & Helbich, M. (2016). SPAWNN: A Toolkit for SPatial Analysis With Self-Organizing Neural Networks. Transactions in GIS, 20(5), 755-775. Other related publications: - Hagenauer, J. (2016). Weighted merge context for clustering and quantizing spatial data with self-organizing neural networks. Journal of Geographical Systems, 18(1), 1-15...
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  • 5
    JCLAL

    JCLAL

    A Java Class Library for Active Learning

    ...It is constructed with a high-level software environment, with a strong object oriented design and use of design patterns, which allow to the developers reuse, modify and extend the framework. If you want to refer to JCLAL in a publication, please cite the following JMLR paper. The full citation is: Oscar Reyes, Eduardo Pérez, María del Carmen Rodríguez-Hernández, Habib M. Fardoun, Sebastián Ventura. JCLAL: A Java Framework for Active Learning. Journal of Machine Learning Research, 17(95):1-5, 2016.
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  • 6

    Automatic cell lineage reconstruction

    Automatic segmentation and tracking for 3D time-lapse microscopy

    ...We demonstrate its (1) generality, by reconstructing cell lineages in four-dimensional, terabyte-sized image data of fruit-fly, zebrafish and mouse embryos, acquired with three different types of fluorescence microscopes, (2) scalability, by analyzing advanced stages of development with up to 20,000 cells per time point, at 26,000 cells min-1 on a single computer workstation, and (3) ease of use, by adjusting only two parameters across all data sets and providing visualization and editing tools for efficient data curation. Our approach achieves on average 97.0% linkage accuracy across all species and imaging modalities." *Please cite this paper if you use this code for your research
    Downloads: 2 This Week
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  • 7
    dotRL

    dotRL

    A platform for rapid Reinforcement Learning methods development

    ...Project goals are: - keep adding new environments and agents as simple as possible - provide a rich set of state-of-art algorithms and problems - integrate with other existing Reinforcement Learning platforms If you found this application useful please cite this work: http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=6643987
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  • 8

    LWPR

    Locally Weighted Projection Regression (LWPR)

    ...At its core, it uses locally linear models, spanned by a small number of univariate regressions in selected directions in input space. A locally weighted variant of Partial Least Squares (PLS) is employed for doing the dimensionality reduction. Please cite: [1] Sethu Vijayakumar, Aaron D'Souza and Stefan Schaal, Incremental Online Learning in High Dimensions, Neural Computation, vol. 17, no. 12, pp. 2602-2634 (2005). [2] Stefan Klanke, Sethu Vijayakumar and Stefan Schaal, A Library for Locally Weighted Projection Regression, Journal of Machine Learning Research (JMLR), vol. 9, pp. 623--626 (2008). ...
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  • 9
    ArabicDiacritizer

    ArabicDiacritizer

    An automatic restoration of Arabic diacritic marks

    ...The algorithm is described in detail in: Ilyes Rebai, and Yassine BenAyed 'Text-to-speech synthesis system with Arabic diacritic recognition system', Computer Speech & Language, 2015. We appreciate it very much if you can cite our related work. ************** Installation *************** - Extract the archive "ArabicDiacritizer Setup.rar". - Install the application using "Setup.exe". - Put an Arabic text in the Text Box. - Start the diacritization process. If the following problem occured: <Access to the path '..\ArabicDiacritizer v1.0\text.data' is denied> - Access to the path "Program Files\ArabicDiacritizer\ArabicDiacritizer v1.0\", - Right click on "ArabicDiacritizer" - Choose "Run as administrator" For further information, please contact: rebai_ily
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  • 10
    Kernel Adaptive Filtering Toolbox

    Kernel Adaptive Filtering Toolbox

    a Matlab benchmarking toolbox for kernel adaptive filtering

    ...This toolbox includes algorithms, demos, and tools to compare their performance. See the included README file for a list of included algorithms and more details. If you use this toolbox in your research please cite: @inproceedings{vanvaerenbergh2013comparative, author = {Van Vaerenbergh, Steven and Santamar{\'i}a, Ignacio}, booktitle = {2013 IEEE Digital Signal Processing (DSP) Workshop and IEEE Signal Processing Education (SPE)}, title = {A Comparative Study of Kernel Adaptive Filtering Algorithms}, year = {2013}, note = {Software available at \url{https://github.com/steven2358/kafbox/}} }
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  • 11

    Graphlet kernel framework

    Calculates similarity between neighborhoods of two vertices in a graph

    ...The graphlet kernel framework can be used for vertex (node) classification in graphs, kernel-based clustering, or community detection. If you use this framework, please cite the following paper: Lugo-Martinez J, Radivojac P. Generalized graphlet kernels for probabilistic inference in sparse graphs. Network Science (2014) 2(2): 254-276.
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  • 12

    LBP in multiple platforms

    LBP implementation in multiple computing platforms (ARM,GPU, DSP...)

    ...This is a software toolbox that collects software implementations of the Local Binary Pattern operator in several platforms: - OpenCL for CPU & GPU - OpenCL for GPU (branchless) - C code optimized for ARM - OpenGL ES 2.0 shaders mobile GPUs - C code for TI C64x DSP core (branchless) - C code for TTA processor synthesis If you use the code somewhere, please cite: Bordallo López M., Nieto A., Boutellier J., Hannuksela J., and Silvén O. "Evaluation of real-time LBP computing in multiple architectures," Journal of Real Time Image Processing, 2014
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  • 13

    LiveVideo

    Real-time Video Analysis Software

    ...For more details, please visit https://www.wonsangyou.com/research/aivision. If you need technical help, please send an email to wyou(at)kaist.ac.kr. When you use this software for your publications, please cite as follows. 1. Wonsang You, M.S. Houari Sabirin, and Munchurl Kim, "Moving object tracking in H.264/AVC bitstream," Lecture Notes in Computer Science, vol. 4577, 2007, pp. 483-492. 2. Wonsang You, M.S. Houari Sabirin, and Munchurl Kim, "Real-time detection and tracking of multiple objects with partial decoding in H.264/AVC bitstream domain," Proceedings of SPIE, vol. 7244, 72440D (February 2009).
    Downloads: 2 This Week
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