Conferences related to Kernel

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2019 IEEE/CVF International Conference on Computer Vision (ICCV)

Early Vision and Sensors Color, Illumination and Texture Segmentation and Grouping Motion and TrackingStereo and Structure from Motion Image -Based Modeling Physics -Based Modeling Statistical Methods and Learning in VisionVideo Surveillance and Monitoring Object, Event and Scene Recognition Vision - Based Graphics Image and Video RetrievalPerformance Evaluation Applications


2018 13th IEEE Conference on Industrial Electronics and Applications (ICIEA)

Industrial Informatics, Computational Intelligence, Control and Systems, Cyber-physicalSystems, Energy and Environment, Mechatronics, Power Electronics, Signal and InformationProcessing, Network and Communication Technologies


2018 14th IEEE International Conference on Signal Processing (ICSP)

ICSP2018 includes sessions on all aspects of theory, design and applications of signal processing. Prospective authors are invited to propose papers in any of the following areas, but not limited to: A. Digital Signal Processing (DSP)B. Spectrum Estimation & ModelingC. TF Spectrum Analysis & WaveletD. Higher Order Spectral AnalysisE. Adaptive Filtering &SPF. Array Signal ProcessingG. Hardware Implementation for Signal ProcessingH Speech and Audio CodingI. Speech Synthesis & RecognitionJ. Image Processing & UnderstandingK. PDE for Image ProcessingL.Video compression &StreamingM. Computer Vision & VRN. Multimedia & Human-computer InteractionO. Statistic Learning & Pattern RecognitionP. AI & Neural NetworksQ. Communication Signal processingR. SP for Internet and Wireless CommunicationsS. Biometrics & AuthentificationT. SP for Bio-medical & Cognitive ScienceU


2018 17th International Conference on Information Technology Based Higher Education and Training (ITHET)

The convergence of current technologies provides the infrastructure for transmitting and storing information faster and cheaper. For information to be used in gaining knowledge, however, environments for collecting, storing, disseminating, sharing and constructing knowledge are needed. Such environments, knowledge media, brings together telecommunication, computer and networking technologies, learning theories and cognitive sciences to form meaningful environments that provides for a variety of learner needs. ITHET 2018 will continue with the traditional themes of previous events. However, our special theme for this year is a fundamental one. We have previously had MOOCs as our special theme, but now they are just infrastructure. Even “Blended Learning” is what we all do anyway. In a time of the unprecedented access to knowledge through IT, it is time for us to revisit the fundamental purpose of our educational system. It is certainly not about knowledge anymore.


2018 18th IEEE/ACM International Symposium on Cluster, Cloud and Grid Computing (CCGRID)

Cluster Computing, Grid Computing, Edge Computing, Cloud Computing, Parallel Computing, Distributed Computing


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Periodicals related to Kernel

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Antennas and Propagation, IEEE Transactions on

Experimental and theoretical advances in antennas including design and development, and in the propagation of electromagnetic waves including scattering, diffraction and interaction with continuous media; and applications pertinent to antennas and propagation, such as remote sensing, applied optics, and millimeter and submillimeter wave techniques.


Automatic Control, IEEE Transactions on

The theory, design and application of Control Systems. It shall encompass components, and the integration of these components, as are necessary for the construction of such systems. The word `systems' as used herein shall be interpreted to include physical, biological, organizational and other entities and combinations thereof, which can be represented through a mathematical symbolism. The Field of Interest: shall ...


Circuits and Systems for Video Technology, IEEE Transactions on

Video A/D and D/A, display technology, image analysis and processing, video signal characterization and representation, video compression techniques and signal processing, multidimensional filters and transforms, analog video signal processing, neural networks for video applications, nonlinear video signal processing, video storage and retrieval, computer vision, packet video, high-speed real-time circuits, VLSI architecture and implementation for video technology, multiprocessor systems--hardware and software-- ...


Communications, IEEE Transactions on

Telephone, telegraphy, facsimile, and point-to-point television, by electromagnetic propagation, including radio; wire; aerial, underground, coaxial, and submarine cables; waveguides, communication satellites, and lasers; in marine, aeronautical, space and fixed station services; repeaters, radio relaying, signal storage, and regeneration; telecommunication error detection and correction; multiplexing and carrier techniques; communication switching systems; data communications; and communication theory. In addition to the above, ...


Computer

Computer, the flagship publication of the IEEE Computer Society, publishes peer-reviewed technical content that covers all aspects of computer science, computer engineering, technology, and applications. Computer is a resource that practitioners, researchers, and managers can rely on to provide timely information about current research developments, trends, best practices, and changes in the profession.


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Most published Xplore authors for Kernel

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Xplore Articles related to Kernel

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Power models supporting energy-efficient co-design on ultra-low power embedded systems

[{u'author_order': 1, u'affiliation': u'Department of Computer Science, UiT The Arctic University of Norway, Tromso, Norway', u'full_name': u'Vi Ngoc-Nha Tran'}, {u'author_order': 2, u'affiliation': u'Movidius Ltd., Dublin, Ireland', u'full_name': u'Brendan Barry'}, {u'author_order': 3, u'affiliation': u'Department of Computer Science, UiT The Arctic University of Norway, Tromso, Norway', u'full_name': u'Phuong Hoai Ha'}] 2016 International Conference on Embedded Computer Systems: Architectures, Modeling and Simulation (SAMOS), None

The energy efficiency of computing systems can be enhanced via power models that provide insights into how the systems consume power. However, there are no application-general, fine-grained and validated power models which can provide insights into how a given application running on an ultra-low power (ULP) embedded system consumes power. In this study, we devise new fine- grained power models ...


CLTune: A Generic Auto-Tuner for OpenCL Kernels

[{u'author_order': 1, u'affiliation': u'SURFsara HPC centre, Amsterdam, Netherlands', u'full_name': u'Cedric Nugteren'}, {u'author_order': 2, u'affiliation': u'SURFsara HPC centre, Amsterdam, Netherlands', u'full_name': u'Valeriu Codreanu'}] 2015 IEEE 9th International Symposium on Embedded Multicore/Many-core Systems-on-Chip, None

This work presents CLTune, an auto-tuner for OpenCL kernels. It evaluates and tunes kernel performance of a generic, user-defined search space of possible parameter-value combinations. Example parameters include the OpenCL workgroup size, vector data-types, tile sizes, and loop unrolling factors. CLTune can be used in the following scenarios: 1) when there are too many tunable parameters to explore manually, 2) ...


Group delay shift covariant quadratic time-frequency representations

[{u'author_order': 1, u'affiliation': u'Dept. of Electr. Eng., Arizona State Univ., Tempe, AZ, USA', u'full_name': u'A. Papandreou-Suppappola'}, {u'author_order': 2, u'full_name': u'R. L. Murray'}, {u'author_order': 3, u'full_name': u'Byeong-Gwan Iem'}, {u'author_order': 4, u'full_name': u'G. F. Boudreaux-Bartels'}] IEEE Transactions on Signal Processing, 2001

We propose classes of quadratic time-frequency representations (QTFRs) that are covariant to group delay shifts (GDSs). The GDS covariance QTFR property is important for analyzing signals propagating through dispersive systems with frequency-dependent characteristics. This is because a QTFR satisfying this property provides a succinct representation whenever the time shift is selected to match the frequency-dependent changes in the signal's group ...


Computation of convergence radius and error bounds of Volterra series for single input systems with a polynomial nonlinearity

[{u'author_order': 1, u'affiliation': u'Ircam - CNRS STMS UMR 9912, 1 place Igor Stravinsky, F-75004 Paris, France', u'full_name': u'Thomas H\xe9lie'}, {u'author_order': 2, u'affiliation': u'Laboratoire des Signaux et Systèemes, Université Paris-Sud, CNRS UMR8506, Supélec, 91405 Gif-Sur-Yvette, France', u'full_name': u'B\xe9atrice Laroche'}] Proceedings of the 48h IEEE Conference on Decision and Control (CDC) held jointly with 2009 28th Chinese Control Conference, None

In this paper, the Volterra series decomposition of a class of time invariant system, polynomial in the state and affine in the input, with an exponentially stable linear part is analyzed. A formal recursive expression of Volterra kernels of the input-to-state system is derived and the singular inversion theorem is used to prove the non-local-in-time convergence of the Volterra series ...


MEI: A Light Weight Memory Error Injection Tool for Validating Online Memory Testers

[{u'author_order': 1, u'affiliation': u'Sch. of Inf. Sci. & Eng., Lanzhou Univ., Lanzhou, China', u'full_name': u'Xiaoqiang Wang'}, {u'author_order': 2, u'affiliation': u'Sch. of Inf. Sci. & Eng., Lanzhou Univ., Lanzhou, China', u'full_name': u'Xuguo Wang'}, {u'author_order': 3, u'affiliation': u'Sch. of Inf. Sci. & Eng., Lanzhou Univ., Lanzhou, China', u'full_name': u'Fangfang Zhu'}, {u'author_order': 4, u'affiliation': u'Sch. of Inf. Sci. & Eng., Lanzhou Univ., Lanzhou, China', u'full_name': u'Qingguo Zhou'}, {u'author_order': 5, u'affiliation': u'Sch. of Inf. Sci. & Eng., Lanzhou Univ., Lanzhou, China', u'full_name': u'Rui Zhou'}] 2016 International Symposium on System and Software Reliability (ISSSR), None

Lots of studies have shown that memory hardware error rates are orders of magnitude higher than previously reported. In order to fight with these memory hardware errors, many memory testing tools have been developed, especially software level online memory testers, which means these memory testers implemented in software can work with the OS (operating system) at the same time. However, ...


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Educational Resources on Kernel

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eLearning

No eLearning Articles are currently tagged "Kernel"

IEEE-USA E-Books

  • Performance Analysis

    This chapter evaluates the small-sample performance of several error estimators under a broad variety of experimental conditions, using performance metrics. Computation time varies widely among the different error estimators. Resubstitution is the fastest estimator. To illustrate regression relating the true and estimated errors, the chapter considers the QDA classification. Error estimation plays a role in feature selection. When selecting features via a wrapper algorithm such as sequential forward floating selection (SFFS), which employs error estimation within it, the choice of error estimator obviously impacts feature selection performance, the degree of impact depending on the classification rule and feature-label distribution. Multiple-data-set bias results from using multiple data sets to evaluate a single classification rule and taking the minimum estimated error. Multiple-rule bias results from applying multiple pattern recognition rules on the same data set. In Yousefi and Dougherty the relationship between reproducibility and classification difficulty is examined.

  • A Proposal for a Kernel Based Algorithm for Large Vocabulary Continuous Speech Recognition

    This chapter contains sections titled: Introduction Segment Models and Hidden Markov Models Kernel Based Model Large Margin Training Implementation Details Discussion Acknowledgements References

  • A Kernel Wrapper for Phoneme Sequence Recognition

    This chapter contains sections titled: Introduction Problem Setting Frame‐based Phoneme Classifier Kernel‐based Iterative Algorithm for Phoneme Recognition Nonlinear Feature Functions Preliminary Experimental Results Discussion: Can we Hope for Better Results? References

  • Digital Hologram Processing in On-Axis Holography

    In the context of fast and 3D quantitative microscopy, on-axis digital holography has a significant potential. This chapter focuses on the numerical reconstruction of digital holograms. After a presentation of classical back- propagation methods, we introduce the general framework of inverse problems that includes recent compressive sensing methods. Inverse problems approaches solve two essential issues in digital holography: the improvement of reconstruction accuracy and the extension of the studied field beyond the physical limit of the sensor size. In addition, the achievable resolution can be derived from the hologram formation model using Cramer-Rao lower bounds. As a drawback of reconstruction methods based on inverse problems is their computational complexity, we suggest two ways to significantly reduce the processing time of holograms.

  • Protein Tertiary Model Assessment

    In structure biology, protein structures are often determined by techniques such as X-ray crystallography, NMR spectroscopy, and electron microscopy. In this chapter, the authors aim to obtain a learning algorithm that studies known structures from Protein Data Bank (PDB) and when given a protein model predicts whether it belongs to the same class as PBD structures. The chapter aims at developing and implementing new granular decision machines to find biologically meaningful features for assessing 3D protein structures accurately and efficiently. This leads to a better understanding of how and why the key biological features and geometric features can dominate a 3D protein structure, and identify these critical sequence features and geometric features. The most familiar assessment techniques are categorized in the chapter, and the importance of each category is discussed. The machine learning techniques considered in the chapter are support vector machines and fuzzy decision trees.

  • Augmented Statistical Models: Using Dynamic Kernels for Acoustic Models

    This chapter contains sections titled: Introduction Temporal Correlation Modeling Dynamic Kernels Augmented Statistical Models Experimental Results Conclusions Acknowledgements References

  • Managing Resources

    This chapter contains sections titled: Resource‐Related Concerns in Mobile Devices Common Concerns MIDP Java Symbian OS Summary Exercises

  • The POSIX Operating System

    This chapter contains sections titled: An Operating Environment Linux 2.6 Kernel

  • Modeling of Nonlinear Systems

  • Support Vector Machines

    This chapter contains sections titled: * Linear Classifiers * The Kernel Function * ¿¿¿¿¿¿-SVM * Multi-class Methods * One-class Methods * Summary



Standards related to Kernel

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(Replaced) IEEE Standard VHDL Language Reference Manual

his standard revises and enhances the VHDL language reference manual (LRM) by including a standard C language interface specification; specifications from previously separate, but related, standards IEEE Std 1164 -1993,1 IEEE Std 1076.2 -1996, and IEEE Std 1076.3-1997; and general language enhancements in the areas of design and verification of electronic systems.



Jobs related to Kernel

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