The Experts below are selected from a list of 22443 Experts worldwide ranked by ideXlab platform

Mansour Jamzad - One of the best experts on this subject based on the ideXlab platform.

  • Robust watermarking against print and scan attack through efficient Modeling Algorithm
    Signal Processing: Image Communication, 2014
    Co-Authors: S. Hamid Amiri, Mansour Jamzad
    Abstract:

    This article proposes a blind discrete wavelet transform-discrete cosine transform (DWT-DCT) composite watermarking scheme that is robust against print and scan distortions. First, two-dimensional DWT is applied to the original image to obtain the mid-frequency subbands. Then, a one-dimensional DCT is applied to the selected mid-frequency subbands to extract the final coefficients for embedding the watermark. To specify watermarking parameters, we utilize a Genetic Algorithm to achieve a predefined image quality after watermark insertion. Suitable locations for watermarking are determined by analyzing the effect of a Modeling Algorithm. This model simulates noise and nonlinear attacks in printers and scanners through noise estimation and system identification methods. The experimental results demonstrate that the proposed Algorithm has a high robustness against print and scan attack such that its robustness is higher than related watermarking Algorithms.

Martin D. F. Wong - One of the best experts on this subject based on the ideXlab platform.

  • A highly compressed timing macro-Modeling Algorithm for hierarchical and incremental timing analysis
    2018 23rd Asia and South Pacific Design Automation Conference (ASP-DAC), 2018
    Co-Authors: Martin D. F. Wong
    Abstract:

    Large-scale hierarchical and incremental timing analysis has driven the need for highly compressed timing macro-models. A small timing macro-model for accelerating hierarchical timing is desired because the size of incremental changes dramatically increases as the macro-models are widely used in the large design process. In fact, it takes days for an incremental timing analysis on millions of gates with thousands of incremental changes. To date, the timing macro-models generated by timing macro-Modeling Algorithms from all the previous works are not compact enough. In this work, we provide four essential techniques in our timing macro-Modeling Algorithm, which are able to generate highly compressed timing macro-models for hierarchical and incremental timing analysis. In addition, our timing macro-model maintain high accuracy and the efficiency in generating our macro-models. Our Algorithm generates timing macro-models where the model sizes are 9% better in the number of nodes and 19% better in the number of edges than the original circuit. Our work outperforms the state of arts significantly in both model size and the runtime in macro-model usage.

  • LibAbs: An efficient and accurate timing macro-Modeling Algorithm for large hierarchical designs
    2017 54th ACM EDAC IEEE Design Automation Conference (DAC), 2017
    Co-Authors: Tsung-wei Huang, Martin D. F. Wong
    Abstract:

    The ever-increasing design complexity is driving the need of fast and accurate macro-Modeling Algorithms to accelerate the hierarchical timing. We introduce LibAbs, an effective macro-Modeling Algorithm that efficiently supports high accuracy, high compression rate, and multi-threading. LibAbs applies tree-based graph reduction techniques to reduce the model size with comparable accuracy values to the flat model under multi-threaded environment. LibAbs outperforms existing tools including top winners from TAU 2016 macro-Modeling contest in terms of model size, accuracy, and runtime on industry benchmarks. The in-context usage of our abstracted model has also demonstrated promising performance for timing-driven optimizations in large hierarchical designs.

Jorg Henseler - One of the best experts on this subject based on the ideXlab platform.

  • on the convergence of the partial least squares path Modeling Algorithm
    Computational Statistics, 2010
    Co-Authors: Jorg Henseler
    Abstract:

    This paper adds to an important aspect of Partial Least Squares (PLS) path Modeling, namely the convergence of the iterative PLS path Modeling Algorithm. Whilst conventional wisdom says that PLS always converges in practice, there is no formal proof for path models with more than two blocks of manifest variables. This paper presents six cases of non-convergence of the PLS path Modeling Algorithm. These cases were estimated using Mode A combined with the factorial scheme or the path weighting scheme, which are two popular options of the Algorithm. As a conclusion, efforts to come to a proof of convergence under these schemes can be abandoned, and users of PLS should triangulate their estimation results.

G. Furlan - One of the best experts on this subject based on the ideXlab platform.

  • An enhancement to universal Modeling Algorithm context for real-time applications to image compression
    [Proceedings] ICASSP 91: 1991 International Conference on Acoustics Speech and Signal Processing, 1991
    Co-Authors: G. Furlan
    Abstract:

    A universal Modeling Algorithm, Context, introduced by J. Rissanen (see IEEE Trans. Info. Theory, vol.29. no.5, 1983) for binary strings, is generalized for nonbinary strings, which makes it applicable to Modeling many types of random processes, such as those encountered in image compression, both lossless and lossy, chaotic systems, and generally whenever prediction is needed. This generalization includes two major improvements, the control of the size of the required tree and a modification of the original context selection rule to improve accuracy and speed, based upon the idea of stochastic complexity, which in the current implementation are combined. In addition to the description of the new version of the Algorithm, its application to image compression is discussed.

Hongye Su - One of the best experts on this subject based on the ideXlab platform.

  • Decomposition-based Modeling Algorithm by CCA-PLS for large scale processes
    2015 American Control Conference (ACC), 2015
    Co-Authors: Lijuan Li, Lu Xiong, Ouguan Xu, Shengxiang Hu, Hongye Su
    Abstract:

    As the crucial part of predictive control, distributed Modeling method is seldom studied due to the absence of efficient methods to system decomposition. In this paper, a process decomposition Algorithm based on canonical correlation analysis (CCA) is proposed. The output variables of all subsystems are firstly determined by the process. And then the maximum correlation coefficient between the outputs of a subsystem and all the process variables are calculated. The variables corresponding to larger elements of axial vector extracted by the maximum correlation coefficient are selected as the input variables. After the decomposition, the sub-models are constructed by PLS Algorithm and the final subsystem models are obtained. The proposed method is experimented in the Modeling of typical Tennessee Eastman (TE) process and the result shows the good performance.