The Experts below are selected from a list of 10563 Experts worldwide ranked by ideXlab platform

Scott Hauck - One of the best experts on this subject based on the ideXlab platform.

  • automated least significant bit datapath optimization for fpgas
    Field-Programmable Custom Computing Machines, 2004
    Co-Authors: Mark L Chang, Scott Hauck
    Abstract:

    In this paper, we present a method for FPGA datapath precision optimization subject to user-defined area and error constraints. This work builds upon our previous research which presented a methodology for optimizing the dynamic range- the most significant bit position. In this work, we present an automated optimization technique for the least-significant bit position of circuit datapaths. We present results describing the effectiveness of our methods on typical signal and image processing kernels.

  • least significant bit optimization techniques for fpgas
    Field Programmable Gate Arrays, 2004
    Co-Authors: Mark L Chang, Scott Hauck
    Abstract:

    We present a methodology for FPGA datapath precision optimization subject to user-defined area and error constraints. This work builds upon our previous research which presented a methodology for optimizing for dynamic range--the most significant bit position. In this work we define the problem of least significant bit optimization and propose optimization techniques that provide finer control of area-to-error tradeoffs than more traditional methods. We present some preliminary results describing the effectiveness of our techniques on typical signal and image processing kernels.

  • FPGA - least-significant bit optimization techniques for FPGAs
    Proceeding of the 2004 ACM SIGDA 12th international symposium on Field programmable gate arrays - FPGA '04, 2004
    Co-Authors: Mark L Chang, Scott Hauck
    Abstract:

    We present a methodology for FPGA datapath precision optimization subject to user-defined area and error constraints. This work builds upon our previous research which presented a methodology for optimizing for dynamic range--the most significant bit position. In this work we define the problem of least significant bit optimization and propose optimization techniques that provide finer control of area-to-error tradeoffs than more traditional methods. We present some preliminary results describing the effectiveness of our techniques on typical signal and image processing kernels.

  • FCCM - Automated least-significant bit datapath optimization for FPGAs
    12th Annual IEEE Symposium on Field-Programmable Custom Computing Machines, 1
    Co-Authors: Mark L Chang, Scott Hauck
    Abstract:

    In this paper, we present a method for FPGA datapath precision optimization subject to user-defined area and error constraints. This work builds upon our previous research which presented a methodology for optimizing the dynamic range- the most significant bit position. In this work, we present an automated optimization technique for the least-significant bit position of circuit datapaths. We present results describing the effectiveness of our methods on typical signal and image processing kernels.

Mark L Chang - One of the best experts on this subject based on the ideXlab platform.

  • automated least significant bit datapath optimization for fpgas
    Field-Programmable Custom Computing Machines, 2004
    Co-Authors: Mark L Chang, Scott Hauck
    Abstract:

    In this paper, we present a method for FPGA datapath precision optimization subject to user-defined area and error constraints. This work builds upon our previous research which presented a methodology for optimizing the dynamic range- the most significant bit position. In this work, we present an automated optimization technique for the least-significant bit position of circuit datapaths. We present results describing the effectiveness of our methods on typical signal and image processing kernels.

  • least significant bit optimization techniques for fpgas
    Field Programmable Gate Arrays, 2004
    Co-Authors: Mark L Chang, Scott Hauck
    Abstract:

    We present a methodology for FPGA datapath precision optimization subject to user-defined area and error constraints. This work builds upon our previous research which presented a methodology for optimizing for dynamic range--the most significant bit position. In this work we define the problem of least significant bit optimization and propose optimization techniques that provide finer control of area-to-error tradeoffs than more traditional methods. We present some preliminary results describing the effectiveness of our techniques on typical signal and image processing kernels.

  • FPGA - least-significant bit optimization techniques for FPGAs
    Proceeding of the 2004 ACM SIGDA 12th international symposium on Field programmable gate arrays - FPGA '04, 2004
    Co-Authors: Mark L Chang, Scott Hauck
    Abstract:

    We present a methodology for FPGA datapath precision optimization subject to user-defined area and error constraints. This work builds upon our previous research which presented a methodology for optimizing for dynamic range--the most significant bit position. In this work we define the problem of least significant bit optimization and propose optimization techniques that provide finer control of area-to-error tradeoffs than more traditional methods. We present some preliminary results describing the effectiveness of our techniques on typical signal and image processing kernels.

  • FCCM - Automated least-significant bit datapath optimization for FPGAs
    12th Annual IEEE Symposium on Field-Programmable Custom Computing Machines, 1
    Co-Authors: Mark L Chang, Scott Hauck
    Abstract:

    In this paper, we present a method for FPGA datapath precision optimization subject to user-defined area and error constraints. This work builds upon our previous research which presented a methodology for optimizing the dynamic range- the most significant bit position. In this work, we present an automated optimization technique for the least-significant bit position of circuit datapaths. We present results describing the effectiveness of our methods on typical signal and image processing kernels.

Chinchen Chang - One of the best experts on this subject based on the ideXlab platform.

  • optimizing least significant bit substitution using cat swarm optimization strategy
    Information Sciences, 2012
    Co-Authors: Zhihui Wang, Chinchen Chang
    Abstract:

    Embedding secret data into a cover image using simple least-significant-bit substitution can degrade the image quality dramatically, especially when a large number of bits are substituted. The exhaustive least-significant-bit substitution method is proposed to solve this problem. However, the idea has no practical application due to its long computation time. This paper adopts the cat swarm optimization (CSO) strategy to obtain the optimal or near optimal solution of the stego-image quality problem. The CSO strategy is generated by observing the behavior of cats, which has been proved to achieve better performance on finding the best global solutions. We revised the CSO strategy in our proposed scheme to make it practicable and suitable to solve the mentioned problem. The experimental results show that the proposed scheme can obtain a better solution with less computation time.

  • Finding optimal least-significant-bit substitution in image hiding by dynamic programming strategy
    Pattern Recognition, 2003
    Co-Authors: Chinchen Chang, Ju-yuan Hsiao, Chi-shiang Chan
    Abstract:

    Abstract The processing of simple least-significant-bit (LSB) substitution embeds the secret image in the least significant bits of the pixels in the host image. This processing may degrade the host image quality so significantly that grabbers can detect that there is something going on in the image that interests them. To overcome this drawback, an exhaustive least-significant-bit substitution scheme was proposed by Wang et al. but it takes huge computation time. Wang et al. then proposed another method that uses a genetic algorithm to search “approximate” optimal solutions and computation time is no longer so huge. In this paper, we shall use the dynamic programming strategy to get the optimal solution. The experimental results will show that our method consumes less computation time and also gets the optimal solution.

Zhongxue Chen - One of the best experts on this subject based on the ideXlab platform.

  • Image complexity and feature mining for steganalysis of least significant bit matching steganography
    Information Sciences, 2008
    Co-Authors: Qingzhong Liu, Andrew H. Sung, Bernardete Ribeiro, Mingzhen Wei, Zhongxue Chen
    Abstract:

    The information-hiding ratio is a well-known metric for evaluating steganalysis performance. In this paper, we introduce a new metric of image complexity to enhance the evaluation of steganalysis performance. In addition, we also present a scheme of steganalysis of least significant bit (LSB) matching steganography, based on feature mining and pattern recognition techniques. Compared to other well-known methods of steganalysis of LSB matching steganography, our method performs the best. Results also indicate that the significance of features and the detection performance depend not only on the information-hiding ratio, but also on the image complexity.

Chi-shiang Chan - One of the best experts on this subject based on the ideXlab platform.

  • Finding optimal least-significant-bit substitution in image hiding by dynamic programming strategy
    Pattern Recognition, 2003
    Co-Authors: Chinchen Chang, Ju-yuan Hsiao, Chi-shiang Chan
    Abstract:

    Abstract The processing of simple least-significant-bit (LSB) substitution embeds the secret image in the least significant bits of the pixels in the host image. This processing may degrade the host image quality so significantly that grabbers can detect that there is something going on in the image that interests them. To overcome this drawback, an exhaustive least-significant-bit substitution scheme was proposed by Wang et al. but it takes huge computation time. Wang et al. then proposed another method that uses a genetic algorithm to search “approximate” optimal solutions and computation time is no longer so huge. In this paper, we shall use the dynamic programming strategy to get the optimal solution. The experimental results will show that our method consumes less computation time and also gets the optimal solution.