نتایج جستجو برای: shifting technique
تعداد نتایج: 638722 فیلتر نتایج به سال:
Data hiding is the technique of embedding data in an image and retrieval of the data with lossless reconstruction of original im age. In this paper, we present data hiding scheme based on histogram modification.This technique is based on differences of adjacent pixels for embedding data and has more hiding capacity compared to existing methods.We exploit a binary tree structure to solve the pro...
1 ALGORITHM FOR POWER MINIMIZATION IN SCAN SEQUENTIAL CIRCUITS 1Harpreet Singh, 2Dr. Sukhwinder Singh 1M.E. (VLSI DESIGN), PEC University of Technology, Chandigarh. 2Professor, PEC University of Technology, Chandigarh Email: [email protected] . Abstract— The paper describes a An ATPG technique is proposed that reduces heat dissipation during testing of sequential circuits that have ...
This paper describes a new technique for minimising power dissipation in full scan sequential circuits during test application. The technique increases the correlation between successive states during shifting in test vectors and shifting out test responses by reducing spurious transitions during test application. The reduction is achieved by freezing the primary input part of the test vector u...
زمینه و هدف : هموفیلی a یک اختلال انعقادی وابسته به جنس مغلوب است که در اثر وقوع ناهنجاری های گوناگون در ژن فاکتور هشت انعقادی حادث می گردد . وارونگی اینترون 22 در50-45% ازموارد مسبب نوع شدید بیماری است. علاوه بر این وارونگی اینترون 1 نیز در بیش از 5% موارد مسئول ایجاد هموفیلی a شدید محسوب می گردد. هدف از این مطالعه ارزیابی دقیق وارونگی اینترون1 ژن فاکتور هشت انعقادی با استفاده از روش is-pcr(in...
There have been many published studies aiming to identify temporal changes in river flow time series, most of which use monotonic trend tests such as the Mann–Kendall test. Although robust to both the distribution of the data and incomplete records, these tests have important limitations and provide no information as to whether a change in variability mirrors a change in magnitude. This study d...
In most on-line learning research the total on-line loss of the algorithm is compared to the total loss of the best o¬-line predictor u from a comparison class of predictors. We call such bounds static bounds. The interesting feature of these bounds is that they hold for an arbitrary sequence of examples. Recently some work has been done where the predictor ut at each trial t is allowed to chan...
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