The Experts below are selected from a list of 17976 Experts worldwide ranked by ideXlab platform
K Shimizu - One of the best experts on this subject based on the ideXlab platform.
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dynamic range Compression Characteristics using an interpolating polynomial for digital audio systems
IEICE Transactions on Fundamentals of Electronics Communications and Computer Sciences, 2005Co-Authors: Shugang Wei, K ShimizuAbstract:An audio signal level compressor is presented, which is based on the approximation algorithm using an interpolating polynomial. To implement a Compression Characteristic in a digital audio system, a power calculation with fractional numbers is required and it is difficult to be performed directly in digital circuits. We introduce a polynomial expression to approximate the power operation, then the gain calculation is easily performed with a number of additions, multiplications and a division. Newton's interpolation formula is used to calculate the Compression Characteristics in a very short time and the obtained Compression Characteristics are very close to the ideal ones.
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audio dynamic range Compression Characteristics based on an interpolating polynomial
The 2nd Annual IEEE Northeast Workshop on Circuits and Systems 2004. NEWCAS 2004., 2004Co-Authors: Shugang Wei, K ShimizuAbstract:An audio signal level compressor is presented, in this paper, which is based on the approximation algorithm using an interpolating polynomial. To implement a Compression Characteristic in a digital audio system, a power calculation with fractional numbers is required and it is difficult to be performed directly in a digital signal processing system. We introduce a polynomial expression to approximate the power operation then the gain calculation is easily performed with a number of additions, multiplications and a division. Newton's interpolation formula is used to calculate the Compression Characteristics in a very short time.
A P Whitmore - One of the best experts on this subject based on the ideXlab platform.
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calculation of the Compression index and preCompression stress from soil Compression test data
Soil & Tillage Research, 2006Co-Authors: A S Gregory, W R Whalley, C W Watts, N R A Bird, Paul D Hallett, A P WhitmoreAbstract:Abstract When compressing soil, there is a Characteristic relationship between compressive stress and volume change that can be used to define important soil mechanical properties. Two defining features can be determined – the Compression index (Cc – the modulus of the slope of the linear virgin Compression curve) and the preCompression stress ( σ ′ p – the transition point between the elastic rebound curve and the virgin Compression curve). These are indicators of compressibility and stress history respectively. The purpose of this paper is to evaluate different ways of estimating these indicators based on laboratory test data. Repacked soils with a range of textures were subjected to sequential Compressions of 50, 100 and 200 kPa, which provided two Compression Characteristics with “known” σ ′ p of 50 and 100 kPa. Three functions were fitted to the measured test data (fourth-order polynomial, symmetrical logistic sigmoidal and asymmetrical Gompertz sigmoidal). Values of Cc were estimated by linear regression (for the data later fitted with a polynomial function) or by the tangent at the inflection point derived from model parameters (logistic and Gompertz functions). Three estimates of σ ′ p were calculated for each of the three functions: the standard Casagrande method (C), the intercept of the virgin Compression curve and the initial (no stress) horizontal line (V–I), and the point of maximum curvature (MC) derived from the curvature function (κ). The accuracy of estimating σ ′ p and the magnitude of Cc generally increased with clay content. Estimates of Cc based on sigmoidal curves did not differ greatly from the linear regression estimate. Sigmoidal curves yielded σ ′ p estimates with lower absolute deviations from known values than polynomial-based estimates. The MC calculation based on the Gompertz function gave the most accurate estimate of σ ′ p . The lower asymptote of sigmoidal curves may also correspond to the water-filled pore space. Thus, despite the fact that all three functions fitted the measured data equally well, Characteristics based on the sigmoidal curves were deemed to be most appropriate. The greater accuracy of the prediction of σ ′ p favoured the Gompertz function. Wider applicability of this was further checked with data selected from an independent database on subsoil compaction. We recommend fitting the Gompertz function to measured soil Compression Characteristic test data, and to define Cc objectively as the modulus of the slope of the tangent at the inflection point, providing this lies within the measured data range, and σ ′ p as the point of maximum curvature as defined by κ.
Shugang Wei - One of the best experts on this subject based on the ideXlab platform.
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dynamic range Compression Characteristics using an interpolating polynomial for digital audio systems
IEICE Transactions on Fundamentals of Electronics Communications and Computer Sciences, 2005Co-Authors: Shugang Wei, K ShimizuAbstract:An audio signal level compressor is presented, which is based on the approximation algorithm using an interpolating polynomial. To implement a Compression Characteristic in a digital audio system, a power calculation with fractional numbers is required and it is difficult to be performed directly in digital circuits. We introduce a polynomial expression to approximate the power operation, then the gain calculation is easily performed with a number of additions, multiplications and a division. Newton's interpolation formula is used to calculate the Compression Characteristics in a very short time and the obtained Compression Characteristics are very close to the ideal ones.
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audio dynamic range Compression Characteristics based on an interpolating polynomial
The 2nd Annual IEEE Northeast Workshop on Circuits and Systems 2004. NEWCAS 2004., 2004Co-Authors: Shugang Wei, K ShimizuAbstract:An audio signal level compressor is presented, in this paper, which is based on the approximation algorithm using an interpolating polynomial. To implement a Compression Characteristic in a digital audio system, a power calculation with fractional numbers is required and it is difficult to be performed directly in a digital signal processing system. We introduce a polynomial expression to approximate the power operation then the gain calculation is easily performed with a number of additions, multiplications and a division. Newton's interpolation formula is used to calculate the Compression Characteristics in a very short time.
Fang Qing - One of the best experts on this subject based on the ideXlab platform.
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Study on the Different Compression Characteristic of High Liquid Limit Clay and High Liquid Limit Silt on Wetting-drying Cycles
Journal of Sichuan University, 2011Co-Authors: Fang QingAbstract:The change rules of high liquid limit clay and high liquid limit silt on wetting-drying cycles were compared and analyzed by analyzing the test results of the height,mass and Compression coefficient of the two soils.The change rules of high liquid limit clay and high liquid limit silt were also studied from the mechanism aspect.From the research results,it can be found that the change rule of mass and height of high liquid limit clay is similar with that of high liquid limit silt.The rate of mass change and height change tend to smooth with the increase in the number of wet and dry cycles in these two soils.The change rule of Compression coefficient is also similar in these two soils.After one wetting-drying cycle,the increase rate of the Compression coefficient of high liquid limit silt and high liquid limit clay is between 40%~180% and 40%~180%,respectively.The Compression coefficient of these two soil tend to smooth after three wetting-drying cycles.The Compression coefficient of these two soil tend to be equal after five wetting-drying cycles.But the Compression coefficient of high liquid limit silt is less than that of high liquid limit clay in wetting-drying cycles.It also can be found that the wetting-drying cycle is an irreversible process for the two soils.
A S Gregory - One of the best experts on this subject based on the ideXlab platform.
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calculation of the Compression index and preCompression stress from soil Compression test data
Soil & Tillage Research, 2006Co-Authors: A S Gregory, W R Whalley, C W Watts, N R A Bird, Paul D Hallett, A P WhitmoreAbstract:Abstract When compressing soil, there is a Characteristic relationship between compressive stress and volume change that can be used to define important soil mechanical properties. Two defining features can be determined – the Compression index (Cc – the modulus of the slope of the linear virgin Compression curve) and the preCompression stress ( σ ′ p – the transition point between the elastic rebound curve and the virgin Compression curve). These are indicators of compressibility and stress history respectively. The purpose of this paper is to evaluate different ways of estimating these indicators based on laboratory test data. Repacked soils with a range of textures were subjected to sequential Compressions of 50, 100 and 200 kPa, which provided two Compression Characteristics with “known” σ ′ p of 50 and 100 kPa. Three functions were fitted to the measured test data (fourth-order polynomial, symmetrical logistic sigmoidal and asymmetrical Gompertz sigmoidal). Values of Cc were estimated by linear regression (for the data later fitted with a polynomial function) or by the tangent at the inflection point derived from model parameters (logistic and Gompertz functions). Three estimates of σ ′ p were calculated for each of the three functions: the standard Casagrande method (C), the intercept of the virgin Compression curve and the initial (no stress) horizontal line (V–I), and the point of maximum curvature (MC) derived from the curvature function (κ). The accuracy of estimating σ ′ p and the magnitude of Cc generally increased with clay content. Estimates of Cc based on sigmoidal curves did not differ greatly from the linear regression estimate. Sigmoidal curves yielded σ ′ p estimates with lower absolute deviations from known values than polynomial-based estimates. The MC calculation based on the Gompertz function gave the most accurate estimate of σ ′ p . The lower asymptote of sigmoidal curves may also correspond to the water-filled pore space. Thus, despite the fact that all three functions fitted the measured data equally well, Characteristics based on the sigmoidal curves were deemed to be most appropriate. The greater accuracy of the prediction of σ ′ p favoured the Gompertz function. Wider applicability of this was further checked with data selected from an independent database on subsoil compaction. We recommend fitting the Gompertz function to measured soil Compression Characteristic test data, and to define Cc objectively as the modulus of the slope of the tangent at the inflection point, providing this lies within the measured data range, and σ ′ p as the point of maximum curvature as defined by κ.