The Experts below are selected from a list of 124788 Experts worldwide ranked by ideXlab platform
T S Fiez - One of the best experts on this subject based on the ideXlab platform.
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a 14 bit current mode spl sigma spl delta dac based upon rotated data Weighted Averaging
IEEE Journal of Solid-state Circuits, 2000Co-Authors: R Radke, A Eshraghi, T S FiezAbstract:A new dynamic element matching (DEM) algorithm, referred to as rotated data Weighted Averaging (RDWA), is implemented in a third-order /spl Sigma//spl Delta/ digital-to-analog converter (DAC) with 64/spl times/ oversampling and a conversion bandwidth of 25 kHz. The systematic and random errors are considered in the design of the 14-bit converter. The /spl Sigma//spl Delta/ DAC is fabricated in a 2-/spl mu/m CMOS process and includes the on-chip reconstruction filter. The prototype was designed to test the performance of the DAC without DEM, with data Weighted Averaging (DWA), and with RDWA. The results show that the new RDWA algorithm is capable of achieving first-order noise shaping while eliminating the signal-dependent harmonic distortion present in DWA.
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a spurious free delta sigma dac using rotated data Weighted Averaging
Custom Integrated Circuits Conference, 1999Co-Authors: R Radke, A Eshraghi, T S FiezAbstract:A new dynamic element matching (DEM) algorithm, referred to as rotated data Weighted Averaging (RDWA), is implemented in a third-order three-bit delta-sigma DAC with 64 times oversampling and a conversion bandwidth of 25 kHz. The systematic and random errors are considered in the design of the 14-bit linear converter. The 2 /spl mu/m CMOS prototype was designed to test the performance of the DAC without DEM, with data Weighted Averaging (DWA), and with RDWA. The results show that the new RDWA algorithm is capable of achieving first-order noise shaping while eliminating the signal-dependent harmonic distortion even for DAC component mismatches as large as 15%.
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improved spl delta spl sigma dac linearity using data Weighted Averaging
International Symposium on Circuits and Systems, 1995Co-Authors: R T Baird, T S FiezAbstract:A new dynamic element matching (DEM) algorithm, referred to as data Weighted Averaging (DWA), is presented for improving DAC linearity in multi-bit delta-sigma (/spl Delta//spl Sigma/) data converters. It out-performs all previously described algorithms. By cycling through the elements of the DAC at a rate dependent on the input to the DAC, the elements of the DAC are exercised at the maximum rate possible. With this algorithm, the distortion spectra are shaped by first-order noise shaping which results in 9 dB/octave improvement in the converter dynamic range when DAC errors dominate the performance. Simulating a third-order 3-bit modulator with an oversampling ratio of 128 and 1% DAC element matching, 110 dB signal-to-noise ratio (18 bits) is achieved by employing DWA DEM.
R Radke - One of the best experts on this subject based on the ideXlab platform.
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a 14 bit current mode spl sigma spl delta dac based upon rotated data Weighted Averaging
IEEE Journal of Solid-state Circuits, 2000Co-Authors: R Radke, A Eshraghi, T S FiezAbstract:A new dynamic element matching (DEM) algorithm, referred to as rotated data Weighted Averaging (RDWA), is implemented in a third-order /spl Sigma//spl Delta/ digital-to-analog converter (DAC) with 64/spl times/ oversampling and a conversion bandwidth of 25 kHz. The systematic and random errors are considered in the design of the 14-bit converter. The /spl Sigma//spl Delta/ DAC is fabricated in a 2-/spl mu/m CMOS process and includes the on-chip reconstruction filter. The prototype was designed to test the performance of the DAC without DEM, with data Weighted Averaging (DWA), and with RDWA. The results show that the new RDWA algorithm is capable of achieving first-order noise shaping while eliminating the signal-dependent harmonic distortion present in DWA.
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a spurious free delta sigma dac using rotated data Weighted Averaging
Custom Integrated Circuits Conference, 1999Co-Authors: R Radke, A Eshraghi, T S FiezAbstract:A new dynamic element matching (DEM) algorithm, referred to as rotated data Weighted Averaging (RDWA), is implemented in a third-order three-bit delta-sigma DAC with 64 times oversampling and a conversion bandwidth of 25 kHz. The systematic and random errors are considered in the design of the 14-bit linear converter. The 2 /spl mu/m CMOS prototype was designed to test the performance of the DAC without DEM, with data Weighted Averaging (DWA), and with RDWA. The results show that the new RDWA algorithm is capable of achieving first-order noise shaping while eliminating the signal-dependent harmonic distortion even for DAC component mismatches as large as 15%.
A Eshraghi - One of the best experts on this subject based on the ideXlab platform.
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a 14 bit current mode spl sigma spl delta dac based upon rotated data Weighted Averaging
IEEE Journal of Solid-state Circuits, 2000Co-Authors: R Radke, A Eshraghi, T S FiezAbstract:A new dynamic element matching (DEM) algorithm, referred to as rotated data Weighted Averaging (RDWA), is implemented in a third-order /spl Sigma//spl Delta/ digital-to-analog converter (DAC) with 64/spl times/ oversampling and a conversion bandwidth of 25 kHz. The systematic and random errors are considered in the design of the 14-bit converter. The /spl Sigma//spl Delta/ DAC is fabricated in a 2-/spl mu/m CMOS process and includes the on-chip reconstruction filter. The prototype was designed to test the performance of the DAC without DEM, with data Weighted Averaging (DWA), and with RDWA. The results show that the new RDWA algorithm is capable of achieving first-order noise shaping while eliminating the signal-dependent harmonic distortion present in DWA.
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a spurious free delta sigma dac using rotated data Weighted Averaging
Custom Integrated Circuits Conference, 1999Co-Authors: R Radke, A Eshraghi, T S FiezAbstract:A new dynamic element matching (DEM) algorithm, referred to as rotated data Weighted Averaging (RDWA), is implemented in a third-order three-bit delta-sigma DAC with 64 times oversampling and a conversion bandwidth of 25 kHz. The systematic and random errors are considered in the design of the 14-bit linear converter. The 2 /spl mu/m CMOS prototype was designed to test the performance of the DAC without DEM, with data Weighted Averaging (DWA), and with RDWA. The results show that the new RDWA algorithm is capable of achieving first-order noise shaping while eliminating the signal-dependent harmonic distortion even for DAC component mismatches as large as 15%.
Ronald R Yager - One of the best experts on this subject based on the ideXlab platform.
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canonical form of ordered Weighted Averaging operators
Annals of Operations Research, 2020Co-Authors: Lesheng Jin, Radko Mesiar, Martin Kalina, Ronald R YagerAbstract:Discrete Ordered Weighted Averaging (OWA) operators as one of the most representative proposals of Yager (1988) have been widely used and studied in both theoretical and application areas. However, there are no effective and systematic corresponding methods for continuous input functions. In this study, using the language of measure (capacity) space we propose a Canonical Form of OWA operators which yield some common properties like Monotonicity and Idempotency and thus serve as a generalization of Discrete OWA operators. We provide also a representation of the Canonical Form by means of asymmetric Choquet integrals. The Canonical Form of OWA operators can effectively handle some input functions defined on ordered sets.
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extended and infinite ordered Weighted Averaging and sum operators with numerical examples
Iranian Journal of Fuzzy Systems, 2020Co-Authors: Zhen Wang, Radko Mesiar, Ronald R Yager, Jindong Qin, Xiangqian Feng, Lesheng JinAbstract:This study discusses some variants of Ordered WeightedAveraging (OWA) operators and related information aggregation methods. Indetail, we define the Extended Ordered Weighted Sum (EOWS) operator and theExtended Ordered Weighted Averaging (EOWA) operator, which are applied inscientometrics evaluation where the preference is over finitely manyrepresentative works. As contrast, we also define the Infinite OrderedWeighted Sum (InOWS) operator and the Infinite Ordered Weighted Averaging(InOWA) operator, which are more suitable for the correspondingscientometrics evaluation where all of works of scholars are considered. Wealso define the family of Infinite Gaussian maxitive OWA weights functionand the family of Infinite Gaussian OWA weights function, and discuss someof their mathematical properties. Some illustrative examples, comparisonsand figures are provided to better expound their applicability inscientometrics evaluation.
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the paradigm of induced ordered Weighted Averaging aggregation process with application in uncertain linguistic evaluation
Granular Computing, 2020Co-Authors: Lesheng Jin, Radko Mesiar, Ronald R YagerAbstract:Induced ordered Weighted Averaging is a powerful tool in decision making, and different inducing variables generally determine different types of IOWA. The existing studies and applications of IOWA often is non-systematical and decision makers may often be confused with several problems such as how to effectively and fast determine and obtain inducing variable, how to handle the situation where tied values appears for inducing values, and how to more flexibly use IOWA in real applications. In this study, to address those problems, we propose the paradigm of Induced Ordered Weighted Averaging aggregation process. The paradigm includes three major stages, information gathering and preparation, information determination, and information aggregation; and each of those stages also includes several detailed steps. An illustrative instance in journal peer reviewing and evaluating problem, including all detailed steps in the paradigm of IOWA process, is also presented.
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ordered Weighted Averaging aggregation on convex poset
IEEE Transactions on Fuzzy Systems, 2019Co-Authors: Lesheng Jin, Radko Mesiar, Ronald R YagerAbstract:Ordered Weighted Averaging (OWA) operators, a family of aggregation functions, are widely used in human decision-making schemes to aggregate data inputs of a decision maker's choosing through a process known as OWA aggregation. The weight allocation mechanism of OWA aggregation employs the principle of linear ordering to order data inputs after the input variables have been rearranged. Thus, OWA operators generally cannot be used to aggregate a collection of $n$ inputs obtained from any given convex partially ordered set (poset). This poses a problem since data inputs are often obtained from various convex posets in the real world. To address this problem, this paper proposes methods that practitioners can use in real-world applications to aggregate a collection of $n$ inputs from any given convex poset. The paper also analyzes properties related to the proposed methods, such as monotonicity and Weighted OWA aggregation on convex posets.
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recent developments in the ordered Weighted Averaging operators theory and practice
Recent developments in the ordered weighted averaging operators : theory and practice, 2011Co-Authors: Ronald R Yager, Janusz Kacprzyk, Gleb BeliakovAbstract:This volume presents the state of the art of new developments, and some interesting and relevant applications of the OWA (ordered Weighted Averaging) operators. The OWA operators were introduced in the early 1980s by Ronald R. Yager as a conceptually and numerically simple, easily implementable, yet extremely powerful general aggregation operator. That simplicity, generality and implementability of the OWA operators, combined with their intuitive appeal, have triggered much research both in the foundations and extensions of the OWA operators, and in their applications to a wide variety of problems in various fields of science and technology.Part I: Methods includes papers on theoretical foundations of OWA operators and their extensions. The papers in Part II: Applications show some more relevant applications of the OWA operators, mostly means, as powerful yet general aggregation operators. The application areas are exemplified by environmental modeling, social networks, image analysis, financial decision making and water resource management.
Steve Juggins - One of the best experts on this subject based on the ideXlab platform.
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Weighted Averaging partial least squares regression wa pls an improved method for reconstructing environmental variables from species assemblages
Hydrobiologia, 1993Co-Authors: C J F Ter Braak, Steve JugginsAbstract:Weighted Averaging regression and calibration form a simple, yet powerful method for reconstructing environmental variables from species assemblages. Based on the concepts of niche-space partitioning and ecological optima of species (indicator values), it performs well with noisy, species-rich data that cover a long ecological gradient (>3 SD units). Partial least squares regression is a linear method for multivariate calibration that is popular in chemometrics as a robust alternative to principal component regression. It successively selects linear components so as to maximize predictive power. In this paper the ideas of the two methods are combined. It is shown that the Weighted Averaging method is a form of partial least squares regression applied to transformed data that uses the first PLS-component only. The new combined method, ast squares, consists of using further components, namely as many as are useful in terms of predictive power. The further components utilize the residual structure in the species data to improve the species parameters (‘optima’) in the final Weighted Averaging predictor. Simulations show that the new method can give 70% reduction in prediction error in data sets with low noise, but only a small reduction in noisy data sets. In three real data sets of diatom assemblages collected for the reconstruction of acidity and salinity, the reduction in prediction error was zero, 19% and 32%.
