The Experts below are selected from a list of 324 Experts worldwide ranked by ideXlab platform
Temkar N Ruckmongathan - One of the best experts on this subject based on the ideXlab platform.
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low power Techniques for gray shades in liquid crystal displays
IEEE\ OSA Journal of Display Technology, 2009Co-Authors: Temkar N RuckmongathanAbstract:The largest transition in the select waveform of successive Approximation Technique is eliminated to reduce power dissipation in liquid crystal displays. Power dissipation in the driver circuit is analyzed for gray scale images by considering three select sequences. They are compared and a new select sequence is proposed to achieve low power dissipation. An additional pulse is introduced in the select waveform to reduce peak amplitude of select and data waveforms to achieve a low supply voltage for the driver circuit.
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a successive Approximation Technique for displaying gray shades in liquid crystal displays lcds
IEEE Transactions on Image Processing, 2007Co-Authors: Temkar N RuckmongathanAbstract:A successive Approximation Technique that is based on the conventional line-by-line-addressing is proposed. A large number of gray shades can be displayed without flicker by using low-cost liquid crystal display drivers that are designed to drive the pixels to either ON or OFF states in bilevel displays
David M Fisher - One of the best experts on this subject based on the ideXlab platform.
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unilateral cleft lip repair anatomic subunit Approximation Technique
2021Co-Authors: Raymond Tse, David M FisherAbstract:This chapter deals with cleft lip repair according to the Fisher anatomic subunit Approximation approach.
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unilateral cleft lip repair an anatomical subunit Approximation Technique
Plastic and Reconstructive Surgery, 2005Co-Authors: David M FisherAbstract:Background:A Technique of unilateral cleft lip repair is described. The repair draws from a variety of previously described repairs and adheres to a concept of anatomical subunits of the lip. Cases from within the spectrum of the deformity have been chosen from a series of 144 consecutive cases to d
David P Williamson - One of the best experts on this subject based on the ideXlab platform.
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a general Approximation Technique for constrained forest problems
Symposium on Discrete Algorithms, 1992Co-Authors: Michel X Goemans, David P WilliamsonAbstract:We present a general Approximation Technique for a large class of graph problems. Our Technique mostly applies to problems of covering, at minimum cost, the vertices of a graph with trees, cycles or paths satisfying certain requirements. In particular, many basic combinatorial optimization problems fit in this framework, including the shortest path, minimum spanning tree, minimum-weight perfect matching, traveling salesman and Steiner tree problems.Our Technique produces Approximation algorithms that run in O(n2 log n) time and come within a factor of 2 of optimal for most of these problems. For instance, we obtain a 2-Approximation algorithm for the minimum-weight perfect matching problem under the triangle inequality. Our running time of O(n2 log n) time compares favorably with the best strongly polynomial exact algorithms running in O(n3) time for dense graphs. A similar result is obtained for the 2-matching problem and its variants.We also derive the first Approximation algorithms for many NP-complete problems, including the non-fixed point-to-point connection problem, the exact path partitioning problem and complex location-design problems. Moreover, for the prize-collecting traveling salesman or Steiner tree problems, we obtain 2-Approximation algorithms, therefore improving the previously best-known performance guarantees of 2.5 and 3, respectively [4].
Martin Branda - One of the best experts on this subject based on the ideXlab platform.
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sample Approximation Technique for mixed integer stochastic programming problems with expected value constraints
Optimization Letters, 2014Co-Authors: Martin BrandaAbstract:This paper deals with the theory of sample Approximation Techniques applied to stochastic programming problems with expected value constraints. We extend the results of Branda (Optimization 61(8):949–968, 2012c) and Wang and Ahmed (Oper Res Lett 36:515–519, 2008) on the rates of convergence to the problems with a mixed-integer bounded set of feasible solutions and several expected value constraints. Moreover, we enable non-iid sampling and consider Holder-calmness of the constraints. We derive estimates on the sample size necessary to get a feasible solution or a lower bound on the optimal value of the original problem using the sample Approximation. We present an application of the estimates to an investment problem with the Conditional Value at Risk constraints, integer allocations and transaction costs.
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sample Approximation Technique for mixed integer stochastic programming problems with several chance constraints
Operations Research Letters, 2012Co-Authors: Martin BrandaAbstract:Abstract The paper deals with sample Approximation applied to stochastic programming problems with chance constraints. We extend results on rates of convergence for problems with mixed-integer bounded sets of feasible solutions and several chance constraints. We derive estimates on the sample size necessary to get a feasible solution of the original problem using sample Approximation. We present an application to a vehicle routing problem with time windows, random travel times, and random demand.
Xinquan Lai - One of the best experts on this subject based on the ideXlab platform.
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oversampling successive Approximation Technique for mems differential capacitive sensor
IEEE Journal of Solid-state Circuits, 2018Co-Authors: Longjie Zhong, Xinquan LaiAbstract:This paper proposed an oversampling successive Approximation (OSSA) Technique to build switched-capacitor capacitance-to-voltage convertor (SC-CVC) for readout circuit of MEMS differential capacitive sensor. The readout circuit employing the OSSA Technique has significantly improved resistance to common-mode parasitic capacitance of the input terminal of the readout circuit. In the OSSA readout circuit, there are five main non-ideal characteristics: holding error, recovery degradation, increment degradation, rise-edge degradation, and charge injection which reduce the accuracy and the settling time of the circuit. These problems are explained in detail and their solutions are given in this paper. The OSSA readout circuit is fabricated in a commercial 0.18- $\mu \text{m}$ BCD process. To show the improvement evidently, a reported traditional readout circuit is also reproduced and fabricated using the same process. Compared with the traditional readout circuit, the proposed readout circuit reduces the effect of common-mode parasitic capacitance on the accuracy of SC-CVC by more than 23.8 dB, power dissipation by 69.3%, and die area by 50%.
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Differential Capacitive Readout Circuit Using Oversampling Successive Approximation Technique
IEEE Transactions on Circuits and Systems I: Regular Papers, 2018Co-Authors: Longjie Zhong, Xinquan Lai, Hongjiang SongAbstract:This paper designs a close loop $\Sigma - \Delta $ readout circuit for differential MEMS accelerometer. A Technique named oversampling successive Approximation (OSA) is employed to build basic amplifiers and integrators. This Technique can largely reduce the gain error and thus low gain amplifier such as single stage amplifier is allowed to be used. As a result, the power consumption and chip area are reduced. However, the OSA-based amplifiers and integrators are vulnerable to the interference caused by charge injection and leakage current from the specific MOSFET switches. This drawback is analyzed in detail and the interference suppressing solutions are given. The OSA-based readout circuit is fabricated in a commercial 0.18 $\mu \text{m}$ BCD process. The measurement results show that the interference is reduced by 20 dB in the circuit with interference suppressing solutions compared with the circuit without interference suppressing solutions. And the noise floor is 24 $\mu \text{g}$ /rtHz. The readout circuit achieves a 0.07% gain error with a low power consumption of 0.5 mW and 9 MHz sampling rate.