The Experts below are selected from a list of 385698 Experts worldwide ranked by ideXlab platform
Rongrong Wang - One of the best experts on this subject based on the ideXlab platform.
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a null space analysis of the l1 Synthesis Method in dictionary based compressed sensing
Applied and Computational Harmonic Analysis, 2014Co-Authors: Xuemei Chen, Haichao Wang, Rongrong WangAbstract:Abstract An interesting topic in compressed sensing aims to recover signals with sparse representations in a dictionary. Recently the performance of the l 1 -analysis Method has been a focus, while some fundamental problems for the l 1 -Synthesis Method are still unsolved. For example, what are the conditions for it to stably recover compressible signals under noise? Do coherent dictionaries allow the existence of sensing matrices that guarantee good performances of the l 1 -Synthesis Method? To answer these questions, we build up a framework for the l 1 -Synthesis Method. In particular, we propose a dictionary-based null space property (D-NSP) which, to the best of our knowledge, is the first sufficient and necessary condition for the success of l 1 -Synthesis without measurement noise. With this new property, we show that when the dictionary D is full spark, it cannot be too coherent otherwise the l 1 -Synthesis Method fails for all sensing matrices. We also prove that in the real case, D-NSP is equivalent to the stability of l 1 -Synthesis under noise.
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L1 Synthesis Method in compressed sensing
2013Co-Authors: Xuemei Chen, Haichao Wang, Rongrong WangAbstract:An interesting topic in compressed sensing aims to recover signals with sparse representations in a dictionary. Recently the performance of the L1-analysis Method has been a focus, while some fundamental problems for the L1-Synthesis Method are still unsolved. For example, what are the conditions for it to stably recover compressible signals under noise? Whether coherent dictionaries allow the existence of sensing matrices that guarantee good performances of the L1-Synthesis Method? To answer these questions, we build up a framework for the L1-Synthesis Method. In particular, we propose a dictionary-based null space property DNSP which, to the best of our knowledge, is the first sufficient and necessary condition for the success of L1-Synthesis without measurement noise. With this new property, we show that when the dictionary D is full spark, it cannot be too coherent otherwise the Method fails for all sensing matrices. We also prove that in the real case, DNSP is equivalent to the stability of L1-Synthesis under noise.
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a null space analysis of the l1 Synthesis Method in dictionary based compressed sensing
arXiv: Information Theory, 2013Co-Authors: Xuemei Chen, Haichao Wang, Rongrong WangAbstract:An interesting topic in compressed sensing aims to recover signals with sparse representations in a dictionary. Recently the performance of the L1-analysis Method has been a focus, while some fundamental problems for the L1-Synthesis Method are still unsolved. For example, what are the conditions for it to stably recover compressible signals under noise? Whether coherent dictionaries allow the existence of sensing matrices that guarantee good performances of the L1-Synthesis Method? To answer these questions, we build up a framework for the L1-Synthesis Method. In particular, we propose a dictionary-based null space property DNSP which, to the best of our knowledge, is the first sufficient and necessary condition for the success of L1-Synthesis without measurement noise. With this new property, we show that when the dictionary D is full spark, it cannot be too coherent otherwise the Method fails for all sensing matrices. We also prove that in the real case, DNSP is equivalent to the stability of L1-Synthesis under noise.
Li Jianhua - One of the best experts on this subject based on the ideXlab platform.
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Characterization of ternary (Na_0.5K_0.5)_1−x Li_ x NbO_3 lead-free piezoelectric ceramics prepared by molten salt Synthesis Method
Journal of Materials Science, 2011Co-Authors: Li JianhuaAbstract:(Na_0.5K_0.5)_1− x Li_ x NbO_3 powders and ceramics were prepared by molten salt Synthesis Method. Pure perovskite-phase powder was obtained at a low temperature of 740 °C with a grain size of below 800 nm. The effects of the LiNbO_3 on phase transition, microstructure, electrical properties, and temperature stability were investigated. A morphotropic phase boundary was identified. The scanning electron microscopy indicated that the (Na_0.5K_0.5)_1− x Li_ x NbO_3 powders and ceramics obtained by the molten salt Synthesis Method have a relatively uniform particle size and microstructure. The results indicate that these materials are promising candidates for lead-free piezoelectric ceramics for practical applications.
Dong Jianning - One of the best experts on this subject based on the ideXlab platform.
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Error analysis on modal Synthesis Method based on CAE
Computer-Aided Engineering, 2012Co-Authors: Dong JianningAbstract:To study the error of NVH analysis on front-end model of engine cabin using modal Synthesis Method,the modes under the cutoff frequencies including 100,200,300,1 000,2 000 and 3 000 Hz are respectively calculated by whole structure calculation and modal Synthesis Methods.Based on these modal calculation results,the driving point transfer function is calculated.The results show that the calculation error of the modal Synthesis Method with residual compensation is lower than that without residual compensation;when the modal calculation cut-off frequency of substructures is twice of the frequency of driving point transfer function,the calculation error of modal Synthesis Method is below 10%;the error can be even controlled less than 5% if the frequency of transfer function is one-third of cut-off frequency.
Feng Gang - One of the best experts on this subject based on the ideXlab platform.
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Symbolic Synthesis Method for Reversible Circuits
Journal of Chinese Computer Systems, 2009Co-Authors: Feng GangAbstract:In this paper,we describe a Synthesis Method based on matrix model and symbolic algebra for reversible circuits considering multiple optimization objectives,including area,delay and crosstalk.We have tested the proposed algorithm on a set of the reversible benchmark circuits.Compared with existing Synthesis Methods,this heuristic reduces crosstalk and path delay by 5-20%.The improvements make Synthesis specially important for high performance and a large number of inputs and outputs designs.
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Considering Crosstalk Symbolic Synthesis Method for Reversible Circuits
2008Co-Authors: Feng GangAbstract:Presently existing Synthesis Methods for reversible circuits were applicable only to reversible circuits with small numbers of inputs and outputs and had neglected the impact of path delay,which could not meet the complex design.The heuristic Synthesis algorithm is presented in this paper.Based on matrix model and symbolic algebra,this paper offers a symbolic Synthesis Method and performs delay and crosstalk optimization simultaneously.Using the cost function the Method steers the Synthesis process,which considers multiple optimization objectives,including area,delay and crosstalk.The proposed algorithm was tested on a set of reversible benchmarks.Compared with existing Synthesis Methods,the algorithm reduces crosstalk by 10.3% and path delay by 5-20%.It has obvious advantages for CPU time and memory cost.The Method can handle large scale reversible circuits in reasonable time.
Xuemei Chen - One of the best experts on this subject based on the ideXlab platform.
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a null space analysis of the l1 Synthesis Method in dictionary based compressed sensing
Applied and Computational Harmonic Analysis, 2014Co-Authors: Xuemei Chen, Haichao Wang, Rongrong WangAbstract:Abstract An interesting topic in compressed sensing aims to recover signals with sparse representations in a dictionary. Recently the performance of the l 1 -analysis Method has been a focus, while some fundamental problems for the l 1 -Synthesis Method are still unsolved. For example, what are the conditions for it to stably recover compressible signals under noise? Do coherent dictionaries allow the existence of sensing matrices that guarantee good performances of the l 1 -Synthesis Method? To answer these questions, we build up a framework for the l 1 -Synthesis Method. In particular, we propose a dictionary-based null space property (D-NSP) which, to the best of our knowledge, is the first sufficient and necessary condition for the success of l 1 -Synthesis without measurement noise. With this new property, we show that when the dictionary D is full spark, it cannot be too coherent otherwise the l 1 -Synthesis Method fails for all sensing matrices. We also prove that in the real case, D-NSP is equivalent to the stability of l 1 -Synthesis under noise.
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L1 Synthesis Method in compressed sensing
2013Co-Authors: Xuemei Chen, Haichao Wang, Rongrong WangAbstract:An interesting topic in compressed sensing aims to recover signals with sparse representations in a dictionary. Recently the performance of the L1-analysis Method has been a focus, while some fundamental problems for the L1-Synthesis Method are still unsolved. For example, what are the conditions for it to stably recover compressible signals under noise? Whether coherent dictionaries allow the existence of sensing matrices that guarantee good performances of the L1-Synthesis Method? To answer these questions, we build up a framework for the L1-Synthesis Method. In particular, we propose a dictionary-based null space property DNSP which, to the best of our knowledge, is the first sufficient and necessary condition for the success of L1-Synthesis without measurement noise. With this new property, we show that when the dictionary D is full spark, it cannot be too coherent otherwise the Method fails for all sensing matrices. We also prove that in the real case, DNSP is equivalent to the stability of L1-Synthesis under noise.
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a null space analysis of the l1 Synthesis Method in dictionary based compressed sensing
arXiv: Information Theory, 2013Co-Authors: Xuemei Chen, Haichao Wang, Rongrong WangAbstract:An interesting topic in compressed sensing aims to recover signals with sparse representations in a dictionary. Recently the performance of the L1-analysis Method has been a focus, while some fundamental problems for the L1-Synthesis Method are still unsolved. For example, what are the conditions for it to stably recover compressible signals under noise? Whether coherent dictionaries allow the existence of sensing matrices that guarantee good performances of the L1-Synthesis Method? To answer these questions, we build up a framework for the L1-Synthesis Method. In particular, we propose a dictionary-based null space property DNSP which, to the best of our knowledge, is the first sufficient and necessary condition for the success of L1-Synthesis without measurement noise. With this new property, we show that when the dictionary D is full spark, it cannot be too coherent otherwise the Method fails for all sensing matrices. We also prove that in the real case, DNSP is equivalent to the stability of L1-Synthesis under noise.
