This paper presents an optimized 3-D Discrete Wavelet Transform (3-DDWT) architecture. 1-DDWT employed for the design of 3-DDWT architecture uses reduced lifting scheme approach. Further the architecture is optimized ...This paper presents an optimized 3-D Discrete Wavelet Transform (3-DDWT) architecture. 1-DDWT employed for the design of 3-DDWT architecture uses reduced lifting scheme approach. Further the architecture is optimized by applying block enabling technique, scaling, and rounding of the filter coefficients. The proposed architecture uses biorthogonal (9/7) wavelet filter. The architecture is modeled using Verilog HDL, simulated using ModelSim, synthesized using Xilinx ISE and finally implemented on Virtex-5 FPGA. The proposed 3-DDWT architecture has slice register utilization of 5%, operating frequency of 396 MHz and a power consumption of 0.45 W.展开更多
Due to advantages in solid modeling with complex geometry and its ideal suitability for 3D printing,the implicit representation has been widely used in recent years.The demand for free-form shapes makes the implicit t...Due to advantages in solid modeling with complex geometry and its ideal suitability for 3D printing,the implicit representation has been widely used in recent years.The demand for free-form shapes makes the implicit tensor-product B-spline representation attract more and more attention.However,it is an important challenge to deal with the storage and transmission requirements of enormous coefficient ten-sor.In this paper,we propose a new compression framework for coefficient tensors of implicit 3D tensor-product B-spline solids.The proposed compression algorithm consists of four steps,i.e.,preprocessing,block splitting,using a lifting-based 3D discrete wavelet transform,and coding with 3D set partitioning in hierarchical trees algorithm.Finally,we manage to lessen the criticism of the implicit tensor-product B-spline representation of surface for its redundancy store of 3D coefficient tensor.Experimental results show that the proposed compression framework not only achieves satisfactory reconstruction quality and considerable compression ratios,but also sup-ports progressive transmissions and random access by employing patch-wise coding strategy.展开更多
Advances in the technology of astronomical spectra acquisition have resulted in an enormous amount of data available in world-wide telescope archives. It is no longer feasible to analyze them using classical approache...Advances in the technology of astronomical spectra acquisition have resulted in an enormous amount of data available in world-wide telescope archives. It is no longer feasible to analyze them using classical approaches, so a new astronomical discipline,astroinformatics, has emerged. We describe the initial experiments in the investigation of spectral line profiles of emission line stars using machine learning with attempt to automatically identify Be and B[e] stars spectra in large archives and classify their types in an automatic manner. Due to the size of spectra collections, the dimension reduction techniques based on wavelet transformation are studied as well. The result clearly justifies that machine learning is able to distinguish different shapes of line profiles even after drastic dimension reduction.展开更多
文摘This paper presents an optimized 3-D Discrete Wavelet Transform (3-DDWT) architecture. 1-DDWT employed for the design of 3-DDWT architecture uses reduced lifting scheme approach. Further the architecture is optimized by applying block enabling technique, scaling, and rounding of the filter coefficients. The proposed architecture uses biorthogonal (9/7) wavelet filter. The architecture is modeled using Verilog HDL, simulated using ModelSim, synthesized using Xilinx ISE and finally implemented on Virtex-5 FPGA. The proposed 3-DDWT architecture has slice register utilization of 5%, operating frequency of 396 MHz and a power consumption of 0.45 W.
基金Thework is supported by theNSFof China(No.11771420)the Fundamental Research Funds for the Central Universities(WK 001046003).
文摘Due to advantages in solid modeling with complex geometry and its ideal suitability for 3D printing,the implicit representation has been widely used in recent years.The demand for free-form shapes makes the implicit tensor-product B-spline representation attract more and more attention.However,it is an important challenge to deal with the storage and transmission requirements of enormous coefficient ten-sor.In this paper,we propose a new compression framework for coefficient tensors of implicit 3D tensor-product B-spline solids.The proposed compression algorithm consists of four steps,i.e.,preprocessing,block splitting,using a lifting-based 3D discrete wavelet transform,and coding with 3D set partitioning in hierarchical trees algorithm.Finally,we manage to lessen the criticism of the implicit tensor-product B-spline representation of surface for its redundancy store of 3D coefficient tensor.Experimental results show that the proposed compression framework not only achieves satisfactory reconstruction quality and considerable compression ratios,but also sup-ports progressive transmissions and random access by employing patch-wise coding strategy.
基金supported by Czech Science Foundation(No.GACR13-08195S)the project Central Register of Research Intentions CEZMSM0021630528 Security-oriented Research in Information Technology,the specific research(No.FIT-S-11-2)+2 种基金the project RVO:67985815the Technological agency of the Czech Republic(TACR)project V3C(No.TE01020415)Grant Agency of the Czech Republic-GACR P103/13/08195S
文摘Advances in the technology of astronomical spectra acquisition have resulted in an enormous amount of data available in world-wide telescope archives. It is no longer feasible to analyze them using classical approaches, so a new astronomical discipline,astroinformatics, has emerged. We describe the initial experiments in the investigation of spectral line profiles of emission line stars using machine learning with attempt to automatically identify Be and B[e] stars spectra in large archives and classify their types in an automatic manner. Due to the size of spectra collections, the dimension reduction techniques based on wavelet transformation are studied as well. The result clearly justifies that machine learning is able to distinguish different shapes of line profiles even after drastic dimension reduction.
