Invnet: A Deep Learning Approach To Invert Complex Deformation Fields
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Inverting a deformation field is a crucial part for numerous image registration methods and has an important impact on the final registration results. There are methods that work well for small and relatively simple deformations. However, a problem arises when the deformation field consists of complex and large deformations, potentially including folding. For such cases, the state-of-the-art methods fail and the inversion results are unpredictable. In this article, we propose a deep network using the encoder-decoder architecture to improve the inverse calculation. The network is trained using deformations randomly generated using various transformation models and their compositions, with a symmetric inverse consistency error as the cost function. The results are validated using synthetic deformations resembling real ones, as well as deformation fields calculated during registration of real histology data. We show that the proposed method provides an approximate inverse with a lower error than the current state-of-the-art methods.Keywords:
Image registration
By analyzing and researching the RS encoder in communication system,it is found the problems of complex circuit structure and low processing speed occurred to traditional RS encoder.Focused on the problems,a new encoder construction method was proposed.A RS(255,223) encoder with symmetric coefficients of generator polynomial was implemented under the Quartus7.0,and test,simulation and validation were conducted under Matlab and ModelSim.The simulation result indicates that the performance of the encoder is better,and the encoder is featured with high speed and low hardware complexity.
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A new theorem of inverse formula is introduced for a kind of infinite series. Thus some new results for important inverse problems in physics are presented in this paper. These are the inverse problems for obtaining the phonon density of states, the inverse blackbody radiation problem for remote sensing, and the solution for inverse Ewald summation. Of more importance, it shows the possibility of the application of number theory to physical problems.
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Abstract Quantum-dot Cellular Automata (QCA) is a transistor-less technology known for its low power consumption and higher clock rate. Serial Concatenated Convolutional Coding (SCCC) encoder is a class of forward error correction. This paper picturizes the implementation of the outer encoder as a (7, 4, 1) Bose Chaudhary Hocquenghem encoder that serves the purpose of burst error correction, a pseudo-random inter-leaver used for permuting of systematic code words and finally the inner encoder which is used for the correction of random errors in QCA. Two different architectures of the SCCC encoder have been proposed and discussed in this study. In the proposed two architectures, the first based on external clock signals whereas the second based on internal clock generation. The sub-blocks outer encoder, pseudo-random inter-leaver and inner encoder of the SCCC encoder are optimized, implemented and simulated using QCADesigner and then integrated to design a compact SCCC encoder. The energy dissipation is computed using QCADesigner-E. The proposed SCCC encoder reduced the total area by 46% and energy dissipation by 50% when compared to the reference SCCC encoder. The proposed encoders are more efficient in terms of cell count, energy dissipation and area occupancy respectively.
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Some examples of inverse problem approach of the phenomena related to materials are introduced. What is the inverse problem?, what could be obtained, identified or estimated by an inverse approach? are discussed based on the inverse processing of elastic waves. Some problems and uncectainities involved in inverse processing are also discussed.
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