4025720
Pricing options under jump diffusion processes with fitted finite volume method
This paper develops a numerical method for a partial integro-differential equation and a partial integro-differential complementarity problem arising from European and American options valuations respectively when the underlying assets are driven by a jump diffusion process. The method is based on a fitted finite volume scheme for the spatial discretization and the Crank–Nicolson scheme for the time discretization. The fully discretized system is solved by an iterative method coupled with an FFT for the evaluation of the discretized integral term, while the constraint in the American option model is imposed by adding a penalty term to the original partial integro-differential complementarity problem. We show that the system matrix of the discretized system is an M-matrix and propose an algorithm for solving the discretized system. Numerical experiments are implemented to show the efficiency and robustness of this method.
5848822
Soft decoder architecture of LT codes
Luby transform (LT) codes, as the first class of efficient rateless codes, attract a lot of attention in the coding theory field. However, the VLSI implementation of LT codes is challenging due to its random code construction characteristic as well as the flexible output length. In this paper, we present an applicable architecture of a soft-decision LT decoder with a block length of 1024 bits and 100 iterations. Partly parallel input node processing and output node processing techniques are both adopted to accelerate decoding speed. An efficient router and reverse router are designed to indicate the graphic connectivity between input nodes and output nodes. The parallel architecture is prototyped on the target FPGA device.
15937167
High-throughput layered decoder implementation for quasi-cyclic LDPC codes
This paper presents a high-throughput decoder design for the Quasi-Cyclic (QC) Low-Density Parity-Check (LDPC) codes. Two new techniques are proposed, including parallel layered decoding architecture (PLDA) and critical path splitting. PLDA enables parallel processing for all layers by establishing dedicated message passing paths among them. The decoder avoids crossbar-based large interconnect network. Critical path splitting technique is based on articulate adjustment of the starting point of each layer to maximize the time intervals between adjacent layers, such that the critical path delay can be split into pipeline stages. Furthermore, min-sum and loosely coupled algorithms are employed for area efficiency. As a case study, a rate-1/2 2304-bit irregular LDPC decoder is implemented using ASIC design in 90 nm CMOS process. The decoder can achieve the maximum decoding throughput of 2.2 Gbps at 10 iterations. The operating frequency is 950 MHz after synthesis and the chip area is 2.9 mm2.
1961988
An Area-Efficient LDPC Decoder Architecture and Implementation for CMMB Systems
This paper presents an area-efficient LDPC decoder architecture for the China Multimedia Mobile Broadcasting (CMMB) standard. Several techniques are adopted to reduce memory size, including the min-sum algorithm (MSA), optimal bit-width quantization of the iterative messages and reduced complexity for the interconnect network. The decoder for the rate-1/2 9216-bit code is implemented using the 90nm 1.0V CMOS technology. It achieves the decoding throughput of 48Mbps at 5 iterations when operating at 60MHz and the power dissipation is only 34mW.
31414473
Improved Learning Algorithm Based on Generalized SOM for Dynamic Non-linear System
This paper proposes an improved learning algorithm based on generalized SOM for dynamical non-linear system identification. To improve the convergent speed and the accuracy of SOM algorithm, we propose the improved self-organizing algorithm, which, at first, applies the multiple local models instead of the global model, and secondly, adjusts the weights of the computing output layers along with the weights of the competing neurons layer during the training process. We prove that the improved algorithm is convergent if the network has suitable initial weights and small positive real parameters. The simulation results using our improved generalized SOM show an improvement for non-linear system compared to traditional neural network control systems.
5878411
Density-Weighted Nystrm Method for Computing Large Kernel Eigensystems
The Nystrm method is a well-known sampling-based technique for approximating the eigensystem of large kernel matrices. However, the chosen samples in the Nystrm method are all assumed to be of equal importance, which deviates from the integral equation that defines the kernel eigenfunctions. Motivated by this observation, we extend the Nystrm method to a more general, density-weighted version. We show that by introducing the probability density function as a natural weighting scheme, the approximation of the eigensystem can be greatly improved. An efficient algorithm is proposed to enforce such weighting in practice, which has the same complexity as the original Nystrm method and hence is notably cheaper than several other alternatives. Experiments on kernel principal component analysis, spectral clustering, and image segmentation demonstrate the encouraging performance of our algorithm.
13407923
The Application of Two-Dimensional Image Reconstruction for Relics' Preservation and Administration
The purpose of this paper is to build a relics database using for preservation and administration for relics. The paper research on the two-dimensional reconstruction images store in the relics database. The reconstruction images of relics can be used to observe and analyze the antique inside that the eye has no way to distinguish without disruption. This paper have make experiments with six relic samples. By using the industrial computed-tomography (CT) obtain the CT scan data of the six relics samples of different materials and after handling the real data, the experiment and analysis of CT image reconstruction have been carried out. The reconstruction images by SIRT algorithm can clearly indicate the internal structure of the relics. This paper accomplished the comparison of image reconstruction by SIRT algorithm with different iterative relaxation coefficient λ values. This job can provide the CT images data of relics for the relics database. The experiments can provide support for preservation and administration of relics and provide information for teaching and learning activities of cultural studies.
4342705
Weighted Ontology-Based Search Exploiting Semantic Similarity
This paper is concerned with the problem of semantic search. By semantic search, we mean searching for instances from knowledge base. Given a query, we are to retrieve ‘relevant’ instances, including those that contain the query keywords and those that do not contain the keywords. This is contrast to the traditional approaches of generating a ranked list of documents that contain the keywords. Specifically, we first employ keyword based search method to retrieve instances for a query; then a proposed method of semantic feedback is performed to refine the search results; and then we conduct re-retrieval by making use of relations and instance similarities. To make the search more effective, we use weighted ontology as the underlying data model in which importances are assigned to different concepts and relations. As far as we know, exploiting instance similarities in search on weighted ontology has not been investigated previously. For the task of instance similarity calculation, we exploit both concept hierarchy and properties. We applied our methods to a software domain. Empirical evaluation indicates that the proposed methods can improve the search performance significantly.
1252619
A fast approximate algorithm for training L1-SVMs in primal space
We propose a novel and fast algorithm to train support vector machines (SVMs) in primal space, which solves an approximate optimization of SVMs with the properties of unconstraint, continuity and twice differentiability by utilizing the Newton optimization technique. Further, we devise a special pre-extracting procedure to speed up the convergence of the algorithm by resorting to a high-quality initial solution. Theoretical studies show that the proposed algorithm produces an @?-approximate solution to standard SVMs and maintains low computational complexity. Experimental results on benchmark data sets demonstrate that our algorithm is much faster than the dual based method such as SVM^l^i^g^h^t while it achieves the similar test accuracy.