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81st IEEE Vehicular Technology Conference (VTC Spring)

作者： Wang, Yanbo Guo, Caili Sun, Xuekang Feng, Chunyan Beijing Univ Posts & Telecommun Sch Informat & Commun Engn Beijing 100876 Peoples R China

ISBN: (纸本)9781479980888

Compressed spectrum sensing (CSS) is proposed to detect spectrum opportunities efficiently over a wideband. However, most of existing CSS approaches will cause high computation costs for signal recovery when spectrum bandwidth goes large. As a result, it prolongs time for spectrum detection, which however runs counter to the original purpose of finding out spectrum opportunities over a wideband as rapidly as possible. To reduce the time consumed in signal reconstruction and realize real-time detection, we propose a novel decomposition compressed spectrum sensing (D-CSS) scheme. In D-CSS, a sparse sampling matrix is constructed first, and then it equivalently means a decomposition of the reconstructing process into two recovery subtasks. In doing so, we can scale down the overall problem and reduce the entire time for wideband spectrum detection compared with current CSS methods for a given desired sensing accuracy. Furthermore, the sparse character of our designed sampling matrix not only facilitates the operations of signal sampling and signal recovery, but also relieves the burden on random seeds generator and memory storage, which alleviates the overall implementation cost in CR practice.

关键词： Compressed spectrum sensing time-efficient signal recovery time D-CSS computation complexity

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EFFICIENT DISCRETE FRACTIONAL HIRSCHMAN OPTIMAL TRANSFORM AND ITS APPLICATION

EFFICIENT DISCRETE FRACTIONAL HIRSCHMAN OPTIMAL TRANSFORM AN...

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IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Hsue, Wen-Liang Pei, Soo-Chang Ding, Jian-Jiun Chung Yuan Christian Univ Dept Elect Engn Chungli Taiwan

ISBN: (纸本)9781457705397

All of the existing N-point discrete fractional signal transforms require O(N-2) computation complexity. In this paper, we propose a new discrete fractional signal transform whose computation complexity can be reduced to O(N-1.5). This new transform is a fractional version of a DFT-based signal transform called as the Hirschman optimal transform (HOT) in the literature. Eigenvalues and eigenvectors properties of the HOT are also developed. Moreover, the proposed discrete fractional HOT transform is extended to further reduce the required computation complexity to linear order O(N). As an application example, we apply this new computationally efficient discrete fractional signal transform to encrypt digital images.

关键词： Fractional Fourier transform discrete fractional Fourier transform computation complexity DFT Hirschman optimal transform

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Belief Function Model for Reliable Optimal Set Estimation of Transition Matrices in Discounted Infinite-Horizon Markov Decision Processes

Belief Function Model for Reliable Optimal Set Estimation of...

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International Joint Conference on Neural Networks (IJCNN)

作者： Li, Baohua Si, Jennie Univ Arkansas Arkansas Inst Nanomat Sci & Engn Fayetteville AR 72701 USA Arizona State Univ Dept Elect Engn Tempe AZ 85287 USA

ISBN: (纸本)9781424496365

We study finite-state, finite-action, discounted infinite-horizon Markov decision processes with uncertain correlated transition matrices in deterministic policy spaces. To efficiently implement an approximate robust policy iteration algorithm for computing a robust optimal or near-optimal policy, a reliable and tight set estimate of the parameters of the transition matrix is needed in advance. However, observation samples on state transitions may be small. Prior information on the parameter space may be incomplete or unavailable. In such cases, a commonly used maximum a posterior (MAP) model may not provide a reliable optimal set estimate of the parameters. In this paper, using the advantages of Dempster-Shafer theory of evidence over Bayesian theory, a belief function model is proposed based on minimizing the cardinality of a set estimate. This new model can give a more reliable optimal solution to cover the true parameters than the MAP model. It degenerates to the MAP model when prior information on the parameter space is complete or prior information is unavailable but observation samples on state transitions are large enough. Moreover, we create a concept of principle components to characterize large observation samples so that both models result in the same reliable and tight results. The computation complexity of the new model is also discussed.

关键词： Approximation algorithms Bayesian methods Bayesian theory computational modeling Dempster-Shafer theory Markov processes Markov processes Probability distribution Robustness approximate robust policy iteration algorithm belief function model belief networks computation complexity computational complexity decision theory discounted infinite-horizon Markov decision processes inference mechanisms maximum a posterior model maximum likelihood estimation optimal set estimation transition matrices uncertain correlated transition matrices

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Grid based method for computing DCT coefficients in arbitrary rectangle region

Grid based method for computing DCT coefficients in arbitrar...

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4th International Symposium on Information Science and Engineering (ISISE)

作者： Lin Xiao Xiao Guoqiang Southwest Univ Comp & Informat Sci Dept Chongqing Peoples R China

ISBN: (纸本)9781467356800;9780769549514

At present, almost all digital images are transferred to the compressed format for storing and communication reason, in which the discrete cosine transform (DCT) based compression method remains an effective and important way for image compression because of the effort from JPEG. It is considerable to introduce image processing techniques such as feature extraction, pattern recognition and image indexing to the compressed image domain. In these image processing techniques, they all involve operations on arbitrary regions in images. So it is critical to find an effective way to calculate the DCT coefficients in arbitrary regions. To this end, we propose a grid based approach to find the most suitable relationship between DCT coefficients in 8*8 macro-blocks and arbitrary rectangle region. Experiment result reveals that the proposed method has a lower computational complexity than the butterfly algorithm (BA) when computing the DCT coefficients in arbitrary rectangle region.

关键词： 2D-DCT grid arbitrary-recetangle computation complexity

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Strong core and Pareto-optimality in the multiple partners matching problem under lexicographic preference domains

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GAMES AND ECONOMIC BEHAVIOR 2024年 145卷 217-238页

作者： Biro, Peter Csaji, Gergely HUN REN KRTK KTI Budapest Hungary Corvinus Univ Budapest Hungary Eotvos Lorand Univ Budapest Hungary

We study strong core and Pareto-optimal solutions for multiple partners matching problem under lexicographic preference domains from a computational point of view. The restriction to the two-sided case is called stable many -to -many matching problem and the general one-sided case is called stable fixtures problem. We provide an example to show that the strong core can be empty even for many -to -many problems, and that deciding the non -emptiness of the strong core is NP -hard. On the positive side, we give efficient algorithms for finding a near feasible strong core solution and for finding a fractional matching in the strong core of fractional matchings. In contrast with the NP -hardness result for the stable fixtures problem, we show that finding a maximum size matching that is Pareto-optimal can be done efficiently for many -to -many problems. Finally, we show that for reverse -lexicographic preferences the strong core is always non -empty in the many -to -many case.

关键词： Stable matchings Many-to-many matching Lexicographic preferences Core computation complexity

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computational aspects of deep learning models for detection of eye retina abnormalities

Computational aspects of deep learning models for detection ...

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Conference on Real-Time Image Processing and Deep Learning

作者： Akil, M. Elloumi, Y. Kachouri, R. Univ Gustave Eiffel LIGM CNRS ESIEE Paris F-77454 Marne La Vallee France Univ Monastir Fac Med LabTIM Monastir Tunisia

ISBN: (纸本)9781510635807

Glaucoma, Cataract, Age-related macular degeneration, (AMD) Diabetic retinopathy (DR) are among the leading retinal diseases. Thus, there is an active effort to create and develop methods to automate screening of retinal diseases. Many CAD (Computer Aided Diagnosis) systems have been expanded and are widely used for ocular diseases. Recently, Deep Neural Networks (DNNs) have been adopted in ophthalmology and applied to fundus images, achieving detection of retinal abnormalities using retinal images. There are essentially two approaches, the first one is based on hybrid method that employs image processing for preprocessing, features extraction and post processing and Deep Neural Network (DNN) is only used for classification. The second is the fully method where DNN is used for both feature extraction and classification. Several DNN models and their variants have been proposed such as AlexNet, VGG, GoogleNet, Inception, U-Net, Residual Net (ResNet), DenseNet for detection of eye retina abnormalities. The aim of this work is to provide the background and the methodology to conduct a benchmarking analysis including the computational aspects and analysis of the representative DNNs proposed in the state of the art for detection DR diseases. For each DNN different characteristics and some performance indices (i.e. model complexity, computation complexity, inference time, memory use) and detection disease performance (i.e. accuracy rate), must be taking into account to find the more accurate model. The public domain datasets used for training and testing the DNN models such as Kaggle, MESSIDOR, and EyePACS are outlined and analyzed in particular in DR detection.

关键词： Deep Neural Networks automated screening and detection of DR disease computation complexity performance analysis fundus images

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A NOVEL PARALLEL RAY-CASTING ALGORITHM 13

A NOVEL PARALLEL RAY-CASTING ALGORITHM

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13th IEEE International Computer Conference on Wavelet Active Media Technology and Information Processing (ICCWAMTIP)

作者： Zhang, Yan Gao, Peng Li, Xiao-Qing Harbin Inst Technol Shenzhen Grad Sch Microelect Ctr Shenzhen 518000 Peoples R China

ISBN: (纸本)9781509061266

The Ray-Casting algorithm is an important method for fast real-time surface display from 3D medical images. Based on Ray-Casting algorithm, a novel parallel Ray-Casting algorithm is proposed in this paper. A novel operation is introduced and defined as a star operation, and star operations can be computed in parallel in the proposed algorithm compared with the serial chain of star operations in the Ray-Casting algorithm. The computation complexity of the proposed algorithm is reduced from O(n) to O(log(2)(n))

关键词： 3D medical image display Ray-Casting algorithm parallel algorithm computation complexity

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Chromatic number of a line with geometric progressions of forbidden distances and the complexity of recognizing distance graphs

Moscow Journal of Combinatorics and Number Theory

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Moscow Journal of Combinatorics and Number Theory 2023年第3期12卷 247-258页

作者： Sokolov, Georgy Department of Discrete Mathematics Moscow Institute of Physics and Technology Moscow Russian Federation

We establish the complexity of recognizing A-distance graphs and estimating the chromatic number of the real line with a set of forbidden distances A, where the set A is a collection of elements of a geometric progression with the common ratio equal to a rational number raised to a rational power. For all sets A of this type we have found the chromatic number of the real line with this set of forbidden distances and proved whether the problem of recognizing nonstrictly noninjectively A-embeddable-in-R graphs is polynomial or NP-hard. © 2023 COPYRIGHT INFORMATION WILL GO HERE.

关键词： chromatic number computation complexity distance graph

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Producing Monomial Sets with Lower Calculation complexity for Polynomial Fitting

Producing Monomial Sets with Lower Calculation Complexity fo...

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International Conference on High Performance Big Data and Intelligent Systems (HPBD and IS)

作者： Liu, Jingyi Yu, Lina Wu, Min Tong, Yuerong Xu, Jian Li, Zhiwei Hu, Xuan Li, Weijun Chinese Acad Sci Inst Semicond Beijing 100083 Peoples R China Shenzhen DAPU Microelect Co Ltd Shenzhen 518100 Peoples R China Beijing Key Lab Semicond Neural Network Intellige Beijing 100083 Peoples R China Univ Chinese Acad Sci Sch Microelect Beijing 100049 Peoples R China Zhongnan Univ Econ & Law Wuhan 430073 Peoples R China

ISBN: (纸本)9781665413275

In the physic world, exploring and discovering the mechanism behind the various phenomenon is crucial for us to know the world better. However, it is hard to discover the principle in case of enormous data and the mechanism may be too hard for a human to figure out. Data science gives us a way of knowing the world and finding the mechanism hidden in the data. Automatic tool like polynomial fitting is a useful method to fit the data well. When the variable number and degree are relatively low, the computation amount of polynomial is small. However, the number of monomials grows exponentially with the increasing variable number and degree. Problems in the real world are always in a high-dimension, and the problem may be complex that needs to use a high degree to fit data well. Plus, with the huge data, the computation complexity is high. Therefore, we think it is necessary to find a way to reduce the computation amount of fitted polynomial. In this paper, we propose to use the PSO algorithm to find the monomial sets that have lower computation amounts. Experiments show the effectiveness of our method.

关键词： polynomial fitting computation complexity optimization problem pattern recognition

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Parallel Computing for Time-Consuming Multicriterial Optimization Problems 14th

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14th International Conference on Parallel Computing Technologies (PaCT)

作者： Gergel, Victor Kozinov, Evgeny Lobachevsky State Univ Nizhny Novgorod Nizhnii Novgorod Russia

ISBN: (纸本)9783319629322;9783319629315

In the present paper, an efficient method for parallel solving the time-consuming multicriterial optimization problems, where the optimality criteria can be multiextremal, and the computation of the criteria values can require a large amount of computations, is proposed. The proposed scheme of parallel computations allows obtaining several efficient decisions of a multicriterial problem. During performing the computations, the maximum use of the search information is provided. The results of the numerical experiments have demonstrated such an approach to allow reducing the computational costs of solving the multicriterial optimization problems essentially - several tens and hundred times.

关键词： Decision making Multicriterial optimization Parallel computing Dimensionality reduction Criteria convolution Algorithm of global search computation complexity

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