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High efficiency video coding (HEVC) standard, introduced by joint collaborative team on video coding (JCT-VC) is the newest international standard of video compression. This standard provides more compression and better video quality, compared to the previous standards such as H.264. The higher compression efficiency is obtained at the cost of an increase in the computational load. One of the portions...
A great number of dimensionality reduction methods are finally reduced to solving generalized eigenvector problems. Optimization techniques are promising ways to solve the parameter selection problems in these dimensionality reduction methods. The most important step in these optimization methods is to compute the objective function with respect to the parameter, which depends on computing the gradient...
The k-core of a graph is the largest subgraph in which every vertex is connected to at least k other vertices within the subgraph. Core decomposition finds the k-core of the graph for every possible k. Past studies have shown important applications of core decomposition such as in the study of the properties of large networks (e.g., sustainability, connectivity, centrality, etc.), for solving NP-hard...
The efficient scheduling of independent computational jobs in a heterogeneous computing (HC) environment is an important problem in domains such as grid computing. Finding optimal schedules for such an environment is (in general) an NP-hard problem, and so heuristic approaches must be used. The goal of grid task scheduling is to achieve high system throughput and to allocate various computing resources...
In this article, a new method to design the flexible-length S-random interleaver is proposed for storage optimization in a turbo coding system. In this method, the high-spread property s-random interleaver is constructed by selecting appropriate position and element that can improve spread property of interleaver in each iteration in which length of interleaver is increased. Using this method, interleavers...
In this paper, we have proposed a novel algorithm based on Ant Colony Optimization (ACO) for finding near-optimal solutions for the Multi-dimensional Multi-choice Knapsack Problem (MMKP). MMKP is a discrete optimization problem, which is a variant of the classical 0-1 Knapsack Problem and is also an NP-hard problem. Due to its high computational complexity, exact solutions of MMKP are not suitable...
In our previous work, the rate-distortion optimized transform (RDOT) is introduced for Intra coding, which is featured by the usage of multiple offline-trained transform matrix candidates. The proposed RDOT achieves remarkable coding gain for KTA Intra coding, while maintaining almost the same computational complexity at the decoder. However, at the encoder, the computational complexity is increased...
Wireless spectrum is a regulated resource, whose control and usage is regulated by government agencies. The allocation of spectrum to interested parties is usually conducted through auctions, and are an important source of income for these regulatory agencies. However, previous spectrum auction design fail to take into consideration the effect of interference, which can adversely affect the truthfulness...
In this paper, we introduce the concept of generalized instantly decodable network coding (G-IDNC) to further minimize decoding delay in wireless broadcast, compared to strict instantly decodable network coding (S-IDNC), studied in. G-IDNC loosens the strict instant decodability constraint in order to target more receivers while preserving the attractive properties of S-IDNC. We show that the minimum...
In this work we propose two decomposition algorithms LR-SVNE and D-SVNE for solving the SVNE problem efficiently. We focus on the design of survivable VN embedding with resource constraints. The VN embedding problem with resource constraints is NP-hard, hence most work focuses on devising heuristic solutions. Simulation results show that our algorithms perform well in terms of both time complexity...
Influence diagrams (IDs) are compact and intuitive models for representation and analysis of decision problems under uncertainty. Influence diagrams have always been imposed on no-forgetting and regularity constraints which guarantee that global optimal strategy can be solved successively by local computations on each decision nodes according to a solution ordering. However, it is difficult to solve...
In order to get the best mode, H.264 computes the cost of every mode for each block by RDO (rate distortion optimization) technique, but it increases a high computational complexity .This paper combines Pan's fast algorithm and an efficient fast mode decision for intra prediction is proposed. First, the intra prediction's types are decided by whether a block is smooth. Second, the Pan's fast algorithm...
The resource-constrained multi-project scheduling (RCMPS) is a NP-hard problem and has been extensively used in manufacturing and engineering fields. In order to solve scheduling of RCMPS, a new algorithm was presented in this paper. The new algorithm combines the some advantages of ACOA and NN . Finally, the algorithm was tested on a case of the RCMPS and the results were presented in the paper....
The master production schedule (MPS) problem is a typical NP-hard problem. In this work, A MPS optimization model whose objectives are maximum utilization of equipment and minimum ratio between storage expenses and overdue fines is established with equipment capacity and product lead time as constraints. Then this model is implemented using a product encoding method and newly designed ant path searching...
We study the dynamic bid optimization problem via a primal dual approach. In the case we have no information about the distribution of queries, we reconstruct the ln(U/L) + 1 competitive algorithm proposed in [ZCL08] through a systematic way and showed the intuition behind this algorithm. In the case of random permutation model, we showed that the learning technique used in [DH09] can give us a (1...
Spectrum sensing is essential for the success of the cognitive radio networks. In traditional spectrum sensing schemes, Secondary Users (SUs) are responsible for the spectrum sensing which could be very time and resource consuming. It leads to a great deal of inefficiency in spectrum usage and introduces many practical challenges. To tackle these challenges and leverage the spectrum opportunity more...
In this paper, we proposed a hybrid algorithm to solve unit commitment optimization problem, which is composed of operation ant colony optimization (ACO) algorithm and Lambda-iteration method. By means of operation encoding, space complexity of ACO algorithm for solving UCP is reduced. Moreover, space complexity can be regulated by adjusting the number of maximum allowable operation in a single time...
We consider the problem of weighted sum-rate maximization (WSRMax) for an arbitrary set of interfering links. This problem is known to be NP-hard; therefore, it is extremely difficult to solve even for a relative small number of links. The main contribution of this paper is to provide a solution method, based on the branch and bound technique, which solves WSRMax problem with an optimality certificate...
Attribute reduction is one of the main issues in the theoretical research of rough set theory which is known as a NP-hard optimization problem. The objective is to find the minimal number of attributes from a large dataset. Hence it is difficult to solve to optimality. This paper proposes a composite neighbourhood structure approach to solve the attribute reduction problem that consists of two versions...
Bayesian network is an uncertainty inference network based on probability. Its structure learning is one of the main research techniques in the field of data mining and knowledge discovering, while constructing Bayesian network structures from data is NP hard. According to the information theory and conditional independence test, a new algorithm is presented for the construction of optimal Bayesian...
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