Sharif Digital Repository

This study deals with the problem of outlier censoring from the secondary data in a radar scenario, where the number of outliers is unknown. To this end, a procedure consisting of joint likelihood estimation and statistical model order selection (MOS) is proposed. Since the maximum likelihood (ML) estimation of the outlier subset requires to solve a combinatorial problem, an approximate ML (AML) method is employed to reduce the complexity. Therefore, to determine the number of outliers, different MOS criteria based on likelihood function are applied. At the analysis stage, the performance of the proposed methods is assessed based on simulated data. The results highlight that the devised... 

This paper presents a particle swarm optimization (PSO) based approach to solve the multi-stage transmission expansion planning problem in a competitive pool-based electricity market. It is a large-scale non-linear combinatorial problem. We have considered some aspects in our modeling including a multi-year time horizon, a number of scenarios based on the future demands of system, investment and operating costs, the N - 1 reliability criterion, and the continuous non-linear functions of market-driven generator offers and demand bids. Also the optimal expansion plan to maximize the cumulative social welfare among the multi-year horizon is searched. Our proposed PSO based approach, namely... 

Synthesis of the optimum distillation column sequence (DCS), which incorporates a huge search space composed of both conventional and complex arrangements, is a highly complicated combinatorial problem in the field of chemical process design and optimisation. In this study, a novel procedure for the synthesis of optimum DCS proposed by Boozarjomehry et al. [Boozarjomehry et al., Can. J. Chem. Eng. 87, 477-492 (2009)] is expanded to include the complex distillation arrangements. The method is based on evolutionary algorithms, and the total annual cost (TAC) is the main criterion used to screen alternatives. Efficient procedure has been proposed for encoding mechanism to include and classify... 

Purpose: Motivated by the high cost of material movements in road construction projects, past studies have used analytical methods to optimize materials logistics plans. A key shortcoming of these methods is their inability to capture the uncertain, dynamic and complex characteristics of the road construction material logistics. Failure to incorporate these characteristics can lead to sub-optimal results. The purpose of this study is to propose the use of discrete event simulation (DES) to address the existing shortfall. Design/methodology/approach: Despite the powerful capabilities of DES models in capturing the operational complexities of construction projects, they have not been... 

The capability of fuzzy systems to solve different kinds of problems has been demonstrated in several previous investigations. Genetic fuzzy systems (GFSs) hybridize the approximate reasoning method of fuzzy systems with the learning capability of evolutionary algorithms. The objective of this paper is to design and analysis of various kinds of genetic fuzzy systems to deal with intrusion detection problem as a new real-world application area which is not previously tackled with GFSs. The resulted intrusion detection system would be capable of detecting normal and abnormal behaviors in computer networks. We have presented three kinds of genetic fuzzy systems based on Michigan, Pittsburgh and... 

Channel equalization is a crucial part of the OFDM communications protocols, which in turn requires channel estimation. In this paper, we consider the problem of orthogonal pilot design in MIMO-OFDM systems for sparse channel estimation. The pilot design in MIMO scenarios compared to the conventional SISO case has the additional constraint that the capability of recovery should be uniformly provided for all single channels. For instance, perfect estimation of a channel at the cost of another one is not permitted. This requirement is even more significant in the emerging Massive-MIMO systems. Our pilot design is based on the compressed sensing technique of minimizing the coherence measure of... 

Transportation Discrete Network Design Problem (TDNDP) aims at choosing a subset of proposed projects to minimize the users' total travel time with respect to a budget constraint. Because TDNDP is a hard combinatorial problem, recent research has widely addressed heuristic approaches and ignored the exact solution. This paper explores how application of parallel computation can affect the performance of an exact algorithm in TDNDP. First, we show that the Branch-and-Bound (B&B) algorithm, proposed by LeBlanc, is well adapted to a parallel design with synchronized Master-Slave (MS) paradigm. Then, we develop a parallel B&B algorithm and implement it with two search strategies of... 

We consider the deterministic pilot design problem for sparse channel estimation in an Orthogonal Frequency Division Multiplexing (OFDM) system. Our design is based on minimizing the coherence measure of the Fourier submatrix associated with the pilot subcarriers. This is done by optimizing over both pilot locations and pilot powers. As finding such global minimizer is a combinatorial problem, we resort to a greedy pilot allocation method. The resulting method achieves a suboptimal solution in a sequential manner and with reasonable computational complexity. Simulation results demonstrate that the proposed scheme performs similar to the existing methods with significantly lower computational...