Additive noise removal using a novel fuzzy-based filter

The paper proposes a new fuzzy-based two-step filter for restoring images corrupted with additive noise. The goal of the first step is to compute the difference between the central pixel and its neighborhood in a selected window and to compute a fuzzy membership degree for each difference value using a Gaussian membership function. Computed fuzzy membership values are appropriately utilized as weights for each pixel and then computes the weighted average representing the modified value for the current central pixel. The second step is used as an augmented step to the first one and its goal is to improve the result obtained in the first step by reducing the noise in the color component differences without destroying the fine details of the image. The experimental analysis shows that the proposed method gives better results compared to existing advanced filters for additive noise reduction. Both visual, quantitative and qualitative analysis have been done to prove the efficiency and effectiveness of the proposed method.     

An improved quantum-inspired evolutionary algorithm for coding resource optimization based network coding multicast scheme

This paper investigates how to minimize the required coding resources in network-coding-based multicast scenarios. An evolutionary algorithm (MEQEA) is proposed to address the above problem. Based on quantum-inspired evolutionary algorithm (QEA), MEQEA introduces multi-granularity evolution mechanism which allows different chromosomes, at each generation, to have different rotation angle step values for update. In virtue of this mechanism, MEQEA significantly improves its capability of exploration and exploitation, since its optimization performance is no longer overly dependant upon the single rotation angle step scheme shared by all chromosomes. MEQEA also presents an adaptive quantum mutation operation which is able to prevent local search efficiently. Simulations are carried out over a number of network topologies. The results show that MEQEA outperforms other heuristic algorithms and is characterized by high success ratio, fast convergence, and excellent global-search capability.     

Throughput and delay analysis of multi-channel wireless infrastructure networks

Wireless infrastructure networks (WINs) provide ubiquitous connectivity to mobile nodes in metro areas. The nodes in such backbone networks are often equipped with multiple transceivers to allow for concurrent transmissions in multiple orthogonal channels. In this study, we develop an analytical model for the estimation of the delay and throughput performance of wireless infrastructure networks employing slotted ALOHA channel access and slotted Carrier Sense Multiple Access with Collision Avoidance (CSMA/CA) over multiple channels. The analytical model, which takes into account the correlation due to multi-hop transmissions, approximates the performance observed through simulations accurately.