This paper identifies a ripple effect problem (REP) that spreads interference to neighbors and proposes a novel channel localization mechanism to decrease the REP in a Wi-Fi system. The proposed mechanism has less blocking probability when compared to a random channel allocation mechanism and also has increased channel reusability. The proposed mechanism in simulation yields less channels B_Em as the number of users and T_used increase.

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In this letter, we propose a innovative threshold receiver for MIMO-OFDM system. The proposed scheme calculates the channel condition number and then selects either combined V algorithm and CLLL or combined QRD-M and DFE detection scheme according to channel information. The complexity of the proposed scheme is about 33.3% of the QRD-M for 4×4 MIMO-OFDM system.

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A new paradigm for building Virtual Controller Model (VCM) for traffic flow simulator is developed. It is based on flight plan data and is applied to Traffic Flow Management System (TFMS) in China. The problem of interest is focused on the sectors of airspace and how restrictions to aircraft movement are applied by air traffic controllers and demand overages or capacity shortfalls in sectors of airspace. To estimate and assess the balance between the traffic flow and the capacity of sector in future, we apply Virtual Controller model, which models by the sectors airspace system and its capacity constraints. Numerical results are presented and illustrated by applying them to air traffic data for a typical day in the Traffic Flow Management System. The results show that the predictive capabilities of the model are successfully validated by showing a comparison between real flow data and simulated sector flow, making this method appropriate for traffic flow management system.

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Existing salient object detection methods either simply use a threshold to detect desired salient objects from saliency map or search the most promising rectangular window covering salient objects on the saliency map. There are two problems in the existing methods: 1) The performance of threshold-dependent methods depends on a threshold selection and it is difficult to select an appropriate threshold value. 2) The rectangular window not only covers the salient object but also contains background pixels, which leads to imprecise salient object detection. For solving these problems, a novel saliency threshold-free method for detecting the salient object with a well-defined boundary is proposed in this paper. We propose a novel window search algorithm to locate a rectangular window on our saliency map, which contains as many as possible pixels belonging the salient object and as few as possible background pixels. Once the window is determined, GrabCut is applied to extract salient object with a well-defined boundary. Compared with existing methods, our approach doesn't need any threshold to binarize the saliency map and additional operations. Experimental results show that our approach outperforms 4 state-of-the-art salient object detection methods, yielding higher precision and better F-Measure.

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