At present, wireless local area networks (WLANs) based on IEEE802.11 are widely deployed in both private premises and public areas. In a public environment offering several access points (APs), a station (STA) needs to choose which AP to associate with. In this paper, we propose a distributed association control scheme with user guidance to increase users' utility based on uplink and downlink throughputs of individual stations. As part of the scheme, we also present a simple throughput estimation method that considers physical data rate, traffic demand, and frame length in both uplink and downlink. Basically, in the proposed scheme, an AP selects a user and suggests that the user moves to another AP if certain conditions are met. The user then decides whether to accept the suggestion or not in a self-interested manner or in a voluntary manner for the benefit of all users including the user's own self. Through simulations under this condition, we confirm that our distributed association control scheme can improve user utility and fairness even though the channel quality of the new AP is unknown in advance.

Several scheduling algorithms have been proposed for the downlink of a Code Division Multiple Access (CDMA) system with High Data Rate (HDR). Modified Largest Weighted Delay First (M-LWDF) scheduling algorithm selects a user according to the user current channel condition, user head-of-line packet delay and user Quality of Service (QoS) requirement. Proportional Fair (PF) scheduling algorithm has also been proposed for CDMA/HDR system and it selects a user according to the ratio of the user current channel rate and the user average channel rate, which provides good performance in terms of fairness. However, when variable bit rate (VBR) traffic is considered under different channel conditions for each user, both schedulers' performance decrease. M-LWDF scheduler can not guarantee the QoS requirement to be achieved and PF scheduler can not achieve a good fairness among the users. In this work, we propose a new scheduling algorithm to enhance M-LWDF and PF schedulers performance. Proposed scheduler selects a user according to the user input traffic characteristic, user current channel condition and user QoS requirement, which consists of a delay value with a maximum violation probability. We consider the well-known effective bandwidth expression, which takes into account the user QoS requirement and the user input traffic characteristics, to select a user to be scheduled. Properties of the proposed scheduling algorithm are investigated through simulations with constant bit rate (CBR) and VBR flows and performance comparisons with M-LWDF and PF schedulers. The results show a better performance of the proposed scheduler compared with M-LWDF and PF schedulers.

Malignant melanoma is a skin cancer that can be mistaken as a harmless mole in the early stages and is curable only in these early stages. Therefore, dermatologists use a microscope that shows the pigment structures of the skin to classify suspicious skin lesions as malignant or benign. This microscope is called "dermoscope." However, even when using a dermoscope a malignant skin lesion can be mistaken as benign or vice versa. Therefore, it seems desirable to analyze dermoscopic images by computer to classify the skin lesion. Before a dermoscopic image can be classified, it should be segmented into regions of the same color. For this purpose, we propose a segmentation method that automatically determines the number of colors by optimizing a cluster validity index. Cluster validity indices can be used to determine how accurately a partition represents the "natural" clusters of a data set. Therefore, cluster validity indices can also be applied to evaluate how accurately a color image is segmented. First the RGB image is transformed into the L*u*v* color space, in which Euclidean vector distances correspond to differences of visible colors. The pixels of the L*u*v* image are used to train a self-organizing map. After completion of the training a genetic algorithm groups the neurons of the self-organizing map into clusters using fuzzy c-means. The genetic algorithm searches for a partition that optimizes a fuzzy cluster validity index. The image is segmented by assigning each pixel of the L*u*v* image to the nearest neighbor among the cluster centers found by the genetic algorithm. A set of dermoscopic images is segmented using the method proposed in this research and the images are classified based on color statistics and textural features. The results indicate that the method proposed in this research is effective for the segmentation of dermoscopic images.

Orthogonal Frequency Division Multiple Access (OFD-MA) is a leading air interface candidate for future generation cellular networks. However, if deployed in a multi-user multi-tier cellular system, it is important to fairly share radio resources such as transmission power and sub-carriers among co-tier and cross-tier users. This paper focuses on a mathematical formulation of cell inner-zone/outer-zone radio resource partitioning variables and considers the case of an FFR-based macrocell underlaid with femtocell. By applying an exhaustive search procedure on the developed formulation, we determine the optimal radio resource partitioning parameter values from the perspectives of macrocell user fairness and femtocell throughput maximization.

Dirty paper coding (DPC) is a strategy to achieve the region capacity of multiple input multiple output (MIMO) downlink channels and a DPC scheduler is throughput optimal if users are selected according to their queue states and current rates. However, DPC is difficult to implement in practical systems. One solution, zero-forcing beamforming (ZFBF) strategy has been proposed to achieve the same asymptotic sum rate capacity as that of DPC with an exhaustive search over the entire user set. Some suboptimal user group selection schedulers with reduced complexity based on ZFBF strategy (ZFBF-SUS) and proportional fair (PF) scheduling algorithm (PF-ZFBF) have also been proposed to enhance the throughput and fairness among the users, respectively. However, they are not throughput optimal, fairness and throughput decrease if each user queue length is different due to different users channel quality. Therefore, we propose two different scheduling algorithms: a throughput optimal scheduling algorithm (ZFBF-TO) and a reduced complexity scheduling algorithm (ZFBF-RC). Both are based on ZFBF strategy and, at every time slot, the scheduling algorithms have to select some users based on user channel quality, user queue length and orthogonality among users. Moreover, the proposed algorithms have to produce the rate allocation and power allocation for the selected users based on a modified water filling method. We analyze the schedulers complexity and numerical results show that ZFBF-RC provides throughput and fairness improvements compared to the ZFBF-SUS and PF-ZFBF scheduling algorithms.