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With the tremendous growth of wireless networks into the next generation to provide better services, Wireless Mesh Networks (WMNs) have emerged to offer ubiquitous communication and seamless broadband applications. WMNs are hybrid networks composed of a mixture of static Wireless Mesh Routers (WMRs) and mobile Wireless Mesh Clients (WMCs) interconnected via wireless links to form a multi-hop wireless Ad Hoc network (WANET). WMNs are self-organized, self-configured, and reliable against single points of failures, and robust against RF interference, obstacles or power outage. This is because WMRs forming wireless backbone mesh networks (WBMNs) are built on advanced physical technologies. Such nodes perform both accessing and forwarding functionality. They are expected to carry huge volumes of traffic and be "on power" at all times. While trying to increase network capacity, problems of the dynamic transmission power control (DTPC) arise in such networks. Such problems include RF Interference, Connectivity and energy-depletion. While there are numerous studies on this topic, contributions in the context of WBMNs are still challenging but interesting research areas. This paper provides an overview of the DTPC algorithms central to the WBMNs framework. The open issues are also highlighted.

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We present an algorithm for performing attributed graph matching. This algorithm is derived from a generalized framework for describing functionally expanded interpolators which is based on the theory of reproducing kernel Hilbert spaces (RKHS). The algorithm incorporates a general approach to a wide class of graph matching problems based on attributed graphs, allowing the structure of the graphs to be based on multiple sets of attributes. No assumption is made about the adjacency structure of the graphs to be matched

We propose a method for estimating depth from images captured with a real aperture camera by fusing defocus and stereo cues. The idea is to use stereo-based constraints in conjunction with defocusing to obtain improved estimates of depth over those of stereo or defocus alone. The depth map as well as the original image of the scene are modeled as Markov random fields with a smoothness prior, and their estimates are obtained by minimizing a suitable energy function using simulated annealing. The main advantage of the proposed method, despite being computationally less efficient than the standard stereo or DFD method, is simultaneous recovery of depth as well as space-variant restoration of the original focused image of the scene.

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