We present a fast voting scheme for localizing circular objects among clutter and occlusion. Typical solutions for the problem are based on Hough transform that evaluates an instance of circle by counting the number of edge points along its boundary. The evaluated value is proportional to radius, making the normalization with respect to the factor necessary for detecting circles with different radii. By representing circle with a number of sampled points, we get rid of the step. To evaluate an instance then involves obtaining the same number of edge points, each close to a sampled point in both spatial position and orientation. The closeness is measured by compatibility function, where a truncating operation is used to suppress noise and deal with occlusion. To evaluate all instances of circle is fulfilled by letting edge point vote in a maximizing way such that any instance possesses a set of maximally compatible edge points. The voting process is further separated into the radius-independent and -dependent parts. The time-consuming independent part can be shared by different radii and outputs the sparse matrices. The radius-dependent part shifts these sparse matrices according to the radius. We present precision-recall curves showing that the proposed approach outperforms the solutions based on Hough transform, at the same time, achieves the comparable time complexity as algorithm of Hough transform using 2D accumulator array.

Round-trip time (RTT) is an important performance metric. Traditional RTT estimation methods usually depend on the cooperation of other networks and particular active or passive measurement platforms, whose global deployments are costly and difficult. Thus a new RTT estimation algorithm, ME algorithm, is introduced. It can estimate the RTT of two hosts communicating through border routers by using TCP CUBIC bulk flow data from those routhers without the use of extra facilities, which makes the RTT estimation in large-scale high-speed networks more effective. In addition, a simpler and more accurate algorithm — AE algorithm — is presented and used when the link has large bandwidth and low packet loss rate. The two proposed algorithms suit sampled flow data because only duration and total packet number of a TCP CUBIC bulk flow are inputs to their calculations. Experimental results show that both algorithms work excellently in real situations. Moreover, they have the potential to be adapted to other TCP versions with slight modification as their basic idea is independent of the TCP congestion control mechanism.

WLAN infrastructure has been deployed densely and extensively in the past few years. Since APs are always kept online, a dense WLAN will waste energy during idle hours. In this paper, we first state some principles for powering on/off APs in order to save energy. Then we design an energy saving mechanism correspondingly. The energy saving mechanism includes three processes: clustering APs, estimating user location, and powering on/off APs, which tries to choose appropriate APs being online according to user location information. Results of trace-driven simulation show that our mechanism could achieve about 42% energy conservation. More importantly, our mechanism can provide better network service for users than previous mechanisms which usually ignore user location information.