In UMTS (universal mobile telecommunications system) networks upgraded with HSPA (high speed packet access) technology, the high access bandwidth and advanced mobile devices make it applicable to share large files among mobile users by peer-to-peer applications. To receive files quickly is essential for mobile users in file sharing applications, mainly because they are subject to unstable signal strength and battery failures. While many researches present peer-to-peer file sharing architectures in mobile environments, few works focus on decreasing the time spent in disseminating files among users. In this paper, we present an efficient peer-to-peer file sharing design for HSPA networks called AFAM -- Adaptive efficient File shAring for uMts networks. AFAM can decrease the dissemination time by efficiently utilizing the upload-bandwidth of mobile nodes. It uses an adaptive rearrangement of a node's concurrent uploads, which causes the count of the node's concurrent uploads to lower while ensuring that the node's upload-bandwidth can be efficiently utilized. AFAM also uses URF -- Upload Rarest First policy for the block selection and receiver selection, which achieves real rarest-first for the spread of blocks and effectively avoids the "last-block" problem in file sharing applications. Our simulations show that, AFAM achieves much less dissemination time than other protocols including BulletPrime and a direct implementation of BitTorrent for mobile environments.

Network tomography is an appealing technology to infer link delay distributions since it only relies on end-to-end measurements. However, most approaches in network delay tomography are usually computationally intractable. In this letter, we propose a Fast link Delay distribution Inference algorithm (FDI). It estimates the node cumulative delay distributions by explicit computations based on a subtree-partitioning technique, and then derives the individual link delay distributions from the estimated cumulative delay distributions. Furthermore, a novel discrete delay model where each link has a different bin size is proposed to efficiently capture the essential characteristics of the link delay. Combining with the variable bin size model, FDI can identify the characteristics of the network-internal link delay quickly and accurately. Simulation results validate the effectiveness of our method.

In this letter, we analyze the influence of motion and out-of-focus blur on both frequency spectrum and cepstrum of an iris image. Based on their characteristics, we define two new discriminative blur features represented by Energy Spectral Density Distribution (ESDD) and Singular Cepstrum Histogram (SCH). To merge the two features for blur detection, a merging kernel which is a linear combination of two kernels is proposed when employing Support Vector Machine. Extensive experiments demonstrate the validity of our method by showing the improved blur detection performance on both synthetic and real datasets.

Facial age estimation requires accurately capturing the mapping relationship between facial features and corresponding ages, so as to precisely estimate ages for new input facial images. Previous works usually use one-layer regression model to learn this complex mapping relationship, resulting in low estimation accuracy. In this letter, we propose a new gender-specific regression model with a two-layer structure for more accurate age estimation. Different from recent two-layer models that use a global regressor to calculate cumulative attributes (CA) and use CA to estimate age, we use gender-specific ones to calculate CA with more flexibility and precision. Extensive experimental results on FG-NET and Morph 2 datasets demonstrate the superiority of our method over other state-of-the-art age estimation methods.