Toan Duc Bui

Efficient data uploading based on network coding in LTE-Advanced heterogeneous networks

LTE-Advanced heterogeneous networks enable a uniform broadband experience to users flexibly anywhere in the network by using a mix of large and small cells — i.e., macro, pico, femto and relay stations. In this paper, we propose a novel network coding-based for mobile content uploading, where multiple user equipments upload their own content toward the eNodeB in LTE-Advanced relay networks. Network coding has been considered as a promising solution in next generation networks because of the significant improvement in the transmission rate and reliability. The network coding enables an intermediate node having the capability of encoding incoming packets rather than simply forwarding. However, the advantages come at the cost of high computational, storage costs and coding vector overhead. The two former drawbacks can be solved easily by the fast development of current smart users and relay with high capability on computation and storage. The last issue of coding vector overhead still remains as many packets are encoded together using a linear combination since each packet needs to carry a large size of the header to store the information of the coding vector. We propose random overlapped chunked code for enhancing the transmission rate and reliability under the constraint of coding vector overhead. Furthermore, the encoding and decoding processes can be operated with low complexity. The complete transmission consists of two phases: users upload the content to the relay; the relay performs the proposed random overlapped chunked code of different coming streams from users and forwards the network-coded packets to the eNodeB. For performance evaluation, we run various simulations along with analysis to show that our proposal outperforms current schemes in terms of decoding probability.

Fully automatic segmentation based on localizing active contour method

Cartilage segmentation is one of challenging issues because knee magnetic resonance (MR) images are consisted of thin sheet structure, intensity inhomogeneity, and low contrast between cartilage and muscle. In this paper, a fully automatic segmentation method for knee cartilage is proposed using spatial fuzzy c-mean clustering (SFCM) and morphological operators. The proposed method modifies the way to generate an approximate boundary of cartilage region, and combines it with localizing region-based active contour method, and overcomes limitations of previous methods. The performance of the proposed method is improved more than 10.8% by Dice similarity coefficient (DSC) in comparison with previous methods.

A stable clustering algorithm based on affinity propagation for VANETs

This paper proposes a stable clustering algorithm based on Affinity Propagation (AP) for Vehicular Ad Hoc Networks (VANETs). In VANETs, vehicles share information for the safe and efficient driving by Dedicated Short Range Communications (DSRC). We present a trajectory-based clustering algorithm for VANETs using AP clustering technique. Our proposed algorithm considers vehicle trajectories to form more stable clusters. Simulation results show the better presentation of our algorithm. The performance of proposed algorithm is measured via cluster life time by choosing appropriate cluster head.

Fast localised active contour for inhomogeneous image segmentation

The localised active contour framework has been widely used for image segmentation because it provides reliable results for inhomogeneous images. However, its computational complexity remains an issue. In this study, the authors introduce a fast algorithm based on the localised active contour framework. A key concept of the proposed algorithm is its consideration of the curve evolution based on the speed function only at active points that change across time, rather than at all points in a narrow band. This approach reduces computational time in the localised active contour. The authors additionally propose a modified speed function to address inhomogeneous image segmentation. The experimental results demonstrate significant advantages of the proposed method over existing methods, both in terms of computational efficiency and segmentation accuracy, for homogeneous and inhomogeneous images.

An efficient resource allocation scheme for scalable video multicast in LTE-advanced networks

We address the challenge of optimizing the radio resource allocation for scalable video multicast over LTE-Advanced. The advantage of multicast service is to utilize the available bandwidth efficiently in delivering the same content to multiple receivers. However, the instantaneous channel condition of each receiver in the multicast group varies independently. To guarantee all receivers experiencing similar performance becomes more challenging. Hence, we need an advanced radio resource management to guarantee the least quality of service levels. In this paper, we propose a novel heuristic strategy which aims to jointly optimize the frequency selectivity, adaptive modulation and coding, and random linear network coding scheme performed at Medium Access Layer. The key aspect is that our proposal enhances the reliability of video services by exploiting random linear network coding in LTE-Advanced. Moreover, the available bandwidth is utilized efficiently by taking the advantage from the frequency selectivity in subgroup formation. We do various simulations for performance evaluation. The results show that our proposed resource allocation outperforms the existing studies regarding resource load, spectral efficiency, recovery probability and the attainable video quality (i.e., peak signal-to-noise ratio).

Content-aware RaptorQ

With a low complexity and high successful probability, RaptorQ code is considered as an efficient solution for media transmission. However, RaptorQ code is optimal for the purpose of single layer transmission. Therefore, RaptorQ code is not suitable for dependent media transmission, where the successful decoding of the least important layer depends on the successful of the more important layer, especially in Scalable video coding (SVC). In this paper, we propose an unequal error protection scheme to protect layers video in SVC using RaptorQ code named content-aware RaptorQ. Specifically, the layers are encoded based on a window selection probability so that the least important media layers is utilized to protect data of more important media layers. The simulation results demonstrate that the successful decoding probability of the proposed method is improved by 105%, relative to that of RaptorQ.

Robust scalable video multicast using triangular network coding in LTE/LTE-Advanced

The recent research studies showed that inter-layered network coding is a promising approach to provide the unequal error protection for scalable video multicast under the channel heterogeneity. The selection of the optimal transmission distribution performed at eNB increases the system performance with the cost of time and computational complexities. In this paper, we propose an optimal transmission strategy for the scalable video multicast using triangular network coding at the application layer in LTE/LTE-Advanced networks. The proposed transmission strategy comprises of two optimization phases: space reduction and performance maximization. The first optimization reduces the number of searching steps in the dictionary of possible transmission distributions by using a proposed performance predictive algorithm. The following optimization not only maximizes the average number of successfully decoded layers among receivers but also maximizes the number of receivers decoding the video base layer successfully in the second phase. We evaluate the proposed transmission strategy through various simulations with the performance metrics regarding the average number of successfully decoded layers among receivers in a multicast group, throughput, and video quality measurement. The simulation results show that our proposed scheme outperforms other recent studies and adapts well with the variable streaming rates of the video under the extreme time constraints.

Unsupervised segmentation of noisy and inhomogeneous images using global region statistics with non-convex regularization

Improving the segmentation of magnetic resonance (MR) images remains challenging because of the presence of noise and inhomogeneous intensity. In this paper, we present an unsupervised, multiphase segmentation model based on a Bayesian framework for both MR image segmentation and bias field correction in the presence of noise. In our model, global region statistics are utilized as segmentation criteria in order to classify regions with similar mean intensities but different variances. Additionally, we propose an edge indicator function based on a guided filter (instead of a Gaussian filter) that can preserve the underlying edges of the image obscured by noise. The proposed edge indicator function is integrated with non-convex regularization to overcome the influence of noise, resulting in more accurate segmentation. Furthermore, the proposed model utilizes a Markov random field to model the spatial correlation between neighboring pixels, which increases the robustness of the model under high-noise conditions. Experimental results demonstrate significant advantages in terms of both segmentation accuracy and bias field correction for inhomogeneous images in the presence of noise.

Knee Cartilage Defect Assessment using Cartilage Thickness Atlas

Abstract: Osteoarthritis is the most common chronic joint disease in the world. With its progression, cartilage thick-ness tends to diminish, which causes severe pain to human being. One way to examine the stage of osteoarthritisis to measure the cartilage thickness. When it comes to inter-subject study, however, it is not easy task to comparecartilage thickness since every human being has different cartilage structure. In this paper, we propose a methodto assess cartilage defect using MRI inter-subject thickness comparison. First, we used manual segmentation methodto build accurate atlas images and each segmented image was labeled as articular surface and bone-cartilage interfacein order to measure the thickness. Secondly, each point in the bone-cartilage interface was assigned the measuredthickness so that the thickness does not change after registration. We used affine transformation and SyGN to getdeformation fields which were then applied to thickness images to have cartilage thickness atlas. In this way, it ispossible to investigate pixel-by-pixel thickness comparison. Lastly, the atlas images were made according to theirosteoarthritis grade which indicates the degree of its progression. The result atlas images were compared using theanalysis of variance in order to verify the validity of our method. The result shows that a significant difference isexisted among them with p < 0.001.Key words: osteoarthritis, Kellgren & Lawrence grade, cartilage thickness, magnetic resonance imaging, inter-sub-ject study, analysis of variance