In medical diagnosis, cone beam CT increases the dose absorbed by a patient. However, the radiographic noise (such as quantum noise) in a CT image increases when radiation exposure is reduced. In this paper, we propose a method to improve the CT image degraded by the quantum mottle based on 2-D wavelet transform modulus sum (WTMS). The noise and regular parts of an image can be observed by tracing the evolution of its 2-D WTMS across scales. Our experimental results show that most of the quantum mottle in the 2-D projections is removed by the proposed method and the edges preserved well. We investigate the relation between the number of X-ray photons and the quality of the denoised images. The result shows the possibility that a patient's dose can be reduced about 10% with the same visual quality by our method.

TCP Friendly Rate Control (TFRC) has been widely used in the Internet multimedia streaming applications. However, performance of traditional TFRC algorithm degrades significantly when deployed over wireless networks. Although numerous TFRC variants have been proposed to improve the performance of TFRC over wireless networks, designing a TFRC algorithm with graceful performance both in throughput and fairness still remains a great challenge. In this paper, a novel TFRC algorithm, named TFRC-FIT, is proposed to improve the performance of TFRC over wireless environments. In the proposed approach, the behavior of multiple TFRC flows is simulated in single connection, while the number of simulated flows is adjusted by the network queuing delay. Through this mechanism, TFRC-FIT can fully utilize the capacity of wireless networks, while maintaining good fairness and TCP friendliness. Both theoretical analysis and extensive experiments over hardware network emulator, Planetlab test bed as well as commercial 3G wireless networks are carried out to characterize and validate the performance of our proposed approach.

In small-aperture high frequency surface wave radar, the main-lobe clutter all can be seen as a more severe space spread clutter under the influence of the smaller array aperture. It compromises the detection performance of moving vessels, especially when the target is submerged in the clutter. To tackle this issue, an improved spread clutter estimated canceller, combining spread clutter estimated canceller, adaptive selection strategy of the optimal training samples and rotating spatial beam method, is presented to suppress main-lobe clutter in both angle domain and range domain. According to the experimental results, the proposed algorithm is shown to have far superior clutter suppression performance based on the real data.

A new method for adaptive digital beamforming technique with compressed sensing (CS) for sparse receiving arrays with gain/phase uncertainties is presented. Because of the sparsity of the arriving signals, CS theory can be adopted to sample and recover receiving signals with less data. But due to the existence of the gain/phase uncertainties, the sparse representation of the signal is not optimal. In order to eliminating the influence of the gain/phase uncertainties to the sparse representation, most present study focus on calibrating the gain/phase uncertainties first. To overcome the effect of the gain/phase uncertainties, a new dictionary optimization method based on the total least squares (TLS) algorithm is proposed in this paper. We transfer the array signal receiving model with the gain/phase uncertainties into an EIV model, treating the gain/phase uncertainties effect as an additive error matrix. The method we proposed in this paper reconstructs the data by estimating the sparse coefficients using CS signal reconstruction algorithm and using TLS method toupdate error matrix with gain/phase uncertainties. Simulation results show that the sparse regularized total least squares algorithm can recover the receiving signals better with the effect of gain/phase uncertainties. Then adaptive digital beamforming algorithms are adopted to form antenna beam using the recovered data.

Key agreement for collaborative groups has become an increasingly popular research area. However, most of previous work requires each member to not only maintain the whole key tree structure whose size is O(N), where N is the size of group, but also involve rekeying operation upon each membership change, resulting in high costs in terms of storage, communication and computation and thus suffers from poor scalability. In this paper, we propose a scalable Distributed and collaborative group key agreement scheme using a Virtual Key Tree (D-VKT). Each group member in D-VKT only reserves and maintains partial information of the whole key tree structure with requirement of O(log N). Furthermore, a distributed tree balancing algorithm is presented to keep the whole key tree as balanced as possible for rekeying efficiency. In addition, a distributed group batch rekeying protocol is proposed to further reduce the computation and communication workload of group rekeying in a highly dynamic environment. The experiment results demonstrate that D-VKT can scale to large and dynamic collaborative groups.

To improve the CT image degraded by radiographic noise (such as quantum mottle), we propose a method based on the wavelet transform modulus sum (WTMS). The noise and regular parts of a signal can be observed by tracing the evolution of its WTMS across scales. Our results show that most of the quantum mottle in the projections of Shepp-Logan phantom has been removed by the proposed method with the supposed cranium well preserved. The denoised CT images show good signal to noise ratio in the region of interest. We also have investigated the relation between the number of X-ray photons and the quality of images reconstructed from denoised projections. From experimental results, this method shows the possibility to reduce a patient's dose about 1/10 with the same visual quality.

The proper allocation of network resources from a common physical substrate to a set of virtual networks (VNs) is one of the key technical challenges of network virtualization. While a variety of state-of-the-art algorithms have been proposed in an attempt to address this issue from different facets, the challenge still remains in the context of large-scale networks as the existing solutions mainly perform in a centralized manner which requires maintaining the overall and up-to-date information of the underlying substrate network. This implies the restricted scalability and computational efficiency when the network scale becomes large. This paper tackles the virtual network mapping problem and proposes a novel hierarchical algorithm in conjunction with a substrate network decomposition approach. By appropriately transforming the underlying substrate network into a collection of sub-networks, the hierarchical virtual network mapping algorithm can be carried out through a global virtual network mapping algorithm (GVNMA) and a local virtual network mapping algorithm (LVNMA) operated in the network central server and within individual sub-networks respectively with their cooperation and coordination as necessary. The proposed algorithm is assessed against the centralized approaches through a set of numerical simulation experiments for a range of network scenarios. The results show that the proposed hierarchical approach can be about 5-20 times faster for VN mapping tasks than conventional centralized approaches with acceptable communication overhead between GVNCA and LVNCA for all examined networks, whilst performs almost as well as the centralized solutions.

To improve the direction-of-arrival estimation performance of the small-aperture array, we propose a source localization method inspired by the Ormia fly’s coupled ears and MUSIC-like algorithm. The Ormia can local its host cricket’s sound precisely despite the tremendous incompatibility between the spacing of its ear and the sound wavelength. In this paper, we first implement a biologically inspired coupled system based on the coupled model of the Ormia’s ears and solve its responses by the modal decomposition method. Then, we analyze the effect of the system on the received signals of the array. Research shows that the system amplifies the amplitude ratio and phase difference between the signals, equivalent to creating a virtual array with a larger aperture. Finally, we apply the MUSIC-like algorithm for DOA estimation to suppress the colored noise caused by the system. Numerical results demonstrate that the proposed method can improve the localization precision and resolution of the array.

To solve the problem of nonhomogeneous clutter suppression for moving target detection in High Frequency Surface Wave Radar (HFSWR), a novel nonhomogeneous clutter detector (NHD) is present in this paper. This novel NHD makes an analysis for the clutter constituents with single snapshot based on the over-determined linear equations in space-time adaptive processing (STAP) and distinguish the nonhomogeneous secondary data from the whole secondary data set through calculating the correlation coefficients of the secondary data.

An improved beamformer, which uses joint estimation of the reconstructed interference-plus-noise (IPN) covariance matrix and array steering vector (ASV), is proposed. It can mitigate the problem of performance degradation in situations where the desired signal exists in the sample covariance matrix and the steering vector pointing has large errors. In the proposed method, the covariance matrix is reconstructed by weighted sum of the exterior products of the interferences' ASV and their individual power to reject the desired signal component, the coefficients of which can be accurately estimated by the compressed sensing (CS) and total least squares (TLS) techniques. Moreover, according to the theorem of sequential vector space projection, the actual ASV is estimated from an intersection of two subspaces by applying the alternating projection algorithm. Simulation results are provided to demonstrate the performance of the proposed beamformer, which is clearly better than the existing robust adaptive beamformers.

In recent years, the inherent effectiveness of Peer-to-Peer (P2P) networks has been advocated to address scalability issues in large scale Internet-based on-Demand streaming services. Most of existing works adopt Cache-and-Relay (CR) scheme to exploit a cooperative paradigm among peers. In this paper, we mainly present our practical evaluation study of the scalability of the CR scheme by taking into account of more than 20,000,000 collected real traces. Based on trace-driven simulations, we conclude that the CR scheme is not as effective as previously reported in terms of saving server bandwidth.

Scheduling plays a key role in MapReduce systems. In this paper, we explore the efficiency of an MapReduce cluster running lots of independent and continuously arriving MapReduce jobs. Data locality and load balancing are two important factors to improve computation efficiency in MapReduce systems for data-intensive computations. Traditional cluster scheduling technologies are not well suitable for MapReduce environment, there are some in-used schedulers for the popular open-source Hadoop MapReduce implementation, however, they can not well optimize both factors. Our main objective is to minimize total flowtime of all jobs, given it's a strong NP-hard problem, we adopt some effective heuristics to seek satisfied solution. In this paper, we formalize the scheduling problem as job selection problem, a load balance aware job selection algorithm is proposed, in task level we design a strict data locality tasks scheduling algorithm for map tasks on map machines and a load balance aware scheduling algorithm for reduce tasks on reduce machines. Comprehensive experiments have been conducted to compare our scheduling strategy with well-known Hadoop scheduling strategies. The experimental results validate the efficiency of our proposed scheduling strategy.