Improving Nonlocal Means Method via a No-reference Image Content Metric for MRI Denoising

The patch matching of the traditional Nonlocal means （NLM） filter mainly depends on structure similarity and cannot adapt to the patch rotation or mirroring transformation. Therefore, designing a measure with rorationally invariant similarity is of significant importance for improving the effectiveness of patch comparison of NLM. This paper proposes to apply a no-reference image content metric with the rotation-invariance to NLM for denoising Magnetic resonance （MR） images. The metric measures quantitatively the content of a patch in an image, including sharpness, contrast, and geometric features such as textures and edges. The metric values for every patch are computed and added into the Gaussian matching kernel of NLM so as to effectively perform patch matching. The main advantage of the proposed method is that it does not need to rotate patches in different orientations during patch matching. Experimental results show that the proposed method is superior to the traditional NLM, the state-of-the-art method Block-matching and 3- D （BM3D） filtering and the Unbiased NLM （UNLM） for MRI denoislng.     

An Improved Combined Multiscale Method for Image Denoising

This paper proposes an improved combined multiscale method for image denoising. It uses the adaptive Bayesian shrinkage on the Undecimated wavelet transform （UWT） coefficients to denoise homogeneous areas, and the hard thresholding on the Fast discrete curvelet transform （FDCT） coefficients to denoise areas with edges. At the same time, it adopted a simple method to fuse two filtered images by UWT and FDCT separately. The experimental results indicate that this method has better performance with easier implementation and lower computational complexity than the existing combined methods.     

Adaptive Method of Velocity Control for Cardiac CT

The image for cardiac CT is blurred because the heart is in motion. Currently, fixed scan velocity for cardiac CT is used and approximate projection can only be achieved due to the non-synchronism between the heartbeat and scan velocity. In order to achieve ideal projection, the control method for selectable scan velocity is proposed according to heartbeat. The velocity varies with the heart beat, temporal resolution and control parameters to achieve a desirable temporal resolution in nonperiodic heartbeat. The selectable scan velocity can provide ideal projection with uniform distribution and the same phases. The simulation result shows that the variable velocity method outperforms the current fixed velocity method.     

Quadratic Regularization Design for 2-D CT

Statistical methods for tomographic image reconstruction have improved noise and spatial resolution properties that may improve image quality in X-ray computed tomography (CT). Penalized weighted least squares (PWLS) methods using conventional quadratic regularization lead to nonuniform and anisotropic spatial resolution due to interactions between the weighting, which is necessary for good noise properties, and the regularizer. Previously, we addressed this problem for parallel-beam emission tomography using matrix algebra methods to design data-dependent, shift-variant regularizers that improve resolution uniformity. This paper develops a fast angular integral mostly analytical (AIMA) regularization design method for 2-D fan-beam X-ray CT imaging, for which parallel-beam tomography is a special case. Simulation results demonstrate that the new method for regularization design requires very modest computation and leads to nearly uniform and isotropic spatial resolution in transmission tomography when using quadratic regularization.     

Robust Quantization of Discrete-Time Signals with Independent Samples

TheN-level uniform quantizer on[-c,c]plus the assignment ofy_{0}deg = -(a _{s}+ c)/2andy_{N+1}deg = (a_{s}+ c)/2to signal values falling in the saturation regions[-a_{s},- c) and (c,a_{s}], respectively, is shown to be the minimax(N + 2)-level quantizer with a nonsaturating input range[-c,c]. The performance criterion considered is the mean weighted quantization error and the input signals are only required to be amplitude bounded bypm a_{s}wherea_{s} > c > 0. The worst case input signal marginal probability distributions are shown to be discrete. From the derivation of this result, the minimax error can be computed. An example is given which illustrates the performance of the minimax quantizer for several input ranges against different input signal probability distributions.     

Image denoising based on multiscale singularity detection for cone beam CT breast imaging

It was recently reported that the real-time flat panel detector-based cone-beam computed tomography (CBCT) breast imaging can help improve the detectability of small breast tumors with an X-ray dose comparable to that of the conventional mammography. In this paper, an efficient denoising algorithm is proposed to further reduce the X-ray exposure level required by a CBCT scan to acquire acceptable image quality. The proposed wavelet-based denoising algorithm possesses three significant characteristics: 1) wavelet coefficients at each scale are classified into two categories: irregular coefficients, and edge-related and regular coefficients; 2) noise in irregular coefficients is reduced as much as possible without producing artifacts to the denoised images; and 3) for the edge-related and regular coefficients, if they are at the first decomposition level, they are further denoised, otherwise, no modifications are made to them so as to obtain good visual quality for diagnosis. By applying the proposed denoising algorithm to the filtered projection images, the X-ray exposure level necessary for the CBCT scan can be reduced by up to 60% while obtaining clinically acceptable image quality. This denoising result indicates that in the clinical application of CBCT breast imaging, the patient radiation dose can be significantly reduced.     

Self-Organization Approaches for Optimization in Cognitive Radio Networks

Cognitive radio （CR） is regarded as a promising technology for providing a high spectral efficiency to mobile users by using heterogeneous wireless network architectures and dynamic spectrum access techniques. However, cognitive radio networks （CRNs） may also impose some challenges due to the ever increasing complexity of network architecture, the increasing complexity with configuration and management of large-scale networks, fluctuating nature of the available spectrum, diverse Quality-of-Service （QoS） requirements of various applications, and the intensifying difficulties of centralized control, etc. Spectrum management functions with self-organization features can be used to address these challenges and realize this new network paradigm. In this paper, fundamentals of CR, including spectrum sensing, spectrum management, spectrum mobility and spectrum sharing, have been surveyed, with their paradigms of self-organization being emphasized. Variant aspects of self- organization paradigms in CRNs, including critical functionalities of Media Access Control （MAC）- and network-layer operations, are surveyed and compared. Furthermore, new directions and open problems in CRNs are also identified in this survey.     

Joint Optimization of Subchannel Reassignment and Power Adaptation for Multicarrier Cooperative Systems

An amplify-and-forward multicarrier cooperative system was proposed in this paper. The optimal resource-allocation problem was investigated in terms of improving data rate. An effective resource-allocation algorithm was proposed based on three techniques, adaptive modulation, subchannel reassignment design and transmit power adaptation. The designed subchannel reassignment not only increases the average data rate, but also raises the threshold of ＂water-filling＂ in optimizing power allocation that can further increase data rate. Since the power allocation is based on the designed subchannel reassignment, the complexity of the proposed algorithm is reduced greatly. Simulation results show that the proposed algorithm effectively increases the system＇s average data rate.     

On the Design and Temporal Petri Net Verification of Grid Commerce Architecture

The emergence of Grid Computing settings lays a solid foundation for designing and constructing a next generation E-commerce environment. This work presents a new E-commerce architecture based on a Grid Computing environment, and models and analyzes the architecture using Temporal Petri nets （TPNs）. It can make sufficiently use of the Grid resources to implement various electronic bargains between the geographically dispersed users. By means of its TPN model, some main temporal properties are represented and verified formally.     

A Fuzzy Similarity-based Clustering Optimized by Particle Swarm Optimization

Traditional fuzzy clustering algorithms based on objective function is unable to determine the optimum number of clusters, sensitive to the initial cluster centers, and easily sunk into the issue of local optimum. A Fuzzy similarity-based clustering （FSBC） algorithm is proposed in this paper. This method consists three phases： first, the objective function is modified by integrating Fuzzy C-means （FCM） and Possibilistic C-means （PCM） method; second, using the density function from data for similarity-based clustering to automatically generate initial prototype without requesting users to specify; finally, the iteration process optimized by Particle swarm optimization （PSO） to obtain appropriate adjustment parameters that can provide better results, which avoids the local minimum problems of traditional methods. The experimental results on the synthetic data and UCI standard data sets show that the proposed algorithm has greater searching capability, less computational complexity, higher clustering precision.     

A Novel Surface Treatment Method for Improving the Antenna System Performance of Mobile Phones with Stainless Steel Frame

A novel surface treatment method of plat-ing Cu＋PPS film/coating on a mobile phone＇s stainless steel frame for improving the antenna system efficiencies is proposed. The mobile phone was measured in free space, in a silicon cover, and in the hand and cover si- multaneously. It＇s found that with this surface treat-ment, the total efficiency of the antenna system can be improved in all the four cases respectively by 14.22%, 1.38%, 15.19% and 1.72% at 940MHz （GSM900：880- 960MHz）, 2.59%, 3.21%, 4.81% and 1.43% at 1720MHz （DCS：1710-1880MHz） and 6.34%, 2.85%, 9.83% and 2.32% at 2100 MHz （WCDMA：1920-2170MHz）. This low- cost surface treatment method is an important break- through to improve antenna system performance of mobile phones especially for those with a stainless steel frame, and suitable for mass production.     

Optimal energy efficiency capacity under joint design of sensing and transmission duration for cognitive radio system

Due to its opportunistic spectrum sharing capability, cognitive radio （CR） has been proposed as a fundamental solution to alleviate the contradiction between spectrum scarcity and inefficient utilization of licensed spectrum. In CR system （CRS）, to efficiently utilize the spectrum resource, one important issue is to allocate the sensing and transmission duration reasonably. In this paper, the evaluation metric of energy efficiency, which represented the total number of bits that were delivered with per joule of energy consumed, is adopted to evaluate the proposed scheme. We study a joint design of energy efficient sensing and transmission durations to maximize energy efficiency capacity （EEC） of CRS. The tradeoff between EEC and sensing and transmission durations are formulized as an optimization problem under constraints on target detection probability of secondary users （SUs） and toleration interference threshold of primary users （PUs）. To obtain the optimal solution, optimizing sensing duration and transmission duration will be first performed separately. Then, a joint optimization iterative algorithm is proposed to search the optimal pair of sensing and transmission durations. Analytical and simulation results show that there exists a unique duration pair where the EEC is maximized, and that the EEC of the proposed joint optimization algorithm outperforms that of existed algorithms. Furthermore, the simulation results also reveal that the performance of the proposed low complexity iterative algorithm is comparable with that of the exhaustive search scheme.     

A Probability Driven Quadri-Stage Contention Protocol for Mobile Ad Hoc Networks

The paper proposes a new contention based Time division multiple access （TDMA） Medium access control （MAC） protocol in wireless ad hoc networks, which can be termed Quadri-stage contention protocol （QSCP）. The QSCP protocol is free of both the ＂hidden terminal＂ and the ＂exposed terminal＂ problems. It is distributive and arbitrarily scalable. In the dedicated contention stage, nodes exchange their ＂Normalized accumulation and dissipation rate of payload （NADROP）＂ messages in 2-hop neighborhood, and then run a contention probability calculation algorithm to decide the probability of sending a contention frame. The algorithm guaran- tees that the number of time slot reserved by a node is proportional to its NADROP value, which can remarkably improve the end-to-end throughput when network load is heavy, and is more efficient in energy consumption. The simulation results demonstrate that QSCP performs better than the existing protocols.     

Mechanism Design Based Nodes Selection Model for Threshold Key Management in MANETs

The design of threshold based distributed Certification authority （CA） has been proposed to provide secure and efficient key management service in Mobile ad hoc networks （MANETs）, but most of previous work ig- nore the efficiency and effectiveness and assuming there are always honest nodes performing the service. Focus- ing on developing a model to select a coalition of nodes dynamically and optimally to carry out the threshold key management service in MANETs, we formulate the dy- namic nodes selection problem as combinatorial optimiza- tion problem, with the objectives of maximizing the success ratio of the service and minimizing the nodes, cost of secu- rity and energy, and then extend the payment structure of the classical Vickrey, Clarke and Groves （VCG） mechanism design framework to ensure truth-telling is the dominant strategy for any node in our scenario. Compared with ex- isting works in the presence of selfish nodes, the proposed model enjoys an improvement of both the success ratio of key management service and lifetime of the network, and a reduction of both the cost of participating nodes and compromising probability of MANETs.     

Labeling Algorithm for Face Detection Using Skin and Hair Characteristics

This research presents an algorithm for face detection based on color images using three main components： skin color characteristics, hair color characteristics, and a decision structure which converts the obtained information from skin and hair regions to labels for identifying the object dependencies and rejecting many of the incorrect decisions. Here we use face color characteristics that have a good resistance against the face rotations and expressions. This algorithm is also capable of being combined with other methods of face recognition in each stage to improve the detection.     

Contour-Based Multi-Source Information Fusion for Motion Segmentation

A motion segmentation framework that effectively exploited the multiple sources of image information and fused these sources of the information synergisti-cally was proposed to serve the purpose of motion segmen- tation. A Markov process was formulated for motion seg- mentation in which two feature spaces were established to estimate the state transition Probability density function （PDF） and the initial state, respectively. An information fusion space was developed such that each motion struc-ture was described as a single distribution in this space. The proposed framework can naturally embed the evolution equations of the active contour methods into the seg-mentation to achieve contour-based segmentation results. Extensive empirical evaluations demonstrate the robust-ness and the promise of this framework.     

Best Effort Spectrum Allocation Scheme for Femtocell Networks in Dense Deployment

In orthogonal frequency division multiple access（OFDMA） based femtocell networks,the co-tier interference among femto base stations（FBS） becomes important in multiuser and densely deployed environment.In order to mitigate the co-tier interference and enhance the system total throughput,this paper proposed a best effort spectrum allocation scheme based on the extension of graph theory.In the scheme,a controller was proposed to collect the channel state information（CSI）of all femtocell user equipments（FUEs） in a certain range.Then,the controller evaluated the signal-to-interference Ratio（SIR） of each FUE and determined the set of its interference neighbors.By calculating the received power matrix（RPM） among FUEs and building interference graph matrix（IGM）,different spectrum resource blocks（RBs） were assigned to the users with interference relation,while users without interference relation shared the same RBs,which could increase the spectrum efficiency.Simulation results show that the proposed algorithm can significantly improve the RB usage efficiency compared with the basic graph coloring theory,and more than 80% improvement can be acquired in dense deployment scenario.Besides,the throughput of both cell edge macro user equipments（MUEs） and cell edge FUEs is guaranteed on the premise of low interference.     

Action-Based Access Control Modelt

Access control is one of the powerful and generalized approaches for restricted resource access. The environmental state is introduced and the term “action” is defined based on roles, temporal states and environmental states. Actions can be used to capture security-relevant aspects of roles, environmental and temporal states in different information systems. Then, the action hierarchy, environmental hierarchy, temporal hierarchy and Action- based access control （ABAC） model are presented. ABAC is compared with the existing models and the result shows that the ABAC model can solve the problem of access control in information systems with mobile computation for its convenient and flexible designs. An application example of ABAC model is described at the end of the paper.     

Construction and Count of 1-Resilient Rotation Symmetric Boolean Functions on pr Variables

Construction and count of 1-resilient Rotation symmetric Boolean functions （RSBFs） on pr variables are demonstrated. It is proved that constructions of 1-resilient RSBFs on pr variables are equivalent to solving an equation system. An accurate enumeration formula of all 1-resilient RSBFs on pr variables is also proposed. Some examples are given, and the exact numbers of 1-resilient RSBFs on 8 and 9 variables are obtained respectively.     

A Scalable Peer-to-Peer Web Services Overlay for Semantic Discovery

The growing number of web services advocates distributed discovery infrastructures which are semantics-enabled. We introduce a novel approach for semantic discovery of web services in structured P2P-based （Peer-to-Peer） registries. We partition concepts into different CGs （Concept groups） and index these ordered concept sets in a two-phase semantic routing mechanism. Each node and query has one concept set as its unique ID. For each concept in the query, current node first route the query to a node with nodeID that shares a prefix as long as with the query, but a longer CG. Second, the query will be routed to a node sharing a longer prefix than that of the current node. Combining with semantic similarity calculation, our proposal guarantees that most existing services matching a query will be found with bounded costs in terms of number of nodes involved. The scalability and reliability of our approach are also confirmed through emulation tests.