Incorporating insights from human visual perception into 3D object processing has become an important research field in computer graphics during the past decades. Many computational models for different applications have been proposed, such as mesh saliency, mesh roughness and mesh skeleton. In this letter, we present a novel Skeleton Modulated Topological Visual Perception Map (SMTPM) integrated with visual attention and visual masking mechanism. A new skeletonisation map is presented and used to modulate the weight of saliency and roughness. Inspired by salient viewpoint selection, a new Loop subdivision stencil decision based rapid viewpoint selection algorithm using our new visual perception is also proposed. Experimental results show that the SMTPM scheme can capture more richer visual perception information and our rapid viewpoint selection achieves high efficiency.

This paper presents a self-clustering algorithm to detect symmetry in images. We combine correlations of orientations, scales and descriptors as a triple feature vector to evaluate each feature pair while low confidence pairs are regarded as outliers and removed. Additionally, all confident pairs are preserved to extract potential symmetries since one feature point may be shared by different pairs. Further, each feature pair forms one cluster and is merged and split iteratively based on the continuity in the Cartesian and concentration in the polar coordinates. Pseudo symmetric axes and outlier midpoints are eliminated during the process. Experiments demonstrate the robustness and accuracy of our algorithm visually and quantitatively.

In this letter, a post-detection signal to noise ratio (SNR) is considered for transmit antenna selection, when a sorted QR decomposition (SQRD) algorithm is used for signal detection in spatial multiplexing (SM) ultra-wideband (UWB) multiple input multiple output systems. The post-detection SNR expression is obtained using a QR factorization algorithm based on a sorted Gram-Schmidt process. The employed antenna selection criterion is to utilize the largest minimum post-detection SNR value. It is shown via simulations that the antenna selection significantly enhances the BER performance of the SQRD-based SM UWB systems on a log-normal multipath fading channel.

Cooperative relay selection, in which one of multiple relays is selected to retransmit the source signal to the destination, has received considerable attention in recent years, because it is a simple way to obtain cooperative diversity in wireless networks. The exact expression of outage probability for a decode-and-forward cooperative relay selection with multiple source and destination antennas over Rayleigh fading channels was recently derived in [9]. In this letter, we derive the exact expressions of outage probability and diversity-multiplexing tradeoff over independent and non-identically distributed Nakagami-m fading channels as an extension of [9]. We then analyze the effects of various parameters such as fading conditions, number of relays, and number of source and destination antennas on the outage probability.

The existing routing protocols for the interplanetary backbone network did not consider future link connection and link congestion. A novel routing protocol named CAMARP for the interplanetary backbone network is proposed in this letter. We use wait delay to consider future link connection and make the best next hop selection. A load balancing mechanism is used to avoid congestion. The proposed method leads to a better and more efficient distribution of traffic, and also leads to lower packet drop rates and higher throughput. CAMARP demonstrates good performance in the experiment.

The conventional semi-orthogonal user pairing algorithm in uplink virtual MIMO systems can be used to improve the total system throughput but it usually fails to maintain good throughput performance for users experiencing relatively poor channel conditions. A novel user paring algorithm is presented in this paper to solve this fairness issue. Based on our analysis of the MMSE receiver, a new criterion called “inverse selection” is proposed for use in conjunction with the semi-orthogonal user selection. Simulation results show that the proposed algorithm can significantly improve the throughput of users with poor channel condition at only a small reduction of the overall throughput.

We propose an optimal access-point (AP) selection algorithm for maximizing the aggregated throughput of each AP (system throughput) while preserving newly arrived-user throughput in multi rate WLAN system. In our algorithm, newly arrived users cooperate with a wireless local area network (WLAN) system they are trying to use, i.e., they are willing to move toward an appropriate AP before the newly arrived user connects to AP. To select the AP by using our AP selection algorithm, the newly arriving users request two novel parameter values, “the minimum acceptable throughput” with which newly arrived users can be satisfied and “the minimum movable distance” in which a user can move to an appropriate AP. While preserving these conditions, we maximize system throughput. When users cannot obtain a throughput greater than “the minimum acceptable throughput” with our proposed AP selection algorithm, they are rejected. Because, if users use streaming applications, which have strict bandwidth demands, with a very low bit-rate connection, they will not be satisfied. Thus, the newly arrived users having low bit-rate connection may be allowed to be rejected before the newly arrived user connects. In this paper, we show the optimal AP by using theoretical proof. We discuss the effectiveness of our proposed AP selection algorithm by using numerical analysis. We also clarify and analyze the characteristics of system throughput. Moreover, we show that a newly arrived user can select the movable distance and acceptable throughput by using examples from graphs depicting every position of newly arrived users. By using the graphs, we also show the relationship between the two parameters (the movable distance and the acceptable throughput) and the optimal AP, and the relationship between the two parameters and optimal system throughput when the movable distance and acceptable throughput are variable.

Joint transmit and receive antenna selection (JTRAS) is proposed for the multiple-input multiple-output (MIMO) two-way relaying channel. A simple and closed-form lower bound on the outage probability of JTRAS is derived. Furthermore, asymptotic analysis reveals that JTRAS can attain the maximum achievable diversity order of the MIMO dual-hop relaying channel.

To improve the observability during the post-silicon validation, it is the key to select the limited trace signals effectively for the data acquisition. This paper proposes an automated trace signal selection algorithm, which uses the pruning-based strategy to reduce the exploration space. First, the restoration range is covered for each candidate signals. Second, the constraints are generated based on the conjunctive normal form (CNF) to avoid the conflict. Finally the candidates are selected through pruning-based enumeration. The experimental results indicate that the proposed algorithm can bring higher restoration ratios and is more effective compared to existing methods.

The quasi-ARX neurofuzzy (Q-ARX-NF) model has shown great approximation ability and usefulness in nonlinear system identification and control. It owns an ARX-like linear structure, and the coefficients are expressed by an incorporated neurofuzzy (InNF) network. However, the Q-ARX-NF model suffers from curse-of-dimensionality problem, because the number of fuzzy rules in the InNF network increases exponentially with input space dimension. It may result in high computational complexity and over-fitting. In this paper, the curse-of-dimensionality is solved in two ways. Firstly, a support vector regression (SVR) based approach is used to reduce computational complexity by a dual form of quadratic programming (QP) optimization, where the solution is independent of input dimensions. Secondly, genetic algorithm (GA) based input selection is applied with a novel fitness evaluation function, and a parsimonious model structure is generated with only important inputs for the InNF network. Mathematical and real system simulations are carried out to demonstrate the effectiveness of the proposed method.

This paper describes a technique for overcoming the model shrinkage problem in automatic speech recognition (ASR), which allows application developers and users to control the model size with less degradation of accuracy. Recently, models for ASR systems tend to be large and this can constitute a bottleneck for developers and users without special knowledge of ASR with respect to introducing the ASR function. Specifically, discriminative language models (DLMs) are usually designed in a high-dimensional parameter space, although DLMs have gained increasing attention as an approach for improving recognition accuracy. Our proposed method can be applied to linear models including DLMs, in which the score of an input sample is given by the inner product of its features and the model parameters, but our proposed method can shrink models in an easy computation by obtaining simple statistics, which are square sums of feature values appearing in a data set. Our experimental results show that our proposed method can shrink a DLM with little degradation in accuracy and perform properly whether or not the data for obtaining the statistics are the same as the data for training the model.

Spatial Multiplexing with precoding provides an opportunity to enhance the capacity and reliability of multi-input multi-output orthogonal frequency division multiplexing (MIMO-OFDM) systems. However, precoder selection may require knowledeg of all subcarriers, which may cause a large amount of feedback if not properly designed. In addition, if the maximum-likelihood (ML) detector is employed, the conventional precoder selection that maximizes the minimum stream SNR is not optimal in terms of the error probability. In this paper, we propose to reduce the feedback overhead by introducing a ML clustering concept in selecting the optimal precoder for ML detector. Numerical results show that the proposed precoder selection based on the ML clustering provides enhanced performance for ML receiver compared with conventional interpolation and clustering algorithms.

We propose an outband sensing-based IEEE 802.11h protocol without a full dynamic frequency selection (DFS) test. This scheme has two features. Firstly, every station performs a cooperative outband sensing, instead of inband sensing during a quiet period. And secondly, as soon as a current channel becomes bad, every station immediately hops to a good channel using the result of outband sensing. Simulation shows the proposed scheme increases network throughput against the legacy IEEE 802.11h.

In this letter, we propose an efficient relay antenna selection algorithm for the amplify and forward (AF) two-way multiple-input multiple-output (MIMO) relay systems with analogue network coding (ANC). The proposed algorithm greedily selects the additional receive-transmit antenna pair that provides the maximum sum-rate. An iterative computation method is also designed to evaluate the sum-rate efficiently.

Feature selection (FS) plays an important role in pattern recognition and machine learning. FS is applied to dimensionality reduction and its purpose is to select a subset of the original features of a data set which is rich in the most useful information. Most existing FS methods based on rough set theory focus on dependency function, which is based on lower approximation as for evaluating the goodness of a feature subset. However, by determining only information from a positive region but neglecting a boundary region, most relevant information could be invisible. This paper, the maximal lower approximation (Max-Certainty) – minimal boundary region (Min-Uncertainty) criterion, focuses on feature selection methods based on rough set and mutual information which use different values among the lower approximation information and the information contained in the boundary region. The use of this idea can result in higher predictive accuracy than those obtained using the measure based on the positive region (certainty region) alone. This demonstrates that much valuable information can be extracted by using this idea. Experimental results are illustrated for discrete, continuous, and microarray data and compared with other FS methods in terms of subset size and classification accuracy.

We describe a multi-step approach for automatic segmentation of the femoral head and the acetabulum in the hip joint from three dimensional (3D) CT images. Our segmentation method consists of the following steps: 1) construction of the valley-emphasized image by subtracting valleys from the original images; 2) initial segmentation of the bone regions by using conventional techniques including the initial threshold and binary morphological operations from the valley-emphasized image; 3) further segmentation of the bone regions by using the iterative adaptive classification with the initial segmentation result; 4) detection of the rough bone boundaries based on the segmented bone regions; 5) 3D reconstruction of the bone surface using the rough bone boundaries obtained in step 4) by a network of triangles; 6) correction of all vertices of the 3D bone surface based on the normal direction of vertices; 7) adjustment of the bone surface based on the corrected vertices. We evaluated our approach on 35 CT patient data sets. Our experimental results show that our segmentation algorithm is more accurate and robust against noise than other conventional approaches for automatic segmentation of the femoral head and the acetabulum. Average root-mean-square (RMS) distance from manual reference segmentations created by experienced users was approximately 0.68 mm (in-plane resolution of the CT data).

We propose a distributed node selection (DNS) scheme that guarantees quality of service (QoS) of the scalable video broadcasting system over wireless channels. The proposed DNS scheme chooses the destination node based on the SVC layer information, and it selects the best relay from a set of competing candidate nodes by considering two factors: 1) wireless channel conditions between destination and relay candidates and 2) scalable video's layer information. In simulations, the performance of the proposed scheme in terms of quality gains, complexity (overhead) and applicability was examined.

In this paper, we consider an amplify-and-forward cooperative wireless network in which network nodes use multiple input multiple output (MIMO) spatial division multiplexing (SDM) to communicate with one another. We examine the problem of distributed cooperative relay selection and signal combining at the destination. First, we propose three distributed relay selection algorithms based on the maximum channel gains, the maximum harmonic mean of the channel gains, and the minimum mean squared error (MSE) of the signal estimation. Second, we propose a minimum mean square error (MMSE) signal combining scheme which jointly serves as the optimal signal combiner and interference canceler. It is shown that the MSE selection together with the MMSE combining achieves the maximal diversity gain. We also show that in MIMO-SDM cooperative networks increasing the number of candidate nodes does not help to improve the BER performance as opposed to the cooperative networks where each node is equipped with only single antenna. A practical approach to implementation of the combiner based on the current wireless access network protocols will also be presented.

This letter studies cellular controlled short-range communication in OFDMA networks. The network needs to decide when to allow direct communication between a closely located device-to-device (D2D) pair instead of conveying data from one device to the other via the base station and when not to, in addition to subchannel and power allocation. Our goal is to maximize the total network throughput while guaranteeing the rate requirements of all users. For that purpose, we formulate an optimization problem subject to subchannel and power constraints. A scheme which combines a joint mode selection and subchannel allocation algorithm based on equal power allocation with a power reallocation scheme is proposed. Simulation results show that our proposed scheme can improve the network throughput and outage probability compared with other schemes.

This letter investigates the problem of distributed channel selection in cognitive radio ad hoc networks (CRAHNs) with heterogeneous spectrum opportunities. Firstly, we formulate this problem as a local congestion game, which is proved to be an exact potential game. Then, we propose a spatial best response dynamic (SBRD) to rapidly achieve Nash equilibrium via local information exchange. Moreover, the potential function of the game reflects the network collision level and can be used to achieve higher throughput.