This letter investigates the outage performance of a joint transmit and receive antenna selection scheme in an amplify-and-forward two-way relaying system with channel estimation error. A closed-form approximate outage probability expression is derived, based on which the asymptotic outage probability expression is derived to get an insight on system's outage performance at high signal-to-noise (SNR) region. Monte Carlo simulation results are presented to verify the analytical results.

Feature selection is a technique to screen out less important features. Many existing supervised feature selection algorithms use redundancy and relevancy as the main criteria to select features. However, feature interaction, potentially a key characteristic in real-world problems, has not received much attention. As an attempt to take feature interaction into account, we propose 1-LSMI, an 1-regularization based algorithm that maximizes a squared-loss variant of mutual information between selected features and outputs. Numerical results show that 1-LSMI performs well in handling redundancy, detecting non-linear dependency, and considering feature interaction.

As the demand for high-throughput communications in wireless LANs (WLAN) increases, the need for expanding channel bandwidth also increases. However, the use of wider band channels results in a decrease in the number of available channels because the total available bandwidth for WLAN is limited. Therefore, if multiple access points (APs) are in proximity and the cells overlap, it is difficult for each AP to use an orthogonal channel and competition increases between APs using the same channel. Coordination of APs is one promising approach; however, it is impractical to control all APs in WLAN systems. To cope with this problem, we proposed to analyze throughput performances of a multibandwidth channel selection by the coordinating APs at Nash equilibria, which can be considered as operating points for independent channel selection by APs. To clarify the effect of coordinating APs, we assume a simple scenario where the cells of three or more APs overlap, and each AP can select multibandwidth channels to maximize their own throughput. Through game-theoretic analysis, we find that the coordinated APs are able to select channels more effectively than if each AP independently selects channels. Consequently, the total throughput of the coordinated APs at Nash equilibria is significantly improved.

Corpus-based concatenative speech synthesis has been widely investigated and deployed in recent years since it provides a highly natural synthesized speech quality. The amount of computation required in the run time, however, can often be quite large. In this paper, we propose early stopping schemes for Viterbi beam search in the unit selection, with which we can stop early in the local Viterbi minimization for each unit as well as in the exploration of candidate units for a given target. It takes advantage of the fact that the space of the acoustic parameters of the database units is fixed and certain lower bounds of the concatenation costs can be precomputed. The proposed method for early stopping is admissible in that it does not change the result of the Viterbi beam search. Experiments using probability-based concatenation costs as well as distance-based costs show that the proposed methods of admissible stopping effectively reduce the amount of computation required in the Viterbi beam search while keeping its result unchanged. Furthermore, the reduction effect of computation turned out to be much larger if the available lower bound for concatenation costs is tighter.

In this paper, we propose four different strategies of node pair selection in multiple input multiple output (MIMO) interference channel where interference alignment (IA) is considered as a transceiver design method. In the first scheme, we consider the maximization of the sum rate by selecting node pairs in a brute force way. We also propose a sub-optimal sum rate maximization scheme with lower complexity than the first scheme. In the third scheme, we aim to minimize the number of links among pairs which incurs the outage in MIMO interference channel. In the fourth scheme, we suggest a max-min node pair selection scheme to enhance both the sum rate and the outage probability. Simulation results demonstrate that all our proposed node pair selection schemes can increase the sum rate but also while also reducing the outage probability compared to the scheme with random node pair selection.

Multiple-input multiple-output (MIMO) systems with antenna selection are practical in that they can alleviate the computational complexity at the receiver and achieve good reception performance. Channel correlation, not just carrier-to-noise ratio (CNR), has a great impact on reception performance in MIMO channels. We propose a practical receive antenna subset selection algorithm with reduced complexity that uses the condition number of the partial channel matrix and a predetermined CNR threshold. This paper describes the algorithm and its performance evaluation by both computer simulation and indoor experiments using a prototype receiver and received signals obtained in an actual mobile outdoor experiment. The results confirm that our proposed method provides good bit error rate performance by setting the CNR threshold properly.

This letter presents the ideas and methods of the winning solution* for the Kaggle Algorithmic Trading Challenge. This analysis challenge took place between 11th November 2011 and 8th January 2012, and 264 competitors submitted solutions. The objective of this competition was to develop empirical predictive models to explain stock market prices following a liquidity shock. The winning system builds upon the optimal composition of several models and a feature extraction and selection strategy. We used Random Forest as a modeling technique to train all sub-models as a function of an optimal feature set. The modeling approach can cope with highly complex data having low Maximal Information Coefficients between the dependent variable and the feature set and provides a feature ranking metric which we used in our feature selection algorithm.

Due to security demand of society development, real-time face recognition has been receiving more and more attention nowadays. In this paper, a real-time face recognition system via Local Binary Pattern (LBP) plus Improved Biomimetic Pattern Recognition (BPR) has been proposed. This system comprises three main steps: real-time color face detection process, feature extraction process and recognition process. Firstly, a color face detector is proposed to detect face with eye alignment and simultaneous performance; while in feature extraction step, LBP method is adopted to eliminate the negative effect of the light heterogeneity. Finally, an improved BPR method with Selective Sampling construction is applied to the recognition system. Experiments on our established database named WYU Database, PUT Database and AR Database show that this real-time face recognition system can work with high efficiency and has achieved comparable performance with the state-of-the-art systems.

The objective of our research is to build a statistical test that can evaluate different risks of a portfolio selection model with fuzzy data. The central points and radiuses of fuzzy numbers are used to determine the portfolio selection model, and we statistically evaluate the best return by a fuzzy statistical test. Empirical studies are presented to illustrate the risk evaluation of the portfolio selection model with interval values. We conclude that the fuzzy statistical test enables us to evaluate a stable expected return and low risk investment with different choices for k, which indicates the risk level. The results of numerical examples show that our method is suitable for short-term investments.

With the wide-spread use of high-speed network connections and high performance mobile/sensor terminals available, new interactive services based on real-time contents have become available over the Internet. In these services, end-nodes (e.g, smart phone, sensors), which are dispersed over the Internet, generates the real-time contents (e.g, live video, sensor data about human activity), and those contents are utilized to support many kinds of human activities seen in the real world. For the services, a new decentralized contents distribution system which can accommodate a large number of content distributions and which can minimize the end-to-end streaming delay between the content publisher and the subscribers is proposed. In order to satisfy the requirements, the proposed content distribution system is equipped with utilizing two distributed resource selection methods. The first method, distributed hash table (DHT)-based contents management, makes it possible for the system to efficiently decide and locate the server managing content distributions in completely decentralized manner. And, the second one, location-aware server selection, is utilized to quickly select the appropriate servers that distribute the streamed contents to all subscribers in real time. This paper considers the performance of the proposed resource selection methods using a realistic computer simulation and shows that the system with the proposed methods has scalability for a large-scale distributed system that attracts a very large number of users, and achieves real-time locating of the contents without degrading end-to-end streaming delay of content.

The design and measured results of a 120 GHz/140 GHz dual-channel OOK (ON-OFF Keying) receiver are presented in this paper. Because a signal with very wide frequency width is difficult to process in a single-channel receiver, a dual-channel configuration with channel selection is adopted in the proposed receiver. The proposed receiver is fabricated using 65 nm CMOS technology. The measured data rate of 3.0 and 3.6 Gbps, minimum sensitivity of -25.6 and -27.1 dBm, communication distance of 0.30 and 0.38 m are achieved in the 120- and 140-GHz receiver, respectively. The correct channel selection is achieved in the 120-GHz receiver. These results indicate the possibility of the CMOS multiband receiver operating at over 100 GHz for low-power high-speed proximity wireless communication systems.

The traditional selection cooperation scheme selects the relay with best instantaneous receive signal-to-noise ratio to forward the message and achieves good outage performance, which may however cause poor fairness among relays. In this letter, we propose two practical selection cooperation schemes in Decode-and-Forward (DF) fashion to improve the fairness of relay selection. Numerical results suggest that both of the proposed schemes can achieve fairness close to the strict fairness scheme without outage performance deterioration. It is also validated that these schemes have lower complexities than traditional ones and therefore are practical for real networks.

In this work, we analyze the performance of cognitive amplify-and-forward (AF) relay networks under the spectrum sharing approach. In particular, by assuming that the AF relay operates in the semi-blind mode (fixed-gain), we derive the exact closed-form expressions of the outage probability for the cognitive relaying (no direct link) and cognitive cooperative (with direct link) systems. Simulation results are presented to verify the theoretical analysis.

In this letter, we analyze the outage performance of cognitive spectrum sharing in two-way relaying systems. We derive expressions of outage probability for the primary and secondary network over independent but not necessarily identically distributed (i.n.i.d.) Rayleigh fading channels. Monte Carlo simulations are presented to verify the theoretical analyses.

In this paper, we study four simple but fundamental cooperative protocols operating in the decode-and-forward (DF) fashion. Intuitively, finding an appropriate relay for such protocols may greatly improve the outage performance in practice. To this end, we investigate the issue of relay selection in this paper. Specifically, using the asymptotic outage probability, we define and derive the cooperative gain (CG) which quantitatively evaluates the superiority of cooperation over direct transmission. To simplify the process of relay selection, we derive the cooperative region (CR) where a relay is necessarily invoked to aid the communication from source to destination. With the aid of CG and CR, we propose our relay selection algorithm requiring the geographical information rather than the instantaneous channel state information (CSI), and predict the optimal relay locations. In addition, two diversity bounds are also prepared and compared. Finally, both simulations and numerical results are provided on the asymptotic outage probability, CG and CR.

This paper proposes a spectrum-overlapped resource management (SORM) technique where each user equipment (UE) can ideally obtain the frequency selection diversity gain under multi-user environments. In the SORM technique for cellular systems, under assumption of adopting a soft canceller with minimum mean square error (SC/MMSE) turbo equalizer, an evolved node B (eNB) accepts overlapped frequency resource allocation. As a result, each UE can use the frequency bins having the highest channel gain. However, the SORM becomes non-orthogonal access when the frequency bins having high channel gain for UEs are partially identical. In this case, the inter-user interference (IUI) caused by overlapping spectra among UEs is eventually canceled out by using the SC/MMSE turbo equalizer. Therefore, SORM can achieve better performance than orthogonal access e.g. FDMA when the IUI is completely canceled. This paper demonstrates that SORM has the potential to improve transmission performance, by extrinsic information transfer (EXIT) analysis. Moreover, this paper evaluates the block error rate (BLER) performance of the SORM and the FDMA. Consequently, this paper shows that the SORM outperforms the FDMA.

In this paper, we examine a new P2P traffic localization approach that exploits peer selection adaptation (i.e., preferring peers who are likely to provide better performance), called Netpherd. Netpherd enables peers to communicate with local domain peers by manipulating networking performance across network domains (i.e., adding an artificial delay to inter-domain traffic). Our feasibility study shows that Netpherd reduces the inter-domain traffic by influencing peer selection adaptation. Netpherd also improves download performance of the peers who know many local domain peers. We discuss one guideline to improve Netpherd based on the feasibility study and verify the guideline with evaluation results.

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.

A novel online speaker clustering method based on a generative model is proposed. It employs an incremental variant of variational Bayesian learning and provides probabilistic (non-deterministic) decisions for each input utterance, on the basis of the history of preceding utterances. It can be expected to be robust against errors in cluster estimation and the classification of utterances, and hence to be applicable to many real-time applications. Experimental results show that it produces 50% fewer classification errors than does a conventional online method. They also show that it is possible to reduce the number of speech recognition errors by combining the method with unsupervised speaker adaptation.

In the letter, we propose an improved histogram shifting (HS) based reversible data hiding scheme for small payload embedding. Conventional HS based schemes are not suitable for low capacity embedding with relatively large distortion due to the inflexible side information selection. From an analysis of the whole HS process, we develop a rate-distortion model and provide an optimal adaptive searching approach for side information selection according to the given payload. Experiments demonstrate the superior performance of the proposed scheme in terms of performance curve for low payload embedding.