In this paper, a threshold-based I-Q diversity combining scheme for ultra-high frequency (UHF) radio frequency identification (RFID) readers with a quadrature receiver is proposed in the aspect of improving the tag detection performance. In addition, the performance of the proposed scheme is evaluated as the closed-form expressions. In particular, its statistical characteristics are detailed and its performance is compared to that of conventional schemes over independent and identically distributed Rician fading conditions in terms of average signal-to-noise ratio (SNR), bit error rate (BER), and the average number of required combining process. Numerical results indicate that the proposed scheme enables processing power control through threshold control while meeting the required quality of service compared to conventional schemes.

In wireless LANs, wireless clients are associated with one of access points (APs) to obtain network connectivity, and the AP performs network traffic relay between the wired infrastructure and wireless clients. If a client with a low transmission rate is associated with an AP, the throughput performance of all the clients that are associated with the AP is significantly degraded because of the long channel usage time of the low-rate client. Therefore, it is important to select an appropriate AP when a new client joins the wireless LAN to prevent the performance degradation. In this paper, we propose a traffic control that determines the feasible data traffic from an AP to the clients on the basis of the trade-off relationship between the equal-throughput and equal-airtime traffic allocation policies. We then propose a network-wide association algorithm that allows a client to be associated with the AP that can provide the highest throughput improvement. Simulation results indicate that the proposed algorithm achieves the better aggregate throughput and throughput fairness performances in IEEE 802.11 WLANs.

In this paper, we propose an efficient tracking method that is formulated as a multi-task reverse sparse representation problem. The proposed method learns the representation of all tasks jointly using a customized APG method within several iterations. In order to reduce the computational complexity, the proposed tracking algorithm starts from a feature selection scheme that chooses suitable number of features from the object and background in the dynamic environment. Based on the selected feature, multiple templates are constructed with a few candidates. The candidate that corresponds to the highest similarity to the object templates is considered as the final tracking result. In addition, we present a template update scheme to capture the appearance changes of the object. At the same time, we keep several earlier templates in the positive template set unchanged to alleviate the drifting problem. Both qualitative and quantitative evaluations demonstrate that the proposed tracking algorithm performs favorably against the state-of-the-art methods.

Multi-hop cooperative communication has been investigated in order to overcome disadvantages such as fading, obstruction and low power. In addition, with the goal of increasing access capacity, the orthogonal frequency division multiplexing (OFDM) modulation is being advanced as a solution. In this paper, we propose the approach of relay ordering in a Decode-and-Forward OFDM scheme. Combining techniques such as maximal ratio combining and selection combining are employed at receivers and approximate outage capacity probabilities are derived for evaluating system performance over frequency selective Rayleigh fading channels. Final, the expressions are validated by Monte-Carlo simulations, and are used to compare with the same scheme based relay selection.

Users of wireless mobile devices need Internet access not only when they stay at home or office, but also when they travel. It may be desirable for such users to select a "longcut route" from their current location to his/her destination that has longer travel time than the shortest route, but provides a better mobile wireless environment. In this paper, we formulate the above situation as the optimization problem of “optimal longcut route selection”, which requires us to find the best route concerning the wireless environment subject to a travel time constraint. For this new problem, we show NP-hardness, propose two pseudo-polynomial time algorithms, and experimental evaluation of the algorithms.

Many wireless communication systems use a relay station for cooperative diversity or cell coverage extension. In this letter, an efficient partial single relay selection scheme is proposed for wireless communications. The conventional schemes such as the best harmonic mean and the threshold-based relay selection should know channel state informaion (CSI), or noise variance at all stations in advance. But the proposed scheme does not require any priori information. It uses a characteristic of the repeated signal pattern at candidates of the relay station. Simulation results show that the performance of proposed scheme is very close to the best harmonic mean relay selection scheme as one of the optimal relay selection schemes.

The indoor fingerprinting localization technology has received more attention in recent years due to the increasing demand of the indoor location based services (LBSs). However, a high quality of the LBS requires a positioning solution with high accuracy and low computational complexity. The particle swarm optimization (PSO) technique, which emulates the social behavior of a flock of birds to search for the optimal solution of a special problem, can provide attractive performance in terms of accuracy, computational efficiency and convergence rate. In this paper, we adopt the PSO algorithm to estimate the location information. First, our system establishes a Bayesian-rule based objective function. It then applies PSO to identify the optimal solution. We also propose a hybrid access point (AP) selection method to improve the accuracy, and analyze the effects of the number and the initial positions of particles on the localization performance. In order to mitigate the estimation error, we use the Kalman Filter to update the initial estimated location via the PSO algorithm to track the trail of the mobile user. Our analysis indicates that our method can reduce the computational complexity and improve the real-time performance. Numerous experiments also demonstrate that our proposed localization and tracking system achieve higher localization accuracy than existing systems.

In this paper, two receive beamforming techniques (Method 1 and Method 2) are proposed for a mobile station (MS) with multiple antenna arrays in an OFDM-based millimeter-wave (mm-wave) cellular communication system. Since the MS in mm-wave cellular communication requires fast processing due to its frequent movement and rotation, a receive beamforming technique with reduced computation complexity and processing time is proposed in Method 2. Of particular interest, estimation techniques for 2-dimensional (2D) direction-of-arrivals (DoAs) corresponding to each cell ID are proposed for uniform circular arrays (UCAs) and uniform rectangular arrays (URAs). Also, a cell selection technique for MSs with multiple antenna arrays is described that use the candidate cell IDs and parameters estimated for all antenna arrays to provide combining gain in addition to array gain in multipath channels. The proposed beamforming techniques are evaluated by computer simulation using a simple model of amm-wave cellular communication system with 3-dimensional spatial channel model (3D SCM).

Feature Selection (FS) is widely used to resolve the problem of selecting a subset of information-rich features; Fuzzy-Rough QuickReduct (FRQR) is one of the most successful FS methods. This paper presents two variants of the FRQR algorithm in order to improve its performance: 1) Combining Fuzzy-Rough Dependency Degree with Correlation-based FS merit to deal with a dilemma situation in feature subset selection and 2) Hybridizing the newly proposed method with the threshold based FRQR. The effectiveness of the proposed approaches are proven over sixteen UCI datasets; smaller subsets of features and higher classification accuracies are achieved.

In wireless communication, it is hard to set the optimal route between a source and a destination through relays, since for optimal relaying, the system operator should know all channel conditions from a source to a destination through relays and determine the path with all channel conditions. In this letter, a multiple relay selection strategy is proposed for the reliability of transmission. The proposed strategy establishes a relaying route to a destination and provides an efficient relay selection process regardless of all channel conditions.

We consider user selection schemes for multi-user MIMO systems with linear precoding. In this work, we apply two user selection schemes based on the orthogonality between the propagation channel of MSs. Indoor transmission experiments are carried out under several scenarios and the performances of user selection schemes are evaluated. It is shown that the transmission performance is improved and the user selection schemes are remarkably affected by the path loss between MSs.

We propose a new multi-label feature selection method that does not require the multi-label problem to be transformed into a single-label problem. Using quadratic programming, the proposed multi-label feature selection algorithm provides markedly better learning performance than conventional methods.

This paper analyzes the correlation between various acoustic features and perceptual voice quality similarity, and proposes a perceptually similar speaker selection technique based on distance metric learning. To analyze the relationship between acoustic features and voice quality similarity, we first conduct a large-scale subjective experiment using the voices of 62 female speakers and perceptual voice quality similarity scores between all pairs of speakers are acquired. Next, multiple linear regression analysis is carried out; it shows that four acoustic features are highly correlated to voice quality similarity. The proposed speaker selection technique first trains a transform matrix based on distance metric learning using the perceptual voice quality similarity acquired in the subjective experiment. Given an input speech, acoustic features of the input speech are transformed using the trained transform matrix, after which speaker selection is performed based on the Euclidean distance on the transformed acoustic feature space. We perform speaker selection experiments and evaluate the performance of the proposed technique by comparing it to speaker selection without feature space transformation. The results indicate that transformation based on distance metric learning reduces the error rate by 53.9%.

Hands-free communications between cellular phones must be robust enough to withstand echo-path variation, and highly nonlinear echoes must be suppressed at low cost, when acoustic echo cancellation or suppression is applied to them. This paper proposes a spectrum-selective nonlinear echo suppression (SS-ES) approach as a solution to these issues. SS-ES is characterized by the selection of either a spectrum of the residual signal from an adaptive filter or a spectrum of the sending input signal depending on the amount of linear echo cancellation in an adaptive filter. Compared to conventional methods, the objective evaluation results of the SS-ES approach show an improvement of approximately 0.8-2.2dB, 0.23-2.39dB, and 0.26-0.50 in average echo return loss enhancement (ERLE), average root-mean-square log-spectral distortion (RMS-LSD), and the perceptual evaluation of speech quality (PESQ) value, respectively, under echo-path variation and double-talk conditions.

In this paper, we present an efficient time-of-arrival (TOA)-based localization method for wireless sensor networks. The goal of a localization system is to accurately estimate the geographic location of a wireless device. In real wireless sensor networks, accurately estimating mobile device location is difficult because of the presence of various errors. Therefore, localization methods have been studied in recent years. In indoor environments, the accuracy of wireless localization systems is affected by non-line-of-sight (NLOS) errors. The presence of NLOS errors degrades the performance of wireless localization systems. In order to effectively estimate the location of the mobile device, NLOS errors should be recognized and mitigated in indoor environments. In the TOA-based ranging method, the distance between the two wireless devices can be computed by multiplying a signal's propagation delay time by the speed of light. TOA-based localization measures the distance between the mobile station (MS) and three or more base stations (BSs). However, each of the NLOS errors of the measured distance between the i-th BS and the MS is different due to dissimilar obstacles in the direct signal path between the two nodes. In order to accurately estimate the location in a TOA-based localization system, an optimized localization algorithm that selects three measured distances with fewer NLOS errors is necessary. We present an efficient TOA-based localization scheme that combines three selected BSs in wireless sensor networks. This localization scheme yields improved localization performance in wireless sensor networks. In this paper, performance tests are performed, and the simulation results are verified through comparisons between various localization methods and the proposed method. As a result, proposed localization scheme using BS selection achieves remarkably better localization performance than the conventional methods. This is verified by experiments in real environments, and demonstrates a performance analysis in NLOS environments. By using BS selection, we will show an efficient and effective TOA-based localization scheme in wireless sensor networks.

Network selection is one of the hot issues in the fusion of heterogeneous wireless networks (HWNs). However, most of previous works only consider selecting single-access network, which wastes other available network resources, rarely take account of multi-access. To make full utilization of available coexisted networks, this paper proposes a novel multi-access selection algorithm based on joint utility optimization for users with multi-mode terminals. At first, the algorithm adopts exponential smoothing method (ESM) to get smoothed values of received signal strength (RSS). Then we obtain network joint utility function under the constraints of bandwidth and number of networks, with the consideration of trade-off between network benefit and cost. At last, Lagrange multiplier and dual optimization methods are used to maximize joint utility. Users select multiple networks according to the optimal association matrix of user and network. The simulation results show that the proposed algorithm can optimize network joint utility, improve throughput, effectively reduce vertical handoff number, and ensure Quality of Service (QoS).

Multi-user MIMO (MU-MIMO) improves the system channel capacity by employing the transmission between a base station and multiple user terminals (UTs). Block Diagonalization (BD) has been proposed in order to realize MU-MIMO broadcast transmission. The BD algorithm cancels inter-user interference by creating the weights so that the channel matrixes for the other users are set to be zero matrixes. However, when the number of transmit antennas is equals to the total number of received antennas, the transmission rate by the BD algorithm is decreased. This paper proposes a new antenna selection method at the UTs to reduce the number of nulls for the other users except an intended user by the BD algorithm. It is verified via bit error rate (BER) evaluation that the proposed method is effective compared to the conventional BD algorithm, especially, when the number of users is increased with a low bit rate. Moreover, this paper evaluates the transmission rate based on IEEE802.11ac standard when considering BD algorithm with ideal user scheduling. Although the number of equivalent receive antenna is only one by the proposed method when the number of antennas at the the UT is two, it is shown that the transmission rate by the proposed method is higher than that by the conventional BD algorithm when the SNR is low even in the condition on user scheduling.

Independent and identically distributed (i.i.d) assumptions are commonly used in the machine learning community. However, social media data violate this assumption due to the linkages. Meanwhile, with the variety of data, there exist many samples, i.e., Universum, that do not belong to either class of interest. These characteristics pose great challenges to dealing with social media data. In this letter, we fully take advantage of Universum samples to enable the model to be more discriminative. In addition, the linkages are also taken into consideration in the means of social dimensions. To this end, we propose the algorithm Semi-Supervised Linked samples Feature Selection with Universum (U-SSLFS) to integrate the linking information and Universum simultaneously to select robust features. The empirical study shows that U-SSLFS outperforms state-of-the-art algorithms on the Flickr and BlogCatalog.

In a multi-user multiple-input multiple-output (MU-MIMO) system that adopts zero-forcing (ZF) as a precoder, the best selection is the combination of users who provide the smallest trace of the inverse of the channel auto-correlation matrix. Noting that the trace of the matrix is closely related to the determinant, we search for users that yield the largest determinant of their channel auto-correlation matrix. The proposed technique utilizes the determinant row-exchange criterion (DREC) for computing the determinant-changing ratio, which is generated whenever a user is replaced by one of a group of pre-selected users. Based on the ratio computed by the DREC, the combination of users providing the largest changing ratio is selected. In order to identify the optimal combination, the DREC procedure is repeated until user replacement provides no increase in the determinant. Through computer simulations of four transmit antennas, we show that the bit error rate (BER) per signal-to-noise ratio (SNR) as well as the sum-rate performance provided by the proposed method is comparable to that of the full search method. Furthermore, using the proposed method, a partial replacement of users can be performed easily with a new user who provides the largest determinant.

In this letter, a new sparse locality preserving projection (SLPP) algorithm is developed and applied to facial expression recognition. In comparison with the original locality preserving projection (LPP) algorithm, the presented SLPP algorithm is able to simultaneously find the intrinsic manifold of facial feature vectors and deal with facial feature selection. This is realized by the use of l1-norm regularization in the LPP objective function, which is directly formulated as a least squares regression pattern. We use two real facial expression databases (JAFFE and Ekman's POFA) to testify the proposed SLPP method and certain experiments show that the proposed SLPP approach respectively gains 77.60% and 82.29% on JAFFE and POFA database.