32866926
Lattice-Reduction-Aided MMSE Tomlinson-Harashima Precoding for MIMO Systems
In this letter, a lattice-reduction-aided (LRA) minimum mean square error (MMSE) Tomlinson-Harashima precoding (THP) is proposed for multiple input multiple output (MIMO) systems. The extended channel is exploited to develop the LRA MMSE-THP based on the lattice reduction method. Simulation results show that the proposed scheme significantly outperforms the conventional MMSE THP and the LRA zero-forcing (ZF) THP and achieves full diversity order.
35354920
Color text image binarization based on binary texture analysis
In this paper, a novel binarization algorithm for color text images is presented. This algorithm effectively integrates color clustering and binary texture analysis, and is capable of handling situations with complex backgrounds. In this algorithm, dimensionality reduction and graph theoretical clustering are first employed. As the result, binary images related to clusters can be obtained. Binary texture analysis is then performed on each candidate binary image. Two kinds of effective texture features, run-length histogram and spatial-size distribution related respectively, are extracted and explored. Cooperating with an LDA classifier, the optimal candidate of the best binarization effect is obtained. Experiments with images collected from Internet has been carried out and compared with existing techniques, both show the effectiveness of the algorithm.
19596582
MMSE vector precoding with joint transmitter and receiver design for MIMO systems
The transmitter side (Tx) vector precoding (VP) shows excellent performance. However, joint transmitter and receiver (Tx-Rx) design might achieve further improvement. In this paper, we propose a novel joint Tx-Rx VP for multiple input multiple output (MIMO) systems. A unitary matrix is used at the receiver to partially equalize the channel. We design the joint Tx-Rx VP with the minimum mean square error (MMSE) criterion and deduce the general closed-form solution. Then we apply several methods including the singular value decomposition, QR decomposition and geometric mean decomposition (GMD) to specify the general joint Tx-Rx VP design. Moreover, by using the extended channel, we achieve an improved scheme to obtain further performance gain. Simulation results show that the specification with GMD method outperforms the other specifications and the Tx MMSE VP. The improved joint Tx-Rx MMSE VP with GMD is superior to other MMSE VP schemes.
26148023
Constant Modulus Based Blind Channel Estimation for OFDM Systems
A novel blind channel estimation scheme is proposed for OFDM systems employing PSK modulation. This scheme minimizes the number of possible channels by exploiting the constant modulus property, chooses a best fit over the possible channels by exploiting the finite alphabet property of information signals, and achieves competitive performance with low computational complexity. Results comparing the new scheme with the finite-alphabet based channel estimation are presented.
41927172
A Wavelets Network Interpolation Algorithm for OFDM Channel Estimation Systems
For the performance deficiency of the pilot symbol aided channel estimation in orthogonal frequency division multiplexing (OFDM) systems, the wavelets network interpolation channel estimator is proposed. By contrast with conventional methods, wavelets network interpolation channel estimator can guarantee the high transmission rate and lower Bit error rates (BER). Computer simulation results demonstrate that the proposed channel estimation method exhibit an improved performance compared to the conventional linear channel estimation methods and is robust to fading rate, especially in fast fading channels.
41711298
Quantitative QoS Management Implement Mechanism in IP-DiffServ
In this paper, a quantitative QoS management implement mechanism in IP-DiffServ is presented. The approach has three merits: (1) it optimizes both the route selected for service class by traffic engineering in network layer and the service class selected for the user services by network plan in service layer, (2) it ensures quantitative QoS for the user service in DiffServ, (3) it achieves high resource utilization, and achieves better performance with less cost for the user service and selected route. Simulation has verified these merits.
15273775
Learning Microarray Gene Expression Data by Hybrid Discriminant Analysis
Microarray technology offers a high-throughput means to study expression networks and gene regulatory networks in cells. The intrinsic nature of high dimensionality and small sample size in microarray data calls for effective computational methods. In this article, we propose a novel hybrid dimension reduction technique for classification that combines principal component analysis (PCA) and linear discriminant analysis (LDA)-hybrid PCA and LDA analysis. This technique effectively solves the singular scatter matrix problem caused by small training samples and increases the effective dimension of the projected subspace. It offers more flexibility and a richer set of alternatives to LDA and PCA in the parametric space. In addition, we propose a boosted hybrid discriminant analysis (HDA), using the AdaBoost algorithm which provides a unified and stable solution to find close to the optimal PCA-LDA prediction result and also reduces computational complexity. Extensive experiments on the yeast cell cycle regulation data set show the superior performance of the hybrid analysis, as we explain.
17985803
Verifying Web Services Composition: A Transformation-Based Approach
Web services composition is an emerging paradigm for enabling application integration. BPEL is a promising language describing composition in form of business processes, but is lack of sound formal semantic. This paper presents the transformation of BPEL to CP-nets in a constructive way. Therefore we can translate composition specified in BPEL into CPnets, which can be analyzed and verified by many specialized tools. So we make tool support available for verifying BPEL composition.