1077673
Low Complexity MMSE Vector Precoding Using Lattice Reduction for MIMO Systems
In this paper, a lattice-reduction-aided (LRA) minimum mean square error (MMSE) vector precoding (VP) is proposed for multiple input multiple output (MIMO) systems. Three schemes are provided for the perturbation vector design by Babai's approximation procedures based on lattice reduction method to reduce the complexity. Performance and complexity analysis are provided. Simulation results show that the proposed schemes significantly outperform the conventional MMSE Tomlinson-Harashima precoding (THP) and the zero forcing (ZF) VP. Compared with the MMSE VP via closest-point search, our LRA approach provides a simple alternative with little performance loss.
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.
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.
15290782
The Performance Analysis of an Improved Robust Beamforming Algorithm
The conventional minimum variance beamformer (MVB) is known to have better resolution and much better interference rejection capability when the array steering vector is accurately known. However, the major problem of the MVB is that it lacks robustness against the unknown array steering vector errors. Therefore, diagonal loading and its extended versions have been a popular approach to improve the robustness of MVB. In this paper, we provide an improved robust MVB (RMVB) algorithm, which has been derived by enforcing a spherical uncertainty set constraint on the array steering vector. The new algorithm has reasonable computation complexity and more numerical stability. Simulation results show that the new algorithm is effective in the presence of array steering vector errors.
21836071
Use clustering to improve neural network in financial time series prediction
In this paper, a time series prediction method using clustering to improve neural network is studied. The big data group is divided into some small parts by clustering. By this way, every small part has a higher conformity, and data in these small parts is used to train corresponding neural network for prediction. The prediction model is constructed from neural network with the addition of clustering and is applied to the financial time series prediction. The experiment results demonstrate the effectiveness of the improvement. Comparison with the primitive neural network prediction model shows that clustering increases neural network's trend accuracy in continuous prediction, while debasing the cost of time and reducing the complexity of the prediction model.
35820562
Named Entity Recognition in Biomedical Literature: A Comparison of Support Vector Machines and Conditional Random Fields
In this paper, we propose two named entity recognition systems for biomedical literature, System1 using support vector machines and System2 using conditional random fields. Through employing several sets of experiments, we make a comprehensive comparison between these two systems. The final results reflect that System2 can achieve higher accuracy than System1, because System2 can catch more essential properties by handling the richer set of features, i.e., adding not only the individual and dynamic features as System1 does but also the combinational features, which can improve the performance further. Furthermore, with carefully designed features, System2 can recognize named entities in biomedical literature with fairly high accuracy, which can achieve the precision of 89.43%, recall of 83.32% and balanced F β= 1 score of 86.28%.
16553815
A Robust Algorithm for Video Based Human Motion Tracking
In this paper, we present a robust algorithm to capture rapid human motion with self-occlusion. Instead of predicting the position of each human feature, the interest-region of full body is estimated. Then candidate features are extracted through the overall search in the interest-region. To establish the correspondence between candidate features and actual features, an adaptive Bayes classifier is constructed based on the time-varied models of feature attributions. At last, a hierarchical human feature model is adopted to verify and accomplish the feature correspondence. To improve the efficiency, we propose a multiresolution search strategy: the initial candidate feature set is estimated at the low resolution image and successively refined at higher resolution levels. The experiment demonstrates the effectiveness of our algorithm.
22925595
A Novel Chase Based Multiuser Detector for MIMO-CDMA Systems
This paper proposes a new chase based multiuser detection scheme for MIMO-CDMA systems. The proposed approach provides performance gain by concentrating on improving the detection accuracy of the weakest symbol. Compared to the layered space-time multiuser detector (LAST-MUD), the proposed scheme can achieve substantial performance improvement, especially, when the number of transmit antennas is equal to the number of receive antennas. The comparison between our scheme and another chase based scheme, the B-Chase detector, which was originally proposed for improving the performance of the vertical Bell Laboratories layered space-time (V-BLAST) system, is also presented. The problem existing in the B-Chase detector is the criterion for selecting the weakest symbol. In our scheme, a more reasonable selection criterion is proposed. We show that the proposed scheme has less complexity than the B-Chase detector but with the tendency of achieving better performance at high SNR.