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.
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.
14796999
Robust color-based tracking
Color as a distinct feature is widely used for object representation and tracking. However, color-based tracking is often influenced by clutter background and illumination variation. This paper presents a robust color-based tracking method, in which robust color feature is extracted for constructing the observation model under the modified particle filter tracking framework. The object is represented by its dominant color, and the weighted histogram with spatial information of the dominant color is used to optimize object models. In the particle filter framework, an extended iterated likelihood weighting scheme is employed to utilize more valuable particles. The experimental results show it is a real-time robust tracker, and it can obtain more than 30 fps with 2.4 G CPU and 512 MRAM.
1518844
Discovering panoramas in web videos
While methods for stitching panoramas have been successful given proper source images, providing these source images still remains a burden. In this paper, we present a method to discover panoramic source images within widely available web videos. The challenge comes from the fact that many of these videos are not recorded intentionally for stitching panoramas. Our method aims to find segments within a video that work as panorama sources. Specifically, we determine a video segment to be a valid panorama source according to the following three criteria. First, its camera motion should cover a wide field-of-view of the scene. Second, its frames should be "mosaicable", which states that the inter-frame motion should observe the underlying conditions for stitching a panorama. Third, its frames should have good image quality. Based on these criteria, we formulate discovering panoramas in a video as an optimization problem that aims to find an optimal set of video segments as panorama sources. After discovering these panorama sources, we synthesize regular scene panoramas using them. When significant dynamics is detected in the sources, we fuse the dynamics into the scene panoramas to make activity synopses to convey the dynamics. Our experiment of querying panoramas from YouTube confirms the feasibility of using web videos as panorama sources and demonstrates the effectiveness of our method.
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%.
122468009
Truth and absolute truth in neutrosophic logic
As a philosophical analysis of some fatal paradoxes, the paper distinguishes the conceptual difference between representation of truth and source of truth, and leads to the conclusion that in order to acquire the genuine source of truth, independently of specific representations possibly belonging to different worlds, one is necessary to ignore all the ideas, logics, conceptions, philosophies and representable knowledge even himself belonging to those misleading worlds, returning to his infant nature, as a preliminary step for his cultivation of unconstrained wisdom. It also carries out some coordinative crucial issues as natural-doctrine, minded-unwitting, logic-infancy, conception-deconception, determinacy-indeterminacy. The paper tries to verify the role of neutrosophy and neutrosophic logic in religious issues and open a gateway toward the oriental classics, excavating the lost treasure.
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.