34929506
Adaptive Receive Antenna Selection for Orthogonal Space-Time Block Codes with Imperfect Channel Estimation
For coherent detection, decoding Orthogonal Space-Time Block Codes (OSTBC) requires full channel state information at the receiver, which basically is obtained by channel estimation. However, in practical systems, channel estimation errors are inevitable and may degrade the system performance more as the number of antennas increases. This letter shows that, using fewer receive antennas can enhance the performance of OSTBC systems in presence of channel estimation errors. Furthermore, a novel adaptive receive antenna selection scheme, which adaptively adjusts the number of receive antennas, is proposed. Performance evaluation and numerical examples show that the proposed scheme improves the performance obviously.
11873989
WLC09-6: Adaptive Orthonormal Random Beamforming for Cellular Downlink with Multiple Antennas
Channel state information (CSI) at transmitter plays an important role for multiuser MIMO broadcast channels, but full CSI at transmitter is not available for many practical systems. Previous work has proposed an orthonormal random beamforming (ORBF) [16] for MIMO broadcast channels with partial channel state information (CSI) feedback, and shown that ORBF achieves the optimal sum-rate capacity for a large number of users. However, for cellular systems with moderate number of users, i.e., no more than 64, ORBF only achieves slight performance gain. Therefore, we analyze the performance of ORBF with moderate number of users and total transmit power constraint and show that ORBF scheme is more efficient under low SNR. Then we propose an adaptive ORBF scheme that selects the number of random beams for simultaneous transmission according to the average SNR. Moreover, a multi-beam selection (MBS) scheme that jointly selects the number and the subset of the multiple beams is proposed to further improve the system performance for low SNR cases. The simulation results show that the proposed schemes achieve significant performance improvement when the number of users is moderate.
14385690
A New Least Squares Subspace Projection Approach to Unmix Hyperspectral Data
Linearly constrained discriminant analysis (LCDA) and orthogonal subspace projection (OSP) are both explored in hyperspectral image classification and have shown promise in signature detection, discrimination and classification. However, the two subspace projection approaches cannot directly estimate the signature abundance. The OSP has been extended by a least squares orthogonal subspace projection (LSOSP) to estimate the signature abundance while LCDA has not. The solution of LCDA turns out to be a constrained version of OSP implemented with a data whitening process and the means of samples as signatures. Due to this fact, following the same idea for extending OSP to LSOSP, in this paper, a modified linearly constrained discriminant analysis (MLCDA) is proposed for unmixing hyperspectral data, which can directly estimate the signature abundance. Experiment results obtained from both artificial simulated and practical remote sensing data demonstrate that the MLCDA algorithm performs better than least squares method and the LSOSP.
389920
Board Forum Crawling: A Web Crawling Method for Web Forum
We present a new method of board forum crawling to crawl Web forum. This method exploits the organized characteristics of the Web forum sites and simulates human behavior of visiting Web forums. The method starts crawling from the homepage, and then enters each board of the site, and then crawls all the posts of the site directly. Board forum crawling can crawl most meaningful information of a Web forum site efficiently and simply. We experimentally evaluated the effectiveness of the method on real Web forum sites by comparing with the traditional breadth-first crawling. We also used this method in a real project, and 12000 Web forum sites have been crawled successfully. These results show the effectiveness of our method
32209678
A Novel Coarse-to-Fine Adaptation Segmentation Approach for Cellular Image Analysis
Cellular image content analysis is one of the most important aspects of the cellular research and often requires collecting a great amount of statistical information and phenomena. Automated segmentation of time-lapse images gradually becomes the key problem in cellular image analysis. To address fuzzy, irregular, and ruffling cell boundaries in time-lapse cellular images, this paper introduces a hierarchical coarse-to-fine approach which is composed of iteration-dependent adaptation procedures with high-level interpretation: initial segmentation, adaptive processing, and refined segmentation. The iteration-dependent adaptation lies in that the adaptive processing and the refined segmentation be deliberately designed without a fixed order and a uniform associated iteration number, to connect coarse segmentation and refined segmentation for locally progressive approximation. The initial segmentation could avoid over-segmentation from watershed transform and converge to some features using a priori information. Experimental results on cellular images with spurious branches, arbitrary gaps, low contrast boundaries and low signal-to-noise ratio, show that the proposed approach provides a close matching to the manual cognition and overcomes several common drawbacks from other existing methods applied on cell migration. The procedure configuration of the proposed approach has a certain potential to serve as a biomedical image content analysis tool.
1653127
Research and Development of the Remote I/O Data Acquisition System Based on Embedded ARM Platform
With the rapid development of the field of industrial process control and the fast popularization of embedded ARM processor, it has been a trend that ARM processor can substitute the single-chip to realize data acquisition and control. A new kind of remote I/O data acquisition system based on embedded ARM platform has been researched and developed in this paper, whose hardware platform use 32-bit embedded ARM microprocessor, and software platform use the uC/OS-II core of real-time multitask operating system which is open-source and can be grafted, cut out and solidified. This system can measure all kinds of electrical and thermal parameters such as voltage, current, thermocouple, RTD, and so on. The measured data can be displayed on LCD of the system, and at the same time can be transmitted through RS485 or Ethernet network to remote DAS or DCS monitoring system by using Modbus/RTU or Modbus/TCP protocol. The system has the dual redundant network and long-distance communication function, which can ensure the disturb rejection capability and reliability of the communication network.
38683441
A Wordnet-Based Approach to Feature Selection in Text Categorization
This paper proposes a new feature selection method for text categorization. In this method, word tendency, which takes related words into consideration, is used to select best terms. Our experiments on binary classification tasks show that our method achieves better than DF and IG when the classes are semantically discriminative. Furthermore, our best performance is usually achieved in fewer features.