In MIMO system, spatial multiplexing (SM) technology indicates the essence of improving the capacity. BLAST is the typical representation of SM, especially VBLAST, which has some detection algorithm such as ML, ZF-DFE...
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In MIMO system, spatial multiplexing (SM) technology indicates the essence of improving the capacity. BLAST is the typical representation of SM, especially VBLAST, which has some detection algorithm such as ML, ZF-DFE, MMSE-DFE and ML-DFE, etc. By considering the correlation of channel matrix, this paper proposed a new algorithm - HPML, which approaches maximum likelihood (ML) performance with lower complexity, and with its reduced algorithm - VML. Theory analysis and simulation results show that HPML and VML can approach ML performance and decrease complexity efficiently at the same time.
This paper presents an unstructured road detection algorithm which can improve the detecting speed and accuracy of unstructured road detection. In this algorithm, first, we process the original images with the median ...
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This paper presents an unstructured road detection algorithm which can improve the detecting speed and accuracy of unstructured road detection. In this algorithm, first, we process the original images with the median value filter, and suppress the stochastic noise; Then an Otsu multi-threshold algorithm based on two-peak method for rapid image segmentation is used, which cause the division effect and the division time to be optimum; Finally the primary edge detection with the Canny operator and mathematics morphology are used, which can obtain the complete and clear path edge image, eventually enhance the accuracy of the path examination obviously. The simulation results show that the unstructured path examination method has the good characteristics of detecting speed and accuracy.
In this work we propose a novel unsupervised algorithm for designing multispectral filters that are tuned for local anomaly detection algorithms. This problem is formulated as a problem of channel reduction in hypersp...
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ISBN:
(纸本)9781617388767
In this work we propose a novel unsupervised algorithm for designing multispectral filters that are tuned for local anomaly detection algorithms. This problem is formulated as a problem of channel reduction in hyperspectral images, which is performed by replacing subsets of adjacent spectral bands by their means. An optimal partition of hyperspectral bands is obtained by minimizing the Maximum of Maha-lanobis Norms (MXMN) of errors, obtained due to misrepresentation of hyperspectral bands by constants. By minimizing the MXMN of errors, one reduces the anomaly contribution to the errors, which allows to retain more anomaly-related information in the reduced channels. We demonstrate that the proposed algorithm produces better results, in terms of the Receiver Operation Characteristic (ROC) curve of a benchmark anomaly detection algorithm (RX) - applied after the dimensionality reduction, as compared to two other dimensionality reduction techniques, including Principal Component Analysis (PCA).
The multiple input multiple output code division multiple access systems can get good performance or high capacity, but the computational complexity of detection is usually high. Although, by way of sphere-constrained...
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The multiple input multiple output code division multiple access systems can get good performance or high capacity, but the computational complexity of detection is usually high. Although, by way of sphere-constrained searching, sphere decoding algorithm and some other methods are able to greatly decrease the detection complexity as compared with that of maximum likelihood algorithm. In order to further decrease the decoding complexity, a new detection algorithm which is based on both sphere decoding method and zero forcing algorithm is put forward. The combinational detector decodes according to the signal-noise-ratio. When the signal-noise-ratio is high enough, it simply outputs the zero forcing detection results, which leads to a faster detecting process. Some simulation results show that the performance of such combinational algorithm has little difference as compared with that of sphere decoding algorithm.
Coverage holes may exist in wireless sensor networks (WSNs) due to presence of obstacles or invalid sensor nodes in the sensing field. Normally, the holes make the data routing failure when the nodes transmit their da...
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Coverage holes may exist in wireless sensor networks (WSNs) due to presence of obstacles or invalid sensor nodes in the sensing field. Normally, the holes make the data routing failure when the nodes transmit their data back to the sink. In this paper, distributed protocols are developed to identify the boundary nodes surrounding the holes of the sensing filed in WSNs without using any location information. Experimental results demonstrate that our algorithm can precisely and correctly identify the boundary nodes even in sparsely sensors deployed regions. Besides, our algorithm can give better performance in terms of control packet overhead and simulation time as compared to previous work.
For a classification problem, noise in real-world data can dramatically lower the predictive accuracy of a learner and increase the time in building model. Researchers have proved that preprocessing noise before learn...
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For a classification problem, noise in real-world data can dramatically lower the predictive accuracy of a learner and increase the time in building model. Researchers have proved that preprocessing noise before learning can bring more advantages. Previous work mostly focus on class noise detection for the difficulties of attribute noise detection. In this paper, we present a cluster based noise detection algorithm, which synthetically considers attribute and class noise detection. Meanwhile, it has the ability of handling different types of datasets. Our algorithm separately detects class and attributes noise by computing the deviation to the center in the same cluster. we test its effect by adding different types of noise and noise level into datasets from the UCI repository, Our approach shows significant effectiveness in improving the predictive accuracy of classification.
This paper presents a real-time robust algorithm to automatically detect and classify round road signs on Chinese highways. As most of road signs on Chinese highways have circular borders, the detection system first t...
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This paper presents a real-time robust algorithm to automatically detect and classify round road signs on Chinese highways. As most of road signs on Chinese highways have circular borders, the detection system first takes advantage of a real-time circle detection algorithm to extract the regions of interest (ROI). Then the color and texture pattern in each ROI is analyzed and compared with sample signs to both validate the candidate road sign and identify their types. The algorithm is implemented in Visual C++ and tested on a laptop powered by an Intel Centrino Duo CPU at 1.8 GHz. The experiment shows the algorithm's speed is fast, 83 ms/images on average for image size of 640 × 480, and its false negative (failure to detect) is 21 signs or 4% (failure to detect) for a pool of 530 different road signs in a pool of 1000 pictures. And there are 3 non-road-sign objects incorrectly recognized as valid road signs.
QR decomposition based M-algorithm (QRD-M) can provide near maximum likelihood detection (MLD) performance and low complexity for MIMO communication. The QRD-M algorithm reduces the complexity by selecting M candidate...
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QR decomposition based M-algorithm (QRD-M) can provide near maximum likelihood detection (MLD) performance and low complexity for MIMO communication. The QRD-M algorithm reduces the complexity by selecting M candidates with the smallest accumulated metrics at each level of the tree search. The trade-off between performance and complexity can be adjusted only by setting the parameter M which cannot provide more valuable tradeoff options with better performance to complexity ratio. Based on the equivalent QRD-M model, a new detection scheme is proposed in this paper for MIMO system on flat fading channel. The detection scheme, named as improved QRD-M (IQRD-M) with two independent parameters, is composed of two parts. After performing QR decomposition of the channel matrix, the MLD with length P is done, the accumulated metrics are calculated and sorted, which gives an ordered set, then IQRD-M algorithm is used to search the left layers with novel termination methods. The proposed algorithm provides better tradeoff options and more near-ML performance with low complexity.
After the characteristics and shortage of some old algorithm about the infrared small weak targets detection being analyzed, a kind of new detection algorithm via high order spectral analysis is proposed for single fr...
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After the characteristics and shortage of some old algorithm about the infrared small weak targets detection being analyzed, a kind of new detection algorithm via high order spectral analysis is proposed for single frame image. Taking the gray level data of the image ground as the noise signal, the gray level data of small weak targets as the harmonic signal mixed in the noise signal, the gray level data of row and arrange pixel in image are processed via power spectral analysis and high order cumulation analysis to restrain noise and detect harmonic. The distinguishability of detection lies on the piece of the rows being spectrum analyzed on the same time. The advantage of the algorithm consists of adopting the same examine theory, easy accomplishment, having the higher accuracy, and needing data limited by the single frame image only. Finally an example applied the detection algorithm is given.
We present a video outage detection algorithm (VODA) that detects catastrophic failures in video systems by mimicking human behavior. VODA uses a continuity detector for video, audio, and motion, along with blackness,...
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We present a video outage detection algorithm (VODA) that detects catastrophic failures in video systems by mimicking human behavior. VODA uses a continuity detector for video, audio, and motion, along with blackness, silence, and stillness detection. An outage is declared when all individual features exhibit a sudden drop; an alarm is generated when an outage lasts over two seconds. We analyze the performance of VODA on a large corpus of movie and TV episodes, and we show it has good performance when applied to 160 hours of recorded broadcast TV.
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