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检索条件"任意字段=The 2010 IEEE International Conference on Progress in Informatics and Computing"

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Modeling AP's Substitutability in Dense-deployed WLANs

Modeling AP's Substitutability in Dense-deployed WLANs

引用

2014 ieee international conference on progress in informatics and computing

作者： Yuntao Fan Hewu Li Wenqi Sun Tsinghua National Laboratory for Infomation Science and Technology(TNList) Tsinghua University

For supporting peak usage, APs(Access Points) are always placed densely in today's WLAN, which leads toexcessive energy consumption at the same time. Clustering APs of the same area and switching off some APs for normal network access are the main solution tosaving energy. How tocluster APs and find the substitutability of APs is the key. RSSI between APs used tocluster APs in related research is not good enough for finding the substitutability of APs, since the substitutability of APs should be from not AP side but user side. This paper introduces a model of the AP's substitutability using RSSI that APs hear from users. Moreover, the relationship of coverage loss and substitutability can be quantified through the model, which can significantly reduce coverage loss while saving nearly the same amount of energy as related works. The strong pointis that nomatter how densely or scarcely APs are deployed, our model can work because it takes coverage intoconsideration inherently. Experiment results show that the model of AP substitutability is valid and stable.

关键词： AP substitutability modeling WLAN

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A Real-time Low-light Enhancement Algorithm for Intelligent Analysis

A Real-time Low-light Enhancement Algorithm for Intelligent ...

引用

2016 ieee international conference on progress in informatics and computing

作者： Xiaochen Hu Li Zhuo Jing Zhang Liying Jiang Signal & Information Processing Laboratory Beijing University of Technology Collaborative Innovation Center of Electric Vehicles in Beijing

Images/videos captured in low-light conditions often present low luminance and contrast. Although the existing algorithms can improve the subjective perception, color distortion and over-enhancement usually appear, which will disturb the subsequent intelligent analysis. Otherwise, due to high computational complexity, the existing algorithms are difficult to process a high resolution（HR） video（1280x720） in real-time. Therefore, a real-time low-light enhancement algorithm for intelligent analysis is proposed in this paper. Firstly, an enhancement model is established in RGB color space. Then, to judge the influence of light intensity, images of Color Checker color chart are captured under a series of light conditions. Finally, the enhancement factor in the proposed model is evaluated by the captured images. Experimental results demonstrate that the proposed algorithm can significantly improve the performance of vehicle license plate localization and skin color detection compared to the existing algorithms. Furthermore, the proposed algorithm can process the HR videos at the speed of 28.3 fps on average.

关键词： intelligent analysis HR video real-time low-light enhancement

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Conceptual Graph Based Text Classification

Conceptual Graph Based Text Classification

引用

2014 ieee international conference on progress in informatics and computing

作者： Yi Wan Tingting He Xinhui Tu School of Computer Science Central China Normal University School of Computer Science Wuhan University of Science and Technology

Most traditional Wikipedia based methods use only article contentinformation. By organizing Wikipedia articles as a graph, multi-information such as category and structure information can be utilized in our method. In this paper, we propose a novel method todoclassification by using knowledge from a conceptual graph which is built from Wikipedia. First, we build a conceptual graph from Wikipedia. Each article is considered as a concept node. Titles, hyperlinks, texts and category information are used as edges tomeasure the relationship between those concepts. Each textis mapped toits respective set of nodes and Personalized PageR ank(random walk) is then used togenerate a set of mostimportant node which can represent the text best. Finally the twosets are scored with a measure of vector similarity. We evaluate our techniques on the standard text classification dataset(20newsgroup), the results show the effectiveness of the proposed approach.

关键词： conceptual garph personalized PageR ank semantic similarity text classification

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Signal Processing Techniques in Medical Ultrasound Imaging

Signal Processing Techniques in Medical Ultrasound Imaging

引用

2016 ieee international conference on progress in informatics and computing

作者： Hideyuki Hasegawa Graduate School of Science and Engineering University of Toyama

Medical diagnostic ultrasound is widely used in clinical situations owing to its preferable properties, such as non-invasiveness and cost effectiveness. In addition, medical ultrasound imaging provides a significantly better temporal resolution than computed tomography and magnetic resonance imaging. Recently, the temporal resolution of ultrasound imaging has been further boosted to several thousand frames per second, and such an extremely high temporal resolution is predominantly useful for measurement of the cardiovascular dynamics. On the other hand, ultrasound imaging is a very flexible modality, which can control the tradeoff between the imaging temporal resolution and the spatial resolution(and contrast). In the current situation of medical ultrasonic imaging, the number of ultrasound transmissions per frame needs to be increased to obtain better spatial resolution and contrast, that degrades the temporal resolution. However, such a tradeoff problem can be solved by the signal processing technique. A key technology to realize high temporal and spatial resolution simultaneously is the adaptive beamforming technology, which realizes a significantly better spatial resolution than the non-adaptive conventional beamforming technology. However, the computational complexity of adaptive beamforming is an issue to be solved to deliver adaptive beamforming techniques in clinical situation. Furthermore, motion estimation techniques are very important to assess the tissue dynamics. However, current motion estimators require multiple ultrasound transmissions to obtain tissue motion and, therefore, the imaging frame rate is degraded. Also, in high-frame-rate ultrasound, several thousand frames per second need to be processed to obtain tissue motion. Therefore, a computationally efficient motion estimator needs to be developed. In this talk, I will present some solutions for such problems in high-frame-rate ultrasonic imaging. I will show that the computational complexity in

关键词： Signal Processing Techniques in Medical Ultrasound Imaging

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A Combined Feature Representation of Deep Feature and Hand-crafted Features for Person Re-identification

A Combined Feature Representation of Deep Feature and Hand-c...

引用

2016 ieee international conference on progress in informatics and computing

作者： Youjiao Li Li Zhuo Xiaochen Hu Jing Zhang Signal & Information Processing Laboratory Beijing University of Technology Department of Computer Science and Technology Shandong University of Technology Collaborative Innovation Center of Electric Vehicles in Beijing

Person re-identification is one of the hot topics in computer vision. How to design a robust feature representation to identify pedestrians is a key problem for person reidentification. In this paper, a feature representation based on Multi-Statistics Cascade on Pyramid(MSCP) is proposed for person re-identification. The MSCP feature is composed of deep PCA network feature and hand-crafted features of Local Maximal Occurrence(LOMO) feature and color *** can characterize the pedestrian images precisely from both global and local views. The Cross-view Quadratic Discriminant Analysis(XQDA) is employed to learn the distance metric of MSCP features. And then a novel re-identification method based on MSCP and XQDA is achieved. Experimental results on VIPeR Dataset demonstrate that our proposed method can achieve superior identification performance compared with six state-of-art methods.

关键词： person re-identification Multi-Statistics Cascade on Pyramid deep feature hand-crafted feature

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An Effective Heuristic for Due-Date-Constrained Bag-of-Tasks Scheduling Problem for Total Cost Minimization on Hybrid Clouds

An Effective Heuristic for Due-Date-Constrained Bag-of-Tasks...

引用

2016 ieee international conference on progress in informatics and computing

作者： Yi Zhang Jin Sun Jie Zhu School of Computer Science & Engineering Nanjing University of Science & Technology School of Computer Science & Technology Nanjing University of Posts & Telecommunications

Bag-of-Tasks(BoT) applications consisting of multiple tasks widely exist in numerous fields. As customers use cloud resources in a pay-as-you-go way, they are willing to execute BoT applications on clouds. Cloud providers and customers establish contracts in which applications' due dates are specified. If an application cannot be finished before the due date, the cloud provider should pay a tardiness penalty. When the private cloud has insufficient available resources to afford all customer-submitted BoT applications, the cloud provider has to outsource some tasks to public clouds with resource-used costs. The key challenge here is how to schedule tasks on hybrid clouds to minimize the total cost, including all applications' tardiness penalties and the cost of using public clouds' resources. We study and formulate this problem as an Integer Programming. Accordingly, we propose an effective greedy heuristic(GH) including two phases(task ordering and task scheduling). GH uses an Earlier Latest Start Time First method(ELSTF) for task ordering with the result that a task sequence is obtained.A Task Dispatching method(TD) is established for the task scheduling, in which each task in the obtained task sequence is scheduled one by one. Experimental results demonstrate that the proposed GH outperforms the baseline(RoundR obin) remarkably. ELSTF and TD are also verified to be effective.

关键词： Bag-of-Tasks Scheduling Hybrid Clouds Due Date Total Cost Minimization

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Automatic Analysis of Tongue Substance Color and Coating Color Using Sparse Representation-Based Classifier

Automatic Analysis of Tongue Substance Color and Coating Col...

引用

2016 ieee international conference on progress in informatics and computing

作者： Panling Qu Hui Zhang Li Zhuo Pei Zhang Jing Zhang Signal and Information Processing Laboratory Beijing University of Technology

Sparse Representation-based Classifier(SRC) is less sensitive to the shortage of data and the selection of feature space. In this paper, SRC is adopted to perform automatic analysis of tongue substance color and coating color which is considered as small dataset classification task. Firstly, for both training samples and testing samples, the tongue body regions are segmented, the regions of tongue substance and coating are separated and the features of these regions are extracted. Then, the feature vectors of testing samples are represented by sparse coding. Finally, the feature vectors of testing samples are reconstructed for each category, the residuals are calculated and the category of the smallest residual is determined as the classification result. Experimental results show that the proposed method can achieve the classification accuracy of 87.80% and 90.51% for the colors of tongue substance and tongue coating respectively. In addition, the classification accuracy of tongue coating color can reach to 91.19% with the assistance of visual samples by SRC. The proposed method provides a new method for automatic analysis of tongue color, and it is also a new application of SRC.

关键词： Traditional Chinese Medical tongue diagnosis substance color coating color sparse representation-based classifier small dataset visual samples

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Fine-grained Vehicle Recognition Using Hierarchical Fine-Tuning Strategy for Urban Surveillance Videos

Fine-grained Vehicle Recognition Using Hierarchical Fine-Tun...

引用

2016 ieee international conference on progress in informatics and computing

作者： Qiang Zhang Li Zhuo Xiaochen Hu Jing Zhang Signal & Information Processing Laboratory Beijing University of Technology Collaborative Innovation Center of Electric Vehicles in Beijing

The Fine-grained Vehicle recognition is easily affected by small visual changes. The existing recognition methods have less robustness to these conditions(such as illumination, weather changes, etc.) and the accuracy of vehicle recognition in complex environments cannot achieve a satisfying result. In this paper, a high-accuracy fine-grained vehicle recognition method using Convolutional Neural Network for urban surveillance videos is proposed. Differing from the conventional CNN based methods, which usually obtain a single classification model, the proposed method combines Pre-training and hierarchical fine-tuning to obtain different classification model that can adapt to the change of illumination conditions. In the recognition phase, the input image is evaluated firstly using the proposed image quality assessment method. Then, the classification model is selected adaptively according to the results of quality evaluation and used for recognition. To test our method, we use a publicly vehicle recognition dataset for Urban Surveillance Videos provide by Yang. Experimental results show that the accuracy of the proposed method is up to 98.89%, which is 0.5% higher than Yang's method.

关键词： fine-grained vehicle recognition hierarchical fine-tuning quality assessment

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Pathological Brain Detection Based on Online Sequential Extreme Learning Machine

Pathological Brain Detection Based on Online Sequential Extr...

引用

2016 ieee international conference on progress in informatics and computing

作者： Siyuan Lu Hainan Wang Xueyan Wu Shuihua Wang School of Computer Science and Technology Nanjing Normal University State Key Lab of CAD & CG Zhejiang University Key laboratory of symbolic computation and knowledge engineering of ministry of education Jilin University Key Laboratory of Statistical information technology and data mining State Statistics Bureau Department of Electrical Engineering The City College of New YorkCUNY

Magnetic resonance imaging(MRI) is a kind of imaging modality, which offers clearer images of soft tissues than computed tomography(CT). It is especially suitable for brain disease detection. It is beneficial to detect diseases automatically and accurately. We proposed a pathological brain detection method based on brain MR images and online sequential extreme learning machine. First, seven wavelet entropies(WE) were extracted from each brain MR image to form the feature vector. Then, an online sequential extreme learning machine(OS-ELM) was trained to differentiate pathological brains from the healthy *** experiment results over 132 brain MRIs showed that the proposed approach achieved a sensitivity of 93.51%, a specificity of 92.22%, and an overall accuracy of 93.33%,which suggested that our method is effective.

关键词： magnetic resonance imaging wavelet entropy online sequential extreme learning machine Classification pattern recognition

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Face Image Illumination Quality Assessment for Surveillance Video Using KPLSR

Face Image Illumination Quality Assessment for Surveillance ...

引用

2016 ieee international conference on progress in informatics and computing

作者： Xiaochen Hu Li Zhuo Jing Zhang Xiaoguang Li Signal & Information Processing Laboratory Beijing University of Technology Collaborative Innovation Center of Electric Vehicles in Beijing

Illumination condition is one of the most important factors that affect the face recognition performance. Face image illumination quality assessment can predict the face recognition performance under various illumination conditions, which will improve the accuracy and efficiency of the face recognition system. However, the quality scores calculated by the existing methods are weakly correlated with the performance of face recognition. Face images with high scores often present bad recognition performance, while face images with low scores unexpectedly present good recognition performance. To predict the recognition performance more accurately, a kernel partial least squares regression(KPLSR) based face image illumination quality assessment method for surveillance video is proposed in this paper. The mapping relationship between illumination conditions and face recognition performance is modeled using KPLSR. Taken the fact that different regions of face image have different importance on face recognition, the features of luminance and contrast of sub-blocks are extracted to reflect the illumination conditions. Matching score of the face recognition system is calculated to measure the recognition performance. Experimental results show that, compared with the existing methods, the quality scores calculated by the proposed method are strong correlated with the recognition performance. The proposed method can also meet the requirements of real time processing.

关键词： face image illumination quality kernel partial least squares regression surveillance video real time

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