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2012 IEEE 11th International Conference on Signal Processing(ICSP 2012)

作者： Dawei Li Lihong Xu Erik Goodman Dept.of Control Science and Engineering Tongji University Dept.of Electrical and Computer Engineering and Beacon Center Michigan State University East Lansing USA 48824

a real-time foreground moving object detection algorithm based on Kernel Density Estimation in srgb color space is proposed in this paper, followed by an iterative noise reduction procedure, which removes dispersed noise and enhances the foreground contour in a binary image mask within a Bayesian framework. A simulated annealing strategy is applied in MRF-type decision making at the texture level to accelerate the convergence of the iterative noise reduction procedure. Experiments show that the proposed algorithm can resist undesirable effects of changing environmental illumination and shadow. Compared to several classical methods, better detection results are achieved on various datasets including both indoor and outdoor cases.

关键词： foreground detection, kernel density estimation, markov random field, noise reduction, simulated annealing

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Increasing Entanglement Monotones by Separable Operations

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Physical Review Letters 2012年第24期108卷 240504-240504页

作者： Eric Chitambar Wei Cui Hoi-Kwong Lo Center for Quantum Information and Quantum Control (CQIQC) Department of Physics and Department of Electrical and Computer Engineering University of Toronto Toronto Ontario M5S 3G4 Canada

Quantum entanglement is fundamentally related to the operational setting of local quantum operations and classical communication (LOCC). A more general class of operations known as separable operations (SEP) is often employed to approximate LOCC, but the exact difference between LOCC and SEP is unknown. In this letter, we compare the two classes in performing particular tripartite to bipartite entanglement conversions and report a gap as large as 12.5% between SEP and LOCC, which is the first known appreciable gap between the classes. Our results rely on constructing a computable entanglement monotone with a clear operational meaning that, unlike all other such monotones previously studied, is not monotonic under SEP. Finally, we prove the curious fact that convergent sequences of LOCC protocols need not be LOCC feasible in the limit.

关键词： MONOTONE operators SEPARABLE algebras QUANTUM entanglement MATHEMATICAL proofs SEQUENCES (Mathematics) STOCHASTIC convergence

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Entanglement monotones for W-type states

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Physical Review A 2012年第6期85卷 062316-062316页

作者： Eric Chitambar Wei Cui Hoi-Kwong Lo []Center for Quantum Information and Quantum Control (CQIQC) Department of Physics and Department of Electrical & Computer Engineering University of Toronto Toronto Ontario M5S 3G4 Canada

In this article, we extend recent results concerning random-pair Einstein-Podolsky-Rosen distillation and the operational gap between separable operations (SEPs) and local operations with classical communication (LOCC). In particular, we consider the problem of obtaining bipartite maximal entanglement from an N-qubit W-class state (i.e., that of the form x0|00⋯0〉+x1|10⋯0〉+⋯+xn|00⋯1〉) when the target pairs are a priori unspecified. We show that when x0=0, the optimal probabilities for SEPs can be computed using semidefinite programming. On the other hand, to bound the optimal probabilities achievable by LOCC, we introduce entanglement monotones defined on the N-qubit W class of states. The LOCC monotones we construct can be increased by SEPs, and in terms of transformation success probability, we are able to quantify a gap as large as 37% between the two classes. Additionally, we demonstrate transformations ρ⊗n→σ⊗n that are feasible by SEP for any n but impossible by LOCC.

关键词： Probability

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Automatic segmentation of brain tumors in magnetic resonance images

Automatic segmentation of brain tumors in magnetic resonance...

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IEEE-EMBS International Conference on Biomedical and Health Informatics, BHI 2012. In Conj. with the 8th Int. Symp.on Medical Devices and Biosensors and the 7th Int. Symp. on Biomedical and Health engineering

作者： Behzadfar, Neda Soltanian-Zadeh, Hamid Control and Intelligent Processing Center of Excellence School of Electrical and Computer Engineering University of Tehran Tehran 14395-515 Iran Pooyandegan Rah Saadat Company Tehran 16756-965 Iran Image Analysis Laboratory Radiology Department HenryFord Healh System Detroit MI 48202 United States

ISBN: (纸本)9781457721779

Segmentation of tumors in magnetic resonance images (MRI) is an important task but is quite time consuming when performed manually by experts. Automating this process is challenging due to the high diversity in appearance of tumor tissue in different patients, and in many cases, similarity between tumor and normal tissues. This paper presents an automatic method for segmentation of brain tumors in MRI. We use images of patients with glioblastoma multiform tumors. After pre-processing and removal of the regions that do not have useful information (e.g., eyes and scalp), we create a projection image for determining the primary location of the tumor. This image provides an overall view of the tumor. Then, we grow the primary region to segment the entire tumor. This method is automatic and independent of the operator. It segments low contrast tumors without requiring their exacta tissue boundaries. The segmentation results obtained by the proposed approach are compared with those of an expert radiologist showing excellent correlations among them (R2=0.97). © 2012 IEEE.

关键词： Tumors

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Sparse online LS-SVM based on modified particle swarm optimization algorithm and application

Sparse online LS-SVM based on modified particle swarm optimi...

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International Conference on Intelligent control and Information Processing (ICICIP)

作者： Weiping Zhang Peifeng Niu Guoqiang Li Institute of Electrical Engineering Qinhuangdao Institute of Technology Yanshan University China key Laboratory of Industrial Computer Control Engineering of Hebei Province National Engineering Research Center for Equipment and Technology of Cold Strip Rolling Rolling China Institute of Electrical Engineering Yanshan University China

In this paper, A simple and effective mechanism is proposed to realize the parsimoniousness of the online least squares support vector machine. Hence, the response time is curtailed. Besides, a modified Particle Swarm Optimization (PSO) algorithm is proposed to ascertain the optimal model parameters. Simulation results show that it outperforms GA and common PSO algorithms and LS-SVM model based on the modified PSO algorithm has the best regression accuracy and generalization ability. The sparse online LS-SVM algorithm is applied to build a turbine heat rate forecasting model, which possesses dynamic prediction functions.

关键词： Prediction algorithms Heating Mathematical model Training Computational modeling Support vector machines Optimization

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A novel system for driver's sleepiness detection using SSVEP

A novel system for driver's sleepiness detection using SSVEP

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International Symposium on Artificial Intelligence and Signal Processing (AISP)

作者： Seyed Navid Resalat Valiallah Saba Fardin Afdideh Control and Intelligent Processing Center of Excellence School of Electrical and Computer Engineering College of Engineering University of Tehran Tehran Iran AJA University of Medical sciences Tehran Iran Tehran Iran

A novel EEG-based system for driver's sleepiness detection is proposed. Driver's sleepiness is an important factor in many accidents. Therefore, real-time sleepiness detection can restrain accidents effectively. In this study, SSVEPs are used for running the proposed system. In order to generate SSVEPs in the brain activities, two experimental setups consisting four single and paired LEDs are proposed. In addition, the effect of two different FFT-based feature extraction methods, and two different classifiers of the LDA and the SVM on the accuracy of the system are studied. Related features are extracted from three different segments (sweep lengths) of 0.5, 1, and 2 seconds. The experimental results show that higher sweep lengths have higher accuracies and the SVM classifier, experimental setup of 4-paired LEDs and sweep length of 1 second has the highest ITR value of 24 bits/min. Therefore, this study demonstrates the feasibility of the proposed system in a practical driving application.

关键词： Light emitting diodes Accuracy Feature extraction Vehicles Sleep Support vector machines Electroencephalography

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Takagi-Sugeno control of the Elevation Channel of A Twin-Rotor System Using Closed-Loop Empirical Data

Takagi-Sugeno Control of the Elevation Channel of A Twin-Rot...

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American control Conference

作者： Hamidreza Azimian Alireza Fatehi Babak N. Araabi Department of Mechanical and Materials Engineering The University of Western Ontario (UWO) London ON Canada Advanced Process Automation and Control (APAC) Research Group Industrial Control Center of Excellence Faculty of Electrical and Computer engineering K. N. Toosi University of Technology Tehran Iran School of Electrical and Computer Engineering University of Tehran Tehran Iran

ISBN: (纸本)9781457710957

In this paper, design of Takagi-Sugeno controllers for a twin-rotor system from empirical data is addressed. First, identification of linear models using Prediction Error Method in the presence of nonlinear distortions and in feedback systems is revisited, and it is demonstrated that feedback nonlinear systems with an integral action can be locally represented by linear models rather than affine models. Moreover, it is demonstrated that how the consistency and unbiasedness of the identified models can be improved by proper design of the experiments. In order to ensure that the unmodeled contributions in the resulting piecewise linear model are sufficiently small, a criterion is proposed based on the small-gain theorem that can be used for unfalsification of the piecewise linear model. Finally, by taking advantage of the separation principle, design of fuzzy Takag-Sugeno state feedback and observer is formulated in terms of Linear Matrix Inequalities (LMIs). The implementation results on the actual system demonstrate significant improvements with respect to the initial controller and previous work on similar systems.

关键词： Nonlinear systems Closed loop systems Observers Uncertainty Data models Predictive models

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A Hierarchal Classifier for Identifying Independent Components

A Hierarchal Classifier for Identifying Independent Componen...

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International Joint Conference on Neural Networks

作者： Chin-Teng Lin Yu-Kai Wang Shi-An Chen Department of Electrical and Control Engineering and Brain Research Center National Chiao-Tung University Department of Computer Science and Brain Research Center National Chiao-Tung University

ISBN: (纸本)9781467314886

Brain-computer interface (BCI) has shown explosive growth for multiple applications in the recently years. Removing artifacts and selecting useful brain sources are essential in BCI research. Independent Component Analysis (ICA) has been proven as an effective technique to remove artifacts and many brain related researches are based on ICA. However, the useful independent components with brain sources are usually selected manually according to the scalp-plots. This is great inconvenience and a barrier for real-time BCI applications of EEG. In this investigation, a two-layer automatic identification model is proposed to select useful brain sources. It is based on neural network including support vector machine with radial basis function (SVMRBF) and self-organizing map (SOM). In the first layer, SVM discriminates useful independent components from the artifact effectively. In the second layer, these selected useful components are automatically classified to different spatial brain sources according to SOM. This study suggests this model to one general application for EEG study. It can reduce the effect of subjective judgment and improve the performance of EEG analysis.

关键词： Component Brain-computer interface Independent component analysis Electroencephalogram Neural network independent components Electroencephalography Independent component analysis self-organising feature maps Support Vector Network Artifacts Cerebrum islet cell antibody Scalp Classifiers

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Sparse MEG Source Localization on Parameterized Cortical Medial Surface

Sparse MEG Source Localization on Parameterized Cortical Med...

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Iranian Conference of Biomedical engineering

作者： Hamid Khosravi Mahsa Mohammadkhani Hamid Soltanian-Zadeh Control and Intelligent Processing Center of Excellence School of Electrical and Computer Engineering Faculty of Engineering University of Tehran Tehran Iran Department of Computing Science University of Alberta Edmonton Alberta (AB) Canada

ISBN: (纸本)9781467331289

The inverse mapping in MagnetoEncephaloGraphy involves localizing neuronal activity of the gray matter tissue from measured signals around the head. The search space of the inverse mapping is conventionally represented in 3-D Cartesian coordinates including the cortical volume, or alternatively, 2-D manifold describing the cortical tissue. 3-D Cartesian coordinates does not suit sparse regularization of the inverse mapping in multi-resolution source domains. On the other hand, boundary surface of the cortex, represented as triangulated mesh, is used as the search space in some other inverse mapping techniques. However, this surface does not represent depth of the cortex. Moreover, to represent neuronal activity in embedded signal dictionaries, convoluted manifold should be gridded. In this paper we propose to use medial surface as the best surface descriptor of the cortical tissue. Furthermore, manifold gridding is constructed by defining bijective mapping between a flattened parameterized domain and convoluted surface. Signal bases are defined conventionally in the parameterized domain and mapped to the cortical volume by means of the bijective mapping. Medial surface of the cortex tissue, represented as triangulated mesh, is extracted by gradient-based search algorithm. Source localization, for simulated neuronal activity, is performed in real head model extracted from magnetic resonance images. Sparse estimated source of the inverse mapping are compared on volume, boundary surface, and medial surface domains. Simulation results confirm advantages of surface over volume domain. Performnace measures of source localization proves the medial surface as the best surface describing the cortex volume.

关键词： Magneto-encephalography (MEG) Inverse mapping Sparse regularization Multi-resolution Medial surface Mesh parameterization

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High-speed SSVEP-based BCI: Study of various frequency pairs and inter-sources distances

High-speed SSVEP-based BCI: Study of various frequency pairs...

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IEEE EMBS International Conference on Information Technology Applications in Biomedicine (ITAB)

作者： Seyed Navid Resalat Valiallah Saba Fardin Afdideh Ali Heidarnejad Control and Intelligent Processing Center of Excellence School of Electrical and Computer Engineering College of Engineering University of Tehran Tehran Iran AJA University of Medical Sciences Iran Biomedical Signal and Image Processing Laboratory (BiSIPL) School of Electrical Engineering College of Engineering Sharif University of Technology Tehran Iran

Brain computer Interface provides a new communication channel for people who have severe brain injuries. Among different types of BCIs, SSVEP-based one has been focused in recent years. In this type of BCI, selection of twinkling frequency of external visual stimulant and the distance between stimulants (in case of more than one stimulant) is so important. In this work, a SSVEP-based BCI with two external stimulants was designed. In order to determine the best twinkling frequency of stimulants and the best distance between them, the classification accuracy for seven different twinkling frequency pairs and five different stimulants distances was calculated. Two methods for feature extraction step were proposed and the Max classifier was used for classification in order to speed up the computational burden of classification step. Features were extracted from four different segment lengths (sweeps). The results showed that nearly in all sweeps and all inter-sources distances the frequency pair of 10-15 Hz has the highest classification accuracy among other frequency pairs, which is 92% in the inter-sources distance of 24 cm and sweep length of 3 seconds. In addition, the results demonstrated that the method 2 feature extraction technique outperforms the method 1. In addition, for determining the best sweep length, Information Transfer Rate (ITR) was computed and the results indicated that the sweep length of 0.5 second has the highest ITR, so would be practical in real-time applications of BCI.

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