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Virtual brain surgery simulation system based on haptic interaction

High Technology Letters 2015年第2期21卷 185-191页

作者：张小瑞 Zhu Jiandong Sun Wei Norman I.Badler Song Aiguo Niu Jianwei Liu Jia School of Computer and Software Nanjing University of Information Science & Technology Jiangsu Engineering Center of Network Monitoring Nanjing University of Information Science and Technology School of Information and Control Nanjing University of Information Science & Technology School of Engineering and Applied Science University of Pennsylvania School of Instrument Science and Engineering Southeast University State key Laboratory of Virtual Reality Technology and Systems Beihang University

To improve the accuracy and interactivity of soft tissue delormatlon simulation, a new plate spring model based on physics is proposed. The model is parameterized and thus can be adapted to simulate different organs. Different soft tissues are modeled by changing the width, number of pieces, thickness, and length of a single plate spring. In this paper, the structural design, calcula- tion of soft tissue deformation and real-time feedback operations of our system are also introduced. To evaluate the feasibility of the system and validate the model, an experimental system of haptic in- teraction, in which users can use virtual hands to pull virtual brain tissues, is built using PHANTOM OMNI devices. Experimental results show that the proposed system is stable, accurate and promising for modeling instantaneous soft tissue deformation.

关键词： haptic feedback human-computer interaction surgery simulation soft tissue deformation

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Tensor object classification via multilinear discriminant analysis network

Tensor object classification via multilinear discriminant an...

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IEEE International Conference on Acoustics, Speech and Signal Processing

作者： Rui Zeng Jiasong Wu L. Senhadji Huazhong Shu LIST Key Laboratory of Computer Network and Information Integration (Southeast University) Ministry of Education Nanjing 210096 China

ISBN: (纸本)9781467369985

This paper proposes an multilinear discriminant analysis network (MLDANet) for the recognition of multidimensional objects, knows as tensor objects. The MLDANet is a variation of linear discriminant analysis network (LDANet) and principal component analysis network (PCANet), both of which are the recently proposed deep learning algorithms. The MLDANet consists of three parts: 1) The encoder learned by MLDA from tensor data. 2) Features maps obtained from decoder. 3) The use of binary hashing and histogram for feature pooling. A learning algorithm for MLDANet is described. Evaluations on UCF11 database indicate that the proposed MLDANet outperforms the PCANet, LDANet, MPCA+LDA, and MLDA in terms of classification for tensor objects.

关键词： Deep learning LDANet MLDANet PCANet tensor object classification

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Gradient Compared lp-LMS Algorithms for Sparse System Identification

Gradient Compared lp-LMS Algorithms for Sparse System Identi...

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第27届中国控制与决策会议

作者： Yong Feng Jiasong Wu Rui Zeng Limin Luo Huazhong Shu School of Biological Science and Medical Engineering Southeast University Laboratory of Image Science and Technology the Key Laboratory of Computer Network and Information IntegrationSoutheast University

ISBN: (纸本)9781479970186

In this paper,we propose two novel p-norm penalty least mean square（l-LMS） algorithms as supplements of the conventional l-LMS algorithm established for sparse adaptive filtering recently.A gradient comparator is employed to selectively apply the zero attractor of p-norm constraint for only those taps that have the same polarity as that of the gradient of the squared instantaneous error,which leads to the new proposed gradient compared p-norm constraint LMS algorithm（lGC-LMS）.We explain that the lGC-LMS can achieve lower mean square error than the standard l-LMS algorithm theoretically and *** further improve the performance of the filter,the lNGC-LMS algorithm is derived using a new gradient comparator which takes the sign-smoothed version of the previous *** performance of the l NGC-LMS is superior to that of the lGC-LMS in theory and in ***,these two comparators can be easily applied to other norm constraint LMS algorithms to derive some new approaches for sparse adaptive *** numerical simulation results show that the two proposed algorithms achieve better performance than the standard LMS algorithm and l-LMS algorithm in terms of convergence rate and steady-state behavior in sparse system identification settings.

关键词： Least Mean Square Algorithm p Norm Constraint Sparse System Identification New Gradient Comparator

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Combo-Pre: A Combination Link Prediction Method in Opportunistic networks

Combo-Pre: A Combination Link Prediction Method in Opportuni...

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International Conference on computer Communications and networks (ICCCN)

作者： Yin Li Sanfeng Zhang College of Software Engineering Southeast University Nanjing China Key Laboratory of Computer Network and Information Integration (Southeast University) Ministry of Education Southeast University Nanjing China

Opportunistic networks have emerged as prospective network architecture for smart mobile devices related applications. The main obstacle for designing routing protocol in opportunistic networks is their inherent dynamic nature. Unknown future link patterns lead to blind and inefficient packet forwarding behavior. Lots of efforts have focused on the future link prediction problem, but contact patterns among node pairs are too different to be predicted by one single method. To this end, we propose a combination link prediction method for opportunistic network routing named Combo-Pre. Combo-Pre applies periodic pattern mining methods to predict frequent and periodic contacts, decision tree method to predict frequent but non-periodic contacts and Adamic-Adar methods in complex networks to predict infrequent contacts. Experimental results show that Combo-Pre outperforms state-of-the-art link prediction methods in opportunistic networks. These results can be applied in routing protocol design to decrease routing cost and promote delivery rate.

关键词： Prediction methods Accuracy Routing Complex networks Jitter Decision trees History

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Tensor object classification via multilinear discriminant analysis network 40

Tensor object classification via multilinear discriminant an...

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40th IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2015

作者： Zeng, Rui Wu, Jiasong Senhadji, Lotfi Shu, Huazhong LIST Key Laboratory of Computer Network and Information Integration Southeast University Ministry of Education Nanjing China INSERM U 1099 Rennes France Laboratoire Traitement du Signal et de l'Image Universit de Rennes 1 Rennes France France

ISBN: (纸本)9781467369978

关键词： Deep learning LDANet MLDANet PCANet tensor object classification

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Enhancing binary relevance for multi-label learning with controlled label correlations exploitation

Lecture Notes in Computer Science (including subseries Lectu...

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Lecture Notes in computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) 2014年 8862卷 91-103页

作者： Li, Yu-Kun Zhang, Min-Ling School of Computer Science and Engineering Southeast University China Key Laboratory of Computer Network and Information Integration Southeast University China

Binary relevance (BR) is regarded as the most intuitive solution to learn from multi-label data, which decomposes the multi-label learning task into a number of independent binary learning tasks (one per class label). To amend its potential weakness of ignoring label correlations, many correlation-enabling extensions to BR have been proposed based on two major strategies, i.e. assuming random correlations with chaining structure or taking full-order correlations with stacking structure. However, in both strategies label correlations are only exploited in an uncontrolled manner, which may be problematic when error-prone and uncorrelated class labels arise. In this paper, to fulfill controlled label correlations exploitation, a novel enhancement to BR is proposed based on a two-stage filtering procedure. In the first stage, error-prone class labels are pruned from the label space based on holdout validation. In the second stage, closely-related class labels are identified based on supervised feature selection by viewing unpruned labels as features. Extensive experiments across fourteen multi-label data sets confirm the superiority of controlled label correlations exploitation, especially when large number class labels exist in the label space. © Springer International Publishing Switzerland 2014.

关键词： Learning systems

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Carriers Selection for Content Dissemination in Social Opportunistic networks

Carriers Selection for Content Dissemination in Social Oppor...

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International Conference on Collaborative Computing: networking, Applications and Worksharing (CollaborateCom)

作者： Yongqiang Dong Wang Yang Taieb Znati Key Laboratory of Computer Network and Information Integration Southeast University Nanjing China Department of Computer Science School of Arts and Sciences Pittsburgh PA USA

Content dissemination in social opportunistic networks has attracted great interests in the field. Most of the researches in literature focused on the data forwarding and routing mechanisms in such network scenarios. There were few studies on the assignment of content originators. This paper is concerned about how to identify a small subset of users as content carriers through which the content can be disseminated more efficiently. The problem is modeled as a dominating set problem in bipartite graph, in which a minimum number of carriers are selected to cover all the venues with required probability, by using available location affinity information. The NP-complete property of the problem is pointed out, along with an exact exponential exhaustive algorithm. Then three polynomial heuristic algorithms are presented to solve the venue coverage problem. Among them, the adaptive metric-based algorithm chooses carriers based on dynamically calculated metric values. Several user-centric and expected contribution based metrics are discussed and examined. The submodular algorithm selects carriers greedily based on the estimation of marginal coverage gain from each user. The reduced-union algorithm asks each venue to select a candidate set separately based on its own individual affinity information. Then the candidate union set is reduced gradually by reasonable greedy method. Synthetized data and real trace experiment results show that the submodular algorithm can be applied in scenarios where coverage threshold is low and small number of carriers is required. In most cases we can rely on metric-based algorithm to solve the carriers selection problem. Among all the metrics, total affinity and proportional contribution perform more valuable than other metrics. Meanwhile, the adaptive operation in metric-based algorithm plays more important role than the metrics in seeking the most qualified carriers. When complete affinity information is not available, the reduced-union algo

关键词： Bipartite graph Algorithm design and analysis Routing Heuristic algorithms Delays network topology

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Deep Label Distribution Learning for Apparent Age Estimation

Deep Label Distribution Learning for Apparent Age Estimation

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International Conference on computer Vision Workshops (ICCV Workshops)

作者： Xu Yang Bin-Bin Gao Chao Xing Zeng-Wei Huo Xiu-Shen Wei Ying Zhou Jianxin Wu Xin Geng School of Computer Science and Engineering Southeast University Nanjing China Key Laboratory of Computer Network and Information Integration Ministry of Education China National Key Laboratory for Novel Software Technology Nanjing University Nanjing China Southeast University Nanjing Jiangsu CN

In the age estimation competition organized by ChaLearn, apparent ages of images are provided. Uncertainty of each apparent age is induced because each image is labeled by multiple individuals. Such uncertainty makes this age estimation task different from common chronological age estimation tasks. In this paper, we propose a method using deep CNN (Convolutional Neural network) with distribution-based loss functions. Using distributions as the training tasks can exploit the uncertainty induced by manual labeling to learn a better model than using ages as the target. To the best of our knowledge, this is one of the first attempts to use the distribution as the target of deep learning. In our method, two kinds of deep CNN models are built with different architectures. After pre-training each deep CNN model with different datasets as one corresponding stream, the competition dataset is then used to fine-tune both deep CNN models. Moreover, we fuse the results of two streams as the final predicted ages. In the final testing dataset provided by competition, the age estimation performance of our method is 0.3057, which is significantly better than the human-level performance (0.34) provided by the competition organizers.

关键词： Face Estimation Streaming media Training Hidden Markov models Uncertainty Standards

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Gradient compared ℓp-LMS algorithms for sparse system identification

Gradient compared ℓp-LMS algorithms for sparse system ident...

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Chinese Control and Decision Conference, CCDC

作者： Yong Feng Jiasong Wu Rui Zeng Limin Luo Huazhong Shu School of Biological Science and Medical Engineering Southeast University Nanjing China Laboratory of Image Science and Technology the Key Laboratory of Computer Network and Information Integration Southeast University Nanjing China Laboratory of Image Science and Technology Southeast University Nanjing China

In this paper, we propose two novel p-norm penalty least mean square (ℓ p -LMS) algorithms as supplements of the conventional ℓ p -LMS algorithm established for sparse adaptive filtering recently. A gradient comparator is employed to selectively apply the zero attractor of p-norm constraint for only those taps that have the same polarity as that of the gradient of the squared instantaneous error, which leads to the new proposed gradient compared p-norm constraint LMS algorithm (ℓ p GC-LMS). We explain that the ℓ p GC-LMS can achieve lower mean square error than the standard ℓ p -LMS algorithm theoretically and experimentally. To further improve the performance of the filter, the ℓ p NGC-LMS algorithm is derived using a new gradient comparator which takes the sign-smoothed version of the previous one. The performance of the ℓpNGC-LMS is superior to that of the ℓpGC-LMS in theory and in simulations. Moreover, these two comparators can be easily applied to other norm constraint LMS algorithms to derive some new approaches for sparse adaptive filtering. The numerical simulation results show that the two proposed algorithms achieve better performance than the standard LMS algorithm and ℓ p -LMS algorithm in terms of convergence rate and steady-state behavior in sparse system identification settings.

关键词： Least squares approximations Standards Convergence System identification Signal processing algorithms Steady-state Signal to noise ratio

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Position Prediction Social-Relationship-Based on Multi-order Markov Model

Position Prediction Social-Relationship-Based on Multi-order...

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International Conference on Advanced Cloud and Big Data (CBD)

作者： Yue Zou Sanfeng Zhang College of Software Engineering Southeast University Nanjing China Key Laboratory of Computer Network and Information Southeast University Nanjing China

Opportunistic network mobile user-nodes location prediction can help to collect the data, improve the dynamic network and help to decrease the problems which include the delay, low success rate, high storage space energy consume and so on, which are the results of the blindness in the process of delivery. In this paper, a mobile node location prediction algorithm base on the Multi-Order Markov prediction model as the initial forecast, and then use social-relationship which is closely linked to the node to optimize the algorithm. Experimental results show that the proposed algorithm is better than the single Multi-Order Markov model.

关键词： Markov processes Prediction algorithms Routing Predictive models Hidden Markov models Whales Delays

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