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Yu J.,Shaanxi Normal University | He X.,Northwest University, China | Geng G.,Northwest University, China | Liu F.,Xidian University | Jiao L.C.,Key Laboratory of Intelligent Perception and Image Understanding of Ministry of Education of China
Computational and Mathematical Methods in Medicine | Year: 2013

Quantitative reconstruction of bioluminescent sources from boundary measurements is a challenging ill-posed inverse problem owing to the high degree of absorption and scattering of light through tissue. We present a hybrid multilevel reconstruction scheme by combining the ability of sparse regularization with the advantage of adaptive finite element method. In view of the characteristics of different discretization levels, two different inversion algorithms are employed on the initial coarse mesh and the succeeding ones to strike a balance between stability and efficiency. Numerical experiment results with a digital mouse model demonstrate that the proposed scheme can accurately localize and quantify source distribution while maintaining reconstruction stability and computational economy. The effectiveness of this hybrid reconstruction scheme is further confirmed with in vivo experiments. © 2013 Jingjing Yu et al. Source


Han H.,Key Laboratory of Intelligent Perception and Image Understanding of Ministry of Education of China | Wu X.,Key Laboratory of Intelligent Perception and Image Understanding of Ministry of Education of China
Tien Tzu Hsueh Pao/Acta Electronica Sinica | Year: 2011

A new method is proposed in this paper for feature extraction based on geometric flow of images and the second generation of Bandelet transformation, where Bandelet coefficients and their statistical values were extracted as the feature of human images. Afterwards the full body and body parts classifier were trained on AdaBoost algorithm. At last, likelihoods of each body parts were computed combined with Bayesian decision-based approach to perform human detection. The results of human detection experiments indicate our proposed feature extraction method's better capability in describing human characteristics while effectively improving the performance of classifier. Combined with body parts detection, our proposed human detection method well enhanced the robustness of human detection task in both static and moving images. Source


Wu J.,China Jiliang University | Wu J.,Xidian University | Wu J.,Key Laboratory of Intelligent Perception and Image Understanding of Ministry of Education of China | Liu F.,Xidian University | And 2 more authors.
Proceedings - 4th International Congress on Image and Signal Processing, CISP 2011 | Year: 2011

The problem of compressive sensing reconstruction can be come down to the problem of solving a sparse linear model. In this paper, the l p(0 Source


Zhu S.-F.,Xidian University | Liu F.,Xidian University | Liu F.,Key Laboratory of Intelligent Perception and Image Understanding of Ministry of Education of China | Qi Y.-T.,Xidian University | And 4 more authors.
Tien Tzu Hsueh Pao/Acta Electronica Sinica | Year: 2011

To solve the joint call admission control problem in heterogeneous network environment of single operator, a novel admission control algorithm based on immune computing was proposed. The mathematical model of joint call admission control problem was expounded, the framework of admission control solution based on immune multi-objective optimization algorithm was given, and simulation experiments were done to validate proposed solution. Experimental result shows that the proposed solution, compared with other solutions, obtains better performance tradeoffs between recent utility and frequency spectrum, balances preferably each radio access network of the same operator, and has the advantage of good application value. Source


Jiao L.C.,Key Laboratory of Intelligent Perception and Image Understanding of Ministry of Education of China | Wang H.,Key Laboratory of Intelligent Perception and Image Understanding of Ministry of Education of China | Shang R.H.,Key Laboratory of Intelligent Perception and Image Understanding of Ministry of Education of China | Liu F.,Key Laboratory of Intelligent Perception and Image Understanding of Ministry of Education of China
Information Sciences | Year: 2013

Most real world multi-objective problems (MOPs) have a complicated solution space. Facing such problems, a direction vectors based co-evolutionary multi-objective optimization algorithm (DVCMOA) that introduces the decomposition idea from MOEA/D to co-evolutionary algorithms is proposed in this paper. It is novel in the sense that DVCMOA applies the concept of direction vectors to co-evolutionary algorithms. DVCMOA first divides the entire population into several subpopulations on the basis of the initial direction vectors in the objective space. Then, it solves MOPs through the co-evolutionary interaction among the subpopulations in which individuals are classified according to their direction vectors. Finally, it explores the less developed regions to maintain the relatively uniform distribution of the solution space. In this way, DVCMOA has advantages in convergence, diversity and uniform distribution of the non-dominated solution set, which are explained through comparison with other state-of-the-art multi-objective optimization evolutionary algorithms (MOEAs) in this paper. DVCMOA is shown to be effective on 6 multi-objective 0-1 knapsack problems. © 2012 Elsevier Inc. All rights reserved. Source

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