When chaotic systems are implemented on finite precision machines, it will lead to the problem of dynamical degradation. Aiming at this problem, most previous related works have been proposed to improve the dynamical ...
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When chaotic systems are implemented on finite precision machines, it will lead to the problem of dynamical degradation. Aiming at this problem, most previous related works have been proposed to improve the dynamical degradation of low-dimensional chaotic maps. This paper presents a novel method to construct high-dimensional digital chaotic systems in the domain of finite computing precision. The model is proposed by coupling a high-dimensional digital system with a continuous chaotic system. A rigorous proof is given that the controlled digital system is chaotic in the sense of Devaney's definition of chaos. Numerical experimental results for different high-dimensional digital systems indicate that the proposed method can overcome the degradation problem and construct high-dimensional digital chaos with complicated dynamical properties. Based on the construction method, a kind of pseudorandom number generator (PRNG) is also proposed as an application.
The paper focuses on two mechanisms, multiscale relevance and visual saliency, in web image search. First, in most current web image search engines, such as Google image Search, Yahoo image Search and so on, people ju...
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ISBN:
(纸本)9781424444625
The paper focuses on two mechanisms, multiscale relevance and visual saliency, in web image search. First, in most current web image search engines, such as Google image Search, Yahoo image Search and so on, people judge the relevance of search results by the thumbnails and then click through the thumbnails to check if the corresponding image is really relevant. Basically the thumbnail and the corresponding image give the multiscale representations of the image. The second is that from visual point of view, it is obvious that salient images would be easier to catch users' eyes and more likely to be clicked than cluttered ones in low-level vision. In this paper, we build a multiscale saliency model and apply it to re-rank the results from web image search engines. Experimental results show that the model can achieve an average precision (AP) [1] of as high as 97%, and it improves the results of Google image search significantly.
We present an unequal decoding power allocation (UDPA) approach for minimization of the receiver power consumption subject to a given quality of service (QoS), by exploiting data partitioning and turbo decoding. W...
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We present an unequal decoding power allocation (UDPA) approach for minimization of the receiver power consumption subject to a given quality of service (QoS), by exploiting data partitioning and turbo decoding. We assign unequal decoding power of forward error correction (FEC) to data partitions with different priority by jointly considering the source coding, channel coding and receiver power consumption. The proposed scheme is applied to H.264 video over additive white Gaussion noise (AWGN) channel, and achieves excellent tradeoff between video delivery quality and power consumption, and yields significant power saving compared with the conventional equal decoding power allocation (EDPA) approach in wireless video transmission.
The biological immune system is a highly parallel and distributed adaptive system. The information processing abilities of the immune system provide important insights into the field of computation. Based on immunodom...
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The biological immune system is a highly parallel and distributed adaptive system. The information processing abilities of the immune system provide important insights into the field of computation. Based on immunodominance in the biological immune system and the clonal selection mechanism, a novel data mining method, Immune Dominance Clonal Multiobjective Clustering algorithm (IDCMC), is presented. The algorithm divides an individual population into three sub-populations according to three different measurements, and adopts different evolution and selection strategies for each sub-population. The update of each sub-population, however, is not carried out in isolation. The periodic combination operation of the analysis of the three sub-populations represents considerable advantages in its global search ability. The clustering task is a multiobjective optimization problem, which is more robust with respect to the variety of cluster structures of different datasets than a single-objective clustering algorithm. In addition, the new algorithm can determine the number of clusters automatically, which should identify the most promising clustering solutions in the candidate set. The experimental results, using artificial datasets with different manifold structure and handwritten digit datasets, show that the IDCMC outperforms the PESA-Ⅱ-based clustering method, the genetic algorithm-based clustering technique and the original K-Means algorithm in solving most of the problems tested.
Artificial immune recognition system (AIRS), as an efficient and successful computational intelligence method, has been widely used for classification. However, this method is seldom used for hyperspectral image class...
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ISBN:
(纸本)9781467321969
Artificial immune recognition system (AIRS), as an efficient and successful computational intelligence method, has been widely used for classification. However, this method is seldom used for hyperspectral image classification due to its complexity. To address this problem, a class-specific model based on AIRS, named as Single Class Learning Network AIRS (SCLN-AIRS), is proposed in this paper to improve the classification accuracy for hyperspectral images compared with AIRS based method. For SCLN-AIRS, the outliers of training samples from irrelevant classes are ignored first. Then, a novel MC evolution strategy is proposed to prevent memory cells being affected by other ones from different classes. In the novel model, the calculation complexity is guaranteed by the fact that the class is expressed only by few memory cells while classification result is improved. Experimental results on AVIRIS dataset demonstrate the effectiveness of the proposed method for hyperspectral image classification.
Due to the numerous important applications of video surveillance and monitoring, video object tracking has been an active research topic in the last decade. This paper makes a survey of approaches to high quality obje...
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The quantum-inspired immune clonal algorithm(QICA) is a rising intelligence *** on evolutionary game theory and QICA,a quantum-inspired immune algorithm embedded with evolutionary game(EGQICA) is proposed to solve com...
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The quantum-inspired immune clonal algorithm(QICA) is a rising intelligence *** on evolutionary game theory and QICA,a quantum-inspired immune algorithm embedded with evolutionary game(EGQICA) is proposed to solve combination optimization *** this paper,we map the quantum antibody’s finding the optimal solution to player’s pursuing maximum utility by choosing strategies in evolutionary *** dynamics is used to model the behavior of the quantum antibody and the memory mechanism is also introduced in this *** results indicate that the proposed approach maintains a good diversity and achieves superior performance.
Panoramic radiography plays a vital role in dental diagnosis and treatment, characterized by low radiation exposure, cost-effectiveness, and high accessibility, rendering it suitable for initial screening of oral dise...
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This paper studies an adaptive regulation problem for the modified FitzHugh-Nagumo neuron model under external electrical stimulation. We first present the solution of the global robust output regulation problem for o...
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This paper studies an adaptive regulation problem for the modified FitzHugh-Nagumo neuron model under external electrical stimulation. We first present the solution of the global robust output regulation problem for output feedback system with an uncertain exosystem which models the external electrical stimulation with unknown frequency and amplitude. Then, we show that the robust control problem for the modified FitzHugh-Nagumo neuron model can be formulated as the global robust output regulation problem and the solvability conditions for the output regulation problem for the modified FitzHugh-Nagumo neuron model are all satisfied. Then, we apply the obtained output regulation result to constructing an output feedback control law for the modified FitzHugh-Nagumo neuron model to achieve global stability of the closed-loop system in the presence of uncertain parameters and external stimulus. An example is given to show that the proposed algorithm can completely reject the external electrical stimulation.
Thermally activated delayed fluorescence(TADF)molecules have attracted great attention as high efficient luminescent *** of TADF molecules possess small energy gap between the first singlet excited state(S_(1))and the...
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Thermally activated delayed fluorescence(TADF)molecules have attracted great attention as high efficient luminescent *** of TADF molecules possess small energy gap between the first singlet excited state(S_(1))and the first triplet excited state(T_(1))to favor the up-conversion from T_(1)to S_(1).In this paper,a new TADF generation mechanism is revealed based on theoretical *** systematic study of the light-emitting properties of SOBF-OMe in both toluene and in aggregation state,we find that the single SOBF-OMe could not realize TADF emission due to large energy gap as well as small up-conversion rates between S_(1)and T_(1).Through analysis of dimers,we find that dimers with intermolecular hydrogen bond(H-bond)are responsible for the generation of TADF,since smaller energy gap between S_(1)and T_(1)is found and the emission wavelength is in good agreement with experimental *** emission properties of SOBF-H are also studied for comparison,which reflect the important role of *** theoretical results agree ith experimental results well and confirm the mechanism of H-bond induced TADF.
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