Example-based automatic music-driven conventional dance motion synthesis

We introduce a novel method for synthesizing dance motions that follow the emotions and contents of a piece of music. Our method employs a learning-based approach to model the music to motion mapping relationship embodied in example dance motions along with those motions' accompanying background music. A key step in our method is to train a music to motion matching quality rating function through learning the music to motion mapping relationship exhibited in synchronized music and dance motion data, which were captured from professional human dance performance. To generate an optimal sequence of dance motion segments to match with a piece of music, we introduce a constraint-based dynamic programming procedure. This procedure considers both music to motion matching quality and visual smoothness of a resultant dance motion sequence. We also introduce a two-way evaluation strategy, coupled with a GPU-based implementation, through which we can execute the dynamic programming process in parallel, resulting in significant speedup. To evaluate the effectiveness of our method, we quantitatively compare the dance motions synthesized by our method with motion synthesis results by several peer methods using the motions captured from professional human dancers' performance as the gold standard. We also conducted several medium-scale user studies to explore how perceptually our dance motion synthesis method can outperform existing methods in synthesizing dance motions to match with a piece of music. These user studies produced very positive results on our music-driven dance motion synthesis experiments for several Asian dance genres, confirming the advantages of our method.     

Feature extraction using orthogonal discriminant local tangent space alignment

A novel algorithm called orthogonal discriminant local tangent space alignment (O-DLTSA) is proposed for supervised feature extraction. Derived from local tangent space alignment (LTSA), O-DLTSA not only inherits the advantages of LTSA which uses local tangent space as a representation of the local geometry so as to preserve the local structure, but also makes full use of class information and orthogonal subspace to improve discriminant power. The experimental results of applying O-DLTSA to standard face databases demonstrate the effectiveness of the proposed method.     

Consensus reaching models of linguistic preference relations based on distance functions

In group decision making (GDM) using linguistic preference relations to obtain the maximum degree of agreement, it is desirable to develop a consensus process prior to the selection process. This paper proposes two consensus models with linguistic information to support the GDM consensus reaching process. Two different distance functions between linguistic preference relations are introduced to measure both individual consistency and group consensus. Based on these measures, the consensus reaching models are developed. The two models presented have the same concept that the expert whose preference is farthest from the group preference needs to update their opinion according to the group preference relation. In addition, the convergence of the models is proved. After achieving the predefined consensus level, each expert’s consistency indexes are still acceptable under the condition that the initial preference relations are of satisfactory consistency. Finally, an example is given to show the effectiveness of the models and to verify the theoretical results.     

Stochastic TCP friendliness: Expanding the design space of TCP-friendly traffic control protocols

While the current definition of TCP friendliness has enabled a wide variety of traffic control protocols other than TCP, it still considerably restricts the design space of TCP-friendly traffic control protocols. For example, some multimedia streaming applications prefer a smooth sending rate on a time scale of tens of seconds, however, a UDP flow maintaining such a long smoothness time scale is naturally not TCP friendly by the current definition. In this paper, we propose an innovative method to relatively compare the degrees of TCP friendliness of different traffic control protocols, and use it to define a new class of TCP friendliness definitions, called stochastic TCP friendliness (STF). STF greatly expands the design space of TCP-friendly traffic control protocols, while still effectively maintaining the desired fairness of the Internet. To demonstrate the expanded design space, we also develop a new congestion control protocol, called TCP-friendly CBR-Like Rate Control (TFCBR), for multimedia streaming applications which do not require a high sending rate but prefer a smooth sending rate on a time scale of tens of seconds. TFCBR is stochastically TCP friendly, however, it is not TCP friendly by the current definition.     

Optimal Performance in Tracking Stochastic Signal Under Disturbance Rejection

The analysis method of optimal tracking performance is proposed for multiple‐input multiple‐output (MIMO) linear time‐invariant (LTI) systems under disturbance rejection. An H₂ criterion of the error signal between the output of the plant and the reference signal is used as a measure for the tracking performance. Spectral factorization is applied to obtain the optimal solution of the system tracking error. The explicit expressions are derived for this minimal tracking error with respect to random reference signals under disturbance rejection. It is shown that the nonminimum phase zeros, the zero direction, the unstable poles, the pole direction of a given plant, statistical characteristics of the reference input signal, and disturbance signal have a negative effect on a feedback system's ability to reduce the system error with disturbance rejection. The results show that the optimal tracking performance will further be damaged because of disturbance rejection. Some typical examples are given to illustrate the theoretical results.     

Delay‐dependent stability for Markovian genetic regulatory networks with time‐varying delays

This paper is concerned with the problem of stochastic stability analysis for a class of genetic regulatory networks with Markovian jump parameters and time‐varying delays. A delay‐dependent stability criterion is derived by using a novel mode‐dependent Lyapunov functional. The derived stability criterion is expressed in terms of linear matrix inequalities and is less conservative than the existing ones in the literature. A numerical example is provided to demonstrate the effectiveness of the proposed stability criterion. Copyright © 2011 John Wiley and Sons Asia Pte Ltd and Chinese Automatic Control Society     

Entropy/cross entropy-based group decision making under intuitionistic fuzzy environment

We study the group decision making problem under intuitionistic fuzzy environment. Based on entropy and cross entropy, we give two methods to determine the optimal weights of attributes, and develop two pairs of entropy and cross entropy measures for intuitionistic fuzzy values. Then, we discuss the properties of these measures and the relations between them and the existing ones. Furthermore, we introduce three new aggregation operators, which treat the membership and non-membership information fairly, to aggregate intuitionistic fuzzy information. Finally, several practical examples are presented to illustrate the developed methods.     

The induced generalized aggregation operators for intuitionistic fuzzy sets and their application in group decision making

In this paper, we present the induced generalized intuitionistic fuzzy ordered weighted averaging (I-GIFOWA) operator. It is a new aggregation operator that generalized the IFOWA operator, including all the characteristics of both the generalized IFOWA and the induced IFOWA operators. It provides a very general formulation that includes as special cases a wide range of aggregation operators for intuitionistic fuzzy information, including all the particular cases of the I-IFOWA operator, GIFOWA operator and the induced intuitionistic fuzzy ordered geometric (I-IFOWG) operator. We also present the induced generalized interval-valued intuitionistic fuzzy ordered weighted averaging (I-GIIFOWA) operator to accommodate the environment in which the given arguments are interval-valued intuitionistic fuzzy sets. Further, we develop procedures to apply them to solve group multiple attribute decision making problems with intuitionistic fuzzy or interval-valued intuitionistic fuzzy information. Finally, we present their application to show the effectiveness of the developed methods.     

A power-free deposited microbead plug-based microfluidic chip for whole-blood immunoassay

We present a deposited microbead plug (DMBP)-based microfluidic chip capable of performing plasma extraction and on-chip immunoassay. The DMBP used as a porous blood filter provides pure blood plasma without the contamination of blood cells or beads. Capillary-driven flow eliminates the requirement of external pumps. The human IgG and goat anti-human IgG sample-to-answer assay was performed in this chip within 600 s using only a 10 μl whole-blood sample. This easy-to-use, rapid, inexpensive, and disposable DMBP-based chip holds a great promise for point-of-care application.     

Adaptive synchronization of the complex dynamical network with double non-delayed and double delayed coupling

To simulate more realistic networks, we introduce a complex dynamical network model with double non-delayed and double delayed coupling and further investigate its synchronization phenomenon in this paper. Based on Lyapunov stability theory, adaptive synchronization criteria is obtained. Analytical result shows that under the designed adaptive controllers, the complex dynamical network with double non-delayed and double delayed coupling can asymptotically synchronize to a given trajectory. What is more, the coupling matrix is not assumed to be symmetric or irreducible. Finally, simulation results show the method is effective.