In this paper, the existence of periodic and partly periodic oscillation for a recurrent neural network with time-varying input and time delays between neural interconnections is investigated. Some theorems to determine the conditions for periodic oscillations are demonstrated. Simple and practical criteria for selecting the parameters in this network are derived. Typical simulation examples are also presented to illustrate the whole methodology.

In this paper, the existence of oscillatory solutions for a four coupled FHN network model with delays is investigated. Some theorems to determine the oscillatory solutions for the system are obtained. The practical criteria for selecting the parameters in this network are provided. Computer simulations are also given to illustrate the effectiveness of the results.

In this paper, the existence of oscillations for a class of recurrent neural networks with time delays between neural interconnections is investigated. By using the fixed point theory and Liapunov functional, we prove that a recurrent neural network might have a unique equilibrium point which is unstable. This particular type of instability, combined with the boundedness of the solutions of the system, will force the network to generate a permanent oscillation. Some necessary and sufficient conditions for these oscillations are obtained. Simple and practical criteria for fixing the range of parameters in this network are also derived. Typical simulation examples are presented.

The existing approaches to the multistability and multiperiodicity of neural networks rely on the strictly excitatory self-interactions of neurons or require constant interconnection weights. For periodically oscillated discrete-time neural networks (DTNNs), it is difficult to discuss multistable dynamics when the connection weights are periodically oscillated around zero. By using transient excitatory self-interactions of neurons and sigmoidal nonlinearities, we develop an approach to investigate multiperiodicity and attractivity of periodically oscillated DTNNs with time-varying and distributed delays. It shows that, under some new criteria, there exist multiplicity results of periodic solutions which are locally or globally exponentially stable. Computer numerical simulations are performed to illustrate the new theories.

This paper presents a study on some approaches in face reconstruction and the methodology used. The results and drawbacks of each method are discussed. To construct the 3D face model there are some methods which select the features automatically or manually. First a method in textured 3D face reconstruction using two 2D images from any angle is discussed which does not need any particular database, but it has to define the feature points manually. Then we describe automatic 2D to 3D face reconstruction from a single frontal face image which needs the use of USF Human ID 3-D database. Afterwards we talk about a Model Based Face Reconstruction for Animation. We also describe briefly about 3D face modeling by fusing multiple 2D images which is fully automatic and via an EM approach which uses the shape and pose parameters. Finally we describe a Rapid Modeling of Animated Faces from Video method and 2D face reconstruction using a minimum set of feature points.

In this paper, we propose a new approach for face pose estimation based on two images from different views under certain conditions. Using a weak-perspective imaging model, six pose parameters were deduced, with four pairs of feature points properly chosen across the two face images. Through the scan-iteration algorithm, a robust performance was achieved, without solving non-linear equations. Comparing to some other methods which estimate rotation matrix based on fundamental matrix (F), our method focuses on the "absolute pose" with respect to front view rather than the "relative pose" between the two face images. "Absolute pose" is indispensable in most situations especially for 3D face modeling. Since our method does not depend on any 3D face models and frontal-view images, it can be applied not only to face recognition and 3D face modeling, but also to other relevant applications. Experimental results demonstrate the efficiency of our method

By using the method of Liapunov functional, a model for bidirectional associative memory networks with time delays is studied. The asymptotic stability is global in the state space of the neuronal activations and is also independent of the delays. Our results can be applied to a variety of situations that arise both in the field of biological and artificial neural networks.