This paper presents a chaotic watermarking scheme for copyright protection. The proposed method employs the singular-value-decomposition (SVD) based watermarking scheme and the encrypted watermark based on the chaotic maps. The rightful owner possesses two secret keys: one related to the owner, another to the original image. Chaotic maps are used with the keys for watermark encryption so as to enhance the anti-counterfeit and noninvertibility properties of the watermark. Examples are illustrated to demonstrate the robustness and security of the proposed watermarking scheme.
This paper presents an object-based adaptive background update algorithm that is suitable for video surveillance in the dynamically changing environment. The background model is updated using information from not only the pixel change but also the currently detected object. The proposed method is able to deal with such problems as ghost and uncovered background. In addition, a shadow detection scheme is proposed to eliminate the shadow effect. Experimental results for three video sequences with different background situations are given to demonstrate the effectiveness of the proposed algorithm.
In this paper, we propose a learning control scheme for direct trajectory control of robotic manipulators. The main features are that we use a priori structure knowledge of robot dynamics in the design and the neural networks are not used to learn inverse dynamic models. The neural network controller is utilized to compensate the deviation due to the approximate models of robotic manipulators. In addition, true teaching signals of the neural network compensators are employed in the learning phase. Simulations are conducted to show the feasibility of the proposed method.
An attentive multidirectional hetero-associative memory network (AMAM) is proposed. The convergence and encoding strategies of AMAM are described. This network enables multiple associations, but with certain associations embedding more attention. This model is inspired by speculation about how associative learning and storage might occur in the nervous system. AMAM has much better error correcting capability and memory capacity than the multidirectional associative memory. Examples are illustrated to show the advantages of this model. In addition, we demonstrate and compare its recall ability for pattern recognition.
This paper presents a scheme that uses a feedforward neural network for the learning and generalization of the dynamic characteristics for the starting of a dc motor. The goal is to build an intelligent motor starter which has a versatility equivalent to that possessed by a human operator. To attain a fast and safe starting from stall for a dc motor a maximum armature current should be maintained during the starting period. This can be achieved by properly adjusting the armature voltage. The network is trained to learn the inverse dynamics of the motor starting characteristics and outputs a proper armature voltage. Simulation was performed to demonstrate the feasibility and effectiveness of the model. This study also addresses the network performance as a function of the number of hidden units and the number of training samples. 1.
Access to the requested content is limited to institutions that have purchased or subscribe to SPIE eBooks.
You are receiving this notice because your organization may not have SPIE eBooks access.*
*Shibboleth/Open Athens users─please
sign in
to access your institution's subscriptions.
To obtain this item, you may purchase the complete book in print or electronic format on
SPIE.org.
INSTITUTIONAL Select your institution to access the SPIE Digital Library.
PERSONAL Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.