Adaptive robust synchronization method is proposed for chaos synchronization of Lurie systems with time-varying delay. To weaken the restrictions on the change rate of time-varying delay in synchronization problems, adaptive estimations related to the derivative's upper bound of the time-varying delay are designed, so this upper bound can be unknown. The Lipschitz constant of the nonlinear link is also adaptively estimated, rather than being pre-calculated. Whole design is in the framework of robust control, and the synchronization can endure disturbance effectively. In addition, the synchronization of chaotic Lurie system with time-varying delay is realized as an example.
With the increasing role of computing devices, facilitating natural human computer interaction (HCI) will have a positive impact on their usage and acceptance as a whole. For long time, research on HCI has been restricted to techniques based on the use of keyboard, mouse, etc. Recently, this paradigm has changed. Techniques such as vision, sound, speech recognition allow for much richer form of interaction between the user and machine. The emphasis is to provide a natural form of interface for interaction. Gestures are one of the natural forms of interaction between humans. As gesture commands are found to be natural for humans, the development of gesture control systems for controlling devices have become a popular research topic in recent years. Researchers have proposed different gesture recognition systems which act as an interface for controlling the applications. One of the drawbacks of present gesture recognition systems is application dependence which makes it difficult to transfer one gesture control interface into different applications. This paper focuses on designing a vision-based hand gesture recognition system which is adaptive to different applications thus making the gesture recognition systems to be application adaptive. The designed system comprises different processing steps like detection, segmentation, tracking, recognition, etc. For making the system as application-adaptive, different quantitative and qualitative parameters have been taken into consideration. The quantitative parameters include gesture recognition rate, features extracted and root mean square error of the system while the qualitative parameters include intuitiveness, accuracy, stress/comfort, computational efficiency, user's tolerance, and real-time performance related to the proposed system. These parameters have a vital impact on the performance of the proposed application adaptive hand gesture recognition system.
Recently, applying control theory to regulate the intracellular mRNA level was introduced as a new direction for gene regulation. However, the high nonlinearity in the gene regulatory networks imposes significant challenges in control design. As a well understood benchmark example, the GAL regulatory network in S. cerevisiae was recently proposed as a test-bed system for validating theoretical control algorithms in cellular systems. A simple proportional feedback control approach was previously proposed for regulating the intracellular mRNA level in the GAL network, however, there were still limitations with its use to control the nonlinear GAL network. To improve the performance and effectiveness, this paper proposes an advanced nonlinear control strategy. The reduced mathematical model for the GAL network is reorganized into a nonlinear affine system. Then, a partial feedback linearization control approach was employed to regulate the concentration of a protein at a desired level. For validating the control approach in experimental studies, we choose Gal1p as a measurable output, instead of GAL1 mRNA used in the previous study. Simulation results demonstrate that this control approach can shorten the convergence time between states comparing with the proportional feedback control.
Many computer vision problems consist of making a suitable content description of images usually aiming to extract the relevant information content. In case of images representing paintings or artworks, the information extracted is rather subject-dependent, thus escaping any universal quantification. However, we proposed a measure of complexity of such kinds of oeuvres which is related to brain processing. The artistic complexity measures the brain inability to categorize complex nonsense forms represented in modern art, in a dynamic process of acquisition that most involves top-down mechanisms. Here, we compare the quantitative results of our analysis on a wide set of paintings of various artists to the cues extracted from a standard bottom-up approach based on visual saliency concept. In every painting inspection, the brain searches for more informative areas at different scales, then connecting them in an attempt to capture the full impact of information content. Artistic complexity is able to quantify information which might have been individually lost in the fruition of a human observer thus identifying the artistic hand. Visual saliency highlights the most salient areas of the paintings standing out from their neighbours and grabbing our attention. Nevertheless, we will show that a comparison on the ways the two algorithms act, may manifest some interesting links, finally indicating an interplay between bottom-up and top-down modalities.
A telemedicine system will provide sustainable, comprehensive, low-cost, fast, private, and convenient access to medical consultation and diagnosis for patients from remote locations. The telemedicine system addressed in this paper consists of a sensor jacket, which is worn by the patient for medical monitoring. The signals sensed through the jacket are processed and transmitted through a public telecommunication link, to a medical professional in a hospital at distance. The medical professional interacts with the patient through audio and video links, and simultaneously examines the data transmitted by the monitoring system. Medical assessment, diagnosis, and prescription are carried out on this basis. Sensing and signal processing are paramount to providing the patient data to the medical professional in an accurate and effective manner. This paper presents some relevant issues and techniques. Specific examples of electrocardiograms and respiratory signals are provided to illustrate the applicable signal conditioning approaches. Results are presented to demonstrate the feasibility and the effectiveness of these methods.
