Predicting complexity perception of real world images

The aim of this work is to predict the complexity perception of real world images.We propose a new complexity measure where different image features, based on spatial, frequency and color properties are linearly combined. In order to find the optimal set of weighting coefficients we have applied a Particle Swarm Optimization. The optimal linear combination is the one that best fits the subjective data obtained in an experiment where observers evaluate the complexity of real world scenes on a web-based interface. To test the proposed complexity measure we have performed a second experiment on a different database of real world scenes, where the linear combination previously obtained is correlated with the new subjective data. Our complexity measure outperforms not only each single visual feature but also two visual clutter measures frequently used in the literature to predict image complexity. To analyze the usefulness of our proposal, we have also considered two different sets of stimuli composed of real texture images. Tuning the parameters of our measure for this kind of stimuli, we have obtained a linear combination that still outperforms the single measures. In conclusion our measure, properly tuned, can predict complexity perception of different kind of images

Intelligent modeling of a piezoelectric tube actuator

Various model-based control methods are currently used in control of piezoelectric tubes, others such as internal model control and model predictive control are anticipated to be employed soon. All these control systems are designed based on black box models. However, systematic black box modeling of piezoelectric tubes has been overlooked in the literature to a large extent or has been presented in a too brief and faulty way. In this article, a novel structure of artificial neural networks is used to model and to assess the nonlinearity of piezoelectric actuators. Apart from nonlinearity, other features of the achieved models like delay time, sampling time, orders as well as system identification process are clearly stated, and more importantly, it is clarified that different definitions of accuracy are needed for different purposes of black box modeling, with change in model features, the accuracy may decrease for one purpose (e.g. predictive control) and increase for another one (e.g. simulation). This highly critical point has never been raised and addressed in modeling of piezoelectric tubes, and a definition of accuracy which suits static systems/models has been widely used in the past to assess models of piezoelectric tubes which are obviously dynamic. Experimental results support the proposed modeling ideas.Morteza Mohammadzaheri, Steven Grainger, Mohsen Bazghaleh, Pouria Yaghmae

Interlayer tuning of X-band frequency-selective surface using liquid crystal

In this paper, a new concept of a voltage-controlled tunable frequency-selective surface (FSS) is introduced based on liquid crystal technology. The designed FSS consists of two periodically patterned metallic layers, separated by a thin dielectric substrate. Tunability is achieved by integrating liquid crystal cells within the substrate for each unit cell, producing interlayer capacitors. By applying a bias voltage between the front and back metallic arrays, the anisotropy axis of the liquid crystal molecules can be re-oriented, and thus the effective relative permittivity of the liquid crystals can be modified to cause a frequency shift in transmission response. Electromagnetic simulations predict 5.6% of continuous frequency tuning for this multi-layer FSS.Amir Ebrahimi, Pouria Yaghmaee, Withawat Withayachumnankul, Christophe Fumeaux, Said Al-Sarawi and Derek Abbot