Adaptive image denoising using cuckoo algorithm

This paper presents a novel denoising approach based on smoothing linear and nonlinear filters combined with an optimization algorithm. The optimization algorithm used was cuckoo search algorithm and is employed to determine the optimal sequence of filters for each kind of noise. Noises that would be eliminated form images using the proposed approach including Gaussian, speckle, and salt and pepper noise. The denoising behaviour of nonlinear filters and wavelet shrinkage threshold methods have also been analysed and compared with the proposed approach. Results show the robustness of the proposed filter when compared with the state-of-the-art methods in terms of peak signal-to-noise ratio and image quality index. Furthermore, a comparative analysis is provided between the said optimization algorithm and the genetic algorithm.     

Comparison of EEG signal decomposition methods in classification of motor-imagery BCI

A brain–computer interface (BCI) provides a link between the human brain and a computer. The task of discriminating four classes (left and right hands and feet) of motor imagery movements of a simple limb-based BCI is still challenging because most imaginary movements in the motor cortex have close spatial representations. We aimed to classify binary limb movements, rather than the direction of movement within one limb. We also investigated joint time-frequency methods to improve classification accuracies. Neither of these, to our knowledge, has been investigated previously in BCI. We recorded EEG data from eleven participants, and demonstrated the classification of four classes of simple-limb motor imagery with an accuracy of 91.46% using intrinsic time-scale decomposition and 88.99% using empirical mode decomposition. In binary classifications, we achieved average accuracies of 89.90% when classifying imaginary movements of left hand versus right hand, 93.1% for left hand versus right foot, 94.00% for left hand versus left foot, 83.82% for left foot versus right foot, 97.62% for right hand versus left foot, and 95.11% for right hand versus right foot. The results show that the binary classification performance is slightly better than that of four-class classification. Our results also show that there is no significant difference in terms of spatial distribution between left and right foot motor imagery movements. There is also no difference in classification performances involving left or right foot movement. This work demonstrates that binary and four-class movements of the left and right feet and hands can be classified using recorded EEG signals of the motor cortex, and an intrinsic time-scale decomposition (ITD) feature extraction method can be used for real time brain computer interface.

     

Numerical investigation on 2D viscoelastic fluid due to exponentially stretching surface with magnetic effects: an application of non-Fourier flux theory

Two-dimensional flow of Casson fluid toward an exponentially stretched surface in view of Cattaneo–Christove flux theory is discoursed in current communication. Flow pattern within boundary layer under the effectiveness of magnetic field is also contemplated in the communication. Non-dimensionalized governing expressions are attained through transformation procedure. To anticipate the fascinating features of present work, solution of resulted nonlinear differential system is computed with the collaborated help of shooting scheme and Runge–Kutta method. The influence of involved variables on velocity and temperature fields is scrutinized. Contribution of thermal relaxation is explicitly pointed out. Evaluation of convective heat transfer and friction factor in the fluid flow is visualized through graphs and tables. Additionally, the assurance of present work is affirmed by developing comparison with previous findings in the literature which sets a trade mark for the implementation of numerical approach. It is inferred from the thorough examination of the analysis that present formulation reduces to classical Fourier’s problem by considering \(\varLambda = 0\). Furthermore, decreasing pattern in temperature distribution is depicted in the presence of Cattaneo–Christove flux law as compared to heat transfer due to the Fourier’s law.

     

Peer to peer trade in HTM5 meta model for agent oriented cloud robotic systems

Cloud computing is a methodology and not a technology. Adaptation of cloud computing services for robotic applications is relatively straightforward while adaptation of underlying ideas will require a new design attitude. Cloud computing is a cost-effective and dynamic business model. Currently cloud robotics is understood as a client server methodology which enables robots utilize resources and services placed at centralized servers. These cloud servers treat robots as any other client computer offering them platform, infrastructure, process or algorithm as a service. HTM5 is an OMG MDA based multi-view meta-model for agent oriented development of cloud robotic systems. HTM5 encourages design of peer-to-peer service ecosystems based on an open registry and matchmaking mechanism. In peer-to-peer cloud robotics, a robot can trade its hardware, software and functional resources as a service to other robots in the ecosystem. The peer-to-peer trade in such systems may be driven by contracts and relationships between its member agents. This article discusses trade-view model of HTM5 methodology and its use in developing a cloud robotic ecosystem that implements peer-to-peer, contract based economy. The article also presents a case study with experiments that implement distributed artificial intelligence and peer-to-peer service oriented trade on simulated and real robot colonies.     

Assessment of diverse image encryption mechanisms under prevalent invasion

Multimedia Tools and Applications - Image encryption mechanisms provide confidentiality and concealment of information (image) in transmission over the alleyway, susceptible to prevalent invasions....     

Conflict-preserving abstraction of discrete event systems using annotated automata

This paper proposes to enhance compositional verification of the nonblocking property of discrete event systems by introducing annotated automata. Annotations store nondeterministic branching information, which would otherwise be stored in extra states and transitions. This succinct representation makes it easier to simplify automata and enables new efficient means of abstraction, reducing the size of automata to be composed and thus the size of the synchronous product state space encountered in verification. The abstractions proposed are of polynomial complexity, and they have been successfully applied to model check the nonblocking property of the same set of large-scale industrial examples as used in related work.     

Adaptive rank weighted switching filter for impulsive noise removal in color images

Journal of Real-Time Image Processing - In this paper, a novel approach to the problem of impulsive noise removal in color digital images is presented. The described switching filter is based on...