Adaptive sliding mode control of n$n$ flexible-joint robot manipulators in the presence of structured and unstructured uncertainties

Multibody System Dynamics - This study investigates a voltage-based adaptive sliding mode control (VB-ASMC) to tracking the position of an $n$ rigid-link flexible-joint (RLFJ) robot manipulator...     

Leaderless Consensus of Non-linear Mixed delay Multi-agent Systems with Random Packet Losses via Sampled-data Control

International Journal of Control, Automation and Systems - This paper inspects the consensus problem of nonlinear mixed delay multi-agent systems with random packet losses through the sampled-data...     

Enhanced sensitivity of cancer cell using one dimensional nano composite material coated photonic crystal

We theoretically analyze the detection of a cancer cell in the one-dimensional photonic crystal by infiltrating different sample cells in the cavity layer. The defect modes appear in their transmission spectra only if the nanocomposite layers are included on either side of the cavity layer. This analysis is carried out by a dielectric constant and the transmittance peak of the cancer cell is compared with the normal cell. The transmittance peak shifts are analyzed with various filling factors for optimization purposes. Through the shifting spectrum, the sensitivity of cancer cell from the normal cell is obtained from a minimum of 42 nm/RIU to a maximum of 43 nm/RIU.

     

Weighted label propagation based on Local Edge Betweenness

The Journal of Supercomputing - In complex networks, especially social networks, networks could be divided into disjoint partitions. However, nodes could be partitioned such that the number of...     

Parametric Formant Modelling and Transformation in Voice Conversion

This paper presents a method for the estimation and mapping of parametric models of speech resonance at formants for voice conversion. The spectral features at formants that contribute to voice characteristics are the trajectories of the frequencies, the bandwidths and intensities of the resonance at formants. The formant features are extracted from the poles of a linear prediction (LP) model of speech. The statistical distributions of formants are modelled by a two-dimensional hidden Markov model (HMM) spanning the time and frequency dimensions. Experimental results are presented which show a close match between HMM-based formant models and the histograms of formants. For voice conversion two alternative methods are explored for mapping the formants of a source speaker to those of a target speaker. The first method is based on an adaptive formant-tracking warping of the frequency response of the LP model and the second method is based on the rotation of the poles of the LP model of speech. Both methods transform all spectral parameters of the resonance at formants of the source speaker towards those of the target speaker. In addition, the issues affecting the selection of the warping ratios for the mapping functions are investigated. Experimental results of formant estimation and perceptual evaluation of voice morphing based on parametric formant models are presented.     

Dependence of Potential Well Depth on the Magnetic Field Intensity in a Polywell Reactor

Using OOPIC-Pro assisted-two dimensional simulation we have considered the dependencies of the electron and ion densities, as well as the central electric potential on the magnetic-field intensity in the Polywell fusion reactor. It is shown that the potential well depth increases with decreasing the magnetic intensity, while much narrower well width (thus more effective deuteron trapping) is achieved with increasing the magnetic field intensity. The results obtained can be employed to adjust the magnetic field intensities at which more effective electron confinement, thus more effective ion-flux convergence, is expected. Furthermore, this study can be used to reach the optimized conditions of the reactor operation as well as to relate to the next generation fusion fuels.     

CCFinder: using Spark to find clustering coefficient in big graphs

Networks with billions of vertices introduce new challenges to perform graph analysis in a reasonable time. Clustering coefficient is an important analytical measure of networks such as social networks and biological networks. To compute clustering coefficient in big graphs, existing distributed algorithms suffer from low efficiency such that they may fail due to demanding lots of memory, or even, if they complete successfully, their execution time is not acceptable for real-world applications. We present a distributed MapReduce-based algorithm, called CCFinder, to efficiently compute clustering coefficient in very big graphs. CCFinder is executed on Apache Spark, a scalable data processing platform. It efficiently detects existing triangles through using our proposed data structure, called FONL, which is cached in the distributed memory provided by Spark and reused multiple times. As data items in the FONL are fine-grained and contain the minimum required information, CCFinder requires less storage space and has better parallelism in comparison with its competitors. To find clustering coefficient, our solution to triangle counting is extended to have degree information of the vertices in the appropriate places. We performed several experiments on a Spark cluster with 60 processors. The results show that CCFinder achieves acceptable scalability and outperforms six existing competitor methods. Four competitors are those methods proposed based on graph processing systems, i.e., GraphX, NScale, NScaleSpark, and Pregel frameworks, and two others are the Cohen’s method and NodeIterator++, introduced based on MapReduce.     

Comparison of stochastic filtering methods for 3D tracking

In the recent years, the 3D visual research has gained momentum with publications appearing for all aspects of 3D including visual tracking. This paper presents a review of the literature published for 3D visual tracking over the past five years. The work particularly focuses on stochastic filtering techniques such as particle filter and Kalman filter. These two filters are extensively used for tracking due to their ability to consider uncertainties in the estimation. The improvement in computational power of computers and increasing interest in robust tracking algorithms lead to increase in the use of stochastic filters in visual tracking in general and 3D visual tracking in particular. Stochastic filters are used for numerous applications in the literature such as robot navigation, computer games and behavior analysis. Kalman filter is a linear estimator which approximates system's dynamics with Gaussian model while particle filter approximates system's dynamics using weighted samples. In this paper, we investigate the implementation of Kalman and particle filters in the published work and we provide comparison between these techniques qualitatively as well as quantitatively. The quantitative analysis is in terms of computational time and accuracy. The quantitative analysis has been implemented using four parameters of the tracked object which are object position, velocity, size of bounding ellipse and orientation angle.