Compact Rack Shaped MIMO Dielectric Resonator Antenna with Improved Axial Ratio for UWB Applications

Wireless Personal Communications - In this paper, a rack shaped two radiator element multiple input multiple output (MIMO) dielectric resonator antenna (DRA) is reported with improved axial ratio...     

Study of automatic text summarization approaches in different languages

Artificial Intelligence Review - Nowadays we see huge amount of information is available on both, online and offline sources. For single topic we see hundreds of articles are available, containing...     

A hybrid edge-based technique for segmentation of renal lesions in CT images

Multimedia Tools and Applications - The entire community of medical experts uses various imaging techniques as the precursor for disease diagnosis with the assistance of computer-aided diagnosis...     

Synthesis of a New Robust Exponential Sliding Mode Differentiator with its Observer Applications

This paper proposes a new robust exponential sliding mode differentiator for estimating the (n ? 1) derivatives of a non‐linear function. Finite time convergence, robustness, and exactness are also ensured analytically with the proposed methodology. In addition, the results of the proposed sliding mode differentiator are extended to derive theorems for the novel state (SO) and extended state observers (ESO), which would estimate the system states as well as uncertainties recursively in finite time. Finally, three examples are implemented to validate the proposed methodologies and the obtained simulations are compared with the previously developed methods in literature.     

An Expert Approach for Data Flow Prediction: Case Study of Wireless Sensor Networks

The data flow is an important parameter used in the optimization problem of Wireless Sensor Networks. This paper presents an expert approach for improved data flow prediction based on data discretization and artificial intelligence. The proposed approach has been implemented on various machine learning methods (a total of 17 methods). This data flow prediction is based on the dataset generated from the simulations with NS-2.35 for multiple Wireless Sensor Networks (5- to -50 nodes). The performance comparison of different machine learning models with continuous data and discretized data is also presented. The proposed approach considerably reduces the execution time of the machine learning models for training purposes and also enhances the accuracy of prediction. The result analysis shows that the proposed approach is better compared to various machine learning methods. Also, the proposed approach is able to handle both continuous and discrete data. The datasets used in this work are available as a supplement at NDS and DDS link.

     

Analysis of Leuconostoc citreum strains using multilocus sequence typing

Food Science and Biotechnology - The objective of this study was to perform genetic diversity analysis of 13 strains isolated from South Korean foods by multilocus sequence typing (MLST). For...     

On the design of neural-fuzzy control system

This article presents a neural–network-based fuzzy logic control (NN–FLC) system. The NN–FLC model has the learning capabilities for constructing membership functions and extracting fuzzy rules from training examples. Both unsupervised and supervised training algorithms are used to find the membership functions of the FLC. Competitive learning algorithms are employed to evaluate fuzzy logic rules. Matlab programs using both neural and fuzzy toolboxes are developed to implement the NN–FLC model. Computer simulations of the inverted pendulum controlled by NN–FLC system were conducted to illustrate the self-learning ability of the network. © 1998 John Wiley & Sons, Inc.13: 11–26, 1998     

Effect of irrigation on the lipid composition of taramira seed

The effect of irrigation on the lipid composition in maturing taramira (Eruca sativa, Mill) seed was studied. Decreasing the number of irrigations had an adverse effect on oil setting. Restricted irrigations affected adversely the biosynthesis of erucic acid in the oil at initial stages, though at later stages of seed development, irrigation had no such effect.     

Optical and electrical characterization of conducting polymer-single walled carbon nanotube composite films

The optical and electrical properties of the conducting polymer poly(3-hexylthiophene) and single walled carbon nanotube (SWCNT) composites have been investigated. The composites were prepared by dispersing carbon nanotubes in the polymer matrix already dissolved in 1,2-dichlorobenzene. The optical absorption, photoluminescence (PL) and electrical conductivity of the composite was studied as a function of SWCNT concentration in the solution. The absorption coefficient of the polymer was found to be unaffected upto a SWCNT concentration 5% w/w. However a minor decrease in the absorption in visible region was observed for higher SWCNT concentrations. The intensity of PL emission from the composite was measured and was found to decrease with the increase in SWCNT concentration. For a SWCNT concentration of 30% w/w, ∼90% of the PL was quenched, indicating an ultra fast transfer of photoinduced charges from donor polymer to acceptor SWCNT. Direct current conductivity of the composite film was found to increase rapidly with the increase in SWCNT concentration and an increase of ∼5 orders of magnitude was observed for a 30% w/w concentration. The enhancement in conductivity is explained in terms of percolation theory with an estimated percolation threshold of 2% w/w.