Development of a varactor -controlled dual-frequency reconfigurable microstrip antenna

A new design for a compact electronically reconffgurable singlefeed dual frequency dual-polarized operation of a square-microstrip antenna capable of achieving tunable frequency ratios in the range 1.1 to 1.37 is proposed and experimentally studied. Varactor diodes inlegruted with the arms of the hexagonal slot and embedded in the square patch are used to tune the operating frequencies by applying reverse-bias voltage. The design has the advantage of size reduction up to 73.21% and 49.86% for the two resonant frequencies, respectively, as compared to standard rectangular patches. The antenna offers good bandwidth of 5.74% and 5.36% for the two operating frequencies. A highly simplified tuning circuitry without any transmission lines adds to the compactness of the design

A Compact Electronically Reconfigurable Dual Frequency Microstrip Antenna for L-Band Applications

A novel design of a computer electronically reconfigurable dual frequency dual polarized single feed hexagonal slot loaded microstrip antenna in L-band is introduced in this chapter. pin diodes are used to switch the operating frequencies considerably without much affecting the radiation characteristics and gain. the antenna can work with a frequency ratio varying in the wide range from 1.2 to 1.4. the proposed design has an added advantage of size reduction up to 72.21% and 46.84% for the two resonating frequencies compared to standard rectangular patches. the design also gives considerable bandwidth of up to 2.82% and 2.42 % for the operating frequencies.

Design of Compact Reconfigurable Dual Frequency Microstrip Antennas Using Varactor Diodes

The design of a compact, single feed, dual frequency dual polarized and electronically reconfigurable microstrip antenna is presented in this paper. A square patch loaded with a hexagonal slot having extended slot arms constitutes the fundamental structure of the antenna. The tuning of the two resonant frequencies is realized by varying the effective electrical length of the slot arms by embedding varactor diodes across the slots. A high tuning range of 34.43% (1.037–1.394 GHz) and 9.27% (1.359–1.485 GHz) is achieved for the two operating frequencies respectively, when the bias voltage is varied from 0 to −30 V. The salient feature of this design is that it uses no matching networks even though the resonant frequencies are tuned in a wide range with good matching below −10 dB. The antenna has an added advantage of size reduction up to 80.11% and 65.69% for the two operating frequencies compared to conventional rectangular patches.

C-shaped slot loaded reconfigurable microstrip antenna

A new electronically reconfigurable dual frequency microstrip patch antenna with highly simplified varactor tuning circuitry is presented. The proposed design allows relatively independent selection of the two operating frequencies. Tuning ranges of 7.1 and 4.1% are realised for the two resonant frequencies without the use of any matching circuits.

Investigations on Reconfigurable Dual Frequency Microstrip Antennas Controlled by PIN Diodes and Varactors

In this work,we investigate novel designs of compact electronically reconfigurable dual frequency microstrip antennas with a single feed,operating mainly in L-band,without using any matching networks and complicated biasing circuitry.These antennas have been designed to operate in very popular frequency range where a great number of wireless communication applications exist.Efforts were carried out to introduce a successful,low cost reconfigurable dual-frequency microstrip antenna design to the wireless and radio frequency design community.

“Reconstruction of Gene Regulatory Network from Expression Profile of Plasma RNA Data of Colorectal Cancer Patients using Soft Computing Techniques”

Microarray data analysis is one of data mining tool which is used to extract meaningful information hidden in biological data. One of the major focuses on microarray data analysis is the reconstruction of gene regulatory network that may be used to provide a broader understanding on the functioning of complex cellular systems. Since cancer is a genetic disease arising from the abnormal gene function, the identification of cancerous genes and the regulatory pathways they control will provide a better platform for understanding the tumor formation and development. The major focus of this thesis is to understand the regulation of genes responsible for the development of cancer, particularly colorectal cancer by analyzing the microarray expression data. In this thesis, four computational algorithms namely fuzzy logic algorithm, modified genetic algorithm, dynamic neural fuzzy network and Takagi Sugeno Kang-type recurrent neural fuzzy network are used to extract cancer specific gene regulatory network from plasma RNA dataset of colorectal cancer patients. Plasma RNA is highly attractive for cancer analysis since it requires a collection of small amount of blood and it can be obtained at any time in repetitive fashion allowing the analysis of disease progression and treatment response.

Cooperative Caching Framework for Mobile Cloud Computing

Due to the advancement in mobile devices and wireless networks mobile cloud computing, which combines mobile computing and cloud computing has gained momentum since 2009. The characteristics of mobile devices and wireless network makes the implementation of mobile cloud computing more complicated than for fixed clouds. This section lists some of the major issues in Mobile Cloud Computing. One of the key issues in mobile cloud computing is the end to end delay in servicing a request. Data caching is one of the techniques widely used in wired and wireless networks to improve data access efficiency. In this paper we explore the possibility of a cooperative caching approach to enhance data access efficiency in mobile cloud computing. The proposed approach is based on cloudlets, one of the architecture designed for mobile cloud computing.

Computing median and antimedian sets in median graphs

The median (antimedian) set of a profile π = (u1, . . . , uk) of vertices of a graphG is the set of vertices x that minimize (maximize) the remoteness i d(x,ui ). Two algorithms for median graphs G of complexity O(nidim(G)) are designed, where n is the order and idim(G) the isometric dimension of G. The first algorithm computes median sets of profiles and will be in practice often faster than the other algorithm which in addition computes antimedian sets and remoteness functions and works in all partial cubes

Soft Computing Approach for Optimization of siRNA Efficiency Prediction for Post-transcriptional Gene Silencing

Post-transcriptional gene silencing by RNA interference is mediated by small interfering RNA called siRNA. This gene silencing mechanism can be exploited therapeutically to a wide variety of disease-associated targets, especially in AIDS, neurodegenerative diseases, cholesterol and cancer on mice with the hope of extending these approaches to treat humans. Over the recent past, a significant amount of work has been undertaken to understand the gene silencing mediated by exogenous siRNA. The design of efficient exogenous siRNA sequences is challenging because of many issues related to siRNA. While designing efficient siRNA, target mRNAs must be selected such that their corresponding siRNAs are likely to be efficient against that target and unlikely to accidentally silence other transcripts due to sequence similarity. So before doing gene silencing by siRNAs, it is essential to analyze their off-target effects in addition to their inhibition efficiency against a particular target. Hence designing exogenous siRNA with good knock-down efficiency and target specificity is an area of concern to be addressed. Some methods have been developed already by considering both inhibition efficiency and off-target possibility of siRNA against agene. Out of these methods, only a few have achieved good inhibition efficiency, specificity and sensitivity. The main focus of this thesis is to develop computational methods to optimize the efficiency of siRNA in terms of “inhibition capacity and off-target possibility” against target mRNAs with improved efficacy, which may be useful in the area of gene silencing and drug design for tumor development. This study aims to investigate the currently available siRNA prediction approaches and to devise a better computational approach to tackle the problem of siRNA efficacy by inhibition capacity and off-target possibility. The strength and limitations of the available approaches are investigated and taken into consideration for making improved solution. Thus the approaches proposed in this study extend some of the good scoring previous state of the art techniques by incorporating machine learning and statistical approaches and thermodynamic features like whole stacking energy to improve the prediction accuracy, inhibition efficiency, sensitivity and specificity. Here, we propose one Support Vector Machine (SVM) model, and two Artificial Neural Network (ANN) models for siRNA efficiency prediction. In SVM model, the classification property is used to classify whether the siRNA is efficient or inefficient in silencing a target gene. The first ANNmodel, named siRNA Designer, is used for optimizing the inhibition efficiency of siRNA against target genes. The second ANN model, named Optimized siRNA Designer, OpsiD, produces efficient siRNAs with high inhibition efficiency to degrade target genes with improved sensitivity-specificity, and identifies the off-target knockdown possibility of siRNA against non-target genes. The models are trained and tested against a large data set of siRNA sequences. The validations are conducted using Pearson Correlation Coefficient, Mathews Correlation Coefficient, Receiver Operating Characteristic analysis, Accuracy of prediction, Sensitivity and Specificity. It is found that the approach, OpsiD, is capable of predicting the inhibition capacity of siRNA against a target mRNA with improved results over the state of the art techniques. Also we are able to understand the influence of whole stacking energy on efficiency of siRNA. The model is further improved by including the ability to identify the “off-target possibility” of predicted siRNA on non-target genes. Thus the proposed model, OpsiD, can predict optimized siRNA by considering both “inhibition efficiency on target genes and off-target possibility on non-target genes”, with improved inhibition efficiency, specificity and sensitivity. Since we have taken efforts to optimize the siRNA efficacy in terms of “inhibition efficiency and offtarget possibility”, we hope that the risk of “off-target effect” while doing gene silencing in various bioinformatics fields can be overcome to a great extent. These findings may provide new insights into cancer diagnosis, prognosis and therapy by gene silencing. The approach may be found useful for designing exogenous siRNA for therapeutic applications and gene silencing techniques in different areas of bioinformatics.