Design of dual-frequency probe-fed microstrip antennas with genetic optimization algorithm

Cataloged from PDF version of article.Dual-frequency operation of antennas has become a necessity for many applications in recent wireless communication systems, such as GPS, GSM services operating at two different frequency bands, and services of PCS and IMT-2000 applications. Although there are various techniques to achieve dual-band operation from various types of microstrip antennas, there is no efficient design tool that has been incorporated with a suitable optimization algorithm. In this paper, the cavity-model based simulation tool along with the genetic optimization algorithm is presented for the design of dual-band microstrip antennas, using multiple slots in the patch or multiple shorting strips between the patch and the ground plane. Since this approach is based on the cavity model, the multiport approach is efficiently employed to analyze the effects of the slots and shorting strips on the input impedance. Then, the optimization of the positions of slots and shorting strips is performed via a genetic optimization algorithm, to achieve an acceptable antenna operation over the desired frequency bands. The antennas designed by this efficient design procedure were realized experimentally, and the results are compared. In addition, these results are also compared to the results obtained by the commercial electromagnetic simulation tool, the FEM-based software HFSS by ANSOFT

Dual functionality of conjugated polymer nanoparticles as an anticancer drug carrier and a fluorescent probe for cell imaging

Cataloged from PDF version of article.Multifunctional nanoparticles based on a green emitting, hydrophobic conjugated polymer, poly[(9,9-bis{propeny}fluorenyl-2,7-diyl)-co-(1,4- benzo-{2,1,3}-thiodiazole)] (PPFBT), that acts both as a fluorescent reporter and a matrix to accommodate an anti-cancer compound, camptothecin (CPT), were prepared, characterized and their potential as a fluorescent probe for cell imaging and as a drug delivery vehicle were evaluated via in vitro cell assays. The cell viability of human hepatocellular carcinoma cell line (Huh7) was investigated in the absence and presence of CPT with sulforhodamine B (SRB) and real-time cell electronic sensing (RT-CES) cytotoxicity assays

The adoption and application of Intelligent Speed Assistance by private motorists: user and non-user perspectives

Intelligent Speed Assistance (ISA) is an in-car system that can help drivers to avoid speeding and therefore reduce crash frequency and severity. ISA systems that intervene to reduce vehicle speeds are increasingly available in new cars. Their efficacy in crash reduction will depend on the extent to which they are adopted and used by motorists. Increasing ISA use is therefore a promising new target for behaviour-change interventions seeking to reduce crash involvement. To provide context for intervention development, this study explored the beliefs and attitudes of 20 car drivers who have intervening ISA systems and 20 that do not. Thematic analysis of interview scripts generated five superordinate themes across both sets of drivers relating to: (1) safety, (2) driver control, (3) choice and enforcement, (4) driver identity and behaviour, and (5) enabling roll-out. ISA acceptability was generally high as long as driver choice around turning off and overriding the system was maintained. Drivers described a number of information needs relating to ISA: increased general awareness of ISA, provision of system-specific information for new ISA drivers and reassurances around non-ISA driver concerns (e.g., the responsiveness of the override and the speed control process). Many drivers indicated that ISA adoption would be facilitated by emphasising personal benefits (e.g., protecting driver licences, reduced insurance premiums, improved fuel efficiency, more relaxed driving) as well as safety advantages

Algorithms for effective querying of compound graph-based pathway databases

<p>Abstract</p> <p>Background</p> <p>Graph-based pathway ontologies and databases are widely used to represent data about cellular processes. This representation makes it possible to programmatically integrate cellular networks and to investigate them using the well-understood concepts of graph theory in order to predict their structural and dynamic properties. An extension of this graph representation, namely hierarchically structured or compound graphs, in which a member of a biological network may recursively contain a sub-network of a somehow logically similar group of biological objects, provides many additional benefits for analysis of biological pathways, including reduction of complexity by decomposition into distinct components or modules. In this regard, it is essential to effectively query such integrated large compound networks to extract the sub-networks of interest with the help of efficient algorithms and software tools.</p> <p>Results</p> <p>Towards this goal, we developed a querying framework, along with a number of graph-theoretic algorithms from simple neighborhood queries to shortest paths to feedback loops, that is applicable to all sorts of graph-based pathway databases, from PPIs (protein-protein interactions) to metabolic and signaling pathways. The framework is unique in that it can account for compound or nested structures and ubiquitous entities present in the pathway data. In addition, the queries may be related to each other through "AND" and "OR" operators, and can be recursively organized into a tree, in which the result of one query might be a source and/or target for another, to form more complex queries. The algorithms were implemented within the querying component of a new version of the software tool P<smcaps>ATIKA</smcaps><it>web </it>(Pathway Analysis Tool for Integration and Knowledge Acquisition) and have proven useful for answering a number of biologically significant questions for large graph-based pathway databases.</p> <p>Conclusion</p> <p>The P<smcaps>ATIKA</smcaps> Project Web site is <url>http://www.patika.org</url>. P<smcaps>ATIKA</smcaps><it>web </it>version 2.1 is available at <url>http://web.patika.org</url>.</p

Modeling and Predicting Surface Roughness via Transformation Optics

Monte Carlo analysis of surface roughness in electromagnetic scattering problems is presented by using the principles of transformation electromagnetics/optics in finite methods. The main motivation in the proposed approach is to eliminate the need of mesh generation for each surface in repeated Monte Carlo realizations, and hence, to devise a faster model in predicting surface roughness. A single, simple and uniform mesh is employed assuming a smooth surface and ignoring the actual surface, and thereafter, a transformation medium is designed on the smooth surface to make this problem equivalent to the original problem with actual surface. The material parameters of the transformation medium are determined by transforming the smooth surface to the actual surface through a specially-defined coordinate transformation. The technique is demonstrated via various finite element simulations

Iterative leap-field domain decomposition method: a domain decomposition finite element algorithm for 3D electromagnetic boundary value problems

The authors introduce the iterative leap-field domain decomposition method that is tailored to the finite element method, by combining the concept of domain decomposition and the Huygens' Principle. In this method, a large-scale electromagnetic boundary value problem is partitioned into a number of suitably-defined 'small' and manageable subproblems whose solutions are assembled to obtain the global solution. The main idea of the method is the iterative application of the Huygens' Principle to the fields radiated by the equivalent currents calculated in each iteration. In the context of the electromagnetic scattering, the method can be applied to cases involving multiple objects, as well as to a 'single' challenging object in a straightforward manner via the locally conformal perfectly matched layer technique. The most attractive feature of the method is the considerable reduction in the memory requirements and computation time. It is observed that convergence is achieved after a few iterations and computation time may further be reduced via parallel processing techniques. After developing the analytical background of this method, we present some numerical results related to the three-dimensional electromagnetic scattering problems

Finite element analysis of electromagnetic scattering problems via iterative leap-field domain decomposition method

We introduce the Iterative Leap-field Domain Decomposition Method (ILF-DDM), which is based on the dual employment of Finite Element Method and Huygens' Principle iteratively, for the solution of electromagnetic boundary value problems. The method can be applied to cases involving both multiple objects and a single 'challenging' object using the locally-conformal perfectly matched layer technique. We report some numerical results for two-dimensional electromagnetic scattering problems