Metamaterial made of paired planar conductors: Particle resonances, phenomena and properties

The properties and characteristics of a recently proposed anisotropic metamaterial based upon layered arrays of tightly coupled pairs of "dogbone" shaped stripe conductors have been explored in detail. It has been found that a metamaterial composed of such stacked layers exhibits artificial magnetism and may support backward wave propagation. The equivalent network models of the constitutive conductor pairs arranged in the periodic array have been devised and applied to the identification of the specific types of resonances, and to the analysis of their contribution into the effective dielectric and magnetic properties of the artificial medium. The proposed "dogbone" configuration of conductor pairs has the advantage of being entirely realizable and assemblable in planar technology. It also appears more prospective than simple cut-wire or metal-plate pairs because the additional geometrical parameters provide an efficient control of separation between the electric and magnetic resonances that, in turn, makes it possible to obtain a fairly broadband left-handed behaviour of the structure at low frequencies.

Enhancing Frequency-Scanning Response of Leaky-Wave Antennas Using High-Impedance Surfaces

The use of high-impedance surfaces (HISs) to increase the frequency-scanning sensitivity of hollow leaky-wave antennas (LWAs) is presented. The LWA consists of a hollow rectangular waveguide with one of its narrow walls replaced by a partially reflective surface, and it is loaded with a metallodielectric HIS to increase its beam-scanning response. Theoretical results based on a simple transverse equivalent network illustrate the physical mechanism responsible for the improvement, and they are verified by experiments on a prototype working in the 11-16 GHz band.

1D-Leaky Wave Antenna Employing Parallel-Plate Waveguide Loaded With PRS and HIS

A new type of one-dimensional leaky-wave antenna (LWA) with independent control of the beam-pointing angle and beamwidth is presented. The antenna is based on a simple structure composed of a bulk parallel-plate waveguide (PPW) loaded with two printed circuit boards (PCBs), each one consisting of an array of printed dipoles. One PCB acts as a partially reflective surface (PRS), and the other grounded PCB behaves as a high impedance surface (HIS). It is shown that an independent control of the leaky-mode phase and leakage rate can be achieved by changing the lengths of the PRS and HIS dipoles, thus resulting in a flexible adjustment of the LWA pointing direction and directivity. The leaky-mode dispersion curves are obtained with a simple Transverse Equivalent Network (TEN), and they are validated with three-dimensional full-wave simulations. Experimental results on fabricated prototypes operating at 15 GHz are reported, demonstrating the versatile and independent control of the LWA performance by changing the PRS and HIS parameters.

Freeze-dried, mucoadhesive system for vaginal delivery of the HIV microbicide, dapivirine: Optimisation by an artificial neural network

Dapivirine mucoadhesive gels and freeze-dried tablets were prepared using a 3 x 3 x 2 factorial design. An artificial neural network (ANN) with multi-layer perception was used to investigate the effect of hydroxypropyl-methylcellulose (HPMC): polyvinylpyrrolidone (PVP) ratio (XI), mucoadhesive concentration (X2) and delivery system (gel or freeze-dried mucoadhesive tablet, X3) on response variables; cumulative release of dapivirine at 24 h (Q(24)), mucoadhesive force (F-max) and zero-rate viscosity. Optimisation was performed by minimising the error between the experimental and predicted values of responses by ANN. The method was validated using check point analysis by preparing six formulations of gels and their corresponding freeze-dried tablets randomly selected from within the design space of contour plots. Experimental and predicted values of response variables were not significantly different (p > 0.05, two-sided paired t-test). For gels, Q(24) values were higher than their corresponding freeze-dried tablets. F-max values for freeze-dried tablets were significantly different (2-4 times greater, p > 0.05, two-sided paired t-test) compared to equivalent gets. Freeze-dried tablets having lower values for X1 and higher values for X2 components offered the best compromise between effective dapivirine release, mucoadhesion and viscosity such that increased vaginal residence time was likely to be achieved. (C) 2009 Elsevier B.V. All rights reserved.

An efficient neural network approach for nanoscale FinFET modelling and circuit simulation

The present paper demonstrates the suitability of artificial neural network (ANN) for modelling of a FinFET in nano-circuit simulation. The FinFET used in this work is designed using careful engineering of source-drain extension, which simultaneously improves maximum frequency of oscillation f(max) because of lower gate to drain capacitance, and intrinsic gain A(V0) = g(m)/g(ds), due to lower output conductance g(ds). The framework for the ANN-based FinFET model is a common source equivalent circuit, where the dependence of intrinsic capacitances, resistances and dc drain current I-d on drain-source V-ds and gate-source V-gs is derived by a simple two-layered neural network architecture. All extrinsic components of the FinFET model are treated as bias independent. The model was implemented in a circuit simulator and verified by its ability to generate accurate response to excitations not used during training. The model was used to design a low-noise amplifier. At low power (J(ds) similar to 10 mu A/mu m) improvement was observed in both third-order-intercept IIP3 (similar to 10 dBm) and intrinsic gain A(V0) (similar to 20 dB), compared to a comparable bulk MOSFET with similar effective channel length. This is attributed to higher ratio of first-order to third-order derivative of I-d with respect to gate voltage and lower g(ds), in FinFET compared to bulk MOSFET. Copyright (C) 2009 John Wiley & Sons, Ltd.

Measuring inconsistency in network intrusion rules

In this preliminary case study, we investigate how inconsistency in a network intrusion detection rule set can be measured. To achieve this, we first examine the structure of these rules which incorporate regular expression (Regex) pattern matching. We then identify primitive elements in these rules in order to translate the rules into their (equivalent) logical forms and to establish connections between them. Additional rules from background knowledge are also introduced to make the correlations among rules more explicit. Finally, we measure the degree of inconsistency in formulae of such a rule set (using the Scoring function, Shapley inconsistency values and Blame measure for prioritized knowledge) and compare the informativeness of these measures. We conclude that such measures are useful for the network intrusion domain assuming that incorporating domain knowledge for correlation of rules is feasible.

Measuring Inconsistency in a Network Intrusion Detection Rule Set Based on Snort

In this preliminary study, we investigate how inconsistency in a network intrusion detection rule set can be measured. To achieve this, we first examine the structure of these rules which are based on Snort and incorporate regular expression (Regex) pattern matching. We then identify primitive elements in these rules in order to translate the rules into their (equivalent) logical forms and to establish connections between them. Additional rules from background knowledge are also introduced to make the correlations among rules more explicit. We measure the degree of inconsistency in formulae of such a rule set (using the Scoring function, Shapley inconsistency values and Blame measure for prioritized knowledge) and compare the informativeness of these measures. Finally, we propose a new measure of inconsistency for prioritized knowledge which incorporates the normalized number of atoms in a language involved in inconsistency to provide a deeper inspection of inconsistent formulae. We conclude that such measures are useful for the network intrusion domain assuming that introducing expert knowledge for correlation of rules is feasible.

Neural Network Based Attack on a Masked Implementation of AES

Masked implementations of cryptographic algorithms are often used in commercial embedded cryptographic devices to increase their resistance to side channel attacks. In this work we show how neural networks can be used to both identify the mask value, and to subsequently identify the secret key value with a single attack trace with high probability. We propose the use of a pre-processing step using principal component analysis (PCA) to significantly increase the success of the attack. We have developed a classifier that can correctly identify the mask for each trace, hence removing the security provided by that mask and reducing the attack to being equivalent to an attack against an unprotected implementation. The attack is performed on the freely available differential power analysis (DPA) contest data set to allow our work to be easily reproducible. We show that neural networks allow for a robust and efficient classification in the context of side-channel attacks.