Evolutions towards a new LSPR particle: Nano-sinusoid Progress in Electromagnetic Research (PIER)

This paper proposes a novel nano-sinusoid particle to be employed in enhanced localized surface plasmon resonance (LSPR) bio-sensing devices. Numerical investigations are carried out to demonstrate advantages o®ered by the proposed nano-particle on LSPR enhancement over other nano-particles including noble nano-triangles and nano-diamonds. Although nano-triangles exhibit high concentration of the electric ¯eld near their tips, when illuminated with a light polarized along the tip axis, they present only one hot spot at the vertex along the polarization direction. To create a structure with two hot spots, which is desired in bio-sensing applications, two nano-triangles can be put back-to-back. Therefore, a nano-diamond particle is obtained which exhibits two hot spots and presents higher enhancements than nano-triangles for the same resonant wavelength. The main drawback of the nano-diamonds is the °uctuation in their physical size-plasmon spectrum relationships, due to a high level of singularity as the result of their four sharp tip points. The proposed nano-sinusoid overcomes this disadvantage while maintaining the bene¯ts of having two hot spots and high enhancement

Miniaturized Quadrature Hybrid Couplers based on Novel U-shaped Transmission Lines

In this paper, a miniaturized microstrip quadrature hybrid coupler (QHC) using U-shaped transmission lines (USTLs) is presented. The proposed approach replaces all arms of the conventional QHC with its equivalent USTL to achieve compactness. The proposed coupler structure is designed to operate in the 1.5 GHz (1427-1518 [MHz]) band which is one of the 5G bands of interest. At such low RF/microwave bands below 3-4 GHz, the size of the conventional coupler is considerably very large which raises a concern for the next generation networks. The pro- posed coupler is designed, simulated and fabricated using Rogers 5880 with thickness of 0.79 mm, dielectric con- stant (εr) of 2.2 and loss tangent of 0.0021. The proposed QHC size is 70% smaller in circuit area (30% relative area) than the conventional equivalent. Simulation and mea- sured results are presented and good matching between the results is observed, confirming the outstanding coupler performance properties. The proposed miniaturized QHC structure will play a vital role for next generation 4G and 5G wireless communication systems operating below 6 GHz

Tuning the Filter Responses with Graphene Based Resonators

Graphene-metal combined waveguide resonators were proposed earlier, as a solution for obtaining frequency tunable resonator responses at the sub-millimeter-wave frequencies. A methodology for combining these waveguide resonators into the frequency tunable filters has been studied subsequently. Here, we discuss the possibilities and limitations of this type of waveguide resonators, illustrated by several examples of tunable filter designs

Design methodology for graphene tunable filters at the sub–millimeter–wave frequencies

Tunable components and circuits, allowing for the fast switching between the states of operation, are among the basic building blocks for future communications and other emerging applications. Based on the previous thorough study of graphene based resonators, the design methodology for graphene tunable filters has been devised, outlined, as well as explained through an example of the fifth order filter. The desired filtering responses can be achieved with the material loss not higher than the loss corresponding to the previously studied single resonators, depending mostly on the quantity of graphene per resonator. The proposed design method relies on the detailed design space mapping; obtained data gives an immediate assessment of the feasibility of specifications with a particular filter order, maximal passband ripple level, desired bandwidth, and acceptable losses. The design process could be further automated by the knowledge based approach using the collected design space data

Dictionary matching in a stream

We consider the problem of dictionary matching in a stream. Given a set of strings, known as a dictionary, and a stream of characters arriving one at a time, the task is to report each time some string in our dictionary occurs in the stream. We present a randomised algorithm which takes O(log log(k + m)) time per arriving character and uses O(k log m) words of space, where k is the number of strings in the dictionary and m is the length of the longest string in the dictionary

Counting approximately-shortest paths in directed acyclic graphs

Given a directed acyclic graph with positive edge-weights, two vertices s and t, and a threshold-weight L, we present a fully-polynomial time approximation-scheme for the problem of counting the s-t paths of length at most L. We extend the algorithm for the case of two (or more) instances of the same problem. That is, given two graphs that have the same vertices and edges and differ only in edge-weights, and given two threshold-weights L_1 and L_2, we show how to approximately count the s-t paths that have length at most L_1 in the first graph and length at most L_2 in the second graph. We believe that our algorithms should find application in counting approximate solutions of related optimization problems, where finding an (optimum) solution can be reduced to the computation of a shortest path in a purpose-built auxiliary graph

Mathematical simulation of memristive for classification in machine learning

Over the last few years, neuromorphic computation has been a widely researched topic. One of the neuromorphic computation elements is the memristor. The memristor is a high density, analogue memory storage, and compliance with Ohm's law for minor potential changes. Memristive behaviour imitates synaptic behaviour. It is a nanotechnology that can reduce power consumption, improve synaptic modeling, and reduce data transmission processes. The purpose of this paper is to investigate a customized mathematical model for machine learning algorithms. This model uses a computing paradigm that differs from standard Von-Neumann architectures, and it has the potential to reduce power consumption and increasing performance while doing specialized jobs when compared to regular computers. Classification is one of the most interesting fields in machine learning to classify features patterns by using a specific algorithm. In this study, a classifier based memristive is used with an adaptive spike encoder for input data. We run this algorithm based on Anti-Hebbian and Hebbian learning rules. These investigations employed two of datasets, including breast cancer Wisconsin and Gaussian mixture model datasets. The results indicate that the performance of our algorithm that has been used based on memristive is reasonably close to the optimal solution

THE LIFE HISTORY AND FEEDING HABITS OF A TRIPOD FISH (TRIACANTHUS :aREVIROSTRIS TEMM.& SCHLEG.) OF THE INDIAN SEA

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Noise-robust method for image segmentation

Segmentation of noisy images is one of the most challenging problems in image analysis and any improvement of segmentation methods can highly influence the performance of many image processing applications. In automated image segmentation, the fuzzy c-means (FCM) clustering has been widely used because of its ability to model uncertainty within the data, applicability to multi-modal data and fairly robust behaviour. However, the standard FCM algorithm does not consider any information about the spatial linage context and is highly sensitive to noise and other imaging artefacts. Considering above mentioned problems, we developed a new FCM-based approach for the noise-robust fuzzy clustering and we present it in this paper. In this new iterative algorithm we incorporated both spatial and feature space information into the similarity measure and the membership function. We considered that spatial information depends on the relative location and features of the neighbouring pixels. The performance of the proposed algorithm is tested on synthetic image with different noise levels and real images. Experimental quantitative and qualitative segmentation results show that our method efficiently preserves the homogeneity of the regions and is more robust to noise than other FCM-based methods

On the relation between the Hartree-Fock and Kohn-Sham approaches

We show that the Hartree-Fock (HF) results cannot be reproduced within the framework of Kohn-Sham (KS) theory because the single-particle densities of finite systems obtained within the HF calculations are not $v$-representable, i.e., do not correspond to any ground state of a $N$ non-interacting electron systems in a local external potential. For this reason, the KS theory, which finds a minimum on a different subset of all densities, can overestimate the ground state energy, as compared to the HF result. The discrepancy between the two approaches provides no grounds to assume that either the KS theory or the density functional theory suffers from internal contradictions.Comment: 7 pages, ReVtex, revised and accepted by Physics Letters