Investigation on localized surface plasmon resonance of different nano-particles for bio-sensor applications

This paper proposes a novel sinusoidal shape nano-particle employed in localized surface plasmon resonance (LSPR) devices. Numerical modeling demonstrates advantages offered by the proposed nano-sinusoid on LSPR enhancement against other nano-particles including noble nano-triangles and nano-diamonds. Although nano-triangles exhibit high concentration of the electric field 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 fluctuation in their physical size-plasmon spectrum relationships, due to a high level of singularity as the result for their four sharp tip points. The proposed nano-sinusoid overcomes this disadvantage while maintaining the benefits of having two hot spots and high enhancements.

Evaluation of segmentation algorithms for extraction of RNFL in OCT images

The thickness of the retinal nerve fiber layer (RFNL) has become a diagnose measure for glaucoma assessment. To measure this thickness, accurate segmentation of the RFNL in optical coherence tomography (OCT) images is essential. Identification of a suitable segmentation algorithm will facilitate the enhancement of the RNFL thickness measurement accuracy. This paper investigates the performance of six algorithms in the segmentation of RNFL in OCT images. The algorithms are: normalised cuts, region growing, k-means clustering, active contour, level sets segmentation: Piecewise Gaussian Method (PGM) and Kernelized Method (KM). The performance of the six algorithms are determined through a set of experiments on OCT retinal images. An experimental procedure is used to measure the performance of the tested algorithms. The measured segmentation precision-recall results of the six algorithms are compared and discussed.

Localized surface plasmon resonance: nano-sinusoid arrays

A new nano-sinusoid shape has recently been proposed, which offers the advantage of more resonance wavelength tunability than that offered by other sharp-tip nano-particles. In this paper, a one-dimensional (1D) chain of the nano-sinusoids is modelled, and results are compared with those describing chains of nano-triangles and nano-diamonds. It is demonstrated that the chain of nano-sinusoids provides more enhancement at hot spots than other examined nano-particle shapes. This enhancement is analytically quantified using the coupling constant values used in the electrostatic eigenmode method for analytically solving Maxwell's equations for the nano-plasmonic devices. In addition, investigating LSPR spectrum of two-dimensional (2D) arrays of NPs demonstrates existence of enhanced surface electric fields on hot spots of the outer rows of the array.

Localized corrosion and inhibition studies using the wire beam electrode in conjunction with the electrochemical noise analysis and the scanning reference electrode techniques

Several new technical developments have been made based on the combined use of the wire beam electrode (WBE), electrochemical noise analysis (ENA) and the scanning reference electrode technique (SRET). These have included: (i) The WBE-R n method- the combined use of the WBE and the noise resistance (Rn) to map the rates and patterns of uniform or localized corrosion; (ii) The WBE-Noise Signatures method- the combined use of the WBE and the noise signature to detect the origination and propagation of localized corrosion; and (iii) The WBE-SRET method- the combined use of the WBE and SRET to investigate localized corrosion from both the metallic and electrolyte phases of a corroding metal surface. This paper presents a brief review on these novel methods and their applications for detecting general and localized corrosion, for mapping the rates of corrosion, and for studying corrosion inhibitors.

Inhibiting localized corrosion of aluminum and aluminum alloy by rare earth metal compounds : behaviors and characteristics observed using an electrochemically integrated multi-electrode array

An electrochemically integrated multi-electrode array namely the wire beam electrode (WBE) has been used to characterize the behavior of cerium chloride (CeCl3) and lanthanum chloride (LaCl3) in inhibiting localized corrosion of AA2024-T3 and AA1100. CeCl3 has been found to inhibit AA2024-T3 corrosion in 0.005 M sodium chloride (NaCl) solution by suppressing galvanic corrosion activities and by creating a large number of insignificant anodes. It has also been shown to inhibit localized corrosion of AA1100 in 0.5 M NaCl solution by promoting the random distribution of minor anodes. LaCl3 has been found to inhibit localized corrosion of AA2024-T3 at 1000 ppm, although its efficiency dropped significantly when its concentration decreased to 500 ppm. The addition of CeCl3 and LaCl3 to corrosion testing cells at later stages was unable to effectively suppress existing corrosion anodes.

Remote centre-of-motion control algorithms of 6-RRCRR parallel robot assisted surgery system (PRAMiSS)

In this paper a 6-RRCRR parallel robot assisted minimally invasive surgery/microsurgery system (PRAMiSS) is introduced. Remote centre-of-motion (RCM) control algorithms of PRAMiSS suitable for minimally invasive surgery and microsurgery are also presented. The programmable RCM approach is implemented in order to achieve manipulation under the constraint of moving through the fixed penetration point. Having minimised the displacements of the mobile platform of the parallel micropositioning robot, the algorithms also apply orientation constraint to the instrument and prevent the tool tip to orient due to the robot movements during the manipulation. Experimental results are provided to verify accuracy and effectiveness of the proposed RCM control algorithms for minimally invasive surgery.

Evaluation and comparison of type reduction algorithms from a forecast accuracy perspective

A variety of type reduction (TR) algorithms have been proposed for interval type-2 fuzzy logic systems (IT2 FLSs). The focus of existing literature is mainly on computational requirements of TR algorithm. Often researchers give more rewards to computationally less expensive TR algorithms. This paper evaluates and compares five frequently used TR algorithms from a forecasting performance perspective. Algorithms are judged based on the generalization power of IT2 FLS models developed using them. Four synthetic and real world case studies with different levels of uncertainty are considered to examine effects of TR algorithms on forecasts accuracies. It is found that Coupland-Jonh TR algorithm leads to models with a better forecasting performance. However, there is no clear relationship between the width of the type reduced set and TR algorithm.

Evaluation of feature selection algorithms for detection of depression from brain sMRI scans

Detection of depression from structural MRI (sMRI) scans is relatively new in the mental health diagnosis. Such detection requires processes including image acquisition and pre-processing, feature extraction and selection, and classification. Identification of a suitable feature selection (FS) algorithm will facilitate the enhancement of the detection accuracy by selection of important features. In the field of depression study, there are very limited works that evaluate feature selection algorithms for sMRI data. This paper investigates the performance of four algorithms for FS of volumetric attributes in sMRI scans. The algorithms are One Rule (OneR), Support Vector Machine (SVM), Information Gain (IG) and ReliefF. The performances of the algorithms are determined through a set of experiments on sMRI brain scans. An experimental procedure is developed to measure the performance of the tested algorithms. The result of the evaluation of the FS algorithms is discussed by using a number of analyses.

Neural network ensemble : evaluation of aggregation algorithms for electricity demand forecasting

This paper examines and analyzes different aggregation algorithms to improve accuracy of forecasts obtained using neural network (NN) ensembles. These algorithms include equal-weights combination of Best NN models, combination of trimmed forecasts, and Bayesian Model Averaging (BMA). The predictive performance of these algorithms are evaluated using Australian electricity demand data. The output of the aggregation algorithms of NN ensembles are compared with a Naive approach. Mean absolute percentage error is applied as the performance index for assessing the quality of aggregated forecasts. Through comprehensive simulations, it is found that the aggregation algorithms can significantly improve the forecasting accuracies. The BMA algorithm also demonstrates the best performance amongst aggregation algorithms investigated in this study.