Design and fabrication of nano-sinusoid LSPR devices.

Applications of LSPR nano-particles in various areas of solar cells, LSPR biosensors, and SERS biosensors, based on interaction of light with noble metal nano-particles is increasing. Therefore, design and nano-fabrication of the LSPR devices is a key step in developing such applications. Design of nano-structures with desirable spectral properties using numerical techniques such as finite difference time domain (FDTD) is the first step in this work. A new structure called nano-sinusoid, satisfying the some desirable LSPR characteristics, is designed and simulated using the FDTD method. In the next stage, analytical method of electro static eigen mode method is used to validate the simulation results. The, nano-fabrications method of electron beam lithography (EBL) is implemented to fabricate the proposed profile with high precision. Finally, atomic force microscopy (AFM) is used to investigate the shape of the fabricated nano-particles, and the dark field microscopy is employed to demonstrate the particular spectral characteristics of the proposed nano-sinusoids.

Compact stacked planar inverted-F antenna for passive deep brain stimulation implants.

A compact meandered three-layer stacked circular planar inverted-F antenna is designed and simulated at the UHF band (902.75 - 927.25 MHz) for passive deep brain stimulation implants. The UHF band is used because it offers small antenna size, and high data rate. The top and middle radiating layers are meandered, and low cost substrate and superstrate materials are used to limit the radius and height of the antenna to 5 mm and 1.64 mm, respectively. A dielectric substrate of FR-4 of ε(r)= 4.7 and δ= 0.018, and a biocompatible superstrate of silicone of er= 3.7 and d= 0.003 with thickness of 0.2 mm are used in the design. The resonance frequency of the proposed antenna is 918 MHz with a bandwidth of 24 MHz at return loss of -10 dB in free space. The antenna parameter such as 3D gain pattern of the designed antenna within a skin-tissue model is evaluated by using the finite element method. The compactness, wide bandwidth, round shape, and stable characteristics in skin make this antenna suitable for DBS. The feasibility of the wireless power transmission to the implant in the human head is also examined.

Investigating nanoparticle-substrate interaction in LSPR biosensing using the image-charge theory.

Localized surface plasmon resonance (LSPR) has been used to develop optical biosensors. Tuning the resonance wavelength to detect target biomolecules with a particular dipolar resonance is essential when designing LSPR biosensors. In this paper, the interaction of nanoparticles (NPs) with glass substrate (SiO(2)) for LSPR wavelength is investigated using the concept of the image-charge theory. Using the FDTD method, it is shown how the NP and substrate size change the plasmon wavelength. Next, this phenomenon is interpreted using the analytical electrostatic eigenvalue method.

Segmentation of multiple sclerosis lesions in MR images: a review.

INTRODUCTION: Multiple sclerosis (MS) is an inflammatory demyelinating disease that the parts of the nervous system through the lesions generated in the white matter of the brain. It brings about disabilities in different organs of the body such as eyes and muscles. Early detection of MS and estimation of its progression are critical for optimal treatment of the disease. METHODS: For diagnosis and treatment evaluation of MS lesions, they may be detected and segmented in Magnetic Resonance Imaging (MRI) scans of the brain. However, due to the large amount of MRI data to be analyzed, manual segmentation of the lesions by clinical experts translates into a very cumbersome and time consuming task. In addition, manual segmentation is subjective and prone to human errors. Several groups have developed computerized methods to detect and segment MS lesions. These methods are not categorized and compared in the past. RESULTS: This paper reviews and compares various MS lesion segmentation methods proposed in recent years. It covers conventional methods like multilevel thresholding and region growing, as well as more recent Bayesian methods that require parameter estimation algorithms. It also covers parameter estimation methods like expectation maximization and adaptive mixture model which are among unsupervised techniques as well as kNN and Parzen window methods that are among supervised techniques. CONCLUSIONS: Integration of knowledge-based methods such as atlas-based approaches with Bayesian methods increases segmentation accuracy. In addition, employing intelligent classifiers like Fuzzy C-Means, Fuzzy Inference Systems, and Artificial Neural Networks reduces misclassified voxels.