We propose some modifications in the conventional index-guided photonic crystal fiber (PCF) structure having circular holes of constant radii. In one of the proposed structures, the hole dimensions of the conventional PCF are modified such that the ratio of the radii of the holes in a particular layer with the nearest layer is constant while maintaining the same refractive index in all the holes. In the other structure, we propose to use different dielectrics in different layers of holes in the conventional PCF structure such that the ratio of the refractive index of the dielectric material in the holes in a particular layer with the nearest layer is constant. The simulations of the proposed structures are carried out using OptiFDTD simulator with full-vector mode solver using finite difference time domain method, and the results are compared with the conventional PCF structure having four layers of air holes. It is observed that the proposed structures exhibit lower waveguide dispersion and confinement loss than the conventional PCF structure over a wide range of wavelengths, making them suitable candidates for applications such as long-distance optical communications or high-data rate data transfer applications. One of the proposed structures exhibits large negative dispersion, and it can be used as a dispersion compensating fiber.
The security of digital data including images has attracted more attention recently, and many different image encryption methods have been proposed in the literature for this purpose. In this paper, a new image encryption method using wavelet packet decomposition and discrete linear canonical transform is proposed. The use of wavelet packet decomposition and DLCT increases the key size significantly making the encryption more robust. Simulation results of the proposed technique are also presented.
Image fusion is the process of combining two or more images of the same scene into a single image which is suitable for human perception and practical applications. This paper investigates the effect of use of different types of masks in
discrete cosine transform (DCT) domain for image fusion applications. Here we have used different types of masks such
as rectangular, triangular, strip and fan shaped mask. In the proposed scheme, the DCT of both the images are taken and mask and its complimentary mask are applied on two transformed images respectively. The masked images are then fused in the transform domain and inverse DCT is applied to obtain the fused image. Simulation results of the proposed technique are also presented and it is observed that fusion based on the fan shaped mask gives better quality of fused image than other masks consider in this paper as well as some of the methods existing in the literature.
Image restoration using Wiener and geometric mean filtering is one of the commonly used techniques in image processing applications. In this paper we propose the use of discrete fractional Fourier transform in place of conventional discrete Fourier transform (DFT) in the Wiener and geometric mean filters. The use of discrete fractional
Fourier transform (DFrFT) provides us additional degree of freedom in terms of the angle parameter of the transforms
which can be exploited for the purpose of image restoration. The proposed restoration filters are applied on both colored and grey images and the simulation results of the proposed technique are presented. The effect of variation of parameters of the transforms and filters are also studied under the presence of noise. It is observed that the results of the conventional Wiener and geometric mean filters are better than the filters using DFrFT except for a specific value of the angle parameter about 0.8.
KEYWORDS: Signal to noise ratio, Associative arrays, Signal processing, Digital image correlation, Continuous wavelet transforms, Electronics, Wavelets, Computer simulations, Interference (communication), Classification systems
This paper presents two new algorithms for fault classification in power signals. The first algorithm is based on empirical mode decomposition (EMD) of the power signals which decomposes a signal into intrinsic mode functions (IMF). In the proposed technique we obtain the IMFs of the power signals and compute the higher order statistical parameters of each IMF, and a dictionary of feature vectors of different types of faults is prepared. To classify the fault in a given signal, its feature vector is computed and its classification is done using the nearest neighbor rule using its Euclidean distance with the feature vectors stored in the dictionary. The simulation results show that we are able to classify the faults accurately using HOS based approach even at signal-to-noise ratio (SNR) value of 10 dB, which is much lower than the values of SNR reported in the literature. The second method is based on computing the histograms of different types of fault signals and computing their distances with histograms of signals stored in the dictionary. It is observed that above SNR value of 30 dB, we are able to classify all types of faults accurately and this method is computationally less demanding.
Proceedings Volume Editor (1)
This will count as one of your downloads.
You will have access to both the presentation and article (if available).
Access to the requested content is limited to institutions that have purchased or subscribe to SPIE eBooks.
You are receiving this notice because your organization may not have SPIE eBooks access.*
*Shibboleth/Open Athens users─please
sign in
to access your institution's subscriptions.
To obtain this item, you may purchase the complete book in print or electronic format on
SPIE.org.
INSTITUTIONAL Select your institution to access the SPIE Digital Library.
PERSONAL Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.