Super-Resolution Reconstruction in Frequency-Domain Optical-Coherence Tomography Using the Finite-Rate-of-Innovation Principle

The standard approach to signal reconstruction in frequency-domain optical-coherence tomography (FDOCT) is to apply the inverse Fourier transform to the measurements. This technique offers limited resolution (due to Heisenberg's uncertainty principle). We propose a new super-resolution reconstruction method based on a parametric representation. We consider multilayer specimens, wherein each layer has a constant refractive index and show that the backscattered signal from such a specimen fits accurately in to the framework of finite-rate-of-innovation (FRI) signal model and is represented by a finite number of free parameters. We deploy the high-resolution Prony method and show that high-quality, super-resolved reconstruction is possible with fewer measurements (about one-fourth of the number required for the standard Fourier technique). To further improve robustness to noise in practical scenarios, we take advantage of an iterated singular-value decomposition algorithm (Cadzow denoiser). We present results of Monte Carlo analyses, and assess statistical efficiency of the reconstruction techniques by comparing their performance against the Cramer-Rao bound. Reconstruction results on experimental data obtained from technical as well as biological specimens show a distinct improvement in resolution and signal-to-reconstruction noise offered by the proposed method in comparison with the standard approach.

ELLIPSE FITTING USING FINITE RATE OF INNOVATION PRINCIPLES

We address the problem of parameter estimation of an ellipse from a limited number of samples. We develop a new approach for solving the ellipse fitting problem by showing that the x and y coordinate functions of an ellipse are finite-rate-of-innovation (FRI) signals. Uniform samples of x and y coordinate functions of the ellipse are modeled as a sum of weighted complex exponentials, for which we propose an efficient annihilating filter technique to estimate the ellipse parameters from the samples. The FRI framework allows for estimating the ellipse parameters reliably from partial or incomplete measurements even in the presence of noise. The efficiency and robustness of the proposed method is compared with state-of-art direct method. The experimental results show that the estimated parameters have lesser bias compared with the direct method and the estimation error is reduced by 5-10 dB relative to the direct method.

Innovation and growth of engineering SMEs in Bangalore: Why do only some innovate and only some grow faster?

This paper probes two research questions by ascertaining the factors which distinguish (i) innovative SMEs from those which are not, and (ii) SMEs which experienced a higher sales growth from those which experienced a lower sales growth, with reference to 197 engineering industry SMEs in Bangalore city. The differentiating factors between innovative and non-innovative SMEs brought out that SMEs must have ``own resources and capabilities'' in the form of internal strength and definite internal strategy if they have to innovate successfully. Younger and smaller firms which are ``entrepreneurial'' in nature and which are innovative contributed to higher sales growth of SMEs compared to older and larger firms which are ``salary-substitute firms'' in nature and which are not innovative. (C) 2015 Elsevier B.V. All rights reserved.

Ellipse Fitting Using the Finite Rate of Innovation Sampling Principle

Standard approaches for ellipse fitting are based on the minimization of algebraic or geometric distance between the given data and a template ellipse. When the data are noisy and come from a partial ellipse, the state-of-the-art methods tend to produce biased ellipses. We rely on the sampling structure of the underlying signal and show that the x- and y-coordinate functions of an ellipse are finite-rate-of-innovation (FRI) signals, and that their parameters are estimable from partial data. We consider both uniform and nonuniform sampling scenarios in the presence of noise and show that the data can be modeled as a sum of random amplitude-modulated complex exponentials. A low-pass filter is used to suppress noise and approximate the data as a sum of weighted complex exponentials. The annihilating filter used in FRI approaches is applied to estimate the sampling interval in the closed form. We perform experiments on simulated and real data, and assess both objective and subjective performances in comparison with the state-of-the-art ellipse fitting methods. The proposed method produces ellipses with lesser bias. Furthermore, the mean-squared error is lesser by about 2 to 10 dB. We show the applications of ellipse fitting in iris images starting from partial edge contours, and to free-hand ellipses drawn on a touch-screen tablet.

Strategic Marketing and Innovation Performance of Indian MSMEs

Micro Small and Medium Enterprises (MSMEs) is an integral part of the Indian industrial sector. The distinctive features of MSMEs are less capital investment and high labour absorption which has created unprecedented importance to this sector. As per the Development Commissioner of MSME, the sector has the credit of being the second highest in employment in India, which stands next to agricultural sector. The MSMEs are very much needed in efficiently allocating the enormous labour supply and scarce capital by implementing labour intensive production processes. Associated with this high growth rates, MSMEs are also facing a number of problems like sub-optimal scale of operation, technological obsolescence, supply chain inefficiencies, increasing domestic and global competition, fund shortages, change in manufacturing & marketing strategies, turbulent and uncertain market scenario. To survive with such issues and compete with large and global enterprises, MSMEs need to adopt innovative approaches in their regular business operations. Among the manufacturing sectors, we find that they are unable to focus themselves in the present competition. This paper presents a brief literature of work done in MSMEs, Innovation and Strategic marketing with reference to Indian manufacturing firms.

Innovation processes within the healthcare industry: Determining critical success factors aligned to product strategies

The impact of differing product strategies on product innovation processes pursued by healthcare firms is discussed. The critical success factors aligned to product strategies are presented. A definite split between pioneering product strategies and late entrant product strategies is also recognised.

Function Innovation in Combined Surface Technologies for Metals

表面技术能够显著提高材料功能而成为工程和产品设计的重要组成部分, 但前提是表面技术必须具有可设计性。为此, 需要开拓和发展表面组合加工技术, 进行创新。该技术的内涵体现了新材料与新技术、基础研究与产业化有机结合的特点, 具有重要的价值和广阔的前景。