Compressed Raman method combining classification and estimation of spectra with optimized binary filters.

An analytic solution for real optimal filters is known, and the special case of optimal binary phase-only filters can be solved by a fast binning algorithm but no analytic solution is known. We establish a geometric solution for the design of optimal binary amplitude filters (OBAF's) and optimal binary phase-only filters (OBPOF's) for any object. The optimal filter is found in terms of maximizing the field strength at the origin in the correlation plane. We found that it is possible to construct a unique convex polygon by using an ordered set of phasors from the filter object's Fourier transform. This process leads eventually to an exact solution for the filter-design problem. We show that the maximum distance across the polygon divides the phasors into two groups: For the OBAF, it determines the group that is passed or blocked; for the OBPOF, it determines which group is passed with a zero or a pi phase shift. The shape of the convex polygon gives qualitative information on the criticalness and the tightness needed in the design process. It provides good insight into the binning-process algorithm and permits us to bound the error in the binning process. Design examples through computer simulation and applications in fingerprint identification are presented.

The practical issues in the design and implementation of the optimal binary filters limit the observation windows down to a size that is usually much smaller than the characteristic features found in document images. To overcome this drawback, a new composite binary filtering method is developed. A preprocessing stage based on linearly separable Boolean functions is used to gather information from a very large window. The pre-filtering results together with the pixel values from a smaller window are then faded into an optimal estimator. The proposed filter structure can be viewed either as a two stage binary filter with linearly separable preprocessing or as a locally adaptive binary filter where the local adaptation is based on the characteristic features extracted from a large window. Both software and hardware implementations of the filter exhibit reasonable complexity even for large windows. Some properties of the filter and a closed form expression of the mean-absolute-error are derived. It is shown that, because there are no restrictions in the second stage, the whole composite structure can be optimally designed. The paper develops a practical design procedure that uses an efficient gradient method. Computer simulations are used to test the proposed filter against the optimal binary filter.

Optimal binary filters estimate an ideal random set by means of an observed random set. By parameterizing the ideal and observation random sets, one can examine the robustness of filter design relative to parameter states. This paper addresses the question as to which states possess the most robust optimal filters. Based on the prior distribution of the states, a measure of robustness is defined for each state and the state possessing maximal robustness is determined. The paper focuses on sparse noise, for which an analytic formulation of robustness is known. It proposes a parametric model from which to approximate robustness by estimating model parameters from image data.

For binary window-based filters, the optimal increasing filter is often derived from the optimal unconstrained (nonincreasing) filter by iteratively switching the filter values at pixels from 0 to 1 or from 1 to 0 so as to make the resulting filter be the optimal increasing filter. This paper gives a corresponding switching algorithm for gray-scale nonlinear filters, and it does so in the context of finite lattices, which makes the algorithm applicable to computational morphology on lattices. The algorithm is minimal in the sense that it involves a minimal search if one wishes to be certain to obtain the optimal increasing filter when beginning with the optimal unconstrained filter.

The experimental and theoretical operation of the dc-blocked phase-only filter correlator is studied for obscured inputs. The performance of the nearly optimal filter is compared with the performances of other varieties of simple optimal filters that are capable of real-time implementation with spatial light modulators. Uses of these correlation filters in associative memories is investigated.

Conventional Boolean logic, which is based on radix-2 binary logic, is no longer suitable to represent complex digital systems. As digital systems continue to grow in complexity, more compact representations become essential to describe these systems. One such possibility is to use multiple-valued logic, with radices larder than two. Many non-conventional technologies are considered as candidates to replace conventional semiconductor based switching circuit technology for computational systems such. Interestingly, some of these technologies are inherently multiple valued. Multiple-valued logic design and optimization methodologies are not yet elaborated, as compared to the well-established binary logic design and optimization and testing scheme. In this paper we are proposing an algorithm, with which functions to describe digital systems are specified and simplified using Multiple valued logic. Representing a digital system with different radices come out with different equivalent specifications for that system. A designer can choose the most suitable specification for his design constrains. The influence of radix increase on the complexity of the algorithm and the resulting specification is also demonstrated and discussed in this paper.

We present a single-capture photometric stereo method using a hyperspectral camera. A spectrally and spatially designed illumination enables a point-wise estimation of reflectance spectra and surface normals from a single hyperspectral image. The illumination works as a reflectance probe in wide spectral regions where reflectance spectra are measured, and the full spectra are estimated by interpolation. It also works as the resource for shadings in other spectral regions. The accuracy of estimation is evaluated in a simulation. Also, we prepare an experimental setup and demonstrate a surface reconstruction against a real scene.

Stokes and anti-Stokes photoluminescence of Rhodamine B in porous silicon

In situ characterisation by anomalous X-ray diffraction of the cationic distribution of dehydrated SrRbX zeolite

We report response of photoluminescence (PL) from GaN nanowires without protection in solutions. The distinct response is not only toward pH but toward ionic concentration under same pH. The nanowires appear to be highly stable under aqueous solution with high ionic concentration and low pH value down to 1. We show that the PL has a reversible interaction with various types of acidic and salt solutions. The quantum states of nanowires are exposed to the external environment and have a direct physical interaction which depends on the anions of the acids. As the ionic concentration increases, the PL intensity goes up or down depending on the chemical species. The response results from a competition of change in surface band bending and charge transfer to redox level in solution. That of GaN films is reported for comparison as the effect of surface band bending can be neglected so that there are only slight variations in PL intensity for GaN films. Additionally, such physical interaction does not impact on the PL peaks in acids and salts, whereas there is a red shift on PL when the nanowires are in basic solution, say NH4OH, due to chemical etching occurred on the nanowires.

This article presents a design methodology for bulk acoustic wave (BAW) filters. First, an overview of BAW physical principles, BAW filter synthesis, and the modified Butterworth-van Dyke model are addressed. Next, design and optimization methodology is presented and applied to a mixed ladder-lattice BAW bandpass filter for the Universal Mobile Telecommunications System (UMTS) TX-band at 1.95 GHz and to ladder and lattice BAW bandpass filters for the DCS1800 TX-band at 1.75 GHz. In each case, BAW filters are based on AlN resonators. UMTS filter is designed with conventional molybdenum electrodes whereas DCS filters electrodes are made with innovative iridium.

An optimal binary-image filter estimates an ideal random set by means of an observed random set. A fundamental and practically important question regards the robustness of a designed filter: to what extent does performance degrade when the filter is applied to a different model than the one for which it has been designed? By parameterizing the ideal and observation random sets, one can analyze the robustness of filter design relative to parameter states. Based on a prior distribution for the states, a robustness mesure is defined for each state in terms of how well its optimal filter performs on models for different states. Not only is filter performance on other states taken into account, but so too is the contribution of other states in terms of their mass relative to the prior state distribution. This paper characterizes maximally robust states, derives performance bounds, treats mean robustness (as opposed to robustness by state), introduces a global filter that is applied across all states, particularizes the entire analysis to a sparse noise model for which there are analytic robustness expressions, and proposes a simplified model for determination of robust states from data. Sufficient conditions are given under which the global filter is uniformly more robust than all state-specific optimal filters.

Model-based optimization of field-scale electrokinetic treatment of dredged sediments

A simple yet robust and computationally efficient methodology for design optimization of microwave filters is presented. The proposed algorithm exploits variable-fidelity electromagnetic (EM) simulation models and adjoint sensitivities for fast evaluation of EM model response derivatives. Most of the operations are performed on the low-fidelity EM model, whose correction is realized by means of space mapping (SM). In the optimization process, the SM parameters are optimized together with the design itself, which allows us to keep the number of both low- and high-fidelity simulations at low levels. The operation and performance of the method is demonstrated using two examples of waveguide filters.

The quest for productive image denoising systems still is a valid challenge, at the intersection of practical investigation and measurements. In spite of the sophistication of the recently proposed systems, most calculations have not yet achieved an attractive level of applicability. In this research, an optimal wavelet filter coefficient design-based methodology is proposed for image denoising. The method utilises new wavelet filter whose coefficients are derived by discrete wavelet (Haar) transform using CPSO optimisation and bilateral filter. The optimal wavelet coefficient based denoising methods minimise the noise, while bilateral filter further decreases the noise and increases the PSNR without any loss of relevant image information. Overall, the proposed approach consists of two stages namely, (i) design of optimal wavelet filter, (ii) image denoising using a bilateral filter. At first, wavelet optimal coefficients are selected using cooperative particle swarm optimiser (CPSO). After that, the hybrid domain based algorithm (wavelet with bilateral filter) is applied to the noisy image which is helpful to obtain the denoised image. A comparative study of the performance of different existing approaches and the proposed denoised approach is made in terms of PSNR, SDME, SSIM and GP. When compared, the proposed algorithm gives better PSNR compared to the existing methods.