Cat Swarm Optimization algorithm for optimal linear phase FIR filter design

In this paper a new meta-heuristic search method, called Cat Swarm Optimization (CSO) algorithm is applied to determine the best optimal impulse response coefficients of FIR low pass, high pass, band pass and band stop filters, trying to meet the respective ideal frequency response characteristics. CSO is generated by observing the behaviour of cats and composed of two sub-models. In CSO, one can decide how many cats are used in the iteration. Every cat has its′ own position composed of M dimensions, velocities for each dimension, a fitness value which represents the accommodation of the cat to the fitness function, and a flag to identify whether the cat is in seeking mode or tracing mode. The final solution would be the best position of one of the cats. CSO keeps the best solution until it reaches the end of the iteration. The results of the proposed CSO based approach have been compared to those of other well-known optimization methods such as Real Coded Genetic Algorithm (RGA), standard Particle Swarm Optimization (PSO) and Differential Evolution (DE). The CSO based results confirm the superiority of the proposed CSO for solving FIR filter design problems. The performances of the CSO based designed FIR filters have proven to be superior as compared to those obtained by RGA, conventional PSO and DE. The simulation results also demonstrate that the CSO is the best optimizer among other relevant techniques, not only in the convergence speed but also in the optimal performances of the designed filters.     

A multi-resolution approach for line-edge roughness detection

A wavelet-based line-edge detection framework is presented that proves to be solely image-dependent. In this analysis, surfaces are considered as a combination of an underlying surface structure and a surface detail, corresponding to low-frequency and high-frequency features, respectively. Through the multi-scale analysis offered by wavelet decomposition, the underlying surface structure is extracted and used to define the line-edge searching region, which, in turn, helps characterize the line-edge roughness (LER), providing valuable information for the evaluation of device fabrication and performance. We focus on exploring the optimal wavelet decomposition, to better separate the underlying structure and the surface detail, using a number of metrics including the Shannon’s entropy, k-means clustering and the flatness factor. The impact of different wavelet functions and resolution levels on line-edge roughness characterization is discussed. An SEM image of a plane diffraction grating is studied to demonstrate the application of the proposed framework.     

Ceramic Filter for Small System Drinking Water Treatment: Evaluation of Membrane Pore Size and Importance of Integrity Monitoring

Ceramic filtration has recently been identified as a promising technology for drinking water treatment in households and small communities. This paper summarizes the results of a pilot-scale study conducted at the U.S. Environmental Protection Agency’s (EPA) Test & Evaluation (T&E) Facility in Cincinnati on two ceramic filtration cartridges with pore sizes of 0.05 and 0.01?μm to evaluate their ability to remove turbidity and microbiological contaminants such as bacteria [Bacillus subtilis ( ≈ 1.0?μm) and Escherichia coli ( ≈ 1.4?μm)], Cryptosporidium oocysts (4–6?μm), polystyrene latex (PSL) beads (2.85?μm) (a surrogate for Cryptosporidium), and MS2 bacteriophage ( ≈ 0.02?μm) (a surrogate for enteric viruses). The results demonstrated that the relatively tighter 0.01-μm cartridge performed better than the 0.05-μm cartridge in removing all the biological contaminants and surrogates. For turbidity removal, the 0.01-μm cartridge performed slightly better than the 0.05-μm cartridge; however, the permeate rate in the 0.01-μm cartridge reduced rapidly at higher feed water turbidity levels indicating that a tighter membrane should only be used with adequate pretreatment or at a low feed water turbidity to prolong membrane life. Microbiological monitoring was identified as a more sensitive indirect integrity monitoring method than turbidity and particle count monitoring to ensure effective treatment of water by ceramic filtration. Both PSL beads and B. subtilis showed potential as effective surrogates for Cryptosporidium, with B. subtilis showing higher degree of conservatism. Any opinions expressed in this article are those of the writer(s) and do not necessarily reflect the official positions and policies of the EPA. Any mention of products or trade names does not constitute recommendation for use by EPA. This document has been reviewed in accordance with EPA’s peer and administrative review policies and approved for publication.     

Statistical Modelling of Vertical Soil Moisture Profile: Coupling of Memory and Forcing

Information of Soil Moisture Content (SMC) at different depths i.e. vertical Soil Moisture (SM) profile is important as it influences several hydrological processes. In the era of microwave remote sensing, spatial distribution of soil moisture information can be retrieved from satellite data for large basins. However, satellite data can provide only the surface (~0–10?cm) soil moisture information. In this study, a methodological framework is proposed to estimate the vertical SM profile knowing the information of SMC at surface layer. The approach is developed by coupling the memory component of SMC within a layer and the forcing component from soil layer lying above by an Auto-Regressive model with an exogenous input (ARX) where forcing component is the exogenous input. The study highlights the mutual reliance between SMC at different depths at a given location assuming the ground water table is much below the study domain. The methodology is demonstrated for three depths: 25, 50 and 80?cm using SMC values of 10?cm depth. Model performance is promising for all three depths. It is further observed that forcing is predominant than memory for near surface layers than deeper layers. With increase in depth, contribution of SM memory increases and forcing dissipates. Potential of the proposed methodology shows some promise to integrate satellite estimated surface soil moisture maps to prepare a fine resolution, 3-dimensional soil moisture profile for large areas, which is kept as future scope of this study.     

Application of Near-Infrared Spectroscopy for the Detection of Metanil Yellow in Turmeric Powder

Turmeric (Curcumina Longa) is a globally traded commodity which is subjected to economically motivated chemically unsafe adulteration, namely metanil yellow. In this work, we report a simplistic and convenient approach to find the adulteration of turmeric with metanil yellow by near-infrared (NIR) spectroscopy coupled with chemometrics. Pure turmeric sample was prepared in the laboratory and spiked with different concentrations of metanil yellow. The reflectance spectra of 248 pure turmeric, metanil yellow, and adulterated samples (1–25%) (w/w) were collected using NIR spectroscopy. The calibration models based on NIR spectra of 144 samples were built for two different regression models, principal component analysis (PCR), and partial least square (PLSR) methods. Another 72 samples were used for external validation. The coefficient of determination (R ²) and root mean square error of calibration for validation and prediction were found to be 0.96–0.99, 0.44–0.91, respectively, for most of the results depending upon different pre-processing techniques and mathematical models used. The original reflectance spectra, the 1st derivative plot, the plot of PLSR regression coefficient (β), and the first three principal component loadings revealed metanil-related absorption regions. To verify the robustness of the models, the figures of merit (FOM) of the models were calculated with the help of net analyte signal (NAS) theory. Overall, it was found that PLSR yielded superior results as compared to the PCR technique. These methods can be applied to other spices also to detect the adulteration rapidly and without any prior sample preparations and with low cost.     

Demographic and behavioral modifiers of arsenic exposure pathways: a Bayesian hierarchical analysis of NHEXAS data

We introduce a Bayesian hierarchical statistical model that describes subpopulation-specific pathways of exposure to arsenic. Our model is fitted to data collected as part of the National Human Exposure Assessment Survey (NHEXAS) and builds on the structural-equation-based analysis of the same data by Clayton et al. (Journal of Exposure Analysis and Environmental Epidemiology, 2002, 12, 29-43). Using demographic information (e.g., gender or age) and surrogates for environmental exposure (e.g., tobacco usage or the average number of minutes spent in an enclosed workshop), we identify subgroup differences in exposure routes. Missing and censored data, as well as uncertainty due to measurement error, are handled systematically in the Bayesian framework. Our analysis indicates that household size, amount of time spent at home, use of tapwater for drinking and cooking, number of glasses of water drunk, use of central air conditioning, and use of gas equipment significantly modify the arsenic exposure pathways.     

Dielectric and impedance spectroscopy of Bi(Ca0.5Ti0.5)O3 ceramic

Bismuth calcium titanate (BiCa_0.5Ti_0.5O₃) ceramic, fabricated by a ceramic processing technique, has been characterized using a variety of experimental techniques. Analysis of basic crystal structure using X-ray diffraction data exhibits the orthorhombic system. Measurements and detailed analysis of some electrical parameters (i.e.,dielectric constant, loss tangent (energy loss), electrical impedance and modulus, conductivity, etc.) of Bi(Ca_0.5Ti_0.5)O₃ in a wide range of frequency (10³–10⁶ Hz) and temperature (30–500 °C) have provided some interesting and useful data and results on structure–properties relationship, conduction mechanism, etc.The role of interface, space charge polarization and Maxwell–Wagner dielectric relaxation in getting high dielectric constant of the material at low frequencies and high temperatures has been discussed. Study of temperature dependence of Nyquist plots clearly shows the contributions of grains in resistive and capacitive properties of the material. The frequency of the applied electric field and temperature strongly affect the dielectric (permittivity and dissipation of energy) and electrical (impedance, electrical modulus and conductivity) characteristics of the material.     

Digital FIR filter design using fitness based hybrid adaptive differential evolution with particle swarm optimization

This paper presents an efficient way of designing linear phase finite impulse response (FIR) low pass and high pass filters using a novel algorithm ADEPSO. ADEPSO is hybrid of fitness based adaptive differential evolution (ADE) and particle swarm optimization (PSO). DE is a simple and robust evolutionary algorithm but sometimes causes instability problem; PSO is also a simple, population based robust evolutionary algorithm but has the problem of sub-optimality. ADEPSO has overcome the above individual disadvantages faced by both the algorithms and is used for the design of linear phase low pass and high pass FIR filters. The simulation results show that the ADEPSO outperforms PSO, ADE, and DE in combination with PSO not only in magnitude response but also in the convergence speed and thus proves itself to be a promising candidate for designing the FIR filters.