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.     

Low-overhead video compression combining partial discrete cosine transform and compressed sensing in WMSNs

Wireless multimedia sensor network (WMSN) is a special wireless sensor network (WSN) made up of several multimedia sensor nodes, specially designed to retrieve multimedia content such as video and audio streams, still images, and scalar sensor data from the environment. Due to strict inherent limitations in terms of processing power, storage and bandwidth, data processing is a challenge in such network. Further, energy is one of the scarcest resources in WSN, especially in WMSN and therefore, saving energy is of utmost importance. Data compression is one of the solutions of such a problem. This paper proposes an energy saving video compression technique for WMSN by judicious combination of partial discrete cosine transform and compressed sensing. This amalgamation exploits the benefits of both the techniques towards fulfilling the objective of saving energy along with achieving desired peak signal to noise ratio (PSNR). When the transform technique ensures low-overhead compression, compressed sensing guarantees the reconstruction of the same video with lesser amount of measurements. Performance of the scheme is measured both qualitatively and quantitatively. In qualitative analysis, overhead of the scheme is measured in terms of storage, computation, and communication overheads and the results are compared with a number of existing schemes including the base scheme. The results show considerable reduction of all such overheads thereby justifying the appropriateness of the proposed scheme for resource-constrained networks like WMSNs. In quantitative analysis, for both ideal and packet loss environment, the scheme is simulated in Cooja, the Contiki network simulator to make it readily implementable in real life mote e.g. MICAz. When compared with the existing state-of-the-art schemes, it performs better not only in terms of 34.31% energy saving but also in getting an acceptable PSNR of 35–37 dB and SSIM of 0.85–0.88 in ideal environment. In packet loss environment, these values are 32.9–35.5 dB and 0.81–0.85 respectively implying acceptable reconstruction even in packet loss environment. Further, it requires the least storage of 51.2 KB. The observation on simulation results is also justified by statistical analysis.

     

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.     

System testing for object‐oriented systems with test case prioritization

This paper presents an approach to generate test cases from UML 2.0 sequence diagrams and subsequently prioritize those test cases using model information encapsulated in the sequence diagrams. The test cases generated according to the proposed approach satisfy the scenario coverage criterion and are suitable for system‐level testing. For prioritizing test cases, three different prioritization metrics are proposed. The values of these prioritization metrics can be analytically computed from the model information only. This paper also presents an approach to generate test data using a concept called rule‐based matrix. The prioritization metrics are used to control the number of test data without compromising the test adequacy. The effectiveness of the proposed approach has been verified using two industrial designs. Copyright © 2009 John Wiley & Sons, Ltd.     

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.     

Botnet traffic identification using neural networks

Advancement of information and communication techniques have led to share big amount of information which is increasing day by day through online activities and creating new added value over the internet services. At the same time threats to the security of cyber world has been increased with increasing number of heterogeneous connection points having powerful computational capacity. Internet being used to interact and control such automatic network devices connected to it. But hackers/crackers can exploit this network environment by putting malicious dummy node(s) or machine(s) called Botnet(s) to co-ordinate the attacks on security such as Denial of Service (DoS) or Distributed Denial of Service (DDoS). The proposed method attempts to identify those mallicious Botnet traffic from regular traffic using novel deep learning approaches like Artificial Neural Networks (ANN), Gatted Recurrent Units (GRU), Long or Short Term Memory (LSTM) model. The proposed model demonstrates significant improvement of all previous works. The testing dataset, Bot-IoT dataset is the latest and one of the largest public domain dataset used to justify improvement. Testing shows 99.7% classification accuracy which is precise and better than all previous works done. Results analysis and comparison shows the accuracy and supremacy over the latest work done on this field.