An improved multimodal medical image fusion scheme based on hybrid combination of nonsubsampled contourlet transform and stationary wavelet transform

This research proposes an improved hybrid fusion scheme for non-subsampled contourlet transform (NSCT) and stationary wavelet transform (SWT). Initially, the source images are decomposed into different sub-bands using NSCT. The locally weighted sum of square of the coefficients based fusion rule with consistency verification is used to fuse the detailed coefficients of NSCT. The SWT is employed to decompose approximation coefficients of NSCT into different sub-bands. The entropy of square of the coefficients and weighted sum-modified Laplacian is employed as the fusion rules with SWT. The final output is obtained using inverse NSCT. The proposed research is compared with existing fusion schemes visually and quantitatively. From the visual analysis, it is observed that the proposed scheme retained important complementary information of source images in a better way. From the quantitative comparison, it is seen that this scheme gave improved edge information, clarity, contrast, texture, and brightness in the fused image.     

Grappling with modern technology: interruptions mediated by mobile devices impact older workers disproportionately

Information Systems and e-Business Management - Mobile technologies have dramatically increased the number of work-related interruptions. In many organizations, employees must remain accessible and...     

Intelligent heart disease prediction in cloud environment through ensembling

Cloud computing is the delivery of on‐demand computing resources. Cloud computing has numerous applications in fields of education, social networking, and medicine. But the benefit of cloud for medical purposes is seamless, particularly because of the enormous data generated by the health care industry. This colossal data can be managed through big data analytics, and hidden patterns can be extracted using machine learning procedures. In particular, the latest issue in the medical domain is the prediction of heart diseases, which can be resolved through culmination of machine learning and cloud computing. Hence, an attempt has been made to propose an intelligent decision support model that can aid medical experts in predicting heart disease based on the historical data of patients. Various machine learning algorithms have been implemented on the heart disease dataset to predict accuracy for heart disease. Naïve Bayes has been selected as an effective model because it provides the highest accuracy of 86.42% followed by AdaBoost and boosted tree. Further, these 3 models are being ensembled, which has increased the overall accuracy to 87.91%. The experimental results have also been evaluated using 10,082 instances that clearly validate the maximum accuracy through ensembling and minimum execution time in cloud environment.     

Performance analysis of a variable‐gain amplify‐and‐forward relayed mixed RF‐FSO system

In this work, an amplify‐and‐forward variable‐gain relayed mixed RF‐FSO system is studied. The considered dual‐hop system consists of a radio frequency (RF) link followed by a free space optical (FSO) channel. The RF link is affected by short‐term multipath fading and long‐term shadowing effects and is assumed to follow the generalized‐K fading distribution that approximates accurately several important distributions often used to model communication channels. The FSO channel experiences fading caused by atmospheric turbulence that is modeled by the gamma‐gamma distribution characterizing moderate and strong turbulence conditions. The FSO channel also suffers path loss and pointing error induced misalignment fading. The performance of the considered system is analyzed under the collective influence of distribution shaping parameters, pointing errors that result in misalignment fading, atmospheric turbulence, and path loss. The moment‐generating function of the Signal power to noise power ratio measured end‐to‐end for this system is derived. The cumulative distribution function for the Signal power to noise power ratio present between the source and destination receiver is also evaluated. Further, we investigate the error and outage performance and the average channel capacity for this system. The analytical expressions in closed form for the outage probability, symbol and bit error rate considering different modulation schemes and channel capacity are also derived. The mathematical expressions obtained are also demonstrated by numerical plots.     

Buccal bioadhesive delivery system of 5-fluorouracil: optimization and characterization

The objective of this work was to apply the response surface approach in the development of buccal bioadhesive tablets of 5-fluorouracil (5-FU). Experiments were performed according to a 3(2) factorial design to evaluate the effects of two polymers, Gantrez MS-955 (X(1)) and hydroxypropylmethyl cellulose (HPMC) K15M (X(2)) on the bioadhesive force, percentage drug release in 8 h (Rel(8 h)), time taken for 50% drug release (t(50%)), and diffusion coefficient (n). The effect of the two independent variables on the response variables was studied by response surface plots and contour plots generated by the Design Expert software. The compatibility between 5-FU and the tablet excipients was confirmed by differential scanning calorimetry (DSC) and Fourier transform infrared (FTIR) studies. Both the polymers were found to have synergistic effect on bioadhesion but the effect of Gantrez was more pronounced. A nonlinear twisted relationship was obtained for Rel(8 h) at the intermediate and high levels of the polymers, which indicated an interaction between them at the corresponding factor levels. Kinetic treatment to the dissolution profiles revealed that the drug release ranged from Fickian to anomalous transport, which was mainly dependent on both the independent variables. The desirability function was used to optimize the response variables, and the observed responses were in agreement with the experimental values.     

The importance of strong carbon-metal adhesion for catalytic nucleation of single-walled carbon nanotubes

Density functional theory is used to show that the adhesion between single-walled carbon nanotubes (SWNTs) and the catalyst particles from which they grow needs to be strong to support nanotube growth. It is found that Fe, Co, and Ni, commonly used to catalyze SWNT growth, have larger adhesion strengths to SWNTs than Cu, Pd, and Au and are therefore likely to be more efficient for supporting growth. The calculations also show that to maintain an open end of the SWNT it is necessary that the SWNT adhesion strength to the metal particle is comparable to the cap formation energy of the SWNT end. This implies that the difference between continued and discontinued SWNT growth to a large extent depends on the carbon-metal binding strength, which we demonstrate by molecular dynamics (MD) simulations. The results highlight that first principles computations are vital for the understanding of the binding strength's role in the SWNT growth mechanism and are needed to get accurate force field parameters for MD.     

Rheology of starch dispersions at high temperatures

In the food industry, many food products experience extreme processing conditions of high temperature and high shear stresses. The measurements of sample behavior for water-based formulations above 100°C is extremely challenging due to changes in material composition from the boiling of volatile ingredients. We have developed a high-sensitivity, pressurized starch pasting cell (up to 5 bar) which utilizes a design free of mechanical bearings and seals, resulting in an order-of-magnitude improvement in torque sensitivity (1 μN.m in oscillatory and 10 μN.m in shear flows) compared to traditional pressure cells. A pressurized atmosphere in the cell suppresses boiling of the volatile components, allowing the characterization of the structure–property relationships of the sample over a range of testing conditions (−5 to 150°C) which simulate industrial processing and storage conditions. This cell is employed to investigate the pasting properties of a commercial starch dispersed in water. In situ gelatinization of starch dispersions of varying starch particle weight fractions (ϕ) subjected to a high temperature (120°C) at elevated pressure and at a fixed shear rate is studied. A phase transition, from an initial flowable starch slurry to a paste, takes place during which the viscosity evolves by several orders of magnitude. Typical parameters associated with the viscosity evolution during gelatinization such as onset temperature, peak temperature, and peak viscosity are analyzed to probe the impact of high temperature on the gelation process and the rheological properties of the final starch paste. Furthermore, yield stresses of the final paste, measured at 120°C, are examined for varying ϕ through traditional rheological methods such as flow ramps, oscillatory shear, and stress growth, demonstrating the capabilities of this cell for studies of steady shear and nonlinear viscoelastic behavior of the starch pastes. The yield stress values are found to be in good agreement when comparing various testing methods. Yield stresses range from 0.25 to 6.5 Pa for ϕ between 0.05 and 0.15, with 0.05 being the minimum starch weight fraction for which there is any measurable yield stress. The yield stress and the paste viscosity both scale with starch particle weight fraction as (ϕ − ϕ_c) ^m, where ϕ_c = 0.04 as no yield stress is observed for ϕ ≤ 0.04. The exponent, m, for yield stress is found to be in the range of 1.15–1.4 depending on the analytical method used and the definition of yield stress while for peak and breakdown viscosities it is noted to be 1.6 and 1.1, respectively. The Herschel-Bulkley model is found to fit the flow curves well. The starch pastes are found to exhibit shear-thinning and significant thixotropic behavior.     

Automated image registration by maximization of a region similarity metric

This article presents a robust algorithm for automated registration of images related by rigid-body transformations. This algorithm uses a new region-based similarity metric, which enables accurate registration of images of large contrast differences. Region segmentation required by the metric is accomplished using a multiscale segmentation algorithm, and minimization of this metric is done using the Powell direction set method. Experimental results are presented to demonstrate that the algorithm is effective for aligning images from single or multiple imaging modalities without the use of any fiducial markers. © 1997 John Wiley & Sons, Inc. Int J Imaging Syst Technol, 8, 513–518, 1997     

On Development of Functionally Graded Material Through Fused Deposition Modelling Assisted Investment Casting from Al<Subscript>2</Subscript>O<Subscript>3</Subscript>/SiC Reinforced Waste Low Density Polyethylene

The recycling of packaging materials such as low density polyethylene (LDPE) into useful product is one of the challenging tasks. Since waste LDPE has some issues like low mechanical strength and thermal degradation; some studies have been reported in recent past to improve these properties with ceramic/metallic reinforcements. In this work reusability of LDPE has been ascertained as functionally graded material (FGM) through aluminum (Al) matrix based investment casting (IC). This study highlights the use of SiC and Al₂O₃ as reinforcement in LDPE for IC applications as a novel method for development of FGM. The master patterns for IC were prepared from reinforced LDPE based feed stock filament (prepared on conventional screw extruder) on open source fused deposition modelling setup. The in-house prepared filament wire was subjected to mechanical and thermal testing to ensure recyclability and stability of the material. The photo micrographs and SEM images were collected to ensure the dispersion of SiC and Al₂O₃ reinforcements in Al based FGM.