A new multiscale Bayesian algorithm for speckle reduction in medical ultrasound images

This paper introduces a new multiscale speckle reduction method based on the extraction of wavelet interscale dependencies to visually enhance the medical ultrasound images and improve clinical diagnosis. The logarithm of the image is first transformed to the oriented dual-tree complex wavelet domain. It is then shown that the adjacent subband coefficients of the log-transformed ultrasound image can be successfully modeled using the general form of bivariate isotropic stable distributions, while the speckle coefficients can be approximated using a zero-mean bivariate Gaussian model. Using these statistical models, we design a new discrete bivariate Bayesian estimator based on minimizing the mean square error (MSE). To assess the performance of the proposed method, four image quality metrics, namely signal-to-noise ratio, MSE, coefficient of correlation, and edge preservation index, were computed on 80 medical ultrasound images. Moreover, a visual evaluation was carried out by two medical experts. The numerical results indicated that the new method outperforms the standard spatial despeckling filters, homomorphic Wiener filter, and new multiscale speckle reduction methods based on generalized Gaussian and symmetric alpha-stable priors.     

Selective adsorption of ketoconazole from aqueous solutions using a new molecularly imprinted polyurethane coated magnetic multiwall carbon nanotubes

New magnetic molecularly imprinted polyurethanes based on magnetic multi-walled carbon nanotubes (MMWNTs-MIPUs) were synthesized with specific selectivity toward ketoconazole (KTZ) as an anti-fungal drug. First, novel N-(4-carboxyphenyl) trimellitimide diisocyanate (NTDIS) was prepared from the reaction of trimellitic anhydride and 4-amino benzoic acid in two steps. Then, NTDIS was functionalized by β-cyclodextrin and methacrylic acid (MAA-NTDIS-β-CD). MAA-NTDIS-β-CD was used as a functional monomer, KTZ as a template, ethylene glycol dimethacrylate (EGDMA) as a cross-linking agent and 2,2′-azobisisobutyronitrile (AIBN) as initiator. Structure and properties of the prepared compounds were characterized by FTIR, ¹H NMR, FESEM, XRD, VSM, BET, and EDX. The influence of parameters such as solution pH, contact time, temperature, and initial concentrations in controlled absorption of KTZ using MMWCNTs-MIPU and MMWCNTs-NIPU were evaluated. The kinetic data were measured using the pseudo-first-order and pseudo-second-order equation. The pseudo-second-order equation displayed the best fit for the kinetic studies (R² 0.9958). The adsorption equilibrium of KTZ using MMWCNTs-MIPU could be well-defined with the Langmuir isotherm model, and the maximum adsorption capacity was calculated as 52.35 mg g^–1. The prepared MMWCNTs-MIPU can be simply separated by an external magnetic field and, MMWCNTs-MIPU can be used after six filtration-regeneration cycles.

     

Deep convolution network for surveillance records super-resolution

Multimedia Tools and Applications - The aim of image super resolution (SR) is to recover low resolution (LR) input image or video to a visually desirable high-resolution (HR) one. The task of...     

Synthesis and characterization of conductive neural tissue engineering scaffolds based on urethane-polycaprolactone

Polymeric biomaterials play a key role in enhancement of lengthy nerve regeneration and various types of scaffolds were used to pave the way for nerve regeneration. Electrospun fibrous scaffolds have special potential applicability in controlling the cell behaviors such as adhesion, growth, proliferation and function. This study attempted to design a conductive and porous fibrous scaffold containing polycaprolactone (PCL) and polyaniline (PANI) with controllable degradation rate by adding urethane groups in scaffold structures. FTIR and NMR analysis was used to characterize the chemical bonds. Morphology, porosity, conductivity and degradation rate of scaffolds were also evaluated. To assess the cell–scaffold interaction, PC-12 cell line was cultured on the scaffolds. Results showed that the degradation rate of composite samples significantly increased in 50 time period. It seems that these results suggest that the composite fibrous scaffolds having proportions of UPCL/PCL/PANI45:20:35 exhibit the most balanced properties that meet all of the required specifications for neural cells and possess a potential application in neural tissue engineering.     

Deep semantic preserving hashing for large scale image retrieval

Multimedia Tools and Applications - Hashing approaches have got a great attention because of its efficient performance for large-scale images. This paper, aims to propose a deep hashing method...     

A multi-stage stochastic programming approach for production planning with uncertainty in the quality of raw materials and demand

Motivated by the challenges encountered in sawmill production planning, we study a multi-product, multi-period production planning problem with uncertainty in the quality of raw materials and consequently in processes yields, as well as uncertainty in products demands. As the demand and yield own different uncertain natures, they are modelled separately and then integrated. Demand uncertainty is considered as a dynamic stochastic data process during the planning horizon, which is modelled as a scenario tree. Each stage in the demand scenario tree corresponds to a cluster of time periods, for which the demand has a stationary behaviour. The uncertain yield is modelled as scenarios with stationary probability distributions during the planning horizon. Yield scenarios are then integrated in each node of the demand scenario tree, constituting a hybrid scenario tree. Based on the hybrid scenario tree for the uncertain yield and demand, a multi-stage stochastic programming (MSP) model is proposed which is full recourse for demand scenarios and simple recourse for yield scenarios. We conduct a case study with respect to a realistic scale sawmill. Numerical results indicate that the solution to the multi-stage stochastic model is far superior to the optimal solution to the mean-value deterministic and the two-stage stochastic models.     

Encapsulation of palladium nanoparticles by multiwall carbon nanotubes‐graft‐poly(citric acid) hybrid materials

Citric acid was polymerized onto the surface of functionalized multiwall carbon nanotubes (MWCNT‐COOH) and MWCNT‐graft‐poly(citric acid) (MWCNT‐g‐PCA) hybrid materials were obtained. Due to the grafted poly(citric acid) branches, MWCNT‐g‐PCA hybrid materials not only were soluble in water but also were able to trap water soluble metal ions. Reduction of trapped metal ions in the polymeric shell of MWCNT‐g‐PCA hybrid materials by reducing agents such as sodium borohydride led to encapsulated metal nanoparticles on the surface of MWCNT. Herein palladium nanoparticles were encapsulated and transported by MWCNT‐g‐PCA hybrid materials (MWCNT‐g‐PCA‐EPN) and their application as nanocatalyst toward Heck reaction in different conditions was investigated. The catalytic activity of palladium ions supported by MWCNT‐g‐PCA hybrid materials (MWCNT‐g‐PCA‐PdCl₂) toward Heck reactions is much more than for MWCNT‐g‐PCA‐EPN. Structure, characteristics and catalytic activity of synthesized systems was investigated using spectroscopy and microscopy methods. © 2010 Wiley Periodicals, Inc. J Appl Polym Sci, 2010     

Trends and challenges of biopolymer-based nanocomposites in food packaging

The ultimate goal of new food packaging technologies, in addition to maintaining the quality and safety of food for the consumer, is to consider environmental concerns and reduce its impacts. In this regard, one of the solutions is to use eco-friendly biopolymers instead of conventional petroleum-based polymers. However, the challenges of using biopolymers in the food packaging industry should be carefully evaluated, and techniques to eliminate or minimize their disadvantages should be investigated. Many studies have been conducted to improve the properties of biopolymer-based packaging materials to produce a favorable product for the food industry. This article reviews the structure of biopolymer-based materials and discusses the trends and challenges of using these materials in food packaging technologies with the focus on nanotechnology and based on recent studies.     

Electromagnetic properties of electrospun Fe3O4/carbon composite nanofibers

In order to develop multifunctional nanofibers, the electrical conductivity and magnetic properties of Fe₃O₄/carbon composite nanofibers have been examined. Polyacrylonitrile (PAN) is used as a matrix to produce magnetic composite nanofibers containing different amounts of magnetite (Fe₃O₄) nanoparticles. Electrospun composite nanofibers were thermally treated to produce electrically conductive and magnetically permeable composite carbon nanofibers. The composite nanofibers were characterized using scanning electron microscopy (SEM), transmission electron microscopy (TEM), X-ray diffractometry (XRD), Raman spectroscopy, four-point probe and Superconducting Quantum Interference Device (SQUID). Uniform nanofibers were obtained with successful transferring of magnetic properties of Fe₃O₄ into the as-spun composite nanofibers. The electromagnetic properties were tuned by adjusting the amount of Fe₃O₄ in the matrix and carbonization process. The electrical conductivity, magnetic moment and also magnetic hysteresis rise up by adding Fe₃O₄ and increasing carbonization temperature. The high surface area provided by the ultrafine fibrous structures, the flexibility and tuneable electromagnetic properties are expected to enable the expansion of the design options for a wide rage of electronic devices.