Shape-driven segmentation of the arterial wall in intravascular ultrasound images

Segmentation of arterial wall boundaries from intravascular images is an important problem for many applications in the study of plaque characteristics, mechanical properties of the arterial wall, its 3-D reconstruction, and its measurements such as lumen size, lumen radius, and wall radius. We present a shape-driven approach to segmentation of the arterial wall from intravascular ultrasound images in the rectangular domain. In a properly built shape space using training data, we constrain the lumen and media-adventitia contours to a smooth, closed geometry, which increases the segmentation quality without any tradeoff with a regularizer term. In addition to a shape prior, we utilize an intensity prior through a nonparametric probability-density-based image energy, with global image measurements rather than pointwise measurements used in previous methods. Furthermore, a detection step is included to address the challenges introduced to the segmentation process by side branches and calcifications. All these features greatly enhance our segmentation method. The tests of our algorithm on a large dataset demonstrate the effectiveness of our approach.     

Preparation and characterization of thermally conductive thermoplastic polyurethane/h‐BN nanocomposites

Polyurethane nanocomposites are versatile engineering polymers with unique properties. In this study, nano hexagonal boron nitride containing thermoplastic polyurethane elastomers were prepared via melt blending and hot‐pressing techniques. The nanocomposites were characterized using Fourier transform infrared, differential scanning calorimetry, thermal gravimetric analysis, tensile tests, and thermal conductivity measurements. The surface morphology of the TPU/h‐BN composites was characterized by scanning electron microscopy. The optical properties of the composites were determined by UV transmittance measurements and as the amount of h‐BN increased, optical transparencies decreased dramatically. Nanocomposites displayed higher E‐modulus values and lower elongation at break values than the pure TPU elastomer. Char yields of TPUs increased with increasing h‐BN percentage. Moreover, thermal conductivity of the composite materials improved with the incorporation of h‐BN. POLYM. COMPOS., 35:530–538, 2014. © 2013 Society of Plastics Engineers     

The hydro-abrasive erosion wear behavior of duplex-treated surfaces of AISI H13 tool steel

The influence of duplex surface treatments consisting of a DC-pulsed plasma nitriding process and subsequent coatings of CrN and TiAlN deposited by physical vapor deposition(PVD)on AISI H13 tool steel was studied in this article.The treated samples were characterized using metallographic techniques,SEM,EDS,and microhardness methods.Hydro-abrasive erosion wear tests were performed in a specifically designed wear tester in which the samples were rotated in a wear tank containing a mixture of distilled water and ceramic abrasive chips with a fixed rotational speed.The wear rates caused by the abrasive particle impacts were assessed based on accumulated weight loss measurements.The worn surfaces were also characterized using optical microscopy,SEM,and EDS.Microhardness measurements indicated a significant increase in the surface hardness of the duplex-treated samples.The surfaces of the samples with the TiAlN coating were approximately 15 times harder than that of the untreated samples and 3 times that of the plasma nitrided samples.Hydro-abrasive erosion wear results showed that the duplex surface treatments,especially the CrN coating,displayed the highest erosion wear resistance.     

Magnetically Guided Self‐Assembly and Coding of 3D Living Architectures

In nature, cells self‐assemble at the microscale into complex functional configurations. This mechanism is increasingly exploited to assemble biofidelic biological systems in vitro. However, precise coding of 3D multicellular living materials is challenging due to their architectural complexity and spatiotemporal heterogeneity. Therefore, there is an unmet need for an effective assembly method with deterministic control on the biomanufacturing of functional living systems, which can be used to model physiological and pathological behavior. Here, a universal system is presented for 3D assembly and coding of cells into complex living architectures. In this system, a gadolinium‐based nonionic paramagnetic agent is used in conjunction with magnetic fields to levitate and assemble cells. Thus, living materials are fabricated with controlled geometry and organization and imaged in situ in real time, preserving viability and functional properties. The developed method provides an innovative direction to monitor and guide the reconfigurability of living materials temporally and spatially in 3D, which can enable the study of transient biological mechanisms. This platform offers broad applications in numerous fields, such as 3D bioprinting and bottom‐up tissue engineering, as well as drug discovery, developmental biology, neuroscience, and cancer research.     

Effect of Fuel on the Synthesis and Properties of Poly(methyl methacrylate) Modified SrFe12O19 Nanoparticles

Nanosized strontium hexaferrite (SrFe₁₂O₁₉) has been synthesized by citrate, urea, oxalic, and glycine precursor via a sol-gel route with poly(methyl methacrylate) (PMMA) as a templating agent. Crystal structure, morphology, and magnetic properties of as-synthesized nanoparticles were characterized by XRD, SEM, FT-IR, and VSM techniques. The formation of strontium hexaferrite and its crystallite size in presence of different fuels were compared. The influence of different fuels was reflected on the phase purity, morphology of the final powders as well as the magnetic properties. Magnetic measurements revealed that samples prepared by citric acid and glycine as fuel have high specific saturation magnetization and moderate coercivity, while urea and oxalic acid fuels resulted in low phase purity, and thus inferior magnetic properties.     

Energy conservation in compressed‐air systems

In this paper, we evaluate and quantify the energy losses associated with compressed‐air systems, and their costs to manufacturers. We also show how to reduce the cost of compressed air in existing facilities by making some modifications with attractive payback periods. Among the measures, we investigate to reduce the compressed air are: (1) repairing air leaks, (2) installing high‐efficiency motors, (3) reducing the average air inlet temperature by using outside air (4) reducing compressor air pressure. We also illustrate the potential saving associated with each measure by using realistic examples. Copyright © 2002 John Wiley & Sons, Ltd.     

Improved prediction methods for scalable predictive animated mesh compression

Animated meshes represented as sequences of static meshes sharing the same connectivity require efficient compression. Among the compression techniques, layered predictive coding methods efficiently encode the animated meshes in a structured way such that the successive reconstruction with an adaptable quality can be performed. The decoding quality heavily depends on how well the prediction is performed in the encoder. Due to this fact, in this paper, three novel prediction structures are proposed and integrated into a state of the art layered predictive coder. The proposed structures are based on weighted spatial prediction with its weighted refinement and angular relations of triangles between current and previous frames. The experimental results show that compared to the state of the art scalable predictive coder, up to 30% bitrate reductions can be achieved with the combination of proposed prediction schemes depending on the content and quantization level.     

Single-frame super resolution of remote-sensing images by convolutional neural networks

Super resolution (SR) refers to generation of a high-resolution (HR) image from a decimated, blurred, low-resolution (LR) image set, which can be either a single-frame or multi-frame that contains a collection of images acquired from slightly different views of the same observation area. In this study, two convolutional neural network (CNN)-based deep learning techniques are adapted in single-frame SR to increase the resolution of remote sensing (RS) images by a factor of 2, 3, and 4. In order to both preserve the colour information and speed up the algorithm, first an intensity hue saturation (IHS) transform is utilized and the SR techniques are only applied to the intensity channel of the images. Colour information is then restored with an inverse IHS transformation. We demonstrate the results of the proposed method on RS images acquired from Satellites Pour l’Observation de la Terre (SPOT) or Earth-observing satellites and Pleiades satellites with different spatial resolution. First synthetic LR images are created by downsampling, then structural similarity (SSIM) Index, peak signal-to-noise ratio (PSNR), Spectral Angle Mapper (SAM) and Erreur Relative Globale Adimensionnelle de Synthese (ERGAS) values are calculated for a quantitative evaluation of the methods. Finally, the method, with better performance results, is tested within a real scenario, that is, with original LR images as the input. The obtained HR images demonstrated visible qualitative enhancements.