Solvothermal synthesis,nanostructural characterization and gas cryo-adsorption studies in a metal–organic framework (IRMOF-1) material

A nanoporous metal–organic framework material, exhibiting an IRMOF-1 type crystalline structure, was prepared by following a direct solvothermal synthesis approach, using zinc nitrate and terephthalic acid as precursors and dimethylformamide as solvent, combined with supercritical CO₂ activation and vacuum outgassing procedures. A series of advanced characterization methods were employed, including scanning electron microscopy, Fourier-transform infrared radiation spectroscopy and X-ray diffraction, in order to study the morphology, surface chemistry and structure of the IRMOF-1 material directly upon its synthesis. Porosity properties, such as Brunauer–Emmet–Teller (BET) specific area (～520 m²/g) and micropore volume (～0.2 cm³/g), were calculated for the activated sample based on N₂ gas sorption data collected at 77 K. The H₂ storage performance was preliminary assessed by low-pressure (0–1 bar) H₂ gas adsorption and desorption measurements at 77 K. The activated IRMOF-1 material of this study demonstrated a fully reversible H₂ sorption behavior combined with an adequate gravimetric H₂ uptake relative to its BET specific area, thus achieving a value of ～1 wt.% under close-to-atmospheric pressure conditions.     

Multiscale quality assessment of Global Human Settlement Layer scenes against reference data using statistical learning

A method for quality assessment of the Global Human Settlement Layer scenes against reference data is presented. It relies on two settlement metrics; the local average and gradient functions that quantify the notions of settlement density and flexible settlement limits respectively. They are both utilized as generalization functions for increasing the level of abstraction of the sets under comparison. Generalization compensates for inaccuracies of the automatic target extraction method and can be computed at multiple scales. The comparison between the target built-up layers and the reference data employs an ordered multi-scale, linear regression computing the goodness of fit measure R²$R^2$. An optimized assessment procedure is investigated in a pilot study and is further employed in a big data exercise. A newly introduced quality metric returns the agreement between automatically extracted built-up from a set of 13605 scenes and the MODIS 500 urban layer, that was found too be as high as 91% for selected sensors. A final experiment attempts a performance increase at lower scales by correlating the target layer with automatically selected training subsets. At 50 m the adjusted R²$R^2$ increases by 3% with a mean squared error improvement of 2% compared to the performance achieved without statistical learning. The experiment suggests that the GHSL assessment at a global scale can be carried out based on limited high resolution reference data of minimal spatial coverage.     

The Weibull manifold in low-level image processing: An application to automatic image focusing

In this paper, we introduce a novel framework for low-level image processing and analysis. First, we process images with very simple, difference-based filter functions. Second, we fit the 2-parameter Weibull distribution to the filtered output. This maps each image to the 2D Weibull manifold. Third, we exploit the information geometry of this manifold and solve low-level image processing tasks as minimisation problems on point sets. For a proof-of-concept example, we examine the image autofocusing task. We propose appropriate cost functions together with a simple implicitly-constrained manifold optimisation algorithm and show that our framework compares very favourably against common autofocus methods from literature. In particular, our approach exhibits the best overall performance in terms of combined speed and accuracy.     

Multi-objective performance optimisation for model predictive control by goal attainment

This article proposes an approach for performance tuning of model predictive control (MPC) using goal-attainment optimisation of the cost function weighting matrices. The approach is developed for three formulations of the control problem: (i) minimal and (ii) non-minimal design based on the same cost function and (iii) a non-minimal MPC approach with an explicit integral-of-error state variable and modified cost function. This approach is based on earlier research into multi-objective optimisation for proportional-integral-plus control systems. Simulation experiments for a 3-input, 3-output Shell heavy oil fractionator model illustrate the feasibility of MPC goal attainment for multivariable decoupling and attainment of a specific output response. For this example, the integral-of-error state variable offers improved design flexibility and hence, when it is combined with the proposed tuning method, yields an improved closed-loop response in comparison to minimal MPC.     

Time series analysis with multiple resolutions

We introduce a new representation for time series, the Multiresolution Vector Quantized (MVQ) approximation, along with a distance function. Similar to Discrete Wavelet Transform, MVQ keeps both local and global information about the data. However, instead of keeping low-level time series values, it maintains high-level feature information (key subsequences), facilitating the introduction of more meaningful similarity measures. The method is fast and scales linearly with the database size and dimensionality. Contrary to previous methods, the vast majority of which use the Euclidean distance, MVQ uses a multiresolution/hierarchical distance function. In our experiments, the proposed technique consistently outperforms the other major methods.     

An adaptive partitioning approach for mining discriminant regions in 3D image data

Mining discriminative spatial patterns in image data is an emerging subject of interest in medical imaging, meteorology, engineering, biology, and other fields. In this paper, we propose a novel approach for detecting spatial regions that are highly discriminative among different classes of three dimensional (3D) image data. The main idea of our approach is to treat the initial 3D image as a hyper-rectangle and search for discriminative regions by adaptively partitioning the space into progressively smaller hyper-rectangles (sub-regions). We use statistical information about each hyper-rectangle to guide the selectivity of the partitioning. A hyper-rectangle is partitioned only if its attribute cannot adequately discriminate among the distinct labeled classes, and it is sufficiently large for further splitting. To evaluate the discriminative power of the attributes corresponding to the detected regions, we performed classification experiments on artificial and real datasets. Our results show that the proposed method outperforms major competitors, achieving 30% and 15% better classification accuracy on synthetic and real data respectively while reducing by two orders of magnitude the number of statistical tests required by voxel-based approaches.     

Feather‐inspired carbon fiber reinforced polymers with nanofibrous fractal interlayer

Much work has been performed in improving carbon fiber‐reinforced polymer (CFRP) composites to prevent delamination, which is the major defect in laminated composites. Nevertheless, there is not much focus on improving conventional CFRP systems in terms of weight, especially when these are used in primary structures. This article explores whether lighter and at the same time stronger CFRP composites can be manufactured to replace conventional CFRP systems in major applications. Under this perspective, and having established the fundamentals for creating the next generation of light weight structural composites—the featherweight composites—this work introduces a feather‐inspired case which uses a controlled interlayer reinforcement in a fractal and reproducible manner at the macro‐, micro‐, and nano‐scales. By extensively describing the matrix system and the manufacturing processes and focusing on analytically and thermomechanically testing the CNT (Carbon Nanotubes) reinforced nanofiber interlayer system, it is shown that this feather‐inspired CFRP achieves significantly higher mechanical properties as well as potential weight savings. POLYM. COMPOS., 168–181, 2016. © 2014 Society of Plastics Engineers     

Still Image Processing on Coarse-Grained Reconfigurable Array Architectures

Due to the increasing demands on efficiency, performance and flexibility reconfigurable computational architectures are very promising candidates in embedded systems design. Recently coarse-grained reconfigurable array architectures (CGRAs), such as the ADRES CGRA and its corresponding DRESC compiler are gaining more popularity due to several technological breakthroughs in this area. We investigate the mapping of two image processing algorithms, Wavelet encoding and decoding, and TIFF compression on this novel type of array architectures in a systematic way. The results of our experiments show that CGRAs based on ADRES and its DRESC compiler technology deliver improved performance levels for these two benchmark applications when compared to results obtained on a state-of-the-art commercial DSP platform, the c64x DSP from Texas Instruments. ADRES/DRESC can beat its performance by at least 50% in cycle count and the power consumption even drops to 10% of the published numbers of the c64x DSP.     

Studies on the usability of recycled PET for food packaging applications

The need of and opportunities for recycling of plastics for food packaging have been recognized, and a lot of work to find meaningful and cost-effective solutions to this issue is in progress. The safety of recycled plastics for food contact use is largely dictated by the ability of post-consumer contaminants to absorb into recycled materials and later diffuse from recycled plastics into the food. The objective of the present study was to establish a suitable analytical approach to identifying and quantifying any chemical substances that derive from the earlier use and remain in the polyethylene terephthalate (PET). A simple gas chromatographic technique using flame ionization detection was developed to allow quantification of solvent extractable compounds in a series of recycled PET samples. Identification of the nature and extent of contaminants in the PET samples was also attempted using GC/MS analysis.