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.     

Optimizations of the naïve-Bayes classifier for the prognosis of B-Chronic Lymphocytic Leukemia incorporating flow cytometry data

Prognosis of B-Chronic Lymphocytic Leukemia (B-CLL) remains a challenging problem in medical research and practice. While the parameters obtained by flow cytometry analysis form the basis of the diagnosis of the disease, the question whether these parameters offer additional prognostic information still remains open. In this work, we attempt to provide computer-assisted support to the clinical experts of the field, by deploying a classification system for B-CLL multiparametric prognosis that combines various heterogeneous (clinical, laboratory and flow cytometry) parameters associated with the disease. For this purpose, we employ the na?ve-Bayes classifier and propose an algorithm that improves its performance. The algorithm discretizes the continuous classification attributes (candidate prognostic parameters) and selects the most useful subset of them to optimize the classification accuracy. Thus, in addition to the high classification accuracy achieved, the proposed approach also suggests the most informative parameters for the prognosis. The experimental results demonstrate that the inclusion of flow cytometry parameters in our system improves prognosis.     

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.     

Interlayer bonding between thermoplastic composites and metals by in-situ polymerization technique

Fiber-metal laminates (FMLs) offer the superior characteristics of polymer composites (i.e., light weight, high strength and stiffness) with the ductility and fracture strength of metals. The bond strength between the two dissimilar materials, composite and metal, dictates the properties and performance of the FMLs. The bonding becomes more critical when the polymer matrix is thermoplastic and hydrophobic in nature. This work employed a novel bonding technique between thermoplastic composites and a metal layer using six different combinations of organic coatings. The flexural, and interlaminar shear strength of the thermoplastic fiber metal laminates (TP-FMLs) were examined to investigate the bond strengths in the different cases along with fracture characteristics revealed from the tested samples using scanning electron microscopy. The viscoelastic performance of the fabricated TP-FMLs were also investigated using the dynamic mechanical thermal analysis method.     

Improving through-thickness conductivity of carbon fiber reinforced polymer using carbon nanotube/polyethylenimine at the interlaminar region

The through-thickness conductivity of carbon fiber reinforced polymer (CFRP) composite was increased by incorporating multiwalled carbon nanotubes in the interlaminar region. Carbon nanotubes (CNTs) were dispersed in a polyethylenimine (PEI) binder, which was then coated onto the carbon fiber fabric. Standard vacuum-assisted resin infusion process was applied to fabricate the composite laminates. This modification technique aims to enhance the electrical conductivity in through-thickness direction for the purpose of nondestructive testing, damage detection, and electromagnetic interference shielding. CNT concentrations ranging from 0 to 0.75 wt% were used and compared to pristine CFRP samples (reference). The through-thickness conductivity of the CFRP exhibited an improvement of up to 781% by adopting this technique. However, the dispersion of CNT in PEI led to a viscosity increase and poor wetting properties which resulted in the formation of voids/defects, poor adhesion (as shown in scanning electron micrographs) and the deterioration of the mechanical properties as manifested by interlaminar shear strength and dynamic mechanical analysis measurements.