Robust control of plants with saturation nonlinearity based on coprime factor representations

We consider the robust control of plants with saturation nonlinearities from an input/output viewpoint. First, we present a parameterization for anti-windup control based on coprime factorizations of the controller. Second, we propose a synthesis method which exploits the freedom to choose a particular coprime factorization.

Epigenetic inheritance in plants.

The function of plant genomes depends on chromatin marks such as the methylation of DNA and the post-translational modification of histones. Techniques for studying model plants such as Arabidopsis thaliana have enabled researchers to begin to uncover the pathways that establish and maintain chromatin modifications, and genomic studies are allowing the mapping of modifications such as DNA methylation on a genome-wide scale. Small RNAs seem to be important in determining the distribution of chromatin modifications, and RNA might also underlie the complex epigenetic interactions that occur between homologous sequences. Plants use these epigenetic silencing mechanisms extensively to control development and parent-of-origin imprinted gene expression.

Enzyme-free glucose biosensor based on low density CNT forest grown directly on a Si/SiO2 substrate

A highly sensitive nonenzymatic amperometric glucose sensor was fabricated by using Ni nanoparticles homogeneously dispersed within and on the top of a vertically aligned CNT forest (CNT/Ni nanocomposite sensor), which was directly grown on a Si/SiO2 substrate. The surface morphology and elemental analysis were characterized using scanning electron microscopy and energy dispersive spectroscopy, respectively. Cyclic voltammetry and chronoamperometry were used to evaluate the catalytic activities of CNT/Ni electrode. The CNT/Ni nanocomposite sensor exhibited a great enhancement of anodic peak current after adding 5 mM glucose in alkaline solution. The sensor can also be applied to the quantification of glucose content with a linear range covering from 5 μM to 7 mM, a high sensitivity of 1433 μA mM-1 cm-2, and a low detection limit of 2 μM. The CNT/Ni nanocomposite sensor exhibits good reproducibility and long-term stability, moreover, it was also relatively insensitive to commonly interfering species, such as uric acid, ascorbic acid, acetaminophen, sucrose and d-fructose. © 2013 Elsevier B.V.

High contrast holograms using nanotube forest

Nanotube forest behaves as highly absorbent material when they are randomly placed in sub-wavelength scales. Furthermore, it is possible to create diffractive structures when these bulks are patterned in a substrate. Here, we introduce an alternative to fabricate intensity holograms by patterning fringes of nanotube forest on a substrate. The result is an efficient intensity hologram that is not restricted to sub-wavelength patterning. Both the theoretical and experimental analysis was performed with good agreement. The produced holograms show a uniform behaviour throughout the visible spectra. © 2013 AIP Publishing LLC.

Unconstrained monocular 3D human pose estimation by action detection and cross-modality regression forest

This work addresses the challenging problem of unconstrained 3D human pose estimation (HPE) from a novel perspective. Existing approaches struggle to operate in realistic applications, mainly due to their scene-dependent priors, such as background segmentation and multi-camera network, which restrict their use in unconstrained environments. We therfore present a framework which applies action detection and 2D pose estimation techniques to infer 3D poses in an unconstrained video. Action detection offers spatiotemporal priors to 3D human pose estimation by both recognising and localising actions in space-time. Instead of holistic features, e.g. silhouettes, we leverage the flexibility of deformable part model to detect 2D body parts as a feature to estimate 3D poses. A new unconstrained pose dataset has been collected to justify the feasibility of our method, which demonstrated promising results, significantly outperforming the relevant state-of-the-arts. © 2013 IEEE.

The Random Forest Kernel and other kernels for big data from random partitions

We present Random Partition Kernels, a new class of kernels derived by demonstrating a natural connection between random partitions of objects and kernels between those objects. We show how the construction can be used to create kernels from methods that would not normally be viewed as random partitions, such as Random Forest. To demonstrate the potential of this method, we propose two new kernels, the Random Forest Kernel and the Fast Cluster Kernel, and show that these kernels consistently outperform standard kernels on problems involving real-world datasets. Finally, we show how the form of these kernels lend themselves to a natural approximation that is appropriate for certain big data problems, allowing $O(N)$ inference in methods such as Gaussian Processes, Support Vector Machines and Kernel PCA.