On neural networks in identification and control of dynamic systems

This paper presents a discussion of the applicability of neural networks in the identification and control of dynamic systems. Emphasis is placed on the understanding of how the neural networks handle linear systems and how the new approach is related to conventional system identification and control methods. Extensions of the approach to nonlinear systems are then made. The paper explains the fundamental concepts of neural networks in their simplest terms. Among the topics discussed are feed forward and recurrent networks in relation to the standard state-space and observer models, linear and nonlinear auto-regressive models, linear, predictors, one-step ahead control, and model reference adaptive control for linear and nonlinear systems. Numerical examples are presented to illustrate the application of these important concepts

Optical characterization of AlAsSb digital alloy and random alloy on GaSb

III-(As, Sb) alloys are building blocks for various advanced optoelectronic devices, but the growth of their ternary or quaternary materials are commonly limited by spontaneous formation of clusters and phase separations during alloying. Recently, digital alloy growth by molecular beam epitaxy has been widely adopted in preference to conventional random alloy growth because of the extra degree of control offered by the ordered alloying. In this article, we provide a comparative study of the optical characteristics of AlAsSb alloys grown lattice-matched to GaSb using both techniques. The sample grown by digital alloy technique showed stronger photoluminescence intensity, narrower peak linewidth, and larger carrier activation energy than the random alloy technique, indicating an improved optical quality with lower density of non-radiative recombination centers. In addition, a relatively long carrier lifetime was observed from the digital alloy sample, consistent with the results obtained from the photoluminescence study

Energy-sensitive GaSb/AlAsSb separate absorption and multiplication avalanche photodiodes for X-Ray and gamma-ray detection

Demonstrated are antimony‐based (Sb‐based) separate absorption and multiplication avalanche photodiodes (SAM‐APDs) for X‐ray and gamma‐ray detection, which are composed of GaSb absorbers and large bandgap AlAsSb multiplication regions in order to enhance the probability of stopping high‐energy photons while drastically suppressing the minority carrier diffusion. Well‐defined X‐ray and gamma‐ray photopeaks are observed under exposure to 241Am radioactive sources, demonstrating the desirable energy‐sensitive detector performance. Spectroscopic characterizations show a significant improvement of measured energy resolution due to reduced high‐peak electric field in the absorbers and suppressed nonradiative recombination on surfaces. Additionally, the GaSb/AlAsSb SAM‐APDs clearly exhibit energy response linearity up to 59.5 keV with a minimum full‐width half‐maximum of 1.283 keV. A further analysis of the spectroscopic measurement suggests that the device performance is intrinsically limited by the noise from the readout electronics rather than that from the photodiodes. This study provides a first understanding of Sb‐based energy‐sensitive SAM‐APDs and paves the way to achieving efficient detection of high‐energy photons for X‐ray and gamma‐ray spectroscopy

Significant suppression of surface leakage in GaSb/AlAsSb heterostructure with Al2O3 passivation

This work develops a (NH4)2S/Al2O3 passivation technique for photodiode-based GaSb/AlAsSb heterostructure. Surface-sulfurated GaSb/AlAsSb heterostructure mesas show a significant suppression of reversed-bias dark current by 4–5 orders of magnitude after they are further passivated by Al2O3 layers. So the mesa sidewalls treated with (NH4)2S/Al2O3 layers can effectively inhibit the shunt path of dark carriers. The activation energies for both bulk and surface components are extracted from temperature-dependent current–voltage characteristics, which suggest that the bulk characteristics remain unchanged, while Fermi-level pinning at surfaces is alleviated. Additionally, temperature coefficients of the breakdown voltage are extracted, confirming that the breakdown process is confined entirely in the large bandgap AlAsSb regions. This study shows that the implementation of (NH4)2S/Al2O3 passivation can lead to room temperature GaSb-based photodiodes and GaSb/AlAsSb-based avalanche photodiodes for highly efficient photodetection

Carbon dioxide and water vapor exchange in a warm temperate grassland

Grasslands cover about 40% of the ice-free global terrestrial surface, but their contribution to local and regional water and carbon fluxes and sensitivity to climatic perturbations such as drought remains uncertain. Here, we assess the direction and magnitude of net ecosystem carbon exchange (NEE) and its components, ecosystem carbon assimilation ( A c ) and ecosystem respiration ( R E ), in a southeastern United States grassland ecosystem subject to periodic drought and harvest using a combination of eddy-covariance measurements and model calculations. We modeled A c and evapotranspiration (ET) using a big-leaf canopy scheme in conjunction with ecophysiological and radiative transfer principles, and applied the model to assess the sensitivity of NEE and ET to soil moisture dynamics and rapid excursions in leaf area index (LAI) following grass harvesting. Model results closely match eddy-covariance flux estimations on daily, and longer, time steps. Both model calculations and eddy-covariance estimates suggest that the grassland became a net source of carbon to the atmosphere immediately following the harvest, but a rapid recovery in LAI maintained a marginal carbon sink during summer. However, when integrated over the year, this grassland ecosystem was a net C source (97 g C m −2 a −1 ) due to a minor imbalance between large A c (−1,202 g C m −2 a −1 ) and R E (1,299 g C m −2 a −1 ) fluxes. Mild drought conditions during the measurement period resulted in many instances of low soil moisture ( θ <0.2 m 3 m −3 ), which influenced A c and thereby NEE by decreasing stomatal conductance. For this experiment, low θ had minor impact on R E . Thus, stomatal limitations to A c were the primary reason that this grassland was a net C source. In the absence of soil moisture limitations, model calculations suggest a net C sink of −65 g C m −2 a −1 assuming the LAI dynamics and physiological properties are unaltered. These results, and the results of other studies, suggest that perturbations to the hydrologic cycle are key determinants of C cycling in grassland ecosystems.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/47701/1/442_2003_Article_1388.pd

Transfer-free growth of graphene on SiO2 insulator substrate from sputtered carbon and nickel films

AbstractHere we demonstrate the growth of transfer-free graphene on SiO2 insulator substrates from sputtered carbon and metal layers with rapid thermal processing in the same evacuation. It was found that graphene always grows atop the stack and in close contact with the Ni. Raman spectra typical of high quality exfoliated monolayer graphene were obtained for samples under optimised conditions with monolayer surface coverage of up to 40% and overall graphene surface coverage of over 90%. Transfer-free graphene is produced on SiO2 substrates with the removal of Ni in acid when Ni thickness is below 100nm, which effectively eliminates the need to transfer graphene from metal to insulator substrates and paves the way to mass production of graphene directly on insulator substrates. The characteristics of Raman spectrum depend on the size of Ni grains, which in turn depend on the thickness of Ni, layer deposition sequence of the stack and RTP temperature. The mechanism of the transfer-free growth process was studied by AFM in combination with Raman. A model is proposed to depict the graphene growth process. Results also suggest a monolayer self-limiting growth for graphene on individual Ni grains

Mitotic spindle disassembly occurs via distinct subprocesses driven by the anaphase-promoting complex, Aurora B kinase, and kinesin-8

A large-scale analysis of spindle disassembly in budding yeast identifies factors required for disengagement of spindle halves, arrest of spindle elongation, and depolymerization of interpolar microtubules

Parameter estimation for robust HMM analysis of ChIP-chip data

Tiling arrays are an important tool for the study of transcriptional activity, protein-DNA interactions and chromatin structure on a genome-wide scale at high resolution. Although hidden Markov models have been used successfully to analyse tiling array data, parameter estimation for these models is typically ad hoc. Especially in the context of ChIP-chip experiments, no standard procedures exist to obtain parameter estimates from the data. Common methods for the calculation of maximum likelihood estimates such as the Baum-Welch algorithm or Viterbi training are rarely applied in the context of tiling array analysis. Results: Here we develop a hidden Markov model for the analysis of chromatin structure ChIP-chip tiling array data, using t emission distributions to increase robustness towards outliers. Maximum likelihood estimates are used for all model parameters. Two different approaches to parameter estimation are investigated and combined into an efficient procedure. Conclusion: We illustrate an efficient parameter estimation procedure that can be used for HMM based methods in general and leads to a clear increase in performance when compared to the use of ad hoc estimates. The resulting hidden Markov model outperforms established methods like TileMap in the context of histone modification studies.13 page(s