Enzyme conversion of lignocellulosic plant materials for resource recovery in a Controlled Ecological Life Support System.

A large amount of inedible plant material composed primarily of the carbohydrate materials cellulose, hemicellulose, and lignin is generated as a result of plant growth in a Controlled Ecological Life-Support System (CELSS). Cellulose is a linear homopolymer of glucose, which when properly processed will yield glucose, a valuable sugar because it can be added directly to human diets. Hemicellulose is a heteropolymer of hexoses and pentoses that can be treated to give a sugar mixture that is potentially a valuable fermentable carbon source. Such fermentations yield desirable supplements to the edible products from hydroponically-grown plants such as rapeseed, soybean, cowpea, or rice. Lignin is a three-dimensionally branched aromatic polymer, composed of phenyl propane units, which is susceptible to bioconversion through the growth of the white rot fungus, Pluerotus ostreatus. Processing conditions, that include both a hot water pretreatment and fungal growth and that lead to the facile conversion of plant polysaccharides to glucose, are presented.     

AFIT neural network research

A brief summary of research done at the Air Force Institute of Technology (AFIT) in the area of neural networks is provided. It has been shown that backpropagation, used for feedforward artificial neural networks, is just a degenerate version of an extended Kalman filter, and that networks can do about as well as the optimum statistical classification technique. A method of finding the importance of features for use by a neural network classifier has been determined. Techniques for using neural networks for image segmentation have been developed. In optical pattern recognition, techniques that allow the processing of real FLIR (forward-looking infrared) images with existing binary spatial light modulators have been devised. An optical direction of arrival detector applicable to laser illumination direction determination has been designed and tested; the design is similar to a fly's eye. Coated mirrors for the optical confocal Fabry-Perot interferometer have been designed, specified, fabricated, and installed. Significant progress has been made in the use of neural networks for processing multiple-feature sets for speech recognition     

A wavelet multiresolution analysis for spatio-temporal signals

The wavelet filters of the conventional 3D multiresolution analysis possess homogeneous spatial and temporal frequency characteristics which limits one's ability to match filter frequency characteristics to signal frequency behavior. Also, the conventional 3D multiresolution analysis employs an oct-tree decomposition structure which restricts the analysis of signal details to identical resolutions in space and time. This paper presents a 3D wavelet multiresolution analysis constructed from nonhomogeneous spatial and temporal filters, and an orthogonal sub-band coding scheme that decouples the spatial and temporal decomposition processes     

Simulation of fast ablation

A brief review of studies on simulation and control of a porous cooling process taking into account material transpiration is given. An approximated solution for an equation of the one-dimensional problem of fast ablation is also presented.     

How much of the solar irradiance variations is caused by the magnetic field at the solar surface?

The contribution to total solar irradiance variations by the magnetic field at the solar surface is estimated. Detailed models of the irradiance changes on the basis of magnetograms show that magnetic features at the solar surface account for over 90% of the irradiance variations on a solar rotation time scale and at least 70% on a solar cycle time scale. If the correction to the VIRGO record proposed by Fröhlich & Finsterle (2001) is accepted, then magnetic features at the solar surface are responsible for over 90% of the solar cycle irradiance variations as well.     

Fiber optic fence sensor developments

Many detection technologies have been employed for perimeter detection, to sensitize a barrier. These outdoor perimeter fence detection sensors must reliably detect intruders attempting to cut or climb the barrier, while ignoring the effects of environmental noise, including nearby activity. In recent conference proceedings, the new IntelliFIBER/spl trade/ fiber optic based product was introduced and compared with previous technologies. IntelliFIBER is designed to provide the advantages of a nonconductive "dielectric" cable sensor, e.g., resistance to electromagnetic interference and the ability to provide longer cable zones. It utilizes the proven processor of the Intelli-FLEX/spl trade/ triboelectric cable fence sensor, which provides relay or bidirectional networked serial communications and is compatible with its calibration module. IntelliFIBER also capitalizes on the Intelli-FLEX's adaptive detection algorithms that were developed through extensive field-testing, to provide exceptional immunity to environmental alarms while still detecting the skilled intruder.     

TerraSAR-X active radar ground calibrator system

In April 2006, the TerraSAR-X satellite was launched. This paper describes the development of a novel and highly integrated, digitally-controlled active SAR system calibrator (DARC). It consists of both an active transponder path for absolute radiometric calibration and a calibrated receiver chain for antenna pattern evaluation of the satellite antenna. A total of 16 active transponder and receiver systems and 17 receiver-only systems will be fabricated for a calibration campaign in 2006.     

Almost instant time inference for hybrid partially dynamic Bayesian networks

A Bayesian network (BN) is a compact representation for probabilistic models and inference. They have been used successfully for many military and civilian applications. It is well known that, in general, the inference algorithms to compute the exact a posterior probability of a target node given observed evidence are either computationally infeasible for dense networks or impossible for general hybrid networks. In those cases, one either computes the approximate results using stochastic simulation methods or approximates the model using discretization or a Gaussian mixture model before applying an exact inference algorithm. This paper combines the concept of simulation and model approximation to propose an efficient algorithm for those cases. The main contribution here is a unified treatment of arbitrary (nonlinear non-Gaussian) hybrid (discrete and continuous) BN inference having both computation and accuracy scalability. The key idea is to precompile the high-dimensional hybrid distribution using a hypercube representation and apply it for both static and dynamic BN inference. Since the inference process essentially becomes a combination of table look-up and some simple operations, the method is shown to be extremely efficient. It can also he scaled to achieve any desirable accuracy given sufficient preprocessing time and memory for the cases where exact inference is not possible     

Recent Breakthroughs in RF Photonics for Radar Systems

Radar systems require transmission of very high purity signals. Photonics is now mature enough to achieve analog transmission with very low noise, strong immunity, and wide-bandwidth even in harsh environments. We present our recent developments of optimized optical links dedicated to radar and multifunction systems     

Performance evaluation for MAP state estimate fusion

This paper presents a quantitative performance evaluation method for the maximum a posteriori (MAP) state estimate fusion algorithm. Under ideal conditions where data association is assumed to be perfect, it has been shown that the MAP or best linear unbiased estimate (BLUE) fusion formula provides the best linear minimum mean squared estimate (LMMSE) given local estimates under the linear Gaussian assumption for a static system. However, for a dynamic system where fusion is recursively performed by the fusion center on local estimates generated from local measurements, it is not obvious how the MAP algorithm will perform. In the past, several performance evaluation methods have been proposed for various fusion algorithms, including simple convex combination, cross-covariance combination, information matrix, and MAP fusion. However, not much has been done to quantify the steady state behavior of these fusion methods for a dynamic system. The goal of this work is to present analytical fusion performance results for MAP state estimate fusion without extensive Monte Carlo simulations, using an approach developed for steady state performance evaluation for track fusion. Two different communication strategies are considered: fusion with and without feedback to the sensors. Analytic curves for the steady state performance of the fusion algorithm for various communication patterns are presented under different operating conditions.