Gamma-ray strength function and pygmy resonance in rare earth nuclei

The gamma-ray strength function for gamma energies in the 1-7 MeV region has been measured for 161,162-Dy and 171,172-Yb using the (3-He,alpha gamma) reaction. Various models are tested against the observed gamma-ray strength functions. The best description is based on the Kadmenskii, Markushev and Furman E1 model with constant temperature and the Lorentzian M1 model. A gamma-ray bump observed at E_gamma=3 MeV is interpreted as the so-called pygmy resonance, which has also been observed previously in (n,gamma) experiments. The parameters for this resonance have been determined and compared to the available systematics.Comment: 11 pages, including 4 figures and 2 table

Optimizing Chemical Sensor Array Sizes

Optimal selection of array sensors for a chemical sensing application is a nontrivial task. It is commonly believed that "more is better" when choosing the number of sensors required to achieve good chemical selectivity. However, cost and system complexity issues point towards the choice of small arrays. A quantitative array optimization is carried out to explore the selectivity of arrays of partially-selective chemical sensors as a function of array size. It is shown that modest numbers (dozens) of target analytes are completely distinguished with a range of arrays sizes. However, the array selectivity and the robustness against sensor sensitivity variability are significantly degraded if the array size is increased above a certain number of sensors, so that relatively small arrays provide the best performance. The results also suggest that data analyses for very large arrays of partially-selective sensors will be optimized by separately anal yzing small sensor subsets

Sensor-fusion-based biometric identity verification

Future generation automated human biometric identification and verification will require multiple features/sensors together with internal and external information sources to achieve high performance, accuracy, and reliability in uncontrolled environments. The primary objective of the proposed research is to develop a theoretical and practical basis for identifying and verifying people using standoff biometric features that can be obtained with minimal inconvenience during the verification process. The basic problem involves selecting sensors and discovering features that provide sufficient information to reliably verify a person`s identity under the uncertainties caused by measurement errors and tactics of uncooperative subjects. A system was developed for discovering hand, face, ear, and voice features and fusing them to verify the identity of people. The system obtains its robustness and reliability by fusing many coarse and easily measured features into a near minimal probability of error decision algorithm

SAW arrays using dendrimers and pattern recognition to detect volatile organics

chemical sensor arrays eliminate the need to develop a high-selectivity material for every analyte. The application of pattern recognition to the simultaneous responses of different microsensors enables the identification and quantification of multiple analytes with a small array. Maximum materials diversity is the surest means to create an effective array for many analytes, but using a single material family simplifies coating development. Here the authors report the successful combination of an array of six dendrimer films with mass-sensitive SAW (surface acoustic wave) sensors to correctly identify 18 organic analytes over wide concentration ranges, with 99.5% accuracy. The set of materials for the array is selected and the results evaluated using Sandia`s Visual-Empirical Region of Influence (VERI) pattern recognition (PR) technique. The authors evaluated eight dendrimer films and one self-assembled monolayer (SAM) as potential SAW array coatings. The 18 organic analytes they examined were: cyclohexane, n-hexane, i-octane, kerosene, benzene, toluene, chlorobenzene, carbon tetrachloride, trichloroethylene, methanol, n-propanol, pinacolyl alcohol, acetone, methyl isobutyl ketone, dimethylmethylphosphate, diisopropylmethylphosphonate, tributylphosphate, and water