8-(3-methylphenoxy)-16H-dinaphtho[2,1-d;1',2'-g][1,3,2]dioxaphosphocine 8-oxide

In the title compound, C28H21O4P, the eight-membered heterocyclic dioxaphosphocine ring has a distorted boat conformation, with the phosphoryl O atom axial and the phenoxy group equatorial. The P=O distance is 1.451 (1) Angstrom and the average length of the three P-O bonds is 1.573 (1) Angstrom. The phenyl ring is nearly perpendicular to both naphthalene planes, making dihedral angles of 91.30 (3) and 97.65 (5)degrees with them. The angle between the two naphthalene planes is 67.73 (3)degrees. The crystal structure is stabilized by van der Waals interactions.

(+/-)-cis-Dichloro[P-(isopropylamino)-dinaphtho[2,1-d:1',2'-f][1,3,2]dioxaphosphepine](triethylphosphine)platinum(II) dichloromethane solvate

In the racemic title compound, [PtCl2(C23H20NO2P)-(C6H15P)].CH2Cl2, the platinum(II) ion, which has approximately square-planar coordination geometry, is coordinated to two different monophosphorus ligands in a cis arrangement along with two chloride ions. A significant shortening of the P-N bond [1.604(7) Angstrom] relative to that in phosphinoamines and their complexes was observed.

2-D and 3-D processing and interpretation of multi-fold ground penetrating radar data:a case history from an archaeological site

A 2.5-D and 3-D multi-fold GPR survey was carried out in the Archaeological Park of Aquileia (northern Italy). The primary objective of the study was the identification of targets of potential archaeological interest in an area designated by local archaeological authorities. The second geophysical objective was to test 2-D and 3-D multi-fold methods and to study localised targets of unknown shape and dimensions in hostile soil conditions. Several portions of the acquisition grid were processed in common offset (CO), common shot (CSG) and common mid point (CMP) geometry. An 8×8 m area was studied with orthogonal CMPs thus achieving a 3-D subsurface coverage with azimuthal range limited to two normal components. Coherent noise components were identified in the pre-stack domain and removed by means of FK filtering of CMP records. Stack velocities were obtained from conventional velocity analysis and azimuthal velocity analysis of 3-D pre-stack gathers. Two major discontinuities were identified in the area of study. The deeper one most probably coincides with the paleosol at the base of the layer associated with activities of man in the area in the last 2500 years. This interpretation is in agreement with the results obtained from nearby cores and excavations. The shallow discontinuity is observed in a part of the investigated area and it shows local interruptions with a linear distribution on the grid. Such interruptions may correspond to buried targets of archaeological interest. The prominent enhancement of the subsurface images obtained by means of multi-fold techniques, compared with the relatively poor quality of the conventional single-fold georadar sections, indicates that multi-fold methods are well suited for the application to high resolution studies in archaeology.

成核剂1,3:2,4-二(亚苄基)-D山梨醇对PET/PEN共混体系结构和结晶形态影响

利用差示扫描量热仪、X射线衍射仪、正交偏光显微镜研究了成核剂 1,3 :2 ,4-二 (亚苄基 ) -D山梨醇(DBS)对聚对苯二甲酸乙二醇酯 (PET) /聚 2 ,6-萘二甲酸乙二醇酯 (PEN)共混体系的结构及结晶形态的影响。结果表明 :成核剂的加入 ,使PET/PEN共混体系熔融起始温度升高 10℃左右 ,结晶峰形变尖锐 ,说明加入成核剂后有效促进了PET/PEN共混体系的结晶。实验结果表明 :成核剂含量低于 1%时 ,PET/PEN共混体系晶体的球晶完整。成核剂含量大于 3 %时 ,PET/PEN /DBS共混体系晶体的球晶碎小。成核剂的加入 ,能够有效地减小球晶尺寸和降低球晶的完善性

Recognizing 3-D Objects Using 2-D Images

We discuss a strategy for visual recognition by forming groups of salient image features, and then using these groups to index into a data base to find all of the matching groups of model features. We discuss the most space efficient possible method of representing 3-D models for indexing from 2-D data, and show how to account for sensing error when indexing. We also present a convex grouping method that is robust and efficient, both theoretically and in practice. Finally, we combine these modules into a complete recognition system, and test its performance on many real images.

Fast Learning VIEWNET Architectures for Recognizing 3-D Objects from Multiple 2-D Views

The recognition of 3-D objects from sequences of their 2-D views is modeled by a family of self-organizing neural architectures, called VIEWNET, that use View Information Encoded With NETworks. VIEWNET incorporates a preprocessor that generates a compressed but 2-D invariant representation of an image, a supervised incremental learning system that classifies the preprocessed representations into 2-D view categories whose outputs arc combined into 3-D invariant object categories, and a working memory that makes a 3-D object prediction by accumulating evidence from 3-D object category nodes as multiple 2-D views are experienced. The simplest VIEWNET achieves high recognition scores without the need to explicitly code the temporal order of 2-D views in working memory. Working memories are also discussed that save memory resources by implicitly coding temporal order in terms of the relative activity of 2-D view category nodes, rather than as explicit 2-D view transitions. Variants of the VIEWNET architecture may also be used for scene understanding by using a preprocessor and classifier that can determine both What objects are in a scene and Where they are located. The present VIEWNET preprocessor includes the CORT-X 2 filter, which discounts the illuminant, regularizes and completes figural boundaries, and suppresses image noise. This boundary segmentation is rendered invariant under 2-D translation, rotation, and dilation by use of a log-polar transform. The invariant spectra undergo Gaussian coarse coding to further reduce noise and 3-D foreshortening effects, and to increase generalization. These compressed codes are input into the classifier, a supervised learning system based on the fuzzy ARTMAP algorithm. Fuzzy ARTMAP learns 2-D view categories that are invariant under 2-D image translation, rotation, and dilation as well as 3-D image transformations that do not cause a predictive error. Evidence from sequence of 2-D view categories converges at 3-D object nodes that generate a response invariant under changes of 2-D view. These 3-D object nodes input to a working memory that accumulates evidence over time to improve object recognition. ln the simplest working memory, each occurrence (nonoccurrence) of a 2-D view category increases (decreases) the corresponding node's activity in working memory. The maximally active node is used to predict the 3-D object. Recognition is studied with noisy and clean image using slow and fast learning. Slow learning at the fuzzy ARTMAP map field is adapted to learn the conditional probability of the 3-D object given the selected 2-D view category. VIEWNET is demonstrated on an MIT Lincoln Laboratory database of l28x128 2-D views of aircraft with and without additive noise. A recognition rate of up to 90% is achieved with one 2-D view and of up to 98.5% correct with three 2-D views. The properties of 2-D view and 3-D object category nodes are compared with those of cells in monkey inferotemporal cortex.