Investigation of vertical radiation loss for whispering-gallery modes in 3-D microresonators by FDTD simulation

The vertical radiation loss of three-dimensional (3-D) microresonators is investigated by 3-D finite-difference time-domain (FDTD) simulation. The simulation shows that the vertical radiation causes an important loss in the microresonators with weak waveguiding, and result in decrease of the quality factors (Q-factors) of whispering-gallery (WG) modes. Through the simulation, we find that TM-like modes have much weaker vertical radiation loss than TE-like modes. High Q-factor TM-like modes are observed in the 3-D microresonators with weak vertical waveguiding, but the Q-factors of TE-like modes decrease greatly.

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.

Phenylthiolate as a sigma- and pi-donor ligand: synthesis of a 3-D organometallic coordination polymer [K2Fe(SPh)(4)](n)

The synthesis and crystal structure of the first mixed-metal organometallic polymer network containing phenylthiolato ligands, [K2Fe(SPh)(4)](n), are investigated. The simple phenyl-thiolate acts as a sigma- and pi-donor ligand to give a 3-D potassium iron coordination polymer with both metal-carbon and metal-sulfur coordination interactions.

Novel polyoxometalate-templated, 3-D supramolecular networks based on lanthanide dimers: Synthesis, structure, and fluorescent properties of [Ln(2)(DNBA)(4)(DMF)(8)][Mo6O19] (DNBA=3,5-dinitrobenzoate)

The spherical Lindquist type polyoxometalate, Mo6O192-, has been used as a noncoordinating anionic template for the construction of novel three-dimensional lanthanide-aromatic monocarboxylate dimer supramolecular networks [Ln(2)(DNBA)(4)(DMF)(8)][Mo6O19] (Ln = La 1, Ce 2, and Eu 3, DNBA = 3,5-dinitrobenzoate, DMF = dimethylformamide). The title compounds are characterized by elemental analyses, IR, and single-crystal X-ray diffractions. X-ray diffraction experiments reveal that two Ln(III) ions are bridged by four 3,5-dinitrobenzoate anions as asymmetrically bridging ligands, leading to dimeric cores, [Ln(2)(DNBA)(4)(DMF)(8)](2+); [Ln(2)(DNBA)(4)(DMF)(8)](2+) groups are joined together by pi-pi stacking interactions between the aromatic groups to form a two-dimensional grid-like network; the 2-D supramolecular layers are further extended into 3-D supramolecular networks with 1-D box-like channels by hydrogen-bonding interactions, in which hexamolybdate polyanions reside. The compounds represent the first examples of 3-D carboxylate-bridged lanthanide dimer supramolecular "host" networks formed by pi-pi stacking and hydrogen-bonding interactions encapsulating noncoordinating "guest" polyoxoanion species. The fluorescent activity of compound 3 is reported.

Stimulus Familiarity Determines Recognition Strategy for Novel 3-D Objects

We describe a psychophysical investigation of the effects of object complexity and familiarity on the variation of recognition time and recognition accuracy over different views of novel 3D objects. Our findings indicate that with practice the response times for different views become more uniform and the initially orderly dependency of the response time on the distance to a "good" view disappears. One possible interpretation of our results is in terms of a tradeoff between memory needed for storing specific-view representations of objects and time spent in recognizing the objects.

Computing 3-D Motion in Custom Analog and Digital VLSI

This thesis examines a complete design framework for a real-time, autonomous system with specialized VLSI hardware for computing 3-D camera motion. In the proposed architecture, the first step is to determine point correspondences between two images. Two processors, a CCD array edge detector and a mixed analog/digital binary block correlator, are proposed for this task. The report is divided into three parts. Part I covers the algorithmic analysis; part II describes the design and test of a 32$\time $32 CCD edge detector fabricated through MOSIS; and part III compares the design of the mixed analog/digital correlator to a fully digital implementation.

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.

A Self-Organizing Neural Network for Learning a Body-Centered Invariant Representation of 3-D Target Position

This paper describes a self-organizing neural network that rapidly learns a body-centered representation of 3-D target positions. This representation remains invariant under head and eye movements, and is a key component of sensory-motor systems for producing motor equivalent reaches to targets (Bullock, Grossberg, and Guenther, 1993).