Ultrasonic synthesis of silica-alumina nanomaterials with controlled mesopore distribution without using surfactants

A novel sol-gel process has been developed for the synthesis of amorphous silica-aluminas with controlled mesopore distribution without the use of organic templating agents, e.g., surfactant molecules. Ultrasonic treatment during the synthesis enables production of precursor sols with narrow particle size distribution. Atomic force microscopy analysis shows that these sol particles are spherical in shape with a narrow size distribution (i.e., 13-25 nm) and their aggregation during the gelation creates clusters containing similar sized interparticle mesopores. A nitrogen physiadsorption study indicates that the mesoporous materials containing different Si/Al ratios prepared by the new synthesis method has a large specific surface area (i.e., 587-692 m(2)/g) and similar pore sizes of 2-11 nm. Solid-state Al-27 magic angle spinning (MAS) NMR shows that most of the aluminum is located in the tetrahedral position. A transmission electron microscopy (TEM) image shows that the mesoporous silica-alumina consists of 12-25 nm spheres. Additionally, high-resolution TEM and electron diffraction indicate that some nanoparticles are characteristic of a crystal, although X-ray diffraction and Si-29 MAS NMR analysis show an amorphous material.

Recognizing Three-Dimensional Objects without the Use of Models

We present an approach to the problem of recognizing three-dimensional objects from line-drawings. In this approach there are no models. The system needs only to be given a single picture of an object; it can then recognize the object in arbitrary orientations.

Intelligence Without Reason

Computers and Thought are the two categories that together define Artificial Intelligence as a discipline. It is generally accepted that work in Artificial Intelligence over the last thirty years has had a strong influence on aspects of computer architectures. In this paper we also make the converse claim; that the state of computer architecture has been a strong influence on our models of thought. The Von Neumann model of computation has lead Artificial Intelligence in particular directions. Intelligence in biological systems is completely different. Recent work in behavior-based Artificial Intelligenge has produced new models of intelligence that are much closer in spirit to biological systems. The non-Von Neumann computational models they use share many characteristics with biological computation.

Multi-Scale Vector-Ridge-Detection for Perceptual Organization Without Edges

We present a novel ridge detector that finds ridges on vector fields. It is designed to automatically find the right scale of a ridge even in the presence of noise, multiple steps and narrow valleys. One of the key features of such ridge detector is that it has a zero response at discontinuities. The ridge detector can be applied to scalar and vector quantities such as color. We also present a parallel perceptual organization scheme based on such ridge detector that works without edges; in addition to perceptual groups, the scheme computes potential focus of attention points at which to direct future processing. The relation to human perception and several theoretical findings supporting the scheme are presented. We also show results of a Connection Machine implementation of the scheme for perceptual organization (without edges) using color.

Feature Extraction Without Edge Detection

Information representation is a critical issue in machine vision. The representation strategy in the primitive stages of a vision system has enormous implications for the performance in subsequent stages. Existing feature extraction paradigms, like edge detection, provide sparse and unreliable representations of the image information. In this thesis, we propose a novel feature extraction paradigm. The features consist of salient, simple parts of regions bounded by zero-crossings. The features are dense, stable, and robust. The primary advantage of the features is that they have abstract geometric attributes pertaining to their size and shape. To demonstrate the utility of the feature extraction paradigm, we apply it to passive navigation. We argue that the paradigm is applicable to other early vision problems.