Molecular Orbital Models of Silica Rings and Their Vibrational Spectra

Amorphous silica (a-silica) has a distribution of various ring sizes. It is generally known that these rings vary in size from 2-membered rings through 6-membered (or possibly higher) rings of silica tetrahedra. These silica rings are modeled by a semiempirical quantum molecular orbital method named AMI. The calculations predict a range of Si-O bond lengths to be from 1.69 to 1.74 Å and a range of Si-O-Si bond angles to be from 102° to 174°. These values compare well with previous ab initio and PM3 results. The predicted infrared spectra also agree well with previous models and experimental results.     

Tensile and Fatigue Behavior of Silicon Carbide Fiber-Reinforced Aluminosilicate Glass

The onset of damage accumulation in ceramic-matrix composites occurs as matrix microcracking and fiber/matrix debonding. Tension tests were used to determine the stress and strain levels to first initiate microcracking in both unidirectional and cross-ply laminates of silicon carbide fiber-reinforced aluminosilicate glass. Tension–tension fatigue tests were then conducted at stress levels below and above the matrix cracking stress level. At stress levels below matrix microcracking, no loss in stiffness occurred. At stresses above matrix cracking, the elastic modulus of the unidirectional specimens exhibited a gradual decrease during the first 10 000 cycles, and then stabilized. However, the cross-ply material sustained most of the damage on the first loading cycle. It is shown that fatigue life can be related to nonlinear stress–strain behavior of the 0° plies, and that the cyclic strain limit was approximately 0.3%.     

Identification of food volatiles attractive toGlischrochilus quadrisignatus andGlischrochilus fasciatus (Coleoptera: Nitidulidae)

Seven volatile compounds identified from the headspace of whole wheat bread dough were investigated for their role in attractingGlischrochilus quadrisignatus andG. fasciatus in the field. Traps baited with either whole wheat bread dough or a synthetic seven-component bread dough odor caught similar numbers of these beetles, suggesting that the seven-compound combination could simulate the behavioral effect of bread dough. A series of trials using traps baited with various combinations of these chemicals showed that five compounds were significantly active in attractingG. quadrisignatus andG. fasciatus, but not all were essential for maximum response. The simplest blend eliciting a level of response comparable to the seven-component combination included ethyl acetate, acetaldehyde, ethanol, and racemic 2-methylbutanol, of which ethyl acetate, ethanol, and acetaldehyde were essential and 2-methylbutanol was replaceable with 2-methylpropanol forG. quadrisignatus attraction. Ethyl acetate and ethanol were essential for comparable attraction ofG. fasciatus. The chemical mediation of food finding in G.quadrisignatus andG. fasciatus is discussed in the context of volatile blends characterized for other nitidulid species.     

Learning to Recognize Volcanoes on Venus

Dramatic improvements in sensor and image acquisition technology have created a demand for automated tools that can aid in the analysis of large image databases. We describe the development of JARtool, a trainable software system that learns to recognize volcanoes in a large data set of Venusian imagery. A machine learning approach is used because it is much easier for geologists to identify examples of volcanoes in the imagery than it is to specify domain knowledge as a set of pixel-level constraints. This approach can also provide portability to other domains without the need for explicit reprogramming; the user simply supplies the system with a new set of training examples. We show how the development of such a system requires a completely different set of skills than are required for applying machine learning to toy world domains. This paper discusses important aspects of the application process not commonly encountered in the toy world, including obtaining labeled training data, the difficulties of working with pixel data, and the automatic extraction of higher-level features.     

Introduction to the special issue on deep learning for biomedical and healthcare applications

Neural Computing and Applications -     

Sequential kernel density approximation and its application to real-time visual tracking

Visual features are commonly modeled with probability density functions in computer vision problems, but current methods such as a mixture of Gaussians and kernel density estimation suffer from either the lack of flexibility, by fixing or limiting the number of Gaussian components in the mixture, or large memory requirement, by maintaining a non-parametric representation of the density. These problems are aggravated in real-time computer vision applications since density functions are required to be updated as new data becomes available. We present a novel kernel density approximation technique based on the mean-shift mode finding algorithm, and describe an efficient method to sequentially propagate the density modes over time. While the proposed density representation is memory efficient, which is typical for mixture densities, it inherits the flexibility of non-parametric methods by allowing the number of components to be variable. The accuracy and compactness of the sequential kernel density approximation technique is illustrated by both simulations and experiments. Sequential kernel density approximation is applied to on-line target appearance modeling for visual tracking, and its performance is demonstrated on a variety of videos.     

A General Method for Sensor Planning in Multi-Sensor Systems: Extension to Random Occlusion

Systems utilizing multiple sensors are required in many domains. In this paper, we specifically concern ourselves with applications where dynamic objects appear randomly and the system is employed to obtain some user-specified characteristics of such objects. For such systems, we deal with the tasks of determining measures for evaluating their performance and of determining good sensor configurations that would maximize such measures for better system performance. We introduce a constraint in sensor planning that has not been addressed earlier: visibility in the presence of random occluding objects. occlusion causes random loss of object capture from certain necessitates the use of other sensors that have visibility of this object. Two techniques are developed to analyze such visibility constraints: a probabilistic approach to determine “average” visibility rates and a deterministic approach to address worst-case scenarios. Apart from this constraint, other important constraints to be considered include image resolution, field of view, capture orientation, and algorithmic constraints such as stereo matching and background appearance. Integration of such constraints is performed via the development of a probabilistic framework that allows one to reason about different occlusion events and integrates different multi-view capture and visibility constraints in a natural way. Integration of the thus obtained capture quality measure across the region of interest yields a measure for the effectiveness of a sensor configuration and maximization of such measure yields sensor configurations that are best suited for a given scenario. The approach can be customized for use in many multi-sensor applications and our contribution is especially significant for those that involve randomly occurring objects capable of occluding each other. These include security systems for surveillance in public places, industrial automation and traffic monitoring. Several examples illustrate such versatility by application of our approach to a diverse set of different and sometimes multiple system objectives. Most of this work was done while A. Mittal was with Real-Time Vision and Modeling Department, Siemens Corporate Research, Princeton, NJ 08540.     

Muscular loading and subjective ratings of muscular tension by novices when typing with standard and split‐design computer keyboards

The objective of this research project was to compare two different computer keyboard designs with respect to their effect on the extent of muscular loading in the right and left trapezius and extensor muscles during typing. The two computer keyboards used in this study were (1) a classic or standard keyboard, and (2) a split‐design keyboard. Evaluation of muscular loading was done using the electromyography (EMG) technique and subjective ratings of muscular tension. Sixteen women, between 18 and 26 years of age, with similar secretarial experience and limited typing abilities, took voluntary part in the experiment. Each subject used both keyboards for 15 min. The EMG signals recorded during typing sessions were those of the left and right sides of trapezius (m. trapezius pars descendeus) and extensor (m. extensor carpi radialis brevis et longus) muscles. Upon completion of each task, subjects were asked to evaluate perceived levels of muscular tension in the shoulder‐neck area and forearms. The results showed that the split‐design computer keyboard significantly reduces muscular loading of the trapezius muscle and subjective feelings of muscular tension in the shoulder‐neck area.