Masoud Asadi-Zeydabadi

Visual sensations during megavoltage radiotherapy to the orbit attributable to Cherenkov radiation

During megavoltage photon and electron beam radiotherapytreatment involving the eye, patients commonly report visual sensations; “nerve stimulation” is the conventional explanation. We propose that the phenomenon can be attributed to Cherenkov radiation inside the eye. The threshold electron energy for Cherenkov radiation in water is 260 keV . The human retina is able to perceive approximately 5–14 visible photons in 0.001 s . A single 500 keV electron traversing 1 mm of water will induce nearly 15 Cherenkov visible range photons. We propose that a portal image involving the eye will produce sufficient Cherenkov radiation to be detected by the retina.

Auto-contractive Maps

This chapter focuses on Auto-Contractive Maps, which is a particularly useful ANN. Moreover, the relationship between Auto-Contractive Map (Auto-CM), which is the main topic of this monograph, its relationship to other ANNs and some illustrative example applications are presented.

One-dimensional relativistic dynamics with scaling formalism

In one dimension, we investigate dynamics using special relativity only, but make no statement regarding any particular insight into the nature of the spacetime continuum. Indeed, we do away completely with any kind of formal tensor algebra or differential topology, but do incorporate scaling into our calculations. The purpose here is primarily pedagogical, as an extension to the standard undergraduate treatment of special relativity, but it is also a calculational tool, to be able to determine the motion of objects in a one-dimensional case even with accelerations and forces, without having to use the full general relativistic treatment. To illustrate this, we directly compare relativistic to Newtonian or non-relativistic motions for particularly instructive selected systems.

Artificial Neural Networks

Artificial Adaptive Systems include Artificial Neural Networks (ANNs or simply neural networks as they are commonly known). The philosophy of neural networks is to extract from data the underlying model that relates this data as an input/output (domain/range) pair. This is quite different from the way most mathematical modeling processes operate. Most mathematical modeling processes normally impose on the given data a model from which the input to output relationship is obtained. For example, a linear model that is a “best fit” in some sense, that relates the input to the output is such a model. What is imposed on the data by artificial neural networks is an a priori architecture rather than an a priori model. From the architecture, a model is extracted. It is clear, from any process that seeks to relate input to output (domain to range), requires a representation of the relationships among data. The advantage of imposing an architecture rather than a data model, is that it allows for the model to adapt. Fundamentally, a neural network is represented by its architecture. Thus, we look at the architecture first followed by a brief introduction of the two types of approaches for implementing the architecture—supervised and unsupervised neural networks. Recall that Auto-CM, which we discuss in Chap. 3, is an unsupervised ANN while K-CM, discussed in Chap. 6, is a supervised version of Auto-CM. However, in this chapter, we show that, in fact, supervised and unsupervised neural networks can be viewed within one framework in the case of the linear perceptron. The chapter ends with a brief look at some theoretical considerations.

Theoretical estimation of retinal oxygenation in chronic diabetic retinopathy

This paper uses computer modeling to estimate the progressive decline in oxygenation that occurs in the human diabetic retina after years of slowly progressive ischemic insult. An established model combines diffusion, saturable consumption, and blood capillary sources to determine the oxygen distribution across the retina. Incorporating long-term degradation of blood supply from the retinal capillaries into the model yields insight into the effects of progressive ischemia associated with prolonged hyperglycemia, suggesting time-scales over which therapeutic mitigation could have beneficial effect. A new extension of the model for oxygen distribution introduces a feedback mechanism for vasodilation and its potential to prolong healthy retinal function.

Auto-CM as a Dynamic Associative Memory

We look at how to use Auto-CM in the context of datasets that are changing in time. We modify our approach while keeping the original philosophy of Auto-CM.

Dataset Transformations and Auto-CM

We have looked at how to visualize the relationships among the elements of a dataset in Chap. 4. This chapter is devoted to the use of Auto-CM in the transformation of datasets for the purpose of extracting further relationships among data elements. The first transformation we call the FS-Transform, which looks beyond an all or nothing, binary relationship that is typical of most ANNs. The extraction of these perhaps more subtle relationships can be thought of as gradual relationships, zero denoting no relationship is present and one denoting a full/complete relationship that is absolutely present. It is thus, akin to a fuzzy set. The second transformation is one, which “morph” the delineation between records and variables within records that we call Hyper-Composition.

Comparison of Auto-CM to Various Other Data Understanding Approaches

We compare Auto-CM with various other methods that extract patterns from data. The way that we measure the results of comparisons uses MST.

Visualization of Auto-CM Output

One of the most powerful aspects of our approach to neural networks is not only the development of the Auto-CM neural network but the visualization of its results. In this chapter we look at two visualization approaches—the Minimal Spanning Tree (MST) and the Maximal Regular Graph (MRG). The resultant from Auto-CM is a matrix of weights. This weight matrix naturally fits into a graph theoretic framework since the weights connecting the nodes will be viewed as edges and the weights as the weights on these edges.

Tailored dielectric and magnetic properties of composite electroceramics with ferroelectric and ferrimagnetic components

High-frequency coaxial waveguide measurements are used to evaluate composition- and frequency-dependent complex permittivities and permeabilities of polycrystalline ferroelectric and ferrimagnetic ceramics. Material dielectric and magnetic property measurements were performed on ferroelectric barium titanate (BaTiO3) and ferrimagnetic yttrium iron garnet (YIG), with the upper measurement frequency bound below gyromagnetic resonance of YIG. These polycrystalline materials were chosen because of their widespread use in voltage- and magnetic field-tunable devices. Wideband frequency material measurements were used to predict dielectric and magnetic properties of various two-phase composite media incorporating BaTiO3 and YIG. Two effective medium theories were used: an asymmetric, modified Maxwell-Garnett type and a symmetric Bruggeman model. The Bruggeman effective medium model is extended to include three-phases. The three-phase Bruggeman predictive model is employed when controlled porosity is introduced into the composite and when two component phases are combined with polytetrafluoroethylene.High-frequency coaxial waveguide measurements are used to evaluate composition- and frequency-dependent complex permittivities and permeabilities of polycrystalline ferroelectric and ferrimagnetic ceramics. Material dielectric and magnetic property measurements were performed on ferroelectric barium titanate (BaTiO3) and ferrimagnetic yttrium iron garnet (YIG), with the upper measurement frequency bound below gyromagnetic resonance of YIG. These polycrystalline materials were chosen because of their widespread use in voltage- and magnetic field-tunable devices. Wideband frequency material measurements were used to predict dielectric and magnetic properties of various two-phase composite media incorporating BaTiO3 and YIG. Two effective medium theories were used: an asymmetric, modified Maxwell-Garnett type and a symmetric Bruggeman model. The Bruggeman effective medium model is extended to include three-phases. The three-phase Bruggeman predictive model is employed when controlled porosity is introduced i...