Machine Learning is the study of methods for programming computers to learn. Computers are applied to a wide range of tasks, and for most of these it is relatively easy for programmers to design and implement the necessary software. However, there are many tasks for which this is difficult or impossible. These can be divided into four general categories. First, there are problems for which there exist no human experts. For example, in modern automated manufacturing facilities, there is a need to predict machine failures before they occur by analyzing sensor readings. Because the machines are new, there are no human experts who can be interviewed by a programmer to provide the knowledge necessary to build a computer system. A machine learning system can study recorded data and subsequent machine failures and learn prediction rules. Second, there are problems where human experts exist, but where they are unable to explain their expertise. This is the case in many perceptual tasks, such as speech recognition, hand-writing recognition, and natural language understanding. Virtually all humans exhibit expert-level abilities on these tasks, but none of them can describe the detailed steps that they follow as they perform them. Fortunately, humans can provide machines with examples of the inputs and correct outputs for these tasks, so machine learning algorithms can learn to map the inputs to the outputs. Third, there are problems where phenomena are changing rapidly. In finance, for example, people would like to predict the future behavior of the stock market, of consumer purchases, or of exchange rates. These behaviors change frequently, so that even if a programmer could construct a good predictive computer program, it would need to be rewritten frequently. A learning program can relieve the programmer of this burden by constantly modifying and tuning a set of learned prediction rules. Fourth, there are applications that need to be customized for each computer user separately. Consider, for example, a program to filter unwanted electronic mail messages. Different users will need different filters. It is unreasonable to expect each user to program his or her own rules, and it is infeasible to provide every user with a software engineer to keep the rules up-to-date. A machine learning system can learn which mail messages the user rejects and maintain the filtering rules automatically. Machine learning addresses many of the same research questions as the fields of statistics, data mining, and psychology, but with differences of emphasis. Statistics focuses on understanding the phenomena that have generated the data, often with the goal of testing different hypotheses about those phenomena. Data mining seeks to find patterns in the data that are understandable by people. Psychological studies of human learning aspire to understand the mechanisms underlying the various learning behaviors exhibited by people (concept learning, skill acquisition, strategy change, etc.).

Machine learning

This paper presents a new reflectance model for rendering computer synthesized images. The model accounts for the relative brightness of different materials and light sources in the same scene. It describes the directional distribution of the reflected light and a color shift that occurs as the reflectance changes with incidence angle. The paper presents a method for obtaining the spectral energy distribution of the light reflected from an object made of a specific real material and discusses a procedure for accurately reproducing the color associated with the spectral energy distribution. The model is applied to the simulation of a metal and a plastic.

A reflectance model for computer graphics

There are many different ways for people to be educated and trained with regard to specific information and skills they need. These methods include classroom lectures with textbooks, computers, handheld devices, and other electronic appliances. The choice of learning innovation is dependent on an individual’s access to various technologies and the infrastructure environment of a person’s surrounding. In a rapidly changing society where there is a great deal of available information and knowledge, adopting and applying information at the right time and right place is needed to main efficiency in both school and business settings. Augmented Reality (AR) is one technology that dramatically shifts the location and timing of education and training. This literature review research describes Augmented Reality (AR), how it applies to education and training, and the potential impact on the future of education.

Augmented Reality in Education and Training

This paper introduces a method to extract 'Shape-DNA', a numerical fingerprint or signature, of any 2d or 3d manifold (surface or solid) by taking the eigenvalues (i.e. the spectrum) of its Laplace-Beltrami operator. Employing the Laplace-Beltrami spectra (not the spectra of the mesh Laplacian) as fingerprints of surfaces and solids is a novel approach. Since the spectrum is an isometry invariant, it is independent of the object's representation including parametrization and spatial position. Additionally, the eigenvalues can be normalized so that uniform scaling factors for the geometric objects can be obtained easily. Therefore, checking if two objects are isometric needs no prior alignment (registration/localization) of the objects but only a comparison of their spectra. In this paper, we describe the computation of the spectra and their comparison for objects represented by NURBS or other parametrized surfaces (possibly glued to each other), polygonal meshes as well as solid polyhedra. Exploiting the isometry invariance of the Laplace-Beltrami operator we succeed in computing eigenvalues for smoothly bounded objects without discretization errors caused by approximation of the boundary. Furthermore, we present two non-isometric but isospectral solids that cannot be distinguished by the spectra of their bodies and present evidence that the spectra of their boundary shells can tell them apart. Moreover, we show the rapid convergence of the heat trace series and demonstrate that it is computationally feasible to extract geometrical data such as the volume, the boundary length and even the Euler characteristic from the numerically calculated eigenvalues. This fact not only confirms the accuracy of our computed eigenvalues, but also underlines the geometrical importance of the spectrum. With the help of this Shape-DNA, it is possible to support copyright protection, database retrieval and quality assessment of digital data representing surfaces and solids. A patent application based on ideas presented in this paper is pending.

/pdf/laplace-beltrami-spectra-as-shape-dna-of-surfaces-and-solids-5djzm2hwdp.pdf

Laplace-Beltrami spectra as 'Shape-DNA' of surfaces and solids

The effects of hot axisymmetric compression to break down the primary carbide network of the H23 tool steels were studied. This current study only focused on one strain rate of 0.01 s -1 . The samples were deformed at 3 different temperatures (1000, 1050 and 1100 o C) with solutioning temperatures 1100 and 1250 o C, respectively. Afterwards, the samples were cooled by water quenching. The techniques used in this current study for investigation were the optical and electron microscopes and Vickers hardness test. The results show that hot axisymmetric compression had broken down the primary carbide network in the direction perpendicular to the compression axis and the carbides became finer. Although the highest hardness (274 HV) was achieved after solutioning at 1250 o C, followed by deformation at 1000 o C, however the microstructure analysis indicated that the optimum hot axisymmetric compression condition was solutioning at 1250 o C and deformation at 1000 o C.

Advanced Materials Research

Depth edge detection by image-based smoothing and morphological operations

Types of humor that robots can play

Automated registration of multi-view point clouds using sphere targets

In 3D computer graphics, it has been an important issue to describe a virtual object realistically and show it in a physically correct manner. Unlike the virtual reality(VR), the augmented reality (AR) is an environment containing virtual and real objects simultaneously. However, the difference in motion between the virtual and real objects is obvious, which makes it hard for AR to be realistic and attractive. To reduce this gap, we propose to apply physical attributes to each virtual object, which can be done by using a physics engine, a software library containing functions related with physics. For this purpose, an ODE (open dynamics engine) library is used. However, the direct use of the library in the AR environment poses a lot of problems. Most of all, the coordination of the coordinate systems between OSG (OpenSceneGraph) and ODE must be taken care of. Next the positions of markers are also needed to be considered. In this paper, such problems are addressed and solutions to them are discussed.

Introduction of Physics Simulation in Augmented Reality

In this paper, two radiation pattern-reconflgurable antennas are designed to operate near the frequency of 1.8GHz. The geometry of the proposed antennas is symmetric with respect to the vertical center line. The electrical shapes of the antennas are composed of a monopole-loaded loop and an open wire. The open wire functions as either a director or re∞ector for the two antennas. Depending on the switching state, the antennas can select between two beam directions with no input impedance difierence. The sizes of each antenna are then optimized to achieve beam switching capability using PIN diodes and FETs. The re∞ection coe-cients and gain patterns for two bias conditions using both switches are measured and compared with the simulated results. The measured results show that the proposed antennas can clearly alternate their beam directions using the switching components.

/pdf/a-compact-beam-reconfigurable-antenna-for-symmetric-beam-81fxsk35hj.pdf

A compact beam reconfigurable antenna for symmetric beam switching

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