2D and 3D Pointing Device Based on a Passive Lights Detection Operation Method Using One Camera

Systems for surface-free pointing and/or command input include a computing device operably linked to an imaging device. The imaging device can be any suitable video recording device including a conventional webcam. At least one pointing/input device is provided including first, second, and third sets of actuable light sources, wherein at least the first and second sets emit differently colored light. The imaging device captures one or more sequential image frames each including a view of a scene including the activated light sources. One or more computer program products calculate a two-dimensional or three-dimensional position and/or a motion and/or an orientation of the pointing/ input device in the captured image frames by identifying a two-dimensional or three-dimensional position of the activated light sources of the first, second, and/or third sets of light sources. Certain activation patterns of light sources are mapped to particular pointing and/or input commands

SVM-Based Negative Data Mining to Binary Classification

The properties of training data set such as size, distribution and the number of attributes significantly contribute to the generalization error of a learning machine. A not well-distributed data set is prone to lead to a partial overfitting model. Two approaches proposed in this dissertation for the binary classification enhance useful data information by mining negative data. First, an error driven compensating hypothesis approach is based on Support Vector Machines (SVMs) with (1+k)-iteration learning, where the base learning hypothesis is iteratively compensated k times. This approach produces a new hypothesis on the new data set in which each label is a transformation of the label from the negative data set, further producing the positive and negative child data subsets in subsequent iterations. This procedure refines the base hypothesis by the k child hypotheses created in k iterations. A prediction method is also proposed to trace the relationship between negative subsets and testing data set by a vector similarity technique. Second, a statistical negative example learning approach based on theoretical analysis improves the performance of the base learning algorithm learner by creating one or two additional hypotheses audit and booster to mine the negative examples output from the learner. The learner employs a regular Support Vector Machine to classify main examples and recognize which examples are negative. The audit works on the negative training data created by learner to predict whether an instance is negative. However, the boosting learning booster is applied when audit does not have enough accuracy to judge learner correctly. Booster works on training data subsets with which learner and audit do not agree. The classifier for testing is the combination of learner, audit and booster. The classifier for testing a specific instance returns the learner\u27s result if audit acknowledges learner\u27s result or learner agrees with audit\u27s judgment, otherwise returns the booster\u27s result. The error of the classifier is decreased to O(e^2) comparing to the error O(e) of a base learning algorithm

Subdivision Surface-Based Geometric Modeling System

A method for surface modeling of images to produce realistic images or to provide simulations with accurate surface information is provided. More particularly, the present invention relates to a new subdivision depth computation technique and to an improved label-driven adaptive subdivision technique for use in Catmull-Clark subdivision surface modeling systems. The method comprises computing a subdivision depth to determine the number of recursive subdivisions which may be performed on a control mesh to generate a plurality of finer mesh elements while preserving a predetermined error tolerance, and using the computed subdivision depth to construct an adaptively refined mesh that is substantially similar to the control mesh within the predetermined error tolerance. Limit control surfaces with and without extraordinary vertices may be analysed using the method of the invention. In another aspect, a software program for accomplishing the method of the present invention is provided

Designing a Multiagent System for Course-Offering Determination

I attended a doctoral symposium of the conference. It was very good to know about how to guide PhD students to conduct high-quality research and complete PhD program. I attended all the keynote sessions of the conference. The presentation on Computational Disaster Management by Professor Pascal Van Hentenryck was very insightful and encouraging. The talk on “Agents might not be people” by Professor Nigel Gilbert was illuminating. The talk on “Satisfiability to Linear Algebra” by Professor Fangzhen Lin was revealing. I attended almost all sections of PRIMA 2013 and some presentations of AI 2013. They reflect the advancement of the field. The discussions with the people on-site were very interesting and helpful to my future research. Also, it was great to talk to active researchers in the field and exchanged ideas of our research and explored the possibility of collaboration.This paper describes the design of a multiagent system that facilitates course-offering decision making for a program in an institution. We first model course offering determination for upcoming semester as a multi-winner election with exogenous constraints which is a problem for computational social choice in multiagent systems, which has rarely been considered. Then, the paper describes the architecture and models of the multiagent system for course offering determination with Gaia role model methodology, TROPOS strategic actor diagram, Agent Unified Modeling Language (AUML) sequence diagram for a multi-agent negotiation interaction protocol, and Pseudo-code algorithms for generating fractional votes and course election protocol. A novel course selection preference model for students has been proposed and described formally. The effectiveness of the approach and the implemented system has been showed with the initial experimental results

Design and Realization of Automatic Warehouse Based on S7-1500PLC

In order to improve the efficiency of automatic warehouse control system,the experimental platform of stereoscopic warehouse with s7-1500plc is designed.The manipulator is driven by stepper motor and servo motor to realize x,y and Z three-axis space motion.The material transmission system is built by general-purpose G120 inverter. HMI KTP700 realizes control and status monitoring.The materials are identified and classified by RFID sensor and other sensors.TIAV15 software build PROFINET communication and PROFIBUS communication network.Using the GRAPH language programming can improve the visualization degree of application and solve the complex problems of program design and debugging of the warehouse control system.Through the design of hardware and software,a set of complete control system design scheme is formed,which has high practical value and provides an excellent teaching and experiment platform for the intelligent storage system