Development of a task-level robot programming and simulation system

An ongoing project in developing a Task-Level Robot Programming and Simulation System (TARPS) is discussed. The objective of this approach is to design a generic TARPS that can be used in a variety of applications. Many robotic applications require off-line programming, and a TARPS is very useful in such applications. Task level programming is object centered in that the user specifies tasks to be performed instead of robot paths. Graphics simulation provides greater flexibility and also avoids costly machine setup and possible damage. A TARPS has three major modules: world model, task planner and task simulator. The system architecture, design issues and some preliminary results are given

Personal navigation via high-resolution gait-corrected inertial measurement units

Journal ArticleAbstract-In this paper, a personal micronavigation system that uses high-resolution gait-corrected inertial measurement units is presented. The goal of this paper is to develop a navigation system that uses secondary inertial variables, such as velocity, to enable long-term precise navigation in the absence of Global Positioning System (GPS) and beacon signals. In this scheme, measured zerovelocity duration from the ground reaction sensors is used to reset the accumulated integration errors from accelerometers and gyroscopes in position calculation. With the described system, an average position error of 4 m is achieved at the end of half-hour walks

Personal navigation via shoe mounted inertial measurement units

Journal ArticleWe are developing a personal micronavigation system that uses high-resolution gait-corrected inertial measurement units. The goal of this project is to develop a navigation system that use secondary inertial variables, such as velocity, to enable long-term precise navigation in the absence of Global Positioning System (GPS) and beacon signals. In this scheme, measured zero velocity durations from the ground reaction sensors are used to reset the accumulated integration errors from the accelerometers and gyroscopes in position calculation. We achieved an average position error of 4 meters at the end of half-hour walks

A novel and robust parameter training approach for HMMs under noisy and partial access to states

Cataloged from PDF version of article.This paper proposes a new estimation algorithm for the parameters of an HMM as to best account for the observed data. In this model, in addition to the observation sequence, we have partial and noisy access to the hidden state sequence as side information. This access can be seen as "partial labeling" of the hidden states. Furthermore, we model possible mislabeling in the side information in a joint framework and derive the corresponding EM updates accordingly. In our simulations, we observe that using this side information, we considerably improve the state recognition performance, up to 70%, with respect to the "achievable margin" defined by the baseline algorithms. Moreover, our algorithm is shown to be robust to the training conditions. (C) 2013 Elsevier B.V. All rights reserved

Morphological evaluation of the mandibular lingula using cone-beam computed tomography

Background: Lingula is a tongue-like flap of bone that overlaps the mandibular foramen antero-medially and location is clinically significant in oral and maxillofacial surgeries. The aim of this study was to assess the shape and precise location of lingula using cone-beam computed tomography (CBCT). Materials and methods: In this study, 3-dimensional images provided by CBCT of 63 patients (28 females, 35 males, age range 25–70 years) were retrospectively evaluated. All CBCT images were performed due to implant planning. From both sides of 63 mandibles were classified in the following shapes: triangular, truncated, nodular and assimilated. The location was determined by 5 distances from the lingula: the anterior and the posterior borders of the mandibular ramus, man­dibular notch and lover border of mandible. Height of the lingula was measured from the lingular tip to the mandibular foramen. Results: Nodular shape of lingula was the most commonly found (32.5%). The mean distance of lingula from anterior and posterior borders of mandibular ramus was 18.5 ± 2.3 and 16.9 ± 3.5 mm, respectively. The lingula was located at 18.1 ± 3.6 mm from the mandibular notch and 38.3 mm from the lover border of mandible. The mean height of lingula was 7.8 ± 2.4 mm. Conclusions: The present study provides the morphological variation and localization of the lingula. The lingula is an important clinical landmark for mandibular osteotomy, and for determining the distance to mandibular foramen entrance

Online Anomaly Detection Under Markov Statistics With Controllable Type-I Error

We study anomaly detection for fast streaming temporal data with real time Type-I error, i.e., false alarm rate, controllability; and propose a computationally highly efficient online algorithm, which closely achieves a specified false alarm rate while maximizing the detection power. Regardless of whether the source is stationary or nonstationary, the proposed algorithm sequentially receives a time series and learns the nominal attributes - in the online setting - under possibly varying Markov statistics. Then, an anomaly is declared at a time instance, if the observations are statistically sufficiently deviant. Moreover, the proposed algorithm is remarkably versatile since it does not require parameter tuning to match the desired rates even in the case of strong nonstationarity. The presented study is the first to provide the online implementation of Neyman-Pearson (NP) characterization for the problem such that the NP optimality, i.e., maximum detection power at a specified false alarm rate, is nearly achieved in a truly online manner. In this regard, the proposed algorithm is highly novel and appropriate especially for the applications requiring sequential data processing at large scales/high rates due to its parameter-tuning free computational efficient design with the practical NP constraints under stationary or non-stationary source statistics. © 2015 IEEE