Status of research at the Institute for Computer Applications in Science and Engineering (ICASE)

Research conducted at the Institute for Computer Applications in Science and Engineering in applied mathematics, numerical analysis and computer science is summarized

Teleoperation experiments with a Utah/MIT hand and a VPL DataGlove

A teleoperation system capable of controlling a Utah/MIT Dextrous Hand using a VPL DataGlove as a master is presented. Additionally the system is capable of running the dextrous hand in robotic (autonomous) mode as new programs are developed. The software and hardware architecture used is presented and the experiments performed are described. The communication and calibration issues involved are analyzed and applications to the analysis and development of automated dextrous manipulations are investigated

MULTI-PLAYER BELIEF CALCULI: MODELS AND APPLICATIONS

In developing methods for dealing with uncertainty in reasoning systems, it is important to consider the needs of the target applications. In particular, when the source of inferential uncertainty can be tracked to distributions of expert opinions, there might be different ways to model the representation and combination of these opinions. In this paper we present the notion of multiplayer belief calculi - a framework that takes into consideration not only the 'regular' type of evidential uncertainty, but also the diversity of expert opinions when the evidence is held fixed. Using several applied examples, we show how the basic framework can be naturally extended to support different application needs and different sets of assumptions about the nature of the inference process.Information Systems Working Papers Serie

Using Neural Networks for Image Classification

This paper will focus on applying neural network machine learning methods to images for the purpose of automatic detection and classification. The main advantage of using neural network methods in this project is its adeptness at fitting non­linear data and its ability to work as an unsupervised algorithm. The algorithms will be run on common, publically available datasets, namely the MNIST and CIFAR­10, so that our results will be easily reproducible

The Last Scientific Revolution

Critically growing problems of fundamental science organisation and content are analysed with examples from physics and emerging interdisciplinary fields. Their origin is specified and new science structure (organisation and content) is proposed as a unified solution

Indoor Semantic Segmentation using depth information

This work addresses multi-class segmentation of indoor scenes with RGB-D inputs. While this area of research has gained much attention recently, most works still rely on hand-crafted features. In contrast, we apply a multiscale convolutional network to learn features directly from the images and the depth information. We obtain state-of-the-art on the NYU-v2 depth dataset with an accuracy of 64.5%. We illustrate the labeling of indoor scenes in videos sequences that could be processed in real-time using appropriate hardware such as an FPGA.Comment: 8 pages, 3 figure