252 research outputs found
Robot Autonomy for Surgery
Autonomous surgery involves having surgical tasks performed by a robot
operating under its own will, with partial or no human involvement. There are
several important advantages of automation in surgery, which include increasing
precision of care due to sub-millimeter robot control, real-time utilization of
biosignals for interventional care, improvements to surgical efficiency and
execution, and computer-aided guidance under various medical imaging and
sensing modalities. While these methods may displace some tasks of surgical
teams and individual surgeons, they also present new capabilities in
interventions that are too difficult or go beyond the skills of a human. In
this chapter, we provide an overview of robot autonomy in commercial use and in
research, and present some of the challenges faced in developing autonomous
surgical robots
Land cover classification using fuzzy rules and aggregation of contextual information through evidence theory
Land cover classification using multispectral satellite image is a very
challenging task with numerous practical applications. We propose a multi-stage
classifier that involves fuzzy rule extraction from the training data and then
generation of a possibilistic label vector for each pixel using the fuzzy rule
base. To exploit the spatial correlation of land cover types we propose four
different information aggregation methods which use the possibilistic class
label of a pixel and those of its eight spatial neighbors for making the final
classification decision. Three of the aggregation methods use Dempster-Shafer
theory of evidence while the remaining one is modeled after the fuzzy k-NN
rule. The proposed methods are tested with two benchmark seven channel
satellite images and the results are found to be quite satisfactory. They are
also compared with a Markov random field (MRF) model-based contextual
classification method and found to perform consistently better.Comment: 14 pages, 2 figure
Strengthening SMEs to make export competitive
The importance of SMEs in any economy cannot be overlooked as they form a major chunk in the economic activity of nations. India has nearly three million SMEs, which account for almost 50 per cent of industrial output. However, SMEs which form the backbone of industrial development in India are not export competitive and contribute only about 34 percent of exports. It is this feature of the SMEs that make it an ideal target to realize its potential export competitive. Drawing from the experiences of countries that have successfully promoted the export competitiveness of SMEs, this paper has identified ways in which SMEs in India can have an access to external markets through exports, which include simplification of procedures, incentives for higher production, preferential treatments to SMEs in the market development fund, linking up SMEs with Transnational Companies or large domestic exporting firms; and formation of clusters and networks in order to reinforce their external competitiveness.Small and Medium Enterprises (SMEs), SWOT Analysis, Export, India
Feature selection simultaneously preserving both class and cluster structures
When a data set has significant differences in its class and cluster
structure, selecting features aiming only at the discrimination of classes
would lead to poor clustering performance, and similarly, feature selection
aiming only at preserving cluster structures would lead to poor classification
performance. To the best of our knowledge, a feature selection method that
simultaneously considers class discrimination and cluster structure
preservation is not available in the literature. In this paper, we have tried
to bridge this gap by proposing a neural network-based feature selection method
that focuses both on class discrimination and structure preservation in an
integrated manner. In addition to assessing typical classification problems, we
have investigated its effectiveness on band selection in hyperspectral images.
Based on the results of the experiments, we may claim that the proposed
feature/band selection can select a subset of features that is good for both
classification and clustering
Nonlinear Dimensionality Reduction for Data Visualization: An Unsupervised Fuzzy Rule-based Approach
Here, we propose an unsupervised fuzzy rule-based dimensionality reduction
method primarily for data visualization. It considers the following important
issues relevant to dimensionality reduction-based data visualization: (i)
preservation of neighborhood relationships, (ii) handling data on a non-linear
manifold, (iii) the capability of predicting projections for new test data
points, (iv) interpretability of the system, and (v) the ability to reject test
points if required. For this, we use a first-order Takagi-Sugeno type model. We
generate rule antecedents using clusters in the input data. In this context, we
also propose a new variant of the Geodesic c-means clustering algorithm. We
estimate the rule parameters by minimizing an error function that preserves the
inter-point geodesic distances (distances over the manifold) as Euclidean
distances on the projected space. We apply the proposed method on three
synthetic and three real-world data sets and visually compare the results with
four other standard data visualization methods. The obtained results show that
the proposed method behaves desirably and performs better than or comparable to
the methods compared with. The proposed method is found to be robust to the
initial conditions. The predictability of the proposed method for test points
is validated by experiments. We also assess the ability of our method to reject
output points when it should. Then, we extend this concept to provide a general
framework for learning an unsupervised fuzzy model for data projection with
different objective functions. To the best of our knowledge, this is the first
attempt to manifold learning using unsupervised fuzzy modeling
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