Outdoor autonomous navigation using SURF features

In this article, we propose a speeded-up robust features (SURF)-based approach for outdoor autonomous navigation. In this approach, we capture environmental images using an omni-directional camera and extract features of these images using SURF. We treat these features as landmarks to estimate a robot’s self-location and direction of motion. SURF features are invariant under scale changes and rotation, and are robust under image noise, changes in light conditions, and changes of viewpoint. Therefore, SURF features are appropriate for the self-location estimation and navigation of a robot. The mobile robot navigation method consists of two modes, the teaching mode and the navigation mode. In the teaching mode, we teach a navigation course. In the navigation mode, the mobile robot navigates along the teaching course autonomously. In our experiment, the outdoor teaching course was about 150 m long, the average speed was 2.9 km/h, and the maximum trajectory error was 3.3 m. The processing time of SURF was several times shorter than that of scale-invariant feature transform (SIFT). Therefore, the navigation speed of the mobile robot was similar to the walking speed of a person.     

Character confusion versus focus word-based correction of spelling and OCR variants in corpora

We present a new approach based on anagram hashing to handle globally the lexical variation in large and noisy text collections. Lexical variation addressed by spelling correction systems is primarily typographical variation. This is typically handled in a local fashion: given one particular text string some system of retrieving near-neighbors is applied, where near-neighbors are other text strings that differ from the particular string by a given number of characters. The difference in characters between the original string and one of its retrieved near-neighbors constitutes a particular character confusion. We present a global way of performing this action: for all possible particular character confusions given a particular edit distance, we sequentially identify all the pairs of text strings in the text collection that display a particular confusion. We work on large digitized corpora, which contain lexical variation due to both the OCR process and typographical or typesetting error and show that all these types of variation can be handled equally well in the framework we present. The character confusion-based prototype of Text-Induced Corpus Clean-up (ticcl) is compared to its focus word-based counterpart and evaluated on 6 years’ worth of digitized Dutch Parliamentary documents. The character confusion approach is shown to gain an order of magnitude in speed on its word-based counterpart on large corpora. Insights gained about the useful contribution of global corpus variation statistics are shown to also benefit the more traditional word-based approach to spelling correction. Final tests on a held-out set comprising the 1918 edition of the Dutch daily newspaper ‘Het Volk’ show that the system is not sensitive to domain variation.     

An Experimental Economics Investigation of Shipper-carrier Interactions in the Choice of Mode and Shipment Size in Freight Transport

The paper discusses the theoretical and empirical evidence on the subject and concludes that freight mode choice can be best understood as the outcome of interactions between shippers and carriers, and that mode choice depends to a large extent on the shipment size that results from shipper-carrier interactions. These conclusions are supported by economic experiments designed to test the hypothesis of cooperative behavior. This was accomplished by conducting two sets of experiments (ones with the shipper playing the lead role in selecting the shipment size; and others in which the shipment size decision was left to the carriers), and by comparing their results to the ones obtained numerically under the assumption of perfect cooperation. The comparison of results indicated that the experiments converged to the perfect cooperation case. This is in line with the conclusion from game theory that indicates that under typical market conditions the shipper and carrier would cooperate. These results also imply that it really does not matter who “makes” the decision about the shipment size and mode to be used at a given time period, as over time the shipper—that is the customer—ends up selecting the bids more consistent with its own interest. In other words, these results do not support the assumption that freight mode choice is solely made by the carriers.     

Iterative delta debugging

Automated debugging attempts to locate the reason for a failure. Delta debugging minimizes the difference between two inputs, where one input is processed correctly while the other input causes a failure, using a series of test runs to determine the outcome of applied changes. Delta debugging is applicable to inputs or to the program itself, as long as a correct version of the program exists. However, complex errors are often masked by other program defects, making it impossible to obtain a correct version of the program through delta debugging in such cases. Iterative delta debugging extends delta debugging and removes a series of defects step by step, until the originally unresolved defect is isolated. The method is automated and managed to localize a bug in some real-life examples.     

Cognitive performance-altering effects of electronic medical records: an application of the human factors paradigm for patient safety

According to the human factors paradigm for patient safety, health care work systems and innovations such as electronic medical records do not have direct effects on patient safety. Instead, their effects are contingent on how the clinical work system, whether computerized or not, shapes health care providers’ performance of cognitive work processes. An application of the human factors paradigm to interview data from two hospitals in the Midwest United States yielded numerous examples of the performance-altering effects of electronic medical records, electronic clinical documentation, and computerized provider order entry. Findings describe both improvements and decrements in the ease and quality of cognitive performance, both for interviewed clinicians and for their colleagues and patients. Changes in cognitive performance appear to have desirable and undesirable implications for patient safety as well as for quality of care and other important outcomes. Cognitive performance can also be traced to interactions between work system elements, including new technology, allowing for the discovery of problems with “fit” to be addressed through design interventions.     

Improving the non-extensive medical image segmentation based on Tsallis entropy

Thresholding techniques for image segmentation is one of the most popular approaches in Computational Vision systems. Recently, M. Albuquerque has proposed a thresholding method (Albuquerque et al. in Pattern Recognit Lett 25:1059–1065, 2004) based on the Tsallis entropy, which is a generalization of the traditional Shannon entropy through the introduction of an entropic parameter q. However, the solution may be very dependent on the q value and the development of an automatic approach to compute a suitable value for q remains also an open problem. In this paper, we propose a generalization of the Tsallis theory in order to improve the non-extensive segmentation method. Specifically, we work out over a suitable property of Tsallis theory, named the pseudo-additive property, which states the formalism to compute the whole entropy from two probability distributions given an unique q value. Our idea is to use the original M. Albuquerque’s algorithm to compute an initial threshold and then update the q value using the ratio of the areas observed in the image histogram for the background and foreground. The proposed technique is less sensitive to the q value and overcomes the M. Albuquerque and k-means algorithms, as we will demonstrate for both ultrasound breast cancer images and synthetic data.     

Localization algorithm using a virtual label for a mobile robot in indoor and outdoor environments

Scanning laser range sensors provide range data consisting of a set of point measurements. The laser sensor URG-04LX has a distance range of approximately 0.02–4 m and a scanning angle range of 240°. Usually, such an image range is acquired from one viewpoint by “moving” the laser beam using rotating mirrors/prisms. The orientation of the laser beam can easily be measured and converted into the coordinates of the image. This article conducts localization using virtual labels with data about distances in the environment obtained from 2D distance laser sensors. This method puts virtual labels on special features and points which are along the mobile robot’s path. The current location is calculated by combining the virtual label and the range image of the laser range finder.