187 research outputs found
Measuring Blood Glucose Concentrations in Photometric Glucometers Requiring Very Small Sample Volumes
Glucometers present an important self-monitoring tool for diabetes patients
and therefore must exhibit high accu- racy as well as good usability features.
Based on an invasive, photometric measurement principle that drastically
reduces the volume of the blood sample needed from the patient, we present a
framework that is capable of dealing with small blood samples, while
maintaining the required accuracy. The framework consists of two major parts:
1) image segmentation; and 2) convergence detection. Step 1) is based on
iterative mode-seeking methods to estimate the intensity value of the region of
interest. We present several variations of these methods and give theoretical
proofs of their convergence. Our approach is able to deal with changes in the
number and position of clusters without any prior knowledge. Furthermore, we
propose a method based on sparse approximation to decrease the computational
load, while maintaining accuracy. Step 2) is achieved by employing temporal
tracking and prediction, herewith decreasing the measurement time, and, thus,
improving usability. Our framework is validated on several real data sets with
different characteristics. We show that we are able to estimate the underlying
glucose concentration from much smaller blood samples than is currently
state-of-the- art with sufficient accuracy according to the most recent ISO
standards and reduce measurement time significantly compared to
state-of-the-art methods
Interaction features for prediction of perceptual segmentation:Effects of musicianship and experimental task
As music unfolds in time, structure is recognised and understood by listeners, regardless of their level of musical expertise. A number of studies have found spectral and tonal changes to quite successfully model boundaries between structural sections. However, the effects of musical expertise and experimental task on computational modelling of structure are not yet well understood. These issues need to be addressed to better understand how listeners perceive the structure of music and to improve automatic segmentation algorithms. In this study, computational prediction of segmentation by listeners was investigated for six musical stimuli via a real-time task and an annotation (non real-time) task. The proposed approach involved computation of novelty curve interaction features and a prediction model of perceptual segmentation boundary density. We found that, compared to non-musicians’, musicians’ segmentation yielded lower prediction rates, and involved more features for prediction, particularly more interaction features; also non-musicians required a larger time shift for optimal segmentation modelling. Prediction of the annotation task exhibited higher rates, and involved more musical features than for the real-time task; in addition, the real-time task required time shifting of the segmentation data for its optimal modelling. We also found that annotation task models that were weighted according to boundary strength ratings exhibited improvements in segmentation prediction rates and involved more interaction features. In sum, musical training and experimental task seem to have an impact on prediction rates and on musical features involved in novelty-based segmentation models. Musical training is associated with higher presence of schematic knowledge, attention to more dimensions of musical change and more levels of the structural hierarchy, and higher speed of musical structure processing. Real-time segmentation is linked with higher response delays, less levels of structural hierarchy attended and higher data noisiness than annotation segmentation. In addition, boundary strength weighting of density was associated with more emphasis given to stark musical changes and to clearer representation of a hierarchy involving high-dimensional musical changes.peerReviewe
A feasibility study on pairing a smartwatch and a mobile device through multi-modal gestures
Pairing is the process of establishing an association between two personal devices. Although such a process is intuitively very simple, achieving a straightforward and secure association is challenging due to several possible attacks and usability-related issues. Indeed, malicious attackers might want to spoof the communication between devices in order to gather sensitive information or harm them. Moreover, offering users simple and usable schemes which attain a high level of security remains a major issue. In addition, due to the great diversity of pairing scenarios and equipment, achieving a single, usable, secure association for all possible devices and use cases is simply not possible.
In this thesis, we study the feasibility of a novel pairing scheme based on multi-modal gestures, namely, gestures involving drawing supported by accelerometer data. In particular, a user can pair a smart-watch on his wrist and a mobile device (e.g., a smart-phone) by simply drawing with a finger on the screen at the device.
To this purpose, we developed mobile applications for smart-watch and smart-phone to sample and process sensed data in support of a secure commitment-based protocol. Furthermore, we performed experiments to verify whether encoded matching-movements have a clear similarity compared to non-matching movements.
The results proved that it is feasible to implement such a scheme which also offers users a natural way to perform secure pairing. This innovative scheme may be adopted by a large number of mobile devices (e.g., smart-watches, smart-phones, tablets, etc.) in different scenarios
The Conditional Cauchy-Schwarz Divergence with Applications to Time-Series Data and Sequential Decision Making
The Cauchy-Schwarz (CS) divergence was developed by Pr\'{i}ncipe et al. in
2000. In this paper, we extend the classic CS divergence to quantify the
closeness between two conditional distributions and show that the developed
conditional CS divergence can be simply estimated by a kernel density estimator
from given samples. We illustrate the advantages (e.g., the rigorous
faithfulness guarantee, the lower computational complexity, the higher
statistical power, and the much more flexibility in a wide range of
applications) of our conditional CS divergence over previous proposals, such as
the conditional KL divergence and the conditional maximum mean discrepancy. We
also demonstrate the compelling performance of conditional CS divergence in two
machine learning tasks related to time series data and sequential inference,
namely the time series clustering and the uncertainty-guided exploration for
sequential decision making.Comment: 23 pages, 7 figure
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