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Speech contains patterns that can be altered by the mood of an individual. There is an increasing focus on automated and distributed methods to collect and monitor speech from large groups of patients suffering from mental health disorders. However, as the scope of these collections increases, the variability in the data also increases. This variability is due in part to the range in the quality of the devices, which in turn affects the quality of the recorded data, negatively impacting the accuracy of automatic assessment.

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In medical imaging, the acquired images are usually analyzed by a human observer and rated with respect to a diagnostic question. However, this procedure is time-demanding and expensive. Furthermore, the lack of a reference image makes this task challenging. In order to support the human observer in assessing image quality and to ensure an objective evaluation, we extend in this paper our previous no-reference magnetic resonance (MR) image quality assessment system with an active learning loop to reduce the amount of necessary labeled training data.

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In this paper, we present Discriminant Correlation Analysis (DCA), a feature level fusion technique that incorporates the class associations in correlation analysis of the feature sets. DCA performs an effective feature fusion by maximizing the pair-wise correlations across the two feature sets, and at the same time, eliminating the between-class correlations and restricting the correlations to be within classes.

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In this paper, we study the problem of generating uniform random
point samples on a domain of d-dimensional space based on a minimum
distance criterion between point samples (Poisson-disk sampling
or PDS). First, we formally define PDS via the pair correlation
function (PCF) to quantitatively evaluate properties of the sampling
process. Surprisingly, none of the existing PDS techniques
satisfy both uniformity and minimum distance criterion, simultaneously.
These approaches typically create an approximate PDS with

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A main goal of data visualization is to find, from among all the available alternatives, mappings to the 2D/3D display which are relevant to the user. Assuming user interaction data, or other auxiliary data about the items or their relationships, the goal is to identify which aspects in the primary data support the user’s input and, equally importantly, which aspects of the user’s potentially noisy input have support in the primary data.

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Crises or large-scale emergencies such as earthquakes and hurricanes cause massive damage to lives and property. Crisis response is an essential task to mitigate the impact of a crisis. An effective response to a crisis necessitates information gathering and analysis. Traditionally, this process has been restricted to the information collected by first responders on the ground in the affected region or by official agencies such as local governments involved in the response.

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Crises and situations of mass emergency such as earthquakes and hurricanes cause massive damage to lives and property. Crisis response is an essential task to mitigate the impact of a crisis. An effective response to a crisis necessitates information gathering and analysis. Traditionally this process has been restricted to the information collected by first responders on the ground in the affected region or official agencies such as local governments involved in the response.

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This work proposes a learning-based energy management policy that takes into consideration the trade-off between the depth-of-discharge (DoD) and the lifetime of batteries. The impact of DoD on the energy management policy is often neglected in the past due to the inability to model its effect on the marginal cost per battery usage. In this work, a novel battery cost evaluation method that takes into consideration the DoD of each battery usage is proposed, and is utilized to devise the day-ahead energy management policy using reinforcement learning and linear value-function approximations.

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