Abstract
Physiological signals are often corrupted by noisy sources. Usually, artificial intelligence algorithms analyze the whole signal, regardless of its varying quality. Instead, experienced cardiologists search for a high-quality signal segment, where more accurate conclusions can be draw. We propose a methodology that simultaneously selects the optimal processing region of a physiological signal and determines its decoding into a state sequence of physiologically meaningful events. Our approach comprises two phases. First, the training of a neural network that then enables the estimation of the state probability distribution of a signal sample. Second, the use of the neural network output within an integer program. The latter models the problem of finding a time window by maximizing a likelihood function defined by the user. Our method was tested and validated in two types of signals, the phonocardiogram and the electrocardiogram. In phonocardiogram and electrocardiogram segmentation tasks, the system's sensitivity increased on average from 95.1% to 97.5% and from 78.9% to 83.8%, respectively, when compared to standard approaches found in the literature.

Abstract

Abstract

Abstract
Chronic wound assessment and wound healing are important for diagnostic, follow up and wound treatment. However, this growing disease affecting nearly 2 thousand million and 5.7 million people in the USA and Europe, costing around $20 billion and $8 thousand million USD per year, still relies on subjective human assessment of wounds. A scoping review allowed us to identify 109 articles that map the literature on the topic of computer vision for chronic wound assessment and healing. These results were carefully analyzed and mapped into relevant clinical challenges associated with this field, identifying the maturity of each different computer vision challenge that needs addressing. Results show that wound size and tissue type classification already have interesting work, but various other clinical areas are in need of larger datasets and computer vision research efforts for achieving a relevant impact in today's clinical routine. © 2020 IEEE.

Abstract
In this paper we study the energy saving potential of smart scale selection methods when using the Viola and Jones face detector running on smartphone devices. Our motivation is that cloud and edge-cloud multi-user environments may provide enough contextual information to create this type of scale selection algorithms. Given their non-trivial design, we must first inspect its actual benefits, before committing important research resources to actually produce relevant smart scale selection methods. Our experimental methodology in this paper assumes the optimum scenario of a perfect selection of scales for each image (drawn from ground truth annotation, using well-known public datasets), comparing it with the typical multi-scale geometrical progression approach of the Viola Jones algorithm, measuring both classification precision and recall, as well as algorithmic execution time and battery consumption on Android smartphone devices. Results show that if we manage to approximate this perfect scale selection, we obtain very significant energy savings, motivating a strong research investment on this topic.