Abstract
Background: Malaria is a leading cause of death and disease in many developing countries, where young children and pregnant women are the most affected groups. In 2012, there were an estimated 207 million cases of malaria, which caused approximately 627 000 malaria deaths. Around 80% of malaria cases occur in Africa, where the lack of access to malaria diagnosis is largely due to a shortage of expertise, being the shortage of equipment the secondary factor. This lack of expertise for malaria diagnosis frequently results on the increase of false positives, since prescription of medication is based only on symptoms. Thus, there is an urgent need of new tools that can facilitate the rapid and easy diagnosis of malaria, especially in areas with limited access to quality healthcare services. Methods: Various image processing and analysis approaches already proposed on the literature for the detection and segmentation of malaria parasites in blood smear microscopic images were collected and reviewed. This timely review aims to support the increasing interest in the development of low cost tools that can facilitate the rapid and easy diagnosis of malaria, especially in areas with limited access to quality healthcare services. Results: Malaria parasites detection and segmentation techniques in microscopic images are, in general, still in need of improvement and further testing. Most of the methodologies reviewed in this work were tested with a limited number of images, and more studies with significantly larger datasets for the evaluation of the proposed approaches are needed. Despite promising results reported during the past years, the great majority of the computer-aided methods found on the literature for malaria diagnosis are based on images acquired under well controlled conditions and with proper microscopic equipment. However, one should take into account that 80% of malaria cases occur in Africa, where this type of equipment is scarce or even nonexistent in common healthcare facilities. Conclusion: This work collects and reviews various image processing and analysis approaches already proposed on the literature for the detection and segmentation of malaria parasites in blood smear microscopic images. This timely review aims to support the increasing interest in the development of image processing-based systems to be used in rural areas of developing countries, which might be the next future trend in malaria computer-aided diagnosis. © 2016 Bentham Science Publishers.

Abstract

Abstract
An estimated 214 million cases of malaria were detected in 2015, which caused approximately 438 000 deaths. Around 90% of those cases occurred in Africa, where the lack of access to malaria diagnosis is largely due to shortage of expertise and equipment. Thus, the importance to develop new tools that facilitate the rapid and easy diagnosis of malaria for areas with limited access to healthcare services cannot be overstated. This paper presents an image processing and analysis methodology using supervised classification to assess the presence of P. falciparum trophozoites and white blood cells in Giemsa stained thick blood smears. The main differential factor is the usage of microscopic images exclusively acquired with low cost and accessible tools such as smartphones, using a dataset of 194 images manually annotated by an experienced parasilogist. Using a SVM classifier and a total of 314 image features extracted for each candidate, the automatic detection of trophozoites detection achieved a sensitivity of 80.5% and a specificity of 93.8%, while the white blood cells achieved 98.2% of sensitivity and 72.1% specificity. (C) 2016 The Authors. Published by Elsevier B.V.

Abstract

Abstract
This paper presents an autonomous navigation and position estimation framework which enables an Unmanned Aerial Vehicle (UAV) to possess the ability to safely navigate in indoor environments. This system uses both the on-board Inertial Measurement Unit (IMU) and the front camera of a AR. Drone platform and a laptop computer were all the data is processed. The system is composed of the following modules: navigation, door detection and position estimation. For the navigation part, the system relies on the detection of the vanishing point using the Hough transform for wall detection and avoidance. The door detection part relies not only on the detection of the contours but also on the recesses of each door using the latter as the main detector and the former as an additional validation for a higher precision. For the position estimation part, the system relies on pre-coded information of the floor in which the drone is navigating, and the velocity of the drone provided by its IMU. Several flight experiments show that the drone is able to safely navigate in corridors while detecting evident doors and estimate its position. The developed navigation and door detection methods are reliable and enable an UAV to fly without the need of human intervention.

Abstract
Parkinson's disease (PD) is often responsible for difficulties in interacting with smartphones; however, research has not yet addressed these issues and how these challenge people with Parkinson's (PwP). This paper specifically investigates the symptoms and characteristics of PD that may influence the interaction with smartphones to then contribute in this direction. The research was based on a literature review of PD symptoms, eight semi-structured interviews with healthcare professionals and observations of PwP, and usability experiments with 39 PwP. Contributions include a list of PD symptoms that may influence the interaction with smartphones, a set of experimental results that evaluated the performance of four gestures tap, swipe, multiple-tap, and drag and 12 user interface design guidelines for creating smartphone user interfaces for PwP. Findings contribute to the work of researchers and practitioners' alike engaged in designing user interfaces for PwP or the broader area of inclusive design.