Strategies for Mobile Web Design

This paper presents a literature review on the topic of web design, specifically with regard to mobile web design. The aim of the review is to identify and analyze major strategies and approaches to design for small-screen-size devices. Three strategies consistently appeared across the reviewed literature, namely, responsive web design, adaptive web design, and separate site. The analysis of these strategies intends to provide a clear understanding of their advantages and disadvantages, in terms of cost and user experience

The Internet of Things in Healthcare. An Overview

La prestación de servicios de salud está experimentando enormes cambios alrededor del mundo. El envejecimiento de la población, la creciente incidencia de enfermedades crónicas, y la escasez de recursos se están convirtiendo en una carga pesada para los actuales sistemas de salud y podrían comprometer la prestación de servicios de salud en las próximas décadas. Por otro lado, la creciente popularidad de dispositivos para el cuidado de la salud y el bienestar, junto con avances en comunicaciones inalámbricas y en sensores abren la puerta a nuevos modelos para la prestación de servicios de salud respaldados por el Internet de las cosas (IoT). Este artículo presenta una revisión general de las tendencias que están impulsando el desarrollo de aplicaciones para el cuidado de la salud basadas en IoT, y las describe brevemente a nivel de sistema.The provision of healthcare is experimenting enormous changes worldwide. Population ageing, rising incidence of chronic diseases, and shortages of resources are placing a heavy burden in current healthcare systems and have the potential to risk the delivery of healthcare in the next few decades. On the other hand, the growing popularity of smart devices for healthcare and wellness, along with advances in wireless communications and sensors are opening the door to novel models of health care delivery supported by the Internet of things (IoT). This paper presents a review of the trends that are driving the development of IoT-based applications for healthcare and briefly describe them at a system level

Compresión Digital en Imágenes Médicas

Imaging technology has long played a principal role in the medical domain, and as such, its use is widespread in the diagnosis and treatment of numerous health conditions. Concurrently, new developments in imaging techniques and sensor technology make possible the acquisition of increasingly detailed images of several organs of the human body. This improvement is indeed advantageous for medical practitioners. However, it comes to a cost in the form of storage and telecommunication infrastructures needed to handle high-resolution images reliably. Ordinarily, digital compression is a mainstay in the efficient management of digital media, including still images and video. From a technical point of view, medical imaging could take full advantage of digital compression technology. However, nuances unique to medical data impose constraints to the application of digital compression in medical images. This paper presents an overview of digital compression in the context of still medical images, along with a brief discussion on related regulatory and legal implications.La Imagenología desempeña un papel protagónico en el campo médico, siendo su uso ampliamente generalizado en el diagnóstico y tratamiento de numerosos trastornos de la salud.Nuevos desarrollos en la adquisición de imágenes y en la tecnología de sensores hacen posible obtener imágenes más detalladas de varios órganos del cuerpo humano. Tal mejora es ciertamente ventajosa para la práctica médica, pero supone un encarecimiento de los recursos tecnológicos necesarios para manejar imágenes de alta resolución de manera confiable. Comúnmente, el manejo eficiente de medios digitales se apoya principalmente en la compresión digital. Desde un punto de vista técnico, las imágenes médicas podrían aprovechar las ventajas de la compresión digital. Sin embargo, peculiaridades de los datos médicos imponen restricciones a su uso. Este artículo presenta un vistazo a la compresión digital en el contexto de las imágenes médicas, y una breve discusión de los aspectos regulatorios y legales asociados a su uso

Estimating Sample Size for Usability Testing

One strategy used to assure that an interface meets user requirements is to conduct usability testing. When conducting such testing one of the unknowns is sample size. Since extensive testing is costly, minimizing the number of participants can contribute greatly to successful resource management of a project. Even though a significant number of models have been proposed to estimate sample size in usability testing, there is still not consensus on the optimal size. Several studies claim that 3 to 5 users suffice to uncover 80% of problems in a software interface. However, many other studies challenge this assertion. This study analyzed data collected from the user testing of a web application to verify the rule of thumb, commonly known as the “magic number 5”. The outcomes of the analysis showed that the 5-user rule significantly underestimates the required sample size to achieve reasonable levels of problem detection

Estimating Sample Size for Usability Testing

One strategy used to assure that an interface meets user requirements is to conduct usability testing. When conducting such testing one of the unknowns is sample size. Since extensive testing is costly, minimizing the number of participants can contribute greatly to successful resource management of a project. Even though a significant number of models have been proposed to estimate sample size in usability testing, there is still not consensus on the optimal size. Several studies claim that 3 to 5 users suffice to uncover 80% of problems in a software interface. However, many other studies challenge this assertion. This study analyzed data collected from the user testing of a web application to verify the rule of thumb, commonly known as the “magic number 5”. The outcomes of the analysis showed that the 5-user rule significantly underestimates the required sample size to achieve reasonable levels of problem detection

El impacto de la automatización en el mejoramiento de procesos

Esta investigación tiene la intención de evaluar el efecto de la automatización en el desempeño de los procesos de una empresa de servicios. Para tal efecto, se analiza los resultados de la automatización de un proceso clave de negocio en un proveedor de servicios de telecomunicaciones. La automatización implementada toma como referencia el ciclo de vida descrito por la metodología BPM (Business Process Management), el cual se compone de cuatro fases: Modelamiento, implementación, ejecución, y análisis. Para la modelación del proceso se utilizó la notación definida en el estándar Business Process Model and Notation (BPMN). La automatización se implementó usando un paquete de herramientas informáticas comercial del tipo BPMS (Business Process Management System)

Epiretinal Membrane Detection in Optical Coherence Tomography Retinal Images Using Deep Learning

Epiretinal membrane (ERM) is an eye disease that affects 7% of the world population, with a higher incidence in people over 75 years old. If left untreated, it can lead to complications in the central vision, resulting in severe vision loss. Early detection is important for progress follow-up, treatment monitoring, and to avoid total vision loss. Optical coherence tomography, a non-invasive retina imaging technique, can be used for effective detection and monitoring of this condition. To date, automatic methods to detect ERM have received little attention in the research literature. This article describes the application of deep learning to the automatic detection of ERM. The proposed solution is based on four widely used convolutional neural network architectures adapted to the task using transfer learning, and ne-tuned with a proprietary dataset. The architectures were specialized by optimizing the network hyperparameters and two loss functions, cross-entropy and focal loss.Adetailed description of the methods is provided, complemented with an exhaustive evaluation of their performance. Overall, the methods reached an accuracy of 99.7%, with sensitivity and speci city of 99.47% and 99.93%, respectively. The results showed that transfer learning enabled a successful use of deep learning to detect ERM in optical coherence tomography retinal images, even when only relatively small training datasets are available