Wifi bluetooth based combined positioning algorithm

Nowadays positioning have become very important for many services (Localization based services) and positioning have become more accurate, despite, there are some territories that basic positioning systems like GPS or even hybrid ones like GPS-WiFi or GPS-WiFi-gsm can’t cover, specially indoor enviroments. In this paper we propose a positioning method merging WLAN and Bluetooth technologies based on trilateration technique. Simulated sceneario demostrate accuracy gains, even when we use a high signal attenuation parameter. A simulated sceneario taken from a real home with the real WLAN and Bluetooth stations validates our WLAN-Bluetooth method. Firstly we calculate each equation from each available station, then we decide how to overdetermine the generated equation system in a reason of 4 to 1 (4 equations for one unknown) and finally solve the system using mathematical methods. This work is a step more to better position in indoor enviroments and localization based service

Digital filter implementation over FPGA platform with LINUX OS

The embedded processors on FPGA's are a good tool to specific propose works. In this work we present how the FPGA is used to apply a Sobel filter to a set of images, also the step needed to set-up the entire system is described. An embedded processor, with a Linux distribution implemented is used to run a special compilation of C filter program, the filter is compared with the results obtained with a PC running the same filter, in the embedded system all the process runs in the FPGA and the exit file can be accessed by ftp or http server embedded into the Linux system

Digital filter implementation over FPGA platform with LINUX OS

The embedded processors on FPGA's are a good tool to specific propose works. In this work we present how the FPGA is used to apply a Sobel filter to a set of images, also the step needed to set-up the entire system is described. An embedded processor, with a Linux distribution implemented is used to run a special compilation of C filter program, the filter is compared with the results obtained with a PC running the same filter, in the embedded system all the process runs in the FPGA and the exit file can be accessed by ftp or http server embedded into the Linux system

Multicast routing and interoperability between wired and wireless ad hoc network

Multicast routing is employed whenever data needs to be delivered from a single source to a group of receivers, in this way, the source can transmit a single flow of data packets which are subsequently delivered to a group of receiver nodes. As a result, multicast routing protocols are extensively used in computer networks, as these techniques allow applications to be able to save bandwidth and reduce the traffic load in the network. In relation to multicast protocols, most of them have been designed for use in wired networks, such as the Internet; similarly, some multicast protocols have been proposed for wireless environments, like wireless ad hoc networks, however they usually deal with the transmission of multicast traffic within the wireless network and do not address those issues related to the interoperability between wired and wireless networks. Nowadays, wireless mobile devices have a large demand of multiple services from the Internet, thus resulting in a need to extend some of the readily available services to wireless network while providing the same level of performance and reliability. This work presents an approach to integrate a wireless ad hoc network with a wired network, while supporting the interoperability of multicast services between the wired network and the wireless ad hoc network. As a result, the multicast traffic generated within the wired network can reach clients located in the wireless ad hoc network

Infrastructure-Less Indoor Localization Using the Microphone, Magnetometer and Light Sensor of a Smartphone

In this paper, we present the development of an infrastructure-less indoor location system (ILS), which relies on the use of a microphone, a magnetometer and a light sensor of a smartphone, all three of which are essentially passive sensors, relying on signals available practically in any building in the world, no matter how developed the region is. In our work, we merge the information from those sensors to estimate the user’s location in an indoor environment. A multivariate model is applied to find the user’s location, and we evaluate the quality of the resulting model in terms of sensitivity and specificity. Our experiments were carried out in an office environment during summer and winter, to take into account changes in light patterns, as well as changes in the Earth’s magnetic field irregularities. The experimental results clearly show the benefits of using the information fusion of multiple sensors when contrasted with the use of a single source of informationIn this paper, we present the development of an infrastructure-less indoor location system (ILS), which relies on the use of a microphone, a magnetometer and a light sensor of a smartphone, all three of which are essentially passive sensors, relying on signals available practically in any building in the world, no matter how developed the region is. In our work, we merge the information from those sensors to estimate the user’s location in an indoor environment. A multivariate model is applied to find the user’s location, and we evaluate the quality of the resulting model in terms of sensitivity and specificity. Our experiments were carried out in an office environment during summer and winter, to take into account changes in light patterns, as well as changes in the Earth’s magnetic field irregularities. The experimental results clearly show the benefits of using the information fusion of multiple sensors when contrasted with the use of a single source of informatio

Uso del campo magnético de la tierra para localizar a las personas en interiores

The location of an individual is a fundamental element of information for some commercial and assistive locationbased applications. Since the global positioning system, the most effective technology for positioning a mobile object in the outdoors does not work in indoor environments, several technological approaches have been proposed to tackle this problem. In this direction, in this paper we present an interesting approach based on the use of earth magnetic-field variations to estimate the localization of an individual in indoor environments

Depression Episodes Detection: A Neural Netand Deep Neural Net Comparison.

Depression is a frequent mental disorder. It is estimated thatit affects more than 300 million people in the world. In this investiga-tion, a motor activity database was used, from which the readings of 55patients (32 control patients and 23 patients with the condition) wereselected, during one week in one minute intervals, obtaining a total of385 observations (participants) and 1440 characteristics (time intervals)from which the most representative one minute intervals were extractedapplying genetic algorithms that reduced the number of data to process,with this strategy it is guaranteed that the most representative genes(characteristics) in the chromosome population is included in a singlemachine learning model of which applied deep neural nets and neuralnets with the aim of creating a comparative between the models gener-ated and determining which model offers better performance to detectingepisodes of depression. The deep neural networks obtained the best per-formance with 0.8086 which is equivalent to 80.86 % of precision, thisdeep neural network was trained with 270 of the participants which isequivalent to 70 % of the observations and was tested with 30 % Remain-ing data which is equal to 115 participants of which 53 were diagnosedas healthy and 40 with depression correctly. Based on these results, itcan be concluded that the implementation of these models in smart de-vices or in some assisted diagnostic tool, it is possible to perform theautomated detection of episodes of depression reliably.La depresión es un trastorno mental frecuente. Se estima que afecta a más de 300 millones de personas en el mundo. En esta investigación se utilizó una base de datos de actividad motora, de la cual se seleccionaron las lecturas de 55 pacientes (32 pacientes control y 23 pacientes con la condición), durante una semana en intervalos de un minuto, obteniendo un total de 385 observaciones (participantes) y 1440 características (intervalos de tiempo) de los cuales se extrajeron los intervalos de un minuto más representativos aplicando algoritmos genéticos que redujeron el número de datos a procesar, con esta estrategia se garantiza que los genes (características) más representativos de la población cromosómica se incluyan en un aprendizaje de una sola máquina modelo del cual se aplicó redes neuronales profundas y redes neuronales con el objetivo de crear una comparativa entre los modelos generados y determinar qué modelo ofrece mejor desempeño para detectar episodios de depresión. Las redes neuronales profundas obtuvieron el mejor desempeño con 0.8086 lo que equivale al 80.86% de precisión, esta red neuronal profunda fue entrenada con 270 de los participantes que es equivalente al 70% de las observaciones y se probó con el 30% de los datos restantes que es igual a 115 participantes de los cuales 53 fueron diagnosticados como sanos y 40 con depresión correctamente. En base a estos resultados, se puede concluir que la implementación de estos modelos en dispositivos inteligentes o en alguna herramienta de diagnóstico asistido, es posible realizar la detección automatizada de episodios de depresión de manera confiable

BookSense an Application for Mental Disorders Diagnosis: A Case Study for User Evaluation and Redesign

Booksense, a mobile application that allows to identify mental disorders such as depression, work stress and postraumatism [13], through a series of questions based on a mental health assessment that allows you to nd out if you have a mental illness, the app can detect if the user shows signs of a mental disorder, being the most important to detect the problem from its stages initials, plus it also has a database of institutions in the country where you can receive care. The World Health Organization (WHO) estimates that there are currently 300 million people on the planet who su er from depression. This is why it is important to have assisted diagnostic tools that help prevent this type of a ectations in the population, as well as keep informed. the people about help centers. All this would not be possible if you do not count an application that has three important aspects that are: E ciency, e ectiveness and satisfaction aspects that are not present in this diagnostic tool is why the importance of the use of usability evaluations. This research aims to generate a redesign of this application based on certain heuristics that ll the gaps in usabilityBooksense, una aplicación móvil que permite identificar trastornos mentales como depresión, estrés laboral y postraumatismo [13], a través de una serie de preguntas basadas en una evaluación de salud mental que te permite saber si tienes una enfermedad mental, la aplicación puede detectar si el usuario muestra signos de un trastorno mental, siendo lo más importante para detectar el problema desde sus etapas iniciales, además también cuenta con una base de datos de instituciones en el país donde puede recibir atención. La Organización Mundial de la Salud (OMS) estima que actualmente hay 300 millones de personas en el planeta que padecen depresión. Por eso es importante contar con herramientas de diagnóstico asistido que ayuden a prevenir este tipo de afectaciones en la población, así como a mantenerse informada. la gente sobre los centros de ayuda. Todo esto no sería posible si no se cuenta una aplicación que tiene tres aspectos importantes que son: Aspectos de eficiencia, efectividad y satisfacción que no están presentes en esta herramienta de diagnóstico de ahí la importancia del uso de evaluaciones de usabilidad. Esta investigación tiene como objetivo generar un rediseño de esta aplicación en base a ciertas heurísticas que llenen los vacíos de usabilida

BookSense an Application for Mental Disorders Diagnosis: A Case Study for User Evaluation and Redesign

Booksense, a mobile application that allows to identify mental disorders such as depression, work stress and postraumatism [13], through a series of questions based on a mental health assessment that allows you to nd out if you have a mental illness, the app can detect if the user shows signs of a mental disorder, being the most important to detect the problem from its stages initials, plus it also has a database of institutions in the country where you can receive care. The World Health Organization (WHO) estimates that there are currently 300 million people on the planet who su er from depression. This is why it is important to have assisted diagnostic tools that help prevent this type of a ectations in the population, as well as keep informed. the people about help centers. All this would not be possible if you do not count an application that has three important aspects that are: E ciency, e ectiveness and satisfaction aspects that are not present in this diagnostic tool is why the importance of the use of usability evaluations. This research aims to generate a redesign of this application based on certain heuristics that ll the gaps in usabilityBooksense, una aplicación móvil que permite identificar trastornos mentales como depresión, estrés laboral y postraumatismo [13], a través de una serie de preguntas basadas en una evaluación de salud mental que te permite saber si tienes una enfermedad mental, la aplicación puede detectar si el usuario muestra signos de un trastorno mental, siendo lo más importante para detectar el problema desde sus etapas iniciales, además también cuenta con una base de datos de instituciones en el país donde puede recibir atención. La Organización Mundial de la Salud (OMS) estima que actualmente hay 300 millones de personas en el planeta que padecen depresión. Por eso es importante contar con herramientas de diagnóstico asistido que ayuden a prevenir este tipo de afectaciones en la población, así como a mantenerse informada. la gente sobre los centros de ayuda. Todo esto no sería posible si no se cuenta una aplicación que tiene tres aspectos importantes que son: Aspectos de eficiencia, efectividad y satisfacción que no están presentes en esta herramienta de diagnóstico de ahí la importancia del uso de evaluaciones de usabilidad. Esta investigación tiene como objetivo generar un rediseño de esta aplicación en base a ciertas heurísticas que llenen los vacíos de usabilida

Métricas de Registro de Imágenes y Predicción de Dolor de Rodilla por Osteoartritis Crónica: Datos de la Osteoarthritis Initiative

Osteoarthritis (OA) is the most common type of arthritis, is a growing disease in the industrialized world. OA is an incapacitate disease that affects more than 1 in 10 adults over 60 years old. X-ray medical imaging is a primary diagnose technique used on staging OA that the expert reads and quantify the stage of the disease. Some Computer-Aided Diagnosis (CADx) efforts to automate the OA detection have been made to aid the radiologist in the detection and control, nevertheless, the pain inherits to the disease progression is left behind. In this research, it’s proposed a CADx system that quantify the bilateral similarity of the patient’s knees to correlate the degree of asymmetry with the pain development. Firstly, the knee images were aligned using a B-spline image registration algorithm, then, a set of similarity measures were quantified, lastly, using this measures it’s proposed a multivariate model to predict the pain development up to 48 months. The methodology was validated on a cohort of 131 patients from the Osteoarthritis Initiative (OAI) database. Results suggest that mutual information can be associated with K&L OAI scores, and Multivariate models predicted knee chronic pain with: AUC 0.756, 0.704, 0.713 at baseline, one year, and two years’ follow-up.Osteoarthritis (OA) is the most common type of arthritis, is a growing disease in the industrialized world. OA is an incapacitate disease that affects more than 1 in 10 adults over 60 years old. X-ray medical imaging is a primary diagnose technique used on staging OA that the expert reads and quantify the stage of the disease. Some Computer-Aided Diagnosis (CADx) efforts to automate the OA detection have been made to aid the radiologist in the detection and control, nevertheless, the pain inherits to the disease progression is left behind. In this research, it’s proposed a CADx system that quantify the bilateral similarity of the patient’s knees to correlate the degree of asymmetry with the pain development. Firstly, the knee images were aligned using a B-spline image registration algorithm, then, a set of similarity measures were quantified, lastly, using this measures it’s proposed a multivariate model to predict the pain development up to 48 months. The methodology was validated on a cohort of 131 patients from the Osteoarthritis Initiative (OAI) database. Results suggest that mutual information can be associated with K&L OAI scores, and Multivariate models predicted knee chronic pain with: AUC 0.756, 0.704, 0.713 at baseline, one year, and two years’ follow-up