Reliably Detecting Connectivity using Local Graph Traits

Local distributed algorithms can only gather sufficient information to identify local graph traits, that is, properties that hold within the local neighborhood of each node. However, it is frequently the case that global graph properties (connectivity, diameter, girth, etc) have a large influence on the execution of a distributed algorithm. This paper studies local graph traits and their relationship with global graph properties. Specifically, we focus on graph k-connectivity. First we prove a negative result that shows there does not exist a local graph trait which perfectly captures graph k-connectivity. We then present three different local graph traits which can be used to reliably predict the k-connectivity of a graph with varying degrees of accuracy. As a simple application of these results, we present upper and lower bounds for a local distributed algorithm which determines if a graph is k-connected. As a more elaborate application of local graph traits, we describe, and prove the correctness of, a local distributed algorithm that preserves k-connectivity in mobile ad hoc networks while allowing nodes to move independently whenever possible

Prototipo de sistema inteligente basado en patrones de ondas cerebrales para prevenir accidentes de tránsito

This article presents the prototype of an intelligent system based on patterns of brain waves to prevent traffic accidents,by which, through a sensor, placed on the driver's head, monitors the patterns of brain waves that are sent in real time via Bluetoothto a Raspberry Pi to be processed with machine learning strategies. In this way it allows to send a visual and sound warning when itdetects the state of drowsiness in the driver. For the prototype construction, data of four people were collected while they were awake,drowsy and asleep. The data set was processed with four supervised learning algorithms: nearest neighbors, support vector machine,decision trees and random forests; the last one was the one that obtained the best result, reaching 82.05% accuracy whendifferentiating the three different states. The estimated cost of the system is 210 USD, resulting an economic system in relation toothers existing in the market.Este artículo presenta el prototipo de sistema inteligente basado en patrones de ondas cerebrales para prevenir accidentesde tránsito, que, mediante un sensor colocado en la cabeza del conductor, monitoriza los patrones de ondas cerebrales los cuales sonenviados en tiempo real vía Bluetooth a una placa Raspberry Pi para ser procesados con estrategias de aprendizaje automático y deesta forma enviar una alerta visual y sonora cuando detecta el estado de somnolencia en el conductor. Para la construcción delprototipo se recogieron datos de cuatro personas en tres estados distintos, mientras estaban despiertas, somnolientas y dormidas. Elconjunto de datos fue procesado con cuatro algoritmos de aprendizaje supervisado: vecinos más cercanos, máquina de soportevectorial, árboles de decisión, bosques aleatorios; siendo este último el que mejores resultados mostró alcanzando un 82.05% deprecisión al diferenciar los tres estados anteriormente mencionados. El costo estimado del sistema es de 210 USD, resultando unsistema económico con relación a otros existentes en el mercado

Prototipo de mano robótica controlada mediante el procesamiento de señales cerebrales utilizando redes neuronales recurrentes

The mapping of electrical activity in the human brain is not only useful in the medical field for the diagnosis of diseases but can also be of great help in other fields such as computer science. This research proposes the use of brain signals as a control mechanism using artificial intelligence techniques. Its main objective is to build a prototype of a 3D printed robotic hand prosthesis monitored through the processing of brain signals using recurrent neural networks. Three different recurrent network architectures were trained and compared using a prototyping methodology (Simple RNN, LSTM, and GRU). This prototyping methodology was trained using a sample size of thirteen people and the non-invasive sensor Mindwave Mobile 2 was used to record the data. The first version, which was in development approximately 3 months, achieved 77% accuracy in classifying new samples using the GRU network model. With further research and development, this prototype may prove very useful in the future for providing people in need of such technology with a higher quality of life.Las señales generadas por la actividad eléctrica producida en el cerebro humano, además de ser utilizadas en el área de medicina para el diagnóstico de enfermedades, pueden ser de gran ayuda en otros campos, como lo son las ciencias computacionales. Esta investigación propone la utilización de señales cerebrales como mecanismo de control, empleando técnicas de inteligencia artificial. La misma tiene como objetivo principal construir un prototipo de una prótesis de mano robótica impresa en 3D, controlada a través del procesamiento de señales cerebrales, utilizando redes neuronales recurrentes. Mediante una metodología de prototipado se entrenaron y compararon tres arquitecturas distintas de redes recurrentes (RNN simple, LSTM y GRU), entrenadas a partir de datos de trece personas, utilizando el sensor no invasivo Mindwave Mobile 2 para la adquisición de estos. La primera versión, desarrollada en un periodo aproximado de tres meses, alcanzó una precisión del 77% al clasificar nuevas muestras utilizando el modelo de red GRU. Este prototipo, al ser una primera aproximación y requerir mayor tiempo de investigación y desarrollo, puede ser de gran utilidad a futuro para personas que así lo necesiten, brindándoles una mayor calidad de vida

Single Spin Asymmetries of Inclusive Hadrons Produced in Electron Scattering from a Transversely Polarized 3^3He Target

We report the first measurement of target single-spin asymmetries (A$_N$) in the inclusive hadron production reaction, $e~$+$~^3\text{He}^{\uparrow}\rightarrow h+X$, using a transversely polarized $^3$He target. The experiment was conducted at Jefferson Lab in Hall A using a 5.9-GeV electron beam. Three types of hadrons ($\pi^{\pm}$, $\text{K}^{\pm}$ and proton) were detected in the transverse hadron momentum range 0.54 $<p_T<$ 0.74 GeV/c. The range of $x_F$ for pions was -0.29 $<x_F<$ -0.23 and for kaons -0.25 $<x_F<$-0.18. The observed asymmetry strongly depends on the type of hadron. A positive asymmetry is observed for $\pi^+$ and $\text{K}^+$. A negative asymmetry is observed for $\pi^{-}$. The magnitudes of the asymmetries follow $|A^{\pi^-}| < |A^{\pi^+}| < |A^{K^+}|$. The K$^{-}$ and proton asymmetries are consistent with zero within the experimental uncertainties. The $\pi^{+}$ and $\pi^{-}$ asymmetries measured for the $^3$He target and extracted for neutrons are opposite in sign with a small increase observed as a function of $p_T$.Comment: Updated version, submitted to Phys. Rev.