Arthur Edwards-Block's research while affiliated with Universidad de Colima and other places

This article discusses the simulation and evaluation of a traffic congestion detection system which combines inter-vehicular communications, fixed roadside infrastructure and infrastructure-to-infrastructure connectivity and big data. The system discussed in this article permits drivers to identify traffic congestion and change their routes accordingly, thus reducing the total emissions of CO2 and decreasing travel time. This system monitors, processes and stores large amounts of data, which can detect traffic congestion in a precise way by means of a series of algorithms that reduces localized vehicular emission by rerouting vehicles. To simulate and evaluate the proposed system, a big data cluster was developed based on Cassandra, which was used in tandem with the OMNeT++ discreet event network simulator, coupled with the SUMO (Simulation of Urban MObility) traffic simulator and the Veins vehicular network framework. The results validate the efficiency of the traffic detection system and its positive impact in detecting, reporting and rerouting traffic when traffic events occur.

This paper presents a system based on WSN technology capable of monitoring heart rate and the rate of motion of seniors within their homes. The system is capable of remotely alerting specialists, caretakers or family members via a smartphone of rapid physiological changes due to falls, tachycardia or bradycardia. This work was carried out using our workgroup's WiSe platform, which we previously developed for use in WSNs. The proposed WSN architecture is flexible, allowing for greater scalability to better allow event-based monitoring. The architecture also provides security mechanisms to assure that the monitored and/or stored data can only be accessed by authorized individuals or devices. The aforementioned characteristics provide the network versatility and solidity required for use in health applications.

Este capítulo describela situación actual de México con respecto a la lectura y cómo puede apoyarse el fomento a la lectura de narra- tiva utilizando ambientes de realidad virtual, pues la mayor parte de la población lee muy poco, en especial narrativa. Por tanto, es necesario incrementar este tipo de lectura, ya que ésta puede mo- tivar a seguir aprendiendo, despertar la imaginación, e incremen- tar el vocabulario, entre otros beneficios. Las tecnologías de información y comunicación ( TIC ) han sido utilizadas para difundir y apoyar ejercicios de lectura en acti- vidades escolares de niveles como primaria y secundaria. En ese sentido, se decidió describir el inicio de un proyecto donde se explora la aplicación de la realidad virtual ( RV ) como soporte para la comprensión de textos literarios, ya que proporciona un entorno multisensorial, inmersivo y experiencial, propiedades intrínsecas que pueden motivar significativamente la lectura.

In this chapter, an intelligent scheduling architecture is presented for the downlink transmission of LTE-Advanced networks to enhance the Quality of Service (QoS) provision to different traffic types while maintaining system level performance such as system throughput and fairness. Hebbian learning process and K-mean clustering algorithm are integrated in the time domain of the proposed scheduling architecture to intelligently allocate the available radio resource to Real Time (RT) and Non-Real Time (NRT) traffic types. The integration of these algorithms allows just enough resource allocation to RT traffic and diverts the remaining resource to NRT traffic to fulfil its minimum throughput requirements. System level simulation is set up for the performance evaluation, and simulation results show that the proposed scheduling architecture reduces average delay, delay violation probability, and average Packet Drop Rate (PDR) of RT traffic while guaranteeing the support of minimum throughput to NRT traffic and maintaining system throughput at good level.

This paper presents a system based on WSN technology capable of monitoring heart rate and the rate of motion of seniors within their homes. The system is capable of remotely alerting specialists, caretakers or family members via a smartphone of rapid physiological changes due to falls, tachycardia or bradycardia. This work was carried out using our workgroup's WiSe platform, which we previously developed for use in WSNs. The proposed WSN architecture is flexible, allowing for greater scalability to better allow event-based monitoring. The architecture also provides security mechanisms to assure that the monitored and/or stored data can only be accessed by authorized individuals or devices. The aforementioned characteristics provide the network versatility and solidity required for use in health applications.

This paper introduces wireless sensor networks for Ambient Assisted Living as a proof of concept. Our workgroup has developed an arrhythmia detection algorithm that we evaluate in a closed space using a wireless sensor network to relay the information collected to where the information can be registered, monitored and analyzed to support medical decisions by healthcare providers. The prototype we developed is then evaluated using the TelosB platform. The proposed architecture considers very specific restrictions regarding the use of wireless sensor networks in clinical situations. The seamless integration of the system architecture enables both mobile node and network configuration, thus providing the versatile and robust characteristics necessary for real-time applications in medical situations. Likewise, this system architecture efficiently permits the different components of our proposed platform to interact efficiently within the parameters of this study.

Driven by the rising demand for underwater operations concerning dam structure monitoring, Hydropower Plant (HPP), reservoir, and lake ecosystem inspection, and mining and oil exploration, underwater robotics applications are increasing rapidly. The increase in exploration, prospecting, monitoring, and security in lakes, rivers, and the sea in commercial applications has led large companies and research centers to invest underwater vehicle development. The purpose of this work is to present the design of an Autonomous Underwater Vehicle (AUV), focusing efforts on dimensioning structural elements and machinery and elaborating the sensory part, which includes navigation sensors and environmental conditions sensors. The integration of these sensors in an intelligent platform provides a satisfactory control of the vehicle, allowing the movement of the submarine on the three spatial axes. Because of the satisfactory fast response of the sensors, one can determine the acceleration and inclination as well as the attitude in relation to the trajectory instantaneously taken. This vehicle will be able to monitor the physical integrity of dams, making acquisition and storage of environmental parameters such as temperature, dissolved oxygen, pH, and conductivity, as well as document images of the biota from reservoir lake HPPs, with minimal cost, high availability, and low dependence on a skilled workforce to operate it.

Sensor-based localization has been found to be one of the most preliminary issues in the world of Mobile/Wireless Robotics. One can easily track a mobile robot using a Kalman Filter, which uses a Phase Locked Loop for tracing via averaging the values. Tracking has now become very easy, but one wants to proceed to navigation. The reason behind this is that tracking does not help one determine where one is going. One would like to use a more precise “Navigation” like Monte Carlo Localization. It is a more efficient and precise way than a feedback loop because the feedback loops are more sensitive to noise, making one modify the external loop filter according to the variation in the processing. In this case, the robot updates its belief in the form of a probability density function (pdf). The supposition is considered to be one meter square. This probability density function expands over the entire supposition. A door in a wall can be identified as peak/rise in the probability function or the belief of the robot. The mobile updates a window of 1 meter square (area depends on the sensors) as its belief. One starts with a uniform probability density function, and then the sensors update it. The authors use Monte Carlo Localization for updating the belief, which is an efficient method and requires less space. It is an efficient method because it can be applied to continuous data input, unlike the feedback loop. It requires less space. The robot does not need to store the map and, hence, can delete the previous belief without any hesitation.

This chapter presents a theoretical and experimental comparison of electromagnetic propagation models for indoor robot communication using mobile ad-hoc IEEE802.11 and IEEE802.15.4. The analysis includes the behavior of the electromagnetic signal using the abovementioned standards in two scenarios, both located inside the building of the College of Telematics of the University of Colima. The results of the propagation of the electromagnetic signals in the two scenarios were then compared with the mathematical model.

This chapter presents the development and implementation of three approaches that contribute to solving the mobile robot path planning problems in dynamic and static environments. The algorithms include some items regarding the implementation of on-line and off-line situations in an environment with static and mobile obstacles. A first technique involves the use of genetic algorithms where a fitness function and the emulation of the natural evolution are used to find a free-collision path. The second and third techniques consider the use of potential fields for path planning using two different ways. Brief descriptions of the techniques and experimental setup used to test the algorithms are also included. Finally, the results applying the algorithms using different obstacle configurations are presented and discussed.