Neuronal-glial populations form functional networks in a biocompatible 3D scaffold

Monolayers of neurons and glia have been employed for decades as tools for the study of cellular physiology and as the basis for a variety of standard toxicological assays. A variety of three dimensional (3D) culture techniques have been developed with the aim to produce cultures that recapitulate desirable features of intact. In this study, we investigated the effect of preparing primary mouse mixed neuron and glial cultures in the inert 3D scaffold, Alvetex. Using planar multielectrode arrays, we compared the spontaneous bioelectrical activity exhibited by neuroglial networks grown in the scaffold with that seen in the same cells prepared as conventional monolayer cultures. Two dimensional (monolayer; 2D) cultures exhibited a significantly higher spike firing rate than that seen in 3D cultures although no difference was seen in total signal power (<50 Hz) while pharmacological responsiveness of each culture type to antagonism of GABAAR, NMDAR and AMPAR was highly comparable. Interestingly, correlation of burst events, spike firing and total signal power (<50 Hz) revealed that local field potential events were associated with action potential driven bursts as was the case for 2D cultures. Moreover, glial morphology was more physiologically normal in 3D cultures. These results show that 3D culture in inert scaffolds represents a more physiologically normal preparation which has advantages for physiological, pharmacological, toxicological and drug development studies, particularly given the extensive use of such preparations in high throughput and high content systems.

Cross layer based protocols for energy aware and critical data delivery related applications using wireless sensor networks

Wireless Sensor Networks (WSNs) have been an exciting topic in recent years. The services offered by a WSN can be classified into three major categories: monitoring, alerting, and information on demand. WSNs have been used for a variety of applications related to the environment (agriculture, water and forest fire detection), the military, buildings, health (elderly people and home monitoring), disaster relief, and area or industrial monitoring. In most WSNs tasks like processing the sensed data, making decisions and generating emergency messages are carried out by a remote server, hence the need for efficient means of transferring data across the network. Because of the range of applications and types of WSN there is a need for different kinds of MAC and routing protocols in order to guarantee delivery of data from the source nodes to the server (or sink). In order to minimize energy consumption and increase performance in areas such as reliability of data delivery, extensive research has been conducted and documented in the literature on designing energy efficient protocols for each individual layer. The most common way to conserve energy in WSNs involves using the MAC layer to put the transceiver and the processor of the sensor node into a low power, sleep state when they are not being used. Hence the energy wasted due to collisions, overhearing and idle listening is reduced. As a result of this strategy for saving energy, the routing protocols need new solutions that take into account the sleep state of some nodes, and which also enable the lifetime of the entire network to be increased by distributing energy usage between nodes over time. This could mean that a combined MAC and routing protocol could significantly improve WSNs because the interaction between the MAC and network layers lets nodes be active at the same time in order to deal with data transmission. In the research presented in this thesis, a cross-layer protocol based on MAC and routing protocols was designed in order to improve the capability of WSNs for a range of different applications. Simulation results, based on a range of realistic scenarios, show that these new protocols improve WSNs by reducing their energy consumption as well as enabling them to support mobile nodes, where necessary. A number of conference and journal papers have been published to disseminate these results for a range of applications.

A Road Following Approach Using Artificial Neural Networks Combinations

Navigation is a broad topic that has been receiving considerable attention from the mobile robotic community over the years. In order to execute autonomous driving in outdoor urban environments it is necessary to identify parts of the terrain that can be traversed and parts that should be avoided. This paper describes an analyses of terrain identification based on different visual information using a MLP artificial neural network and combining responses of many classifiers. Experimental tests using a vehicle and a video camera have been conducted in real scenarios to evaluate the proposed approach.

Adaptive, Decentralized, And Real-Time Sampling Strategies For Resource Constrained Hydraulic And Hydrologic Sensor Networks

We discuss the development and performance of a low-power sensor node (hardware, software and algorithms) that autonomously controls the sampling interval of a suite of sensors based on local state estimates and future predictions of water flow. The problem is motivated by the need to accurately reconstruct abrupt state changes in urban watersheds and stormwater systems. Presently, the detection of these events is limited by the temporal resolution of sensor data. It is often infeasible, however, to increase measurement frequency due to energy and sampling constraints. This is particularly true for real-time water quality measurements, where sampling frequency is limited by reagent availability, sensor power consumption, and, in the case of automated samplers, the number of available sample containers. These constraints pose a significant barrier to the ubiquitous and cost effective instrumentation of large hydraulic and hydrologic systems. Each of our sensor nodes is equipped with a low-power microcontroller and a wireless module to take advantage of urban cellular coverage. The node persistently updates a local, embedded model of flow conditions while IP-connectivity permits each node to continually query public weather servers for hourly precipitation forecasts. The sampling frequency is then adjusted to increase the likelihood of capturing abrupt changes in a sensor signal, such as the rise in the hydrograph – an event that is often difficult to capture through traditional sampling techniques. Our architecture forms an embedded processing chain, leveraging local computational resources to assess uncertainty by analyzing data as it is collected. A network is presently being deployed in an urban watershed in Michigan and initial results indicate that the system accurately reconstructs signals of interest while significantly reducing energy consumption and the use of sampling resources. We also expand our analysis by discussing the role of this approach for the efficient real-time measurement of stormwater systems.

Inovações tecnológicas no setor de telecomunicações no Brasil: desafios e oportunidades do LTE para expansão da telefonia móvel.

O telefone celular teve uma grande evolução nos últimos anos, o que antes era utilizado exclusivamente para transmissão de voz, hoje tem características avançadíssimas incluindo várias evoluções tecnológicas. Dentro deste aspecto, a inovação que o LTE (Long Term Evolution) vem demonstrando em suas características realmente se destacam em relação as tecnologias que a antecederam e representa grande evolução se comparada com as outras. Foi desenvolvida no âmbito do projeto 3GPP e (3rd Generation Partners Project) promovido pelo Instituto Europeu de Normalização na área de Telecomunicações ETSI (European Telecommunications Standard Institute). As operadoras que demonstram interesse em disponibilizar esta tecnologia buscam introduzir a flexibilidade do LTE para ir ao encontro dos objetivos de suas redes existentes, espectro e negócios para banda larga móvel e serviços multimídia. O LTE promete taxas de download de 326,4Mbps, taxas de upload de 86,4Mbps, RTT ( ROUND TRIP TIME ) menos de 10 mile segundos e raio das células podendo atingir até 100km. O sistema 4G (LTE) é um sistema integrado completamente baseado em IP, que é resultado de tecnologias conectadas por fios e sem fios disponibilizando um custo acessível, atendendo as exigências de uma rede de comunicação (Wireless), serviços de transferência de mensagens multimídias, conversa com vídeo, televisão móvel de alta definição, serviços mínimos como voz e dados entre outras vantagens. Desta forma, este estudo tem por objetivo analisar quais são as oportunidades e os desafios no mercado de telefonia móvel de Telecomunicações ao implantar o sistema de tecnologia LTE demonstrando o benefício dos fabricantes e operadoras no sentido econômico e tecnológico.

Breadcrumbs: location and context aware mobile platform for story sharing

With the current proliferation of sensor equipped mobile devices such as smartphones and tablets, location aware services are expanding beyond the mere efficiency and work related needs of users, evolving in order to incorporate fun, culture and the social life of users. Today people on the move have more and more connectivity and are expected to be able to communicate with their usual and familiar social networks. That means communications not only with their peers and colleagues, friends and family but also with unknown people that might share their interests, curiosities or happen to use the same social network. Through social networks, location aware blogging, cultural mobile applications relevant information is now available at specific geographical locations and open to feedback and conversations among friends as well as strangers. In fact, nowadays smartphone technologies aloud users to post and retrieve content while on the move, often relating to specific physical landmarks or locations, engaging and being engaged in conversations with strangers as much as their own social network. The use of such technologies and applications while on the move can often lead people to serendipitous discoveries and interactions. Throughout our thesis we are engaging on a two folded investigation: how can we foster and support serendipitous discoveries and what are the best interfaces for it? In fact, to read and write content while on the move is a cognitively intensive task. While the map serves the function of orienting the user, it also absorbs most of the user’s concentration. In order to address this kind of cognitive overload issue with Breadcrumbs we propose a 360 degrees interface that enables the user to find content around them by means of scanning the surrounding space with the mobile device. By using a loose metaphor of a periscope, harnessing the power of the smartphone sensors we designed an interactive interface capable of detecting content around the users and display it in the form of 2 dimensional bubbles which diameter depends on their distance from the users. Users will navigate the space in relation to the content that they are curious about, rather than in relation to the traditional geographical map. Through this model we envisage alleviating a certain cognitive overload generated by having to continuously confront a two dimensional map with the real three dimensional space surrounding the user, but also use the content as a navigational filter. Furthermore this alternative mean of navigating space might bring serendipitous discovery about places that user where not aware of or intending to reach. We hence conclude our thesis with the evaluation of the Breadcrumbs application and the comparison of the 360 degrees interface with a traditional 2 dimensional map displayed on the devise screen. Results from the evaluation are compiled in findings and insights for future use in designing and developing context aware mobile applications.

Desenvolvimento de um protocolo de comunicação para automação de subestações móveis via satélite

One of the most important decisions to turn a substation automatic and no attended it relates to the communication media between this substation and Operation Center. Generally energy companies uses radio or optic fiber, depending of distances and infrastructure of each situation. This rule applies to common substations. Mobile substations are a particular case, therefore they are conceived for use at provisional situations, emergencies, preventive or corrective maintenance. Thus the telecommunication solution used at common substations are not applied so easily to mobile substations, due absence of infrastructure (media) or difficulty to insert the mobile substation data in existing automation network not long. The ideal media must supply covering in a great geographic area to satisfy presented requirements. The implantation costs of this big infrastructure are expensive, however a existing operator may be used. Two services that fulfill that requirements are satellite and cellular telephony. This work presents a solution for automation of mobile substations through satellite. It was successfully implanted at a brazilian electric energy concessionaire named COSERN. The operation became transparent to operators. Other gotten benefits had been operational security, quality in the supply of electric energy and costs reduction. The project presented is a new solution, designed to substations and general applications where few data should be transmitted, but there is difficulties in relation to the media. Despite the satellite having been used, the same resulted can be gotten using celullar telephony, through Short Messages or packet networks as GPRS or EDGE.

Determination of resonant frequencies of triangular and rectangular microstrip antennas, using artificial neural networks

This paper presents models that can be used in the design of microstrip antennas for mobile communications. The antennas can be triangular or rectangular. The presented models are compared with deterministic and empirical models based on artificial neural networks (ANN) presented in the literature. The models are based on Perceptron Multilayer (PML) and Radial Basis Function (RBF) ANN. RBF based models presented the best results. Also, the models can be embedded in CAD systems, in order to design microstrip antennas for mobile communications.

Environment mapping for mobile robots navigation using hierarchical neural network and omnivision

Autonomous robots must be able to learn and maintain models of their environments. In this context, the present work considers techniques for the classification and extraction of features from images in joined with artificial neural networks in order to use them in the system of mapping and localization of the mobile robot of Laboratory of Automation and Evolutive Computer (LACE). To do this, the robot uses a sensorial system composed for ultrasound sensors and a catadioptric vision system formed by a camera and a conical mirror. The mapping system is composed by three modules. Two of them will be presented in this paper: the classifier and the characterizer module. The first module uses a hierarchical neural network to do the classification; the second uses techiniques of extraction of attributes of images and recognition of invariant patterns extracted from the places images set. The neural network of the classifier module is structured in two layers, reason and intuition, and is trained to classify each place explored for the robot amongst four predefine classes. The final result of the exploration is the construction of a topological map of the explored environment. Results gotten through the simulation of the both modules of the mapping system will be presented in this paper. © 2008 IEEE.

Weight vector estimation of circular arrays of antennas using Artificial Neural Networks

This paper presents a model for the control of the radiation pattern of a circular array of antennas, shaping it to address the radiation beam in the direction of the user, in order to reduce the transmitted power and to attenuate interference. The control of the array is based on Artificial Neural Networks (ANN) of the type RBF (Radial Basis Functions), trained from samples generated by the Wiener equation. The obtained results suggest that the objective was reached.

New contributions to the determination of resonance frequencies of rectangular microstrip antennas by neural networks

This paper uses artificial neural networks (ANN) to compute the resonance frequencies of rectangular microstrip antennas (MSA), used in mobile communications. Perceptron Multi-layers (PML) networks were used, with the Quasi-Newton method proposed by Broyden, Fletcher, Goldfarb and Shanno (BFGS). Due to the nature of the problem, two hundred and fifty networks were trained, and the resonance frequency for each test antenna was calculated by statistical methods. The estimate resonance frequencies for six test antennas were compared with others results obtained by deterministic and ANN based empirical models from the literature, and presented a better agreement with the experimental values.

JSEG-based image segmentation in computer vision for agricultural mobile robot navigation

This project aims to apply image processing techniques in computer vision featuring an omnidirectional vision system to agricultural mobile robots (AMR) used for trajectory navigation problems, as well as localization matters. To carry through this task, computational methods based on the JSEG algorithm were used to provide the classification and the characterization of such problems, together with Artificial Neural Networks (ANN) for pattern recognition. Therefore, it was possible to run simulations and carry out analyses of the performance of JSEG image segmentation technique through Matlab/Octave platforms, along with the application of customized Back-propagation algorithm and statistical methods in a Simulink environment. Having the aforementioned procedures been done, it was practicable to classify and also characterize the HSV space color segments, not to mention allow the recognition of patterns in which reasonably accurate results were obtained.

ANN statistical image recognition method for computer vision in agricultural mobile robot navigation

The main application area in this project, is to deploy image processing and segmentation techniques in computer vision through an omnidirectional vision system to agricultural mobile robots (AMR) used for trajectory navigation problems, as well as localization matters. Thereby, computational methods based on the JSEG algorithm were used to provide the classification and the characterization of such problems, together with Artificial Neural Networks (ANN) for image recognition. Hence, it was possible to run simulations and carry out analyses of the performance of JSEG image segmentation technique through Matlab/Octave computational platforms, along with the application of customized Back-propagation Multilayer Perceptron (MLP) algorithm and statistical methods as structured heuristics methods in a Simulink environment. Having the aforementioned procedures been done, it was practicable to classify and also characterize the HSV space color segments, not to mention allow the recognition of segmented images in which reasonably accurate results were obtained. © 2010 IEEE.

Pattern recognition structured heuristics methods for image processing in mobile robot navigation

In this project, the main focus is to apply image processing techniques in computer vision through an omnidirectional vision system to agricultural mobile robots (AMR) used for trajectory navigation problems, as well as localization matters. To carry through this task, computational methods based on the JSEG algorithm were used to provide the classification and the characterization of such problems, together with Artificial Neural Networks (ANN) for pattern recognition. Therefore, it was possible to run simulations and carry out analyses of the performance of JSEG image segmentation technique through Matlab/Octave platforms, along with the application of customized Back-propagation algorithm and statistical methods as structured heuristics methods in a Simulink environment. Having the aforementioned procedures been done, it was practicable to classify and also characterize the HSV space color segments, not to mention allow the recognition of patterns in which reasonably accurate results were obtained. ©2010 IEEE.

A proposed neural control for the trajectory tracking of a nonholonomic mobile robot with disturbances

In this paper, a trajectory tracking control problem for a nonholonomic mobile robot by the integration of a kinematic neural controller (KNC) and a torque neural controller (TNC) is proposed, where both the kinematic and dynamic models contains disturbances. The KNC is a variable structure controller (VSC) based on the sliding mode control theory (SMC), and applied to compensate the kinematic disturbances. The TNC is a inertia-based controller constituted of a dynamic neural controller (DNC) and a robust neural compensator (RNC), and applied to compensate the mobile robot dynamics, and bounded unknown disturbances. Stability analysis with basis on Lyapunov method and simulations results are provided to show the effectiveness of the proposed approach. © 2012 Springer-Verlag.