A Computational Architecture Based on RFID Sensors for Traceability in Smart Cities

Information Technology and Communications (ICT) is presented as the main element in order to achieve more efficient and sustainable city resource management, while making sure that the needs of the citizens to improve their quality of life are satisfied. A key element will be the creation of new systems that allow the acquisition of context information, automatically and transparently, in order to provide it to decision support systems. In this paper, we present a novel distributed system for obtaining, representing and providing the flow and movement of people in densely populated geographical areas. In order to accomplish these tasks, we propose the design of a smart sensor network based on RFID communication technologies, reliability patterns and integration techniques. Contrary to other proposals, this system represents a comprehensive solution that permits the acquisition of user information in a transparent and reliable way in a non-controlled and heterogeneous environment. This knowledge will be useful in moving towards the design of smart cities in which decision support on transport strategies, business evaluation or initiatives in the tourism sector will be supported by real relevant information. As a final result, a case study will be presented which will allow the validation of the proposal.

The Relationship between Adolescent Social Network Structure and Perceptions of School Climate

Thesis (Ph.D.)--University of Washington, 2016-06

Tracking the states of a nonlinear system in the weight-space of a feed-forward neural network

Nonlinear, non-stationary signals are commonly found in a variety of disciplines such as biology, medicine, geology and financial modeling. The complexity (e.g. nonlinearity and non-stationarity) of such signals and their low signal to noise ratios often make it a challenging task to use them in critical applications. In this paper we propose a new neural network based technique to address those problems. We show that a feed forward, multi-layered neural network can conveniently capture the states of a nonlinear system in its connection weight-space, after a process of supervised training. The performance of the proposed method is investigated via computer simulations.

A scatterometer neural network sensor model with input noise

The ERS-1 satellite carries a scatterometer which measures the amount of radiation scattered back toward the satellite by the ocean's surface. These measurements can be used to infer wind vectors. The implementation of a neural network based forward model which maps wind vectors to radar backscatter is addressed. Input noise cannot be neglected. To account for this noise, a Bayesian framework is adopted. However, Markov Chain Monte Carlo sampling is too computationally expensive. Instead, gradient information is used with a non-linear optimisation algorithm to find the maximum em a posteriori probability values of the unknown variables. The resulting models are shown to compare well with the current operational model when visualised in the target space.

A scatterometer neural network sensor model with input noise

The ERS-1 satellite carries a scatterometer which measures the amount of radiation scattered back toward the satellite by the ocean's surface. These measurements can be used to infer wind vectors. The implementation of a neural network based forward model which maps wind vectors to radar backscatter is addressed. Input noise cannot be neglected. To account for this noise, a Bayesian framework is adopted. However, Markov Chain Monte Carlo sampling is too computationally expensive. Instead, gradient information is used with a non-linear optimisation algorithm to find the maximum em a posteriori probability values of the unknown variables. The resulting models are shown to compare well with the current operational model when visualised in the target space.

A novel silicon membrane-based biosensing platform using distributive sensing strategy and artificial neural networks for feature analysis

A novel biosensing system based on a micromachined rectangular silicon membrane is proposed and investigated in this paper. A distributive sensing scheme is designed to monitor the dynamics of the sensing structure. An artificial neural network is used to process the measured data and to identify cell presence and density. Without specifying any particular bio-application, the investigation is mainly concentrated on the performance testing of this kind of biosensor as a general biosensing platform. The biosensing experiments on the microfabricated membranes involve seeding different cell densities onto the sensing surface of membrane, and measuring the corresponding dynamics information of each tested silicon membrane in the form of a series of frequency response functions (FRFs). All of those experiments are carried out in cell culture medium to simulate a practical working environment. The EA.hy 926 endothelial cell lines are chosen in this paper for the bio-experiments. The EA.hy 926 endothelial cell lines represent a particular class of biological particles that have irregular shapes, non-uniform density and uncertain growth behaviour, which are difficult to monitor using the traditional biosensors. The final predicted results reveal that the methodology of a neural-network based algorithm to perform the feature identification of cells from distributive sensory measurement has great potential in biosensing applications.

Gene network and proteomic analyses of cardiac responses to pathological and physiological stress

Background—The molecular mechanisms underlying similarities and differences between physiological and pathological left ventricular hypertrophy (LVH) are of intense interest. Most previous work involved targeted analysis of individual signaling pathways or screening of transcriptomic profiles. We developed a network biology approach using genomic and proteomic data to study the molecular patterns that distinguish pathological and physiological LVH. Methods and Results—A network-based analysis using graph theory methods was undertaken on 127 genome-wide expression arrays of in vivo murine LVH. This revealed phenotype-specific pathological and physiological gene coexpression networks. Despite >1650 common genes in the 2 networks, network structure is significantly different. This is largely because of rewiring of genes that are differentially coexpressed in the 2 networks; this novel concept of differential wiring was further validated experimentally. Functional analysis of the rewired network revealed several distinct cellular pathways and gene sets. Deeper exploration was undertaken by targeted proteomic analysis of mitochondrial, myofilament, and extracellular subproteomes in pathological LVH. A notable finding was that mRNA–protein correlation was greater at the cellular pathway level than for individual loci. Conclusions—This first combined gene network and proteomic analysis of LVH reveals novel insights into the integrated pathomechanisms that distinguish pathological versus physiological phenotypes. In particular, we identify differential gene wiring as a major distinguishing feature of these phenotypes. This approach provides a platform for the investigation of potentially novel pathways in LVH and offers a freely accessible protocol (http://sites.google.com/site/cardionetworks) for similar analyses in other cardiovascular diseases.

General Regression Neuro–Fuzzy Network for Identification of Nonstationary Plants

General Regression Neuro-Fuzzy Network, which combines the properties of conventional General Regression Neural Network and Adaptive Network-based Fuzzy Inference System is proposed in this work. This network relates to so-called “memory-based networks”, which is adjusted by one-pass learning algorithm.

Outliers Resistant Learning Algorithm for Radial-basis-fuzzy-wavelet-neural Network in Stomach Acute Injury Diagnosis Tasks

In this paper an outliers resistant learning algorithm for the radial-basis-fuzzy-wavelet-neural network based on R. Welsh criterion is proposed. Suggested learning algorithm under consideration allows the signals processing in presence of significant noise level and outliers. The robust learning algorithm efficiency is investigated and confirmed by the number of experiments including medical applications.

A mixture density network approach to modelling and exploiting uncertainty in nonlinear control problems

In nonlinear and stochastic control problems, learning an efficient feed-forward controller is not amenable to conventional neurocontrol methods. For these approaches, estimating and then incorporating uncertainty in the controller and feed-forward models can produce more robust control results. Here, we introduce a novel inversion-based neurocontroller for solving control problems involving uncertain nonlinear systems which could also compensate for multi-valued systems. The approach uses recent developments in neural networks, especially in the context of modelling statistical distributions, which are applied to forward and inverse plant models. Provided that certain conditions are met, an estimate of the intrinsic uncertainty for the outputs of neural networks can be obtained using the statistical properties of networks. More generally, multicomponent distributions can be modelled by the mixture density network. Based on importance sampling from these distributions a novel robust inverse control approach is obtained. This importance sampling provides a structured and principled approach to constrain the complexity of the search space for the ideal control law. The developed methodology circumvents the dynamic programming problem by using the predicted neural network uncertainty to localise the possible control solutions to consider. A nonlinear multi-variable system with different delays between the input-output pairs is used to demonstrate the successful application of the developed control algorithm. The proposed method is suitable for redundant control systems and allows us to model strongly non-Gaussian distributions of control signal as well as processes with hysteresis. © 2004 Elsevier Ltd. All rights reserved.

Secure routing and trust computation in multihop infrastructureless networks

Today's wireless networks rely mostly on infrastructural support for their operation. With the concept of ubiquitous computing growing more popular, research on infrastructureless networks have been rapidly growing. However, such types of networks face serious security challenges when deployed. This dissertation focuses on designing a secure routing solution and trust modeling for these infrastructureless networks. ^ The dissertation presents a trusted routing protocol that is capable of finding a secure end-to-end route in the presence of malicious nodes acting either independently or in collusion, The solution protects the network from active internal attacks, known to be the most severe types of attacks in an ad hoc application. Route discovery is based on trust levels of the nodes, which need to be dynamically computed to reflect the malicious behavior in the network. As such, we have developed a trust computational model in conjunction with the secure routing protocol that analyzes the different malicious behavior and quantifies them in the model itself. Our work is the first step towards protecting an ad hoc network from colluding internal attack. To demonstrate the feasibility of the approach, extensive simulation has been carried out to evaluate the protocol efficiency and scalability with both network size and mobility. ^ This research has laid the foundation for developing a variety of techniques that will permit people to justifiably trust the use of ad hoc networks to perform critical functions, as well as to process sensitive information without depending on any infrastructural support and hence will enhance the use of ad hoc applications in both military and civilian domains. ^

A Multi-Criteria Decision-Making Approach to Compare and Contrast the Websites of China-based and U.S-based Hotels

The improvement in living standards and the development of telecommunications have led to a large increase in the number of Internet users in China. It has been reported by China National Network Information Center that the number of Internet users in China has reached 33.7 million in 2001, ranting the country third in the world. This figure also shows that more and more Chinese residents have accepted the Internet and use it to obtain information and compete their travel planning. Milne and Ateljevic stated that the integration of computing and telecommunications would create a global information network based mostly on the Internet. The Internet, especially the World Wide Web, has had a great impact on the hospitality and tourism industry in recent years. The WWW plays an important role in mediating between customers and hotel companies as a place to acquire information acquisition and transact business.

C-MEMS Based Micro Enzymatic Biofuel Cells

Miniaturized, self-sufficient bioelectronics powered by unconventional micropower may lead to a new generation of implantable, wireless, minimally invasive medical devices, such as pacemakers, defibrillators, drug-delivering pumps, sensor transmitters, and neurostimulators. Studies have shown that micro-enzymatic biofuel cells (EBFCs) are among the most intuitive candidates for in vivo micropower. In the fisrt part of this thesis, the prototype design of an EBFC chip, having 3D intedigitated microelectrode arrays was proposed to obtain an optimum design of 3D microelectrode arrays for carbon microelectromechanical systems (C-MEMS) based EBFCs. A detailed modeling solving partial differential equations (PDEs) by finite element techniques has been developed on the effect of 1) dimensions of microelectrodes, 2) spatial arrangement of 3D microelectrode arrays, 3) geometry of microelectrode on the EBFC performance based on COMSOL Multiphysics. In the second part of this thesis, in order to investigate the performance of an EBFC, behavior of an EBFC chip performance inside an artery has been studied. COMSOL Multiphysics software has also been applied to analyze mass transport for different orientations of an EBFC chip inside a blood artery. Two orientations: horizontal position (HP) and vertical position (VP) have been analyzed. The third part of this thesis has been focused on experimental work towards high performance EBFC. This work has integrated graphene/enzyme onto three-dimensional (3D) micropillar arrays in order to obtain efficient enzyme immobilization, enhanced enzyme loading and facilitate direct electron transfer. The developed 3D graphene/enzyme network based EBFC generated a maximum power density of 136.3 μWcm-2 at 0.59 V, which is almost 7 times of the maximum power density of the bare 3D carbon micropillar arrays based EBFC. To further improve the EBFC performance, reduced graphene oxide (rGO)/carbon nanotubes (CNTs) has been integrated onto 3D mciropillar arrays to further increase EBFC performance in the fourth part of this thesisThe developed rGO/CNTs based EBFC generated twice the maximum power density of rGO based EBFC. Through a comparison of experimental and theoretical results, the cell performance efficiency is noted to be 67%.

Response toolkit to provide an active response against intrusions using Ontology-Based IRS

Los sistemas de respuesta activa tienen por objetivo ejecutar una respuesta en contra de una intrusión de forma automática. Sin embargo, ejecutar una respuesta automáticamente no es una tarea trivial ya que el costo de ejecutar una respuesta podría ser más grande que el efecto que cause la intrusión propiamente dicha. También, el sistema debe contar con un amplio conjunto de acciones de respuesta y un algoritmo que seleccione la respuesta óptima. Este artículo propone un toolkit de respuestas que será integrado a un IRS basado en Ontologías para permitir la ejecución automática de la mejor respuesta cuando una intrusión es detectada. Se presenta un conjunto de respuestas basadas en host y basadas en red que pueden ser ejecutadas por el IRS, dicha ejecución es llevada a cabo mediante agentes basados en plugins que han sido distribuidos en la red. Finalmente, se realiza la verificación del sistema propuesto, tomando como caso de uso un ataque de defacement obteniéndose resultados satisfactorios.

Resúmenes de los Trabajos Finales de Graduación para Optar por el Título de Licenciatura en Bibliotecología y Documentación, 1998-1999

El objetivo de este Trabajo Final de Graduación fue formular una propuesta de diseño y construcción de una Intranet, que permita al Centro de Documentación "Alvaro Castro Jenkins" del Banco Central de Costa Rica, administrar los recursos, servicios, y productos de información de acceso confidencial, discrecional y restringido a los funcionarios del Banco, con una red privada de comunicaciones, basada en los protocolos TCP/IP y tecnologías de World Wide Web (WWW). La implementación de la Unidad de Información del OVSICORI se propuso a partir del diagnóstico de la infraestructura, de los recursos tecnológicos, recursos documentales, financieros, humanos y necesidades de los usuarios reales y potenciales. Este Trabajo Final de Graduación consistió en una investigación descriptiva en la que se realizó un diagnóstico de los servicios de información existentes en la Biblioteca Conjunta de la Corte Interamericana de Derechos Humanos y del Instituto Interamericano de Derechos Humanos, con el fin de analizar la pertinencia y utilidad de los mismos y proponer un programa de mejoramiento continuo para los servicios y productos existentes, así como el diseño de nuevos servicios y productos de información.