Modeling connectivity in terms of network activity

A new complex network model is proposed which is founded on growth, with new connections being established proportionally to the current dynamical activity of each node, which can be understood as a generalization of the Barabasi-Albert static model. By using several topological measurements, as well as optimal multivariate methods (canonical analysis and maximum likelihood decision), we show that this new model provides, among several other theoretical kinds of networks including Watts-Strogatz small-world networks, the greatest compatibility with three real-world cortical networks.

A complex network-based approach for boundary shape analysis

This paper introduces a novel methodology to shape boundary characterization, where a shape is modeled into a small-world complex network. It uses degree and joint degree measurements in a dynamic evolution network to compose a set of shape descriptors. The proposed shape characterization method has all efficient power of shape characterization, it is robust, noise tolerant, scale invariant and rotation invariant. A leaf plant classification experiment is presented on three image databases in order to evaluate the method and compare it with other descriptors in the literature (Fourier descriptors, Curvature, Zernike moments and multiscale fractal dimension). (C) 2008 Elsevier Ltd. All rights reserved.

Concentric characterization and classification of complex network nodes: Application to an institutional collaboration network

Differently from theoretical scale-free networks, most real networks present multi-scale behavior, with nodes structured in different types of functional groups and communities. While the majority of approaches for classification of nodes in a complex network has relied on local measurements of the topology/connectivity around each node, valuable information about node functionality can be obtained by concentric (or hierarchical) measurements. This paper extends previous methodologies based on concentric measurements, by studying the possibility of using agglomerative clustering methods, in order to obtain a set of functional groups of nodes, considering particular institutional collaboration network nodes, including various known communities (departments of the University of Sao Paulo). Among the interesting obtained findings, we emphasize the scale-free nature of the network obtained, as well as identification of different patterns of authorship emerging from different areas (e.g. human and exact sciences). Another interesting result concerns the relatively uniform distribution of hubs along concentric levels, contrariwise to the non-uniform pattern found in theoretical scale-free networks such as the BA model. (C) 2008 Elsevier B.V. All rights reserved.

Road network and GPS tracking with data processing and quality assessment

GPS technology has been embedded into portable, low-cost electronic devices nowadays to track the movements of mobile objects. This implication has greatly impacted the transportation field by creating a novel and rich source of traffic data on the road network. Although the promise offered by GPS devices to overcome problems like underreporting, respondent fatigue, inaccuracies and other human errors in data collection is significant; the technology is still relatively new that it raises many issues for potential users. These issues tend to revolve around the following areas: reliability, data processing and the related application. This thesis aims to study the GPS tracking form the methodological, technical and practical aspects. It first evaluates the reliability of GPS based traffic data based on data from an experiment containing three different traffic modes (car, bike and bus) traveling along the road network. It then outline the general procedure for processing GPS tracking data and discuss related issues that are uncovered by using real-world GPS tracking data of 316 cars. Thirdly, it investigates the influence of road network density in finding optimal location for enhancing travel efficiency and decreasing travel cost. The results show that the geographical positioning is reliable. Velocity is slightly underestimated, whereas altitude measurements are unreliable.Post processing techniques with auxiliary information is found necessary and important when solving the inaccuracy of GPS data. The densities of the road network influence the finding of optimal locations. The influence will stabilize at a certain level and do not deteriorate when the node density is higher.

Speed control and policing in a cellular mobile network: speedNet

In this paper, we describe SpeedNet, a GSM network variant which resembles an ad hoc wireless mobile network where base stations keep track of the velocities of mobile users (cars). SpeedNet is intended to track mobile users and their speed passively for both speed policing and control of traffic. The speed of the vehicle is controlled in a speed critical zone by means of an electro-mechanical control system, suitably referred to as VVLS (Vehicular Velocity Limiting System). VVLS is mounted on the vehicle and responds to the command signals generated by the base station. It also determines the next base station to handoff, in order to improve the connection reliability and bandwidth efficiency of the underlying network. Robust Extended Kalman Filter (REKF) is used as a passive velocity estimator of the mobile user with the widely used proportional and integral controller speed control. We demonstrate through simulation and analysis that our prediction algorithm can successfully estimate the mobile user’s velocity with low system complexity as it requires two closest mobile base station measurements and also it is robust against system uncertainties due to the inherent deterministic nature in the mobility model.

Sensor fusion based localization of a mobile user in a wireless network

This paper applies sensor fusion to the localization problem of a mobile user. We propose that the use of direction of arrival (DOA) estimations along with received signal strength measurements can increase the accuracy and robustness of location estimations. The DOA estimations are incapable of providing multi-dimensional positioning alone, while signal strength methods are prone to high uncertainties. A Robust Extended Kalman Filter (REKF) is used to derive the state estimate of the mobile user's position, and successfully track the mobile users with less system complexity, as it requires measurements from only one base station. Therefore, localization of mobile users can be performed at the single base station. Furthermore, the technique is robust against system uncertainties caused by the inherent deterministic nature of the mobility model. Through simulation, we show the accuracy of our prediction algorithm and the simplicity of its implementation.

Location based power control for mobile devices in a cellular network

This paper provides location estimation based power control strategy for cellular radio systems via a location based interference management scheme. Our approach considers the carrier-to-interference as dependent on the transmitter and receiver separation distance and therefore an accurate estimation of the precise locations can provide the power critical mobile user to control the transition power accordingly. In this fully
distributed algorithms, we propose using a Robust Extended Kalman Filter (REKF) to derive an estimate of the mobile user’s closest mobile base station from the user’s location, heading and altitude. Our analysis demonstrates that this algorithm can successfully track the mobile users with less system complexity, as it requires measurements from only one or two closest mobile base stations and hence enable the user to transmit at the rate that is sufficient for the interference management. Our power control
algorithms based on this estimation converges to the desired power trajectory. Further, the technique is robust against system uncertainties caused by the inherent deterministic nature of the mobility model. Through simulation, we show the accuracy of our prediction algorithm and the simplicity of its implementation.

Location based power control for energy critical sensors in a disconnected network

This paper provides a location based power control strategy for disconnected sensory nodes deployed for long term service. Power conservation is of importance particularly when sensors communicate with a mobile robot used for data collection. The proposed algorithm uses estimations from a Robust Extended Kalman Filter (REKF) with RSSI measurements, in implementing a sigmoid function based power control algorithm which essentially approaches a desired power emission trajectory based on carrier-to-interference ratios(CIR) to ensure interferenceless reception. The more realistic modelling we use incorporates physical dynamics between the mobile robot and the sensors together with the wireless propagation parameters between the transmitter and receiver to formulate a sophisticated and effective power control strategy for the exclusive usage of energy critical disconnected nodes in a sensory network increasing their life span.

A discussion on passive location discovery in emitter networks using angle-only measurements

In this paper we discuss the ghost node problem found when triangulation of 2 or more nodes is required. We present and discuss a simple algorithm, termed ABLE (Angle Based Location Estimation), that will position randomly placed emitters in a wireless sensor network using a mobile antenna array. The individual nodes in the network are relieved of the localization task by the mobile antenna system and require no modifications to account for location determination. Furthermore, no beacon nodes (i.e. nodes that know their own position) are required. We provide analysis that indicates a reasonably small number of measurements are required to guarantee the successful
localization of the emitting nodes and demonstrate our results through simulation.

Investigation of the postcure reaction and surface energy of epoxy resins using time-of-flight secondary ion mass spectrometry and contact-angle measurements

Time-of-flight secondary ion mass spectrometry (ToF-SIMS) was used to investigate correlations between the molecular changes and postcuring reaction on the surface of a diglycidyl ether of bisphenol A and diglycidylether of bisphenol F based epoxy resin cured with two different amine-based hardeners. The aim of this work was to present a proof of concept that ToF-SIMS has the ability to provide information regarding the reaction steps, path, and mechanism for organic reactions in general and for epoxy resin curing and postcuring reactions in particular. Contact-angle measurements were taken for the cured and postcured epoxy resins to correlate changes in the surface energy with the molecular structure of the surface. Principal components analysis (PCA) of the ToFSIMS positive spectra explained the variance in the molecular information, which was related to the resin curing and postcuring reactions with different hardeners and to the surface energy values. The first principal component captured information related to the chemical phenomena of the curing reaction path, branching, and network density based on changes in the relative ion density of the aliphatic hydrocarbon and the C₇H₇O⁺ positive ions. The second principal component captured information related to the difference in the surface energy, which was correlated to the difference in the relative intensity of the C_xH_yN_z⁺ ions of the samples. PCA of the negative spectra provided insight into the extent of consumption of the hardener molecules in the curing and postcuring reactions of both systems based on the relative ion intensity of the nitrogen-containing negative ions and showed molecular correlations with the sample surface energy.

Network traffic classification using correlation information

Traffic classification has wide applications in network management, from security monitoring to quality of service measurements. Recent research tends to apply machine learning techniques to flow statistical feature based classification methods. The nearest neighbor (NN)-based method has exhibited superior classification performance. It also has several important advantages, such as no requirements of training procedure, no risk of overfitting of parameters, and naturally being able to handle a huge number of classes. However, the performance of NN classifier can be severely affected if the size of training data is small. In this paper, we propose a novel nonparametric approach for traffic classification, which can improve the classification performance effectively by incorporating correlated information into the classification process. We analyze the new classification approach and its performance benefit from both theoretical and empirical perspectives. A large number of experiments are carried out on two real-world traffic data sets to validate the proposed approach. The results show the traffic classification performance can be improved significantly even under the extreme difficult circumstance of very few training samples.

Classifying network traffic in the big data era

With the arrival of Big Data Era, properly utilizing the power of big data is becoming increasingly essential for the strength and competitiveness of businesses and organizations. We are facing grand challenges from big data from different perspectives, such as processing, communication, security, and privacy. In this talk, we discuss the big data challenges in network traffic classification and our solutions to the challenges. The significance of the research lies in the fact that each year the network traffic increase exponentially on the current Internet. Traffic classification has wide applications in network management, from security monitoring to quality of service measurements. Recent research tends to apply machine-learning techniques to flow statistical feature based classification methods. In this talk, we propose a series of novel approaches for traffic classification, which can improve the classification performance effectively by incorporating correlated information into the classification process. We analyze the new classification approaches and their performance benefit from both theoretical and empirical perspectives. A large number of experiments are carried out on two real-world traffic datasets to validate the proposed approach. The results show the traffic classification performance can be improved significantly even under the extreme difficult circumstance of very few training samples. Our work has significant impact on security applications.

Poly(triazine imide) with intercalation of Lithium and ChlorideiIons [(C3N3)2(NHxLi1−x)3⋅LiCl]: A Crystalline 2D Carbon Nitride network

Poly(triazine imide) with intercalation of lithium and chloride ions (PTI/Li+Cl−) was synthesized by temperature-induced condensation of dicyandiamide in a eutectic mixture of lithium chloride and potassium chloride as solvent. By using this ionothermal approach the well-known problem of insufficient crystallinity of carbon nitride (CN) condensation products could be overcome. The structural characterization of PTI/Li+Cl− resulted from a complementary approach using spectroscopic methods as well as different diffraction techniques. Due to the high crystallinity of PTI/Li+Cl− a structure solution from both powder X-ray and electron diffraction patterns using direct methods was possible; this yielded a triazine-based structure model, in contrast to the proposed fully condensed heptazine-based structure that has been reported recently. Further information from solid-state NMR and FTIR spectroscopy as well as high-resolution TEM investigations was used for Rietveld refinement with a goodness-of-fit (χ2) of 5.035 and wRp=0.05937. PTI/Li+Cl− (P63cm (no. 185); a=846.82(10), c=675.02(9) pm) is a 2D network composed of essentially planar layers made up from imide-bridged triazine units. Voids in these layers are stacked upon each other forming channels running parallel to [001], filled with Li+ and Cl− ions. The presence of salt ions in the nanocrystallites as well as the existence of sp2-hybridized carbon and nitrogen atoms typical of graphitic structures was confirmed by electron energy-loss spectroscopy (EELS) measurements. Solid-state NMR spectroscopy investigations using 15N-labeled PTI/Li+Cl− proved the absence of heptazine building blocks and NH2 groups and corroborated the highly condensed, triazine-based structure model.

Identify kinetic features of fibers growing, branching, and bundling in microstructure engineering of crystalline fiber network

Fibers growing, branching, and bundling are essential for the development of crystalline fiber networks of molecular gels. In this work, for two typical crystalline fiber networks, i.e. the network of spherulitic domains and the interconnected fibers network, related kinetic information is obtained using dynamic rheological measurements and analysis in terms of the Avrami theory. In combination with microstructure characterizations, we establish the correlation of the Avrami derived kinetic parameter not only with the nucleation nature and growth dimensionality of fibers and branches, but also with the fiber bundles induced by fiber-fiber interactions. Our study highlights the advantage of simple dynamic rheological measurements over other spectroscopic methods used in previous studies for providing more kinetic information on fiber-fiber interactions, enabling the Avrami analyses to extract distinct kinetic features not only for fibers growing and branching, but also for bundling in the creation of strong interconnected fibers networks. This work may be helpful for the implementation of precise kinetic control of crystalline fiber network formations for achieving desirable microstructures and rheological properties for advanced applications of gel materials. This journal is © the Partner Organisations 2014.

A novel intelligent system to nitrogen content prediction in plants using indirect chlorophyll measurements

The accurate identification of the nitrogen content in plants is extremely important since it involves economic aspects and environmental impacts, Several experimental tests have been carried out to obtain characteristics and parameters associated with the health of plants and its growing. The nitrogen content identification in plants involves a lot of non-linear parameters and complexes mathematical models. This paper describes a novel approach for identification of nitrogen content thought SPAD index using artificial neural networks (ANN). The network acts as identifier of relationships among, crop varieties, fertilizer treatments, type of leaf and nitrogen content in the plants (target). So, nitrogen content can be generalized and estimated and from an input parameter set. This approach can form the basis for development of an accurate real time system to predict nitrogen content in plants.