A modular traffic sampling architecture for flexible network measurements

Dissertação de Mestrado (Programa Doutoral em Informática)

CH4, CO, and H2O spectroscopy for the Sentinel-5 Precursor mission: an assessment with the Total Carbon Column Observing Network measurements

The TROPOspheric Monitoring Instrument (TROPOMI) will be part of ESA's Sentinel-5 Precursor (S5P) satellite platform scheduled for launch in 2015. TROPOMI will monitor methane and carbon monoxide concentrations in the Earth's atmosphere by measuring spectra of back-scattered sunlight in the short-wave infrared (SWIR). S5P will be the first satellite mission to rely uniquely on the spectral window at 4190–4340 cm−1 (2.3 μm) to retrieve CH4 and CO. In this study, we investigated if the absorption features of the three relevant molecules CH4, CO, and H2O are adequately known. To this end, we retrieved total columns of CH4, CO, and H2O from absorption spectra measured by two ground-based Fourier transform spectrometers that are part of the Total Carbon Column Observing Network (TCCON). The retrieval results from the 4190–4340 cm−1 range at the TROPOMI resolution (0.45 cm−1) were then compared to the CH4 results obtained from the 6000 cm−1 region, and the CO results obtained from the 4190–4340 cm−1 region at the higher TCCON resolution (0.02 cm−1). For TROPOMI-like settings, we were able to reproduce the CH4 columns to an accuracy of 0.3% apart from a constant bias of 1%. The CO retrieval accuracy was, through interference, systematically influenced by the shortcomings of the CH4 and H2O spectroscopy. In contrast to CH4, the CO column error also varied significantly with atmospheric H2O content. Unaddressed, this would introduce seasonal and latitudinal biases to the CO columns retrieved from TROPOMI measurements. We therefore recommend further effort from the spectroscopic community to be directed at the H2O and CH4 spectroscopy in the 4190–4340 cm−1 region.

Generalized connectivity between any two nodes in a complex network

This article focuses on the identification of the number of paths with different lengths between pairs of nodes in complex networks and how these paths can be used for characterization of topological properties of theoretical and real-world complex networks. This analysis revealed that the number of paths can provide a better discrimination of network models than traditional network measurements. In addition, the analysis of real-world networks suggests that the long-range connectivity tends to be limited in these networks and may be strongly related to network growth and organization.

Sensitivity of complex networks measurements

Complex networks obtained from real-world networks are often characterized by incompleteness and noise, consequences of imperfect sampling as well as artifacts in the acquisition process. Because the characterization, analysis and modeling of complex systems underlain by complex networks are critically affected by the quality and completeness of the respective initial structures, it becomes imperative to devise methodologies for identifying and quantifying the effects of the sampling on the network structure. One way to evaluate these effects is through an analysis of the sensitivity of complex network measurements to perturbations in the topology of the network. In this paper, measurement sensibility is quantified in terms of the relative entropy of the respective distributions. Three particularly important kinds of progressive perturbations to the network are considered, namely, edge suppression, addition and rewiring. The measurements allowing the best balance of stability (smaller sensitivity to perturbations) and discriminability (separation between different network topologies) are identified with respect to each type of perturbation. Such an analysis includes eight different measurements applied on six different complex networks models and three real-world networks. This approach allows one to choose the appropriate measurements in order to obtain accurate results for networks where sampling bias cannot be avoided-a very frequent situation in research on complex networks.

A complex network approach to text summarization

Automatic summarization of texts is now crucial for several information retrieval tasks owing to the huge amount of information available in digital media, which has increased the demand for simple, language-independent extractive summarization strategies. In this paper, we employ concepts and metrics of complex networks to select sentences for an extractive summary. The graph or network representing one piece of text consists of nodes corresponding to sentences, while edges connect sentences that share common meaningful nouns. Because various metrics could be used, we developed a set of 14 summarizers, generically referred to as CN-Summ, employing network concepts such as node degree, length of shortest paths, d-rings and k-cores. An additional summarizer was created which selects the highest ranked sentences in the 14 systems, as in a voting system. When applied to a corpus of Brazilian Portuguese texts, some CN-Summ versions performed better than summarizers that do not employ deep linguistic knowledge, with results comparable to state-of-the-art summarizers based on expensive linguistic resources. The use of complex networks to represent texts appears therefore as suitable for automatic summarization, consistent with the belief that the metrics of such networks may capture important text features. (c) 2008 Elsevier Inc. All rights reserved.

Extensive cross-talk and global regulators identified from an analysis of the integrated transcriptional and signaling network in Escherichia coli

To understand the regulatory dynamics of transcription factors (TFs) and their interplay with other cellular components we have integrated transcriptional, protein-protein and the allosteric or equivalent interactions which mediate the physiological activity of TFs in Escherichia coli. To study this integrated network we computed a set of network measurements followed by principal component analysis (PCA), investigated the correlations between network structure and dynamics, and carried out a procedure for motif detection. In particular, we show that outliers identified in the integrated network based on their network properties correspond to previously characterized global transcriptional regulators. Furthermore, outliers are highly and widely expressed across conditions, thus supporting their global nature in controlling many genes in the cell. Motifs revealed that TFs not only interact physically with each other but also obtain feedback from signals delivered by signaling proteins supporting the extensive cross-talk between different types of networks. Our analysis can lead to the development of a general framework for detecting and understanding global regulatory factors in regulatory networks and reinforces the importance of integrating multiple types of interactions in underpinning the interrelationships between them.

Classificação de sistemas meteorológicos e comparação da precipitação estimada pelo radar e medida pela rede telemétrica na bacia hidrográfica do alto Tietê

Foram analisadas características da precipitação estimada a partir de 145.194 campos de refletividade, de um total de 827 dias entre 1998 e 2003, obtidos do Radar Meteorológico de São Paulo (RSP). Os eventos foram classificados de acordo com intensidades de precipitação; em Convectivos (EC) e Estratiformes (EE). Quanto à morfologia, cinco tipos de sistemas foram identificados; Convecção Isolada (CI), Brisa Marítima (BM), Linhas de Instabilidade (LI), Bandas Dispersas (BD) e Frentes Frias (FF). Eventos convectivos dominam na primavera e verão e estratiformes no outono e inverno. A CI e a BM tiveram maiores picos de atuação entre outubro e março enquanto as FF de abril a setembro. BD atuam durante todo o ano e as LI só não foram observadas nos meses de junho e julho. Uma comparação pontual entre a precipitação medida pela telemetria e estimada com o radar foi realizada e, mostrou haver, na maioria dos casos, um viés positivo do RSP, para acumulações de 10, 30 e 60 minutos. Com o objetivo de integrar as estimativas de precipitação do radar com as medidas da rede telemétrica, por meio de uma análise objetiva estatística, foram obtidas dos campos de precipitação do radar as estruturas das correlações espaciais em função da distância para acumulações de chuva de 15, 30, 60 e 120 minutos para os cinco tipos de sistemas precipitantes que foram caracterizados. As curvas das correlações espaciais médias de todos os eventos de precipitação de cada sistema foram ajustadas por funções polinomiais de sexta ordem. Os resultados indicam diferenças significativas na estrutura espacial das correlações entre os sistemas precipitantes.

Green ICT metrics and Biomimicry

Sustainability issue of ICT have gathered attention in recent years, and researchers are working on this problem. Sustainability incorporates numerous interconnected aspects as well as methods to achieve it in ICT, so it is quite complicated to have a general view on a problem without a proper framework. However, a general methodology for such a research is missing. In this work it is proposed to use Biomimicry approach as a framework for sustainability research and development, as it introduces systematics and also forces to account sustainable aspects. Additionally, an interesting problem of green network measurements for enhancing sustainability in ICT will be researched using mentioned approach. The goal is to investigate Biomimicry as a systemic approach for developing sustainable systems, as well as to apply it in green network measurements study. Comparative study is performed for examining Biomimicry approach, as well as a use case of green network measurements research is presented. As a result, green network measurement can potentially improve sustainability, but only to a limited extent as it cannot incorporate all the aspects; within Biomimicry approach, two methodologies exist. It is possible to conclude that Biomimicry is a good framework for developing sustainable systems, nevertheless, another methodology has to be introduced within it; new methodology has to incorporate advantages of two existing ones.

Complex networks analysis of manual and machine translations

Complex networks have been increasingly used in text analysis, including in connection with natural language processing tools, as important text features appear to be captured by the topology and dynamics of the networks. Following previous works that apply complex networks concepts to text quality measurement, summary evaluation, and author characterization, we now focus on machine translation (MT). In this paper we assess the possible representation of texts as complex networks to evaluate cross-linguistic issues inherent in manual and machine translation. We show that different quality translations generated by NIT tools can be distinguished from their manual counterparts by means of metrics such as in-(ID) and out-degrees (OD), clustering coefficient (CC), and shortest paths (SP). For instance, we demonstrate that the average OD in networks of automatic translations consistently exceeds the values obtained for manual ones, and that the CC values of source texts are not preserved for manual translations, but are for good automatic translations. This probably reflects the text rearrangements humans perform during manual translation. We envisage that such findings could lead to better NIT tools and automatic evaluation metrics.

A wireless sensor network platform for structural health monitoring: enabling accurate and synchronized measurements through COTS+custom-based design

Structural health monitoring has long been identified as a prominent application of Wireless Sensor Networks (WSNs), as traditional wired-based solutions present some inherent limitations such as installation/maintenance cost, scalability and visual impact. Nevertheless, there is a lack of ready-to-use and off-the-shelf WSN technologies that are able to fulfill some most demanding requirements of these applications, which can span from critical physical infrastructures (e.g. bridges, tunnels, mines, energy grid) to historical buildings or even industrial machinery and vehicles. Low-power and low-cost yet extremely sensitive and accurate accelerometer and signal acquisition hardware and stringent time synchronization of all sensors data are just examples of the requirements imposed by most of these applications. This paper presents a prototype system for health monitoring of civil engineering structures that has been jointly conceived by a team of civil, and electrical and computer engineers. It merges the benefits of standard and off-the-shelf (COTS) hardware and communication technologies with a minimum set of custom-designed signal acquisition hardware that is mandatory to fulfill all application requirements.

Cloud droplet size and liquid water path retrievals from zenith radiance measurements: examples from the Atmospheric Radiation Measurement Program and the Aerosol Robotic Network

The ground-based Atmospheric Radiation Measurement Program (ARM) and NASA Aerosol Robotic Net- work (AERONET) routinely monitor clouds using zenith ra- diances at visible and near-infrared wavelengths. Using the transmittance calculated from such measurements, we have developed a new retrieval method for cloud effective droplet size and conducted extensive tests for non-precipitating liquid water clouds. The underlying principle is to combine a liquid-water-absorbing wavelength (i.e., 1640 nm) with a non-water-absorbing wavelength for acquiring information on cloud droplet size and optical depth. For simulated stratocumulus clouds with liquid water path less than 300 g m−2 and horizontal resolution of 201 m, the retrieval method underestimates the mean effective radius by 0.8μm, with a root-mean-squared error of 1.7 μm and a relative deviation of 13%. For actual observations with a liquid water path less than 450 g m−2 at the ARM Oklahoma site during 2007– 2008, our 1.5-min-averaged retrievals are generally larger by around 1 μm than those from combined ground-based cloud radar and microwave radiometer at a 5-min temporal resolution. We also compared our retrievals to those from combined shortwave flux and microwave observations for relatively homogeneous clouds, showing that the bias between these two retrieval sets is negligible, but the error of 2.6 μm and the relative deviation of 22 % are larger than those found in our simulation case. Finally, the transmittance-based cloud effective droplet radii agree to better than 11 % with satellite observations and have a negative bias of 1 μm. Overall, the retrieval method provides reasonable cloud effective radius estimates, which can enhance the cloud products of both ARM and AERONET.

Network optimization for enhanced resilience of urban heat island measurements

The urban heat island is a well-known phenomenon that impacts a wide variety of city operations. With greater availability of cheap meteorological sensors, it is possible to measure the spatial patterns of urban atmospheric characteristics with greater resolution. To develop robust and resilient networks, recognizing sensors may malfunction, it is important to know when measurement points are providing additional information and also the minimum number of sensors needed to provide spatial information for particular applications. Here we consider the example of temperature data, and the urban heat island, through analysis of a network of sensors in the Tokyo metropolitan area (Extended METROS). The effect of reducing observation points from an existing meteorological measurement network is considered, using random sampling and sampling with clustering. The results indicated the sampling with hierarchical clustering can yield similar temperature patterns with up to a 30% reduction in measurement sites in Tokyo. The methods presented have broader utility in evaluating the robustness and resilience of existing urban temperature networks and in how networks can be enhanced by new mobile and open data sources.

An application of neural network technique to correct the dome temperature effects on pyrgeometer measurements

This work describes an application of a multilayer perceptron neural network technique to correct dome emission effects on longwave atmospheric radiation measurements carried out using an Eppley Precision Infrared Radiometer (PIR) pyrgeometer. It is shown that approximately 7-month-long measurements of dome and case temperatures and meteorological variables available in regular surface stations (global solar radiation, air temperature, and air relative humidity) are enough to train the neural network algorithm and correct the observed longwave radiation for dome temperature effects in surface stations with climates similar to that of the city of São Paulo, Brazil. The network was trained using data from 15 October 2003 to 7 January 2004 and verified using data, not present during the network-training period, from 8 January to 30 April 2004. The longwave radiation values generated by the neural network technique were very similar to the values obtained by Fairall et al., assumed here as the reference approach to correct dome emission effects in PIR pyrgeometers. Compared to the empirical approach the neural network technique is less limited to sensor type and time of day (allows nighttime corrections).

Chlorophyll a spatial inference using artificial neural network from multispectral images and in situ measurements

Considering the importance of monitoring the water quality parameters, remote sensing is a practicable alternative to limnological variables detection, which interacts with electromagnetic radiation, called optically active components (OAC). Among these, the phytoplankton pigment chlorophyll a is the most representative pigment of photosynthetic activity in all classes of algae. In this sense, this work aims to develop a method of spatial inference of chlorophyll a concentration using Artificial Neural Networks (ANN). To achieve this purpose, a multispectral image and fluorometric measurements were used as input data. The multispectral image was processed and the net training and validation dataset were carefully chosen. From this, the neural net architecture and its parameters were defined to model the variable of interest. In the end of training phase, the trained network was applied to the image and a qualitative analysis was done. Thus, it was noticed that the integration of fluorometric and multispectral data provided good results in the chlorophyll a inference, when combined in a structure of artificial neural networks.

What drives the seasonality of photosynthesis across the Amazon basin? A cross-site analysis of eddy flux tower measurements from the Brasil flux network

We investigated the seasonal patterns of Amazonian forest photosynthetic activity, and the effects thereon of variations in climate and land-use, by integrating data from a network of ground-based eddy flux towers in Brazil established as part of the ‘Large-Scale Biosphere Atmosphere Experiment in Amazonia’ project. We found that degree of water limitation, as indicated by the seasonality of the ratio of sensible to latent heat flux (Bowen ratio) predicts seasonal patterns of photosynthesis. In equatorial Amazonian forests (5◦ N–5◦ S), water limitation is absent, and photosynthetic fluxes (or gross ecosystem productivity, GEP) exhibit high or increasing levels of photosynthetic activity as the dry season progresses, likely a consequence of allocation to growth of new leaves. In contrast, forests along the southern flank of the Amazon, pastures converted from forest, and mixed forest-grass savanna, exhibit dry-season declines in GEP, consistent with increasing degrees of water limitation. Although previous work showed tropical ecosystem evapotranspiration (ET) is driven by incoming radiation, GEP observations reported here surprisingly show no or negative relationships with photosynthetically active radiation (PAR). Instead, GEP fluxes largely followed the phenology of canopy photosynthetic capacity (Pc), with only deviations from this primary pattern driven by variations in PAR. Estimates of leaf flush at three