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.

Can retired galaxies mimic active galaxies? Clues from the Sloan Digital Sky Survey

The classification of galaxies as star forming or active is generally done in the ([O III]/H beta, [N II]/H alpha) plane. The Sloan Digital Sky Survey (SDSS) has revealed that, in this plane, the distribution of galaxies looks like the two wings of a seagull. Galaxies in the right wing are referred to as Seyfert/LINERs, leading to the idea that non-stellar activity in galaxies is a very common phenomenon. Here, we argue that a large fraction of the systems in the right wing could actually be galaxies which stopped forming stars. The ionization in these `retired` galaxies would be produced by hot post-asymptotic giant branch stars and white dwarfs. Our argumentation is based on a stellar population analysis of the galaxies via our STARLIGHT code and on photoionization models using the Lyman continuum radiation predicted for this population. The proportion of LINER galaxies that can be explained in such a way is, however, uncertain. We further show how observational selection effects account for the shape of the right wing. Our study suggests that nuclear activity may not be as common as thought. If retired galaxies do explain a large part of the seagull`s right wing, some of the work concerning nuclear activity in galaxies, as inferred from SDSS data, will have to be revised.

Photometric redshifts and k-corrections for the Sloan Digital Sky Survey Data Release 7

We present a catalogue of galaxy photometric redshifts and k-corrections for the Sloan Digital Sky Survey Data Release 7 (SDSS-DR7), available on the World Wide Web. The photometric redshifts were estimated with an artificial neural network using five ugriz bands, concentration indices and Petrosian radii in the g and r bands. We have explored our redshift estimates with different training sets, thus concluding that the best choice for improving redshift accuracy comprises the main galaxy sample (MGS), the luminous red galaxies and the galaxies of active galactic nuclei covering the redshift range 0 < z < 0.3. For the MGS, the photometric redshift estimates agree with the spectroscopic values within rms = 0.0227. The distribution of photometric redshifts derived in the range 0 < z(phot) < 0.6 agrees well with the model predictions. k-corrections were derived by calibration of the k-correct_v4.2 code results for the MGS with the reference-frame (z = 0.1) (g - r) colours. We adopt a linear dependence of k-corrections on redshift and (g - r) colours that provide suitable distributions of luminosity and colours for galaxies up to redshift z(phot) = 0.6 comparable to the results in the literature. Thus, our k-correction estimate procedure is a powerful, low computational time algorithm capable of reproducing suitable results that can be used for testing galaxy properties at intermediate redshifts using the large SDSS data base.

The indirect amygdala-dorsal striatum pathway mediates conditioned freezing: Insights on emotional memory networks

The dorsal striatum (DS) is involved in various forms of learning and memory such as procedural learning, habit learning, reward-association and emotional learning. We have previously reported that bilateral DS lesions disrupt tone fear conditioning (TFC), but not contextual fear conditioning (CFC) [Ferreira TL, Moreira KM, Ikeda DC, Bueno OFA, Oliveira MGM (2003) Effects of dorsal striatum lesions in tone fear conditioning and contextual fear conditioning. Brain Res 987:17-24]. To further elucidate the participation of DS in emotional learning, in the present study, we investigated the effects of bilateral pretest (postraining) electrolytic DS lesions on TFC. Given the well-acknowledged role of the amygdala in emotional learning, we also examined a possible cooperation between DS and the amygdala in TFC, by using asymmetrical electrolytic lesions, consisting of a unilateral lesion of the central amygdaloid nucleus (CeA) combined to a contralateral DS lesion. The results show that pre-test bilateral DS lesions disrupt TFC responses, suggesting that DS plays a role in the expression of TFC. More importantly, rats with asymmetrical pre-training lesions were impaired in TFC, but not in CFC tasks. This result was confirmed with muscimol asymmetrical microinjections in DS and CeA, which reversibly inactivate these structures. On the other hand, similar pretest lesions as well as unilateral electrolytic lesions of CeA and DS in the same hemisphere did not affect TFC. Possible anatomical substrates underlying the observed effects are proposed. Overall, the present results underscore that other routes, aside from the well-established CeA projections to the periaqueductal gray, may contribute to the acquisition/consolidation of the freezing response associated to a TFC task. It is suggested that CeA may presumably influence DS processing via a synaptic relay on dopaminergic neurons of the substantia nigra compacta and retrorubral nucleus. The present observations are also in line with other studies showing that TFC and CFC responses are mediated by different anatomical networks. (C) 2008 IBRO. Published by Elsevier Ltd. All rights reserved.

Modularity and robustness of bone networks

Cortical bones, essential for mechanical support and structure in many animals, involve a large number of canals organized in intricate fashion. By using state-of-the art image analysis and computer graphics, the 3D reconstruction of a whole bone (phalange) of a young chicken was obtained and represented in terms of a complex network where each canal was associated to an edge and every confluence of three or more canals yielded a respective node. The representation of the bone canal structure as a complex network has allowed several methods to be applied in order to characterize and analyze the canal system organization and the robustness. First, the distribution of the node degrees (i.e. the number of canals connected to each node) confirmed previous indications that bone canal networks follow a power law, and therefore present some highly connected nodes (hubs). The bone network was also found to be partitioned into communities or modules, i.e. groups of nodes which are more intensely connected to one another than with the rest of the network. We verified that each community exhibited distinct topological properties that are possibly linked with their specific function. In order to better understand the organization of the bone network, its resilience to two types of failures (random attack and cascaded failures) was also quantified comparatively to randomized and regular counterparts. The results indicate that the modular structure improves the robustness of the bone network when compared to a regular network with the same average degree and number of nodes. The effects of disease processes (e. g., osteoporosis) and mutations in genes (e.g., BMP4) that occur at the molecular level can now be investigated at the mesoscopic level by using network based approaches.

Border trees of complex networks

The comprehensive characterization of the structure of complex networks is essential to understand the dynamical processes which guide their evolution. The discovery of the scale-free distribution and the small-world properties of real networks were fundamental to stimulate more realistic models and to understand important dynamical processes related to network growth. However, the properties of the network borders (nodes with degree equal to 1), one of its most fragile parts, remained little investigated and understood. The border nodes may be involved in the evolution of structures such as geographical networks. Here we analyze the border trees of complex networks, which are defined as the subgraphs without cycles connected to the remainder of the network (containing cycles) and terminating into border nodes. In addition to describing an algorithm for identification of such tree subgraphs, we also consider how their topological properties can be quantified in terms of their depth and number of leaves. We investigate the properties of border trees for several theoretical models as well as real-world networks. Among the obtained results, we found that more than half of the nodes of some real-world networks belong to the border trees. A power-law with cut-off was observed for the distribution of the depth and number of leaves of the border trees. An analysis of the local role of the nodes in the border trees was also performed.

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.

Resilience of protein-protein interaction networks as determined by their large-scale topological features

The relationship between the structure and function of biological networks constitutes a fundamental issue in systems biology. Particularly, the structure of protein-protein interaction networks is related to important biological functions. In this work, we investigated how such a resilience is determined by the large scale features of the respective networks. Four species are taken into account, namely yeast Saccharomyces cerevisiae, worm Caenorhabditis elegans, fly Drosophila melanogaster and Homo sapiens. We adopted two entropy-related measurements (degree entropy and dynamic entropy) in order to quantify the overall degree of robustness of these networks. We verified that while they exhibit similar structural variations under random node removal, they differ significantly when subjected to intentional attacks (hub removal). As a matter of fact, more complex species tended to exhibit more robust networks. More specifically, we quantified how six important measurements of the networks topology (namely clustering coefficient, average degree of neighbors, average shortest path length, diameter, assortativity coefficient, and slope of the power law degree distribution) correlated with the two entropy measurements. Our results revealed that the fraction of hubs and the average neighbor degree contribute significantly for the resilience of networks. In addition, the topological analysis of the removed hubs indicated that the presence of alternative paths between the proteins connected to hubs tend to reinforce resilience. The performed analysis helps to understand how resilience is underlain in networks and can be applied to the development of protein network models.

Intrinsically Multivariate Predictive Genes

Canalizing genes possess such broad regulatory power, and their action sweeps across a such a wide swath of processes that the full set of affected genes are not highly correlated under normal conditions. When not active, the controlling gene will not be predictable to any significant degree by its subject genes, either alone or in groups, since their behavior will be highly varied relative to the inactive controlling gene. When the controlling gene is active, its behavior is not well predicted by any one of its targets, but can be very well predicted by groups of genes under its control. To investigate this question, we introduce in this paper the concept of intrinsically multivariate predictive (IMP) genes, and present a mathematical study of IMP in the context of binary genes with respect to the coefficient of determination (CoD), which measures the predictive power of a set of genes with respect to a target gene. A set of predictor genes is said to be IMP for a target gene if all properly contained subsets of the predictor set are bad predictors of the target but the full predictor set predicts the target with great accuracy. We show that logic of prediction, predictive power, covariance between predictors, and the entropy of the joint probability distribution of the predictors jointly affect the appearance of IMP genes. In particular, we show that high-predictive power, small covariance among predictors, a large entropy of the joint probability distribution of predictors, and certain logics, such as XOR in the 2-predictor case, are factors that favor the appearance of IMP. The IMP concept is applied to characterize the behavior of the gene DUSP1, which exhibits control over a central, process-integrating signaling pathway, thereby providing preliminary evidence that IMP can be used as a criterion for discovery of canalizing genes.

PERFORMANCE EVOLUTION OF PEER TO PEER NETWORKS

This thesis work concerns about the Performance evolution of peer to peer networks, where we used different distribution technique’s of peer distribution like Weibull, Lognormal and Pareto distribution process. Then we used a network simulator to evaluate the performance of these three distribution techniques.During the last decade the Internet has expanded into a world-wide network connecting millions of hosts and users and providing services for everyone. Many emerging applications are bandwidth-intensive in their nature; the size of downloaded files including music and videos can be huge, from ten megabits to many gigabits. The efficient use of network resources is thus crucial for the survivability of the Internet. Traffic engineering (TE) covers a range of mechanisms for optimizing operational networks from the traffic perspective. The time scale in traffic engineering varies from the short-term network control to network planning over a longer time period.Here in this thesis work we considered the peer distribution technique in-order to minimise the peer arrival and service process with three different techniques, where we calculated the congestion parameters like blocking time for each peer before entering into the service process, waiting time for a peers while the other peer has been served in the service block and the delay time for each peer. Then calculated the average of each process and graphs have been plotted using Matlab to analyse the results

Conservation and development interventions as networks: The case of the India ecodevelopment project, Karnataka

Experience with community-based biodiversity conservation programs in the 1980s and 1990s contributed to the conviction among donor agencies and researchers that such programs must be based on the active support of local resource users, appropriate incentives, and institutional support. Yet the continuing struggles of practitioners to implement conservation interventions that are socially and ecologically sustainable point to difficulty in realizing these principles on the ground. Actor-oriented research in rural development and actor network theory emphasize that the capacity of facilitators to engage effectively in negotiation processes and establish strong networks with key actors is critical in mediating intervention outcomes. Drawing on the case of the India Ecodevelopment Project at Rajiv Gandhi (Nagarahole) National Park in Karnataka, India, this paper explores the role of relationships and networks between actors in a conservation and development intervention, finding that practitioners need to focus on negotiation and network building as a central rather than subsidiary part of the intervention process. Associated with this is the need for change in the way donor and implementing agencies conduct themselves, to promote communication and greater flexibility in intervention processes.

Distribution of disease caused by Phytophthora cinnamomi in Wilsons Promontory National Park and potential for further impact

The extent of disease caused by Phytophthora cinnamomi was determined within vegetation communities of Wilsons Promontory National Park. Aerial survey of visible symptoms by helicopter and systematic survey along all roads and tracks followed by isolation of the pathogen from soil found that in total 551 ha of moist foothill forest, heath and heathy woodland broad vegetation types were affected by the disease. P. cinnamomi was isolated from 93% of sites that, based on the presence of visible symptoms, were expected to yield the pathogen. The species-rich heathy woodland was most affected with 6.5% of the total area of this type showing symptoms of disease. The size of infestation ranged from 229 ha on the slopes of the Vereker Range in the north to less than 1 ha along the Sealers Cove Walking Track in the south. The potential for disease to spread into uninfested vegetation was estimated for all sites from which P. cinnamomi was isolated. Eight of 18 sites where evidence of disease was found were estimated to have a high potential for further disease spread. This study indicates that even though the disease may be waning in some areas of the Park, the pathogen is active and easily isolated from others and provides a continuing threat to susceptible vegetation communities.

Using geometric shape variations to create an inverse model for a sheet metal process

The output of the sheet metal forming process is subject to much variation. This paper develops a method to measure shape variation in channel forming and relate this back to the corresponding process parameter levels of the manufacturing set-up to create an inverse model. The shape variation in the channels is measured using a modified form of the point distribution model (also known as the active shape model). This means that channels can be represented by a weighting vector of minimal linear dimension that contains all the shape variation information from the average formed channel.

The inverse models were created using classifiers that related the weighting vectors to the process parameter levels for the blank holder force (BHF), die radii (DR) and tool gap (TG) of the parameters. Several classifiers were tested: linear, quadratic Gaussian and artificial neural networks. The quadratic Gaussian classifiers were the most accurate and the most consistent type of classifier over all the parameters.

An active stereo vision-based learning approach for robotic tracking, fixating and grasping control

In this paper, an active stereo vision-based learning approach is proposed for a robot to track, fixate and grasp an object in unknown environments. First, the functional mapping relationships between the joint angles of the active stereo vision system and the spatial representations of the object are derived and expressed in a three-dimensional workspace frame. Next, the self-adaptive resonance theory-based neural networks and the feedforward neural networks are used to learn the mapping relationships in a self-organized way. Then, the approach is verified by simulation using the models of an active stereo vision system which is installed in the end-effector of a robot. Finally, the simulation results confirm the effectiveness of the present approach.

A review of current mobility prediction techniques for ad hoc networks

Next Generation Networks (3G & beyond) will support real-time multimedia applications through traditional wide-area networking concepts as well as hot-spot (WLAN) and ad hoc networking concepts. In order to fulfil the vision of Next Generation Networks a method of maintaining a real-time flow despite frequent topology changes and irregularity in user movement is required. Mobility Prediction has been identified as having applications in the areas of link availability estimation and pro-active routing in ad hoc networks. In this work we present an overview of current mobility prediction schemes that have been proposed. Simulation results are also presented.