Hybrid energy storage for energy harvesting powered wireless sensor networks

Science Foundation Ireland (CSET - Centre for Science, Engineering and Technology, grant 07/CE/I1147); Scientific Foundation Ireland (ITOBO (398-CRP))

Burst-mode electronic dispersion compensation in long reach PONs

Long reach passive optical networks (LR-PONs), which integrate fibre-to-the-home with metro networks, have been the subject of intensive research in recent years and are considered one of the most promising candidates for the next generation of optical access networks. Such systems ideally have reaches greater than 100km and bit rates of at least 10Gb/s per wavelength in the downstream and upstream directions. Due to the limited equipment sharing that is possible in access networks, the laser transmitters in the terminal units, which are usually the most expensive components, must be as cheap as possible. However, the requirement for low cost is generally incompatible with the need for a transmitter chirp characteristic that is optimised for such long reaches at 10Gb/s, and hence dispersion compensation is required. In this thesis electronic dispersion compensation (EDC) techniques are employed to increase the chromatic dispersion tolerance and to enhance the system performance at the expense of moderate additional implementation complexity. In order to use such EDC in LR-PON architectures, a number of challenges associated with the burst-mode nature of the upstream link need to be overcome. In particular, the EDC must be made adaptive from one burst to the next (burst-mode EDC, or BM-EDC) in time scales on the order of tens to hundreds of nanoseconds. Burst-mode operation of EDC has received little attention to date. The main objective of this thesis is to demonstrate the feasibility of such a concept and to identify the key BM-EDC design parameters required for applications in a 10Gb/s burst-mode link. This is achieved through a combination of simulations and transmission experiments utilising off-line data processing. The research shows that burst-to-burst adaptation can in principle be implemented efficiently, opening the possibility of low overhead, adaptive EDC-enabled burst-mode systems.

Local network-based measures to assess the inferability of different regulatory networks

The purpose of this study is to compare the inferability of various synthetic as well as real biological regulatory networks. In order to assess differences we apply local network-based measures. That means, instead of applying global measures, we investigate and assess an inference algorithm locally, on the level of individual edges and subnetworks. We demonstrate the behaviour of our local network-based measures with respect to different regulatory networks by conducting large-scale simulations. As inference algorithm we use exemplarily ARACNE. The results from our exploratory analysis allow us not only to gain new insights into the strength and weakness of an inference algorithm with respect to characteristics of different regulatory networks, but also to obtain information that could be used to design novel problem-specific statistical estimators.

Inferring the conservative causal core of gene regulatory networks

Background
Inferring gene regulatory networks from large-scale expression data is an important problem that received much attention in recent years. These networks have the potential to gain insights into causal molecular interactions of biological processes. Hence, from a methodological point of view, reliable estimation methods based on observational data are needed to approach this problem practically.

Results
In this paper, we introduce a novel gene regulatory network inference (GRNI) algorithm, called C3NET. We compare C3NET with four well known methods, ARACNE, CLR, MRNET and RN, conducting in-depth numerical ensemble simulations and demonstrate also for biological expression data from E. coli that C3NET performs consistently better than the best known GRNI methods in the literature. In addition, it has also a low computational complexity. Since C3NET is based on estimates of mutual information values in conjunction with a maximization step, our numerical investigations demonstrate that our inference algorithm exploits causal structural information in the data efficiently.

Conclusions
For systems biology to succeed in the long run, it is of crucial importance to establish methods that extract large-scale gene networks from high-throughput data that reflect the underlying causal interactions among genes or gene products. Our method can contribute to this endeavor by demonstrating that an inference algorithm with a neat design permits not only a more intuitive and possibly biological interpretation of its working mechanism but can also result in superior results.

Rapid design of a single chip adaptive beamformer

This paper presents the design of a single chip adaptive beamformer which contains 5 million transistors and can perform 50 GigaFlops. The core processor of the adaptive beamformer is a QR-array processor implemented on a fully efficient linear systolic architecture. The paper highlights a number of rapid design techniques that have been used to realize the design. These include an architecture synthesis tool for quickly developing the circuit architecture and the utilization of a library of parameterizable silicon intellectual property (IP) cores, to rapidly develop the circuit layouts.

Object-Oriented Genetic Programming for the Automatic Inference of Graph Models for Complex Networks

Complex networks are systems of entities that are interconnected through meaningful relationships. The result of the relations between entities forms a structure that has a statistical complexity that is not formed by random chance. In the study of complex networks, many graph models have been proposed to model the behaviours observed. However, constructing graph models manually is tedious and problematic. Many of the models proposed in the literature have been cited as having inaccuracies with respect to the complex networks they represent. However, recently, an approach that automates the inference of graph models was proposed by Bailey [10] The proposed methodology employs genetic programming (GP) to produce graph models that approximate various properties of an exemplary graph of a targeted complex network. However, there is a great deal already known about complex networks, in general, and often specific knowledge is held about the network being modelled. The knowledge, albeit incomplete, is important in constructing a graph model. However it is difficult to incorporate such knowledge using existing GP techniques. Thus, this thesis proposes a novel GP system which can incorporate incomplete expert knowledge that assists in the evolution of a graph model. Inspired by existing graph models, an abstract graph model was developed to serve as an embryo for inferring graph models of some complex networks. The GP system and abstract model were used to reproduce well-known graph models. The results indicated that the system was able to evolve models that produced networks that had structural similarities to the networks generated by the respective target models.

An improved method for discovering link criticality in transport networks

Quantitatively assessing the importance or criticality of each link in a network is of practical value to operators, as that can help them to increase the network's resilience, provide more efficient services, or improve some other aspect of the service. Betweenness is a graph-theoretical measure of centrality that can be applied to communication networks to evaluate link importance. However, as we illustrate in this paper, the basic definition of betweenness centrality produces inaccurate estimations as it does not take into account some aspects relevant to networking, such as the heterogeneity in link capacity or the difference between node-pairs in their contribution to the total traffic. A new algorithm for discovering link centrality in transport networks is proposed in this paper. It requires only static or semi-static network and topology attributes, and yet produces estimations of good accuracy, as verified through extensive simulations. Its potential value is demonstrated by an example application. In the example, the simple shortest-path routing algorithm is improved in such a way that it outperforms other more advanced algorithms in terms of blocking ratio

Mediterranean agriculture under climate change: adaptive capacity, adaptation, and ethics

In the coming decades, the Mediterranean region is expected to experience various climate impacts with negative consequences on agricultural systems and which will cause uneven reductions in agricultural production. By and large, the impacts of climate change on Mediterranean agriculture will be heavier for southern areas of the region. This unbalanced distribution of negative impacts underscores the significance and role of ethics in such a context of analysis. Consequently, the aim of this article is to justify and develop an ethical approach to agricultural adaptation in the Mediterranean and to derive the consequent implications for adaptation policy in the region. In particular, we define an index of adaptive capacity for the agricultural systems of the Mediterranean region on whose basis it is possible to group its different sub-regions, and we provide an overview of the suitable adaptation actions and policies for the sub-regions identified. We then vindicate and put forward an ethical approach to agricultural adaptation, highlighting the implications for the Mediterranean region and the limitations of such an ethical framework. Finally, we emphasize the broader potential of ethics for agricultural adaptation policy.

Modeling worldwide highway networks

This Letter addresses the problem of modeling the highway systems of different countries by using complex networks formalism. More specifically, we compare two traditional geographical models with a modified geometrical network model where paths, rather than edges, are incorporated at each step between the origin and the destination vertices. Optimal configurations of parameters are obtained for each model and used for the comparison. The highway networks of Australia, Brazil, India, and Romania are considered and shown to be properly modeled by the modified geographical model. (C) 2009 Elsevier B.V. All rights reserved.