Simulating event related potentials in tasks involving conflict resolution: a neural network

In this thesis, the main Executive Control theories are exposed. Methods typical of Cognitive and Computational Neuroscience are introduced and the role of behavioural tasks involving conflict resolution in the response elaboration, after the presentation of a stimulus to the subject, are highlighted. In particular, the Eriksen Flanker Task and its variants are discussed. Behavioural data, from scientific literature, are illustrated in terms of response times and error rates. During experimental behavioural tasks, EEG is registered simultaneously. Thanks to this, event related potential, related with the current task, can be studied. Different theories regarding relevant event related potential in this field - such as N2, fERN (feedback Error Related Negativity) and ERN (Error Related Negativity) – are introduced. The aim of this thesis is to understand and simulate processes regarding Executive Control, including performance improvement, error detection mechanisms, post error adjustments and the role of selective attention, with the help of an original neural network model. The network described here has been built with the purpose to simulate behavioural results of a four choice Eriksen Flanker Task. Model results show that the neural network can simulate response times, error rates and event related potentials quite well. Finally, results are compared with behavioural data and discussed in light of the mentioned Executive Control theories. Future perspective for this new model are outlined.

Performance analysis of reconstruction algorithms for breast microwave imaging

The problem of localizing a scatterer, which represents a tumor, in a homogeneous circular domain, which represents a breast, is addressed. A breast imaging method based on microwaves is considered. The microwave imaging involves to several techniques for detecting, localizing and characterizing tumors in breast tissues. In all such methods an electromagnetic inverse scattering problem exists. For the scattering detection method, an algorithm based on a linear procedure solution, inspired by MUltiple SIgnal Classification algorithm (MUSIC) and Time Reversal method (TR), is implemented. The algorithm returns a reconstructed image of the investigation domain in which it is detected the scatterer position. This image is called pseudospectrum. A preliminary performance analysis of the algorithm vying the working frequency is performed: the resolution and the signal-to-noise ratio of the pseudospectra are improved if a multi-frequency approach is considered. The Geometrical Mean-MUSIC algorithm (GM- MUSIC) is proposed as multi-frequency method. The performance of the GMMUSIC is tested in different real life computer simulations. The performed analysis shows that the algorithm detects the scatterer until the electrical parameters of the breast are known. This is an evident limit, since, in a real life situation, the anatomy of the breast is unknown. An improvement in GM-MUSIC is proposed: the Eye-GMMUSIC algorithm. Eye-GMMUSIC algorithm needs no a priori information on the electrical parameters of the breast. It is an optimizing algorithm based on the pattern search algorithm: it searches the breast parameters which minimize the Signal-to-Clutter Mean Ratio (SCMR) in the signal. Finally, the GM-MUSIC and the Eye-GMMUSIC algorithms are tested on a microwave breast cancer detection system consisting of an dipole antenna, a Vector Network Analyzer and a novel breast phantom built at University of Bologna. The reconstruction of the experimental data confirm the GM-MUSIC ability to localize a scatterer in a homogeneous medium.

A QoS Controller Framework Compliant with the ETSI Network Function Virtualization Specification

The 5th generation of mobile networking introduces the concept of “Network slicing”, the network will be “sliced” horizontally, each slice will be compliant with different requirements in terms of network parameters such as bandwidth, latency. This technology is built on logical instead of physical resources, relies on virtual network as main concept to retrieve a logical resource. The Network Function Virtualisation provides the concept of logical resources for a virtual network function, enabling the concept virtual network; it relies on the Software Defined Networking as main technology to realize the virtual network as resource, it also define the concept of virtual network infrastructure with all components needed to enable the network slicing requirements. SDN itself uses cloud computing technology to realize the virtual network infrastructure, NFV uses also the virtual computing resources to enable the deployment of virtual network function instead of having custom hardware and software for each network function. The key of network slicing is the differentiation of slice in terms of Quality of Services parameters, which relies on the possibility to enable QoS management in cloud computing environment. The QoS in cloud computing denotes level of performances, reliability and availability offered. QoS is fundamental for cloud users, who expect providers to deliver the advertised quality characteristics, and for cloud providers, who need to find the right tradeoff between QoS levels that has possible to offer and operational costs. While QoS properties has received constant attention before the advent of cloud computing, performance heterogeneity and resource isolation mechanisms of cloud platforms have significantly complicated QoS analysis and deploying, prediction, and assurance. This is prompting several researchers to investigate automated QoS management methods that can leverage the high programmability of hardware and software resources in the cloud.

Hierarchical Approach for Survivable Network Design

A central design challenge facing network planners is how to select a cost-effective network configuration that can provide uninterrupted service despite edge failures. In this paper, we study the Survivable Network Design (SND) problem, a core model underlying the design of such resilient networks that incorporates complex cost and connectivity trade-offs. Given an undirected graph with specified edge costs and (integer) connectivity requirements between pairs of nodes, the SND problem seeks the minimum cost set of edges that interconnects each node pair with at least as many edge-disjoint paths as the connectivity requirement of the nodes. We develop a hierarchical approach for solving the problem that integrates ideas from decomposition, tabu search, randomization, and optimization. The approach decomposes the SND problem into two subproblems, Backbone design and Access design, and uses an iterative multi-stage method for solving the SND problem in a hierarchical fashion. Since both subproblems are NP-hard, we develop effective optimization-based tabu search strategies that balance intensification and diversification to identify near-optimal solutions. To initiate this method, we develop two heuristic procedures that can yield good starting points. We test the combined approach on large-scale SND instances, and empirically assess the quality of the solutions vis-à-vis optimal values or lower bounds. On average, our hierarchical solution approach generates solutions within 2.7% of optimality even for very large problems (that cannot be solved using exact methods), and our results demonstrate that the performance of the method is robust for a variety of problems with different size and connectivity characteristics.

Interest in a national research network in surgery in Switzerland

Networks are known to improve performance and create synergies. A research network can provide a significant advantage for all parties involved in research in surgery by systematically tracking the outcome of a huge number of patients over a long period of time. The aim of the present study was to investigate the experiences of surgeons with respect to research activities, to evaluate the opinions of surgeons with regard to the development of a national network for research in the field of surgery in Switzerland and to obtain data on how such a network should be designed.

Analysis Methodology for Flow-level Evaluation of a Hybrid Mobile-Sensor Network

Our society uses a large diversity of co-existing wired and wireless networks in order to satisfy its communication needs. A cooper- ation between these networks can benefit performance, service availabil- ity and deployment ease, and leads to the emergence of hybrid networks. This position paper focuses on a hybrid mobile-sensor network identify- ing potential advantages and challenges of its use and defining feasible applications. The main value of the paper, however, is in the proposed analysis approach to evaluate the performance at the mobile network side given the mixed mobile-sensor traffic. The approach combines packet- level analysis with modelling of flow-level behaviour and can be applied for the study of various application scenarios. In this paper we consider two applications with distinct traffic models namely multimedia traffic and best-effort traffic.

Identifying Resiliency Performance Measures for Megaregional Planning Case Study of the Transportation Corridor Between Boston, Massachusetts, and Washington, DC

Transportation corridors in megaregions present a unique challenge for planners because of the high concentration of development, complex interjurisdictional issues, and history of independent development of core urban centers. The concept of resilience, as applied to megaregions, can be used to understand better the performance of these corridors. Resiliency is the ability to recover from or adjust easily to change. Resiliency performance measures can be expanded on for application to megaregions throughout the United States. When applied to transportation corridors in megaregions and represented by performance measures such as redundancy, continuity, connectivity, and travel time reliability, the concept of resiliency captures the spatial and temporal relationships between the attributes of a corridor, a network, and neighboring facilities over time at the regional and local levels. This paper focuses on the development of performance measurements for evaluating corridor resiliency as well as a plan for implementing analysis methods at the jurisdictional level. The transportation corridor between Boston, Massachusetts, and Washington, D.C., is used as a case study to represent the applicability of these measures to megaregions throughout the country.

Evidence for a cognitive control network for goal-directed attention in simple sustained attention

The deterioration of performance over time is characteristic for sustained attention tasks. This so-called "performance decrement" is measured by the increase of reaction time (RT) over time. Some behavioural and neurobiological mechanisms of this phenomenon are not yet fully understood. Behaviourally, we examined the increase of RT over time and the inter-individual differences of this performance decrement. On the neurophysiological level, we investigated the task-relevant brain areas where neural activity was modulated by RT and searched for brain areas involved in good performance (i.e. participants with no or moderate performance decrement) as compared to poor performance (i.e. participants with a steep performance decrement). For this purpose, 20 healthy, young subjects performed a carefully designed task for simple sustained attention, namely a low-demanding version of the Rapid Visual Information Processing task. We employed a rapid event-related functional magnetic resonance imaging (fMRI) design. The behavioural results showed a significant increase of RT over time in the whole group, and also revealed that some participants were not as prone to the performance decrement as others. The latter was statistically significant comparing good versus poor performers. Moreover, high BOLD-responses were linked to longer RTs in a task-relevant bilateral fronto-cingulate-insular-parietal network. Among these regions, good performance was associated with significantly higher RT-BOLD correlations in the pre-supplementary motor area (pre-SMA). We concluded that the task-relevant bilateral fronto-cingulate-insular-parietal network was a cognitive control network responsible for goal-directed attention. The pre-SMA in particular might be associated with the performance decrement insofar that good performers could sustain activity in this brain region in order to monitor performance declines and adjust behavioural output.

TCP Performance Optimizations for Wireless Sensor Networks

Since the appearance of downsized and simplified TCP/IP stacks, single nodes from Wireless Sensor Networks (WSNs) have become directly accessible from the Internet with commonly used networking tools and applications (e.g., Telnet or SMTP). However, TCP has been shown to perform poorly in wireless networks, especially across multiple wireless hops. This paper examines TCP performance optimizations based on distributed caching and local retransmission strategies of intermediate nodes in a TCP connection, and proposes extended techniques to these strategies. The paper studies the impact of different radio duty-cycling MAC protocols on the end-to-end TCP performance when using the proposed TCP optimization strategies in an extensive experimental evaluation on a real-world sensor network testbed.

Body sensor network for in-home personal healthcare

A body sensor network solution for personal healthcare under an indoor environment is developed. The system is capable of logging the physiological signals of human beings, tracking the orientations of human body, and monitoring the environmental attributes, which covers all necessary information for the personal healthcare in an indoor environment. The major three chapters of this dissertation contain three subsystems in this work, each corresponding to one subsystem: BioLogger, PAMS and CosNet. Each chapter covers the background and motivation of the subsystem, the related theory, the hardware/software design, and the evaluation of the prototype’s performance.

A 5-year comparison of performance of sentinel and mandatory notification surveillance systems for measles in Switzerland

A sensitive, specific and timely surveillance is necessary to monitor progress towards measles elimination. We evaluated the performance of sentinel and mandatory-based surveillance systems for measles in Switzerland during a 5-year period by comparing 145 sentinel and 740 mandatory notified cases. The higher proportion of physicians who reported at least one case per year in the sentinel system suggests underreporting in the recently introduced mandatory surveillance for measles. Accordingly, the latter reported 2-36-fold lower estimates for incidence rates than the sentinel surveillance. However, these estimates were only 0.6-12-fold lower when we considered confirmed cases alone, which indicates a higher specificity of the mandatory surveillance system. In contrast, the sentinel network, which covers 3.5% of all outpatient consultations, detected only weakly and late a major national measles epidemic in 2003 and completely missed 2 of 10 cantonal outbreaks. Despite its better timeliness and greater sensitivity in case detection, the sentinel system, in the current situation of low incidence, is insufficient to perform measles control and to monitor progress towards elimination.

IDENTIFICATION OF GENES CONTROLLING BIOLOGICAL PROCESSES AND PATHWAYS THROUGH STATISTICAL ANALYSIS AND NETWORK RECONSTRUCTION

The developmental processes and functions of an organism are controlled by the genes and the proteins that are derived from these genes. The identification of key genes and the reconstruction of gene networks can provide a model to help us understand the regulatory mechanisms for the initiation and progression of biological processes or functional abnormalities (e.g. diseases) in living organisms. In this dissertation, I have developed statistical methods to identify the genes and transcription factors (TFs) involved in biological processes, constructed their regulatory networks, and also evaluated some existing association methods to find robust methods for coexpression analyses. Two kinds of data sets were used for this work: genotype data and gene expression microarray data. On the basis of these data sets, this dissertation has two major parts, together forming six chapters. The first part deals with developing association methods for rare variants using genotype data (chapter 4 and 5). The second part deals with developing and/or evaluating statistical methods to identify genes and TFs involved in biological processes, and construction of their regulatory networks using gene expression data (chapter 2, 3, and 6). For the first part, I have developed two methods to find the groupwise association of rare variants with given diseases or traits. The first method is based on kernel machine learning and can be applied to both quantitative as well as qualitative traits. Simulation results showed that the proposed method has improved power over the existing weighted sum method (WS) in most settings. The second method uses multiple phenotypes to select a few top significant genes. It then finds the association of each gene with each phenotype while controlling the population stratification by adjusting the data for ancestry using principal components. This method was applied to GAW 17 data and was able to find several disease risk genes. For the second part, I have worked on three problems. First problem involved evaluation of eight gene association methods. A very comprehensive comparison of these methods with further analysis clearly demonstrates the distinct and common performance of these eight gene association methods. For the second problem, an algorithm named the bottom-up graphical Gaussian model was developed to identify the TFs that regulate pathway genes and reconstruct their hierarchical regulatory networks. This algorithm has produced very significant results and it is the first report to produce such hierarchical networks for these pathways. The third problem dealt with developing another algorithm called the top-down graphical Gaussian model that identifies the network governed by a specific TF. The network produced by the algorithm is proven to be of very high accuracy.

Bluetooth Enabled Ad-hoc Networks: Performance Evaluation of a Self-healing Scatternet Formation Protocol

Bluetooth wireless technology is a robust short-range communications system designed for low power (10 meter range) and low cost. It operates in the 2.4 GHz Industrial Scientific Medical (ISM) band and it employs two techniques for minimizing interference: a frequency hopping scheme which nominally splits the 2.400 - 2.485 GHz band in 79 frequency channels and a time division duplex (TDD) scheme which is used to switch to a new frequency channel on 625 μs boundaries. During normal operation a Bluetooth device will be active on a different frequency channel every 625 μs, thus minimizing the chances of continuous interference impacting the performance of the system. The smallest unit of a Bluetooth network is called a piconet, and can have a maximum of eight nodes. Bluetooth devices must assume one of two roles within a piconet, master or slave, where the master governs quality of service and the frequency hopping schedule within the piconet and the slave follows the master’s schedule. A piconet must have a single master and up to 7 active slaves. By allowing devices to have roles in multiple piconets through time multiplexing, i.e. slave/slave or master/slave, the Bluetooth technology allows for interconnecting multiple piconets into larger networks called scatternets. The Bluetooth technology is explored in the context of enabling ad-hoc networks. The Bluetooth specification provides flexibility in the scatternet formation protocol, outlining only the mechanisms necessary for future protocol implementations. A new protocol for scatternet formation and maintenance - mscat - is presented and its performance is evaluated using a Bluetooth simulator. The free variables manipulated in this study include device activity and the probabilities of devices performing discovery procedures. The relationship between the role a device has in the scatternet and it’s probability of performing discovery was examined and related to the scatternet topology formed. The results show that mscat creates dense network topologies for networks of 30, 50 and 70 nodes. The mscat protocol results in approximately a 33% increase in slaves/piconet and a reduction of approximately 12.5% of average roles/node. For 50 node scenarios the set of parameters which creates the best determined outcome is unconnected node inquiry probability (UP) = 10%, master node inquiry probability (MP) = 80% and slave inquiry probability (SP) = 40%. The mscat protocol extends the Bluetooth specification for formation and maintenance of scatternets in an ad-hoc network.

Learning flexible sensori-motor mappings in a complex network

Given the complex structure of the brain, how can synaptic plasticity explain the learning and forgetting of associations when these are continuously changing? We address this question by studying different reinforcement learning rules in a multilayer network in order to reproduce monkey behavior in a visuomotor association task. Our model can only reproduce the learning performance of the monkey if the synaptic modifications depend on the pre- and postsynaptic activity, and if the intrinsic level of stochasticity is low. This favored learning rule is based on reward modulated Hebbian synaptic plasticity and shows the interesting feature that the learning performance does not substantially degrade when adding layers to the network, even for a complex problem.