Methodologies for Synthesizable Programmable Devices based on Multi-Stage Switching Networks

Nowadays the rise of non-recurring engineering (NRE) costs associated with complexity is becoming a major factor in SoC design, limiting both scaling opportunities and the flexibility advantages offered by the integration of complex computational units. The introduction of embedded programmable elements can represent an appealing solution, able both to guarantee the desired flexibility and upgradabilty and to widen the SoC market. In particular embedded FPGA (eFPGA) cores can provide bit-level optimization for those applications which benefits from synthesis, paying on the other side in terms of performance penalties and area overhead with respect to standard cell ASIC implementations. In this scenario this thesis proposes a design methodology for a synthesizable programmable device designed to be embedded in a SoC. A soft-core embedded FPGA (eFPGA) is hence presented and analyzed in terms of the opportunities given by a fully synthesizable approach, following an implementation flow based on Standard-Cell methodology. A key point of the proposed eFPGA template is that it adopts a Multi-Stage Switching Network (MSSN) as the foundation of the programmable interconnects, since it can be efficiently synthesized and optimized through a standard cell based implementation flow, ensuring at the same time an intrinsic congestion-free network topology. The evaluation of the flexibility potentialities of the eFPGA has been performed using different technology libraries (STMicroelectronics CMOS 65nm and BCD9s 0.11μm) through a design space exploration in terms of area-speed-leakage tradeoffs, enabled by the full synthesizability of the template. Since the most relevant disadvantage of the adopted soft approach, compared to a hardcore, is represented by a performance overhead increase, the eFPGA analysis has been made targeting small area budgets. The generation of the configuration bitstream has been obtained thanks to the implementation of a custom CAD flow environment, and has allowed functional verification and performance evaluation through an application-aware analysis.

Enhancing quality of service in software-defined networks

Resource management is of paramount importance in network scenarios and it is a long-standing and still open issue. Unfortunately, while technology and innovation continue to evolve, our network infrastructure system has been maintained almost in the same shape for decades and this phenomenon is known as “Internet ossification”. Software-Defined Networking (SDN) is an emerging paradigm in computer networking that allows a logically centralized software program to control the behavior of an entire network. This is done by decoupling the network control logic from the underlying physical routers and switches that forward traffic to the selected destination. One mechanism that allows the control plane to communicate with the data plane is OpenFlow. The network operators could write high-level control programs that specify the behavior of an entire network. Moreover, the centralized control makes it possible to define more specific and complex tasks that could involve many network functionalities, e.g., security, resource management and control, into a single framework. Nowadays, the explosive growth of real time applications that require stringent Quality of Service (QoS) guarantees, brings the network programmers to design network protocols that deliver certain performance guarantees. This thesis exploits the use of SDN in conjunction with OpenFlow to manage differentiating network services with an high QoS. Initially, we define a QoS Management and Orchestration architecture that allows us to manage the network in a modular way. Then, we provide a seamless integration between the architecture and the standard SDN paradigm following the separation between the control and data planes. This work is a first step towards the deployment of our proposal in the University of California, Los Angeles (UCLA) campus network with differentiating services and stringent QoS requirements. We also plan to exploit our solution to manage the handoff between different network technologies, e.g., Wi-Fi and WiMAX. Indeed, the model can be run with different parameters, depending on the communication protocol and can provide optimal results to be implemented on the campus network.

Increased phase synchronization during continuous face integration measured simultaneously with EEG and fMRI

Gamma zero-lag phase synchronization has been measured in the animal brain during visual binding. Human scalp EEG studies used a phase locking factor (trial-to-trial phase-shift consistency) or gamma amplitude to measure binding but did not analyze common-phase signals so far. This study introduces a method to identify networks oscillating with near zero-lag phase synchronization in human subjects.

INTEGRATION D’UN OBSERVATOIRE URBAIN SUR GOOGLE MAPS : Cas des infrastructures de la santé de la ville de Douala Cameroun

The spectacular advances computer science applied to geographic information systems (GIS) in recent times has favored the emergence of several technological solutions. These developments have given rise to enormous opportunities for digital management of the territory. Among the technological solutions, the most famous Google Maps offers free online mapping dynamic exhaustive of the Maps. In addition to meet the enormous needs of urban indicators geotagged information, we did work on this project “Integration of an urban observatory on Google Maps.” The problem of geolocation in the urban observatory is particularly relevant in the sense that there is currently no data (descriptive and geographical) reliable on the urban sector; we must stick to extrapolate from data old and obsolete. This helps to curb the effectiveness of urban management to make difficult investment programming and to prevent the acquisition of knowledge to make cities engines of growth. The use of a geolocation tool coupled to the data would allow better monitoring of indicators Our project's objective is to develop an interactive map server (WebMapping) which map layer is formed from the resources of the Google Maps servers and match information from the field to produce maps of urban equipment and infrastructure of a city data to the client's request To achieve this goal, we will participate in a study of a GPS location of strategic sites in our core sector (health facilities), on the other hand, using information from the field, we will build a postgresql database that will link the information from the field to map from Google Maps via KML scripts and PHP appropriate. We will limit ourselves in our work to the city of Douala Cameroon with the sectors of health facilities with the possibility of extension to other areas and other cities. Keywords: Geographic Information System (GIS), Thematic Mapping, Web Mapping, data mining, Google API.

Development of Socio-Economic Behaviour and Attitudes of Baltic Nationalities: Perspectives for Integration with Europe

This project is the third stage of a comparative research project, The New Baltic Barometer, which was carried out simultaneously with the "New Democracies Barometer" of the Paul Lazerfeld Society (Vienna) and The Russian Barometer. It studied the opinion and behaviour of the largest Baltic ethnic groups (Estonians, Latvians, Lithuanians). The main focus was on the attitudes of Baltic residents towards the changes in the economic and political system, attitudes towards political values, political trust, and attitudes to the Baltic countries joining the European Union. An analysis of macroeconomic indicators of the Baltic states made it possible to deduce the link between the country's economic development, and satisfaction with the political regime and attitudes towards democratic values. The study analysed the conditions for the democratisation of society, i.e. the development of culture and public opinion in the Baltic states. Attention was also paid to the development of a social network of individuals, showing the transition from informal networks to impersonal institutions. The group concluded that the participation of residents in formal organisations, NGOs in particular, considerably fosters political trust and also increases political efficacy. Participation in formal organisations also reduces the importance of esteem for an authoritarian leader.

A BAYESIAN SHRINKAGE MODEL FOR INCOMPLETE LONGITUDINAL BINARY DATA WITH APPLICATION TO THE BREAST CANCER PREVENTION TRIAL

We consider inference in randomized studies, in which repeatedly measured outcomes may be informatively missing due to drop out. In this setting, it is well known that full data estimands are not identified unless unverified assumptions are imposed. We assume a non-future dependence model for the drop-out mechanism and posit an exponential tilt model that links non-identifiable and identifiable distributions. This model is indexed by non-identified parameters, which are assumed to have an informative prior distribution, elicited from subject-matter experts. Under this model, full data estimands are shown to be expressed as functionals of the distribution of the observed data. To avoid the curse of dimensionality, we model the distribution of the observed data using a Bayesian shrinkage model. In a simulation study, we compare our approach to a fully parametric and a fully saturated model for the distribution of the observed data. Our methodology is motivated and applied to data from the Breast Cancer Prevention Trial.

Energy-Efficient Management Of Heterogeneous Wireless Sensor Networks

Various applications for the purposes of event detection, localization, and monitoring can benefit from the use of wireless sensor networks (WSNs). Wireless sensor networks are generally easy to deploy, with flexible topology and can support diversity of tasks thanks to the large variety of sensors that can be attached to the wireless sensor nodes. To guarantee the efficient operation of such a heterogeneous wireless sensor networks during its lifetime an appropriate management is necessary. Typically, there are three management tasks, namely monitoring, (re) configuration, and code updating. On the one hand, status information, such as battery state and node connectivity, of both the wireless sensor network and the sensor nodes has to be monitored. And on the other hand, sensor nodes have to be (re)configured, e.g., setting the sensing interval. Most importantly, new applications have to be deployed as well as bug fixes have to be applied during the network lifetime. All management tasks have to be performed in a reliable, time- and energy-efficient manner. The ability to disseminate data from one sender to multiple receivers in a reliable, time- and energy-efficient manner is critical for the execution of the management tasks, especially for code updating. Using multicast communication in wireless sensor networks is an efficient way to handle such traffic pattern. Due to the nature of code updates a multicast protocol has to support bulky traffic and endto-end reliability. Further, the limited resources of wireless sensor nodes demand an energy-efficient operation of the multicast protocol. Current data dissemination schemes do not fulfil all of the above requirements. In order to close the gap, we designed the Sensor Node Overlay Multicast (SNOMC) protocol such that to support a reliable, time-efficient and energy-efficient dissemination of data from one sender node to multiple receivers. In contrast to other multicast transport protocols, which do not support reliability mechanisms, SNOMC supports end-to-end reliability using a NACK-based reliability mechanism. The mechanism is simple and easy to implement and can significantly reduce the number of transmissions. It is complemented by a data acknowledgement after successful reception of all data fragments by the receiver nodes. In SNOMC three different caching strategies are integrated for an efficient handling of necessary retransmissions, namely, caching on each intermediate node, caching on branching nodes, or caching only on the sender node. Moreover, an option was included to pro-actively request missing fragments. SNOMC was evaluated both in the OMNeT++ simulator and in our in-house real-world testbed and compared to a number of common data dissemination protocols, such as Flooding, MPR, TinyCubus, PSFQ, and both UDP and TCP. The results showed that SNOMC outperforms the selected protocols in terms of transmission time, number of transmitted packets, and energy-consumption. Moreover, we showed that SNOMC performs well with different underlying MAC protocols, which support different levels of reliability and energy-efficiency. Thus, SNOMC can offer a robust, high-performing solution for the efficient distribution of code updates and management information in a wireless sensor network. To address the three management tasks, in this thesis we developed the Management Architecture for Wireless Sensor Networks (MARWIS). MARWIS is specifically designed for the management of heterogeneous wireless sensor networks. A distinguished feature of its design is the use of wireless mesh nodes as backbone, which enables diverse communication platforms and offloading functionality from the sensor nodes to the mesh nodes. This hierarchical architecture allows for efficient operation of the management tasks, due to the organisation of the sensor nodes into small sub-networks each managed by a mesh node. Furthermore, we developed a intuitive -based graphical user interface, which allows non-expert users to easily perform management tasks in the network. In contrast to other management frameworks, such as Mate, MANNA, TinyCubus, or code dissemination protocols, such as Impala, Trickle, and Deluge, MARWIS offers an integrated solution monitoring, configuration and code updating of sensor nodes. Integration of SNOMC into MARWIS further increases performance efficiency of the management tasks. To our knowledge, our approach is the first one, which offers a combination of a management architecture with an efficient overlay multicast transport protocol. This combination of SNOMC and MARWIS supports reliably, time- and energy-efficient operation of a heterogeneous wireless sensor network.

Effects of study precision and risk of bias in networks of interventions: a network meta-epidemiological study

BACKGROUND Empirical research has illustrated an association between study size and relative treatment effects, but conclusions have been inconsistent about the association of study size with the risk of bias items. Small studies give generally imprecisely estimated treatment effects, and study variance can serve as a surrogate for study size. METHODS We conducted a network meta-epidemiological study analyzing 32 networks including 613 randomized controlled trials, and used Bayesian network meta-analysis and meta-regression models to evaluate the impact of trial characteristics and study variance on the results of network meta-analysis. We examined changes in relative effects and between-studies variation in network meta-regression models as a function of the variance of the observed effect size and indicators for the adequacy of each risk of bias item. Adjustment was performed both within and across networks, allowing for between-networks variability. RESULTS Imprecise studies with large variances tended to exaggerate the effects of the active or new intervention in the majority of networks, with a ratio of odds ratios of 1.83 (95% CI: 1.09,3.32). Inappropriate or unclear conduct of random sequence generation and allocation concealment, as well as lack of blinding of patients and outcome assessors, did not materially impact on the summary results. Imprecise studies also appeared to be more prone to inadequate conduct. CONCLUSIONS Compared to more precise studies, studies with large variance may give substantially different answers that alter the results of network meta-analyses for dichotomous outcomes.

Do Integration Policies Affect Immigrants' Voluntary Engagement? An Exploration at Switzerland's Subnational Level

This paper investigates whether integration policies influence immigrants' propensity to volunteer, the latter being an important element of immigrants' integration into the host society. By distinguishing different categories of integration policies at Switzerland's subnational level and applying a Bayesian multilevel approach, our results suggest varying policy effects: while policies fostering socio-structural rights enhance immigrants' propensity to volunteer, we observe a negative curvilinear relationship between cultural rights and obligations and immigrants' volunteerism implying that a combination of cultural entitlements and obligations is most conducive to immigrants' civic engagement.

Context-Aware Adaptive Opportunistic Routing in Mobile Ad-hoc and Sensor Networks

Mobile ad-hoc networks (MANETs) and wireless sensor networks (WSNs) have been attracting increasing attention for decades due to their broad civilian and military applications. Basically, a MANET or WSN is a network of nodes connected by wireless communication links. Due to the limited transmission range of the radio, many pairs of nodes in MANETs or WSNs may not be able to communicate directly, hence they need other intermediate nodes to forward packets for them. Routing in such types of networks is an important issue and it poses great challenges due to the dynamic nature of MANETs or WSNs. On the one hand, the open-air nature of wireless environments brings many difficulties when an efficient routing solution is required. The wireless channel is unreliable due to fading and interferences, which makes it impossible to maintain a quality path from a source node to a destination node. Additionally, node mobility aggravates network dynamics, which causes frequent topology changes and brings significant overheads for maintaining and recalculating paths. Furthermore, mobile devices and sensors are usually constrained by battery capacity, computing and communication resources, which impose limitations on the functionalities of routing protocols. On the other hand, the wireless medium possesses inherent unique characteristics, which can be exploited to enhance transmission reliability and routing performance. Opportunistic routing (OR) is one promising technique that takes advantage of the spatial diversity and broadcast nature of the wireless medium to improve packet forwarding reliability in multihop wireless communication. OR combats the unreliable wireless links by involving multiple neighboring nodes (forwarding candidates) to choose packet forwarders. In opportunistic routing, a source node does not require an end-to-end path to transmit packets. The packet forwarding decision is made hop-by-hop in a fully distributed fashion. Motivated by the deficiencies of existing opportunistic routing protocols in dynamic environments such as mobile ad-hoc networks or wireless sensor networks, this thesis proposes a novel context-aware adaptive opportunistic routing scheme. Our proposal selects packet forwarders by simultaneously exploiting multiple types of cross-layer context information of nodes and environments. Our approach significantly outperforms other routing protocols that rely solely on a single metric. The adaptivity feature of our proposal enables network nodes to adjust their behaviors at run-time according to network conditions. To accommodate the strict energy constraints in WSNs, this thesis integrates adaptive duty-cycling mechanism to opportunistic routing for wireless sensor nodes. Our approach dynamically adjusts the sleeping intervals of sensor nodes according to the monitored traffic load and the estimated energy consumption rate. Through the integration of duty cycling of sensor nodes and opportunistic routing, our protocol is able to provide a satisfactory balance between good routing performance and energy efficiency for WSNs.

The structural substrate of brain function: analysis of brain structural networks based on diffusion-weighted imaging

The brain is a complex neural network with a hierarchical organization and the mapping of its elements and connections is an important step towards the understanding of its function. Recent developments in diffusion-weighted imaging have provided the opportunity to reconstruct the whole-brain structural network in-vivo at a large scale level and to study the brain structural substrate in a framework that is close to the current understanding of brain function. However, methods to construct the connectome are still under development and they should be carefully evaluated. To this end, the first two studies included in my thesis aimed at improving the analytical tools specific to the methodology of brain structural networks. The first of these papers assessed the repeatability of the most common global and local network metrics used in literature to characterize the connectome, while in the second paper the validity of further metrics based on the concept of communicability was evaluated. Communicability is a broader measure of connectivity which accounts also for parallel and indirect connections. These additional paths may be important for reorganizational mechanisms in the presence of lesions as well as to enhance integration in the network. These studies showed good to excellent repeatability of global network metrics when the same methodological pipeline was applied, but more variability was detected when considering local network metrics or when using different thresholding strategies. In addition, communicability metrics have been found to add some insight into the integration properties of the network by detecting subsets of nodes that were highly interconnected or vulnerable to lesions. The other two studies used methods based on diffusion-weighted imaging to obtain knowledge concerning the relationship between functional and structural connectivity and about the etiology of schizophrenia. The third study integrated functional oscillations measured using electroencephalography (EEG) and functional magnetic resonance imaging (fMRI) as well as diffusion-weighted imaging data. The multimodal approach that was applied revealed a positive relationship between individual fluctuations of the EEG alpha-frequency and diffusion properties of specific connections of two resting-state networks. Finally, in the fourth study diffusion-weighted imaging was used to probe for a relationship between the underlying white matter tissue structure and season of birth in schizophrenia patients. The results are in line with the neurodevelopmental hypothesis of early pathological mechanisms as the origin of schizophrenia. The different analytical approaches selected in these studies also provide arguments for discussion of the current limitations in the analysis of brain structural networks. To sum up, the first studies presented in this thesis illustrated the potential of brain structural network analysis to provide useful information on features of brain functional segregation and integration using reliable network metrics. In the other two studies alternative approaches were presented. The common discussion of the four studies enabled us to highlight the benefits and possibilities for the analysis of the connectome as well as some current limitations.

Structural and Institutional Determinants of Influence Reputation: A Comparison of Collaborative and Adversarial Policy Networks in Decision Making and Implementation

The central assumption in the literature on collaborative networks and policy networks is that political outcomes are affected by a variety of state and nonstate actors. Some of these actors are more powerful than others and can therefore have a considerable effect on decision making. In this article, we seek to provide a structural and institutional explanation for these power differentials in policy networks and support the explanation with empirical evidence. We use a dyadic measure of influence reputation as a proxy for power, and posit that influence reputation over the political outcome is related to vertical integration into the political system by means of formal decision-making authority, and to horizontal integration by means of being well embedded into the policy network. Hence, we argue that actors are perceived as influential because of two complementary factors: (a) their institutional roles and (b) their structural positions in the policy network. Based on temporal and cross-sectional exponential random graph models, we compare five cases about climate, telecommunications, flood prevention, and toxic chemicals politics in Switzerland and Germany. The five networks cover national and local networks at different stages of the policy cycle. The results confirm that institutional and structural drivers seem to have a crucial impact on how an actor is perceived in decision making and implementation and, therefore, their ability to significantly shape outputs and service delivery.

Chow–Liu trees are sufficient predictive models for reproducing key features of functional networks of periictal EEG time-series

Seizure freedom in patients suffering from pharmacoresistant epilepsies is still not achieved in 20–30% of all cases. Hence, current therapies need to be improved, based on a more complete understanding of ictogenesis. In this respect, the analysis of functional networks derived from intracranial electroencephalographic (iEEG) data has recently become a standard tool. Functional networks however are purely descriptive models and thus are conceptually unable to predict fundamental features of iEEG time-series, e.g., in the context of therapeutical brain stimulation. In this paper we present some first steps towards overcoming the limitations of functional network analysis, by showing that its results are implied by a simple predictive model of time-sliced iEEG time-series. More specifically, we learn distinct graphical models (so called Chow–Liu (CL) trees) as models for the spatial dependencies between iEEG signals. Bayesian inference is then applied to the CL trees, allowing for an analytic derivation/prediction of functional networks, based on thresholding of the absolute value Pearson correlation coefficient (CC) matrix. Using various measures, the thus obtained networks are then compared to those which were derived in the classical way from the empirical CC-matrix. In the high threshold limit we find (a) an excellent agreement between the two networks and (b) key features of periictal networks as they have previously been reported in the literature. Apart from functional networks, both matrices are also compared element-wise, showing that the CL approach leads to a sparse representation, by setting small correlations to values close to zero while preserving the larger ones. Overall, this paper shows the validity of CL-trees as simple, spatially predictive models for periictal iEEG data. Moreover, we suggest straightforward generalizations of the CL-approach for modeling also the temporal features of iEEG signals.

The role of Preferential Trade Agreements (PTAs) in facilitating global production networks

This paper examines the effects of preferential trade agreements (PTAs) in facilitating international trade flows connecting production networks. We consider over 250 PTAs with trade flows distinguished into parts and components and final goods for the period 1979-2008. The gravity equation estimates suggest that the concurrent year effects of PTA formation on trade in parts and components are unseen, whereas PTAs have positive and pervasive effects on both types of trade flows 6 and 9 years after the PTA formation.

Chapter 1 Economic Integration and Industry Location: Theories, Empirical Results, and Implications for CLMV

Trade affects the internal location of industry in two ways: it induces firms to specialize and it expands the set of markets that firms serve. If there are industry-specific external economies, firms in related industries will spatially agglomerate (Hanson 1996a). In the context of economic integration, diminished barriers to trade affect industry location particularly in less developed countries. As described below, regional agreements in North America and Europe have caused frontier regions to expand. These regions, which include border regions and port cities, have advantages over internal regions in terms of access to foreign markets. Since trade liberalization induces many firms in developing countries to participate in production networks and to specialize in labor-intensive activities such as assembling and processing of foreign-made components, their inputs as well as final products need to be carried across borders. Therefore, the best industry location, one that minimizes transport costs, is likely to shift to frontier regions. In East Asia, China has developed rapidly since it opened up to international trade. Simultaneously, a large amount of foreign direct investment (FDI) has been attracted and industry agglomerations have been formed in coastal regions, that is, frontier regions linked to the global market by sea, leaving many internal regions behind. Similarly, Cambodia, Laos, Myanmar, and Vietnam (CLMV) have joined AFTA and/or the WTO and liberalized international trade since the 1990s. Moreover, transport infrastructures such as the East-West Economic Corridor, the Southern Economic Corridor, and the North-South Economic Corridor have been built and narrowed economic distances in the Greater Mekong Subregion (GMS). As a result, frontier regions are likely to increase their location advantages and lure labor-intensive operations from neighboring countries. It is expected that, as has happened in North America and Europe, economic integration in East Asia will significantly affect internal geography in CLMV. In this study, I first review theories relevant to economic integration and industry location within a country. In particular, emphasis is placed on the new economic geography (NEG). Secondly, empirical results for North America and Europe are surveyed since they have preceded East Asia in regional integration and a substantial number of studies have been conducted on these regions. The final section summarizes and discusses implications for internal geography in CLMV.