Middleware for Mobile Sensing Applications in Urban Environments

Sensor networks represent an attractive tool to observe the physical world. Networks of tiny sensors can be used to detect a fire in a forest, to monitor the level of pollution in a river, or to check on the structural integrity of a bridge. Application-specific deployments of static-sensor networks have been widely investigated. Commonly, these networks involve a centralized data-collection point and no sharing of data outside the organization that owns it. Although this approach can accommodate many application scenarios, it significantly deviates from the pervasive computing vision of ubiquitous sensing where user applications seamlessly access anytime, anywhere data produced by sensors embedded in the surroundings. With the ubiquity and ever-increasing capabilities of mobile devices, urban environments can help give substance to the ubiquitous sensing vision through Urbanets, spontaneously created urban networks. Urbanets consist of mobile multi-sensor devices, such as smart phones and vehicular systems, public sensor networks deployed by municipalities, and individual sensors incorporated in buildings, roads, or daily artifacts. My thesis is that "multi-sensor mobile devices can be successfully programmed to become the underpinning elements of an open, infrastructure-less, distributed sensing platform that can bring sensor data out of their traditional close-loop networks into everyday urban applications". Urbanets can support a variety of services ranging from emergency and surveillance to tourist guidance and entertainment. For instance, cars can be used to provide traffic information services to alert drivers to upcoming traffic jams, and phones to provide shopping recommender services to inform users of special offers at the mall. Urbanets cannot be programmed using traditional distributed computing models, which assume underlying networks with functionally homogeneous nodes, stable configurations, and known delays. Conversely, Urbanets have functionally heterogeneous nodes, volatile configurations, and unknown delays. Instead, solutions developed for sensor networks and mobile ad hoc networks can be leveraged to provide novel architectures that address Urbanet-specific requirements, while providing useful abstractions that hide the network complexity from the programmer. This dissertation presents two middleware architectures that can support mobile sensing applications in Urbanets. Contory offers a declarative programming model that views Urbanets as a distributed sensor database and exposes an SQL-like interface to developers. Context-aware Migratory Services provides a client-server paradigm, where services are capable of migrating to different nodes in the network in order to maintain a continuous and semantically correct interaction with clients. Compared to previous approaches to supporting mobile sensing urban applications, our architectures are entirely distributed and do not assume constant availability of Internet connectivity. In addition, they allow on-demand collection of sensor data with the accuracy and at the frequency required by every application. These architectures have been implemented in Java and tested on smart phones. They have proved successful in supporting several prototype applications and experimental results obtained in ad hoc networks of phones have demonstrated their feasibility with reasonable performance in terms of latency, memory, and energy consumption.

Characterization of Failure Modes in CFRP Composites - An ANN Approach

Carbon fiber reinforced polymer (CFRP) composite specimens with different thickness, geometry, and stacking sequences were subjected to fatigue spectrum loading in stages. Another set of specimens was subjected to static compression load. On-line acoustic Emission (AE) monitoring was carried out during these tests. Two artificial neural networks, Kohonen-self organizing feature map (KSOM), and multi-layer perceptron (MLP) have been developed for AE signal analysis. AE signals from specimens were clustered using the unsupervised learning KSOM. These clusters were correlated to the failure modes using available a priori information such as AE signal amplitude distributions, time of occurrence of signals, ultrasonic imaging, design of the laminates (stacking sequences, orientation of fibers), and AE parametric plots. Thereafter, AE signals generated from the rest of the specimens were classified by supervised learning MLP. The network developed is made suitable for on-line monitoring of AE signals in the presence of noise, which can be used for detection and identification of failure modes and their growth. The results indicate that the characteristics of AE signals from different failure modes in CFRP remain largely unaffected by the type of load, fiber orientation, and stacking sequences, they being representatives of the type of failure phenomena. The type of loading can have effect only on the extent of damage allowed before the specimens fail and hence on the number of AE signals during the test. The artificial neural networks (ANN) developed and the methods and procedures adopted show significant success in AE signal characterization under noisy environment (detection and identification of failure modes and their growth).

Multivariate Techniques for Identifying Diffractive Interactions at the LHC

Close to one half of the LHC events are expected to be due to elastic or inelastic diffractive scattering. Still, predictions based on extrapolations of experimental data at lower energies differ by large factors in estimating the relative rate of diffractive event categories at the LHC energies. By identifying diffractive events, detailed studies on proton structure can be carried out. The combined forward physics objects: rapidity gaps, forward multiplicity and transverse energy flows can be used to efficiently classify proton-proton collisions. Data samples recorded by the forward detectors, with a simple extension, will allow first estimates of the single diffractive (SD), double diffractive (DD), central diffractive (CD), and non-diffractive (ND) cross sections. The approach, which uses the measurement of inelastic activity in forward and central detector systems, is complementary to the detection and measurement of leading beam-like protons. In this investigation, three different multivariate analysis approaches are assessed in classifying forward physics processes at the LHC. It is shown that with gene expression programming, neural networks and support vector machines, diffraction can be efficiently identified within a large sample of simulated proton-proton scattering events. The event characteristics are visualized by using the self-organizing map algorithm.

Governance theory as a framework for empirical research : a case study on local environmental policy-making in Helsinki, Finland

Governance has been one of the most popular buzzwords in recent political science. As with any term shared by numerous fields of research, as well as everyday language, governance is encumbered by a jungle of definitions and applications. This work elaborates on the concept of network governance. Network governance refers to complex policy-making situations, where a variety of public and private actors collaborate in order to produce and define policy. Governance is processes of autonomous, self-organizing networks of organizations exchanging information and deliberating. Network governance is a theoretical concept that corresponds to an empirical phenomenon. Often, this phenomenon is used to descirbe a historical development: governance is often used to describe changes in political processes of Western societies since the 1980s. In this work, empirical governance networks are used as an organizing framework, and the concepts of autonomy, self-organization and network structure are developed as tools for empirical analysis of any complex decision-making process. This work develops this framework and explores the governance networks in the case of environmental policy-making in the City of Helsinki, Finland. The crafting of a local ecological sustainability programme required support and knowledge from all sectors of administration, a number of entrepreneurs and companies and the inhabitants of Helsinki. The policy process relied explicitly on networking, with public and private actors collaborating to design policy instruments. Communication between individual organizations led to the development of network structures and patterns. This research analyses these patterns and their effects on policy choice, by applying the methods of social network analysis. A variety of social network analysis methods are used to uncover different features of the networked process. Links between individual network positions, network subgroup structures and macro-level network patterns are compared to the types of organizations involved and final policy instruments chosen. By using governance concepts to depict a policy process, the work aims to assess whether they contribute to models of policy-making. The conclusion is that the governance literature sheds light on events that would otherwise go unnoticed, or whose conceptualization would remain atheoretical. The framework of network governance should be in the toolkit of the policy analyst.

Aluekehittämisverkostojen arviointi : Verkostotoimijanäkökulma MAL- ja hyvinvointiverkostoihin aluekehittämisen välineinä

The Finnish regional development system has gone through structural reforms from state centered governed system to multi-actor governance, based on negotiation and cooperation. One of the reforms has been the regional cohesion and competitiveness program (COCO) established in 2010. The aim of the program is to increase competitiveness through all the regions and balance the regional development by supporting networking. The main focus of the program is to improve the methods and tools for regional development. In the program there are seven thematic networks founded around topics seen important national wide. This thesis explores regional development networks and their evaluation COCO:s two thematic networks, Wellbeing and Land use, housing and transportation as examples. The aim of the thesis is to explore the network actors understanding of thematic networks as tools for regional development. In particular, the study focuses on how the actors see the possible network level outcomes and wider effects of the networking activity. In addition, the central themes of the study are the prerequisite for successful network processes and improvement of the network process effectiveness by evaluation. The research material in this study consist the interviews of the network coordinators and national and regional actors participating in the network activities. The interviews were conducted in spring 2011. Based on the research results, the networks act on national regional and network level and across them. The cooperation is based on official and unofficial relations. The structure of the networks seemed to be self-organizing and controlled at the same time. The structural elements were found to set the framework for the network process and evaluation. According to the results, the networks enabled the more effective operation of the national development system, support of the regions and political lobbying. For the regions the networks offered support for areal development, new resources and possibility to influence national discourse. As conclusion, the role of the network was to disseminate information, create possibilities for collaboration and execute projects and studies and to effect on national policy making. These factors determined the effectiveness of the networks. However, because different regions were satisfied with different network level outcomes, the utilization of the networks in the regions should be evaluated by their own objectives. Resources, motivation, competence and perceptions of the effects were found to affect the successful implementation of the network process and cooperation in networks. Some network level obstacles could be overcome with coordination, but the challenge was the ability and motivation of the areas to utilize the networks as resources and see them as part of the comprehensive development agenda. Thus, the development should focus on how to increase awareness on how to improve regional cooperation processes and how multiple regional actors could enhance their working by utilizing the networks.

Regional Flood Frequency Analysis using Two-level Clustering Approach

Clustering techniques are used in regional flood frequency analysis (RFFA) to partition watersheds into natural groups or regions with similar hydrologic responses. The linear Kohonen's self‐organizing feature map (SOFM) has been applied as a clustering technique for RFFA in several recent studies. However, it is seldom possible to interpret clusters from the output of an SOFM, irrespective of its size and dimensionality. In this study, we demonstrate that SOFMs may, however, serve as a useful precursor to clustering algorithms. We present a two‐level. SOFM‐based clustering approach to form regions for FFA. In the first level, the SOFM is used to form a two‐dimensional feature map. In the second level, the output nodes of SOFM are clustered using Fuzzy c‐means algorithm to form regions. The optimal number of regions is based on fuzzy cluster validation measures. Effectiveness of the proposed approach in forming homogeneous regions for FFA is illustrated through application to data from watersheds in Indiana, USA. Results show that the performance of the proposed approach to form regions is better than that based on classical SOFM.

Contributions to unsupervised and supervised learning with applications in digital image processing

311 p. : il.

A genealogical map of the concept of habit

The notion of information processing has dominated the study of the mind for over six decades. However, before the advent of cognitivism, one of the most prominent theoretical ideas was that of Habit. This is a concept with a rich and complex history, which is again starting to awaken interest, following recent embodied, enactive critiques of computationalist frameworks. We offer here a very brief history of the concept of habit in the form of a genealogical network-map. This serves to provide an overview of the richness of this notion and as a guide for further re-appraisal. We identify 77 thinkers and their influences, and group them into seven schools of thought. Two major trends can be distinguished. One is the associationist trend, starting with the work of Locke and Hume, developed by Hartley, Bain, and Mill to be later absorbed into behaviorism through pioneering animal psychologists (Morgan and Thorndike). This tradition conceived of habits atomistically and as automatisms (a conception later debunked by cognitivism). Another historical trend we have called organicism inherits the legacy of Aristotle and develops along German idealism, French spiritualism, pragmatism, and phenomenology. It feeds into the work of continental psychologists in the early 20th century, influencing important figures such as Merleau-Ponty, Piaget, and Gibson. But it has not yet been taken up by mainstream cognitive neuroscience and psychology. Habits, in this tradition, are seen as ecological, self-organizing structures that relate to a web of predispositions and plastic dependencies both in the agent and in the environment. In addition, they are not conceptualized in opposition to rational, volitional processes, but as transversing a continuum from reflective to embodied intentionality. These are properties that make habit a particularly attractive idea for embodied, enactive perspectives, which can now re-evaluate it in light of dynamical systems theory and complexity research.

Paintable band-edge liquid crystal lasers.

In this paper we demonstrate photonic band-edge laser emission from emulsion-based polymer dispersed liquid crystals. The lasing medium consists of dye-doped chiral nematic droplets dispersed within a polymer matrix that spontaneously align as the film dries. Such lasers can be easily formed on single substrates with no alignment layers. The system combines the self-organizing periodic structure of chiral nematic liquid crystals with the simplicity of the emulsion procedure so as to produce a material that retains the emission characteristics of band-edge lasers yet can be readily coated. Sequential and stacked layers demonstrate the possibility of achieving simultaneous multi-wavelength laser output from glass, metallic, and flexible substrates.

Microsatellites assessment of Chinese sturgeon (Acipenser sinensis Gray) genetic variability

Four microsatellites were used to examine the genetic variability of the spawning stocks of Chinese sturgeon, Acipenser sinensis, from the Yangtze River sampled over a 3-year period (1999-2001). Within 60 individuals, a total of 28 alleles were detected over four polymorphic microsatellite loci. The number of alleles per locus ranged from 4 to 15, with an average allele number of 7. The number of genotypes per locus ranged from 6 to 41. The genetic diversity of four microsatellite loci varied from 0.34 to 0.67, with an average value of 0.54. For the four microsatellite loci, the deviation from the Hardy-Weinberg equilibrium was mainly due to null alleles. The mean number of alleles per locus and the mean heterozygosity were lower than the average values known for anadromous fishes. Fish were clustered according to their microsatellite characteristics using an unsupervised 'Artificial Neural Networks' method entitled 'Self-organizing Map'. The results revealed no significant genetic differentiation considering genetic distance among samples collected during different years. Lack of heterogeneity among different annual groups of spawning stocks was explained by the complex age structure (from 8 to 27 years for males and 12 to 35 years for females) of Chinese sturgeon, leading to formulate an hypothesis about the maintenance of genetic diversity and stability in long-lived animals.

Morphological computation of multi-gaited robot locomotion based on free vibration.

In recent years, there has been increasing interest in the study of gait patterns in both animals and robots, because it allows us to systematically investigate the underlying mechanisms of energetics, dexterity, and autonomy of adaptive systems. In particular, for morphological computation research, the control of dynamic legged robots and their gait transitions provides additional insights into the guiding principles from a synthetic viewpoint for the emergence of sensible self-organizing behaviors in more-degrees-of-freedom systems. This article presents a novel approach to the study of gait patterns, which makes use of the intrinsic mechanical dynamics of robotic systems. Each of the robots consists of a U-shaped elastic beam and exploits free vibration to generate different locomotion patterns. We developed a simplified physics model of these robots, and through experiments in simulation and real-world robotic platforms, we show three distinctive mechanisms for generating different gait patterns in these robots.

Learning legged locomotion

Legged locomotion of biological systems can be viewed as a self-organizing process of highly complex system-environment interactions. Walking behavior is, for example, generated from the interactions between many mechanical components (e.g., physical interactions between feet and ground, skeletons and muscle-tendon systems), and distributed informational processes (e.g., sensory information processing, sensory-motor control in central nervous system, and reflexes) [21]. An interesting aspect of legged locomotion study lies in the fact that there are multiple levels of self-organization processes (at the levels of mechanical dynamics, sensory-motor control, and learning). Previously, the self-organization of mechanical dynamics was nicely demonstrated by the so-called Passive Dynamic Walkers (PDWs; [18]). The PDW is a purely mechanical structure consisting of body, thigh, and shank limbs that are connected by passive joints. When placed on a shallow slope, it exhibits natural bipedal walking dynamics by converting potential to kinetic energy without any actuation. An important contribution of these case studies is that, if designed properly, mechanical dynamics can generate a relatively complex locomotion dynamics, on the one hand, and the mechanical dynamics induces self-stability against small disturbances without any explicit control of motors, on the other. The basic principle of the mechanical self-stability appears to be fairly general that there are several different physics models that exhibit similar characteristics in different kinds of behaviors (e.g., hopping, running, and swimming; [2, 4, 9, 16, 19]), and a number of robotic platforms have been developed based on them [1, 8, 13, 22]. © 2009 Springer London.

Controlled synthesis of nanoporous Au microsheet via surfactant emulsion template

A facile and wet-chemical approach was employed to control synthesis of self-organizing, hyperbranched nanoporous Au microsheet with high quality in bulk quantity. This method produced nanoporous Au microsheets with a thickness of 7-15 nm. The microsheets were composed of irregularly interconnected planar Au nanoplates with interstices, i.e. nanopores of 10-50 nm. And the nanoporous Au microsheets were enveloped in 10-30 nm thick polyaniline (PANI) sheaths. The morphology of the nanostructured Au composites could also be easily tuned by changing the concentration of aniline and chlorauric acid. The dendritic and epitaxial growth of nanoporous Au microsheet was believed as the diffusion-limited process confined in the lamellar emulsion phase through self-assembly of aniline and dodecylsulfate. The solution reaction proceeded at a mild condition (room temperature and aqueous solutions), and less toxic reagents were employed instead of extreme toxic and corrosive chemicals.

Solvent-vapor treatment induced performance enhancement of poly(3-hexylthiophene): methanofullerene bulk-heterojunction photovoltaic cells

The authors report enhanced poly(3-hexylthiophene) (P3HT):methanofullerene (PCBM) bulk-heterojunction photovoltaic cells via 1,2-dichlorobenzene (DCB) vapor treatment and thermal annealing. DCB vapor treatment can induce P3HT self-organizing into ordered structure leading to enhanced absorption and high hole mobility. Further annealing the device at a high temperature, PCBM molecules begin to diffuse into aggregates and together with the ordered P3HT phase form bicontinuous pathways in the entire layer for efficient charge separation and transport. Compared to the control device that is merely annealed, optical absorption, short-circuit current, and power conversion efficiency are increased for the DCB vapor-treated cell.