Wireless Network Analytics for Next Generation Spectrum Awareness

The importance of networks, in their broad sense, is rapidly and massively growing in modern-day society thanks to unprecedented communication capabilities offered by technology. In this context, the radio spectrum will be a primary resource to be preserved and not wasted. Therefore, the need for intelligent and automatic systems for in-depth spectrum analysis and monitoring will pave the way for a new set of opportunities and potential challenges. This thesis proposes a novel framework for automatic spectrum patrolling and the extraction of wireless network analytics. It aims to enhance the physical layer security of next generation wireless networks through the extraction and the analysis of dedicated analytical features. The framework consists of a spectrum sensing phase, carried out by a patrol composed of numerous radio-frequency (RF) sensing devices, followed by the extraction of a set of wireless network analytics. The methodology developed is blind, allowing spectrum sensing and analytics extraction of a network whose key features (i.e., number of nodes, physical layer signals, medium access protocol (MAC) and routing protocols) are unknown. Because of the wireless medium, over-the-air signals captured by the sensors are mixed; therefore, blind source separation (BSS) and measurement association are used to estimate the number of sources and separate the traffic patterns. After the separation, we put together a set of methodologies for extracting useful features of the wireless network, i.e., its logical topology, the application-level traffic patterns generated by the nodes, and their position. The whole framework is validated on an ad-hoc wireless network accounting for MAC protocol, packet collisions, nodes mobility, the spatial density of sensors, and channel impairments, such as path-loss, shadowing, and noise. The numerical results obtained by extensive and exhaustive simulations show that the proposed framework is consistent and can achieve the required performance

Data based identification and prediction of nonlinear and complex dynamical systems

We thank Dr. R. Yang (formerly at ASU), Dr. R.-Q. Su (formerly at ASU), and Mr. Zhesi Shen for their contributions to a number of original papers on which this Review is partly based. This work was supported by ARO under Grant No. W911NF-14-1-0504. W.-X. Wang was also supported by NSFC under Grants No. 61573064 and No. 61074116, as well as by the Fundamental Research Funds for the Central Universities, Beijing Nova Programme.Peer reviewedPostprin

Information-theoretic Reasoning in Distributed and Autonomous Systems

The increasing prevalence of distributed and autonomous systems is transforming decision making in industries as diverse as agriculture, environmental monitoring, and healthcare. Despite significant efforts, challenges remain in robustly planning under uncertainty. In this thesis, we present a number of information-theoretic decision rules for improving the analysis and control of complex adaptive systems. We begin with the problem of quantifying the data storage (memory) and transfer (communication) within information processing systems. We develop an information-theoretic framework to study nonlinear interactions within cooperative and adversarial scenarios, solely from observations of each agent's dynamics. This framework is applied to simulations of robotic soccer games, where the measures reveal insights into team performance, including correlations of the information dynamics to the scoreline. We then study the communication between processes with latent nonlinear dynamics that are observed only through a filter. By using methods from differential topology, we show that the information-theoretic measures commonly used to infer communication in observed systems can also be used in certain partially observed systems. For robotic environmental monitoring, the quality of data depends on the placement of sensors. These locations can be improved by either better estimating the quality of future viewpoints or by a team of robots operating concurrently. By robustly handling the uncertainty of sensor model measurements, we are able to present the first end-to-end robotic system for autonomously tracking small dynamic animals, with a performance comparable to human trackers. We then solve the issue of coordinating multi-robot systems through distributed optimisation techniques. These allow us to develop non-myopic robot trajectories for these tasks and, importantly, show that these algorithms provide guarantees for convergence rates to the optimal payoff sequence

DEVELOPING MACHINE LEARNING TECHNIQUES FOR NETWORK CONNECTIVITY INFERENCE FROM TIME-SERIES DATA

Inference of the connectivity structure of a network from the observed dynamics of the states of its nodes is a key issue in science, with wide-ranging applications such as determination of the synapses in nervous systems, mapping of interactions between genes and proteins in biochemical networks, distinguishing ecological relationships between different species in their habitats etc. In this thesis, we show that certain machine learning models, trained for the forecasting of experimental and synthetic time-series data from complex systems, can automatically learn the causal networks underlying such complex systems. Based on this observation, we develop new machine learning techniques for inference of causal interaction network connectivity structures underlying large, networked, noisy, complex dynamical systems, solely from the time-series of their nodal states. In particular, our approach is to first train a type of machine learning architecture, known as the ‘reservoir computer’, to mimic the measured dynamics of an unknown network. We then use the trained reservoir computer system as an in silico computational model of the unknown network to estimate how small changes in nodal states propagate in time across that network. Since small perturbations of network nodal states are expected to spread along the links of the network, the estimated propagation of nodal state perturbations reveal the connections of the unknown network. Our technique is noninvasive, but is motivated by the widely used invasive network inference method, whereby the temporal propagation of active perturbations applied to the network nodes are observed and employed to infer the network links (e.g., tracing the effects of knocking down multiple genes, one at a time, can be used infer gene regulatory networks). We discuss how we can further apply this methodology to infer causal network structures underlying different time-series datasets and compare the inferred network with the ground truth whenever available. We shall demonstrate three practical applications of this network inference procedure in (1) inference of network link strengths from time-series data of coupled, noisy Lorenz oscillators, (2) inference of time-delayed feedback couplings in opto-electronic oscillator circuit networks designed the laboratory, and, (3) inference of the synaptic network from publicly-available calcium fluorescence time-series data of C. elegans neurons. In all examples, we also explain how experimental factors like noise level, sampling time, and measurement duration systematically affect causal inference from experimental data. The results show that synchronization and strong correlation among the dynamics of different nodal states are, in general, detrimental for causal network inference. Features that break synchrony among the nodal states, e.g., coupling strength, network topology, dynamical noise, and heterogeneity of the parameters of individual nodes, help the network inference. In fact, we show in this thesis that, for parameter regimes where the network nodal states are not synchronized, we can often achieve perfect causal network inference from simulated and experimental time-series data, using machine learning techniques, in a wide variety of physical systems. In cases where effects like observational noise, large sampling time, or small sampling duration hinder such perfect network inference, we show that it is possible to utilize specially-designed surrogate time-series data for assigning statistical confidence to individual inferred network links. Given the general applicability of our machine learning methodology in time-series prediction and network inference, we anticipate that such techniques can be used for better model-building, forecasting, and control of complex systems in nature and in the lab

Social evolution : opinions and behaviors in face-to-face networks

Thesis (Ph. D.)--Massachusetts Institute of Technology, School of Architecture and Planning, Program in Media Arts and Sciences, 2010.Cataloged from PDF version of thesis.Includes bibliographical references (p. 133-143).Exposure to new ideas and opinions, and their diffusion within social networks, are important questions in education, business, and government. However until recently there has been no method to automatically capture fine-grained face-to-face interactions between people, to better model the diffusion process. In this thesis, we describe the use of colocation and communication sensors in 'socially aware' mobile phones to model the spread of opinions and behaviors of 78 residents of an undergraduate residence hall for an entire academic year, based on over 320,000 hours of behavior data. Political scientists (Huckfeldt and Sprague, APSR, 1983) have noted the problem of mutual causation between face-to-face networks and political opinions. During the last three months of the 2008 US presidential campaigns of Barack Obama and John McCain, we find that political discussants have characteristic interaction patterns that can be used to recover the self-reported 'political discussant' ties within the community. Automatically measured mobile phone features allow us to estimate exposure to different types of opinions in this community. We propose a measure of 'dynamic homophily' which reveals surprising short-term, population-wide behavior changes around external political events such as election debates and Election Day. To our knowledge, this is the first time such dynamic homophily effects have been measured. We find that social exposure to peers in the network predicts individual future opinions (R 2 ~ 0.8, p < 0.001). The use of mobile phone based dynamic exposure increases the explained variance for future political opinions by up to 30%. It is well known that face-to-face networks are the main vehicle for airborne contagious diseases (Elliott, Spatial Epidemiology, 2000). However, epidemiologists have not had access to tools to quantitatively measure the likelihood of contagion, as a function of contact/exposure with infected individuals, in realistic scenarios (Musher, NEJM, 2003), since it requires data about both symptoms and social interactions between individuals. We use of co-location and communication sensors to understand the role of face-to-face interactions in the contagion process. We find that there are characteristic changes in behavior when individuals become sick, reflected in features like total communication, temporal structure in communication (e.g., late nights and weekends), interaction diversity, and movement entropy (both within and outside the university). These behavior variations can be used to infer the likelihood of an individual being symptomatic, based on their network interactions alone, without the use of health-reports. We use a recently-developed signal processing approach (Nolte, Nature, 2008) to better understand the temporal information flux between physical symptoms (i.e., common colds, influenza), measured behavior variations and mental health symptoms (i.e., stress and early depression). Longitudinal studies indicate that health-related behaviors from obesity (Christakis and Fowler, 2007) to happiness (Fowler and Christakis, 2008) may spread through social ties. The effects of social networks and social support on physical health are well-documented (Berkman, 1994; Marmot and Wilkinson, 2006). However, these studies do not quantify actual face-to-face interactions that lead to the adoption of health-related behaviors. We study the variations in BMI, weight (in lbs), unhealthy eating habits, diet and exercise, and find that social exposure measured using mobile phones is a better predictor of BMI change over a semester, than self-report data, in stark contrast to previous work. From a smaller pilot study of social exposure in face-to-face networks and the propagation of viral music, we find that phone communication and location features predict the sharing of music between people, and also identify social ties that are 'close friends' or 'casual acquaintances'. These interaction and music sharing features can be used to model latent influences between participants in the music sharing process.by Anmol Madan.Ph.D

Applications in computational finance with a focus on approximation of financial time series by neurocomputing

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26th Annual Computational Neuroscience Meeting (CNS*2017): Part 3 - Meeting Abstracts - Antwerp, Belgium. 15–20 July 2017

This work was produced as part of the activities of FAPESP Research,\ud Disseminations and Innovation Center for Neuromathematics (grant\ud 2013/07699-0, S. Paulo Research Foundation). NLK is supported by a\ud FAPESP postdoctoral fellowship (grant 2016/03855-5). ACR is partially\ud supported by a CNPq fellowship (grant 306251/2014-0)