Performances of passive electric networks and piezoelectric transducers for beam vibration control

This thesis is focused on beam vibration control using piezoelectric transducers and passive electric networks. The first part of this study deals with the modeling and the analysis of stepped piezoelectric beams. A refined one-dimensional model is derived and experimentally validated. The modal properties are determined with four numerical methods. A homogenized model of stepped periodic piezoelectric beams is derived by using two-scale convergence. The second part deals with the performance analysis of three passive circuits in damping structural vibrations: the piezoelectric shunting, the second order transmission line and the fourth order transmission line. The effects of uncertainties of the electric parameters on the system performances are analyzed. Theoretical predictions are validated through different experimental setup

Fluorescent particle tracers in surface hydrology: a proof of concept in a semi-natural hillslope

Abstract. In this paper, a proof of concept experiment is conducted to assess the feasibility of tracing overland flow on an experimental hillslope plot via a novel fluorescent particle tracer. Experiments are performed by using beads of diameters ranging from 75 to 1180 μm. Particles are sensed through an experimental apparatus comprising a light source and a video acquisition unit. Runoff on the experimental plot is artificially simulated by using a custom-built rainfall system. Particle transits are detected through supervised methodologies requiring the presence of operators and unsupervised procedures based on image analysis techniques. Average flow velocity estimations are executed based on travel time measurements of the particles as they are dragged by the overland flow on the hillslope. Velocities are compared to flow measurements obtained using rhodamine dye. Experimental findings demonstrate the potential of the methodology for understanding overland flow dynamics in complex natural settings. In addition, insights on the optimization of particle size are presented based on the visibility of the beads and their accuracy in flow tracing

Performances of passive electric networks and piezoelectric transducers for beam vibration control

This thesis is focused on beam vibration control using piezoelectric transducers and passive electric networks. The first part of this study deals with the modeling and the analysis of stepped piezoelectric beams. A refined one-dimensional model is derived and experimentally validated. The modal properties are determined with four numerical methods. A homogenized model of stepped periodic piezoelectric beams is derived by using two-scale convergence. The second part deals with the performance analysis of three passive circuits in damping structural vibrations: the piezoelectric shunting, the second order transmission line and the fourth order transmission line. The effects of uncertainties of the electric parameters on the system performances are analyzed. Theoretical predictions are validated through different experimental setup

Identifying manifolds underlying group motion in Vicsek agents

Collective motion of animal groups often undergoes changes due to perturbations. In a topological sense, we describe these changes as switching between low-dimensional embedding manifolds underlying a group of evolving agents. To characterize such manifolds, first we introduce a simple mapping of agents between time-steps. Then, we construct a novel metric which is susceptible to variations in the collective motion, thus revealing distinct underlying manifolds. The method is validated through three sample scenarios simulated using a Vicsek model, namely switching of speed, coordination, and structure of a group. Combined with a dimensionality reduction technique that is used to infer the dimensionality of the embedding manifold, this approach provides an effective model-free framework for the analysis of collective behavior across animal species.Comment: 12 pages, 6 figures, journal articl

An information-theoretic approach to study spatial dependencies in small datasets

From epidemiology to economics, there is a fundamental need of statistically principled approaches to unveil spatial patterns and identify their underpinning mechanisms. Grounded in network and information theory, we establish a non-parametric scheme to study spatial associations from limited measurements of a spatial process. Through the lens of network theory, we relate spatial patterning in the dataset to the topology of a network on which the process unfolds. From the available observations of the spatial process and a candidate network topology, we compute a mutual information statistic that measures the extent to which the measurement at a node is explained by observations at neighbouring nodes. For a class of networks and linear autoregressive processes, we establish closed-form expressions for the mutual information statistic in terms of network topological features. We demonstrate the feasibility of the approach on synthetic datasets comprising 25–100 measurements, generated by linear or nonlinear autoregressive processes. Upon validation on synthetic processes, we examine datasets of human migration under climate change in Bangladesh and motor vehicle deaths in the United States of America. For both these real datasets, our approach is successful in identifying meaningful spatial patterns, begetting statistically-principled insight into the mechanisms of important socioeconomic problems.This study is part of the collaborative activities carried out under the programs of the Region of Murcia (Spain): ‘Groups of Excellence of the Region of Murcia, the Fundación Séneca, Science and Technology Agency project 19884/GERM/15 and ‘Call for Fellowships for Guest Researcher Stays at Universities andOPIS’ project 21144/IV/19. M.P. would like to express his gratitude to the Technical University of Cartagena for hosting him during a Sabbatical leave and to acknowledge support from the National Science Foundation under grant no.CMMI 1561134. M.R.M. would like to acknowledge support from Ministerio de Ciencia, Innovación y Universidades under grant number PID2019-107800GB-I00/AEI/10.13039/50110001103

Inference of time-varying networks through transfer entropy, the case of a Boolean network model

Inferring network topologies from the time series of individual units is of paramount importance in the study of biological and social networks. Despite considerable progress, our success in network inference is largely limited to static networks and autonomous node dynamics, which are often inadequate to describe complex systems. Here, we explore the possibility of reconstructing time-varying weighted topologies through the information-theoretic notion of transfer entropy. We focus on a Boolean network model in which the weight of the links and the spontaneous activity periodically vary in time. For slowly-varying dynamics, we establish closed-form expressions for the stationary periodic distribution and transfer entropy between each pair of nodes. Our results indicate that the instantaneous weight of each link is mapped into a corresponding transfer entropy value, thereby affording the possibility of pinpointing the dominant weights at each time. However, comparing transfer entropy readings at different times may provide erroneous estimates of the strength of the links in time, due to a counterintuitive modulation of the information flow by the non-autonomous dynamics. In fact, this time variation should be used to scale transfer entropy values toward the correct inference of the time evolution of the network weights. This study constitutes a necessary step toward a mathematically-principled use of transfer entropy to reconstruct time-varying networks.This work was supported by the National Science Foundation under Grant Nos. CMMI 1433670, CMMI 1561134, and CBET 1547864, the US Army Research Office under Grant No. W911NF-15-1-0267 with Dr. Samuel C. Stanton and Dr. Alfredo Garcia as the program managers, and Ministerio de Economía y Competitividad de Espana and FEDER funds under Grant No. ECO2015-65637-P. This study is part of the collaborative activities carried out under the program Groups of Excellence of the Region of Murcia, the Fundacion Seneca, Science and Technology Agency of the Region of Murcia Project No. 19884/GERM/15

Inferring the size of a collective of self-propelled Vicsek particles from the random motion of a single unit

nferring the size of a collective from the motion of a few accessible units is a fundamental problem in network science and interdisciplinary physics. Here, we recognize stochasticity as the commodity traded in the units’ interactions. Drawing inspiration from the work of Einstein-Perrin-Smoluchowski on the discontinuous structure of matter, we use the random motion of one unit to identify the footprint of every other unit. Just as the Avogadro’s number can be determined from the Brownian motion of a suspended particle in a liquid, the size of the collective can be inferred from the random motion of any unit. For self-propelled Vicsek particles, we demonstrate an inverse proportionality between the diffusion coefficient of the heading of any particle and the size of the collective. We provide a rigorous method to infer the size of a collective from measurements of a few units, strengthening the link between physics and collective behavior

Inferring causal relationships in zebrafish-robot interactions through transfer entropy: a small lure to catch a big fish

In the field of animal behavior, effective methods to apprehend causal relationships that underlie the interactions between animals are in dire need. How to identify a leader in a group of social animals or quantify the mutual response of predator and prey are exemplary questions that would benefit from an improved understanding of causality. Information theory offers a potent framework to objectively infer cause-and-effect relationships from raw experimental data, in the form of behavioral observations or individual trajectory tracks. In this targeted review, we summarize recent advances in the application of the information-theoretic concept of transfer entropy to animal interactions. First, we offer an introduction to the theory of transfer entropy, keeping a balance between fundamentals and practical implementation. Then, we focus on animal-robot experiments as a means for the validation of the use of transfer entropy to measure causal relationships. We explore a test battery of robotics-based protocols designed for studying zebrafish social behavior and fear response. Grounded in experimental evidence, we demonstrate the potential of transfer entropy to assist in the detection and quantification of causal relationships in animal interactions. The proposed robotics-based platforms offer versatile, controllable, and customizable stimuli to generate a priori known cause-and-effect relationships, which would not be feasible with live stimuli. We conclude the paper with an outlook on possible applications of transfer entropy to study group behavior and clarify the determinants of leadership in social animals

Zebrafish Adjust Their Behavior in Response to an Interactive Robotic Predator

Zebrafish (Danio rerio) constitutes a valuable experimental species for the study of the biological determinants of emotional responses, such as fear and anxiety. Fear-related test paradigms traditionally entail the interaction between focal subjects and live predators, which may show inconsistent behavior throughout the experiment. To address this technical challenge, robotic stimuli are now frequently integrated in behavioral studies, yielding repeatable, customizable, and controllable experimental conditions. While most of the research has focused on open-loop control where robotic stimuli are preprogrammed to execute a priori known actions, recent work has explored the possibility of two-way interactions between robotic stimuli and live subjects. Here, we demonstrate a "closed-loop control" system to investigate fear response of zebrafish in which the response of the robotic stimulus is determined in real-time through a finite-state Markov chain constructed from independent observations on the interactions between zebrafish and their predator. Specifically, we designed a 3D-printed robotic replica of the zebrafish allopatric predator red tiger Oscar fish (Astronotus ocellatus), instrumented to interact in real-time with live subjects. We investigated the role of closed-loop control in modulating fear response in zebrafish through the analysis of the focal fish ethogram and the information-theoretic quantification of the interaction between the subject and the replica. Our results indicate that closed-loop control elicits consistent fear response in zebrafish and that zebrafish quickly adjust their behavior to avoid the predator's attacks. The augmented degree of interactivity afforded by the Markov-chain-dependent actuation of the replica constitutes a fundamental advancement in the study of animal-robot interactions and offers a new means for the development of experimental paradigms to study fear