Modeling the ballistic-to-diffusive transition in nematode motility reveals variation in exploratory behavior across species

A quantitative understanding of organism-level behavior requires predictive models that can capture the richness of behavioral phenotypes, yet are simple enough to connect with underlying mechanistic processes. Here we investigate the motile behavior of nematodes at the level of their translational motion on surfaces driven by undulatory propulsion. We broadly sample the nematode behavioral repertoire by measuring motile trajectories of the canonical lab strain $C. elegans$ N2 as well as wild strains and distant species. We focus on trajectory dynamics over timescales spanning the transition from ballistic (straight) to diffusive (random) movement and find that salient features of the motility statistics are captured by a random walk model with independent dynamics in the speed, bearing and reversal events. We show that the model parameters vary among species in a correlated, low-dimensional manner suggestive of a common mode of behavioral control and a trade-off between exploration and exploitation. The distribution of phenotypes along this primary mode of variation reveals that not only the mean but also the variance varies considerably across strains, suggesting that these nematode lineages employ contrasting ``bet-hedging'' strategies for foraging.Comment: 46 pages, 18 figures, 6 table

Control theoretic models of pointing

This article presents an empirical comparison of four models from manual control theory on their ability to model targeting behaviour by human users using a mouse: McRuer’s Crossover, Costello’s Surge, second-order lag (2OL), and the Bang-bang model. Such dynamic models are generative, estimating not only movement time, but also pointer position, velocity, and acceleration on a moment-to-moment basis. We describe an experimental framework for acquiring pointing actions and automatically fitting the parameters of mathematical models to the empirical data. We present the use of time-series, phase space, and Hooke plot visualisations of the experimental data, to gain insight into human pointing dynamics. We find that the identified control models can generate a range of dynamic behaviours that captures aspects of human pointing behaviour to varying degrees. Conditions with a low index of difficulty (ID) showed poorer fit because their unconstrained nature leads naturally to more behavioural variability. We report on characteristics of human surge behaviour (the initial, ballistic sub-movement) in pointing, as well as differences in a number of controller performance measures, including overshoot, settling time, peak time, and rise time. We describe trade-offs among the models. We conclude that control theory offers a promising complement to Fitts’ law based approaches in HCI, with models providing representations and predictions of human pointing dynamics, which can improve our understanding of pointing and inform design

Diffraction Free, Self-Bending Airy Wave Arrangement

Methods and systems for Airy waves and wavepackets or beams having all the advantages of a diffraction-free or dispersion free wave but at the same time, its intensity features accelerate or self-bend during propagation. These beams can perform ballistic dynamics akin to projectiles moving under the action of gravity. The Airy waves are highly asymmetric and as a result their energy is more tightly confined in one quadrant thus increasing the energy density in the main lobes. These wavepackets can be one, two, and three-dimensional waves. In addition they tend to self-heal themselves which is important in adverse environments

Ball 3D Localization From A Single Calibrated Image

Ball 3D localization in team sports has various applications including automatic offside detection in soccer, or shot release localization in basketball. Today, this task is either resolved by using expensive multi-views setups, or by restricting the analysis to ballistic trajectories. In this work, we propose to address the task on a single image from a calibrated monocular camera by estimating ball diameter in pixels and use the knowledge of real ball diameter in meters. This approach is suitable for any game situation where the ball is (even partly) visible. To achieve this, we use a small neural network trained on image patches around candidates generated by a conventional ball detector. Besides predicting ball diameter, our network outputs the confidence of having a ball in the image patch. Validations on 3 basketball datasets reveals that our model gives remarkable predictions on ball 3D localization. In addition, through its confidence output, our model improves the detection rate by filtering the candidates produced by the detector. The contributions of this work are (i) the first model to address 3D ball localization on a single image, (ii) an effective method for ball 3D annotation from single calibrated images, (iii) a high quality 3D ball evaluation dataset annotated from a single viewpoint. In addition, the code to reproduce this research is be made freely available at https://github.com/gabriel-vanzandycke/deepsport.Comment: 9 pages, CVSports202

Adaptive Extended Kalman Filter for Ballistic Missile Tracking

In the current work, adaptive extended Kalman filter (AEKF) is presented for solution of ground radar based ballistic missile (BM) tracking problem in re-entry phase with unknown ballistic coefficient. The estimation of trajectory of any BM in re-entry phase is extremely difficult, because of highly non-linear motion of BM. The estimation accuracy of AEKF has been tested for a typical test target tracking problem adopted from literature. Further, the approach of AEKF is compared with extended Kalman filter (EKF). The simulation result indicates the superiority of the AEKF in solving joint parameter and state estimation problems

Vision-based autonomous aircraft payload delivery system

This research sought to design and develop an autonomous aircraft payload delivery system which utilised an onboard computer vision system for drop-zone identification. The research was tasked at achieving a modular system which could be used in the delivery of a given payload within a 5 m radius of designated drop-zone identifier. An integrated system was developed, where an autonomous flight controller, an onboard companion computer and computer vision system formed the physical hardware utilised to achieve the desired objectives. A Linux-based Robotic Operating System software architecture was used to develop the control algorithms which governed the autonomous flight control, object recognition and tracking through image processing, and payload release trajectory modelling of the system. The hardware and software architectures were integrated into a remote control fixed-wing aircraft for testing. Implementation of the system through simulation and physical testing proved successful and payload delivery was achieved at an altitude of 75 m, within an average displacement of 1.82 m from the true drop-zone location, where drop-zone detection and location were determined through autonomous survey over the approximate drop-zone’s location. This research furthered the development of autonomous aircraft delivery systems by introducing computer vision as a means of drop-zone location confirmation and authentication, allowing for greater payload delivery security and efficiency. The results gathered in this research illustrated the possible applications of modular airborne payload delivery systems into Industry 4.0 through the use of such a system in the service delivery sector

High-sensitivity tracking of optically trapped particles in gases and liquids : observation of Brownian motion in velocity space

textThe thermal velocity fluctuations of microscopic particles mediate the transition from microscopic statistical mechanics to macroscopic long-time diffusion. Prior to this work, detection methods lacked the sensitivity necessary to resolve motion at the length and time scales at which thermal velocity fluctuations occur. This dissertation details two experiments which resulted in velocity measurement of the thermal motion of dielectric microspheres suspended by an optical trap in gases and liquids. First, optical tweezers were used to trap glass microspheres in air over a wide range of pressures and a detection system was developed to track the trapped microspheres' trajectories with MHz bandwidth and 50 MHz bandwidth and <3 fm/rt(Hz) sensitivity. Brownian motion in a liquid is influenced by hydrodynamic, time-retarded coupling between the particle and the fluid flow its motion generates. Our measurements allowed for instantaneous velocity measurement with an SNR of up to 16 dB and confirmed the Maxwell Boltzmann distribution for Brownian motion in a liquid. The measurements also revealed several unusual features predicted for Brownian motion in the regime of hydrodynamic coupling, including faster-than-exponential decay of the velocity autocorrelation function, correlation of the thermal force and non-zero cross-correlation between the particle's velocity and the thermal force preceding it.Physic