Joint Estimation and Control for Multi-Target Passive Monitoring with an Autonomous UAV Agent

This work considers the problem of passively monitoring multiple moving targets with a single unmanned aerial vehicle (UAV) agent equipped with a direction-finding radar. This is in general a challenging problem due to the unobservability of the target states, and the highly non-linear measurement process. In addition to these challenges, in this work we also consider: a) environments with multiple obstacles where the targets need to be tracked as they manoeuvre through the obstacles, and b) multiple false-alarm measurements caused by the cluttered environment. To address these challenges we first design a model predictive guidance controller which is used to plan hypothetical target trajectories over a rolling finite planning horizon. We then formulate a joint estimation and control problem where the trajectory of the UAV agent is optimized to achieve optimal multi-target monitoring

Application of chemometric methods for assessment and modelling of microbiological quality data concerning coastal bathing water in Greece

Background. Worldwide, the aim of managing water is to safeguard human health whilst maintaining sustainable aquatic and associated terrestrial, ecosystems. Because human enteric viruses are the most likely pathogens responsible for waterborne diseases from recreational water use, but detection methods are complex and costly for routine monitoring, it is of great interest to determine the quality of coastal bathing water with a minimum cost and maximum safety. Design and methods. This study handles the assessment and modelling of the microbiological quality data of 2149 seawater bathing areas in Greece over 10-year period (1997-2006) by chemometric methods. Results. Cluster analysis results indicated that the studied bathing beaches are classified in accordance with the seasonality in three groups. Factor analysis was applied to investigate possible determining factors in the groups resulted from the cluster analysis, and also two new parameters were created in each group; VF1 includes E. coli, faecal coliforms and total coliforms and VF2 includes faecal streptococci/enterococci. By applying the cluster analysis in each seasonal group, three new groups of coasts were generated, group A (ultraclean), group B (clean) and group C (contaminated). Conclusions. The above analysis is confirmed by the application of discriminant analysis, and proves that chemometric methods are useful tools for assessment and modeling microbiological quality data of coastal bathing water on a large scale, and thus could attribute to effective and economical monitoring of the quality of coastal bathing water in a country with a big number of bathing coasts, like Greece

Cooperative Simultaneous Tracking and Jamming for Disabling a Rogue Drone

This work investigates the problem of simultaneous tracking and jamming of a rogue drone in 3D space with a team of cooperative unmanned aerial vehicles (UAVs). We propose a decentralized estimation, decision and control framework in which a team of UAVs cooperate in order to a) optimally choose their mobility control actions that result in accurate target tracking and b) select the desired transmit power levels which cause uninterrupted radio jamming and thus ultimately disrupt the operation of the rogue drone. The proposed decision and control framework allows the UAVs to reconfigure themselves in 3D space such that the cooperative simultaneous tracking and jamming (CSTJ) objective is achieved; while at the same time ensures that the unwanted inter-UAV jamming interference caused during CSTJ is kept below a specified critical threshold. Finally, we formulate this problem under challenging conditions i.e., uncertain dynamics, noisy measurements and false alarms. Extensive simulation experiments illustrate the performance of the proposed approach.Comment: 2020 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS

A step-by-step diagnosis of exclusion in a twin pregnancy with acute respiratory failure due to non-fatal amniotic fluid embolism: a case report

<p>Abstract</p> <p>Introduction</p> <p>Respiratory failure may develop during the later stages of pregnancy and is usually associated with tocolysis or other co-existing conditions such as pneumonia, sepsis, pre-eclampsia or amniotic fluid embolism syndrome.</p> <p>Case presentation</p> <p>We present the case of a 34-year-old healthy woman with a twin pregnancy at 31 weeks and 6 days who experienced acute respiratory failure, a few hours after administration of tocolysis (ritodrine), due to preterm premature rupture of the membranes. Her chest discomfort was significantly ameliorated after the ritodrine infusion was stopped and a Cesarean section was performed 48 hours later under spinal anesthesia; however, 2 hours after surgery she developed severe hypoxemia, hypotension, fever and mild coagulopathy. The patient was intubated and transferred to the intensive care unit where she made a quick and uneventful recovery within 3 days. As there was no evidence for drug- or infection-related thromboembolic or myocardial causes of respiratory failure, we conclude that our patient experienced a rare type of non-fatal amniotic fluid embolism.</p> <p>Conclusion</p> <p>In spite of the lack of solid scientific support for our diagnosis, we conclude that our patient suffered an uncommon type of amniotic fluid embolism syndrome and we believe that this report highlights the need for extreme vigilance and a high index of suspicion for such a diagnosis in any pregnant individual.</p

AgeDB: the first manually collected, in-the-wild age database

Over the last few years, increased interest has arisen with respect to age-related tasks in the Computer Vision community. As a result, several "in-the-wild" databases annotated with respect to the age attribute became available in the literature. Nevertheless, one major drawback of these databases is that they are semi-automatically collected and annotated and thus they contain noisy labels. Therefore, the algorithms that are evaluated in such databases are prone to noisy estimates. In order to overcome such drawbacks, we present in this paper the first, to the best of knowledge, manually collected "in-the-wild" age database, dubbed AgeDB, containing images annotated with accurate to the year, noise-free labels. As demonstrated by a series of experiments utilizing state-of-the-art algorithms, this unique property renders AgeDB suitable when performing experiments on age-invariant face verification, age estimation and face age progression "in-the-wild"

Integrated Ray-Tracing and Coverage Planning Control using Reinforcement Learning

In this work we propose a coverage planning control approach which allows a mobile agent, equipped with a controllable sensor (i.e., a camera) with limited sensing domain (i.e., finite sensing range and angle of view), to cover the surface area of an object of interest. The proposed approach integrates ray-tracing into the coverage planning process, thus allowing the agent to identify which parts of the scene are visible at any point in time. The problem of integrated ray-tracing and coverage planning control is first formulated as a constrained optimal control problem (OCP), which aims at determining the agent's optimal control inputs over a finite planning horizon, that minimize the coverage time. Efficiently solving the resulting OCP is however very challenging due to non-convex and non-linear visibility constraints. To overcome this limitation, the problem is converted into a Markov decision process (MDP) which is then solved using reinforcement learning. In particular, we show that a controller which follows an optimal control law can be learned using off-policy temporal-difference control (i.e., Q-learning). Extensive numerical experiments demonstrate the effectiveness of the proposed approach for various configurations of the agent and the object of interest.Comment: 2022 IEEE 61st Conference on Decision and Control (CDC), 06-09 December 2022, Cancun, Mexic

Distributed Search Planning in 3-D Environments With a Dynamically Varying Number of Agents

In this work, a novel distributed search-planning framework is proposed, where a dynamically varying team of autonomous agents cooperate in order to search multiple objects of interest in three-dimension (3-D). It is assumed that the agents can enter and exit the mission space at any point in time, and as a result the number of agents that actively participate in the mission varies over time. The proposed distributed search-planning framework takes into account the agent dynamical and sensing model, and the dynamically varying number of agents, and utilizes model predictive control (MPC) to generate cooperative search trajectories over a finite rolling planning horizon. This enables the agents to adapt their decisions on-line while considering the plans of their peers, maximizing their search planning performance, and reducing the duplication of work.Comment: IEEE Transactions on Systems, Man, and Cybernetics: Systems, 202