Cardiac Autonomic Function in Adults Born Preterm

Objective To evaluate cardiac autonomic function in adults born preterm. Study design We studied the association between prematurity and cardiac autonomic function using heart rate variability measurements in 600 adults (mean age of 23.3 years) from a geographically based cohort in Northern Finland. There were 117 young adults born early preterm (= 37 weeks, controls). Autonomic function was analyzed by calculating time and frequency domain heart rate variability measurements using linear regression. Results Compared with controls, the mean difference in root mean square of successive differences (indicating cardiac vagal activity) was -12.0% (95% CI -22.2%, -0.5%, adjusted for sex, age, source cohort, and season P = .04) for the early preterm group and -7.8% (-16.8%, 2.0%, P = .12) for the late preterm group. Mean differences with controls in low frequency power (indicating cardiac vagal activity, including some sympathetic- and baroreflex-mediated effects) were -13.6% (-26.7%, 1.8%, P = .08) for the early pretermgroup and -16.4% (-27.0%, -4.3%, P = .01) for the late preterm group. Mean differences in high frequency power (quantifying cardiac vagal modulation in respiratory frequency) were -19.2% (-36.6%, 2.9%, P = .09) for the early preterm group and -13.8% (-29.4%, 5.3%, P = .15) for the late preterm group. Differences were attenuated when controlled for body mass index and physical activity. Conclusions Our results suggest altered autonomic regulatory control in adults born preterm, including those born late preterm. Altered autonomic regulatory control may contribute to increased cardiovascular risk in adults born preterm.Peer reviewe

Postexercise Heart Rate Recovery in Adults Born Preterm

Objective To evaluate postexercise heart rate recovery (HRR) in adults born preterm. Study design We studied the association between preterm birth and postexercise HRR in 545 adults (267 women) at 23.3 years of age (range 19.9-26.3 years). One hundred three participants were born early preterm ( Results Mean peak HR was 159.5 bpm in the early preterm (P = .16 with controls), 157.8 bpm in the late preterm (P = .56), and 157.0 bpm in the control group. Mean HRR 30 seconds after exercise was 3.2 bpm (95% CI 1.1-5.2) lower in the early preterm group and 2.1 bpm (0.3-3.8) lower in the late preterm group than the full term controls. Mean 60s HRR was 2.5 (-0.1 to 5.1) lower in the early preterm group and 2.8 bpm (0.6-4.9) lower in the late preterm group. Mean maximum slope after exercise was 0.10 beats/s (0.02-0.17) lower in the early preterm group and 0.06 beats/s (0.00-0.12) lower in the late preterm group. Conclusions Our results suggest reduced HRR after exercise in adults born preterm, including those born late preterm. This suggests altered reactivation of the parasympathetic nervous system, which may contribute to cardiovascular risk among adults born preterm.Peer reviewe

Unmanned Aircraft Systems - Education Activities in Finland, UCNDrone Perspective

Non peer reviewe

A Distributed Architecture for Executing Complex Tasks with Multiple Robots

Abstract — This paper presents a software architecture for the network-transparent control of distributed robotic systems. The system consists of two main components: a generic and easily extensible CORBA-based interface to distributed services, and a high-level XML-based description language for specifying the behavior of the robots. The architecture makes it possible to create dynamically modifiable, extensible control software with ease. It is successfully utilized in implementing a coffee serving system in which the co-operation of two very different robots and two other distributed services are needed. I

All-around 3D reconstruction from spherical images with semantic segmentation

Abstract Lightweight, affordable spherical cameras can be utilized in mobile robotics to build a 3D map of the working environment of a robot. This paper demonstrates how to use the optical flow between two spherical images to quickly construct a semantically meaningful 3D model spanning all directions. The main contribution of the paper is the use of semantic segmentation to increase the robustness of the reconstruction method in both indoor and outdoor applications

Garbot — Semantic Segmentation for Material Recycling and 3D Reconstruction Utilizing Robotics

Abstract Semantic segmentation directly from the images of landfills can be utilized in the earth movers to segregate the garbage autonomously. Generally, Various segregation methods are available for garbage segregation such as IOT based waste segregation, Conveyor belt segregation in which none of them are directly from landfills. Semantic segmentation is one of the important tasks that maps the path towards the complete scene understanding. The aim of this paper is to present a smart segregation method for garbage by using semantic segmentation with DeepLab V3+ Model using the framework(Backbone model) of Xception-65 with the mean accuracy of 75.01%. This paper features the segmentation with the GarbotV1dataset which has major classifications such as Plastic, Cart-board, Wood, Metal, Sponge. The paper also contributes a method for reconstructing the segmented images to build a 3D map and this exploits the use of earth moving vehicles to navigate autonomously by localizing the segmented objects

Development of task-oriented ROS-based autonomous UGV with 3D object detection

Abstract In a scenario where fire accidents takes place the priority is always human safety and acting swiftly to contain the fire from further spreading. The modern autonomous systems can promise both human safety and can perform actions rapidly. One such scenario which is motivated by urban firefighting was designed in challenge 3 of MBZIRC 2020 competition. In this challenge the UAV’s and UGV collaborate autonomously to detect the fire and quench the flames with water. So, in this project we have developed Robot Operating System (ROS)-based autonomous system to solve the challenge for UGV criteria by detecting targeted objects in real-time, in our case its simulated fire and red colored softballs. Then finally localize those targets as markers in the map and navigate autonomously to all those targets. This work has two sections, in the first section mapping and localizing the fire and softballs in highly cluttered environment and then reaching those targets autonomously. Robustly mapping the area with adequate sensors and detection of targets with optimally trained CNN based network is the key to localizing of the targeted objects in a highly cluttered environments