A Machine Learning Approach to Robot Localization Using Fiducial Markers in RobotAtFactory 4.0 Competition.

Localization is a crucial skill in mobile robotics because the robot needs to make reasonable navigation decisions to complete its mission. Many approaches exist to implement localization, but artificial intelligence can be an interesting alternative to traditional localization techniques based on model calculations. This work proposes a machine learning approach to solve the localization problem in the RobotAtFactory 4.0 competition. The idea is to obtain the relative pose of an onboard camera with respect to fiducial markers (ArUcos) and then estimate the robot pose with machine learning. The approaches were validated in a simulation. Several algorithms were tested, and the best results were obtained by using Random Forest Regressor, with an error on the millimeter scale. The proposed solution presents results as high as the analytical approach for solving the localization problem in the RobotAtFactory 4.0 scenario, with the advantage of not requiring explicit knowledge of the exact positions of the fiducial markers, as in the analytical approach.

Optimizing Resources and Increasing the Coverage of Internet-of-Things (IoT) Networks: An Approach Based on LoRaWAN.

A resource optimization methodology is proposed for application in long range wide area networks (LoRaWANs). Using variable neighborhood search (VNS) and a minimum-cost spanning tree algorithm, it reduces the implementation and the maintenance costs of such low power networks. Performance evaluations were conducted in LoRaWANs with LoRa repeaters to increase coverage, in scenario where the number and the location of the repeaters are determined by the VNS metaheuristic. Parameters such as spread factor (SF), bandwidth and transmission power were adjusted to minimize the network's total energy per useful bit (Ebit) and the total data collection time. The importance of the SF in the trade-off between (Ebit) and time on-air is evaluated, considering a device scaling factor. Simulation results, obtained after model adjustments with experimental data, show that, in networks with few associated devices, there is a preference for small values of SF aiming at reduction of Ebit. The usage of large SF's becomes relevant when reach extensions are required. The results also demonstrate that, for networks with high number of nodes, the scaling of devices over time become relevant in the fitness function, forcing an equal distribution of time slots per SF to avoid discrepancies in the time data collection.

Thermal comfort in urban green spaces: a survey on a Dutch university campus.

To better understand the influence of urban green infrastructure (UGI) on outdoor human thermal comfort, a survey and physical measurements were performed at the campus of the University of Groningen, The Netherlands, in spring and summer 2015. Three hundred eighty-nine respondents were interviewed in five different green spaces. We aimed to analyze people's thermal comfort perception and preference in outdoor urban green spaces, and to specify the combined effects between the thermal environmental and personal factors. The results imply that non-physical environmental and subjective factors (e.g., natural view, quiet environment, and emotional background) were more important in perceiving comfort than the actual thermal conditions. By applying a linear regression and probit analysis, the comfort temperature was found to be 22.2 °C and the preferred temperature was at a surprisingly high 35.7 °C. This can be explained by the observation that most respondents, who live in temperate regions, have a natural tendency to describe their preferred state as "warmer" even when feeling "warm" already. Using the Kruskal-Wallis H test, the four significant factors influencing thermal comfort were people's exposure time in green spaces, previous thermal environment and activity, and their thermal history. However, the effect of thermal history needs further investigation due to the unequal sample sizes of respondents from different climate regions. By providing evidence for the role of the objective and subjective factors on human thermal comfort, the relationship between UGI, microclimate, and thermal comfort can assist urban planning to make better use of green spaces for microclimate regulation.

Assessment of the masking effects of birdsong on the road traffic noise environment.

This study aims to explore how the soundscape quality of traffic noise environments can be improved by the masking effects of birdsong in terms of four soundscape characteristics, i.e., perceived loudness, naturalness, annoyance and pleasantness. Four factors that may influence the masking effects of birdsong (i.e., distance of the receiver from a sound source, loudness of masker, occurrence frequencies of masker, and visibility of sound sources) were examined by listening tests. The results show that the masking effects are more significant in the road traffic noise environments with lower sound levels (e.g., <52.5 dBA), or of distance from traffic (e.g., >19 m). Adding birdsong can indeed increase the naturalness and pleasantness of the traffic noise environment at different distances of the receiver from a road. Naturalness, annoyance, and pleasantness, but not perceived loudness, can be altered by increasing the birdsong loudness (i.e., from 37.5 to 52.5 dBA in this study). The pleasantness of traffic noise environments increases significantly from 2.7 to 6.7, when the occurrence of birdsong over a period of 30 s is increased from 2 to 6 times. The visibility of the sound source also influences the masking effects, but its effect is not as significant as the effects of the three other factors.

Effects of urban green infrastructure (UGI) on local outdoor microclimate during the growing season.

This study analyzed how the variations of plant area index (PAI) and weather conditions alter the influence of urban green infrastructure (UGI) on microclimate. To observe how diverse UGIs affect the ambient microclimate through the seasons, microclimatic data were measured during the growing season at five sites in a local urban area in The Netherlands. Site A was located in an open space; sites B, C, and D were covered by different types and configurations of green infrastructure (grove, a single deciduous tree, and street trees, respectively); and site E was adjacent to buildings to study the effects of their façades on microclimate. Hemispherical photography and globe thermometers were used to quantify PAI and thermal comfort at both shaded and unshaded locations. The results showed that groves with high tree density (site B) have the strongest effect on microclimate conditions. Monthly variations in the differences of mean radiant temperature (∆Tmrt) between shaded and unshaded areas followed the same pattern as the PAI. Linear regression showed a significant positive correlation between PAI and ∆Tmrt. The difference of daily average air temperature (∆T a ) between shaded and unshaded areas was also positively correlated to PAI, but with a slope coefficient below the measurement accuracy (±0.5 °C). This study showed that weather conditions can significantly impact the effectiveness of UGI in regulating microclimate. The results of this study can support the development of appropriate UGI measures to enhance thermal comfort in urban areas.

Quantifying Postural Control during Exergaming Using Multivariate Whole-Body Movement Data: A Self-Organizing Maps Approach.

BACKGROUND: Exergames are becoming an increasingly popular tool for training balance ability, thereby preventing falls in older adults. Automatic, real time, assessment of the user's balance control offers opportunities in terms of providing targeted feedback and dynamically adjusting the gameplay to the individual user, yet algorithms for quantification of balance control remain to be developed. The aim of the present study was to identify movement patterns, and variability therein, of young and older adults playing a custom-made weight-shifting (ice-skating) exergame. METHODS: Twenty older adults and twenty young adults played a weight-shifting exergame under five conditions of varying complexity, while multi-segmental whole-body movement data were captured using Kinect. Movement coordination patterns expressed during gameplay were identified using Self Organizing Maps (SOM), an artificial neural network, and variability in these patterns was quantified by computing Total Trajectory Variability (TTvar). Additionally a k Nearest Neighbor (kNN) classifier was trained to discriminate between young and older adults based on the SOM features. RESULTS: Results showed that TTvar was significantly higher in older adults than in young adults, when playing the exergame under complex task conditions. The kNN classifier showed a classification accuracy of 65.8%. CONCLUSIONS: Older adults display more variable sway behavior than young adults, when playing the exergame under complex task conditions. The SOM features characterizing movement patterns expressed during exergaming allow for discriminating between young and older adults with limited accuracy. Our findings contribute to the development of algorithms for quantification of balance ability during home-based exergaming for balance training.

Suitability of Kinect for measuring whole body movement patterns during exergaming.

Exergames provide a challenging opportunity for home-based training and evaluation of postural control in the elderly population, but affordable sensor technology and algorithms for assessment of whole body movement patterns in the home environment are yet to be developed. The aim of the present study was to evaluate the use of Kinect, a commonly available video game sensor, for capturing and analyzing whole body movement patterns. Healthy adults (n=20) played a weight shifting exergame under five different conditions with varying amplitudes and speed of sway movement, while 3D positions of ten body segments were recorded in the frontal plane using Kinect and a Vicon 3D camera system. Principal Component Analysis (PCA) was used to extract and compare movement patterns and the variance in individual body segment positions explained by these patterns. Using the identified patterns, balance outcome measures based on spatiotemporal sway characteristics were computed. The results showed that both Vicon and Kinect capture >90% variance of all body segment movements within three PCs. Kinect-derived movement patterns were found to explain variance in trunk movements accurately, yet explained variance in hand and foot segments was underestimated and overestimated respectively by as much as 30%. Differences between both systems with respect to balance outcome measures range 0.3-64.3%. The results imply that Kinect provides the unique possibility of quantifying balance ability while performing complex tasks in an exergame environment.

Pyridyl-1,2,4-triazole diphenyl boron complexes as efficient tuneable blue emitters.

The detection of nuclear radiation necessitates the availability of new generations of tunable blue emitting fluorophores with high emission quantum yields. Here we show that pyridyl-1,2,4-triazole based diphenyl boron complexes can provide highly tuneable emission through facile modification of the C5 position of the 1,2,4-triazolato ring. The series of complexes prepared show a wide range of emission from near-UV to green, enabling fine control over the spectral overlap with detectors used in scintillator technology.