Real-time object detection and tracking in mixed reality using Microsoft HoloLens

This paper presents a mixed reality system that, using the sensors mounted on the Microsoft Hololens headset and a cloud service, acquires and processes in real-time data to detect and track different kinds of objects and finally superimposes geographically coherent holographic texts on the detected objects. Such a goal has been achieved dealing with the intrinsic headset hardware limitations, by performing part of the overall computation in an edge/cloud environment. In particular, the heavier object detection algorithms, based on Deep Neural Networks (DNNs), are executed in the cloud. At the same time, we compensate for cloud transmission and computation latencies by running light scene detection and object tracking onboard the headset. The proposed pipeline allows meeting the real-time constraint by exploiting at the same time the power of state of art DNNs and the potential of Microsoft Hololens. This paper presents the design choices and describes the original algorithmic steps we devised to achieve real-time tracking in mixed reality. Finally, the proposed system is experimentally validated

A Machine Learning Approach to Monitor Air Quality from Traffic and Weather data

Knowing the amount of air pollutants in our cities is of great importance to help decision makers in the definition of effective strategies aimed at maintaining a good air quality, which is a key factor for a healthy life, especially in urban environments. Using a data set from a big metropolitan city, we realize the uAQE: urban Air Quality Evaluator, which is a supervised machine learning model able to estimate air pollutants values using only weather and traffic data. We evaluate the performance of our solution by comparing the predicted pollutant values with the real measurements provided by professional air monitoring stations. We use the predicted pollutants to compute a standard Air Quality Index (AQI) and we map it into a set of five qualitative AQI classes, which can be used for decision making at the city level. uAQE is able to predict the AQI class value with an accuracy of 0.8

Adaptation in toxic environments: Arsenic genomic islands in the bacterial genus Thiomonas:

Acid mine drainage (AMD) is a highly toxic environment for most living organisms due to the presence of many lethal elements including arsenic (As). Thiomonas (Tm.) bacteria are found ubiquitously in AMD and can withstand these extreme conditions, in part because they are able to oxidize arsenite. In order to further improve our knowledge concerning the adaptive capacities of these bacteria, we sequenced and assembled the genome of six isolates derived from the Carnoulès AMD, and compared them to the genomes of Tm. arsenitoxydans 3As (isolated from the same site) and Tm. intermedia K12 (isolated from a sewage pipe). A detailed analysis of the Tm. sp. CB2 genome revealed various rearrangements had occurred in comparison to what was observed in 3As and K12 and over 20 genomic islands (GEIs) were found in each of these three genomes. We performed a detailed comparison of the two arsenic-related islands found in CB2, carrying the genes required for arsenite oxidation and As resistance, with those found in K12, 3As, and five other Thiomonas strains also isolated from Carnoulès (CB1, CB3, CB6, ACO3 and ACO7). Our results suggest that these arsenic-related islands have evolved differentially in these closely related Thiomonas strains, leading to divergent capacities to survive in As rich environments

A machine learning approach to GNSS scintillation detection: automatic soft inspection of the events

Classical approaches for the automatic detection of ionospheric scintillation events in Global Navigation Satellite System (GNSS) receivers are based on the observation of indices (e.g. S4) that are obtained by processing parameters assessed at the signal processing stages of the receiver. Such values are the result of algorithms that imply specific processing choices (such as detrending, averaging and threshold operations) which influence the final performance of the detection. To reach good levels of accuracy and generalization for the identification and classification of the physical phenomenon, these approaches may require an additional human effort to refine the detection results by means of a manual inspection of the events, which is expensive and time consuming. This paper proposes a new methodology for the detection of ionospheric scintillation events based on Machine Learning techniques applied to GNSS data. This method, based on Decision Trees algorithms, aims at overcoming the limitation of the classical approaches by identifying scintillation events “as if” done by a human operator through visual inspection. This approach is automatic, unbound from traditional scintillation indices and features improved detection, false alarm, and missed detection rates when compared to standard methods

uAQE: Urban Air Quality Evaluator

Knowing the amount of air pollutants in our cities is of great importance to help decision-makers in the definition of effective strategies aimed at maintaining a good air quality, which is a key factor for a healthy life, especially in urban environments. Using a data set from a big metropolitan city, we realize the uAQE: urban Air Quality Evaluator, which is a supervised machine learning model able to estimate air pollutants values using only weather and traffic data. We evaluate the performance of our solution by comparing the predicted pollutant values with the real measurements provided by professional air monitoring stations. We use the predicted pollutants to compute a standard Air Quality Index (AQI) and we map it into a set of five qualitative AQI classes, which can be used for decision making at the city level. uAQE is able to predict the AQI class value with an accuracy of 0.8

Ferriphaselus amnicola strain GF-20, a new iron- and thiosulfate-oxidizing bacterium isolated from a hard rock aquifer

International audienceFerriphaselus amnicola GF-20 is the first Fe-oxidizing bacterium isolated from the continental subsurface. It was isolated from groundwater circulating at 20 m depth in the fractured-rock catchment observatory of Guidel-Ploemeur (France). Strain GF-20 is a neutrophilic, iron- and thiosulfate-oxidizer and grows autotrophically. The strain shows a preference for low oxygen concentrations, which suggests an adaptation to the limiting oxygen conditions of the subsurface. It produces extracellular stalks and dreads when grown with Fe(II) but does not secrete any structure when grown with thiosulfate. Phylogenetic analyses and genome comparisons revealed that strain GF-20 is affiliated with the species F. amnicola and is strikingly similar to F. amnicola strain OYT1, which was isolated from a groundwater seep in Japan. Based on the phenotypic and phylogenetic characteristics, we propose that GF-20 represents a new strain within the species F. amnicola

Spatio-Temporal Detection of the Thiomonas Population and the Thiomonas Arsenite Oxidase Involved in Natural Arsenite Attenuation Processes in the Carnoulès Acid Mine Drainage

International audienceThe acid mine drainage (AMD) impacted creek of the Carnoulès mine (Southern France) is characterized by acid waters with a high heavy metal content. The microbial community inhabiting this AMD was extensively studied using isolation, metagenomic and metaproteomic methods, and the results showed that a natural arsenic (and iron) attenuation process involving the arsenite oxidase activity of several Thiomonas strains occurs at this site. A sensitive quantitative Selected Reaction Monitoring (SRM)-based proteomic approach was developed for detecting and quantifying the two subunits of the arsenite oxidase and RpoA of two different Thiomonas groups. Using this approach combined with 16S rRNA gene sequence analysis based on pyrosequencing and FISH, it was established here for the first time that these Thiomonas strains are ubiquitously present in minor proportions in this AMD and that they express the key enzymes involved in natural remediation processes at various locations and time points. In addition to these findings, this study also confirms that targeted proteomics applied at the community level can be used to detect weakly abundant proteins in situ