Where am i? scene recognition for mobile robots using audio features

Automatic recognition of unstructured environments is an important problem for mobile robots. We focus on using audio features to recognize different auditory environments, where they are characterized by different types of sounds. The use of audio information provides a complementary means of scene recognition that can effectively augment visual information. In particular, audio can be used toward both the analysis and characterization of the environment at a higher level of abstraction. We begin our investigation of recognizing different auditory environments with the audio information. In this paper, we utilize low-level audio features from a mobile robot and investigate using highlevel features based on spectral analysis for scene characterization, and a recognition system was built to discriminate between different environments based on these audio features found. 1

Autonomous sampling of ocean submesoscale fronts with ocean gliders and numerical model forecasting

Submesoscale fronts arising from mesoscale stirring are ubiquitous in the ocean and have a strong impact on upper-ocean dynamics. This work presents a method for optimizing the sampling of ocean fronts with autonomous vehicles at meso- and submesoscales, based on a combination of numerical forecast and autonomous planning. This method uses a 48-h forecast from a real-time high-resolution data-assimilative primitive equation ocean model, feature detection techniques, and a planner that controls the observing platform. The method is tested in Monterey Bay, off the coast of California, during a 9-day experiment focused on sampling subsurface thermohaline-compensated structures using a Seaglider as the ocean observing platform. Based on model estimations, the sampling “gain,” defined as the magnitude of isopycnal tracer variability sampled, is 50% larger in the feature-chasing case with respect to a non-feature-tracking scenario. The ability of the model to reproduce, in space and time, thermohaline submesoscale features is evaluated by quantitatively comparing the model and glider results. The model reproduces the vertical (~50–200 m thick) and lateral (~5–20 km) scales of subsurface subducting fronts and near-bottom features observed in the glider data. The differences between model and glider data are, in part, attributed to the selected glider optimal interpolation parameters and to uncertainties in the forecasting of the location of the structures. This method can be exported to any place in the ocean where high-resolution data-assimilative model output is available, and it allows for the incorporation of multiple observing platforms

Autonomous sampling of ocean submesoscale fronts with ocean gliders and numerical model forecasting

Submesoscale fronts arising from mesoscale stirring are ubiquitous in the ocean and have a strong impact on upper-ocean dynamics. This work presents a method for optimizing the sampling of ocean fronts with autonomous vehicles at meso- and submesoscales, based on a combination of numerical forecast and autonomous planning. This method uses a 48-h forecast from a real-time high-resolution data-assimilative primitive equation ocean model, feature detection techniques, and a planner that controls the observing platform. The method is tested in Monterey Bay, off the coast of California, during a 9-day experiment focused on sampling subsurface thermohaline-compensated structures using a Seaglider as the ocean observing platform. Based on model estimations, the sampling “gain,” defined as the magnitude of isopycnal tracer variability sampled, is 50% larger in the feature-chasing case with respect to a non-feature-tracking scenario. The ability of the model to reproduce, in space and time, thermohaline submesoscale features is evaluated by quantitatively comparing the model and glider results. The model reproduces the vertical (~50–200 m thick) and lateral (~5–20 km) scales of subsurface subducting fronts and near-bottom features observed in the glider data. The differences between model and glider data are, in part, attributed to the selected glider optimal interpolation parameters and to uncertainties in the forecasting of the location of the structures. This method can be exported to any place in the ocean where high-resolution data-assimilative model output is available, and it allows for the incorporation of multiple observing platforms

Satellites to Seafloor: Toward Fully Autonomous Ocean Sampling

Future ocean observing systems will rely heavily on autonomous vehicles to achieve the persistent and heterogeneous measurements needed to understand the ocean’s impact on the climate system. The day-to-day maintenance of these arrays will become increasingly challenging if significant human resources, such as manual piloting, are required. For this reason, techniques need to be developed that permit autonomous determination of sampling directives based on science goals and responses to in situ, remote-sensing, and model-derived information. Techniques that can accommodate large arrays of assets and permit sustained observations of rapidly evolving ocean properties are especially needed for capturing interactions between physical circulation and biogeochemical cycling. Here we document the first field program of the Satellites to Seafloor project, designed to enable a closed loop of numerical model prediction, vehicle path-planning, in situ path implementation, data collection, and data assimilation for future model predictions. We present results from the first of two field programs carried out in Monterey Bay, California, over a period of three months in 2016. While relatively modest in scope, this approach provides a step toward an observing array that makes use of multiple information streams to update and improve sampling strategies without human intervention

Satellites to seafloor : toward fully autonomous ocean sampling

Author Posting. © The Oceanography Society, 2017. This article is posted here by permission of The Oceanography Society for personal use, not for redistribution. The definitive version was published in Oceanography 30, no. 2 (2017): 160–168, doi:10.5670/oceanog.2017.238.Future ocean observing systems will rely heavily on autonomous vehicles to achieve the persistent and heterogeneous measurements needed to understand the ocean’s impact on the climate system. The day-to-day maintenance of these arrays will become increasingly challenging if significant human resources, such as manual piloting, are required. For this reason, techniques need to be developed that permit autonomous determination of sampling directives based on science goals and responses to in situ, remote-sensing, and model-derived information. Techniques that can accommodate large arrays of assets and permit sustained observations of rapidly evolving ocean properties are especially needed for capturing interactions between physical circulation and biogeochemical cycling. Here we document the first field program of the Satellites to Seafloor project, designed to enable a closed loop of numerical model prediction, vehicle path-planning, in situ path implementation, data collection, and data assimilation for future model predictions. We present results from the first of two field programs carried out in Monterey Bay, California, over a period of three months in 2016. While relatively modest in scope, this approach provides a step toward an observing array that makes use of multiple information streams to update and improve sampling strategies without human intervention.This work is funded by the Keck Institute for Space Studies (generously supported by the W.M. Keck Foundation) through the project “Science-driven Autonomous and Heterogeneous Robotic Networks: A Vision for Future Ocean Observation

Autonomous sampling of ocean submesoscale fronts with ocean gliders and numerical model forecasting

Submesoscale fronts arising from mesoscale stirring are ubiquitous in the ocean and have a strong impact on upper-ocean dynamics. This work presents a method for optimizing the sampling of ocean fronts with autonomous vehicles at meso- and submesoscales, based on a combination of numerical forecast and autonomous planning. This method uses a 48-h forecast from a real-time high-resolution data-assimilative primitive equation ocean model, feature detection techniques, and a planner that controls the observing platform. The method is tested in Monterey Bay, off the coast of California, during a 9-day experiment focused on sampling subsurface thermohaline-compensated structures using a Seaglider as the ocean observing platform. Based on model estimations, the sampling “gain,” defined as the magnitude of isopycnal tracer variability sampled, is 50% larger in the feature-chasing case with respect to a non-feature-tracking scenario. The ability of the model to reproduce, in space and time, thermohaline submesoscale features is evaluated by quantitatively comparing the model and glider results. The model reproduces the vertical (~50–200 m thick) and lateral (~5–20 km) scales of subsurface subducting fronts and near-bottom features observed in the glider data. The differences between model and glider data are, in part, attributed to the selected glider optimal interpolation parameters and to uncertainties in the forecasting of the location of the structures. This method can be exported to any place in the ocean where high-resolution data-assimilative model output is available, and it allows for the incorporation of multiple observing platforms

Satellites to Seafloor: Toward Fully Autonomous Ocean Sampling

Future ocean observing systems will rely heavily on autonomous vehicles to achieve the persistent and heterogeneous measurements needed to understand the ocean’s impact on the climate system. The day-to-day maintenance of these arrays will become increasingly challenging if significant human resources, such as manual piloting, are required. For this reason, techniques need to be developed that permit autonomous determination of sampling directives based on science goals and responses to in situ, remote-sensing, and model-derived information. Techniques that can accommodate large arrays of assets and permit sustained observations of rapidly evolving ocean properties are especially needed for capturing interactions between physical circulation and biogeochemical cycling. Here we document the first field program of the Satellites to Seafloor project, designed to enable a closed loop of numerical model prediction, vehicle path-planning, in situ path implementation, data collection, and data assimilation for future model predictions. We present results from the first of two field programs carried out in Monterey Bay, California, over a period of three months in 2016. While relatively modest in scope, this approach provides a step toward an observing array that makes use of multiple information streams to update and improve sampling strategies without human intervention

Edge states in a two-dimensional honeycomb lattice of massive magnetic skyrmions

We study the collective dynamics of a two-dimensional honeycomb lattice of magnetic skyrmions. By performing large-scale micromagnetic simulations, we find multiple chiral and non-chiral edge modes of skyrmion oscillations in the lattice. The non-chiral edge states are due to the Tamm-Shockley mechanism, while the chiral ones are topologically protected against structure defects and hold different handednesses depending on the mode frequency. To interpret the emerging multiband nature of the chiral edge states, we generalize the massless Thiele's equation by including a second-order inertial term of skyrmion mass as well as a third-order non-Newtonian gyroscopic term, which allows us to model the band structure of skrymion oscillations. Theoretical results compare well with numerical simulations. Our findings uncover the importance of high order effects in strongly coupled skyrmions and are helpful for designing novel topological devices.Comment: 6 pages,4 figures,accepted by Physical Review B as a Rapid Communicatio

Worldwide surveillance of self-reported sitting time: a scoping review

Abstract: Background: Prolonged sitting time is a risk factor for chronic disease, yet recent global surveillance is not well described. The aims were to clarify: (i) the countries that have collected country-level data on self-reported sitting time; (ii) the single-item tools used to collect these data; and (iii) the duration of sitting time reported across low- to high-income countries. Methods: Country-level data collected within the last 10 years using single-item self-report were included. The six-stage methodology: (1) reviewing Global Observatory for Physical Activity! Country Cards; (2–4) country-specific searches of PubMed, the Demographic and Health Survey website and Google; (5) analysing the Eurobarometer 88.4; and (6) country-specific searches for World Health Organization STEPwise reports. Results: A total of 7641 records were identified and screened for eligibility. Sixty-two countries (29%) reported sitting time representing 47% of the global adult population. The majority of data were from high-income (61%) and middle income (29%) countries. The tools used were the International Physical Activity Questionnaire (IPAQ; n = 34), a modified IPAQ (n = 1) or the Global Physical Activity Questionnaire (GPAQ; n = 27). The median of mean daily sitting times was 4.7 (IQR: 3.5–5.1) hours across all countries. Higher-income countries recorded a longer duration of sitting time than lower-income countries (4.9 vs 2.7 h). Conclusions: This study provides an updated collation of countries collecting self-reported sitting time data. The daily sitting time findings should be interpreted cautiously. Current surveillance of sitting time is limited by a lack of coverage. Measures of population sitting time that are valid, feasible and sensitive to change should be embedded within global surveillance systems, to help guide future policy, research and practice. Trial registration: Not applicable