13,991 research outputs found

    ShakingBot: Dynamic Manipulation for Bagging

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    Bag manipulation through robots is complex and challenging due to the deformability of the bag. Based on dynamic manipulation strategy, we propose a new framework, ShakingBot, for the bagging tasks. ShakingBot utilizes a perception module to identify the key region of the plastic bag from arbitrary initial configurations. According to the segmentation, ShakingBot iteratively executes a novel set of actions, including Bag Adjustment, Dual-arm Shaking, and One-arm Holding, to open the bag. The dynamic action, Dual-arm Shaking, can effectively open the bag without the need to account for the crumpled configuration.Then, we insert the items and lift the bag for transport. We perform our method on a dual-arm robot and achieve a success rate of 21/33 for inserting at least one item across various initial bag configurations. In this work, we demonstrate the performance of dynamic shaking actions compared to the quasi-static manipulation in the bagging task. We also show that our method generalizes to variations despite the bag's size, pattern, and color.Comment: Manipulating bag through robots to baggin

    Event-based tracking of human hands

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    This paper proposes a novel method for human hands tracking using data from an event camera. The event camera detects changes in brightness, measuring motion, with low latency, no motion blur, low power consumption and high dynamic range. Captured frames are analysed using lightweight algorithms reporting 3D hand position data. The chosen pick-and-place scenario serves as an example input for collaborative human-robot interactions and in obstacle avoidance for human-robot safety applications. Events data are pre-processed into intensity frames. The regions of interest (ROI) are defined through object edge event activity, reducing noise. ROI features are extracted for use in-depth perception. Event-based tracking of human hand demonstrated feasible, in real time and at a low computational cost. The proposed ROI-finding method reduces noise from intensity images, achieving up to 89% of data reduction in relation to the original, while preserving the features. The depth estimation error in relation to ground truth (measured with wearables), measured using dynamic time warping and using a single event camera, is from 15 to 30 millimetres, depending on the plane it is measured. Tracking of human hands in 3D space using a single event camera data and lightweight algorithms to define ROI features (hands tracking in space)

    Audio-Visual Automatic Speech Recognition Towards Education for Disabilities

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    Education is a fundamental right that enriches everyone’s life. However, physically challenged people often debar from the general and advanced education system. Audio-Visual Automatic Speech Recognition (AV-ASR) based system is useful to improve the education of physically challenged people by providing hands-free computing. They can communicate to the learning system through AV-ASR. However, it is challenging to trace the lip correctly for visual modality. Thus, this paper addresses the appearance-based visual feature along with the co-occurrence statistical measure for visual speech recognition. Local Binary Pattern-Three Orthogonal Planes (LBP-TOP) and Grey-Level Co-occurrence Matrix (GLCM) is proposed for visual speech information. The experimental results show that the proposed system achieves 76.60 % accuracy for visual speech and 96.00 % accuracy for audio speech recognition

    Kurcuma: a kitchen utensil recognition collection for unsupervised domain adaptation

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    The use of deep learning makes it possible to achieve extraordinary results in all kinds of tasks related to computer vision. However, this performance is strongly related to the availability of training data and its relationship with the distribution in the eventual application scenario. This question is of vital importance in areas such as robotics, where the targeted environment data are barely available in advance. In this context, domain adaptation (DA) techniques are especially important to building models that deal with new data for which the corresponding label is not available. To promote further research in DA techniques applied to robotics, this work presents Kurcuma (Kitchen Utensil Recognition Collection for Unsupervised doMain Adaptation), an assortment of seven datasets for the classification of kitchen utensils—a task of relevance in home-assistance robotics and a suitable showcase for DA. Along with the data, we provide a broad description of the main characteristics of the dataset, as well as a baseline using the well-known domain-adversarial training of neural networks approach. The results show the challenge posed by DA on these types of tasks, pointing to the need for new approaches in future work.Open Access funding provided thanks to the CRUE-CSIC agreement with Springer Nature. This work was supported by the I+D+i project TED2021-132103A-I00 (DOREMI), funded by MCIN/AEI/10.13039/501100011033. Some of the computing resources were provided by the Generalitat Valenciana and the European Union through the FEDER funding program (IDIFEDER/2020/003). The second author is supported by grant APOSTD/2020/256 from “Programa I+D+i de la Generalitat Valenciana”

    A real-time smart sensing system for automatic localization and recognition of vegetable plants for weed control

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    Tomato is a globally grown vegetable crop with high economic and nutritional values. Tomato production is being threatened by weeds. This effect is more pronounced in the early stages of tomato plant growth. Thus weed management in the early stages of tomato plant growth is very critical. The increasing labor cost of manual weeding and the negative impact on human health and the environment caused by the overuse of herbicides are driving the development of smart weeders. The core task that needs to be addressed in developing a smart weeder is to accurately distinguish vegetable crops from weeds in real time. In this study, a new approach is proposed to locate tomato and pakchoi plants in real time based on an integrated sensing system consisting of camera and color mark sensors. The selection scheme of reference, color, area, and category of plant labels for sensor identification was examined. The impact of the number of sensors and the size of the signal tolerance region on the system recognition accuracy was also evaluated. The experimental results demonstrated that the color mark sensor using the main stem of tomato as the reference exhibited higher performance than that of pakchoi in identifying the plant labels. The scheme of applying white topical markers on the lower main stem of the tomato plant is optimal. The effectiveness of the six sensors used by the system to detect plant labels was demonstrated. The computer vision algorithm proposed in this study was specially developed for the sensing system, yielding the highest overall accuracy of 95.19% for tomato and pakchoi localization. The proposed sensor-based system is highly accurate and reliable for automatic localization of vegetable plants for weed control in real time

    The VLT/SPHERE view of the ATOMIUM cool evolved star sample

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    Context. Low- and intermediate-mass asymptotic giant stars and massive red supergiant stars are important contributors to the chemical enrichment of the Universe. They are among the most efficient dust factories of the Galaxy, harboring chemically rich circumstellar environments. Yet, the processes that lead to dust formation or the large-scale shaping of the mass loss still escape attempts at modeling. Aims. Through the ATOMIUM project, we aim to present a consistent view of a sample of 17 nearby cool evolved stars. Our goals are to unveil the dust-nucleation sites and morphologies of the circumstellar envelope of such stars and to probe ambient environments with various conditions. This will further enhance our understanding of the roles of stellar convection and pulsations, and that of companions in shaping the dusty circumstellar medium. Methods. Here we present and analyze VLT/SPHERE-ZIMPOL polarimetric maps obtained in the visible (645–820 nm) of 14 out of the 17 ATOMIUM sources. They were obtained contemporaneously with the ALMA high spatial resolution data. To help interpret the polarized signal, we produced synthetic maps of light scattering by dust, through 3D radiative transfer simulations with the RADMC3D code. Results. The degree of linear polarization (DoLP) observed by ZIMPOL spreads across several optical filters. We infer that it primarily probes dust located just outside of the point spread function of the central source, and in or near the plane of the sky. The polarized signal is mainly produced by structures with a total optical depth close to unity in the line of sight, and it represents only a fraction of the total circumstellar dust. The maximum DoLP ranges from 0.03–0.38 depending on the source, fractions that can be reproduced by our 3D pilot models for grains composed of olivine, melilite, corundum, enstatite, or forsterite. The spatial structure of the DoLP shows a diverse set of shapes, including clumps, arcs, and full envelopes. Only for three sources do we note a correlation between the ALMA CO υ = 0, J = 2−1 and SiO υ = 0, J = 5−4 lines, which trace the gas density, and the DoLP, which traces the dust. Conclusions. The clumpiness of the DoLP and the lack of a consistent correlation between the gas and the dust location show that, in the inner environment, dust formation occurs at very specific sites. This has potential consequences for the derived mass-loss rates and dust-to-gas ratio in the inner region of the circumstellar environment. Except for π1 Gru and perhaps GY Aql, we do not detect interactions between the circumstellar wind and the hypothesized companions that shape the wind at larger scales. This suggests that the orbits of any other companions are tilted out of the plane of the sky

    Reinforcing optimization enabled interactive approach for liver tumor extraction in computed tomography images

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    Detecting liver abnormalities is a difficult task in radiation planning and treatment. The modern development integrates medical imaging into computer techniques. This advancement has monumental effect on how medical images are interpreted and analyzed. In many circumstances, manual segmentation of liver from computerized tomography (CT) imaging is imperative, and cannot provide satisfactory results. However, there are some difficulties in segmenting the liver due to its uneven shape, fuzzy boundary and complicated structure. This leads to necessity of enabling optimization in interactive segmentation approach. The main objective of reinforcing optimization is to search the optimal threshold and reduce the chance of falling into local optimum with survival of the fittest (SOF) technique. The proposed methodology makes use of pre-processing stage and reinforcing meta heuristics optimization based fuzzy c-means (FCM) for obtaining detailed information about the image. This information gives the optimal threshold value that is used for segmenting the region of interest with minimum user input. Suspicious areas are recognized from the segmented output. Both public and simulated dataset have been taken for experimental purposes. To validate the effectiveness of the proposed strategy, performance criteria such as dice coefficient, mode and user interaction level are taken and compared with state-of-the-art algorithms

    SOFIA and ALMA Investigate Magnetic Fields and Gas Structures in Massive Star Formation: The Case of the Masquerading Monster in BYF 73

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    We present SOFIA+ALMA continuum and spectral-line polarisation data on the massive molecular cloud BYF 73, revealing important details about the magnetic field morphology, gas structures, and energetics in this unusual massive star formation laboratory. The 154μ\mum HAWC+ polarisation map finds a highly organised magnetic field in the densest, inner 0.55×\times0.40 pc portion of the cloud, compared to an unremarkable morphology in the cloud's outer layers. The 3mm continuum ALMA polarisation data reveal several more structures in the inner domain, including a pc-long, \sim500 M_{\odot} "Streamer" around the central massive protostellar object MIR 2, with magnetic fields mostly parallel to the east-west Streamer but oriented north-south across MIR 2. The magnetic field orientation changes from mostly parallel to the column density structures to mostly perpendicular, at thresholds NcritN_{\rm crit} = 6.6×\times1026^{26} m2^{-2}, ncritn_{\rm crit} = 2.5×\times1011^{11} m3^{-3}, and BcritB_{\rm crit} = 42±\pm7 nT. ALMA also mapped Goldreich-Kylafis polarisation in 12^{12}CO across the cloud, which traces in both total intensity and polarised flux, a powerful bipolar outflow from MIR 2 that interacts strongly with the Streamer. The magnetic field is also strongly aligned along the outflow direction; energetically, it may dominate the outflow near MIR 2, comprising rare evidence for a magnetocentrifugal origin to such outflows. A portion of the Streamer may be in Keplerian rotation around MIR 2, implying a gravitating mass 1350±\pm50 M_{\odot} for the protostar+disk+envelope; alternatively, these kinematics can be explained by gas in free fall towards a 950±\pm35 M_{\odot} object. The high accretion rate onto MIR 2 apparently occurs through the Streamer/disk, and could account for \sim33% of MIR 2's total luminosity via gravitational energy release.Comment: 33 pages, 32 figures, accepted by ApJ. Line-Integral Convolution (LIC) images and movie versions of Figures 3b, 7, and 29 are available at https://gemelli.spacescience.org/~pbarnes/research/champ/papers

    The MeerKAT Galaxy Cluster Legacy Survey: Survey overview and highlights

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    MeerKAT’s large number (64) of 13.5 m diameter antennas, spanning 8 km with a densely packed 1 km core, create a powerful instrument for wide-area surveys, with high sensitivity over a wide range of angular scales. The MeerKAT Galaxy Cluster Legacy Survey (MGCLS) is a programme of long-track MeerKAT L-band (900−1670 MHz) observations of 115 galaxy clusters, observed for ∼6−10 h each in full polarisation. The first legacy product data release (DR1), made available with this paper, includes the MeerKAT visibilities, basic image cubes at ∼8″ resolution, and enhanced spectral and polarisation image cubes at ∼8″ and 15″ resolutions. Typical sensitivities for the full-resolution MGCLS image products range from ∼3−5 μJy beam−1. The basic cubes are full-field and span 2° × 2°. The enhanced products consist of the inner 1.2° × 1.2° field of view, corrected for the primary beam. The survey is fully sensitive to structures up to ∼10′ scales, and the wide bandwidth allows spectral and Faraday rotation mapping. Relatively narrow frequency channels (209 kHz) are also used to provide H I mapping in windows of 0 < z < 0.09 and 0.19 < z < 0.48. In this paper, we provide an overview of the survey and the DR1 products, including caveats for usage. We present some initial results from the survey, both for their intrinsic scientific value and to highlight the capabilities for further exploration with these data. These include a primary-beam-corrected compact source catalogue of ∼626 000 sources for the full survey and an optical and infrared cross-matched catalogue for compact sources in the primary-beam-corrected areas of Abell 209 and Abell S295. We examine dust unbiased star-formation rates as a function of cluster-centric radius in Abell 209, extending out to 3.5 R 200. We find no dependence of the star-formation rate on distance from the cluster centre, and we observe a small excess of the radio-to-100 μm flux ratio towards the centre of Abell 209 that may reflect a ram pressure enhancement in the denser environment. We detect diffuse cluster radio emission in 62 of the surveyed systems and present a catalogue of the 99 diffuse cluster emission structures, of which 56 are new. These include mini-halos, halos, relics, and other diffuse structures for which no suitable characterisation currently exists. We highlight some of the radio galaxies that challenge current paradigms, such as trident-shaped structures, jets that remain well collimated far beyond their bending radius, and filamentary features linked to radio galaxies that likely illuminate magnetic flux tubes in the intracluster medium. We also present early results from the H I analysis of four clusters, which show a wide variety of H I mass distributions that reflect both sensitivity and intrinsic cluster effects, and the serendipitous discovery of a group in the foreground of Abell 3365
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