98 research outputs found
Structured light techniques for 3D surface reconstruction in robotic tasks
Robotic tasks such as navigation and path planning can be greatly enhanced by a vision system capable of providing depth perception from fast and accurate 3D surface reconstruction. Focused on robotic welding tasks we present a comparative analysis of a novel mathematical formulation for 3D surface reconstruction and discuss image processing requirements for reliable detection of patterns in the image. Models are presented for a parallel and angled configurations of light source and image sensor. It is shown that the parallel arrangement requires 35\% fewer arithmetic operations to compute a point cloud in 3D being thus more appropriate for real-time applications. Experiments show that the technique is appropriate to scan a variety of surfaces and, in particular, the intended metallic parts for robotic welding tasks
Optical techniques for 3D surface reconstruction in computer-assisted laparoscopic surgery
One of the main challenges for computer-assisted surgery (CAS) is to determine the intra-opera- tive morphology and motion of soft-tissues. This information is prerequisite to the registration of multi-modal patient-specific data for enhancing the surgeon’s navigation capabilites by observ- ing beyond exposed tissue surfaces and for providing intelligent control of robotic-assisted in- struments. In minimally invasive surgery (MIS), optical techniques are an increasingly attractive approach for in vivo 3D reconstruction of the soft-tissue surface geometry. This paper reviews the state-of-the-art methods for optical intra-operative 3D reconstruction in laparoscopic surgery and discusses the technical challenges and future perspectives towards clinical translation. With the recent paradigm shift of surgical practice towards MIS and new developments in 3D opti- cal imaging, this is a timely discussion about technologies that could facilitate complex CAS procedures in dynamic and deformable anatomical regions
DHX9 Helicase promotes R-loop formation in cells with impaired RNA splicing
Unresolved R-loops can represent a threat to genome stability. Here the authors reveal that DHX9 helicase can promote R-loop formation in the absence of splicing factors SFPQ and SF3B3
Validation of the HERA Phase i Epoch of Reionization 21 cm Power Spectrum Software Pipeline
We describe the validation of the HERA Phase I software pipeline by a series of modular tests, building up to an end-to-end simulation. The philosophy of this approach is to validate the software and algorithms used in the Phase I upper-limit analysis on wholly synthetic data satisfying the assumptions of that analysis, not addressing whether the actual data meet these assumptions. We discuss the organization of this validation approach, the specific modular tests performed, and the construction of the end-to-end simulations. We explicitly discuss the limitations in scope of the current simulation effort. With mock visibility data generated from a known analytic power spectrum and a wide range of realistic instrumental effects and foregrounds, we demonstrate that the current pipeline produces power spectrum estimates that are consistent with known analytic inputs to within thermal noise levels (at the 2σ level) for k > 0.2h Mpc-1 for both bands and fields considered. Our input spectrum is intentionally amplified to enable a strong "detection"at k ∼ 0.2 h Mpc-1 - at the level of ∼25σ - with foregrounds dominating on larger scales and thermal noise dominating at smaller scales. Our pipeline is able to detect this amplified input signal after suppressing foregrounds with a dynamic range (foreground to noise ratio) of ⪆107. Our validation test suite uncovered several sources of scale-independent signal loss throughout the pipeline, whose amplitude is well-characterized and accounted for in the final estimates. We conclude with a discussion of the steps required for the next round of data analysis
Methods of Error Estimation for Delay Power Spectra in 21 cm Cosmology
Precise measurements of the 21 cm power spectrum are crucial for understanding the physical processes of hydrogen reionization. Currently, this probe is being pursued by low-frequency radio interferometer arrays. As these experiments come closer to making a first detection of the signal, error estimation will play an increasingly important role in setting robust measurements. Using the delay power spectrum approach, we have produced a critical examination of different ways that one can estimate error bars on the power spectrum. We do this through a synthesis of analytic work, simulations of toy models, and tests on small amounts of real data. We find that, although computed independently, the different error bar methodologies are in good agreement with each other in the noise-dominated regime of the power spectrum. For our preferred methodology, the predicted probability distribution function is consistent with the empirical noise power distributions from both simulated and real data. This diagnosis is mainly in support of the forthcoming HERA upper limit and also is expected to be more generally applicable
First Results from HERA Phase I: Upper Limits on the Epoch of Reionization 21 cm Power Spectrum
We report upper limits on the Epoch of Reionization 21 cm power spectrum at redshifts 7.9 and 10.4 with 18 nights of data (∼36 hr of integration) from Phase I of the Hydrogen Epoch of Reionization Array (HERA). The Phase I data show evidence for systematics that can be largely suppressed with systematic models down to a dynamic range of ∼109 with respect to the peak foreground power. This yields a 95% confidence upper limit on the 21 cm power spectrum of 212≤(30.76)2mK2 at k = 0.192 h Mpc-1 at z = 7.9, and also 212≤(95.74)2mK2 at k = 0.256 h Mpc-1 at z = 10.4. At z = 7.9, these limits are the most sensitive to date by over an order of magnitude. While we find evidence for residual systematics at low line-of-sight Fourier k π modes, at high k π modes we find our data to be largely consistent with thermal noise, an indicator that the system could benefit from deeper integrations. The observed systematics could be due to radio frequency interference, cable subreflections, or residual instrumental cross-coupling, and warrant further study. This analysis emphasizes algorithms that have minimal inherent signal loss, although we do perform a careful accounting in a companion paper of the small forms of loss or bias associated with the pipeline. Overall, these results are a promising first step in the development of a tuned, instrument-specific analysis pipeline for HERA, particularly as Phase II construction is completed en route to reaching the full sensitivity of the experiment
A Real Time Processing system for big data in astronomy: Applications to HERA
As current- and next-generation astronomical instruments come online, they will generate an unprecedented deluge of data. Analyzing these data in real time presents unique conceptual and computational challenges, and their long-term storage and archiving is scientifically essential for generating reliable, reproducible results. We present here the real-time processing (RTP) system for the Hydrogen Epoch of Reionization Array (HERA), a radio interferometer endeavoring to provide the first detection of the highly redshifted 21 cm signal from Cosmic Dawn and the Epoch of Reionization by an interferometer. The RTP system consists of analysis routines run on raw data shortly after they are acquired, such as calibration and detection of radio-frequency interference (RFI) events. RTP works closely with the Librarian, the HERA data storage and transfer manager which automatically ingests data and transfers copies to other clusters for post-processing analysis. Both the RTP system and the Librarian are public and open source software, which allows for them to be modified for use in other scientific collaborations. When fully constructed, HERA is projected to generate over 50 terabytes (TB) of data each night, and the RTP system enables the successful scientific analysis of these data
Identification of an N-terminal 27 kDa fragment of Mycoplasma pneumoniae P116 protein as specific immunogen in M. pneumoniae infections
<p>Abstract</p> <p>Background</p> <p><it>Mycoplasma pneumoniae </it>is an important cause of respiratory tract infection and is increasingly being associated with other diseases such as asthma and extra-pulmonary complications. Considerable cross-reactivity is known to exist between the whole cell antigens used in the commercial serological testing assays. Identification of specific antigens is important to eliminate the risk of cross-reactions among different related organisms. Adherence of <it>M. pneumoniae </it>to human epithelial cells is mediated through a well defined apical organelle to which a number of proteins such as P1, P30, P116 and HMW1-3 have been localized, and are being investigated for adhesion, gliding and immunodiagnostic purposes.</p> <p>Methods</p> <p>A 609 bp fragment P116<sub>(N-27), </sub>corresponding to the N-terminal region of <it>M. pneumoniae </it>P116 gene was cloned and expressed. A C-terminal fragment P1<sub>(C-40), </sub>of P1 protein of <it>M. pneumoniae </it>was also expressed. Three IgM ELISA assays based on P116<sub>(N-27), </sub>P1<sub>(C-40) </sub>and (P116 <sub>(N-27) </sub>+ P1<sub>(C-40)</sub>) proteins were optimized and a detailed analysis comparing the reactivity of these proteins with a commercial kit was carried out. Comparative statistical analysis of these assays was performed with the SPSS version 15.0.</p> <p>Results</p> <p>The expressed P116<sub>(N-27) </sub>protein was well recognized by the patient sera and was immunogenic in rabbit. P1<sub>(C-40) </sub>of <it>M. pneumoniae </it>was also immunogenic in rabbit. In comparison to the reference kit, which is reported to be 100% sensitive and 75% specific, ELISA assay based on purified P116<sub>(N-27), </sub>P1<sub>(C-40) </sub>and (P116<sub>(N-27) </sub>+ P1<sub>(C-40)</sub>) proteins showed 90.3%, 87.1% and 96.8% sensitivity and 87.0%, 87.1% and 90.3% specificity respectively. The p value for all the three assays was found to be < 0.001, and there was a good correlation and association between them.</p> <p>Conclusion</p> <p>This study shows that an N-terminal fragment of P116 protein holds a promise for serodiagnosis of <it>M. pneumoniae </it>infection. The IgM ELISA assays based on the recombinant proteins seem to be suitable for the use in serodiagnosis of acute <it>M. pneumoniae </it>infections. The use of short recombinant fragments of P116 and P1 proteins as specific antigens may eliminate the risk of cross-reactions and help to develop a specific and sensitive immunodiagnostic assay for <it>M. pneumoniae </it>detection.</p
Optimizing sparse RFI prediction using deep learning
Radio frequency interference (RFI) is an ever-present limiting factor among radio telescopes even in the most remote observing locations. When looking to retain the maximum amount of sensitivity and reduce contamination for Epoch of Reionization studies, the identification and removal of RFI is especially important. In addition to improved RFI identification, we must also take into account computational efficiency of the RFI-Identification algorithm as radio interferometer arrays such as the Hydrogen Epoch of Reionization Array (HERA) grow larger in number of receivers. To address this, we present a deep fully convolutional neural network (DFCN) that is comprehensive in its use of interferometric data, where both amplitude and phase information are used jointly for identifying RFI. We train the network using simulated HERA visibilities containing mock RFI, yielding a known \u2018ground truth\u2019 data set for evaluating the accuracy of various RFI algorithms. Evaluation of the DFCN model is performed on observations from the 67 dish build-out, HERA-67, and achieves a data throughput of 1.6
7 105 HERA time-ordered 1024 channelled visibilities per hour per GPU. We determine that relative to an amplitude only network including visibility phase adds important adjacent time\u2013frequency context which increases discrimination between RFI and non-RFI. The inclusion of phase when predicting achieves a recall of 0.81, precision of 0.58, and F2 score of 0.75 as applied to our HERA-67 observations
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