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Purpose This paper aims to present object or feature segmentation from an ordered 3D point cloud range data obtained from a laser scanner for the purpose of robot navigation.Design/methodology/approach Rotating multi-beam laser scanners provide ordered 3D range data. Differences between consecutive ranges in radial direction are used to compute a novel measure of terrain unevenness at each data point. Computed over a complete rotation, an unevenness field is formed surrounding the scanner. A part of this field staying below a threshold is recognized as ground and removed. Remaining non-ground points are segmented into objects by region growing with points whose unevenness lies within pre-specified limiting values.Findings The proposed unevenness attribute is simple and efficient for segmenting distinct objects or features. The fineness of surface features can be regulated by adjusting the threshold value of difference in unevenness between neighbouring points that triggers an onset of new segments.Research limitations/implications The angles between neighbouring laser range data are assumed to be known.Practical implications Segmented objects or features can be used for scan registration, object tracking and robot navigation.Social implications The method may find use in autonomous robots and driverless cars.Originality/value Differences between consecutive range data are used imaginatively to derive a novel measure of terrain unevenness, which in turn, is used for efficient segmentation of objects and features. [ABSTRACT FROM AUTHOR]

We introduce a novel measure of terrain unevenness, which is computed in terms of ranges of neighbouring laser beams of a 3D laser scanner mounted on a mobile robot. The unevenness so computed over all sampled points forms an unevenness field around the robot. We explore the nature of this measure of unevenness through analysis, and arrive at a reasonable policy for setting thresholds on unevenness in order to detect obstacles, and in the process mark out the traversable region around the mobile robot. The traversable region is obtained as a connectivity graph over 180 × 32 cells in about 30 ms time on an Intel i3 processor. This connectivity graph can be potentially used for path planning of the mobile robot. Conceptually and computationally the measure is simple and efficient. It has an added advantage of being robust against small tilts of the sensor during locomotion. It also works well on slopes. Although there are numerous ways of detecting traversable regions in the literature, it is the novelty of the measure and its analysis, which we believe is our contribution through this paper. We demonstrate its usefulness through experimental results and compare its performance with a standard method that uses both height and slope between neighbouring range points to detect obstacles. [ABSTRACT FROM AUTHOR]

Automated human emotion recognition from facial expressions is a well-studied problem and still remains a very challenging task. Some efficient or accurate deep learning models have been presented in the literature. However, it is quite difficult to design a model that is both efficient and accurate at the same time. Moreover, identifying the minute feature variations in facial regions for both macro and micro-expressions requires expertise in network design. In this paper, we proposed to search for a highly efficient and robust neural architecture for both macro and micro-level facial expression recognition. To the best of our knowledge, this is the first attempt to design a NAS-based solution for both macro and micro-expression recognition. We produce lightweight models with a gradient-based architecture search algorithm. To maintain consistency between macro and micro-expressions, we utilize dynamic imaging and convert micro-expression sequences into a single frame, preserving the spatiotemporal features in the facial regions. The EmoNAS has evaluated over 13 datasets (7 macro expression datasets: CK+, DISFA, MUG, ISED, OULU-VIS CASIA, FER2013, RAF-DB, and 6 micro-expression datasets: CASME-I, CASME-II, CAS(ME)2̂, SAMM, SMIC, MEGC2019 challenge). The proposed models outperform the existing state-of-the-art methods and perform very well in terms of speed and space complexity. [ABSTRACT FROM AUTHOR]

Background: The long‐term outcomes of patients with congenital and childhood complete atrioventricular block (CCAVB/CAVB) after pacemaker implantation are unclear. Methods: We performed a meta‐analysis of all the studies of CCAVB. A systematic search of PubMed and CENTRAL databases from January 1, 1967 to January 31, 2020 was performed. The quality of studies included was critically appraised using the Newcastle–Ottawa scale, and outcome data were analyzed using the restricted maximum likelihood function. Results: Twenty‐nine studies were eligible for analysis, with a total of 1553 patients. The all‐cause‐mortality was 5.7% (95% confidence interval [CI]: 2.5%–9.9%), while pacing‐induced cardiomyopathy (PICM) was seen in 3.8% (95% CI: 1.2–7.2). Diagnosis at birth (effect size [ES] [95%CI]: −2.23 [−0.36 to −0.10]; p <.001), presence of congenital heart disease (ES [95%CI]: −0.67 [0.41–0.93]; p <.001), younger age at pacemaker implantation (ES [95%CI]: −0.01 [−0.02 to −0.001]; p =.02), and duration of pacing (ES [95%CI]: −0.03 [−0.05 to −0.003]; p =.03), were associated with an higher mortality on binominal logistic regression. None of the parameters were significant on multivariate analysis. Conclusion: Pooled proportional mortality in patients with CCAVB and CAVB is 5.7% with an infrequent incidence of PICM (3.8%) in the paced patients with AVB suggesting that pacing in these patients is an effective management strategy with a low incidence of long‐term side effects. Registry and randomized data can throw additional light regarding the natural history and appropriate management strategy in these patients. [ABSTRACT FROM AUTHOR]

We report a case of giant ureteric calculus in an adult male with bladder exstrophy who presented with urinary incontinence and recurrent urinary tract infection. Investigations revealed a 16-cm ureteric calculus, a vesical calculus and a left staghorn renal calculus. Ureteric stone was removed by open ureterolithotomy. [ABSTRACT FROM AUTHOR]