125 research outputs found

    Self-Attention Dense Depth Estimation Network for Unrectified Video Sequences

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    The dense depth estimation of a 3D scene has numerous applications, mainly in robotics and surveillance. LiDAR and radar sensors are the hardware solution for real-time depth estimation, but these sensors produce sparse depth maps and are sometimes unreliable. In recent years research aimed at tackling depth estimation using single 2D image has received a lot of attention. The deep learning based self-supervised depth estimation methods from the rectified stereo and monocular video frames have shown promising results. We propose a self-attention based depth and ego-motion network for unrectified images. We also introduce non-differentiable distortion of the camera into the training pipeline. Our approach performs competitively when compared to other established approaches that used rectified images for depth estimation


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    Minimising impact of wire resistance in low-power crossbar array write scheme

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    This paper presents a circuit level analysis of write operation in memristor crossbar memory array with and without line resistance. Three write schemes: floating line, V/2 and V/3 are investigated. Analysis shows that floating line scheme could also be considered reliable in arrays with aspect ratio of 1:1 and negligible line resistance just like the latter two schemes. Further analysis also shows that high density crossbar structures cannot be designed using any of the three schemes with worst case line resistance and data distribution within the array. To solve this problem, we propose a voltage compensating technique for write voltage degradation caused by line resistance during write operation on crossbar array. This technique is able to enhance write voltage in the presence of worst case line resistance and thus enable the design of higher density and reliable crossbar array


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    A sensor comprises a plurality of sensor elements arranged in an array . Each sensor element is memristive and has an electrical resistance characteristic related to exposure to a species to be sensed . The sensor elements are arranged to be connectable such that at least one sensor element is connected in parallel with at least one other sensor element . By using appropriate connections , the array of sensor elements can be read

    AutoML accurately predicts endovascular mechanical thrombectomy in acute large vessel ischemic stroke

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    Background and objectiveAutomated machine learning or autoML has been widely deployed in various industries. However, their adoption in healthcare, especially in clinical settings is constrained due to a lack of clear understanding and explainability. The aim of this study is to utilize autoML for the prediction of functional outcomes in patients who underwent mechanical thrombectomy and compare it with traditional ML models with a focus on the explainability of the trained models.MethodsA total of 156 patients of acute ischemic stroke with Large Vessel Occlusion (LVO) who underwent mechanical thrombectomy within 24 h of stroke onset were included in the study. A total of 34 treatment variables including clinical, demographic, imaging, and procedure-related data were extracted. Various conventional machine learning models such as decision tree classifier, logistic regression, random forest, kNN, and SVM as well as various autoML models such as AutoGluon, MLJAR, Auto-Sklearn, TPOT, and H2O were used to predict the modified Rankin score (mRS) at the time of patient discharge and 3 months follow-up. The sensitivity, specificity, accuracy, and AUC for traditional ML and autoML models were compared.ResultsThe autoML models outperformed the traditional ML models. For the prediction of mRS at discharge, the highest testing accuracy obtained by traditional ML models for the decision tree classifier was 74.11%, whereas for autoML which was obtained through AutoGluon, it showed an accuracy of 88.23%. Similarly, for mRS at 3 months, the highest testing accuracy of traditional ML was that of the SVM classifier at 76.5%, whereas that of autoML was 85.18% obtained through MLJAR. The 24-h ASPECTS score was the most important predictor for mRS at discharge whereas for prediction of mRS at 3 months, the most important factor was mRS at discharge.ConclusionAutomated machine learning models based on multiple treatment variables can predict the functional outcome in patients more accurately than traditional ML models. The ease of clinical coding and deployment can assist clinicians in the critical decision-making process. We have developed a demo application which can be accessed at https://mrs-score-calculator.onrender.com/