Potential For Real-time Applications Research Articles

Online Calibration of Extrinsic Parameters for Solid-State LIDAR Systems.

This work addresses the challenge of calibrating multiple solid-state LIDAR systems. The study focuses on three different solid-state LIDAR sensors that implement different hardware designs, leading to distinct scanning patterns for each system. Consequently, detecting corresponding points between the point clouds generated by these LIDAR systems-as required for calibration-is a complex task. To overcome this challenge, this paper proposes a method that involves several steps. First, the measurement data are preprocessed to enhance its quality. Next, features are extracted from the acquired point clouds using the Fast Point Feature Histogram method, which categorizes important characteristics of the data. Finally, the extrinsic parameters are computed using the Fast Global Registration technique. The best set of parameters for the pipeline and the calibration success are evaluated using the normalized root mean square error. In a static real-world indoor scenario, a minimum root mean square error of 7 cm was achieved. Importantly, the paper demonstrates that the presented approach is suitable for online use, indicating its potential for real-time applications. By effectively calibrating the solid-state LIDAR systems and establishing point correspondences, this research contributes to the advancement of multi-LIDAR fusion and facilitates accurate perception and mapping in various fields such as autonomous driving, robotics, and environmental monitoring.

Advancing vector-borne disease prediction through functional classifier integration: A novel approach for enhanced modeling

This paper evaluates various machine learning models for predicting vector-borne diseases, focusing on performance metrics that reveal insights into their efficacy. The Multilayer Perceptron (MLP) model demonstrated the highest accuracy at 92%, surpassing the Simple Logistic (SL) and Support Vector Machine (SVM) models, which achieved 88% and 90.87% accuracy, respectively. Notably, the MLP model excelled in precision, recall, and F-Measure, indicating superior classification accuracy. Conversely, the SVM model exhibited noteworthy computational efficiency with the lowest processing time at 0.3 seconds, emphasizing its potential for real-time applications in public health interventions. In contrast, the Radial Basis Function Network (RBFN) lagged in accuracy and other metrics. The results underscore the trade-offs between accuracy and computational efficiency, emphasizing the need for a nuanced model selection. Considering the holistic evaluation, the SVM model emerged as a compelling choice, balancing high accuracy and efficient processing, making it promising for real-time public health applications. This study contributes valuable insights into machine learning model performance, emphasizing the importance of selecting models tailored to the specific needs of vector-borne disease prediction. As we confront emerging infectious diseases, the SVM model stands as an indispensable tool, supporting a proactive and data-driven approach to mitigate the global health impact of vector- borne diseases.

Machine Learning-assisted Prediction and Optimization of Exergy Efficiency and Destruction of Cumene Plant under Uncertainty

Machine Learning (ML)'s growing role in process industries during the digitalization era is notable. This study combines Artificial Neural Networks (ANNs) and Aspen Plus to predict exergy efficiency, exergy destruction, and potential improvements in a cumene plant under uncertain process conditions. An optimization framework, using Particle Swarm Optimization (PSO) and Genetic Algorithm (GA), was developed to enhance exergy efficiency amid uncertainty. Initially, a steady-state Aspen model evaluates exergy efficiency, irreversibility, and potential improvements. The proposed model is transitioned to a dynamic mode, introducing artificial uncertainties into key variables. An ANN model predicts exergy efficiency and exergy destruction under uncertainty. The PSO and GA-based optimization methods improve exergy efficiency and reduce exergy destruction. This work demonstrates the potential real-time application of intelligent methods in plant analysis.

Goethite (α-FeOOH) nanoparticles wrapped on reduced graphene oxide nanosheet for sensitive electrochemical detection of arsenic(III)

The goethite (α-FeOOH) nanoparticles were wrapped on the reduced graphene oxide (rGO) to synthesize the α-FeOOH/rGO nanocomposites. The nanocomposites (NCs) were initially examined for their optical, structural, and morphological properties. The XRD data obtained the crystallite size of the α-FeOOH, showed that the average crystal size for pristine α-FeOOH and α-FeOOH/rGO nanocomposites were about 85 and 90 nm, respectively. The transmission electron microscope confirmed the nanoparticles (NPs) were evenly distributed throughout the reduced graphene oxide sheets. The nanocomposites improved glassy carbon electrodes (GCE), making them efficient sensors for detecting the arsenic(III) (As+3) in a pH 5 phosphate buffer solution with an Ag/AgCl reference electrode. The detection limit for As+3 was 0.07 μgL−1 and the resulting sensitivity was 0.39 μA−1 μgL−1 in the linear dynamic range of 0.1–10 μgL−1. The α-FeOOH/rGO/GCE was more sensitive than its original and showed a synergistic effect due to the influence of α-FeOOH on the properties of rGO. The α-FeOOH/rGO NCs-modified GCE electrode performed as a promising sensor, by separating the common interfering ions. Moreover, the modified electrode exhibited remarkable stability, repeatability, and potential real-time application towards the detection of arsenic(III). Additionally, the proposed approach has been successfully applied to the detection of As+3 in the real water sample.

Deep learning-based framework for the observation of real-time melt pool and detection of anomaly in wire-arc additive manufacturing

ABSTRACT Object detection has become a popular tool of deep learning in the era of digital manufacturing. In this study, the most powerful and efficient object detection algorithm, i.e., You Only Look Once (YOLO) algorithm, was used to detect anomalies in deposited beads of wire-arc additive manufacturing (WAAM) using melt pool images. This study used the latest version of YOLO algorithm to train and validate the custom image dataset of the melt pool obtained by conducting experiments using a robotic-controlled WAAM. The mean average precision (mAP) for the “Regular bead” class and the “Irregular bead” class reached 99% at an Intersection over Union (IoU) threshold of 0.5, for both training and validation. When the model was tested for new or unseen datasets by conducting four new experimental trials, the mAP value for the “Regular bead” class reached 98.47% and for the “Irregular bead” class reached 96.68% at an average processing time of 0.014 s/frame. The object detection algorithm YOLO has shown an excellent processing time of 15 ms per frame, which shows its potential for real-time application in the manufacturing industry.

Real-Time Water Level Monitoring Based on GNSS Dual-Antenna Attitude Measurement

Real-time and high-precision water level monitoring is crucial for the fields of hydrology, hydraulic engineering, and disaster prevention and control. The most prevalent method for measuring water level is through the use of water level gauges, which can be costly and have limited coverage. In recent years, Global Navigation Satellite System Reflectometry (GNSS-R) technology has emerged as a promising approach for water level monitoring due to its low cost and high coverage. However, a limitation of current GNSS-R technology is the extended time required to record signals, which hinders its potential for real-time application. This paper introduces a novel real-time water level monitoring method based on GNSS dual-antenna attitude measurement and develops a model to invert water level based on baseline vector. This method uses double-difference observations to eliminate errors caused by various factors, such as satellite and receiver clock, and ionospheric and tropospheric delay. To avoid the impact of detecting and correcting cycle slips during real-time operations, a single-epoch calculation method is introduced. In order to verify the stability and reliability of our method, field tests were carried out at Dongshahe Station in Beijing. We obtained water level data with a time resolution of 1 Hz through field experiments. Experimental data collected from 12 May to 8 June 2022 and from 4 July to 8 August 2022 showed good agreement with on-site water gauge measurements, with root mean square errors of 2.77 cm and 2.54 cm, respectively. Experimental results demonstrate that this method can achieve high-precision, high-temporal-resolution water level monitoring.

Predictive Cruise Controller for Electric Vehicle to Save Energy and Extend Battery Lifetime

Electric vehicles are considered the most effective solution to the petroleum crisis and reduction of air pollution. In order to enhance energy efficiency and battery lifetime, this paper designs a predictive cruise controller (EC) for electric vehicles. Road information and traffic preview are employed for velocity planning while minimizing energy usage, maintaining battery health, and avoiding collision with a lead vehicle. To enable real-time implementation, we apply a model predictive control (MPC) framework formulated in space domain, and approximation and relaxation are introduced to obtain a smooth nonlinear program. Simulation results indicate that the proposed controller yields suboptimal performance as compared to the globally optimal solution. For higher practicability on real-life scenarios, we develop an enhanced EC that is capable of optimizing the stopping of the ego vehicle. According to the car-following studies where the lead vehicle is driven using real-life data, the enhanced EC achieves <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$7.14 \%$</tex-math></inline-formula> energy saving and <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$29.81 \%$</tex-math></inline-formula> battery life extension when compared to the intelligent driving model. The computation time of <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$40 \,\mathrm{ms}$</tex-math></inline-formula> per MPC update also demonstrates its potential for real-time applications.

Application of Generative Adversarial Network-Based Inversion Algorithm in Imaging 2-D Lossy Biaxial Anisotropic Scatterer

An effective quasi-real-time inversion algorithm based on the super-resolution generative adversarial network (SR-GAN) is proposed to quantitatively image the 2-D biaxial anisotropic scatterers. The SR-GAN was originally proposed for the purpose of super-resolution image reconstruction, which exactly fits the need for inverse problem. In addition, Visual Geometry Group (VGG) loss is introduced to extract the high-level features of the object instead of the low-level pixel-wise error measures. The angle-dependent reconstruction effect due to the dipole radiation as in the traditional inversion methods is effectively resolved by the machine learning method. Numerical results using both synthetic data and experimental data are given to validate the effectiveness of the proposed method. Both the imaging quality and resolution are greatly improved by the proposed SR-GAN algorithm, compared to the traditional iterative inversion algorithm. In addition, the computational time is reduced significantly and the quasi-real-time imaging is finally realized, which promises a potential real-time application of the inverse scattering method.

Three staged integrated community-based water filter system for potable water by effective removal of contaminants from ground water

Potable water scarcity is a dire problem faced worldwide due to contaminated ground water. Microbial contaminants, heavy metals chromium (Cr) and metalloid such as arsenic (As) are some of the major pollutants of ground water and pose serious health hazards. The present work is focused to design a multifunctional three compartment integrated water filtration system for community use and its validation at both lab and pilot scale. The top compartment is mainly for removal of flocculating contamination with improvement of taste and odor of drinking water. The second compartment consists of functionalized sand layers for removal of metal and microbial contaminants and the third compartment comprises of nanoparticle embedded ceramic candles to further eliminate any remaining contaminants. The developed integrated water filter containing various types of materials were characterized through flow rate, scanning electron microscopy (SEM) and energy dispersive spectrometry (EDS) before and after filtration. Moreover, the analysis of various physiological parameters such as pH, turbidity, total dissolved solids and hardness pre and post filtration elucidated stable and improved results. Micro-computed tomography (micro-CT) analysis of baked ceramic candle showed substantial increased porosity of ~83.11 ± 2.17 % in comparison to unbaked candle (26.09 ± 1.33 %). The inductively coupled plasma mass spectrometry (ICP-MS) analysis demonstrated significant reduction of As and Cr levels before and after filtration, which was further confirmed by EDS mapping. Microbial testing showed hierarchal decrease in microbial load after each stage at lab scale. This low-cost, multistage, gravity-based filtration system proved effective in removal of heavy metal and microbial contaminants in water and provides the potable filtrate for daily use. Thus, the integrated system with simple design without requiring major infrastructure and promising results may lead to its potential real-time application for supplying potable water to large populations, particularly in rural populations who cannot afford sophisticated water purification system.

Power Management of a Plug-in Hybrid Electric Vehicle Using Neural Networks with Comparison to Other Approaches

Many researchers spent much effort on the online power management strategies for plug-in hybrid vehicles (PHEVs) and hybrid electric vehicles (HEVs). Nowadays, artificial neural networks (ANNs), one of the machine learning techniques, have also been applied to this problem due to their good performance in learning non-linear and complicated multi-inputs multi-outputs (MIMO) dynamic systems. In this paper, an ANN is applied to the online power management for a plug-in hybrid electric vehicle (PHEV) by predicting the torque split between an internal combustion engine (ICE) and an electric motor (e-Motor) to optimize the greenhouse gas (GHG) emissions by using dynamic programming (DP) results as training data. Dynamic programming can achieve a global minimum solution while it is computationally intensive and requires prior knowledge of the entire drive cycle. As such, this method cannot be implemented in real-time. The DP-based ANN controller can get the benefit of using an ANN to fit the DP solution so that it can be implemented in real-time for an arbitrary drive cycle. We studied the hyper-parameters’ effects on the ANN model and different structures of ANN models are compared. The minimum training mean square error (MSE) models in each comparison set are selected for comparison with DP and equivalent consumption minimization strategy (ECMS). The total GHG emissions and state of charge (SOC) are the metrics used for the analysis and comparison. All the selected ANNs provide results that are comparable to the optimal DP solution, which indicates that ANNs are almost as good as the DP solution. It is found that the multiple hidden-layer ANN shows more efficiency in the training process than the single hidden-layer ANN. By comparing the results with ECMS, the ANN shows great potential in real-time application with the smallest deviation from the results of DP. In addition, our approach does not require any additional trip information, and its output (torque split) is more directly implementable on real vehicles.

Wearable Inertial Sensor-Based Limb Lameness Detection and Pose Estimation for Horses

Accurate objective, automated limb lameness detection and pose estimation play an important role for animal well-being and precision livestock farming. We present a wearable sensor-based limb lameness detection and pose estimation for horse walk and trot locomotion. The gait event and lameness detection are first built on a recurrent neural network (RNN) with long short-term memory (LSTM) cells. Its outcomes are used in the limb pose estimation. A learned low-dimensional motion manifold is parameterized by a phase variable with a Gaussian process dynamic model. We compare the RNN-LSTM-based lameness detection method with a feature-based multi-layer classifier (MLC) and a multi-class classifier (MCC) that are built on support vector machine/K-nearest-neighbors and deep convolutional neural network methods, respectively. Experimental results show that using only accelerometer measurements, the RNN-LSTM-based approach achieves 95% lameness detection accuracy and also outperforms the feature-based MLC or MCC in terms of several assessment criteria. The pose estimation scheme can predict the 24 limb joint angles in the sagittal plane with average errors less than 5 and 10 degs under normal and induced lameness conditions, respectively. The presented work demonstrate the successful use of machine learning techniques for high performance lameness detection and pose estimation in equine science. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Note to Practitioners</i> —Automation technologies are increasingly used for precision agriculture but few have focused on monitoring individual animals in open field for precision livestock farming. Limb lameness detection and pose estimation in open field is labor-intensive, unsafe for farmers, and inefficient. The presented machine learning-enabled, wearable inertial sensor-based design provides an effective and efficient approach for horse limb lameness detection and pose estimation applications. We present an RNN-LSTM for lameness detection and an integrated manifold learning model is used to predict the horse limb joint angles in walk and trot gaits under normal and induced lameness conditions. We also present a systematic analysis and experiments to demonstrate the impacts of the wearable sensor locations and signal information on lameness detection and pose estimation performance. Several other machine learning-based lameness detection methods are also presented and compared. The extensive multi-horse testing results are presented to demonstrate the superior accuracy and higher performance than other types of machine learning methods. One attractive feature of the proposed design lies in its high performance and fast computational capability for potential real-time applications in open field.

3D walnut-like rare-earth gadolinium molybdate encapsulated with thermo-responsive hydrogel for sensitive electrochemical detection of anticancer drug Niftolide in human urine

Prostate cancer is ranked as the second most common type of cancer in men and has a high mortality rate. Niftolide (NIFT), a non-steroidal antiandrogen drug, has been extensively used in the treatment of prostate cancer; however, its improper and excessive use can lead to unwanted side effects and complications to the body. In this study, we synthesized a novel rare-earth gadolinium molybdate encapsulated with thermo-responsive hydrogel composite (PNIPAM/GdMo) using a simple precipitation technique for electro-catalytic detection of NIFT in human urine samples. Urea (NH2CONH2) and Ethylene glycol (EG; (CH2OH)2) used in the synthesis process exhibited a bifunctional behavior by simultaneously acting as a reducing agent and enhancing the 3D morphological structure. In addition, the encapsulation of the PNIPAM with GdMo particles showed a significant synergic effect and a high surface area. The morphological, structural, chemical components, and electro-catalytic properties of the composite are studied by various analytical and electro-catalytic techniques. The electro-catalytic studies indicated a significant improvement in the current response of the sensor at 40 °C as compared to 20 °C, which makes it a temperature-enabled reversible sensor. The proposed sensor exhibited excellent limit of detection (LOD) of 5 nM, linear range (LR) of 0.01–4, 8 − 400 µM, and sensitivity of 1.1858 µA µM−1 cm−2. Moreover, the anti-interference ability, reproducibility, and stability reinforced the practical applicability of the proposed sensor for the electro-catalytic detection of NIFT quantity in human urine samples. The sensor showed an excellent recovery rate with RSD of less than 3%, which authenticates its potential for real-time applications.

Real-Time Fluorescent Monitoring of Kinetically Controlled Supramolecular Self-Assembly of Atom-Precise Cu8 Nanocluster.

Kinetically stable and long-lived intermediates are crucial in monitoring the progress and understanding of supramolecular self-assembly of diverse aggregated structures with collective functions. Herein, the complex dynamics of an atomically precise CuI nanocluster [Cu8 (t BuC6 H4 S)8 (PPh3 )4 ] (Cu8a) is systematically investigated. Remarkably, by monitoring the aggregation-induced emission (AIE) and electron microscopy of the kinetically stable intermediates in real time, the directed self-assembly (DSA) process of Cu8a is deduced. The polymorphism and different emission properties of Cu NCs aggregates were successfully captured, allowing the structure-optical property relationship to be established. More importantly, the utilization of a mathematical "permutation and combination" ideology by introducing a heterogeneous luminescent agent of a carbon dot (CD) to Cu8a aggregates enriches the "visualization" fluorescence window, which offers great potential in real time application for optical sensing of materials.

Real‐Time Fluorescent Monitoring of Kinetically Controlled Supramolecular Self‐Assembly of Atom‐Precise Cu8 Nanocluster

AbstractKinetically stable and long‐lived intermediates are crucial in monitoring the progress and understanding of supramolecular self‐assembly of diverse aggregated structures with collective functions. Herein, the complex dynamics of an atomically precise CuI nanocluster [Cu8(tBuC6H4S)8(PPh3)4] (Cu8a) is systematically investigated. Remarkably, by monitoring the aggregation‐induced emission (AIE) and electron microscopy of the kinetically stable intermediates in real time, the directed self‐assembly (DSA) process of Cu8a is deduced. The polymorphism and different emission properties of Cu NCs aggregates were successfully captured, allowing the structure–optical property relationship to be established. More importantly, the utilization of a mathematical “permutation and combination” ideology by introducing a heterogeneous luminescent agent of a carbon dot (CD) to Cu8a aggregates enriches the “visualization” fluorescence window, which offers great potential in real time application for optical sensing of materials.

Technical considerations on the use of Granger causality in neuromonitoring

Neuromonitoring-derived indices play an important role in implementing personalised medicine for traumatic brain injury patients. A well-established example is the pressure reactivity index (PRx), calculated from spontaneous fluctuations of arterial blood pressure (ABP) and intracranial pressure (ICP). PRx assumes causal relationship between ABP and ICP but lacks the check for this assumption. Granger causality (GC) — a method of assessing causal interactions between time series data — is gaining popularity in neurosciences. In our work, we used ABP and ICP data recorded at the frequency of 100 Hz or higher from 235 traumatic brain injury patients. We focused on time domain GC. Analysis was first performed directly on high-resolution data, which included pulse waves. We showed that due to the measurement delay in high-resolution ABP data, GC analysis may erroneously indicate strong ICP→ABP causal relation. Subsequently, the data were downsampled to 0.1 Hz, effectively removing pulse and respiratory waves. We aimed to investigate how different ways of calculating GC influence results and which way should be recommended for ABP-ICP recordings. We considered aspects like selecting autoregressive model order and dealing with data non-stationarity. In addition, we generated simulated signals to investigate the influence of gaps and different procedures of missing data imputation on GC estimation. We showed that unlike methods which interpolate missing data, replacing missing data by white Gaussian noise did not increase the rate of false GC detection. Python source code used in this study is available at: https://github.com/m-m-placek/python-icmplus-granger-causality. Statement of significanceAssessing causality between time series data is of particular interest when neuromonitoring indices are derived from those time series and causal interaction between them is assumed. Causality assessment can improve reliability of such indices and open pathways for their safe clinical implementation. Granger Causality (GC) has recently been investigated in data collected from traumatic brain injury patients. However, there are two main issues related to applications suggested in these studies. Firstly, they considered GC for entire multi-day data recordings or for 24-h long episodes. There is interest in considering causal relationships in finer granularity, also in terms of their potential real-time applications at the bedside. Secondly, GC calculation requires selecting some parameters and there is no unique nor standardised way of doing that. Many papers often provide very brief description of data pre-processing and GC calculation. For this reason, it can be harder to reproduce and compare results derived from GC application. Different ways of obtaining GC may potentially lead to inconsistent results. Here, we attempted to explore possibility of time-varying GC of finer granularity and to provide general guidelines for application of GC to neurocritical care time series affected by periods of missing values.

Design of green emitting CaZrO3:Tb3+ nanophosphor: Luminescence based platform for real-time ultrasensitive detection of latent fingerprints and anti-counterfeiting applications

Rare earth doped phosphors are widely applied to the numerous fields, such as luminescent paints, white-light emitting devices, photovoltaics, security inks, etc., owing to their inherent properties, such as sharp emission profiles, substantial stoke shifts, and long luminescence life time. Here, green emitting CaZrO3:Tb3+ (x = 1–11 mol %) nanophosphors have been successfully prepared by solution combustion route using Cordia myxa (lasura) leaves extract as fuel. The powder X-ray diffraction patterns shows orthorhombic crystal structure. Surface morphology of the samples showed porous and flakes type with large void spaces. Photoluminescence spectra exhibits a series of sharp peaks in the wavelength range of 475–650 nm, which ascribed to 5D4→7F6-3 transitions of the Tb3+ ions. The Commission Internationale de I'Eclairage chromaticity coordinates are evaluated from the photoluminescence emission spectra and exhibited co-ordinates (0.32, 0.40) located within the plane green region of the diagram. Highest color purity was achieved for 5 mol % of Tb3+ ions doped CaZrO3 nanophosphors with an average correlated color temperature value ~6025 K. Thermoluminescence properties of the prepared nanophosphors exposed with ultraviolet light exhibit two glow peaks at ~166 and 328 °C. A linear and sub-linear responses of the thermoluminescence intensity after ultraviolet light exposure on samples confirms that the samples are more suitable for dosimetry applications. Latent fingerprints are developed using optimized CaZrO3:Tb3+(5 mol %) nanophosphor exhibit high sensitivity and selectivity, high contrast and low background interference on different surfaces. More importantly, the single information encryption strategy was designed and used for high-level anti-counterfeiting. The results clearly indicate that the optimized nanophosphor has great potential for real time applications in white light emitting diodes, dosimetry and advanced forensic applications.

Modeling of cooling and heat conduction in permanent mold casting process

Motivated by the need to understand the heat transfer process in permanent mold casting, the heat conduction problem in the casting and the mold is modeled as transient one-dimensional heat flow in a double-layer cylinder with radial interfacial heat flux to emulate the cooling process of the casting. The cylinder is cooled down by dissipating heat to surrounding by convection. Green's function method, which obtained by separation of variables technique, is used to obtain a closed form solution of temperature distribution. The results of the derived analytical expressions are verified with numerical results of finite-element analysisand the published experimental results. The simulations are performed for different casting/mold materials with diversified thermo-physical properties to figure out the relationship between those properties and heat transfer process. The analytical results are justified by their good agreement with both of numerical and experimental results and its time efficiency in computation which offers advantages in potential real-time application to casting process monitoring. The mean absolute percentage error between the obtained temperatures using the developed analytical model and the measured ones was 14.5% and 2.4% for the casting and mold, respectively.

Real-time Semantic Segmentation with Context Aggregation Network

With the increasing demand of autonomous systems, pixelwise semantic segmentation for visual scene understanding needs to be not only accurate but also efficient for potential real-time applications. In this paper, we propose Context Aggregation Network, a dual branch convolutional neural network, with significantly lower computational costs as compared to the state-of-the-art, while maintaining a competitive prediction accuracy. Building upon the existing dual branch architectures for high-speed semantic segmentation, we design a high resolution branch for effective spatial detailing and a context branch with light-weight versions of global aggregation and local distribution blocks, potent to capture both long-range and local contextual dependencies required for accurate semantic segmentation, with low computational overheads. We evaluate our method on two semantic segmentation datasets, namely Cityscapes dataset and UAVid dataset. For Cityscapes test set, our model achieves state-of-the-art results with mIOU of 75.9%, at 76 FPS on an NVIDIA RTX 2080Ti and 8 FPS on a Jetson Xavier NX. With regards to UAVid dataset, our proposed network achieves mIOU score of 63.5% with high execution speed (15 FPS).

Intraprocedural Angiographic Signs Observed During Endovascular Thrombectomy in Patients With Acute Ischemic Stroke: A Systematic Review.

In the real-world practice of endovascular thrombectomy (EVT) for acute ischemic stroke (AIS), the analysis of intraprocedural angiographic signs (IPASs) still challenges neurointerventionists. This review provides insights into the significance of these subtle changes for predicting underlying etiology, technical feasibility, and patient prognosis, thus promoting the potential real-time application of these signs. A systematic literature search was conducted using PubMed, Ovid Medline/Embase, and Cochrane. The search focused on studies published between January 1995 and August 2020 that reported findings related to intraprocedural angiographic manifestations in endovascular recanalization therapy for AIS. We identified 12 IPASs in 22 studies involving 1,683 patients. The IPASs were assigned into 3 subsets according to their clinical meanings. The systematic analysis of IPAS in clinical trials and practice will lead to a better understanding of treatment effects, responses, and mechanisms during EVT. Studies of larger cohorts using more robust statistical methods are needed.

RAPIQUE: Rapid and Accurate Video Quality Prediction of User Generated Content

Blind or no-reference video quality assessment of user-generated content (UGC) has become a trending, challenging, heretofore unsolved problem. Accurate and efficient video quality predictors suitable for this content are thus in great demand to achieve more intelligent analysis and processing of UGC videos. Previous studies have shown that natural scene statistics and deep learning features are both sufficient to capture spatial distortions, which contribute to a significant aspect of UGC video quality issues. However, these models are either incapable or inefficient for predicting the quality of complex and diverse UGC videos in practical applications. Here we introduce an effective and efficient video quality model for UGC content, which we dub the Rapid and Accurate Video Quality Evaluator (RAPIQUE), which we show performs comparably to state-of-the-art (SOTA) models but with orders-of-magnitude faster runtime. RAPIQUE combines and leverages the advantages of both quality-aware scene statistics features and semantics-aware deep convolutional features, allowing us to design the first general and efficient spatial and temporal (space-time) bandpass statistics model for video quality modeling. Our experimental results on recent large-scale UGC video quality databases show that RAPIQUE delivers top performances on all the datasets at a considerably lower computational expense. We hope this work promotes and inspires further efforts towards practical modeling of video quality problems for potential real-time and low-latency applications. To promote public usage, an implementation of RAPIQUE has been made freely available online: \url{https://github.com/vztu/RAPIQUE}.