scholarly journals Correction of respiratory artifacts in MRI head motion estimates

2018 ◽  
Author(s):  
Damien A. Fair ◽  
Oscar Miranda-Dominguez ◽  
Abraham Z. Snyder ◽  
Anders Perrone ◽  
Eric A. Earl ◽  
...  
Keyword(s):  
Real Time ◽  
Functional Mri ◽  
Brain Mri ◽  
Body Motion ◽  
Head Motion ◽  
Single Shot ◽  
Resting State Functional Connectivity ◽  
Novel Approach ◽  
Field Fluctuations ◽  
Functional Connectivity Mri

AbstractHead motion represents one of the greatest technical obstacles for brain MRI. Accurate detection of artifacts induced by head motion requires precise estimation of movement. However, this estimation may be corrupted by factitious effects owing to main field fluctuations generated by body motion. In the current report, we examine head motion estimation in multiband resting state functional connectivity MRI (rs-fcMRI) data from the Adolescent Brain and Cognitive Development (ABCD) Study and a comparison ‘single-shot’ dataset from Oregon Health & Science University. We show unequivocally that respirations contaminate movement estimates in functional MRI and that respiration generates apparent head motion not associated with degraded quality of functional MRI. We have developed a novel approach using a band-stop filter that accurately removes these respiratory effects. Subsequently, we demonstrate that utilizing this filter improves post-processing data quality. Lastly, we demonstrate the real-time implementation of motion estimate filtering in our FIRMM (Framewise Integrated Real-Time MRI Monitoring) software package.

scholarly journals Integrating Task-Based Functional MRI Across Tasks Markedly Boosts Prediction and Reliability of Brain-Cognition Relationship

2021 ◽  
Author(s):  
Alina Tetereva ◽  
Jean Li ◽  
Jeremiah Deng ◽  
Argyris Stringaris ◽  
Narun Pat
Keyword(s):  
Individual Differences ◽  
Functional Mri ◽  
Cognitive Abilities ◽  
Brain Mri ◽  
Integration Theory ◽  
Sources Of Information ◽  
Resting State Functional Connectivity ◽  
Retest Reliability ◽  
Human Connectome Project ◽  
Test Retest Reliability

Capturing individual differences in cognitive abilities is central to human neuroscience. Yet our ability to estimate cognitive abilities via brain MRI is still poor in both prediction and reliability. Our study tested if this inability was partly due to the over-reliance on 1) non-task MRI modalities and 2) single modalities. We directly compared predictive models comprising of different sets of MRI modalities (e.g., task vs. non-task). Using the Human Connectome Project (n=873 humans, 473 females, after exclusions), we integrated task-based functional MRI (tfMRI) across seven tasks along with other non-task MRI modalities (structural MRI, resting-state functional connectivity) via a machine-learning, stacking approach. The model integrating all modalities provided unprecedented prediction (r=.581) and excellent test-retest reliability (ICC>.75) in capturing general cognitive abilities. Importantly, comparing to the model integrating among non-task modalities (r=.367), integrating tfMRI across tasks led to significantly higher prediction (r=.544) while still providing excellent test-retest reliability (ICC>.75). The model integrating tfMRI across tasks was driven by areas in the frontoparietal network and by tasks that are cognition-related (working-memory, relational processing, and language). This result is consistent with the parieto-frontal integration theory of intelligence. Accordingly, our results sharply contradict the recently popular notion that tfMRI is not appropriate for capturing individual differences in cognition. Instead, our study suggests that tfMRI, when used appropriately (i.e., by drawing information across the whole brain and across tasks and by integrating with other modalities), provides predictive and reliable sources of information for individual differences in cognitive abilities, more so than non-task modalities.

A novel approach to the real-time modelling of large urban drainage systems

1997 ◽  
Vol 36 (8-9) ◽  
pp. 19-24 ◽  
Author(s):  
Richard Norreys ◽  
Ian Cluckie
Keyword(s):  
Real Time ◽  
Dynamic Models ◽  
Drainage System ◽  
Radar Data ◽  
Urban Drainage ◽  
Real Time Control ◽  
Empirical Modelling ◽  
Time Control ◽  
Novel Approach ◽  
Non Linear Systems

Conventional UDS models are mechanistic which though appropriate for design purposes are less well suited to real-time control because they are slow running, difficult to calibrate, difficult to re-calibrate in real time and have trouble handling noisy data. At Salford University a novel hybrid of dynamic and empirical modelling has been developed, to combine the speed of the empirical model with the ability to simulate complex and non-linear systems of the mechanistic/dynamic models. This paper details the ‘knowledge acquisition module’ software and how it has been applied to construct a model of a large urban drainage system. The paper goes on to detail how the model has been linked with real-time radar data inputs from the MARS c-band radar.

A novel approach for phishing URLs detection using lexical based machine learning in a real-time environment

2021 ◽  
Author(s):  
Brij B. Gupta ◽  
Krishna Yadav ◽  
Imran Razzak ◽  
Konstantinos Psannis ◽  
Arcangelo Castiglione ◽  
...  
Keyword(s):  
Machine Learning ◽  
Real Time ◽  
Novel Approach

SFSSD: A Select Feature Single-shot Detector for Real-time Target detection

2020 ◽  
Author(s):  
Shengyang Wang ◽  
Zhi Li ◽  
Xinpin Rao
Keyword(s):  
Real Time ◽  
Target Detection ◽  
Single Shot

scholarly journals A novel approach to optimize the production cost of railway coaches of India using situational-based composite triangular and trapezoidal fuzzy LPP models

2021 ◽  
Author(s):  
Rakesh Kumar ◽  
Gaurav Dhiman ◽  
Neeraj Kumar ◽  
Rajesh Kumar Chandrawat ◽  
Varun Joshi ◽  
...  
Keyword(s):  
Comparative Study ◽  
Real Time ◽  
Production Cost ◽  
Upper Bounds ◽  
Production Costs ◽  
Cost Data ◽  
Novel Approach ◽  
Membership Grade ◽  
Minimum Costs

AbstractThis article offers a comparative study of maximizing and modelling production costs by means of composite triangular fuzzy and trapezoidal FLPP. It also outlines five different scenarios of instability and has developed realistic models to minimize production costs. Herein, the first attempt is made to examine the credibility of optimized cost via two different composite FLP models, and the results were compared with its extension, i.e., the trapezoidal FLP model. To validate the models with real-time phenomena, the Production cost data of Rail Coach Factory (RCF) Kapurthala has been taken. The lower, static, and upper bounds have been computed for each situation, and then systems of optimized FLP are constructed. The credibility of each model of composite-triangular and trapezoidal FLP concerning all situations has been obtained, and using this membership grade, the minimum and the greatest minimum costs have been illustrated. The performance of each composite-triangular FLP model was compared to trapezoidal FLP models, and the intense effects of trapezoidal on composite fuzzy LPP models are investigated.

scholarly journals Machine Learning Solutions for Bridge Scour Forecast Based on Monitoring Data

2021 ◽  
pp. 036119812110126
Author(s):  
Negin Yousefpour ◽  
Steve Downie ◽  
Steve Walker ◽  
Nathan Perkins ◽  
Hristo Dikanski
Keyword(s):  
Machine Learning ◽  
Real Time ◽  
Monitoring Data ◽  
Sensor Data ◽  
Monitoring Systems ◽  
Bridge Scour ◽  
Scour Monitoring ◽  
Novel Approach ◽  
Bayesian Inference Method ◽  
Water Level Variations

Bridge scour is a challenge throughout the U.S.A. and other countries. Despite the scale of the issue, there is still a substantial lack of robust methods for scour prediction to support reliable, risk-based management and decision making. Throughout the past decade, the use of real-time scour monitoring systems has gained increasing interest among state departments of transportation across the U.S.A. This paper introduces three distinct methodologies for scour prediction using advanced artificial intelligence (AI)/machine learning (ML) techniques based on real-time scour monitoring data. Scour monitoring data included the riverbed and river stage elevation time series at bridge piers gathered from various sources. Deep learning algorithms showed promising in prediction of bed elevation and water level variations as early as a week in advance. Ensemble neural networks proved successful in the predicting the maximum upcoming scour depth, using the observed sensor data at the onset of a scour episode, and based on bridge pier, flow and riverbed characteristics. In addition, two of the common empirical scour models were calibrated based on the observed sensor data using the Bayesian inference method, showing significant improvement in prediction accuracy. Overall, this paper introduces a novel approach for scour risk management by integrating emerging AI/ML algorithms with real-time monitoring systems for early scour forecast.

scholarly journals Deep Reinforcement Learning for End-to-End Local Motion Planning of Autonomous Aerial Robots in Unknown Outdoor Environments: Real-Time Flight Experiments

Sensors ◽  
2021 ◽  
Vol 21 (7) ◽  
pp. 2534
Author(s):  
Oualid Doukhi ◽  
Deok-Jin Lee
Keyword(s):  
Reinforcement Learning ◽  
Real Time ◽  
Autonomous Navigation ◽  
Control Technique ◽  
Local Motion ◽  
Aerial Robots ◽  
Novel Approach ◽  
Outdoor Environments ◽  
Aerial Vehicle ◽  
High Level

Autonomous navigation and collision avoidance missions represent a significant challenge for robotics systems as they generally operate in dynamic environments that require a high level of autonomy and flexible decision-making capabilities. This challenge becomes more applicable in micro aerial vehicles (MAVs) due to their limited size and computational power. This paper presents a novel approach for enabling a micro aerial vehicle system equipped with a laser range finder to autonomously navigate among obstacles and achieve a user-specified goal location in a GPS-denied environment, without the need for mapping or path planning. The proposed system uses an actor–critic-based reinforcement learning technique to train the aerial robot in a Gazebo simulator to perform a point-goal navigation task by directly mapping the noisy MAV’s state and laser scan measurements to continuous motion control. The obtained policy can perform collision-free flight in the real world while being trained entirely on a 3D simulator. Intensive simulations and real-time experiments were conducted and compared with a nonlinear model predictive control technique to show the generalization capabilities to new unseen environments, and robustness against localization noise. The obtained results demonstrate our system’s effectiveness in flying safely and reaching the desired points by planning smooth forward linear velocity and heading rates.

Design of an Intelligent Data Cache with Replacement Policy

2019 ◽  
Vol 10 (2) ◽  
pp. 87-107
Author(s):  
B. Shameedha Begum ◽  
N. Ramasubramanian
Keyword(s):  
Real Time ◽  
Replacement Policy ◽  
Data Cache ◽  
Cache Performance ◽  
Performance Requirements ◽  
Novel Approach ◽  
Reconfigurable Caches ◽  
Real Time Applications ◽  
Hard Real Time ◽  
Replacement Algorithms

Embedded systems are designed for a variety of applications ranging from Hard Real Time applications to mobile computing, which demands various types of cache designs for better performance. Since real-time applications place stringent requirements on performance, the role of the cache subsystem assumes significance. Reconfigurable caches meet performance requirements under this context. Existing reconfigurable caches tend to use associativity and size for maximizing cache performance. This article proposes a novel approach of a reconfigurable and intelligent data cache (L1) based on replacement algorithms. An intelligent embedded data cache and a dynamic reconfigurable intelligent embedded data cache have been implemented using Verilog 2001 and tested for cache performance. Data collected by enabling the cache with two different replacement strategies have shown that the hit rate improves by 40% when compared to LRU and 21% when compared to MRU for sequential applications which will significantly improve performance of embedded real time application.

scholarly journals The Use of Infrared Thermography as a Novel Approach for Real-Time Validation of PCR Thermocyclers

2009 ◽  
Vol 3 (2) ◽  
pp. 116-119 ◽  
Author(s):  
Hugo Ahlm Grønlund ◽  
Charlotta Löfström ◽  
Jens Bue Helleskov ◽  
Jeffrey Hoorfar
Keyword(s):  
Real Time ◽  
Infrared Thermography ◽  
Novel Approach

Free circulating nucleic acids in plasma and serum as a novel approach to the use of internal controls in real time PCR based detection

2014 ◽  
Vol 207 ◽  
pp. 133-137 ◽  
Author(s):  
Ersin Karataylı ◽  
Yasemin Çelik Altunoğlu ◽  
Senem Ceren Karataylı ◽  
Cihan Yurdaydın ◽  
A. Mithat Bozdayı
Keyword(s):  
Nucleic Acids ◽  
Real Time ◽  
Real Time Pcr ◽  
Internal Controls ◽  
Circulating Nucleic Acids ◽  
Novel Approach
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