Purpose: The present study reviews the available scientific literature on artificial intelligence (AI)-assisted ultrasound-guided regional anaesthesia (UGRA) and evaluates the reported intraprocedural parameters and p...
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Purpose: The present study reviews the available scientific literature on artificial intelligence (AI)-assisted ultrasound-guided regional anaesthesia (UGRA) and evaluates the reported intraprocedural parameters and postprocedural outcomes. Methods: A literature search was performed on 19 September 2023, using the Medline, EMBASE, CINAHL, Cochrane Library and Google Scholar databases by experts in electronic searching. All study designs were considered with no restrictions regarding patient characteristics or cohort size. Outcomes assessed included the accuracy of AI-model tracking, success at the first attempt, differences in outcomes between AI-assisted and unassisted UGRA, operator feedback and case-report data. Results: A joint adaptive median binary pattern (JAMBP) has been applied to improve the tracking procedure, while a particle filter (PF) is involved in feature extraction. JAMBP combined with PF was most accurate on all images for landmark identification, with accuracy scores of 0.83, 0.93 and 0.93 on original, preprocessed and filtered images, respectively. Evaluation of first-attempt success of spinal needle insertion revealed first-attempt success in most patients. When comparing AI application versus UGRA alone, a significant statistical difference (p < 0.05) was found for correct block view, correct structure identification and decrease in mean injection time, needle track adjustments and bone encounters in favour of having AI assistance. Assessment of operator feedback revealed that expert and nonexpert operator feedback was overall positive. Conclusion: AI appears promising to enhance UGRA as well as to positively influence operator training. AI application of UGRA may improve the identification of anatomical structures and provide guidance for needle placement, reducing the risk of complications and improving patient outcomes. Level of EvidenceLevel IV.
This paper introduces a novel approach for the segmentation, detection, and tracking small animals and insects like butterflies and squirrels using the YOLOv8 object detection model and Deep SORT tracking algorithm. W...
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Among those most important resources for a particular country's expansion and expansion was electrical power. According to the most recent sustainability the growth paper, the continent continues to have a signifi...
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Wildlife Hazard Management is nowadays a very serious problem, mostly at airports and wind farms. If ignored, it may lead to repercussions in human safety, ecology, and economics. One of the approaches that is widely ...
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Wildlife Hazard Management is nowadays a very serious problem, mostly at airports and wind farms. If ignored, it may lead to repercussions in human safety, ecology, and economics. One of the approaches that is widely implemented in small and medium-size airports, as well as on wind turbines is based on a stereo-vision. However, to provide long-term observations allowing the determination of the hot spots of birds' activity and forecast future events, a robust tracking algorithm is required. The aim of this paper is to review tracking algorithms widely used in Radar Science and assess the possibilities of application of these algorithms for the purpose of tracking birds with a stereo-vision system. We performed a survey-of-related works and simulations determined five state-of-the art algorithms: Kalman Filter, Nearest-Neighbour, Joint-Probabilistic Data Association, and Interacting Multiple Model with the potential for implementation in a stereo-vision system. These algorithms have been implemented and simulated in the proposed case study
The paper presents a statistical analysis of long-lived detected mesoscale eddies in the Lofoten Basin (LB). An automated eddy iden-tification and tracking method is applied to the altimeter data during the time-perio...
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The paper presents a statistical analysis of long-lived detected mesoscale eddies in the Lofoten Basin (LB). An automated eddy iden-tification and tracking method is applied to the altimeter data during the time-period 1993-2017 to detect and track anticyclonic (ACEs) and cyclonic eddies (CEs) in the LB. Our analysis found that only one percent of the eddies detected are long-lived (eddy-life > 35 days). Even though only 1%, the detected 330 long-lived mesoscale eddies (CEs, 120;ACEs, 210) account for >11,550 daily individual eddy-observations. The lifetime, occurrence, generation sites, size, intensity, and drift of these long-lived eddies are quantified. The average drift speed of long-lived eddies is found to show a pronounced seasonal variation with a maximum from October to March. Long-lived eddies in the LB are further divided into four groups based on their region of generation and dissipation. Long-lived eddies gen-erated and dissipated outside the region of the Lofoten Vortex (group 2 eddies) are found to be the predominant type (CEs, 73.3%;ACEs, 69.5%). The eddies generated and dissipated in the region of the permanent Lofoten Vortex form the second dominant type (CEs, 14.3%;ACEs, 26.2%). Based on their lifetime, properties of the two predominant groups of eddies are examined in detail. The difference found in the temporal variability of the eddy characteristics of the two groups reflects their different genesis. The analysis revealed that the mesoscale eddies of group 2 have a longer life than eddies of group 1, and ACEs are more long-lived in comparison to CEs. The analysis also found three main areas of eddy generation in the frontal zone of the NwASC from where mesoscale eddies propagate to the north-west, forming three main corridors of trajectories. The study further provides evidence of long-lived cyclonic CEs surrounding the large quasi-permanent Lofoten Vortex (LV) and forming a shield around it. Small CEs located in two areas with centers at 69.5
The goal of maritime situational awareness (MSA) is to provide a seamless wide-area operational picture of ship traffic in coastal areas and the oceans in real time. Radar is a central sensing modality for MSA. In par...
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The goal of maritime situational awareness (MSA) is to provide a seamless wide-area operational picture of ship traffic in coastal areas and the oceans in real time. Radar is a central sensing modality for MSA. In particular, oceanographic high-frequency surface-wave (HFSW) radars are attractive for surveying large sea areas at over-the-horizon distances, due to their low environmental footprint and low power requirements. However, their design is not optimal for the challenging conditions prevalent in MSA applications, thus calling for the development of dedicated information fusion and multisensor-multitarget tracking algorithms. In this study, the authors show how the multisensor-multitarget tracking problem can be formulated in a Bayesian framework and efficiently solved by running the loopy sum-product algorithm on a suitably devised factor graph. Compared to previously proposed methods, this approach is advantageous in terms of estimation accuracy, computational complexity, implementation flexibility, and scalability. Moreover, its performance can be further enhanced by estimating unknown model parameters in an online fashion and by fusing automatic identification system (AIS) data and context-based information. The effectiveness of the proposed Bayesian multisensor-multitarget tracking and information fusion algorithms is demonstrated through experimental results based on simulated data as well as real HFSW radar data and real AIS data.
Functional near infrared spectroscopy (fNIRS) is a neuroimaging technique that allows to monitor the functional hemoglobin oscillations related to cortical activity. One of the main issues related to fNIRS application...
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Functional near infrared spectroscopy (fNIRS) is a neuroimaging technique that allows to monitor the functional hemoglobin oscillations related to cortical activity. One of the main issues related to fNIRS applications is the motion artefact removal, since a corrupted physiological signal is not correctly indicative of the underlying biological process. A novel procedure for motion artifact correction for fNIRS signals based on wavelet transform and video tracking developed for infrared thermography (IRT) is presented. In detail, fNIRS and IRT were concurrently recorded and the optodes' movement was estimated employing a video tracking procedure developed for IRT recordings. The wavelet transform of the fNIRS signal and of the optodes' movement, together with their wavelet coherence, were computed. Then, the inverse wavelet transform was evaluated for the fNIRS signal excluding the frequency content corresponding to the optdes' movement and to the coherence in the epochs where they were higher with respect to an established threshold. The method was tested using simulated functional hemodynamic responses added to real resting-state fNIRS recordings corrupted by movement artifacts. The results demonstrated the effectiveness of the procedure in eliminating noise, producing results with higher signal to noise ratio with respect to another validated method.
In neuroscience research, laboratory animals and especially monkeys are often used in experiments. Besides the neural signals provided by radio from a sensor array mounted on the monkey head, also the head position an...
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ISBN:
(纸本)9781728188034
In neuroscience research, laboratory animals and especially monkeys are often used in experiments. Besides the neural signals provided by radio from a sensor array mounted on the monkey head, also the head position and orientation are needed for a complete analysis. In this paper, a monkey tracking and head orientation computation method is proposed. The procedure was validated using a database of four videos, visualizing experiments of a total duration of 3 hours and 48 minutes.
The capability of detecting and tracking targets can play a significant role in mobile robot navigation systems. Visual tracking systems may control the direction and speed of motion of a robot to keep the target in i...
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The capability of detecting and tracking targets can play a significant role in mobile robot navigation systems. Visual tracking systems may control the direction and speed of motion of a robot to keep the target in its field of view either by moving the robot itself or the vision sensors. In this work, a compact size tracking and guiding system that could be mounted on weight-sensitive UAV platforms is presented. The system combines a motion detection technique that could be used with non-static cameras in addition to color filtering to detect and track objects in the field of view of a UAV. This hybrid system provides a reliable tracking system for low resolution images taken by a UAV camera. The proposed system implements keypoint detection algorithms including SIFT, SURF and FAST, a motion detection method using frame subtraction and object detection algorithms using color back projection in a hybrid approach that utilizes the best of each algorithm and avoids heavy usage of computing resources. Keypoint detectors SURF, SIFT and FAST are tested and implemented for the purpose of image alignment and frame subtractions. Experimental tests showed the system's ability to detect and track low detailed targets. The system is tested on a UAV using a Raspberry Pi 2 mini-computer running OpenCV libraries and was able to process eleven frames per second implementing object detection and tracking. The test objects were mainly cars monitored from different altitudes through a UAV downward pointing camera.
This paper describes the track-finding algorithm that is used for event reconstruction in the Belle II experiment operating at the SuperKEKB B-factory in Tsukuba, Japan. The algorithm is designed to balance the requir...
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This paper describes the track-finding algorithm that is used for event reconstruction in the Belle II experiment operating at the SuperKEKB B-factory in Tsukuba, Japan. The algorithm is designed to balance the requirements of a high efficiency to find charged particles with a good track parameter resolution, a low rate of spurious tracks, and a reasonable demand on CPU resources. The software is implemented in a flexible, modular manner and employs a diverse selection of global and local track-finding algorithms to achieve an optimal performance. (C) 2020 Elsevier B.V. All rights reserved.
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