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Tsai, Yi-Chang (James), Yi-Ching Wu, and Geoffrey Price. "A Cost-Effective and Objective Full-Depth Patching Identification Method using 3D Sensing Technology with Automated Crack Detection and Classification." Transportation Research Record: Journal of the Transportation Research Board 2672, no. 40 (2018): 50–58. http://dx.doi.org/10.1177/0361198118798474.
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Full-depth patching is one of the commonly used asphalt pavement maintenance and rehabilitation methods in which deteriorated base and surface layers are repaired to restore strength and improve ride quality. During resurfacing projects, areas requiring full-depth patching are identified and quantified as construction priorities because of the high costs associated with the labor and materials for the procedure. Currently, the manual surveys conducted to identify these locations are time-consuming and labor-intensive. Thus, large projects often cannot easily quantify the full-depth patching need because of the significant labor that would be required. This paper proposes a method that uses emerging 3D laser technology to identify the full-depth patching need by processing and analyzing the pavement distresses automatically extracted from 3D laser images. The proposed method consists of five steps: (1) 3D data acquisition, calibration, and validation, (2) crack detection, (3) crack classification, (4) rutting detection and measurement, and (5) determination of image-based patching need using the established decision tree. A case study of one mile of 3D pavement images, collected from US 80/S.R. 26, was conducted to demonstrate the use and feasibility of the proposed method. Results show the proposed method is capable of correctly classifying 95.4% of the images that show pavements requiring patching and 84.2% of the images showing pavements not requiring patching for a combined accuracy of 94.1%. The method shows promise for identifying patch locations in a cost-effective manner and will save money and time for transportation agencies.
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Zhong, Haidong, Xianyi Chen, and Qinglong Tian. "An Improved Reversible Image Transformation Using K-Means Clustering and Block Patching." Information 10, no. 1 (2019): 17. http://dx.doi.org/10.3390/info10010017.
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Recently, reversible image transformation (RIT) technology has attracted considerable attention because it is able not only to generate stego-images that look similar to target images of the same size, but also to recover the secret image losslessly. Therefore, it is very useful in image privacy protection and reversible data hiding in encrypted images. However, the amount of accessorial information, for recording the transformation parameters, is very large in the traditional RIT method, which results in an abrupt degradation of the stego-image quality. In this paper, an improved RIT method for reducing the auxiliary information is proposed. Firstly, we divide secret and target images into non-overlapping blocks, and classify these blocks into K classes by using the K-means clustering method. Secondly, we match blocks in the last (K-T)-classes using the traditional RIT method for a threshold T, in which the secret and target blocks are paired with the same compound index. Thirdly, the accessorial information (AI) produced by the matching can be represented as a secret segment, and the secret segment can be hided by patching blocks in the first T-classes. Experimental results show that the proposed strategy can reduce the AI and improve the stego-image quality effectively.
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Gong, Yan. "Panoramic Image Patching Algorithm Based on Global Optimization." Journal of Information and Computational Science 12, no. 14 (2015): 5523–30. http://dx.doi.org/10.12733/jics20150019.
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Celaya-Padilla, Jose M., Carlos E. Galvan T, J. Ruben Delgado C, Issac Galvan-Tejada, and Ernesto Ivan Sandoval. "Multi-seed texture synthesis to fast image patching." Procedia Engineering 35 (2012): 210–16. http://dx.doi.org/10.1016/j.proeng.2012.04.182.
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Totsuka, Satoru, Tomoya Handa, Hitoshi Ishikawa, and Nobuyuki Shoji. "Improvement of Adherence with Occlu-Pad Therapy for Pediatric Patients with Amblyopia." BioMed Research International 2018 (November 22, 2018): 1–5. http://dx.doi.org/10.1155/2018/2394562.
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We aimed to examine visual acuity improvement effect and adherence in amblyopia training using tablet type vision training equipment (Occlu-pad). The subjects were 138 patients with amblyopia (average age of 5.5 ± 1.6 years old); their amblyopic visual acuity at the start of training was logMAR 0.15 to 1.3. Occlu-pad is a device that processes images such that amblyopic eyes can only view the image as it passes through polarized glasses; this is achieved by peeling off the polarizing film layer in the liquid crystal display of an iPad (Apple). Amblyopia training comprised either the instructional training with Occlu-pad or the eye patch (Patching) as a family training, after wearing perfectly corrected glasses. Visual acuity improvement following amblyopia training by Occlu-pad and Patching was significantly different after 6 months in patients with anisometropic amblyopia (p <0.05). In patients with strabismic amblyopia, a significant difference between training methods was observed after 9 months (p <0.05). Use of the Occlu-pad resulted in better adherence for patients with either anisometropic amblyopia or strabismic amblyopia; a significant difference in adherence was observed after 3 months, compared with Patching (p <0.05). Amblyopia training with Occlu-pad supports greater visual acuity improvement and adherence than Patching.
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Dan, Han-Cheng, Hao-Fan Zeng, Zhi-Heng Zhu, Ge-Wen Bai, and Wei Cao. "Methodology for Interactive Labeling of Patched Asphalt Pavement Images Based on U-Net Convolutional Neural Network." Sustainability 14, no. 2 (2022): 861. http://dx.doi.org/10.3390/su14020861.
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Image recognition based on deep learning generally demands a huge sample size for training, for which the image labeling becomes inevitably laborious and time-consuming. In the case of evaluating the pavement quality condition, many pavement distress patching images would need manual screening and labeling, meanwhile the subjectivity of the labeling personnel would greatly affect the accuracy of image labeling. In this study, in order for an accurate and efficient recognition of the pavement patching images, an interactive labeling method is proposed based on the U-Net convolutional neural network, using active learning combined with reverse and correction labeling. According to the calculation results in this paper, the sample size required by the interactive labeling is about half of the traditional labeling method for the same recognition precision. Meanwhile, the accuracy of interactive labeling method based on the mean intersection over union (mean_IOU) index is 6% higher than that of the traditional method using the same sample size and training epochs. In addition, the accuracy analysis of the noise and boundary of the prediction results shows that this method eliminates 92% of the noise in the predictions (the proportion of noise is reduced from 13.85% to 1.06%), and the image definition is improved by 14.1% in terms of the boundary gray area ratio. The interactive labeling is considered as a significantly valuable approach, as it reduces the sample size in each epoch of active learning, greatly alleviates the demand for manpower, and improves learning efficiency and accuracy.
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TOKUDA, Kenichi, Tetsuya KINUGASA, Ryota HAYASHI, Takafumi HAJI, and Hisanori AMANO. "Shredded Image Patching of Inner Crawler Cameras for Disaster Robot." Proceedings of Mechanical Engineering Congress, Japan 2016 (2016): G1500504. http://dx.doi.org/10.1299/jsmemecj.2016.g1500504.
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Takahashi, Ryo, Takashi Matsubara, and Kuniaki Uehara. "Data Augmentation Using Random Image Cropping and Patching for Deep CNNs." IEEE Transactions on Circuits and Systems for Video Technology 30, no. 9 (2020): 2917–31. http://dx.doi.org/10.1109/tcsvt.2019.2935128.
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Maeda, Keisuke, Saya Takada, Tomoki Haruyama, Ren Togo, Takahiro Ogawa, and Miki Haseyama. "Distress Detection in Subway Tunnel Images via Data Augmentation Based on Selective Image Cropping and Patching." Sensors 22, no. 22 (2022): 8932. http://dx.doi.org/10.3390/s22228932.
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Distresses, such as cracks, directly reflect the structural integrity of subway tunnels. Therefore, the detection of subway tunnel distress is an essential task in tunnel structure maintenance. This paper presents the performance improvement of deep learning-based distress detection to support the maintenance of subway tunnels through a new data augmentation method, selective image cropping and patching (SICAP). Specifically, we generate effective data for training the distress detection model by focusing on the distressed regions via SICAP. After the data augmentation, we train a distress detection model using the expanded training data. The new image generated based on SICAP does not change the pixel values of the original image. Thus, there is little loss of information, and the generated images are effective in constructing a robust model for various subway tunnel lines. We conducted experiments with some comparative methods. The experimental results show that the detection performance can be improved by our data augmentation.
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WANG, DACHENG, and SARGUR N. SRIHARI. "ANALYSIS OF FORM IMAGES." International Journal of Pattern Recognition and Artificial Intelligence 08, no. 05 (1994): 1031–52. http://dx.doi.org/10.1142/s0218001494000528.
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Automatic analysis of images of forms is a problem of both practical and theoretical interest; due to its importance in office automation, and due to the conceptual challenges posed for document image analysis, respectively. We describe an approach to the extraction of text, both typed and handwritten, from scanned and digitized images of filled-out forms. In decomposing a filled-out form into three basic components of boxes, line segments and the remainder (handwritten and typed characters, words, and logos), the method does not use a priori knowledge of form structure. The input binary image is first segmented into small and large connected components. Complex boxes are decomposed into elementary regions using an approach based on key-point analysis. Handwritten and machine-printed text that touches or overlaps guide lines and boxes are separated by removing lines. Characters broken by line removal are rejoined using a character patching method. Experimental results with filled-out forms, from several different domains (insurance, banking, tax, retail and postal) are given.
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Wu (吴秋雨), Qiuyu, Ilya Kolb, Brendan M. Callahan, et al. "Integration of autopatching with automated pipette and cell detection in vitro." Journal of Neurophysiology 116, no. 4 (2016): 1564–78. http://dx.doi.org/10.1152/jn.00386.2016.
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Patch clamp is the main technique for measuring electrical properties of individual cells. Since its discovery in 1976 by Neher and Sakmann, patch clamp has been instrumental in broadening our understanding of the fundamental properties of ion channels and synapses in neurons. The conventional patch-clamp method requires manual, precise positioning of a glass micropipette against the cell membrane of a visually identified target neuron. Subsequently, a tight “gigaseal” connection between the pipette and the cell membrane is established, and suction is applied to establish the whole cell patch configuration to perform electrophysiological recordings. This procedure is repeated manually for each individual cell, making it labor intensive and time consuming. In this article we describe the development of a new automatic patch-clamp system for brain slices, which integrates all steps of the patch-clamp process: image acquisition through a microscope, computer vision-based identification of a patch pipette and fluorescently labeled neurons, micromanipulator control, and automated patching. We validated our system in brain slices from wild-type and transgenic mice expressing channelrhodopsin 2 under the Thy1 promoter (line 18) or injected with a herpes simplex virus-expressing archaerhodopsin, ArchT. Our computer vision-based algorithm makes the fluorescent cell detection and targeting user independent. Compared with manual patching, our system is superior in both success rate and average trial duration. It provides more reliable trial-to-trial control of the patching process and improves reproducibility of experiments.
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Du, Juan. "AIVMAF: Automatic Image Quality Estimation Based on Improved VMAF and YOLOv4." Journal of Physics: Conference Series 2289, no. 1 (2022): 012020. http://dx.doi.org/10.1088/1742-6596/2289/1/012020.
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Abstract The current most widely used way of image quality estimation relies heavily on the subjective assessment, while majority of past objective estimation methods are not satisfactory on accuracy. To solve them and realize unsupervised image quality estimation with high precision, this paper creates a linear way with “Proportional Partition” controlled by horizontal and vertical rates of extracted pixel to get best representations of the image with patching, balance the uneven distribution of image quality in each source image, and offer dynamic compatibility to images with high resolution. Besides, it estimates the image quality automatically with a model trained by current best artificial intelligence (AI) algorithm for target detection YOLOv4 with 1000 images random selected from ImageNet2013 database. The proposal also uses the spirit of joint indices from the current widely used method named Video Multimethod Assessment Fusion (VMAF). But we replace its Visual Information Fidelity (VIF) with Visual Saliency-induced Index (VSI) and add VSI to our target function because of VIF’s dependence on subjective assessment, and also for VSI’s better performance surpassing most recent IQA estimators as TOP3 best model in recent world. Besides, contrast masking is also included by objective function for the KL-divergence to simulate the human visual perception better. A creative “Batch Learning” way is found to address patches for less calculation and faster speed. All source images are pretreated with colour space transformation and normalization to improve descriptiveness of images and reduce the redundant points, and a threshold is devised to formulate suppression mechanisms. The proposed solution is tested to be a good image quality assessor in many aspects such as correctness, consistency, linearity, monotonicity and speed, and performs well on even HD images.
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Hu, Yi, Yuyi Yao, and Zhen Cheng. "Low-Rank Tensor Patching Based on Convolutional Sparse Coding for Communication Data Repair." Mobile Information Systems 2022 (September 5, 2022): 1–14. http://dx.doi.org/10.1155/2022/8194834.
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The study aimed to solve the common problem that hardware limitations and degradation make the data obtained in reality usually incomplete and improve the quality of communication transmission. In this paper, we propose a new low-rank tensor complementation model LRTC-CSC, which is based on tensor kernel parametrization (TNN), preserves the low-rank structure of information while restoring the detail features, and finally solves the problem using the efficient alternating direction multiplier method (ADMM). Based on the low-rank nature of the tensor, adding convolutional sparse coding (CSC) can well represent the characteristics of the high-frequency part of the information to handle the details while recovering the global information. The experimental results show that the training set of this paper saves much time compared with other models in several metrics by using only ten images of similar color for each data. At the same time, the data recovery effect is much higher than the novel TV canonical prior. In particular, the LRTC-CSC model is 5.18 dB higher than the LRTC-TV model in terms of PSNR value for image recovery at a 70% missing rate. The LRTC-CSC model proposed in this paper is more accurate and efficient for communication data restoration.
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Li, Wansong, Xianyu Su, and Zhongbao Liu. "Large-scale three-dimensional object measurement: a practical coordinate mapping and image data-patching method." Applied Optics 40, no. 20 (2001): 3326. http://dx.doi.org/10.1364/ao.40.003326.
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Spiecker, Heinrich, Martin G. Schinker, Jörg Hansen, Young-In Park, Tilman Ebding, and Walter Döll. "CELL STRUCTURE IN TREE RINGS: NOVEL METHODS FOR PREPARATION AND IMAGE ANALYSIS OF LARGE CROSS SECTIONS." IAWA Journal 21, no. 3 (2000): 361–73. http://dx.doi.org/10.1163/22941932-90000253.
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A new method for the preparation of wood samples for high-resolution reflected-light microscopy in combination with a species-specific image analysis system is introduced. The method is based on an ultraprecise single-point diamond flycutter equipped with air bearings and functions on the basis of a rotating diamond edge. Extensive and very smooth surfaces can be produced on increment cores as well as entire cross sections. Hence, this technique provides the prerequisites for a continuous microscopic analysis across large tree-ring sequences, or entire cross sections. Patching or sub-sectioning is not required. The high-resolution analysis of cell structures of spruce and oak presented here introduces new approaches for describing and predicting reactions of diameter growth to various environmental events.
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Pan, Wu Min. "Dynamic Update Mechanism in Wireless Sensor Networks." Applied Mechanics and Materials 526 (February 2014): 267–72. http://dx.doi.org/10.4028/www.scientific.net/amm.526.267.
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Many researches use diff environmental conditions or application requirements in WSN. In the default Deluge mechanism, it will recover a sensor node from an updating error by reloading the stored full image again or waiting for the host machine to retransmit the full image again. This strategy is easy to implement and intuitive, but replacing the current executing image by retransmitting a full image file again is resource-consuming. To avoid retransmitting the full image when performing recovery, at the time when diff-based updating procedures have been finished, sensor nodes using our recovery mechanisms will backup the received patch files (i.e. diff script) in flash memory. Our mechanisms would effectively utilize flash memory space to store several backup patch files. When recovery is needed, our mechanismswould incrementally recover a failed node by patching up the system with each of the backup patch files. In the design of our recovery mechanisms, the failed sensor node will first try its best to recover itself without the assistance of the host machine, in order to avoid affecting the operations of other normal nodes when performing recovery procedures. Thus, compared with the full image replacement strategy, our mechanisms can save many computing resources.
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Ayres, Nicholas, Lipika Deka, and Daniel Paluszczyszyn. "Continuous Automotive Software Updates through Container Image Layers." Electronics 10, no. 6 (2021): 739. http://dx.doi.org/10.3390/electronics10060739.
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The vehicle-embedded system also known as the electronic control unit (ECU) has transformed the humble motorcar, making it more efficient, environmentally friendly, and safer, but has led to a system which is highly dependent on software. As new technologies and features are included with each new vehicle model, the increased reliance on software will no doubt continue. It is an undeniable fact that all software contains bugs, errors, and potential vulnerabilities, which when discovered must be addressed in a timely manner, primarily through patching and updates, to preserve vehicle and occupant safety and integrity. However, current automotive software updating practices are ad hoc at best and often follow the same inefficient fix mechanisms associated with a physical component failure of return or recall. Increasing vehicle connectivity heralds the potential for over the air (OtA) software updates, but rigid ECU hardware design does not often facilitate or enable OtA updating. To address the associated issues regarding automotive ECU-based software updates, a new approach in how automotive software is deployed to the ECU is required. This paper presents how lightweight virtualisation technologies known as containers can promote efficient automotive ECU software updates. ECU functional software can be deployed to a container built from an associated image. Container images promote efficiency in download size and times through layer sharing, similar to ECU difference or delta flashing. Through containers, connectivity and OtA future software updates can be completed without inconveniences to the consumer or incurring expense to the manufacturer.
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Haverstick, D. M., and M. Glaser. "Visualization of domain formation in the inner and outer leaflets of a phospholipid bilayer." Journal of Cell Biology 106, no. 6 (1988): 1885–92. http://dx.doi.org/10.1083/jcb.106.6.1885.
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Large vesicles (5-10-micron in diameter) were formed in the presence of phospholipids fluorescently labeled on the acyl chain and visualized using a fluorescence microscope, charge-coupled-device camera and digital image processor. When such vesicles contained a fluorescent phosphatidic acid (PA) and were exposed to 2 mM CaCl2 or 0.5 mM PrCl3, it was possible to visualize PA-enriched domains within the vesicles. Calcium-induced domain formation was reversible in the presence of 4 mM EGTA. Vesicles were formed containing fluorescent PA on either the inner or outer leaflet of the bilayer and the patching and dissolution of patching were studied under conditions where calcium was present on the outside of the vesicle and where calcium was distributed across the bilayer. In addition, vesicles were formed with two different fluorescent PA's, one on the inner leaflet and a different one on the outer leaflet of the bilayer. The results of the experiments show that in vesicles formed primarily with naturally occurring phospholipids such as egg phosphatidylcholine or brain phosphatidylethanolamine, there was no coordinate action of the two leaflets of the bilayer. An exception to this was found, however, if the vesicles were formed in the presence of primarily dioleoyl phospholipids (greater than 95 mol %). In these vesicles there was a coordinate or coupled response to calcium by the two leaflets of the bilayer. In most cases, however, the two leaflets of the bilayer showed independent or uncoupled domain formation.
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Tehrani, Ali, Ivan Rosado-Mendez, and Hassan Rivaz. "Deep estimation of viscoelastic and backscatter quantitative ultrasound." Journal of the Acoustical Society of America 152, no. 4 (2022): A74. http://dx.doi.org/10.1121/10.0015594.
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Deep learning is an ideal tool to solve inverse problems, which are often ill-posed and require incorporation of a priori information. We focus on solving two well-known inverse problems that entail the estimation of (1) viscoelastic (VE) and (2) backscatter quantitative ultrasound (QUS). These properties are of critical clinical value but are not currently available from B-mode images. On the first front, we propose a novel technique called PICTURE (Physically Inspired ConsTraint for Unsupervised Regularized Elastography) [Tehrani, Rivaz, MICCAI, (2022)], where we impose additional physics-based constraints on the deformation vector field within our loss function and show that it substantially improves the quality of lateral displacement estimation. We develop semi- and unsupervised methods to tackle the problem of lack of ground truth training datasets in real experiments. On the second front, we propose a novel method for segmenting regions of ultrasound images without any patching based on scatterer number densities [Tehrani et al. TUFFC (2022)]. Our segmentation maps can divide the image into irregular regions of fully developed speckle (FDS) or underdeveloped speckle. When moving from simulation to real datasets, we exploit domain adaptation methods using concepts similar to the popular reference phantom method in QUS.
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Kurent, Alma, and Dragica Kosec. "Amblyopia." Slovenian Medical Journal 88, no. 1-2 (2019): 71–76. http://dx.doi.org/10.6016/zdravvestn.2834.
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Amblyopia is a reduction of vision in one or both eyes due to a failure of normal neural development in the immature visual system.Amblyopia occurs due to two basic conditions – abnormal binocular interaction (e.g., strabismus) and blurring or distortion of visual image due to uncorrected refractive errors or media opacities. Best-corrected visual acuities in amblyopic eyes range from mild deficits to severe vision loss.The principle of treating amblyopia involves clearing any image blur and encouraging use of the amblyopic eye with occlusion of the better-seeing eye. Paediatric Eye Disease Investigator Group (PEDIG) studies show that both part-time and full-time occlusions can produce similar results in the eye with severe amblyopia and occlusion can be prescribed initially at 2 hours per day for the moderate amblyope. Studies show that daily atropine and patching for 6 hours/day can be equivalent treatment options and that if pharmacologic blurring is used for treatment, initial treatment can begin with just weekend use of atropine for moderate amblyopia.Amblyopia can lead to permanent loss of vision; therefore vision screening is strongly recommended over the course of childhood to detect amblyopia early enough to allow successful treatment.
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Nishio, Mizuho, Shunjiro Noguchi, and Koji Fujimoto. "Automatic Pancreas Segmentation Using Coarse-Scaled 2D Model of Deep Learning: Usefulness of Data Augmentation and Deep U-Net." Applied Sciences 10, no. 10 (2020): 3360. http://dx.doi.org/10.3390/app10103360.
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Combinations of data augmentation methods and deep learning architectures for automatic pancreas segmentation on CT images are proposed and evaluated. Images from a public CT dataset of pancreas segmentation were used to evaluate the models. Baseline U-net and deep U-net were chosen for the deep learning models of pancreas segmentation. Methods of data augmentation included conventional methods, mixup, and random image cropping and patching (RICAP). Ten combinations of the deep learning models and the data augmentation methods were evaluated. Four-fold cross validation was performed to train and evaluate these models with data augmentation methods. The dice similarity coefficient (DSC) was calculated between automatic segmentation results and manually annotated labels and these were visually assessed by two radiologists. The performance of the deep U-net was better than that of the baseline U-net with mean DSC of 0.703–0.789 and 0.686–0.748, respectively. In both baseline U-net and deep U-net, the methods with data augmentation performed better than methods with no data augmentation, and mixup and RICAP were more useful than the conventional method. The best mean DSC was obtained using a combination of deep U-net, mixup, and RICAP, and the two radiologists scored the results from this model as good or perfect in 76 and 74 of the 82 cases.
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Azrad, Syaril, Mohammad Fadhil, Farid Kendoul, and Kenzo Nonami. "Quadrotor UAV Indoor Localization Using Embedded Stereo Camera." Applied Mechanics and Materials 629 (October 2014): 270–77. http://dx.doi.org/10.4028/www.scientific.net/amm.629.270.
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Localization of Small-Size Unmanned Air Vehicles (UAVs) such as the Quadrotors inGlobal Positioning System (GPS)-denied environment such as indoors has been done using varioustechniques. Most of the experiment indoors that requires localization of UAVs, used cameras or ultrasonicsensors installed indoor or applied indoor environment modification such as patching (InfraRed) IR and visual markers. While these systems have high accuracy for the UAV localization, theyare expensive and have less practicality in real situations. We propose a system consisting of a stereocamera embedded on a quadrotor UAV for indoor localization. The optical flow data from the stereocamera then are fused with attitude and acceleration data from our sensors to get better estimationof the quadrotor location. Using stereo camera capabilities the quadrotor altitude are estimated usingSIFT Feature Stereo Matching are used in addition to the altitude estimation computed using opticalflow. To avoid latency due to computational time, image processing and the quadrotor control areprocessed threads and core allocation.
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Usman, Opeyemi Lateef, Ravie Chandren Muniyandi, Khairuddin Omar, and Mazlyfarina Mohamad. "Gaussian smoothing and modified histogram normalization methods to improve neural-biomarker interpretations for dyslexia classification mechanism." PLOS ONE 16, no. 2 (2021): e0245579. http://dx.doi.org/10.1371/journal.pone.0245579.
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Achieving biologically interpretable neural-biomarkers and features from neuroimaging datasets is a challenging task in an MRI-based dyslexia study. This challenge becomes more pronounced when the needed MRI datasets are collected from multiple heterogeneous sources with inconsistent scanner settings. This study presents a method of improving the biological interpretation of dyslexia’s neural-biomarkers from MRI datasets sourced from publicly available open databases. The proposed system utilized a modified histogram normalization (MHN) method to improve dyslexia neural-biomarker interpretations by mapping the pixels’ intensities of low-quality input neuroimages to range between the low-intensity region of interest (ROIlow) and high-intensity region of interest (ROIhigh) of the high-quality image. This was achieved after initial image smoothing using the Gaussian filter method with an isotropic kernel of size 4mm. The performance of the proposed smoothing and normalization methods was evaluated based on three image post-processing experiments: ROI segmentation, gray matter (GM) tissues volume estimations, and deep learning (DL) classifications using Computational Anatomy Toolbox (CAT12) and pre-trained models in a MATLAB working environment. The three experiments were preceded by some pre-processing tasks such as image resizing, labelling, patching, and non-rigid registration. Our results showed that the best smoothing was achieved at a scale value, σ = 1.25 with a 0.9% increment in the peak-signal-to-noise ratio (PSNR). Results from the three image post-processing experiments confirmed the efficacy of the proposed methods. Evidence emanating from our analysis showed that using the proposed MHN and Gaussian smoothing methods can improve comparability of image features and neural-biomarkers of dyslexia with a statistically significantly high disc similarity coefficient (DSC) index, low mean square error (MSE), and improved tissue volume estimations. After 10 repeated 10-fold cross-validation, the highest accuracy achieved by DL models is 94.7% at a 95% confidence interval (CI) level. Finally, our finding confirmed that the proposed MHN method significantly outperformed the normalization method of the state-of-the-art histogram matching.
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Fedden, Leon, Zhenning Zhang, Khan Baykaner, Qin Li, and Lucas Bordeaux. "Abstract 1937: DIME-CT: Self-supervised learning for medical image analysis using patch-based embeddings." Cancer Research 82, no. 12_Supplement (2022): 1937. http://dx.doi.org/10.1158/1538-7445.am2022-1937.
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Abstract Whether tracking patient progress for clinical decision making, or investigating novel therapies, automated analysis of Computed Tomography (CT) imaging data is essential for the future of digital radiomics. In digital radiomics, as in all medical imaging, well annotated data is scarce, whereas unlabelled images are relatively plentiful. In other fields of image processing and medical imaging, the application of self-supervised learning (SSL) to large quantities of unlabelled data has resulted in great strides forward for fast, scalable, and interpretable image analysis. In this work we present a new approach applying SSL to CT imaging data which allows for: 1) Improved performance on image classification tasks, based on 2) dramatically reduced quantity of annotated CT imaging data, whilst also 3) enabling easy exploration and interpretation of the image regions. We applied a selection of self-supervised approaches (BYOL, DINO, SimCLR, & inpainting) to CT imaging data. Because the high dimensionality of CT data prevents us from using them directly to out-of the box SSL models, we adopt a 3D patching approach to reduce the dimensionality of the neural net input, and process each patch independently. We train our self-supervised models on public datasets (DeepLesion, NSCLC), and we specialize these models for tumor classification tasks that we evaluate on AstraZeneca sponsored clinical trials. The specialization is done in two different ways: 1) we use the pre-trained SSL model as an encoder that transforms the images of the clinical study into embeddings, on which we apply supervised classification models; 2) we use transfer learning to fine-tune supervised classification models that take the patches directly as inputs. We find that self-supervised pre-training significantly improves the accuracy on tumor classification tasks compared against a supervised learning baseline. Additionally, using the SSL embeddings we build an interactive map of CT imaging data enabling quick and intuitive inspection of the relevant regions. Our findings show that SSL constitutes an important tool for medical imaging analysis. SSL results in models that generalize better, and enable improved downstream interpretability and predictions. Furthermore, well trained SSL models can be re-applied to multiple indications because they are pre-trained on broad and diverse CT imaging data. Citation Format: Leon Fedden, Zhenning Zhang, Khan Baykaner, Qin Li, Lucas Bordeaux. DIME-CT: Self-supervised learning for medical image analysis using patch-based embeddings [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2022; 2022 Apr 8-13. Philadelphia (PA): AACR; Cancer Res 2022;82(12_Suppl):Abstract nr 1937.
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Kim, Hoi-Eun. "Patching Up a Diplomatic Fissure: Reassessing Richard Wunsch, a German Physician to the Korean Court, 1901–1905*." German History 37, no. 4 (2019): 478–99. http://dx.doi.org/10.1093/gerhis/ghz013.
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Abstract In 1901 the Korean imperial court invited Greifswald-educated medical doctor Richard Wunsch to be personal physician to Emperor Kojong, only to abandon him upon his arrival in Seoul. Previous scholarship has understood Wunsch’s wasteful four-year engagement as a typical example from an incompetent regime on the cusp of its forceful transformation into Japan’s protectorate in 1905. Based upon careful analysis of hitherto unexplored diplomatic documents from both German and Korean sides, I argue that the appointment of Wunsch needs be understood in the whirlwind of diplomatic tension between Germany and Korea that arose over the issue of a disputed mining concession in 1898. The mutually agreed arrangement to install a German doctor in the Korean court in 1901 was a symbol of the patched-up relationship, with each side harbouring a different agenda—the German government wanted to put a powerful person in the court to gain an edge in imperial politics in Korea, and Koreans wished to have a German doctor who would function as a counterweight to the more dominant presence of American and British doctors in the court. By reconstructing the impact of convoluted factors, both local and global, in the invitation of a German doctor to Korea, this article provides a detailed case study of the use of medical science by the German imperial government as a chip in the global game of influence. It also functions as an overdue corrective to a commercially inspired and prevalent image of Wunsch that has capitalized upon a self-orientalizing tendency in contemporary Korea.
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Hanh, Nguyen Hoang, Mai Sy Tuan, Pham Hong Tinh, and Nguyen Thi Hong Hanh. "Characteristics of gap regeneration in mangrove forests of Dong Rui, Tien Yen, Quang Ninh." TAP CHI SINH HOC 40, no. 2 (2018): 129–37. http://dx.doi.org/10.15625/0866-7160/v40n2.13243.
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Field surveys using unmanned aerial vehicle monitoring were conducted in combination with satellite image analysis to evaluate the characteristics of gaps inside mangrove forest vegetation in Dong Rui commune, Tien Yen district, Quang Ninh province as well as the natural regeneration inside these gaps. A total of 15 gaps was located in four survey plots totaling 22,500 m2, among which four different types of mangrove forest communities were found, being dominated by (1) Bruguiera gymnorrhiza; (2) Aegriceras corniculatum, Kandelia obovata, Rhizophora stylosa and Bruguiera gymnorrhiza; (3) Bruguiera gymnorrhiza, Rhizophora stylosa; and (4): Aegriceras corniculatum, Kandelia obovata, respectively. Based on observations from 2012 to 2018, changes in regenerated tree densities were similar between gaps with a poor diversity of 2–5 species resembling the composition of the surrounding canopy. In some gaps, species not found in the nearby vegetation were observed with lower densities. Thus, the regeneration and patching of the mangrove forest gaps in Dong Rui were quite uniform at a midium rate. Species composition inside gaps did not differ significantly from the surrounding magrove forest communities. However, the ecological dominance of each species varied over time, depending on several natural factors and human actitvities where gaps were formed..
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Zhang, Tuanfeng, Sebastien Bombarde, Sebastien B. Strebelle, and Emily Oatney. "3D Porosity Modeling of a Carbonate Reservoir Using Continuous Multiple-Point Statistics Simulation." SPE Journal 11, no. 03 (2006): 375–79. http://dx.doi.org/10.2118/96308-pa.
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Summary Training images are numerical representations of geological conceptual models that provide prior information on reservoir architecture. A new emerging geostatistical approach named multiple-point statistics (MPS) simulation allows extracting multiple-point structures from such training images and anchoring these structures to the data actually observed in the reservoir. By reproducing multiple-point statistics inferred from training images, MPS enables the modeling of complex curvilinear structures (e.g., sinuous channels) in a much more geologically realistic way than traditional two-point statistics (variogram-based) techniques. However, in the original MPS implementation, all multiple-point statistics moments computed from the training image are exported to the reservoir model without processing, which allows simulating only categorical or discretized variables. This implementation is appropriate with clastic reservoirs for which, typically, depositional facies are simulated first using MPS, then porosity and permeability are distributed within each simulated facies using traditional variogram-based techniques. But for reservoirs, in particular in carbonate environments, where porosity and permeability trends/cycles are not closely tied to any facies distribution, simulating porosity/permeability directly using corresponding continuous training images appears to be a more suitable approach. In this paper, a new filter-based implementation of MPS, named filtersim, is proposed to reproduce features from continuous variable training images. First, a set of general filters is applied to the training image to transform (summarize) each training pattern into a set of scores accounting for different aspects of the pattern, such as north-south and east-west gradients and curvatures. The training patterns are classified in the score space and grouped into a small number of similarity classes. The simulation consists then of visiting each grid node along a random path, identifying the similarity class that best fits to the local conditioning data, and patching a pattern drawn from that selected similarity class. In our study, this new approach was applied to model the 3D porosity distribution of a carbonate reservoir in Kazakhstan. First, the original "categorical" MPS program snesim was used to model the two main reservoir regions, platform and slope, where the spatial porosity distribution is characterized by significantly different features. Interpreted well markers and seismic data were used to condition the modeling of these two regions. Then porosity was distributed in the platform region using the "continuous" filter-based MPS program filtersim, as described previously. The 3D training images used in that second step displayed porosity trends/cycles controlled by the type of geological sedimentation process believed to have occurred in the reservoir.
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Salerni, Annabella, Gloria Gambini, Chiara Fedeli, et al. "OCT-Angiography Findings in Patients with Amblyopia: Comparison between Healthy Controls, Treatment-Responsive, and Treatment-Unresponsive Amblyopic Patients." Diagnostics 11, no. 10 (2021): 1751. http://dx.doi.org/10.3390/diagnostics11101751.
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There is no consensus on whether amblyopia affects the retinal vascular plexus and morphology. Previous studies focused on the differences between amblyopic patients and normal controls without evaluating amblyopic eyes after patching. To evaluate differences in the superficial vascular density of amblyopic eyes, normal eyes, and amblyopic eyes reaching normal BCVA after patch therapy, OCTA was used. All patients underwent a comprehensive ophthalmological examination, including visual acuity, refraction, ocular motility tests, and anterior and posterior segment examination. OCTA was performed by an expert physician using the Zeiss Cirrus 5000-HD-OCT Angioplex (Carl Zeiss, Meditec, Inc., Dublin, OH, USA). OCTA scans were performed using a 3 × 3 mm2 and 6 × 6 mm2 fovea-centered image setting. The mean outer macular vessel density in the previously amblyopic group was 19.15 ± 0.51%. This was statistically significantly higher than in both the amblyopic group (18.70 ± 1.14%) and the normal controls (18.18 ± 1.40%) (p = 0.014). The previously amblyopic group also significantly differed from both normal controls and amblyopic eyes with regards to the inner (p = 0.011), outer (p = 0.006), and full (p = 0.003) macular perfusion. Finally, linear regression analysis revealed that BCVA was linearly correlated to outer perfusion in amblyopic (p = 0.003) and ex amblyopic eyes (p < 0.001). Considering the cross-sectional nature of our study, from our results, we can only hypothesize a possible correlation between light stimulation and retinal vasculature development. However, further longitudinal studies are needed to support this hypothesis.
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Zhou, Jiawei, Alexandre Reynaud, Yeon Jin Kim, Kathy T. Mullen, and Robert F. Hess. "Chromatic and achromatic monocular deprivation produce separable changes of eye dominance in adults." Proceedings of the Royal Society B: Biological Sciences 284, no. 1867 (2017): 20171669. http://dx.doi.org/10.1098/rspb.2017.1669.
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Temporarily depriving one eye of its input, in whole or in part, results in a transient shift in eye dominance in human adults, with the patched eye becoming stronger and the unpatched eye weaker. However, little is known about the role of colour contrast in these behavioural changes. Here, we first show that the changes in eye dominance and contrast sensitivity induced by monocular eye patching affect colour and achromatic contrast sensitivity equally. We next use dichoptic movies, customized and filtered to stimulate the two eyes differentially. We show that a strong imbalance in achromatic contrast between the eyes, with no colour content, also produces similar, unselective shifts in eye dominance for both colour and achromatic contrast sensitivity. Interestingly, if this achromatic imbalance is paired with similar colour contrast in both eyes, the shift in eye dominance is selective, affecting achromatic but not chromatic contrast sensitivity and revealing a dissociation in eye dominance for colour and achromatic image content. On the other hand, a strong imbalance in chromatic contrast between the eyes, with no achromatic content, produces small, unselective changes in eye dominance, but if paired with similar achromatic contrast in both eyes, no changes occur. We conclude that perceptual changes in eye dominance are strongly driven by interocular imbalances in achromatic contrast, with colour contrast having a significant counter balancing effect. In the short term, eyes can have different dominances for achromatic and chromatic contrast, suggesting separate pathways at the site of these neuroplastic changes.
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Menang PhD, Ophilia Abianji. "The Modern African Woman and the Politics of Reconciling Career and Domestic Activities." International Journal of Social Sciences and Humanities Invention 6, no. 4 (2019): 5384–90. http://dx.doi.org/10.18535/ijsshi/v6i4.03.
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In early African male fiction, women were not allowed to go to school. They were pushed to the periphery where they occupied marginal spaces. They had no voice in public. They were expected to perform household duties concerning childbearing and domestic functions. These are roles that domesticated women and made them dependent on their husbands for survival. However, urbanization and the spread of female education has given women more space and opportunities for survival and livelihood. In addition to culturally assigned roles, women are now educated and have acquired skills which enable them to have paid jobs and pursue a career in different walks of life thereby rendering them economically empowered and making positive contributions to the growth of their communities and families. This blend of domestic activities and pursuing a career is not without its own challenges. This article aims at examining the politics of reconciling career and domestic activities through the prism of Alobwed’Epie’s Patching the Broken Dream. It looks at the challenges that women/widows go through as wives, mothers and being career women. It reveals how the woman/widow rises above these challenges and reconstructs her image. Informed by the womanist ideology of Micere Mugo, E Modupe Kolawole and Chikwenye Ogunyemi, this paper justifies the view that domestic activities and career in the novel under study is challenging for women especially widows. However, these challenges do not limit the woman. They make her strong and develop hidden potentials that change their perception about life and people’s view about women and widows.
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Chavarro, Adrian F., Diego Renza, and Dora M. Ballesteros. "Influence of Hyperparameters in Deep Learning Models for Coffee Rust Detection." Applied Sciences 13, no. 7 (2023): 4565. http://dx.doi.org/10.3390/app13074565.
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Most of the world’s crops can be attacked by various diseases or pests, affecting their quality and productivity. In recent years, transfer learning with deep learning (DL) models has been used to detect diseases in maize, tomato, rice, and other crops. In the specific case of coffee, some recent works have used fixed hyperparameters to fine-tune the pre-trained models with the new dataset and/or applied data augmentation, such as image patching, to improve classifier performance. However, a detailed evaluation of the impact of architecture (e.g., backbone) and training (e.g., optimizer and learning rate) hyperparameters on the performance of coffee rust classification models has not been performed. Therefore, this paper presents a comprehensive study of the impact of five types of hyperparameters on the performance of coffee rust classification models. Specifically, eight pre-trained models are compared, each with four different amounts of transferred layers and three different numbers of neurons in the fully-connected (FC) layer, and the models are fine-tuned with three types of optimizers, each with three learning rate values. Comparing more than 800 models in terms of F1-score and accuracy, it is identified that the type of backbone is the hyperparameter with the greatest impact (with differences between models of up to 70%), followed by the optimizer (with differences of up to 20%). At the end of the study, specific recommendations are made on the values of the most suitable hyperparameters for the identification of this type of disease in coffee crops.
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Kesidis, Anastasios L., Vassilios Krassanakis, Loukas-Moysis Misthos, and Nikolaos Merlemis. "patchIT: A Multipurpose Patch Creation Tool for Image Processing Applications." Multimodal Technologies and Interaction 6, no. 12 (2022): 111. http://dx.doi.org/10.3390/mti6120111.
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Patch-based approaches in image processing are often preferable to working with the entire image. They provide an alternative representation of the image as a set of partial local sub-images (patches) which is a vital preprocessing step in many image processing applications. In this paper, a new software tool called patchIT is presented, providing an integrated framework suitable for the systematic and automatized extraction of patches from images based on user-defined geometrical and spatial criteria. Patches can be extracted in both a sliding and random manner and can be exported either as images, MATLAB .mat files, or raw text files. The proposed tool offers further functionality, including masking operations that act as spatial filters, identifying candidate patch areas, as well as geometric transformations by applying patch value indexing. It also efficiently handles issues that arise in large-scale patch processing scenarios in terms of memory and time requirements. In addition, a use case in cartographic research is presented that utilizes patchIT for map evaluation purposes based on a visual heterogeneity indicator. The tool supports all common image file formats and efficiently processes bitonal, grayscale, color, and multispectral images. PatchIT is freely available to the scientific community under the third version of GNU General Public License (GPL v3) on the GitHub platform.
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Françoso, Maria Teresa, Carolina Oyama Mota, Tadeu Rosanti Sugahara Medeiros Lima, and Creso De Franco Peixoto. "Nondestructive Testing in Asphalt Pavements Using Ground Penetrating Radar (GPR)." Applied Mechanics and Materials 303-306 (February 2013): 525–28. http://dx.doi.org/10.4028/www.scientific.net/amm.303-306.525.
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This paper presents the results of using the system GPR (Ground Penetrating Radar), as an alternative that uses radio waves at frequencies from 10 to 2500 MHz to get data that enable non-destructive conduct inspections of underground or concrete structures in real time. The research aims to investigate asphalt pavements, analyzing the variations in the responses, which can reveal the presence of pathologies or defects. A case study was made acquiring data, at the State University of Campinas - UNICAMP, in Campinas – SP – Brazil, with antennae 270 and 1600 MHz, in pavements with visible defects of patching and alligator cracks, initially with the dry structure and later, wet. The results were processed in specialized software (Radan 7.0) to generate terrain profiles. The GPR showed effective when there is a change in material employed as in the case of patching, because it was possible to detect layers compromised by intensive request of traffic, the start and end of application of the patching and even deformations in the new layer. In contrast, the alligator cracking did not reach the same result, not presenting accuracy in recognizing the defect. The extent of defect was the only well defined feature in the images.
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Zwiers, Marcel P. "Patching cardiac and head motion artefacts in diffusion-weighted images." NeuroImage 53, no. 2 (2010): 565–75. http://dx.doi.org/10.1016/j.neuroimage.2010.06.014.
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Guo, H., J. Stach, R. Panaccione, and P. J. Belletrutti. "A118 UPPER GASTROINTESTINAL HEMORRHAGE SECONDARY TO SUPERIOR MESENTERIC ARTERY PSEUDOANEURYSM: CASE REPORT AND REVIEW OF LITERATURE." Journal of the Canadian Association of Gastroenterology 5, Supplement_1 (2022): 137–38. http://dx.doi.org/10.1093/jcag/gwab049.117.
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Abstract Background Visceral artery pseudoaneurysms (VAPAs) are rare with an estimated incidence of 0.1%-0.2%. Due to various etiologies, a tear occurs in the vessel wall with subsequent formation of a peri-artery hematoma. A ruptured VAPA is a clinical emergency due to life-threatening hemorrhage and is associated with mortality rates of 25%-75%. Aims We report a case of upper gastrointestinal (GI) hemorrhage secondary to a ruptured superior mesenteric artery (SMA) pseudoaneurysm. A review of the literature regarding management of VAPAs and SMA pseudoaneurysms was performed using relevant medical subject headings on PubMed. Methods A 66-year-old woman presented to hospital with sudden large volume hematemesis and melena. Her daily medications included Aspirin and Atorvastatin. She had no prior history of peptic ulcers or chronic liver disease. She was found to be tachycardic and hypotensive. Initial investigations demonstrated a hemoglobin of 42g/L and a blood urea nitrogen of 17.5mmol/L. She was resuscitated and referred for an emergent upper endoscopy. On endoscopy, in the third portion of the duodenum, a 4cm solid-appearing subepithelial lesion with central umbilication and an apparent visible vessel was identified. Upon inspection of the lesion, the umbilicated area spontaneously began spurting blood (Image 1). A hemoclip was immediately placed next to the lesion for localization, then hemostatic powder was applied to the area. An immediate computerized tomography (CT) angiography of the abdomen revealed a 3.9 x 2.1 cm pseudoaneurysm arising from the superior mesenteric artery impressing upon the duodenum. Results Transcathether arterial embolization of the SMA pseudoaneurysm was performed, during which two Nester coils were deposited in the ileocolic outflow vessel. A covered endovascular stent was also deployed across the culprit arterial branch to exclude the pseudoaneurysm. Following the procedure, the patient stabilized and had no further GI bleeding. Traditionally, visceral angiography has been the gold standard diagnostic test for VAPAs, but has now been supplanted by CT angiography. Treatment strategies of VAPAs can be broadly separated into endovascular methods (coils, vascular plugs, stents, liquid embolic agents) and surgical methods (aneurysmectomy with patching, end-to-end anastomosis, bypass grafting). Conclusions SMA pseudoaneurysms are a rare yet life-threatening cause of GI bleeding. Endoscopically, they resemble solid subepithelial masses, such as GI stromal tumor, nerve sheath tumor or a lipoma, which may lead to inappropriate attempts to biopsy the lesion or apply direct endoscopic therapy. Prompt diagnosis with imaging, such as CT angiography, is paramount with a view to definitive treatment of the pseudoaneurysm via endovascular methods. Actively hemorrhagic SMA pseudoaneurysm Funding Agencies None
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Zheng, Jinghong, Zhengguo Li, Zijian Zhu, Shiqian Wu, and Susanto Rahardja. "Hybrid Patching for a Sequence of Differently Exposed Images With Moving Objects." IEEE Transactions on Image Processing 22, no. 12 (2013): 5190–201. http://dx.doi.org/10.1109/tip.2013.2283401.
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Elnagar, A. "Patching and Projection of Multiple Overlapping Images onto 3-D Developable Structures." International Journal of Modelling and Simulation 21, no. 3 (2001): 201–8. http://dx.doi.org/10.1080/02286203.2001.11442203.
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Bai, Yuanchao, Xu Yang, Xianming Liu, et al. "Towards End-to-End Image Compression and Analysis with Transformers." Proceedings of the AAAI Conference on Artificial Intelligence 36, no. 1 (2022): 104–12. http://dx.doi.org/10.1609/aaai.v36i1.19884.
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We propose an end-to-end image compression and analysis model with Transformers, targeting to the cloud-based image classification application. Instead of placing an existing Transformer-based image classification model directly after an image codec, we aim to redesign the Vision Transformer (ViT) model to perform image classification from the compressed features and facilitate image compression with the long-term information from the Transformer. Specifically, we first replace the patchify stem (i.e., image splitting and embedding) of the ViT model with a lightweight image encoder modelled by a convolutional neural network. The compressed features generated by the image encoder are injected convolutional inductive bias and are fed to the Transformer for image classification bypassing image reconstruction. Meanwhile, we propose a feature aggregation module to fuse the compressed features with the selected intermediate features of the Transformer, and feed the aggregated features to a deconvolutional neural network for image reconstruction. The aggregated features can obtain the long-term information from the self-attention mechanism of the Transformer and improve the compression performance. The rate-distortion-accuracy optimization problem is finally solved by a two-step training strategy. Experimental results demonstrate the effectiveness of the proposed model in both the image compression and the classification tasks.
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Sjafri, Fifi Susanti, Khairul Nizam Abdul Maulud, Wan Shafrina Wan Mohd Jaafar, Faiz Arif, Abdul Aziz Ab Rahman, and Muhammad Mukhlisin. "Development of Road Maintenance Inventory in UKM by Using Aerial Images." Jurnal Kejuruteraan si1, no. 2 (2018): 73–78. http://dx.doi.org/10.17576/jkukm-2018-si1(2)-09.
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Nowadays, all data and information management is conducted in integrated digital form. The same goes for inventory management of road maintenance to ensure the updated road information. Geographical Information System (GIS) is a powerful application and able to analyse and store all the geospatial data. The objective of this study is to identify the physical condition of the roads in UKM based on information from the unmanned aerial vehicle (UAV) technology, to produce spatial data storage of road distresses and to analyse the frequency of road distress in UKM. This study was carried out in the field and a desktop study. The results of the study proved that there is road damage occurring repeatedly in the same location yearly. The results from UAV found that in a period of one year, the types of distresses decrease from 11 types to 5 types, where patching is the highest occurring road distress. Lingkungan Ilmu indicates the highest number of road distress occurring along the period at 63 distresses in 2015 and 15 distresses in 2016. All the recorded information can be stored easily by using geospatial technology. This study shows that UAV and GIS are utilisable in maintaining road inventory management.
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Sudarsana, I. Ketut, I. Wayan Lali Yogantara, and Ni Wayan Ekawati. "Cyber Bullying Prevention And Handling Through Hindu Family Education." Jurnal Penjaminan Mutu 5, no. 2 (2019): 170. http://dx.doi.org/10.25078/jpm.v5i2.1090.
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<p><em>Technological developments have positive and negative impact on teenagers in particular. The most common negative impact of technology is cyber bullying. In globalization era and technological development, nowadays computer can produce multifunctional internet. All good and evil behavior can be done on social media. Before the development of the internet, cyber bullying actor committed their crimes in the real world. Cyber bullying actor can hurt and make many individuals uncomfortable in their lives. The crime is called bullying. Nowadays, the bullying can be done through any other media indirectly such as social media and it called cyber bullying. According to the Hinduja and Patching Cyber-Bullying researcher, the behavior is described as intentional action by sending electronic text (e-mail), or recording images that are usually uploaded to social media (youtube). The contents uploaded such as mocking, harassing, threatening, harassing, or insulting.<strong></strong></em></p>
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Okawa, Marina, Takafumi Taketomi, Goshiro Yamamoto, et al. "A model-based tracking framework for textureless 3D rigid curved objects." Journal on Interactive Systems 3, no. 2 (2013): 1. http://dx.doi.org/10.5753/jis.2012.611.
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This paper addresses the problem of tracking texturelessrigid curved objects. A common approach uses polygonalmeshes to represent curved objects inside an edge-based trackingsystem. However, in order to accurately recover their shape,high quality meshes are required, creating a trade-off betweencomputational efficiency and tracking accuracy. To solve thisissue, we suggest the use of quadrics calculated for each patchin the mesh to give local approximations of the object contour.This representation reduces considerably the level of detail of thepolygonal mesh while maintaining tracking accuracy. The noveltyof our research lies in using curves to represent the quadrics’projection in the current viewpoint for distance evaluation insteadof comparing directly the edges from the mesh and detectededges in the video image. In our tracking framework, we alsoinclude a method to calculate the measurable Degrees of Freedom(DoF) of the target object. This is used to recover the poseparameters when the object has less than 6DoF. Experimentalresults compare our approach to the traditional method ofusing sparse and dense meshes. Finally, we present a potentialAugmented Reality application of the proposed method.
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Delgado-López, Pedro David, Cecilia Gil-Polo, Vicente Martín-Velasco, Javier Martín-Alonso, Ana María Galacho-Harriero, and Elena Araus-Galdós. "Spinal cord herniation repair with microstaples: case report." Journal of Neurosurgery: Spine 26, no. 3 (2017): 384–87. http://dx.doi.org/10.3171/2016.8.spine16318.
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Idiopathic spinal cord herniation (ISCH) is a relatively rare and frequently misdiagnosed condition. It preferentially affects women and causes progressive thoracic myelopathy that presents as a Brown-Séquard syndrome or as spastic paraparesis. Although its etiology and pathogenesis are controversial, ISCH is characterized by the presence of an anterior dural defect that allows the incarceration of a segment of the cord. Typically, a C-shaped ventral displacement and kinking of the cord are visible on sagittal MRI. Surgery aimed at stopping or reversing myelopathic symptoms is usually recommended for symptomatic patients. Surgical options include reduction of the hernia and direct suturing, or enlargement of the dural defect, with or without patching. Suturing under the cord in a very tight space can be troublesome and may lead to neurological deterioration. The authors present the case of a symptomatic ISCH in which nonpenetrating titanium microstaples were used to close the dural defect after cord reduction. The patient experienced a good outcome, and the follow-up MRI study showed adequate cord repositioning and stability of the suture. The use of microstaples, which allows for an easier and faster dural closure than conventional suturing, is a novel technical adjunct that has not been previously reported for this condition. In addition, microstaples produce minimal metallic artifact that does not hinder the quality of follow-up MR images.
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Abdel-Basset, Mohamed, Hossam Hawash, Khalid Abdulaziz Alnowibet, Ali Wagdy Mohamed, and Karam M. Sallam. "Interpretable Deep Learning for Discriminating Pneumonia from Lung Ultrasounds." Mathematics 10, no. 21 (2022): 4153. http://dx.doi.org/10.3390/math10214153.
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Lung ultrasound images have shown great promise to be an operative point-of-care test for the diagnosis of COVID-19 because of the ease of procedure with negligible individual protection equipment, together with relaxed disinfection. Deep learning (DL) is a robust tool for modeling infection patterns from medical images; however, the existing COVID-19 detection models are complex and thereby are hard to deploy in frequently used mobile platforms in point-of-care testing. Moreover, most of the COVID-19 detection models in the existing literature on DL are implemented as a black box, hence, they are hard to be interpreted or trusted by the healthcare community. This paper presents a novel interpretable DL framework discriminating COVID-19 infection from other cases of pneumonia and normal cases using ultrasound data of patients. In the proposed framework, novel transformer modules are introduced to model the pathological information from ultrasound frames using an improved window-based multi-head self-attention layer. A convolutional patching module is introduced to transform input frames into latent space rather than partitioning input into patches. A weighted pooling module is presented to score the embeddings of the disease representations obtained from the transformer modules to attend to information that is most valuable for the screening decision. Experimental analysis of the public three-class lung ultrasound dataset (PCUS dataset) demonstrates the discriminative power (Accuracy: 93.4%, F1-score: 93.1%, AUC: 97.5%) of the proposed solution overcoming the competing approaches while maintaining low complexity. The proposed model obtained very promising results in comparison with the rival models. More importantly, it gives explainable outputs therefore, it can serve as a candidate tool for empowering the sustainable diagnosis of COVID-19-like diseases in smart healthcare.
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Marsik, Matthew, Forrest R. Stevens, and Jane Southworth. "Amazon deforestation: Rates and patterns of land cover change and fragmentation in Pando, northern Bolivia, 1986 to 2005." Progress in Physical Geography: Earth and Environment 35, no. 3 (2011): 353–74. http://dx.doi.org/10.1177/0309133311399492.
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Much research has focused on deforestation in the Amazon, particularly with proximity to roads and population centers as proximate causes. This research presents the analysis of rates and patterns of land cover change in Pando, northern Bolivia, an area with most of its tropical humid forest still intact. Using a decision tree classifier, five forest/non-forest (FNF) classifications were created for 1986, 1991, 1996, 2000, and 2005 from 40 Landsat images that were preprocessed and mosaicked. FNF trajectory images were created for each date pair to indicate areas of stable forest and non-forest, and areas and rates of de/reforestation. Mean patch size, perimeter-area ratio, fractal dimension, and aggregation index metrics were calculated for the FNF trajectory images based on increasing buffer distances from road and along the main access road. In 2005, forest covered 95% of the area in Pando. Large areas of aggregated deforestation occur nearest the department capital of Cobija, along the border with Brazil, and about 50 km west and east of Cobija along the principal access road. Deforestation becomes patchier with increased distance from the population center and laterally from the road. Multiple non-linear relationships exist between the fragmentation metrics and distance from road. The results have implications for understanding and managing the spatial contiguity of these forests, which provide valuable ecological services as well as the livelihood base for many inhabitants.
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Aljuboori, Zaid, Margaret McGrath, Muhammed Amir Essibayi, Saif Zaidi, Danial Hallam, and Basavaraj Ghodke. "Spontaneous spinal cerebrospinal fluid venous-fistula treated with transvenous embolization: A case report." Surgical Neurology International 12 (September 30, 2021): 492. http://dx.doi.org/10.25259/sni_878_2021.
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Background: Spinal cerebrospinal fluid venous fistula (CVF) is a recognized cause of chronic positional headache and spontaneous intracranial hypotension (SIH). It occurs due to an aberrant connection formed between the spinal subarachnoid space and an adjacent spinal epidural vein. The diagnosis of CVF can be difficult to establish but can be documented utilizing advanced imaging techniques (e.g., enhanced MR myelography/digital subtraction myelography). Their treatment involves surgical ligation of the involved nerve root, imaging-guided epidural blood patching, and/or endovascular embolization. Here, we report a 40-year-old male who presented with a symptomatic lumbar CVF successfully treated with transvenous embolization. Case Description: A 40-year-old male presented with several months of positional headaches. The MRI of the brain showed diffuse pachymeningeal enhancement consistent with the diagnosis of SIH. Although the MR of the lumbar spine was unremarkable, the MR myelogram with digital subtraction imaging showed a CVF at the L2 level. Following transvenous embolization (i.e., through the Azygous vein), the patient’s symptoms fully resolved. Conclusion: Spinal CVF are rare and may cause chronic headaches and symptoms/signs of SIH. In this case, an MR myelogram with digital subtraction images demonstrated the anomalous connection between the spinal subarachnoid space and an adjacent spinal epidural vein at the L2 level. Although open surgical ablation of this connection may be feasible, less invasive techniques such as endovascular embolization should become the treatment of choice for the future management of CVF.
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Irwin, Mitchell T., Steig E. Johnson, and Patricia C. Wright. "The state of lemur conservation in south-eastern Madagascar: population and habitat assessments for diurnal and cathemeral lemurs using surveys, satellite imagery and GIS." Oryx 39, no. 2 (2005): 204–18. http://dx.doi.org/10.1017/s0030605305000451.
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The unique primates of south-eastern Madagascar face threats from growing human populations. The country's extant primates already represent only a subset of the taxonomic and ecological diversity existing a few thousand years ago. To prevent further losses remaining taxa must be subjected to effective monitoring programmes that directly inform conservation efforts. We offer a necessary first step: revision of geographic ranges and quantification of habitat area and population size for diurnal and cathemeral (active during both day and night) lemurs. Recent satellite images are used to develop a forest cover geographical information system, and censuses are used to establish range boundaries and develop estimates of population density and size. These assessments are used to identify regions and taxa at risk, and will be a useful baseline for future monitoring of habitat and populations. Precise estimates are impossible for patchily-distributed taxa (especially Hapalemur aureus, H. simus and Varecia variegata variegata); these taxa require more sophisticated modelling.
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Anwar, Hirra, Saad Ullah Khan, Muhammad Mohsin Ghaffar, et al. "The NWRD Dataset: An Open-Source Annotated Segmentation Dataset of Diseased Wheat Crop." Sensors 23, no. 15 (2023): 6942. http://dx.doi.org/10.3390/s23156942.
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Wheat stripe rust disease (WRD) is extremely detrimental to wheat crop health, and it severely affects the crop yield, increasing the risk of food insecurity. Manual inspection by trained personnel is carried out to inspect the disease spread and extent of damage to wheat fields. However, this is quite inefficient, time-consuming, and laborious, owing to the large area of wheat plantations. Artificial intelligence (AI) and deep learning (DL) offer efficient and accurate solutions to such real-world problems. By analyzing large amounts of data, AI algorithms can identify patterns that are difficult for humans to detect, enabling early disease detection and prevention. However, deep learning models are data-driven, and scarcity of data related to specific crop diseases is one major hindrance in developing models. To overcome this limitation, in this work, we introduce an annotated real-world semantic segmentation dataset named the NUST Wheat Rust Disease (NWRD) dataset. Multileaf images from wheat fields under various illumination conditions with complex backgrounds were collected, preprocessed, and manually annotated to construct a segmentation dataset specific to wheat stripe rust disease. Classification of WRD into different types and categories is a task that has been solved in the literature; however, semantic segmentation of wheat crops to identify the specific areas of plants and leaves affected by the disease remains a challenge. For this reason, in this work, we target semantic segmentation of WRD to estimate the extent of disease spread in wheat fields. Sections of fields where the disease is prevalent need to be segmented to ensure that the sick plants are quarantined and remedial actions are taken. This will consequently limit the use of harmful fungicides only on the targeted disease area instead of the majority of wheat fields, promoting environmentally friendly and sustainable farming solutions. Owing to the complexity of the proposed NWRD segmentation dataset, in our experiments, promising results were obtained using the UNet semantic segmentation model and the proposed adaptive patching with feedback (APF) technique, which produced a precision of 0.506, recall of 0.624, and F1 score of 0.557 for the rust class.
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Chang, Yet-Chung, Shu-Kun Hsu, and Ching-Hui Tsai. "SIDESCAN SONAR IMAGE PROCESSING:CORRECTING BRIGHTNESS VARIATION AND PATCHING GAPS." Journal of Marine Science and Technology 18, no. 6 (2010). http://dx.doi.org/10.51400/2709-6998.1935.
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Sreejith, S., R. Subramanian, and S. Karthik. "Using patching asymmetric regions to assess ischemic stroke lesion in neuro imaging." Journal of Intelligent & Fuzzy Systems, March 7, 2022, 1–10. http://dx.doi.org/10.3233/jifs-212457.
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Ischemic stroke is a universal ailment that endangers the life of patients and makes them bedridden until death. Over a decade, doctors and radiologists have been dissecting patient status straightforwardly from the printouts of the slice images delivered by different diagnostic imaging modalities. Computed Tomography (CT) is a frequently used imaging strategy for therapeutic analysis and neuroanatomical investigations. The main objective of the paper is to develop a simple technique with less architectural complication and power consumption. The proposed work is to section the ischemic stroke lesion more efficiently from multi-succession CT images using patching the asymmetric region. The Hough transform segment and extracts the features from the asymmetric region of the CT image and finally, the random forest is implemented to classify the unusual tissues from the CT image dependent on their pathological properties. RF classifier has been trained for different parts of the cerebrum for fragmenting the stroke lesion. The acquired outcomes produce better segmentation accuracy when compared with different strategies. The overall efficiency of the proposed method determines the Ischemic stroke with an accuracy of 95% with an RF classifier. Hence this method can be used in the segmentation process of stroke lesions.
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Manning, Jeffrey, and Jack Shotton. "Ihara’s Lemma for Shimura curves over totally real fields via patching." Mathematische Annalen, September 25, 2020. http://dx.doi.org/10.1007/s00208-020-02048-8.
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Abstract We prove Ihara’s lemma for the mod l cohomology of Shimura curves, localized at a maximal ideal of the Hecke algebra, under a large image hypothesis on the associated Galois representation. This was proved by Diamond and Taylor, for Shimura curves over $$\mathbb {Q}$$ Q , under various assumptions on l. Our method is totally different and can avoid these assumptions, at the cost of imposing the large image hypothesis. It uses the Taylor–Wiles method, as improved by Diamond and Kisin, and the geometry of integral models of Shimura curves at an auxiliary prime.