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"Subtraction."
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A property of irrational numbers and its application in music
The concept of irrational envelope numbers and some properties of their polynomial range were introduced in this paper. The paper proves the number of two intervals forming a unit circle of Circumference 1, proposes a method of rolling subtraction to find the number of musical tones needed for any small comma in music, and finally gives the cent value of the interval corresponding to some envelope numbers.
Journal Article
Subtracting with seals
Introduces the concept of subtraction through examples of counting and subtracting the different types of seals in the illustrations, including information about the physical characteristics and behavior of seals.
Book
Calculation of virtual 3D subtraction angiographies using conditional generative adversarial networks (cGANs)
Objective
Subtraction angiographies are calculated using a native and a contrast-enhanced 3D angiography images. This minimizes both bone and metal artifacts and results in a pure image of the vessels. However, carrying out the examination twice means double the radiation dose for the patient. With the help of generative AI, it could be possible to simulate subtraction angiographies from contrast-enhanced 3D angiographies and thus reduce the need for another dose of radiation without a cutback in quality. We implemented this concept by using conditional generative adversarial networks.
Methods
We selected all 3D subtraction angiographies from our PACS system, which had performed between 01/01/2018 and 12/31/2022 and randomly divided them into training, validation, and test sets (66%:17%:17%). We adapted the pix2pix framework to work on 3D data and trained a conditional generative adversarial network with 621 data sets. Additionally, we used 158 data sets for validation and 164 for testing. We evaluated two test sets with (
n
= 72) and without artifacts (
n
= 92). Five (blinded) neuroradiologists compared these datasets with the original subtraction dataset. They assessed similarity, subjective image quality, and severity of artifacts.
Results
Image quality and subjective diagnostic accuracy of the virtual subtraction angiographies revealed no significant differences compared to the original 3D angiographies. While bone and movement artifact level were reduced, artifact level caused by metal implants differed from case to case between both angiographies without one group being significant superior to the other.
Conclusion
Conditional generative adversarial networks can be used to simulate subtraction angiographies in clinical practice, however, new artifacts can also appear as a result of this technology.
Journal Article
Application of Posture Recognition Model Based on Improved CORDIC Algorithm in Spatial Health Monitoring of the Elderly Living Alone
- In an aging society, accidental falls are highly likely to occur due to external factors or the incidence of diseases in the elderly. Serious consequences can arise when an elderly person living alone falls and is unable to get up to call for help. To address the issue of automatic assistance for elderly individuals living alone when they fall by accident, and the problem of behavioral analysis in daily life, this paper designs and develops an elderly monitoring system through the study of feature extraction algorithms and posture recognition algorithms. Furthermore, this paper has also developed a set of three-dimensional spatial angle calculation methods suitable for implementation on a microcontroller. The CORDIC algorithm can compute two-dimensional spatial angles through simple addition, subtraction, and bit shifting operations. Through careful study of the CORDIC algorithm, it was found that applying the CORDIC algorithm twice can achieve the calculation of three-dimensional spatial angles. Through experimental testing, we can see that the algorithms proposed in this paper have achieved good experimental results in terms of computational accuracy and operation time, reaching a relatively advanced level.
Journal Article
Subtraction in action
\"Friends share their strategies for figuring out how many objects remain as some are taken away.\"-- Provided by publisher.
Book
A Quantitative Digital Subtraction Angiography Technique for Characterizing Reduction in Hepatic Arterial Blood Flow During Transarterial Embolization
ObjectiveThere is no standardized and objective method for determining the optimal treatment endpoint (sub-stasis) during transarterial embolization. The objective of this study was to demonstrate the feasibility of using a quantitative digital subtraction angiography (qDSA) technique to characterize intra-procedural changes in hepatic arterial blood flow velocity in response to transarterial embolization in an in vivo porcine model.Materials and MethodsEight domestic swine underwent bland transarterial embolizations to partial- and sub-stasis angiographic endpoints with intraprocedural DSA acquisitions. Embolized lobes were assessed on histopathology for ischemic damage and tissue embolic particle density. Analysis of target vessels used qDSA and a commercially available color-coded DSA (ccDSA) tool to calculate blood flow velocities and time-to-peak, respectively.ResultsBlood flow velocities calculated using qDSA showed a statistically significant difference (p < 0.01) between partial- and sub-stasis endpoints, whereas time-to-peak calculated using ccDSA did not show a significant difference. During the course of embolizations, the average correlation with volume of particles delivered was larger for qDSA (− 0.86) than ccDSA (0.36). There was a statistically smaller mean squared error (p < 0.01) and larger coefficient of determination (p < 0.01) for qDSA compared to ccDSA. On pathology, the degree of embolization as calculated by qDSA had a moderate, positive correlation (p < 0.01) with the tissue embolic particle density of ischemic regions within the embolized lobe.ConclusionsqDSA was able to quantitatively discriminate angiographic embolization endpoints and, compared to a commercially available ccDSA method, improve intra-procedural characterization of blood flow changes. Additionally, the qDSA endpoints correlated with tissue-level changes.
Journal Article
Novel Deep Learning Domain Adaptation Approach for Object Detection Using Semi-Self Building Dataset and Modified YOLOv4
Moving object detection is a vital research area that plays an essential role in intelligent transportation systems (ITSs) and various applications in computer vision. Recently, researchers have utilized convolutional neural networks (CNNs) to develop new techniques in object detection and recognition. However, with the increasing number of machine learning strategies used for object detection, there has been a growing need for large datasets with accurate ground truth used for the training, usually demanding their manual labeling. Moreover, most of these deep strategies are supervised and only applicable for specific scenes with large computational resources needed. Alternatively, other object detection techniques such as classical background subtraction need low computational resources and can be used with general scenes. In this paper, we propose a new a reliable semi-automatic method that combines a modified version of the detection-based CNN You Only Look Once V4 (YOLOv4) technique and background subtraction technique to perform an unsupervised object detection for surveillance videos. In this proposed strategy, background subtraction-based low-rank decomposition is applied firstly to extract the moving objects. Then, a clustering method is adopted to refine the background subtraction (BS) result. Finally, the refined results are used to fine-tune the modified YOLO v4 before using it in the detection and classification of objects. The main contribution of this work is a new detection framework that overcomes manual labeling and creates an automatic labeler that can replace manual labeling using motion information to supply labeled training data (background and foreground) directly from the detection video. Extensive experiments using real-world object monitoring benchmarks indicate that the suggested framework obtains a considerable increase in mAP compared to state-of-the-art results on both the CDnet 2014 and UA-DETRAC datasets.
Journal Article