With the outstanding superposition and entanglement properties of quantum computing, quantum machine learning has attracted widespread attention in many fields, such as medical image analysis, password cracking, and p...
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With the outstanding superposition and entanglement properties of quantum computing, quantum machine learning has attracted widespread attention in many fields, such as medical image analysis, password cracking, and pattern recognition. Although classical machine learning is widely used and has shown great potential in medical image analysis, the bottlenecks of insufficient labeled data and low processing efficiency still exist. To overcome these challenges, massive studies combined quantum computing with machine learning to explore more advanced algorithms, which have achieved distin-guished improvements in parameter optimization, execution efficiency, and the reduction of error rates. Quantum machine learning provides new insights for the intersectional research of quantum technology and medical image analysis and contributes to the future development of medical image analysis. This review delivers an overview of the definition and taxonomy of quantum machine learning, as well as summarizes various quantum machine learning methods and their applications in medical image analy-sis over the past decade.(c) 2023 Elsevier B.v. All rights reserved.
PurposeThis study aims to introduce a vision-based model to generate droplets with auto-tuned parameters. The model can auto-adjust the inherent uncertainties and errors involved with the fabrication and operating par...
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PurposeThis study aims to introduce a vision-based model to generate droplets with auto-tuned parameters. The model can auto-adjust the inherent uncertainties and errors involved with the fabrication and operating parameters in microfluidic platform, attaining precise size and frequency of droplet ***/methodology/approachThe photolithography method is utilized to prepare the microfluidic devices used in this study, and various experiments are conducted at various flow-rate and viscosity ratios. Data for droplet shape is collected to train the artificial intelligence (AI) *** phase of droplets demonstrated a unique spring back effect in droplet size. The fully developed droplet sizes in the microchannel were modeled using least absolute shrinkage and selection operators (LASSO) regression model, Gaussian support vector machine (SvM), long short term memory (LSTM) and deep neural network models. Mean absolute percentage error (MAPE) of 0.05 and R2 = 0.93 were obtained with a deep neural network model on untrained flow data. The shape parameters of the droplets are affected by several uncontrolled parameters. These parameters are instinctively captured in the ***/valueExperimental data set is generated for varying viscosity values and flow rates. The variation of flow rate of continuous phase is observed here instead of dispersed phase. An automated computation routine is developed to read the droplet shape parameters considering the transient growth phase of droplets. The droplet size data is used to build and compare various AI models for predicting droplet sizes. A predictive model is developed, which is ready for automated closed loop control of the droplet generation.
With the characteristics of high I/O packaging density and excellent electrothermal performance, ceramic column grid array (CCGA) packaging has been widely used in highly reliable applications such as aerospace. For C...
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ISBN:
(纸本)9798400709234
With the characteristics of high I/O packaging density and excellent electrothermal performance, ceramic column grid array (CCGA) packaging has been widely used in highly reliable applications such as aerospace. For CCGA solder column, defect detection needs to be applied before it leaves the factory. The traditional manual detection method has low detection efficiency and the detect accuracy is greatly influenced by human subjective factors. Aiming at this problem, a set of algorithm consists of digital imageprocessing method, Yolov3 network and U-Net network has been combined to realize the surface and inner defect detection for CCGA solder column. The whole algorithm has been embedded into industrial software system based on Qt environment and field experiments have been applied. The experiment results show that the whole algorithm has good real-time performance and the detection accuracy is consistent with manual detection accuracy. The algorithm proposed in this paper can meet the needs of online defect detection for CCGA solder column.
In traditional industry, the detection of the automobile components quality is mainly completed by human eyes. Low detection accuracy, high labor consumption, and slow detection speed are important reasons for the slo...
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Heart rate is a crucial metric in health monitoring. Traditional computer vision solutions estimate cardiac signals by detecting physical manifestations of heartbeats, such as facial discoloration caused by blood oxyg...
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Heart rate is a crucial metric in health monitoring. Traditional computer vision solutions estimate cardiac signals by detecting physical manifestations of heartbeats, such as facial discoloration caused by blood oxygenation changes, from subject videos using regression methods. As continuous signals are more complex and expensive to de-noise, this study introduces an alternative approach, employing end-to-end classification models to remotely derive a discrete representation of cardiac signals from face videos. These visual cardiac signal classifiers are trained on discretized cardiac signals, a novel pre-processing method with limited precedent in health monitoring literature. Consequently, various methods to convert continuous cardiac signals into binary form are presented, and their impact on training is evaluated. An implementation of this approach, the temporal shift convolutional attention binary classifier, is presented using the regression-based convolutional attention network architecture. The classifier and a baseline regression model are trained and tested using publicly available and locally collected datasets designed for heart signal detection from face video. The model performance is then assessed based on the heart rate error from the extracted cardiac signals. Results show the proposed method outperforms the baseline on the UBFC-rPPG dataset, reducing cross-dataset root mean square error from 2.33 to 1.63 beats per minute. However, both models struggled to generalize to the PURE dataset, with root mean square errors of 12.40 and 16.29 beats per minute, respectively. Additionally, the proposed approach reduces the computational complexity of model output post-processing, enhancing its suitability for real-time applications and deployment on systems with restricted resources.
Leaf wetness duration is a crucial factor in plant disease management. Current optical methods use standard RGB images to classify leaf wetness as a binary problem, i.e., wet or dry. Green leaves absorb red light, whe...
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Leaf wetness duration is a crucial factor in plant disease management. Current optical methods use standard RGB images to classify leaf wetness as a binary problem, i.e., wet or dry. Green leaves absorb red light, whereas water reflects it. Based on this difference, an experimental platform was built to semi-automatically measure droplet deposition on grape leaves while capturing red laser images using an RGB camera. The setup measured changes in leaf mass and area of scanned leaves to determine the water mass per leaf area as a measure of leaf wetness. A sprayer was used to apply water droplets to the leaves. As the amount of deposited water increased, the mean red channel intensity decreased, with more bright spots in the images. These bright spots were more distinguishable as droplets in the green channel. Segmented leaf area, mean red channel intensity, and the number of identified droplets were used as image features. A generalised additive model was employed to predict the leaf wetness value with extracted features. The R-squared value for the prediction of the validation dataset was 0.71. image resolution and leaf orientation were identified as factors that influenced the model accuracy. The measurement method introduced in this study shows potential for accurately quantifying leaf wetness, and implies that in practice detecting leaf wetness can be integrated into a multi-classification problem, thereby broadening the potential applications of optical methods.
Depth image spatial clustering is an important task in the fields of computer vision and machine learning, aiming to group pixels or point cloud data of depth images into clusters with similar features. This is crucia...
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Diabetes-oriented diabetic retinopathy impacts the blood vessels in the region of the retina to enlarge and leak blood and other fluids. In most cases, diabetic retinopathy affects both eyes. If left untreated, it wou...
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machinevision technology has the characteristics of high precision, fast speed, non-contact and high degree of automation in displacement detection. It is feasible to apply machinevision technology to the detection ...
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In this paper, a computer vision-based approach for optimizing component test benches in endurance testing of automotive components. As a use case, the paper explores testing of automotive throttle position sensor usi...
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