This paper mainly discusses how to effectively use multi-core embedded digital signal processor (DSP) technology to realize parallel computation of image tracking algorithm and memory optimization in high precision mi...
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ISBN:
(数字)9798350374315
ISBN:
(纸本)9798350374322
This paper mainly discusses how to effectively use multi-core embedded digital signal processor (DSP) technology to realize parallel computation of image tracking algorithm and memory optimization in high precision missile terminal guidance system. With the increasing requirement of missile strike accuracy in modern war, the vision-based terminal guidance technology has been widely concerned because of its strong autonomy and anti-jamming ability. However, such imageprocessingalgorithms are usually highly computation-intensive and data dependent, which requires high real-time and hardware resources. In this paper, the key steps of image tracking algorithm are analyzed and reconstructed deeply, and the architecture suitable for parallel processing of multi-core DSP is designed by modularization. The parallel computing advantages of multi-core DSP are given full play through task division and scheduling strategies to improve the execution efficiency and real-time performance of the algorithm. Secondly, the research also focuses on memory optimization, including the design of data cache strategy, reducing data redundancy, improving memory access locality, etc., and strives to maximize the data reading speed and reduce the memory bandwidth pressure under the condition of limited embedded system resources. To sum up, this research is committed to building an efficient and stable multi-core embedded DSP environment, parallel computing model and memory optimization scheme for high-precision missile terminal guidance image tracking algorithm, which has important theoretical value and practical significance for improving the intelligence level and combat efficiency of missile terminal guidance system.
Forest fires pose a significant threat to ecosystems and wildlife, exacerbating global warming and environmental degradation. The contemporary challenge lies in the difficulty of timely detection by modern organizatio...
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ISBN:
(数字)9798350375190
ISBN:
(纸本)9798350375206
Forest fires pose a significant threat to ecosystems and wildlife, exacerbating global warming and environmental degradation. The contemporary challenge lies in the difficulty of timely detection by modern organizations. In response, the development of an automated forest fire detection system becomes imperative. The system involves the integration of various technologies, including Arduino microcontrollers, cameras, smoke detectors, flame detectors, and imageprocessingalgorithms. When a fire is detected, the system promptly sends alert messages to nearby forest departments, facilitating rapid response and mitigation efforts. This research work explores the design, implementation, and functionality of such an automated forest fire detection system, emphasizing the role of each component in enhancing the overall efficacy of early fire detection. The integration of Arduino technology, coupled with advanced sensors and imageprocessing, exemplifies a multidimensional approach to addressing the pressing issue of forest fires.
Computer blockchain technology provides a possible means to enhance data security, transparency, and overall efficiency. This study focuses on the design and optimization ideas of using blockchain technology in studen...
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ISBN:
(数字)9798350360240
ISBN:
(纸本)9798350384161
Computer blockchain technology provides a possible means to enhance data security, transparency, and overall efficiency. This study focuses on the design and optimization ideas of using blockchain technology in student management systems. The architecture plan for this system was developed during the design phase, emphasizing the use of smart contracts to automate registration, classification, and authentication processes, as well as decentralized data storage and encrypted hashing of irreversible records. License blockchain can be used to provide privacy protection and access restrictions, ensuring that student data is secure and accessible to authorized parties. The goal of optimizing the solution is to improve scalability and transaction speed, while reducing the amount of energy used in traditional blockchain systems. The improvement of consensus algorithms and second layer extension technologies such as sidechain or off chain protocols can achieve faster data processing without compromising security. In addition, metadata management and advanced encryption technologies have improved system performance and minimized data to the greatest extent possible.
Deep Neural Networks (DNNs) have the potential for making various clinical procedures more time-efficient by automating medical image segmentation. Due to their strong, in some cases human-level, performance, they hav...
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ISBN:
(数字)9781510649408
ISBN:
(纸本)9781510649408;9781510649392
Deep Neural Networks (DNNs) have the potential for making various clinical procedures more time-efficient by automating medical image segmentation. Due to their strong, in some cases human-level, performance, they have become the standard approach in this field. The design of the best possible medical image segmentation DNNs, however, is task-specific. Neural Architecture Search (NAS), i.e., the automation of neural network design, has been shown to have the capability to outperform manually designed networks for various tasks. However, the existing NAS methods for medical image segmentation have explored a quite limited range of types of DNN architectures that can be discovered. In this work, we propose a novel NAS search space for medical image segmentation networks. This search space combines the strength of a generalised encoder-decoder structure, well known from U-Net, with network blocks that have proven to have a strong performance in image classification tasks. The search is performed by looking for the best topology of multiple cells simultaneously with the configuration of each cell within, allowing for interactions between topology and cell-level attributes. From experiments on two publicly available datasets, we find that the networks discovered by our proposed NAS method have better performance than well-known handcrafted segmentation networks, and outperform networks found with other NAS approaches that perform only topology search, and topology-level search followed by cell-level search.
In this paper, we propose Online LASSO - a version of LASSO that is configured for streaming data. In standard LASSO, the penalty parameter is typically chosen by cross-validation, a procedure which requires the entir...
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ISBN:
(数字)9798350368741
ISBN:
(纸本)9798350368758
In this paper, we propose Online LASSO - a version of LASSO that is configured for streaming data. In standard LASSO, the penalty parameter is typically chosen by cross-validation, a procedure which requires the entire dataset upfront and repeated fitting. The main contribution of this work is in finding an easy and principled choice for the penalty parameter for every incoming data point, in cases where the input features are uncorrelated. The proposed Online LASSO has several benefits: i) it is memory and time efficient ii) it is easy to implement, iii) it does not require an initial batch of data to start, iv) it does not require any tuning (e.g., step size or tolerance), and finally v) it converges to the performance of the optimal predictor and correct selection of features. We demonstrate these capabilities and compare Online LASSO with standard LASSO as well as other adaptive LASSO variations and provide a discussion on their performances.
Medical image classification and segmentation based on deep learning (DL) are emergency research topics for diagnosing variant viruses of the current COVID-19 situation. In COVID-19 computed tomography (CT) images of ...
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ISBN:
(纸本)9781665462198
Medical image classification and segmentation based on deep learning (DL) are emergency research topics for diagnosing variant viruses of the current COVID-19 situation. In COVID-19 computed tomography (CT) images of the lungs, ground glass turbidity is the most common finding that requires specialist diagnosis. Based on this situation, some researchers propose the relevant DL models which can replace professional diagnostic specialists in clinics when lacking expertise. However, although DL methods have a stunning performance in medical imageprocessing, the limited datasets can be a challenge in developing the accuracy of diagnosis at the human level. In addition, deep learning algorithms face the challenge of classifying and segmenting medical images in three or even multiple dimensions and maintaining high accuracy rates. Consequently, with a guaranteed high level of accuracy, our model can classify the patients' CT images into three types: Normal, Pneumonia and COVID. Subsequently, two datasets are used for segmentation, one of the datasets even has only a limited amount of data (20 cases). Our system combined the classification model and the segmentation model together, a fully integrated diagnostic model was built on the basis of ResNet50 and 3D U-Net algorithm. By feeding with different datasets, the COVID image segmentation of the infected area will be carried out according to classification results. Our model achieves 94.52% accuracy in the classification of lung lesions by 3 types: COVID, Pneumonia and Normal. For 2 labels (ground truth, lung lesions) segmentation, the model gets 99.57% of accuracy, 0.2191 of train loss and 0.78 +/- 0.03 of MeanDice +/- Std, while the 4 labels (ground truth, left lung, right lung, lung lesions) segmentation achieves 98.89% of accuracy, 0.1132 of train loss and 0.83 +/- 0.13 of MeanDice +/- Std. For future medical use, embedding the model into the medical facilities might be an efficient way of assisting or substituting do
Media such as audio, images, and videos, which occupy significant storage space in digital environments, are often compressed to save space, particularly outside professional settings. The compression process aims to ...
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ISBN:
(数字)9798350379433
ISBN:
(纸本)9798350379440
Media such as audio, images, and videos, which occupy significant storage space in digital environments, are often compressed to save space, particularly outside professional settings. The compression process aims to achieve space savings while maintaining an acceptable level of quality. Many standard image compression algorithms are based on the principle of eliminating data that the human eye cannot perceive and selectively removing certain data after performing spatial transformation on the obtained image. In recent years, compression algorithms utilizing autoencoders or generative adversarial network (GAN) architectures have emerged. These algorithms fundamentally aim to reduce the dimensionality of data and create a representation of it. The dimension reduction stage used is considered equivalent to the compression process. In this study, the performance measurement of three different models using autoencoders and GANs is conducted, and the results are compared with the performance of the JPEG algorithm in terms of speeds and ratio. Upon examining the results, it is observed that learned image compression methods are catching up with JPEG in terms of quality. The methods achieve better results in terms of compression ratio compared to JPEG, but they operate much slower than JPEG in terms of processing time and have some artificial artifacts.
Watermelon is a commonly cultivated fruit worldwide, especially in Southeast Asia. As one of the top exports in Asia, the commercialization of watermelon has its market. Its consumer appeal makes it one of the most so...
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ISBN:
(数字)9798350372106
ISBN:
(纸本)9798350372113
Watermelon is a commonly cultivated fruit worldwide, especially in Southeast Asia. As one of the top exports in Asia, the commercialization of watermelon has its market. Its consumer appeal makes it one of the most sought-after fruits globally. With the watermelon fruit consisting of different varieties, consumers usually have difficulty in classifying watermelons due to their similar external appearances, especially when labeled under the same name. This study is conducted to implement a system that detects and classifies three red watermelon varieties, Red Export, Orchid Sweet, and Dixie Queen, with imageprocessingalgorithms. The researchers utilized Canny Edge Detection for the dataset’s preprocessing phase and Convolutional Neural Network (CNN) for its classification. The Raspberry Pi is also applied to this study. Moreover, the researchers created and collected their datasets for testing and validation data. The model used in this study has acquired 84.71% overall accuracy.
The main purpose of this study is to explore the issues of real-time, accurate, and unmarked recognition of sports movements in recent years. By reviewing the relevant research on machine learning or deep learning for...
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ISBN:
(数字)9798350374407
ISBN:
(纸本)9798350374414
The main purpose of this study is to explore the issues of real-time, accurate, and unmarked recognition of sports movements in recent years. By reviewing the relevant research on machine learning or deep learning for specific sports or target actions based on computer vision image data input, the aim is to provide references for the application of unmarked motion capture technology in the field of sports motion recognition. The research employed a literature review methodology, conducting searches in six databases, namely Web of Science, PubMed, Scopus, Google Scholar, IEEE Xplore, and China National Knowledge Infrastructure (CNKI), covering publications from January 2000 to June 2020. Through boolean logic operations on the retrieved literature, key information such as first author/publication year, types/targets of motion, participant information, camera parameters, image feature extraction techniques, action recognition algorithms, evaluation methods for action recognition quality, training and validation methods for image data, and performance metrics for action recognition were extracted. After screening, a total of 23 articles were included in the study. The findings revealed that $39 \%$ of the studies utilized machine learning algorithms based on support vector machines, while $35 \%$ employed deep learning algorithms based on convolutional neural networks. Commonly used evaluation metrics for action recognition quality included classification accuracy, confusion matrix, and displacement error. The development of computer vision motion capture, models, and algorithms demonstrated promising applications in areas such as action technique recognition and sports performance analysis. Traditional machine learning algorithms like support vector machines and principal component analysis remain dominant in action recognition technology; however, in certain scenarios, the performance of deep learning algorithms surpassed that of traditional machine learning methods.
Uncertainty in AI refers to the degree of confidence or probability associated with the accuracy or effectiveness of an AI system's output. It is a measure of how much the AI system is unsure about the correctness...
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ISBN:
(数字)9798331509828
ISBN:
(纸本)9798331509835
Uncertainty in AI refers to the degree of confidence or probability associated with the accuracy or effectiveness of an AI system's output. It is a measure of how much the AI system is unsure about the correctness of its output or decision-making. Uncertainty can arise in AI systems due to a variety of factors, including the quality of input data, variations in data formats, variations in context or environment, and the complexity of the underlying algorithms. Different AI applications can have specific types of uncertainty associated with them, as we discussed in previous questions about uncertainty in AI image recognition, natural language processing, data extraction, expert systems, planning and optimization, and robotics. To mitigate uncertainty in AI systems, researchers and developers use various methods, such as incorporating more data and diverse inputs, improving the quality of input data, enhancing the algorithms, and using feedback mechanisms to learn from errors and improve the system over time. However, complete elimination of uncertainty is often challenging or impossible, and users should always be aware of the potential for errors or unexpected behaviour in AI systems. In this paper, analysis of artificial intelligence uncertainty in various fields.
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