Reconfigurable intelligent surfaces (RISs) have been recognized as a revolutionary technology for future wireless networks. However, RIS-assisted communications have to continuously tune phase-shifts relying on accura...
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Non-overlapping codes are a set of codewords such that the prefix of each codeword is not a suffix of any codeword in the set, including itself. If the lengths of the codewords are variable, it is additionally require...
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This paper tackles the deployment challenges of Simultaneous Transmitting and Reflecting Reconfigurable Intelligent Surface (STAR-RIS) in communication systems. Unlike existing works that use fixed deployment setups o...
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CAA software is developed to extend the scope of applications of CATIA, whereas the CAA provides no API to implement the encryption of CAA software, which is to the disadvantage of protecting the security of CAA softw...
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The development of modern marine technology promotes frequent marine activities and marine traffic, and at the same time brings maritime security issues that cannot be ignored. Traditional optical and radio detection ...
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ISBN:
(数字)9798350306569
ISBN:
(纸本)9798350306576
The development of modern marine technology promotes frequent marine activities and marine traffic, and at the same time brings maritime security issues that cannot be ignored. Traditional optical and radio detection technologies are usually affected by many factors, and the detection effect is poor in bad weather and nighttime environments. In order to improve maritime safety requirements, infrared camera detection is regarded as an effective supplementary means. Considering that the current infrared detection capability is relatively weak, an orthogonal experimental design method is proposed. Through simulation, it is obtained that the detection distance is 120km of A at level 1, the spatial angle is 30-60° of B at level 2, the camera angle of view is the elevation angle of C at level 1, and the scanning period is D at level 3. The combination of 5s is the combination with the best recognition efficiency among the combinations of each factor level, that is, A
1
B
2
C
1
D
3
. First, briefly introduce the working principle of offshore infrared camera detection and the steps of orthogonal test design, then determine the level of factors that affect the detection ability, and design a factor level table. On this basis, the orthogonal test design was carried out, and 27 sets of test results were obtained. Finally, through the analysis and verification of the test results, the primary and secondary order of the factors affecting the detection ability were obtained, and then the optimal detection scheme was obtained.
Cellular proliferation in the lung tissues is a hallmark of lung cancer. Lung cancer is particularly dangerous since the lungs are responsible for both breathing in oxygen and exhaling carbon dioxide—two of the body&...
Cellular proliferation in the lung tissues is a hallmark of lung cancer. Lung cancer is particularly dangerous since the lungs are responsible for both breathing in oxygen and exhaling carbon dioxide—two of the body's most vital functions. The application of deep-learning (DL) for the identification of lymph node involvement on histopathology slides has gained a lot of attention due to the potential impact it could have on patient diagnosis and therapy. Recognition accuracy, precision, sensitivity, F-Score, specificity, etc., are all significantly lower with the current approach. Convolution-Neural-Network (CNN), CNN Gradient-Descent (CNN GD), VGG-16, VGG-19, and Resnet-50 are just few of the deep learning algorithms that exhibit improved performance in the metrics with the proposed methodology. CT scan pictures and histopathology images are used to evaluate the suggested method. When histopathological tissues are analyzed, the results demonstrate that detection accuracy improves.
Due to the scarcity and specific imaging characteristics in medical images, light-weighting Vision Transformers (ViTs) for efficient medical image segmentation is a significant challenge, and current studies have not ...
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A micro-electrode array (MEA) is essential in the bio-medical field to measure various bio-signals in vitro and in vivo environments. The transparent MEA allows imaging of cell surfaces and organs inside the body. Als...
A micro-electrode array (MEA) is essential in the bio-medical field to measure various bio-signals in vitro and in vivo environments. The transparent MEA allows imaging of cell surfaces and organs inside the body. Also, when we perform light-based modulation, such as optogenetics, higher efficiency in response to light can be obtained with a transparent MEA. Here, instead of well-known direct electrode material printing, we print a polymer seed layer that can induce the formation of transparent ultrathin $(< 10\ \text{nm})$ metal electrodes with the merits of fabrication simplicity, low processing temperature, and design customizability. We optimized Au deposition thickness and metal film morphology to form conductive and transparent electrodes on selectively printed polymer seed layer regions. These electrodes show improved impedance at low frequencies compared to well-known thick Au-based electrodes. Finally, we successfully recorded brain signals in vivo by placing the flexible electrode array on the surface of the mouse brain.
The purpose of the research is to compare Random Forest (RF) Algorithm methods to Extra Trees (ET) Classification algorithms for detecting the Disk Filtration attacks in air gapped computers. Materials and Methods: Th...
The purpose of the research is to compare Random Forest (RF) Algorithm methods to Extra Trees (ET) Classification algorithms for detecting the Disk Filtration attacks in air gapped computers. Materials and Methods: The sample set comprises 609 positive records and 612 negative records were taken for conducting the experiment to compare Random Forest Algorithm with Extra Trees Algorithm for Disk Filtration Attacks. This sample size was obtained using clinical analysis, which had alpha and beta values of 0.05 and 1, an enrollment ratio of 1, pre-test G power of 80%, and 0.5, respectively, with a 95% confidence level. Random Forest and ET classifiers were built using a framework for disk filtration detection. Results: The Disk Filtration attacks on the data set is detected with 99.40% accuracy by the Novel Random Forest classifier,the ET generates 99.20%, in contrast. With a 95% confidence interval, there is a statistically significant difference between the two groups (p=0.002; p0.05). Therefore, RF classifiers outperform ET classifiers. Conclusion: The outcomes demonstrate that RF performs more accurately when compared to ET.
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