It's It's been so crucial lately to emphasize data security applying as the world depends on data exchange extensively almost in all domains. Steganography is one of the main techniques used to ensure informat...
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When a pipeline is inserted into the joint coupler, frequent fires and uneven fusion occur in a trial of fusion without reaching to a desired position. To solve the matters, a positioning sensor and sensor system were...
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As users create and share their own content, the importance of data is increasing. However, there has been a situation in which users' content is subordinated to major Internet service companies and users are unab...
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With its inherent modular structure, modular multilevel converters (MMCs) have outstanding scalability and application flexibility, but it also introduces the submodule (SM) voltage balancing issue. Numerous SM voltag...
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Beam management is the defacto approach for configuring the antennas in 5G MIMO communication systems. Extending the beam management framework to larger arrays— also known as extreme MIMO systems—is challenging as t...
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ISBN:
(数字)9798350393187
ISBN:
(纸本)9798350393194
Beam management is the defacto approach for configuring the antennas in 5G MIMO communication systems. Extending the beam management framework to larger arrays— also known as extreme MIMO systems—is challenging as the overheads grow with the array dimensions. One solution is to make use of the wealth of sensor data that is becoming available in integrated sensing and communication (ISAC) systems. In this paper, we propose a neural architecture for codebook design using environmental context derived from sensor data. In particular, we combine beamspace transformations with local occupancy grids obtained through network sensing to maximize the achievable rate in vehicular operations. Our results show significant performance gains over traditional codebooks while requiring less overhead than standard 5G beam management.
In modern power systems, there is a possibility that the power system topology may change due to the trip of power lines or distributed generations due to maintenance or fault. Since changes in the topology of the pow...
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Compared to other semiconductor quantum computing platform, it is relatively untouched about the verification of linear-optical quantum computing (LOQC) platform. To overcome this problem, we propose a universal and v...
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This paper presents a hardware simulator for a Frequency-Watt control in smart inverters. The power plant generator rotates at a constant speed, which is related to the frequency of the grid. The frequency of the grid...
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In the dynamic landscape of retail, understanding customer behavior is paramount for businesses seeking to optimize marketing strategies and enhance the shopping experience. This study explores the utilization of hier...
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Metastatic cancer, defined by the spread of cancer cells from their original site to distant body regions, presents considerable diagnostic hurdles. Precise identification in histopathological images is essential for ...
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ISBN:
(数字)9798350357509
ISBN:
(纸本)9798350357516
Metastatic cancer, defined by the spread of cancer cells from their original site to distant body regions, presents considerable diagnostic hurdles. Precise identification in histopathological images is essential for optimal patient care. This research introduces an innovative hybrid framework integrating ResNet50, self-attention modules, and Gated Recurrent Units (GRUs) to enhance binary classification precision in metastatic cancer detection. In opposite to CNN-GRU, CNN-LSTM, and AlexNet-GRU models, our approach showcased remarkable performance across two datasets. For the Histopathologic Cancer Detection BreakHis dataset, the model achieved 96.00% accuracy, 95.32% precision, 96.15% sensitivity, and 95.32% specificity. On the BACH dataset, it attained 98.00% accuracy, 98.44% precision, 98.44% sensitivity, and 98.44% specificity. In multi-class classification tasks, the model achieved an impeccable score of 1.00 on both the BreakHis and BACH datasets. Those outcomes highlight the model's capacity to significantly decrease diagnostic errors and improve pathologists' diagnostic accuracy surpassing other approaches in the field.
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