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Titlebook: Intelligent Signal Processing and RF Energy Harvesting for State of art 5G and B5G Networks; Javaid A. Sheikh,Taimoor Khan,Binod Kumar Kan

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樓主: 海市蜃樓
21#
發(fā)表于 2025-3-25 03:33:11 | 只看該作者
22#
發(fā)表于 2025-3-25 09:57:43 | 只看該作者
23#
發(fā)表于 2025-3-25 14:18:02 | 只看該作者
Antenna Designs for Development of Cognitive Radio Systems,y commercial bands. Hence, spectrum scarcity has been a major challenge to be addressed. The cognitive radio addresses this issue in an efficient manner. This chapter also presents materials used for fabrication and simulation technologies.
24#
發(fā)表于 2025-3-25 17:26:07 | 只看該作者
Recent Developments of Network Monitoring Systems and Challenges,ring, and privacy preservation are also addressed. By examining the advancements and challenges in network monitoring systems, this paper aims to provide a comprehensive understanding of the current state of the field and identify future research directions.
25#
發(fā)表于 2025-3-25 23:43:22 | 只看該作者
An Insight into Content-Based Image Retrieval Techniques, Datasets, and Evaluation Metrics,atasets, evaluation metrics, and pros and cons of different CBIR techniques is presented. The paper concludes by discussing current research challenges and future opportunities to improve and apply CBIR to various fields.
26#
發(fā)表于 2025-3-26 02:51:25 | 只看該作者
Data Collection and Analysis: The Foundation of Evidence-Based Research in Various Disciplines,es a detailed summary of data collection, scrappers, crawlers, and APIs. It also gives a view of data pre-processing, data annotation, data evaluation, and also highlight its various challenges and issues.
27#
發(fā)表于 2025-3-26 05:18:45 | 只看該作者
28#
發(fā)表于 2025-3-26 12:33:06 | 只看該作者
Deep Learning-Based Segmentation of MRI Images: Concepts, Challenges, Deep Learning Architectures, l neural networks (CNNs) being the most commonly used approach in the medical image analysis community. In this paper, we provide a comprehensive study of deep learning-based segmentation of MRI images, covering the fundamental concepts, challenges, deep neural network architectures, and future directions.
29#
發(fā)表于 2025-3-26 13:51:23 | 只看該作者
30#
發(fā)表于 2025-3-26 19:55:26 | 只看該作者
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