Recent advances in the diffusion model mark a significant leap in AI-generated image technology while extending its application to the Internet of Things (IoT). However, deploying these models on resource-constrained ...
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Multiple secondary users(SUs) perform collaborative spectrum sensing(CSS) in cognitive radio networks to improve the sensing performance. However,this system severely degrades with spectrum sensing data falsification(...
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Multiple secondary users(SUs) perform collaborative spectrum sensing(CSS) in cognitive radio networks to improve the sensing performance. However,this system severely degrades with spectrum sensing data falsification(SSDF) attacks from a large number of malicious secondary users, i.e., massive SSDF attacks. To mitigate such attacks, we propose a joint spectrum sensing and spectrum access framework. During spectrum sensing,each SU compares the decisions of CSS and independent spectrum sensing(IndSS), and then the reliable decisions are adopted as its final decisions. Since the transmission slot is divided into several tiny slots, at the stage of spectrum access, each SU is assigned with a specific tiny time slot. In accordance with its independent final spectrum decisions, each node separately accesses the tiny time *** results verify effectiveness of the proposed algorithm.
Researchers have recently created several deep learning strategies for various tasks, and facial recognition has made remarkable progress in employing these techniques. Face recognition is a noncontact, nonobligatory,...
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Researchers have recently created several deep learning strategies for various tasks, and facial recognition has made remarkable progress in employing these techniques. Face recognition is a noncontact, nonobligatory, acceptable, and harmonious biometric recognition method with a promising national and social security future. The purpose of this paper is to improve the existing face recognition algorithm, investigate extensive data-driven face recognition methods, and propose a unique automated face recognition methodology based on generative adversarial networks (GANs) and the center symmetric multivariable local binary pattern (CS-MLBP). To begin, this paper employs the center symmetric multivariant local binary pattern (CS-MLBP) algorithm to extract the texture features of the face, addressing the issue that C2DPCA (column-based two-dimensional principle component analysis) does an excellent job of removing the global characteristics of the face but struggles to process the local features of the face under large samples. The extracted texture features are combined with the international features retrieved using C2DPCA to generate a multifeatured face. The proposed method, GAN-CS-MLBP, syndicates the power of GAN with the robustness of CS-MLBP, resulting in an accurate and efficient face recognition system. Deep learning algorithms, mainly neural networks, automatically extract discriminative properties from facial images. The learned features capture low-level information and high-level meanings, permitting the model to distinguish among dissimilar persons more successfully. To assess the proposed technique’s GAN-CS-MLBP performance, extensive experiments are performed on benchmark face recognition datasets such as LFW, YTF, and CASIA-WebFace. Giving to the findings, our method exceeds state-of-the-art facial recognition systems in terms of recognition accuracy and resilience. The proposed automatic face recognition system GAN-CS-MLBP provides a solid basis for a
Currently,mobile communication is one of the widely used means of ***,it is quite challenging for a telecommunication company to attract new *** recent concept of mobile number portability has also aggravated the pro...
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Currently,mobile communication is one of the widely used means of ***,it is quite challenging for a telecommunication company to attract new *** recent concept of mobile number portability has also aggravated the problem of customer *** need to identify beforehand the customers,who could potentially churn out to the *** the telecommunication industry,such identification could be done based on call detail *** research presents an extensive experimental study based on various deep learning models,such as the 1D convolutional neural network(CNN)model along with the recurrent neural network(RNN)and deep neural network(DNN)for churn *** use the mobile telephony churn prediction dataset obtained from ***,containing the data for around 100,000 individuals,out of which 86,000 are non-churners,whereas 14,000 are churned *** imbalanced data are handled using undersampling and *** accuracy for CNN,RNN,and DNN is 91%,93%,and 96%,***,DNN got 99%for ROC.
A three-phase three-wire LCL grid-connected inverter is usually used as an interface between renewable-energy sources and grid. However, grid voltage is always distorted and results in grid-current distortion when the...
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Existing studies solve software engineering tasks using code infilling through LLMC. They utilize context information, which refers to data near the target code of infilling, as input prompts. Although prompts are ess...
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We propose and realize a 1D photonic crystal nanocavity laser embedded in a polydimethylsiloxane (PDMS) thin film. The nanolaser in PDMS exhibits a significant optical response to structural deformation. It can be att...
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Reticle optimization is a computationally demanding task in optical microlithography for advanced semiconductor fabrication. In this study, we explore the effectiveness of D-Wave's quantum annealing (QA) and hybri...
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We consider a multi-user joint rate adaptation and computation distribution problem in a millimeter wave (mmWave) virtual reality (VR) system. The VR system that we consider comprises an edge computing unit (ECU) that...
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Reconfigurable Intelligent Surfaces (RIS) also known as Intelligent Reflecting Surfaces (IRS) often depend upon metasurfaces. These typically comprise of a large array of passive elements that can be fabricated to mod...
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