Thyroid disease represents a significant contributor to challenges in both medical diagnosis and the prediction of its onset, making it a complex area of study within medical research. This research thoroughly analyse...
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Rate-splitting multiple access(RSMA) has recently gained attention as a potential robust multiple access(MA)scheme for upcoming wireless networks. Given its ability to efficiently utilize wireless resources and design...
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Rate-splitting multiple access(RSMA) has recently gained attention as a potential robust multiple access(MA)scheme for upcoming wireless networks. Given its ability to efficiently utilize wireless resources and design interference management strategies, it can be applied to unmanned aerial vehicle(UAV) networks to provide convenient services for large-scale access ground users. However, due to the line-of-sight(LoS) broadcast nature of UAV transmission, information is susceptible to eavesdropping in RSMA-based UAV networks. Moreover, the superposition of signals at the receiver in such networks becomes complicated. To cope with the challenge, we propose a two-user multi-input single-output(MISO) RSMA-based UAV secure transmission framework in downlink communication networks. In a passive eavesdropping scenario, our goal is to maximize the sum secrecy rate by optimizing the transmit beamforming and deployment location of the UAV-base station(UAV-BS),while considering quality-of-service(QoS) constraints, maximum transmit power, and flight space limitations. To address the non-convexity of the proposed problem, the optimization problem is first decoupled into two subproblems. Then, the successive convex approximation(SCA) method is employed to solve each subproblem using different propositions. In addition, an alternating optimization(AO)-based location RSMA(L-RSMA) beamforming algorithm is developed to implement joint optimization to obtain the suboptimal solution. Numerical results demonstrate that(1) the proposed L-RSMA scheme yields a28.97% higher sum secrecy rate than the baseline L-space division multiple access(SDMA) scheme;(2) the proposed L-RSMA scheme improves the security performance by 42.61% compared to the L-non-orthogonal multiple access(NOMA) scheme.
In multiuser multiple-input multiple-output (MU-MIMO) systems, the selection of a subset of users to achieve the maximum sum rate is critical when resources are limited. In addition, designing suitable precoder and de...
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III-nitride nanowires have emerged as an important semiconductor device technology development platform, leveraging the unique physical properties of III-nitride semiconductors such as widely tunable bandgap energies,...
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True-time-delay (TTD) arrays can implement frequency-dependent rainbow beams and enable fast beam alignment in wideband millimeter-wave (mmWave) systems. In this paper, we consider 3D rainbow beam training with planar...
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Increase in electricity generation is caused due to population increase, which leads to the depletion of fossil fuels, and increased pollution. This leads to focusing on alternate renewable energy, mainly solar photov...
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Recent advancements in wearable and Internet of Things (IoT) technologies have yet to be fully realized in combination with Mixed Reality (MR) for comprehensive real-time health monitoring systems. This paper introduc...
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In this paper,a robust and consistent COVID-19 emergency decision-making approach is proposed based on q-rung linear diophantine fuzzy set(q-RLDFS),differential evolutionary(DE)optimization principles,and evidential r...
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In this paper,a robust and consistent COVID-19 emergency decision-making approach is proposed based on q-rung linear diophantine fuzzy set(q-RLDFS),differential evolutionary(DE)optimization principles,and evidential reasoning(ER)*** proposed approach uses q-RLDFS in order to represent the evaluating values of the alternatives corresponding to the *** optimization is used to obtain the optimal weights of the attributes,and ER methodology is used to compute the aggregated q-rung linear diophantine fuzzy values(q-RLDFVs)of each *** the score values of alternatives are computed based on the aggregated *** alternative with the maximum score value is selected as a better *** applicability of the proposed approach has been illustrated in COVID-19 emergency decision-making system and sustainable energy planning ***,we have validated the proposed approach with a numerical ***,a comparative study is provided with the existing models,where the proposed approach is found to be robust to perform better and consistent in uncertain environments.
In recent years, deep neural networks have achieved remarkable accuracy in computer vision tasks. With inference time being a crucial factor, particularly in dense prediction tasks such as semantic segmentation, knowl...
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Climate change poses significant challenges to agricultural management,particularly in adapting to extreme weather conditions that impact agricultural *** works with traditional Reinforcement Learning(RL)methods often...
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Climate change poses significant challenges to agricultural management,particularly in adapting to extreme weather conditions that impact agricultural *** works with traditional Reinforcement Learning(RL)methods often falter under such extreme *** address this challenge,our study introduces a novel approach by integrating Continual Learning(CL)with RL to form Continual Reinforcement Learning(CRL),enhancing the adaptability of agricultural management *** the Gym-DSSAT simulation environment,our research enables RL agents to learn optimal fertilization strategies based on variable weather *** incorporating CL algorithms,such as Elastic Weight Consolidation(EWC),with established RL techniques like Deep Q-Networks(DQN),we developed a framework in which agents can learn and retain knowledge across diverse weather *** CRL approach was tested under climate variability to assess the robustness and adaptability of the induced policies,particularly under extreme weather events like severe *** results showed that continually learned policies exhibited superior adaptability and performance compared to optimal policies learned through the conventional RL methods,especially in challenging conditions of reduced rainfall and increased *** pioneering work,which combines CL with RL to generate adaptive policies for agricultural management,is expected to make significant advancements in precision agriculture in the era of climate change.
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