8 research outputs found

    Cooperative Multi-Type Multi-Agent Deep Reinforcement Learning for Resource Management in Space-Air-Ground Integrated Networks

    Full text link
    The Space-Air-Ground Integrated Network (SAGIN), integrating heterogeneous devices including low earth orbit (LEO) satellites, unmanned aerial vehicles (UAVs), and ground users (GUs), holds significant promise for advancing smart city applications. However, resource management of the SAGIN is a challenge requiring urgent study in that inappropriate resource management will cause poor data transmission, and hence affect the services in smart cities. In this paper, we develop a comprehensive SAGIN system that encompasses five distinct communication links and propose an efficient cooperative multi-type multi-agent deep reinforcement learning (CMT-MARL) method to address the resource management issue. The experimental results highlight the efficacy of the proposed CMT-MARL, as evidenced by key performance indicators such as the overall transmission rate and transmission success rate. These results underscore the potential value and feasibility of future implementation of the SAGIN

    Lightweight Neural Network with Knowledge Distillation for CSI Feedback

    Full text link
    Deep learning (DL) has shown promise in enhancing channel state information (CSI) feedback. However, many studies indicate that better feedback performance often accompanies higher computational complexity. Pursuing better performance-complexity tradeoffs is crucial to facilitate practical deployment, especially on computation-limited devices, which may have to use lightweight autoencoder with unfavorable performance. To achieve this goal, this paper introduces knowledge distillation (KD) to achieve better tradeoffs, where knowledge from a complicated teacher autoencoder is transferred to a lightweight student autoencoder for performance improvement. Specifically, two methods are proposed for implementation. Firstly, an autoencoder KD-based method is introduced by training a student autoencoder to mimic the reconstructed CSI of a pretrained teacher autoencoder. Secondly, an encoder KD-based method is proposed to reduce training overhead by performing KD only on the student encoder. Additionally, a variant of encoder KD is introduced to protect user equipment and base station vendor intellectual property. Numerical simulations demonstrate that the proposed KD methods can significantly improve the student autoencoder's performance, while reducing the number of floating point operations and inference time to 3.05%-5.28% and 13.80%-14.76% of the teacher network, respectively. Furthermore, the variant encoder KD method effectively enhances the student autoencoder's generalization capability across different scenarios, environments, and bandwidths.Comment: 28 pages, 4 figure

    Serum protein coating enhances the antisepsis efficacy of silver nanoparticles against multidrug-resistant Escherichia coli infections in mice

    Get PDF
    Antimicrobial resistance poses a significant threat to public health and social development worldwide. This study aimed to investigate the effectiveness of silver nanoparticles (AgNPs) in treating multidrug-resistant bacterial infections. Eco-friendly spherical AgNPs were synthesized using rutin at room temperature. The biocompatibility of both polyvinyl pyrrolidone (PVP) and mouse serum (MS)-stabilized AgNPs was evaluated at 20 μg/mL and showed a similar distribution in mice. However, only MS-AgNPs significantly protected mice from sepsis caused by the multidrug-resistant Escherichia coli (E. coli) CQ10 strain (p = 0.039). The data revealed that MS-AgNPs facilitated the elimination of Escherichia coli (E. coli) in the blood and the spleen, and the mice experienced only a mild inflammatory response, as interleukin-6, tumor necrosis factor-α, chemokine KC, and C-reactive protein levels were significantly lower than those in the control group. The results suggest that the plasma protein corona strengthens the antibacterial effect of AgNPs in vivo and may be a potential strategy for combating antimicrobial resistance

    A broad range triboelectric stiffness sensor for variable inclusions recognition

    No full text
    With the development of artificial intelligence, stiffness sensors are extensively utilized in various fields, and their integration with robots for automated palpation has gained significant attention. This study presents a broad range self-powered stiffness sensor based on the triboelectric nanogenerator (Stiff-TENG) for variable inclusions in soft objects detection. The Stiff-TENG employs a stacked structure comprising an indium tin oxide film, an elastic sponge, a fluorinated ethylene propylene film with a conductive ink electrode, and two acrylic pieces with a shielding layer. Through the decoupling method, the Stiff-TENG achieves stiffness detection of objects within 1.0 s. The output performance and characteristics of the TENG for different stiffness objects under 4 mm displacement are analyzed. The Stiff-TENG is successfully used to detect the heterogeneous stiffness structures, enabling effective recognition of variable inclusions in soft object, reaching a recognition accuracy of 99.7%. Furthermore, its adaptability makes it well-suited for the detection of pathological conditions within the human body, as pathological tissues often exhibit changes in the stiffness of internal organs. This research highlights the innovative applications of TENG and thereby showcases its immense potential in healthcare applications such as palpation which assesses pathological conditions based on organ stiffness.</p

    Supplementary information files for A broad range triboelectric stiffness sensor for variable inclusions recognition

    No full text
    Supplementary files for article A broad range triboelectric stiffness sensor for variable inclusions recognitionWith the development of artificial intelligence, stiffness sensors are extensively utilized in various fields, and their integration with robots for automated palpation has gained significant attention. This study presents a broad range self-powered stiffness sensor based on the triboelectric nanogenerator (Stiff-TENG) for variable inclusions in soft objects detection. The Stiff-TENG employs a stacked structure comprising an indium tin oxide film, an elastic sponge, a fluorinated ethylene propylene film with a conductive ink electrode, and two acrylic pieces with a shielding layer. Through the decoupling method, the Stiff-TENG achieves stiffness detection of objects within 1.0 s. The output performance and characteristics of the TENG for different stiffness objects under 4 mm displacement are analyzed. The Stiff-TENG is successfully used to detect the heterogeneous stiffness structures, enabling effective recognition of variable inclusions in soft object, reaching a recognition accuracy of 99.7%. Furthermore, its adaptability makes it well-suited for the detection of pathological conditions within the human body, as pathological tissues often exhibit changes in the stiffness of internal organs. This research highlights the innovative applications of TENG and thereby showcases its immense potential in healthcare applications such as palpation which assesses pathological conditions based on organ stiffness.</p
    corecore