In recent years, most of the studies have shown that the generalized iterated shrinkage thresholdings (GISTs) have become the commonly used first-order optimization algorithms in sparse learning problems. The nonconve...
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To expedite the design process of the platform-based dual-antenna mutually coupled layout, the inverse artificial neural networks(ANNs) model is proposed, which can directly generate the layout to match the expected e...
To expedite the design process of the platform-based dual-antenna mutually coupled layout, the inverse artificial neural networks(ANNs) model is proposed, which can directly generate the layout to match the expected electromagnetic response. In this ANNs model, the two antenna coupling performance S-parameter is regarded as the input, and the corresponding discrete points are the output. Once the training is completed, the proposed inverse model can provide the corresponding antennas located point directly without an optimization process. In the last, two examples are presented to demonstrate the accuracy and efficiency of the proposed method.
In many earlier works,perfect quantum state transmission over the butterfly network can be achieved via quantum network coding protocols with the assist of maximally entangled ***,in actual quantum networks,a maximall...
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In many earlier works,perfect quantum state transmission over the butterfly network can be achieved via quantum network coding protocols with the assist of maximally entangled ***,in actual quantum networks,a maximally entangled state as auxiliary resource is hard to be obtained or easily turned into a non-maximally entangled state subject to all kinds of environmental ***,we propose a more practical quantum network coding scheme with the assist of non-maximally entangled *** this paper,a practical quantum network coding protocol over grail network is proposed,in which the non-maximally entangled resource is assisted and even the desired quantum state can be perfectly *** achievable rate region,security and practicability of the proposed protocol are discussed and *** practical quantum network coding protocol proposed over the grail network can be regarded as a useful attempt to help move the theory of quantum network coding towards practicability.
With the evolution of self-supervised learning, the pre-training paradigm has emerged as a predominant solution within the deep learning landscape. Model providers furnish pre-trained encoders designed to function as ...
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With the evolution of self-supervised learning, the pre-training paradigm has emerged as a predominant solution within the deep learning landscape. Model providers furnish pre-trained encoders designed to function as versatile feature extractors, enabling downstream users to harness the benefits of expansive models with minimal effort through fine-tuning. Nevertheless, recent works have exposed a vulnerability in pre-trained encoders, highlighting their susceptibility to downstream-agnostic adversarial examples (DAEs) meticulously crafted by attackers. The lingering question pertains to the feasibility of fortifying the robustness of downstream models against DAEs, particularly in scenarios where the pre-trained encoders are publicly accessible to the attackers. In this paper, we initially delve into existing defensive mechanisms against adversarial examples within the pre-training paradigm. Our findings reveal that the failure of current defenses stems from the domain shift between pre-training data and downstream tasks, as well as the sensitivity of encoder parameters. In response to these challenges, we propose Genetic Evolution-Nurtured Adversarial Fine-tuning (Gen-AF), a two-stage adversarial fine-tuning approach aimed at enhancing the robustness of downstream models. Gen-AF employs a genetic-directed dual-track adversarial fine-tuning strategy in its first stage to effectively inherit the pre-trained encoder. This involves optimizing the pre-trained encoder and classifier separately while incorporating genetic regularization to preserve the model’s topology. In the second stage, Gen-AF assesses the robust sensitivity of each layer and creates a dictionary, based on which the top-k robust redundant layers are selected with the remaining layers held fixed. Upon this foundation, we conduct evolutionary adaptability fine-tuning to further enhance the model’s generalizability. Our extensive experiments, conducted across ten self-supervised training methods and six
Mobile edge computing has been a promising technology that enables diverse applications of computation-intensive yet latency-sensitive in marine Internet of Things networks. In this paper, we propose a framework of hi...
Mobile edge computing has been a promising technology that enables diverse applications of computation-intensive yet latency-sensitive in marine Internet of Things networks. In this paper, we propose a framework of hierarchical computing offloading with the assistance of high altitude platforms (HAPs), and a hybrid transmission scheme of non-orthogonal multiple access (NOMA) and frequency division multiple access (FDMA) is designed for achieving efficient computation offloading. Specifically, the offshore sensing devices (SDs) initially perform computation offloading to the HAPs by forming NOMA groups, and the HAPs further offload partial workload to the onshore base station (BS) via FDMA. For efficient calculation, we aim to minimize the overall delay in completing all the workload processing of these SDs by jointly optimizing the durations of NOMA and FDMA transmission as well as the hierarchical computation offloading workload. Though the problem is in the form of non-convexity, we design an efficient SCA-based algorithm to tackle it. Finally, numerical results demonstrate the optimality and convergence of the proposed algorithm, as well as the performance gains of the proposed scheme.
With the growing demand for wireless communication networks, achieving efficient and equitable channel access schemes has become paramount. In this paper, we delve into the realm of distributed channel access in homog...
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ISBN:
(数字)9798350396034
ISBN:
(纸本)9798350396041
With the growing demand for wireless communication networks, achieving efficient and equitable channel access schemes has become paramount. In this paper, we delve into the realm of distributed channel access in homogeneous wireless networks, focusing on both fairness and efficiency. We introduce a media access control protocol, named Fair and Efficient Multiple Access (FEMA), by leveraging decomposed Multi-Agent Deep Deterministic Policy Gradient (MADDPG). FEMA decouples the dual objectives into self-oriented efficiency reward and team-oriented fairness reward. Correspondingly, local critic networks and global critic networks target self-oriented reward and team-oriented reward, respectively. Extensive simulations demonstrate FEMA’s superiority over conventional MAC protocols and other multiple access techniques based on deep reinforcement learning, showcasing its ability to simultaneously enhance fairness and efficiency.
In this paper, a novel scheme is proposed by introducing the concept of generalized spatial modulation (GSM) and space-time block code (STBC) into simultaneously transmitting and reflecting reconfigurable intelligent ...
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In recent years, various methods for processing and analyzing thermographic data have been applied in non-destructive testing to improve the visibility of defects. Representatively, traditional linear dimensionality r...
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ISBN:
(数字)9798350361674
ISBN:
(纸本)9798350361681
In recent years, various methods for processing and analyzing thermographic data have been applied in non-destructive testing to improve the visibility of defects. Representatively, traditional linear dimensionality reduction methods (e.g. principal component analysis) have achieved good results in thermographic data analysis. In this work, a slow feature thermography (SFT) method for non-destructive evaluation of defects in composite materials. Thermographic data can be regarded as a continuous time series generated by an infrared imager at different moments. Slow feature analysis algorithm is subsequently used to extract slow features of the thermal image sequence. Slow features refer to features that are unchanged or slowly changing in time-series data, such as the background of a thermal image. By extracting the slow features of data, the inhomogeneous background, noise and defect features contained in thermal images can be effectively distinguished. Test results on carbon fiber reinforced polymer specimens demonstrate the feasibility and effectiveness of the SFT method.
This paper proposes a novel passive location parameter estimator using multiple satellites for moving aerial targets. Specifically, we consider target detection in the ground dual receiver system, which is divided int...
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In this paper, hypotheses of target detection features is proposed when constructing the signal model. Under this condition, the Rao detection method is extended to weak echo signal detection using multi-satellite ill...
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