Depending on the applications based on graphene, single-layer or few-layer graphene would be more beneficial. Ideally, graphene could be nucleated directly with the required thickness. However, some aspects related to...
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Energy efficiency is an important criterion for routing algorithms in the wireless sensor network. Cooperative routing can reduce energy consumption effectively stemming from its diversity gain advantage. To solve the...
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Energy efficiency is an important criterion for routing algorithms in the wireless sensor network. Cooperative routing can reduce energy consumption effectively stemming from its diversity gain advantage. To solve the energy consumption problem and maximize the network lifetime, this paper proposes a Virtual Multiple Input Multiple Output based Cooperative Routing algorithm(VMIMOCR). VMIMOCR chooses cooperative relay nodes based on Virtual Multiple Input Multiple Output Model, and balances energy consumption by reasonable power allocation among transmitters, and decides the forwarding path finally. The experimental results show that VMIMOCR can improve network lifetime from 37% to 348% in the medium node density, compared with existing routing algorithms.
Disk arrays and shared-memory multiprocessors are new technologies that are rapidly becoming pervasive. They are complementary because disk arrays naturally balance the I/O workload by interleaving data across all dis...
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Disk arrays and shared-memory multiprocessors are new technologies that are rapidly becoming pervasive. They are complementary because disk arrays naturally balance the I/O workload by interleaving data across all disks while a shared memory multiprocessor balances the processing workload across multiple processors. In this paper, we examine how disk arrays and shared memory multiprocessors lead to an effective method for constructing database machines for general-purpose complex query processing. We show that disk arrays can lead to cost-effective storage systems if they are configured from suitably small formfactor disk drives. We introduce the storage system metric data temperature (IO/s/Gbyte) as a way to evaluate how well a disk configuration can sustain its workload, and we show that disk arrays can sustain the same data temperature as a more expensive mirrored-disk configuration. We use the metric to evaluate the performance of disk arrays in XPRS, an operational shared-memory multiprocessor database system being developed at the University of California, Berkeley.
This paper studies event-triggered control for disturbed nonlinear systems. A new dual-stage Lyapunov-based event-triggering condition is proposed to cope with the time-varying transmission *** the first stage, the ra...
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This paper studies event-triggered control for disturbed nonlinear systems. A new dual-stage Lyapunov-based event-triggering condition is proposed to cope with the time-varying transmission *** the first stage, the ratio of Lyapunov function values at the last two triggering instants is *** based on the ratio, the corresponding threshold function is selected from two candidate forms. It is proved that the designed event-triggered control system is input-to-state practically stable with respect to the measurement errors and disturbances. Moreover, Zeno behavior is excluded successfully by calculating the lower bound of the minimum inter-event times. Finally, a simulation example is provided to show the feasibility and the effectiveness of the proposed approach.
The effect of intergrain exchange and magnetostatic interactions on remanence and coercivity is calculated numerically for an isotropic Nd-Fe-B model magnet composed of 8000 grains. The intergrain exchange interaction...
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The effect of intergrain exchange and magnetostatic interactions on remanence and coercivity is calculated numerically for an isotropic Nd-Fe-B model magnet composed of 8000 grains. The intergrain exchange interaction affects the magnetizing process markedly when the grain size L is small, but the magnetostatic interaction does not have substantial effects. The calculated remanence which takes the intergrain exchange interaction into account increases with decreasing L from M(s)/2 (M(s): the saturation magnetization) to M(s) and agrees roughly with the experimental results. The calculated coercivity decreases with decreasing L from 0.48 H(A) (H(A): the anisotropy field) to 0. Our results suggest that the large remanence above M(s)/2 observed experimentally in isotropic Nd-Fe-B magnets can be explained by the intergrain exchange interaction between Nd2Fe14B grains.
Multi-agent collaborative perception, an emerging technology in intelligent driving, has attracted considerable attention in recent years. Despite advancements in previous works, challenges remain due to inevitable lo...
Multi-agent collaborative perception, an emerging technology in intelligent driving, has attracted considerable attention in recent years. Despite advancements in previous works, challenges remain due to inevitable localization errors, data sparsity, and bandwidth limitations. To address these challenges, a collaborative detection and tracking method, CoTrack, is proposed to balance perception effectiveness with communication efficiency. Specifically, a spatio-temporal aggregation module, consisting of a spatial cross-agent collaboration submodule and a temporal ego-agent enhancement submodule, is presented. The former dynamically integrates spatial semantics from multiple agents to alleviate feature misalignment caused by localization errors, while the latter captures the historical context of the ego-agent to compensate for the insufficiency of single-frame observations resulting from data sparsity. Additionally, an unsupervised feature compressor is designed to reduce communication volume. Furthermore, a two-stage online association strategy is developed to improve the matching success rate of detection-track pairs in collaborative tracking task. Experimental results on both simulated and real datasets demonstrate that CoTrack achieves state-of-the-art performance in collaborative 3D object detection and tracking tasks while maintaining robustness in harsh and noisy environments.
Breast cancer is a deadly disease that usually affects women. Since a higher mammographic density is linked to a higher risk of developing breast cancer, it is crucial to consider this factor when determining an indiv...
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ISBN:
(数字)9798331518523
ISBN:
(纸本)9798331518530
Breast cancer is a deadly disease that usually affects women. Since a higher mammographic density is linked to a higher risk of developing breast cancer, it is crucial to consider this factor when determining an individual's risk for the illness. This makes it possible to guarantee thorough breast cancer screening and detection. With a focus on their possible applications in breast cancer prediction, this work investigates machine learning (ML) and deep learning (DL) methods for categorizing and segmenting mammography breast density. We demonstrate their applicability in therapeutic scenarios by comparing the performance of different algorithms. Its methods for categorizing and dividing mammographic breast density have shown to be incredibly effective. By offering accurate and consistent evaluations, these techniques can help radiologists make well-informed decisions. This survey summarizes the most recent state-of-the-art methods and covers developments in ML and DL algorithms for breast density segmentation and classification.
Advances in artificial intelligence (AI) have had a major impact on natural language processing (NLP), even more so with the emergence of large-scale language models like ChatGPT. This paper aims to provide a critical...
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It has been shown that analog-to-information conversion(AIC) is an efficient scheme to perform sub-Nyquist sampling of pulsed radar echoes. However, it is often impractical, if not infeasible, to reconstruct full-rang...
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It has been shown that analog-to-information conversion(AIC) is an efficient scheme to perform sub-Nyquist sampling of pulsed radar echoes. However, it is often impractical, if not infeasible, to reconstruct full-range Nyquist samples because of huge storage and computational load requirements. Based on the analyses of AIC measurement system, this paper develops a novel segment-sliding reconstruction(Seg SR) scheme to effectively reconstruct the Nyquist samples. The Seg SR performs segment-by-segment reconstruction in a sliding mode and can be implemented in real time. An important characteristic that distinguishes the proposed Seg SR from existing methods is that the measurement matrix in each segment satisfies the restricted isometry property(RIP) condition. Partial support in the previous segment can be incorporated into the estimation of the Nyquist samples in the current segment. The effect of interference introduced from adjacent segments is theoretically analyzed, and it is revealed that the interference consists of two interference levels with different impacts to the signal reconstruction performance. With these observations, a two-step orthogonal matching pursuit(OMP)procedure is proposed for segment reconstruction, which takes into account different interference levels and partially known support of the previous segment. The proposed Seg SR scheme achieves near-optimal reconstruction performance with a significant reduction of computational loads and storage requirements. Theoretical analyses and simulations verify its effectiveness.
In this article, a new dynamic gyrator model is proposed for transient analysis of inductive power transfer (IPT). The well-known dynamic phasor is applied to an IPT system and then the dynamic gyrator is derived from...
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