In the field of object detection in large-scale remote sensing images, achieving a good tradeoff between model accuracy and speed has been a long-standing challenge. The majority of inference time is spent on background regions without objects, making real-time detection difficult in practical applications. Common approaches involve partitioning large-scale remote sensing images into smaller patches, followed by using additional classification networks or detectors on the final layer of the backbone's feature map to identify and filter out patches devoid of objects, ultimately enhancing detection efficiency. This paper proposes a novel model, called OPD-Swin-Transformer, for ship detection in large-scale remote sensing images. This model integrates a simple and lightweight Object Presence Detector (OPD) at each stage of the Swin-Transformer and uses a step-by-step, progressively challenging strategy to filter out background image patches, achieving an overall improvement in detection speed. The model optimizes the entire network end-to-end using a multi-task loss function, leading to simultaneous improvements in detection accuracy. By employing an optimal threshold generation strategy based on the weighted Youden index, the model effectively maintains a higher recall rate for ships while filtering out background images, achieving an optimal balance between speed and accuracy. Our OPD-Swin-Transformer is integrated into two mainstream detectors and evaluated on two popular benchmarks for ship detection. The experiments demonstrate that, when compared to other state-of-the-art methods, this approach increases inference speed by more than 40% while also improving detection accuracy. The code and models are publicly available at https://github.com/foreverfire01/OPD .

Graph visualization with proper layout is widely applied to understand the relationship between entities in a complex system and the topological structure information is mainly used. Real-world graphs often have the community structures property which is ignored in many existing graph layout methods. Thus, we propose a multivariate network layout method using the force-directed method with attribute constraints. This method can effectively take into account the hierarchical structure, connection strength, and quantitative comparison between communities. First, the layout of community centers is generated by a force-directed algorithm in which node count of the community is taken as constraints to enable area balance of community; Second, community force based on node attribute is added in the force-directed algorithm to maintain the community clarity. A visualization system is also developed to allow users to interactively generate community structure-aware layout results. qualitative and quantitative evaluation of the results verifies the usability and effectiveness of the proposed method by comparing it with other methods.

We propose and experimentally demonstrate a tunable frequency-doubling optoelectronic oscillator (FD-OEO) based on a single-bandpass dispersion-induced microwave photonic filter (MPF) consisting of a Mach–Zehnder modulator (MZM), a linearly chirped fiber Bragg grating and polarization-multiplexed dual-loop. Thanks to the polarization dependence of the MZM, a special double sideband modulation is implemented where the optical carrier (OC) and subcarriers are orthogonally polarized. By simply tuning the PC in the OEO loop, the phase difference between the orthogonal polarization carrier and two sidebands can be controlled, and thus the center frequency of the fundamental OEO can be tuned. Furthermore, a PC and a polarizer are placed outside the OEO to achieve optical carrier suppression (OCS) modulation, which ensures that a frequency-tunable microwave signal at the second-harmonic frequency is generated. In the experiment, a fundamental frequency signal with tunable frequency from 3.6 to 6.85 GHz and FD-OEO with a tunable frequency range from 7.2 to 13.7 GHz are generated.

Existing model watermarking methods fail to provide adequate protection for edge intelligence models. This paper innovatively integrates the characteristics of model fingerprinting, proposing a model watermarking method named FingerMarks that enables both model attribution and traceability of edge node users. The method initially constructs a uniform trigger set and an encoding scheme through fingerprint extraction, which effectively distinguishes the host model from independently trained models. Based on the encoding scheme, distinct user IDs are converted and mapped into specific labels, thereby generating distinct watermark-embedded trigger sets. Watermarks are embedded using a progressive adversarial training strategy. Comprehensive evaluation across multiple datasets confirms the method’s performance, uniqueness, and robustness. Experimental results show that FingerMarks effectively identifies the watermarked model while maintaining superior robustness compared to state-of-the-art alternatives.