Open multi-agent systems (OMASs), characterized by the dynamic joining and leaving of agents, possess distinct attributes such as agent-level autonomy, time-varying network topologies, and environmental openness. These characteristics make them highly applicable to dynamic scenarios like robotic swarms, smart grids, and vehicular networks. However, such dynamism introduces core challenges in maintaining system stability, achieving efficient collaboration, and guaranteeing decision robustness. This paper presents a brief overview of recent advances in distributed control and decision-making algorithms for OMASs. First, the fundamental concepts and control strategies of OMASs are systematically reviewed. Second, distributed decision-making mechanisms encompassing distributed consensus optimization, separable resource allocation, and Nash equilibrium (NE) seeking in non-cooperative games are discussed, highlighting key technologies and typical methods. Finally, an outlook on future perspectives in the field is presented.

Three-dimensional convolutional neural networks (3D CNNs) show considerable promise for lung nodule detection. However, their high computational complexity and memory demands present substantial challenges for acceleration on a single field-programmable gate array (FPGA). To address this, we propose efficient mapping schemes for a multi-FPGA platform, leveraging its massive parallelism to maximize computational efficiency. Our system, integrating six customized FPGA boards, achieves state-of-the-art performance, delivering approximately 15.9 tera operations per second (TOPS) for nodule segmentation and approximately 3.8 TOPS for nodule classification. Compared to a central processing unit baseline, it achieves a 128.2× speedup while exhibiting 6.7× higher energy efficiency than a graphics processing unit implementation. Furthermore, the system attains a state-of-the-art recall rate of 87.1% on the real-world clinical benchmark.

Reconstructing high dynamic range (HDR) images from a single low dynamic range (LDR) input requires recovering missing information in highlight-clipped and shadow-distorted regions. Existing methods generally rely on sufficient ground-truth HDR images as supervision signals or multi-exposure LDR sequences to improve quality, limiting their flexibility. To address this, we propose USME-HDR, a framework for single-image HDR reconstruction based on multi-exposure priors, where the HDR reconstruction stage is learned without ground-truth HDR supervision. Specifically, an exposure-adjustment network (EAN) is trained in a supervised manner to map a single LDR image to over/under-exposure pairs. Inspired by the Retinex theory, we further decompose the input into a light map and a light feature, which are fed into the EAN as auxiliary inputs for luminance-aware exposure generation. An exposure time ratio guidance mechanism is further introduced to improve luminance fidelity. Finally, the HDR image is synthesized by fusing the original LDR image with generated multi-exposure images, refined through self-supervised optimization. Experiments demonstrate that during the test phase, USME-HDR reconstructs visually compelling HDR images from only a single LDR input, without requiring real low- or high-exposure images.

This study proposes a fast zeroing neural network algorithm to solve time-varying Laplacian linear systems arising from the modeling of superstructure quadrilateral dynamic resistor networks on hammock surfaces. By incorporating the intrinsic structure of the underlying special-form matrices into the core neurodynamic design, the proposed algorithm enables efficient real-time computation of electric potentials under dynamic conditions. The Lyapunov-based analysis proves global exponential convergence. Numerical simulations on resistor networks of various scales demonstrate high computational efficiency and verify convergence to solutions from arbitrary initial conditions. Furthermore, by integrating the proposed algorithm as a potential field solver with a directional potential field path planning algorithm and exploiting the natural descent property of resistor network node potentials, we propose a fast path planning algorithm for robotic navigation on hammock surfaces. Compared with conventional path planning approaches, the proposed algorithm achieves higher computational efficiency in the aforementioned hammock surface path planning task, and this advantage becomes increasingly pronounced as the scale increases. The proposed algorithm is also applied to dynamic path planning tasks, further validating its potential in robotics and control applications. Finally, we present two conjectures.

User-level file systems are widely adopted in research and production environments due to their flexibility and reduced risk of kernel crashes. Filesystem in Userspace (FUSE) is a general-purpose framework for developing user-level file systems in Linux. Compared with library-based file systems, FUSE adopts a cooperative architecture between kernel modules and userspace libraries, ensuring metadata security and compliance with standard portable operating system interface (POSIX) semantics. However, this architecture introduces substantial context-switching overhead. Existing optimization approaches for high-performance computing environments often improve input/output (I/O) performance at the expense of FUSE's cross-environment compatibility and kernel-level security guarantees. To address this limitation, this study proposes SplitFUSE, an I/O-acceleration framework for user-level file systems that preserves high compatibility and strong security. SplitFUSE introduces a split architecture that decouples metadata and data-request processing. Specifically, the kernel maintains full metadata consistency, while the userspace retains only a minimal metadata subset required to validate and process data requests that bypass the kernel securely. Implemented as a self-contained mechanism, SplitFUSE preserves the same cross-environment compatibility as conventional FUSE. Experimental results demonstrate that, under I/O-intensive small-write workloads, SplitFUSE achieves up to 4-6 times higher write bandwidth than native FUSE and outperforms state-of-the-art alternatives. For common file system workloads, it delivers substantial performance improvements with minimal migration overhead.

This study proposes an efficiency-enhanced dual-mode orthogonal Doherty power amplifier (ODPA) architecture. The operation theory, design process, and implementation of the proposed ODPA are introduced in detail. By introducing an orthogonal architecture with control signal power injection and incorporating a modified output matching network, the efficiency of Doherty power amplifiers under active load modulation can be enhanced. Furthermore, owing to the additional degrees of freedom provided by the orthogonal architecture, which enables independent amplitude and phase control and dynamic reshaping of the load modulation trajectories, and the dual-mode reciprocal bias configuration, the proposed ODPA exhibits high adaptability to load mismatch. To validate the proposed architecture, a 2.1-GHz dual-mode ODPA is designed and fabricated. With a matched load, the fabricated dual-mode ODPA exhibites over 60% drain efficiency throughout the power range from 6-dB output back-off (OBO) to saturation. For mismatched loads with a 2:1 voltage standing wave ratio, a saturated output power of 42.8-44.6 dBm and OBO efficiency of 47.2%-58.1% are obtained through mode reconfiguration.

The dual transponder carrier ranging system, an enhancement over the dual one-way ranging system, provides high-precision relative distance measurements without imposing stringent time synchronization requirements. However, a critical limitation to its use in absolute distance measurement is the initial value change phenomenon, which manifests as abrupt phase changes after every system restart. Through theoretical analysis, the root causes of this phenomenon are identified as a fractional output-to-input frequency ratio in the frequency synthesizer and a non-integer coherent turnaround ratio. To validate the theoretical analysis, simulations are conducted using Simulink, and experimental verification is performed on an S-band and C-band hardware platform. Both simulation and experimental results demonstrate that configuring both ratios as integers effectively suppresses these abrupt phase changes. These findings establish an integer-ratio design rule that is independent of specific frequency plans, providing a general design guideline for coherent transponder ranging systems intended for absolute distance measurement, as well as a reference for frequency planning in other absolute distance measurement systems of the same kind.