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lssue 6, 2026
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Yunhe Pan, Aiguo Fei
Advisor Xicheng Lu
ISSN 3069-8928
CN 33-1439/TP
IF (2025 JCR) 3.2
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Volume 27 Issue 6，2026
Regular Papers;Review
Distributed control and decision-making algorithms for open multi-agent systems: a brief overview Enhanced Publication AI Introduction
Abstract：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.Keywords：Open multi-agent system;Time-varying topology;Distributed control;Distributed optimization;Nash equilibrium seeking10|4|0Updated：2026-07-17Regular Papers;Research Article
Efficient mapping and flexible interconnects: accelerating 3D CNN-based lung nodule segmentation and classification on multi-FPGA Enhanced Publication AI Introduction
Abstract：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.Keywords：Lung nodule detection;Three-dimensional convolutional neural networks (3D CNNs);Field-programmable gate array (FPGA);Multi-FPGA systems;Hardware acceleration;Parallel mapping9|4|0Updated：2026-07-17Regular Papers;Research Article
Unsupervised single-image high dynamic range rendering via multi-exposure priors Enhanced Publication AI Introduction
Abstract：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.Keywords：High dynamic range (HDR);HDR reconstruction;Single-image HDR;Unsupervised learning;Multi-exposure prior7|4|0Updated：2026-07-17Regular Papers;Research Article
A neural network algorithm for superstructure quadrilateral dynamic resistor networks on hammock surfaces with application to artificial intelligence Enhanced Publication AI Introduction
Abstract：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.Keywords：Zeroing neural network;Resistor network;Laplacian system;Equivalent resistance;Potential;Path planning8|4|0Updated：2026-07-17Regular Papers;Research Article
SplitFUSE: a highly compatible framework for I/O acceleration of user-level file systems AI Introduction
Abstract：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.Keywords：Filesystem in Userspace (FUSE);User-level file system;Input/Output (I/O) acceleration;Kernel bypass;Split architecture6|6|0Updated：2026-07-17Regular Papers;Research Article
Dual-mode orthogonal Doherty power amplifier with efficiency enhancement and load mismatch adaptability AI Introduction
Abstract：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.Keywords：Doherty power amplifier;Dual mode;High efficiency;Load mismatch;Orthogonal;Reciprocal bias5|5|0Updated：2026-07-17
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