論文 - 大槻　知明

Generalized Wideband Power Amplifier Modeling for Vehicular Wireless Transceivers Using Kolmogorov-Arnold Convolutional Liquid Neural Networks

Wu Y., Wang Q., Ohtsuki T., Niyato D., Adachi F., Gui G.

IEEE Transactions on Vehicular Technology 2026年

ISSN  00189545

　概要を見る

Accurate behavioral modeling of power amplifiers (PAs) is crucial for digital pre-distortion and energy-efficient vehicular wireless transceivers, yet remains challenging due to bandwidth-dependent nonlinearities and fast-varying drive conditions in practical RF front-ends. Conventional PA models struggle to capture coupled temporal memory effects and spatial signal dependencies, limiting their effectiveness in broadband vehicular transmitters. To address this issue, we propose a Kolmogorov–Arnold Convolutional Liquid Neural Network (KACLN) that jointly leverages Kolmogorov–Arnold representation learning and liquid neuron adaptive dynamics to model nonlinear distortion with strong temporal generalization. The proposed KACLN integrates one-dimensional and two-dimensional convolutional operator kernels to simultaneously characterize long- and short-term PA memory, frequency-selective distortion patterns, and cross-sample bandwidth interactions. Experiments over diverse vehicular RF operating regimes demonstrate that KACLN improves modeling precision and robustness, achieving 1.789 dB normalized mean square error (NMSE) reduction and 2.639 dB adjacent channel error power ratio (ACEPR) suppression gains compared with the best performed MCLDNN. The proposed KACLN further mitigates efficiency degradation associated with static power back-off, enhancing effective output utilization under fast-varying channel and wideband signal envelopes. These results validate KACLN as a practical, real-time capable nonlinear surrogate suitable for next-generation vehicular PA linearization, DPD-aided waveform shaping, and low-latency wireless transmitter design.

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Efficient Voxel-Based mmWave Radar HAR with Early Temporal Fusion and a Compact 3D–2D Hybrid Network

Zhao R., Miao F., Liu Y., Yin Y., Ohtsuki T., Gui G., Adachi F.

IEEE Internet of Things Journal 2026年

　概要を見る

Millimeter-wave (mmWave) radar has emerged as a powerful sensing modality for human activity recognition (HAR) owing to its capability to capture 3D point cloud sequences without privacy concerns. However, effectively modeling the sparse, irregular, and non-uniform nature of radar data remains a major challenge. Existing approaches often rely on highly complex network architectures to improve accuracy, which leads to excessive computational overhead and poor scalability. To overcome these limitations, this paper proposes a compact hybrid feature extraction network for voxelized radar point cloud classification, which performs early-stage spatio-temporal fusion and is termed STFusionNet (Spatial-Temporal Fusion Network). The STFusionNet comprises (i) a lightweight 3D convolutional front-end, which treats consecutive temporal frames as input channels to encode motion dynamics into a compact volumetric representation and further aggregates features along the depth axis; (ii) a minimalist 2D convolutional backbone after a Depth-to-Channel Folding operation, which captures spatial features with minimal computational cost. Extensive experiments on the public MMActivity and MiliPoint datasets demonstrate that our model achieves competitive accuracy (e.g., 92.20% on MMActivity and 72.86% on MiliPoint) with only about 50K parameters and 46 MMac, outperforming or matching representative spatio-temporal baselines under a much lower computational budget. Extensive ablation experiments verify the contribution of key components, while statistical significance tests confirm the reliability of the performance improvements. These results confirm that STFusionNet offers a robust, efficient, and generalizable solution for mmWave radar-based HAR in Internet of Things (IoT) applications.

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Dual-Stream Multimodal Fusion With Local–Global Attention for Remote-Sensing Object Detection

Huang Y., Wang Z., Tang T., Ohtsuki T., Gui G.

IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 19   1691 - 1702 2026年

ISSN  19391404

　概要を見る

Object detection in remote-sensing imagery plays a crucial role in providing precise geospatial information for urban planning and environmental monitoring. However, real-world remote-sensing scenarios often involve complex conditions, such as varying illumination, weather interference, and low signal-to-noise ratios, which significantly degrade the performance of traditional single-modal detection methods. To overcome these limitations, multimodal object detection has developed, demonstrating great potential by integrating complementary information from multiple modalities. Nevertheless, existing multimodal frameworks still face challenges, such as insufficient cross-modal interaction, limited learning of complementary features, and high computational costs, due to redundant fusion in complex environments. To overcome these challenges, we propose an enhanced multimodal fusion strategy aimed at maximizing cross-modal feature learning capabilities. Our method employs a dual-backbone architecture to extract mode-specific representations independently, integrating a direction attention module at an early stage of each backbone to enhance discriminative feature extraction. We then introduce a dual-stream feature fusion network to effectively fuse cross-modal features, generating rich representations for the detection head. In addition, we embed a local–global channel attention mechanism in the head stage to strengthen feature learning in the channel dimension before generating the final prediction. Extensive experiments on the widely used vehicle detection in aerial imagery multimodal remote-sensing dataset demonstrate that our method achieves state-of-the-art performance, while evaluations on single-modal datasets confirm its exceptional generalization capability.

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Delay-Constrained Sum-Rate Maximization in STAR-RIS V2X Communications Using Lyapunov Framework

Ganeshkumar L., Ramachandran R., Ohtsuki T.

IEEE Access 14   857 - 874 2026年

　概要を見る

The emerging technology of simultaneous transmitting and reflecting reconfigurable intelligent surfaces (STAR-RIS) enables 360° full-space signal coverage, making it a promising approach for resilient and sustainable smart cities in next-generation wireless networks. This work investigates long-term downlink communication in STAR-RIS-assisted vehicular networks, aiming to maximize the sum-rate while ensuring queue stability under high vehicle mobility. To model the stochastic variations arising from vehicle mobility and random data arrivals, we employ a Lyapunov optimization framework, which reformulates the long-term stochastic objective into a sequence of per-slot deterministic problems. This method is effective in vehicular networks, as it adapts decisions to rapidly changing channel conditions while guaranteeing queue stability without requiring mobility predictions. A joint optimization strategy is developed, wherein the roadside unit (RSU) beamforming vectors and the STAR-RIS phase-shift coefficients are alternately optimized using zeroforcing beamforming and projected gradient descent (PGD), respectively. Extensive simulations demonstrate that the proposed framework significantly improves the long-term throughput, fairness, and queue stability compared with five benchmark STAR-RIS schemes. The results remain robust across different mobility speeds, transmit powers, and STAR-RIS element configurations, confirming the effectiveness of the proposed approach in dynamic Vehicle-to-Everything (V2X) environments.

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Coverage and Rate Performance Analysis of Multi-RIS-Assisted Dual-Hop mmWave Networks

Cao Y., Wu X., He J., Ohtsuki T., Quek T.Q.S.

IEEE Transactions on Wireless Communications 25   3137 - 3152 2026年

ISSN  15361276

　概要を見る

Millimeter-wave (mmWave) communication, which operates at high frequencies, has gained extensive research interest due to its significantly wide spectrum and short wavelengths. However, mmWave communication suffers from the notable drawbacks as follows: i) The mmWave signals are sensitive to the blockage, which is caused by the weak diffraction ability of mmWave propagation; ii) Even though the introduction of reconfigurable intelligent surfaces (RISs) can overcome the performance degradation caused by serve path loss, the location of users and RISs as well as their densities incur a significant impact on the coverage and rate performance; iii) When the RISs’ density is very high, i.e., the network becomes extremely dense, a user sees several line-of-sight RISs and thus experiences significant interference, which degrades the system performance. Motivated by the challenges above, we first analyze distributed multi-RIS-aided mmWave communication system over Nakagami-m fading from the stochastic geometry perspective. To be specific, we analyze the end-to-end (E2E) signal-to-interference-plus-noise-ratio (SINR) coverage and rate performance of the system. To improve the system performance in terms of the E2E SINR coverage probability and rate, we study the optimization of the phase-shifting control of the distributed RISs and optimize the E2E SINR coverage particularly when deploying a large number of reflecting elements in RISs. To facilitate the study, we optimize the dynamic association criterion between the RIS and destination. Furthermore, we optimize the multi-RIS-user association based on the physical distances between the RISs and destination by exploiting the maximum-ratio transmission. Numerical and simulation results indicate that the deployment of distributed RISs can significantly improve the E2E SINR coverage probability and achievable rate of the system compared to the selected benchmarks.

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Bio-Inspired Dendritic Liquid Neural Network for Robust Near-Field Positioning in B5G/6G

Zhu Q., Wang Y., Ohtsuki T., Mao S., Niyato D., Gui G.

IEEE Wireless Communications Letters 15   350 - 354 2026年

ISSN  21622337

　概要を見る

In B5G and 6G wireless networks, large-scale antenna arrays introduce spherical wavefronts in the near-field region, presenting both opportunities and challenges for communication and positioning. To address this, we propose a novel near-field positioning method using a bio-inspired dendritic liquid neural network (DLNN). The DLNN features a multi-branch dendritic structure, with each neuron processing signals through multiple parallel dendrites and a dynamic gating mechanism to adapt the weighting and fusion of outputs. This enhances its ability to capture complex spatiotemporal features. Additionally, the liquid layer with leaky integration processes time-series data, enabling strong adaptability and robust temporal memory. Simulation results show that DLNN outperforms other deep learning models in localization accuracy and robustness, with RMSE values of 0.1706 meters at 40 dBm and 0.2642 meters at 10 dBm, compared to 0.2836 meters and 0.2986 meters from other models. Its inference latency of 0.0580 seconds demonstrates excellent accuracy, speed, and energy efficiency, making DLNN highly suitable for real-time positioning in resource-constrained environments.

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Beyond Contact: An Open-Set Biometric Identification System Using Radar-Extracted Heart Signals

Xing Z., Bouazizi M., Ohtsuki T.

IEEE Journal of Biomedical and Health Informatics 30 （ 3 ） 2718 - 2731 2026年

ISSN  21682194

　概要を見る

This paper proposes a novel radar-based framework for non-contact biometric identification through heart signal extraction, targeting secure and privacy-conscious identification scenarios. Traditional biometric methods, such as fingerprint and facial recognition, face challenges including privacy concerns, vulnerability to spoofing, and the requirement for close proximity or direct line-of-sight. Our framework addresses these issues by reconstructing electrocardiogram (ECG) signals from radar-extracted cardiac motion data and implementing an open-set person identification system. Specifically, the framework integrates ECGReconNet, a specialized deep learning model for reconstructing ECG signals from human chest wall displacement, the InceptionTime model enhanced with fixed-Class Anchor Clustering (fixed-CAC) loss for robust feature anchoring, and a hypersphere-based delineation method to differentiate known from unknown individuals. Experimental results on a public dataset demonstrate state-of-the-art performance, achieving 99.61% accuracy in closed-set identification (27 subjects) and 93.97% accuracy under challenging open-set conditions (14 known and 13 unknown subjects). However, the proposed approach exhibits limitations, including sensitivity to abrupt body movements and environmental noise, potential performance degradation under severe cardiac irregularities, and reduced efficacy with increased numbers of unknown identities.

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Adaptive Task Offloading for Edge Computing in Internet of Energy via Retrieval-Augmented Generation

Zhou X., Guan X., Wang N., Chen H., Ohtsuki T., Zhang Y.

IEEE Network 2026年

ISSN  08908044

　概要を見る

Traditional task offloading in Internet of Energy (IoE) edge environments struggles to accommodate heterogeneous user tasks and dynamic system conditions. Although Large Language Models (LLMs) offer potential solutions, their deployment is hindered by the dynamic resource constraints and stringent Quality of Service (QoS) requirements of IoE. Retrieval-augmented generation (RAG) enables LLM to overcome these limitations. In this paper, we propose an RAG-empowered adaptive task offloading framework. First, the framework transforms raw IoE-collected data into LLM-comprehensible textual inputs. Second, we design an intelligent preference-matching module that guides RAG to retrieve relevant knowledge and cases using key QoS features. Third, we build a composite knowledge vector database with both positive and negative experiences. We introduce a failure severity value to quantify the importance of counterexamples and enhance decision-making robustness. Finally, we design a hybrid knowledge prompt generation module. It utilizes a hierarchical filtering strategy and optimizes the ranking of retrieved knowledge for comprehensive prompts. Experimental results demonstrate that our proposed framework significantly outperforms baselines, increasing cumulative reward by 48.4% while reducing latency and energy consumption by 44.8% and 50.9%, respectively.

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LRGD: Low-Rank Guided Diffusion for Robust Image Transmission in Semantic Communication

Zhao Z., Wu C., Lin Y., Zhong L., Ji Y., Ohtsuki T., Bennis M.

IEEE Transactions on Cognitive Communications and Networking 12   2439 - 2454 2026年

　概要を見る

Semantic communication has gained significant attention in modern data-intensive applications due to its efficient information transmission capabilities. While recent advancements in large language models and diffusion models have enabled generative image semantic communication in text-based and multimodal settings, existing approaches struggle with visual fidelity due to the inherent limitations of textual descriptions and the lack of dedicated design in multimodal methods. To address these issues, we propose a compressed sensing-driven multimodal semantic communication framework that transmits saliency-adaptive sparse sampling, edge contours, and textual descriptions of images at the transmitter and reconstructs images at the receiver using guided diffusion models. We innovatively design a low-rank guided diffusion (LRGD) model to adapt to this framework, employing a dynamic smoothing low-rank guidance process in the latent space using low-quality reconstructed images. This approach enables highly accurate restoration of visual details under a low sampling ratio without additional training. Compared to state-of-the-art methods, our framework uses model-independent and interpretable universal semantic representations for communication, significantly enhancing cross-model compatibility without model sharing between the transmitter and receiver. Extensive experiments on multiple datasets demonstrate that the proposed method achieves robust and superior image reconstruction performance across various noisy environments while compressing data to 5.7% of the original image size.

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Yours or Mine? Overwriting Attacks Against Neural Audio Watermarking

Yao L., Huang C., Wang S., Xue J., Guo H., Liu J., Lin P., Ohtsuki T., Pan M.

Proceedings of the Aaai Conference on Artificial Intelligence 40 （ 33 ） 27756 - 27763 2026年

ISSN  21595399

　概要を見る

As generative audio models are rapidly evolving, AIgenerated audios increasingly raise concerns about copyright infringement and misinformation spread. Audio watermarking, as a proactive defense, can embed secret messages into audio for copyright protection and source verification. However, current neural audio watermarking methods focus primarily on the imperceptibility and robustness of watermarking, while ignoring its vulnerability to security attacks. In this paper, we develop a simple yet powerful attack: the overwriting attack that overwrites the legitimate audio watermark with a forged one and makes the original legitimate watermark undetectable. Based on the audio watermarking information that the adversary has, we propose three categories of overwriting attacks, i.e., white-box, gray-box, and black-box attacks. We also thoroughly evaluate the proposed attacks on stateof-the-art neural audio watermarking methods. Experimental results demonstrate that the proposed overwriting attacks can effectively compromise existing watermarking schemes across various settings and achieve a nearly 100% attack success rate. The practicality and effectiveness of the proposed overwriting attacks expose security flaws in existing neural audio watermarking systems, underscoring the need to enhance security in future audio watermarking designs.

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VMamba-SLAM: Real-Time Dynamic Scene Adaptation Via Vision Mamba and Selective Feature Preservation for Enhanced Robotic Localization

Sun J., Shen X., Huang H., Li R., Wang Q., Zhao H., Ohtsuki T., Gui G.

IEEE Transactions on Vehicular Technology 2026年

ISSN  00189545

　概要を見る

Dynamic Simultaneous Localization and Mapping (SLAM) remains a challenging problem in robotics, particularly due to the interference of moving objects with feature-based localization and mapping. In this paper, we introduce a novel dynamic object-aware SLAM system built upon ORB-SLAM3, which integrates a computationally efficient Vision Mamba (VMamba) model with an advanced dynamic object filtering strategy. The proposed VMamba model, comprising only 8.8 million parameters, achieves high-precision object detection with minimal computational overhead, thereby enabling real-time operation on embedded platforms. A key contribution of this work is the development of an improved dynamic object filtering strategy that selectively retains static features within dynamic object bounding boxes. By leveraging Harris corners, optical flow, and epipolar geometry, our approach effectively eliminates dynamic outliers while preserving essential static scene information—a significant advancement over conventional methods that indiscriminately remove features. Experimental evaluations on the TUM Dynamic and BONN datasets demonstrate that our method attains superior Absolute Trajectory Error (ATE) performance compared to state-of-the-art dynamic SLAM approaches, including ORB-SLAM3, DynaSLAM, and YOLO-SLAM. Furthermore, real-time deployment on an NVIDIA Jetson Orin NX 16 GB platform incurs only a 6.45% reduction in Frames Per Second (FPS) relative to YOLO-SLAM, while achieving a 28.68% improvement in average ATE. These results underscore the computational efficiency of VMamba and the robust real-time capabilities of our SLAM system. Our study validates that integrating a lightweight, high-precision object detection model with an effective dynamic object filtering strategy can significantly enhance the robustness, accuracy, and real-time performance of dynamic SLAM, even on resource-constrained platforms. Future work will focus on further optimizing VMamba and extending the dynamic filtering techniques to better accommodate increasingly complex dynamic environments, thereby broadening its applicability in real-world robotic systems.

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Secrecy Rate Enhancement for Active RIS-Assisted MISO Wiretap Systems: A QoS-Aware Optimization Approach

Li W., Cao Y., Bouazizi M., Ohtsuki T., Mumtaz S.

IEEE Wireless Communications Letters 15   2398 - 2402 2026年

ISSN  21622337

　概要を見る

Reconfigurable intelligent surface (RIS)-assisted communication has emerged as a critical enabler for sixth-generation (6G) wireless networks. In particular, the active RIS architecture, by virtue of its signal amplification capability, overcomes the double-fading bottleneck inherent to passive RIS and provides a new avenue for secure physical-layer transmission. However, existing studies have not adequately addressed the secrecy performance of RIS in delay-constrained quality-of-service (QoS) scenarios. This letter investigates the problem of maximization of effective secrecy rate (ESR) in an active RIS-assisted multiple-input single-output (MISO) wiretap channel, subject to both transmit power and active RIS power constraints, under delay-limited QoS requirements. We tackle the nonconvex problem using an alternating optimization (AO)-based iterative algorithm. An analysis of the algorithmic complexity is also presented. Experiment results verify the performance gains achieved by the optimized design.

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Interpretability-Oriented UAV Recognition via Frequency-Aware Networks: A Coarse-to-Fine Framework for Enhanced Accuracy and Insight

Lu G., Wang S., Ohtsuki T., Sari H., Gui G.

IEEE Transactions on Information Forensics and Security 21   3312 - 3324 2026年

ISSN  15566013

　概要を見る

With the rapid proliferation of uncrewed aerial vehicles (UAVs) in civilian and industrial applications, the risk of malicious or unauthorized UAV use has become a critical security concern. Existing machine learning (ML)-based UAV recognition methods offer a certain degree of interpretability, but their performance is often limited in complex environments and across diverse UAV types. In contrast, deep learning (DL)-based methods exhibit strong representation capability, yet they generally lack physical interpretability. To address this issue, we propose an interpretable UAV recognition framework, termed frequency-aware network for UAV recognition (FANet-UAV), which performs coarse-to-fine feature learning in the frequency domain. Specifically, a multiplication filter module (MFM) is first designed to capture coarse-grained spectral patterns by exploiting multi-mode and multi-scale frequency characteristics of UAV signals. Based on these coarse representations, a convolutional neural network (CNN) is further employed to extract fine-grained discriminative features for accurate classification. Experimental results on two public UAV datasets demonstrate the effectiveness of the proposed method. In particular, FANet-UAV improves the recognition accuracy from 90.45% to 96.82% on DroneRFa and from 94.15% to 98.83% on DroneRF. Moreover, visualization results and channel-wise SHAP analysis provide both pre-hoc and post-hoc interpretability, revealing that FANet-UAV mainly relies on flight control signal (FCS) features for decision-making, while video transmission signal (VTS) features contribute less to the final recognition results.

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Twin-Timescale Traffic Signal Timing Method with Joint Communication Resource Allocation

Wang T., Yang G., Wang L., Mathiopoulos P.T., Ohtsuki T., Ouyang M.

IEEE Internet of Things Journal 2026年

　概要を見る

In multi-intersection traffic signal timing (TST) the use of a uniform cycle time reduces efficiency at low-volume intersections and signal changes which can alter vehicle flows and degrade communication quality. This paper presents a solution to this problem introducing a Twin-Timescale model whereby a multi-intersection TST method is jointly considered with communication resource allocation (CRA) strategies. For each intersection, a Twin-Timescale Reinforcement Learning (RL) algorithm is proposed, by considering both CRA and TST as the Short- and the Long- Timescale models, respectively, and jointly formulating their rewards. Based on the characteristics of this Twin-Timescale model, the reward function is constructed as a singularly perturbed model (SPM), and a state feedback controller that can ensure asymptotic stability is designed. Furthermore, a novel asynchronous time Multi-Agent Coordinated RL algorithm is proposed, which allows each intersection to generate each own joint parameters for its decision strategy which are based upon information obtained from neighboring intersections at asynchronous time. In addition, the use of Federated Learning (FL) in conjunction with the proposed algorithms is investigated, and its advantages in enhancing the resistance to malicious attacks are discussed. Using real traffic data, various performance evaluation results obtained by means of extensive computer simulation experiments have confirmed that the proposed TST-CRA joint optimization approach significantly improves traffic indicators and maintains the quality of the overall communication performance. Through numerical calculations, the state feedback controller coefficients have been determined.

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Toward Robust Radio Frequency Fingerprint Identification: A Federated Learning Framework With Feature Alignment

Wang Y., Cai Z., Tang T., Ohtsuki T., Gui G.

IEEE Transactions on Information Forensics and Security 21   2641 - 2651 2026年

ISSN  15566013

　概要を見る

With the growing adoption of Internet of Things (IoT) devices, ensuring the security of wireless communications has become increasingly critical. Radio frequency fingerprint identification (RFFI) has shown promise in this regard due to its capability of uniquely identifying devices. Although deep learning (DL) approaches have significantly improved RFFI performance, they typically rely on large-scale centralized data. This poses challenges in terms of privacy preservation and heterogeneous data distributions. To address the performance degradation caused by non-independent and identically distributed (non-IID) data in cross-receiver scenarios, this paper proposes a feature alignment strategy based on federated learning (FL) for RFFI. In such scenarios, due to differences in receiver hardware characteristics, deployment locations, and channel conditions, the signals captured by different receivers often exhibit distribution shifts, resulting in misaligned feature spaces across clients. The proposed method guides each client to learn aligned intermediate feature representations during local training, effectively mitigating the resulting adverse impact on model generalization. Experiments conducted on a real-world RF dataset demonstrate that the proposed method achieves higher identification accuracy and improved stability compared with representative federated baselines, including FedAvg and FedProx. The highest identification accuracy reaches 90.83%, and the performance gains are accompanied by generally reduced variance across different client configurations, highlighting the robustness and generalization capability of the proposed approach in heterogeneous wireless environments.

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Sum-Rate Maximization in Irregular STAR-RIS-Driven V2X Systems

Ganeshkumar L., Ramachandran R., Ohtsuki T.

IEEE Wireless Communications Letters 15   1504 - 1508 2026年

ISSN  21622337

　概要を見る

The reconfigurable intelligent surface (RIS) plays a crucial role in enabling sustainable communication infrastructure for 6G networks. This letter presents a long-term net weighted sum-rate maximization problem with queue stability for irregular simultaneous transmission and reflection RIS (ISTAR-RIS)-assisted vehicular networks. The model incorporates time-varying and mobility-induced channel dynamics. A joint optimization problem of the road side unit (RSU) beamforming vector, topological selection and the phase shift matrix of ISTAR-RIS elements is formulated. The simulation results demonstrate the effectiveness of the proposed approach in maximizing the sum-rate while comparing with existing benchmark methods in ISTAR-RIS-aided wireless and device-to-device (D2D) communication.

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Ship Classification in SAR Images Based on Full-Attention ViT and Image Reconstruction

Wang L., Zhao C., Wang T., Sun B., Qi Y., Qi B., Ohtsuki T.

Lecture Notes in Electrical Engineering 1366 LNEE   501 - 509 2026年

ISSN  18761100

　概要を見る

Classification of ships in synthetic aperture radar (SAR) images plays a critical role in maritime search and rescue as well as national defense security. Over the past few years, the swift advancement rapid of deep learning has opened up innovative technological directions for precise classification of SAR ship images. However, the unique imaging mechanism of SAR images, resulting in a lack of ship samples and insufficient feature information, continues to make accurate classification of SAR ships challenging. In this study, we propose an improved vision transformer (ViT) method using a full attention (FA) mechanism, incorporating channel attention after Self-Attention, dynamically selecting channels through reweighting to enhance comprehensive feature representation. To address the issue of insufficient training samples, we designed an image reconstruction module based on SRGAN, which generates high-resolution and detailed image data through adversarial learning, effectively compensating for the sample quantity in the dataset. Ultimately, the results section demonstrates that the method introduced delivers enhanced classification accuracy relative to current methods.

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Self-Supervised Federated Learning for UAV-IoT Systems With Dynamic Non-IID Data via Model Correlation

Li Z., Bouazizi M., Ohtsuki T., Gui G.

IEEE Internet of Things Journal 13 （ 4 ） 5827 - 5841 2026年

　概要を見る

Federated learning (FL) offers significant advantages in preserving data privacy and enhancing communication efficiency, making it especially suitable for Internet of Things (IoT) networks supported by uncrewed aerial vehicles (UAVs). However, most existing FL approaches rely on assumptions of uniformly distributed, well-labeled, and large-scale datasets—conditions that rarely hold in practical UAV-based IoT scenarios. These environments typically feature small-scale, nonindependent and identically distributed (non-IID), and dynamically changing data. To address these challenges, we propose a novel self-supervised federated unsupervised learning (FUL) framework tailored for UAV-assisted IoT systems. The proposed framework comprises three key components: 1) a realistic UAV data collection model that considers limited onboard storage and mobility constraints; 2) a robust local training strategy that incorporates self-supervised regularization and a centered kernel alignment (CKA)-based similarity loss to mitigate the effects of data heterogeneity and rapid distribution shifts; and 3) an importance-aware hybrid normalized aggregation method at the global server, which leverages model divergence-based metrics to evaluate local model reliability and integrates both current and historical gradient information for stable model updates. Experimental results demonstrate that our framework achieves classification accuracies of 30.5%, 62.8%, and 70.5% under memory constraints of 500, 1000, and 2000 samples, respectively—outperforming the best baseline by 18.1%, 26.5%, and 9.1% under the same conditions. These results highlight the effectiveness of the proposed FUL framework in handling data heterogeneity and dynamic sample variations inherent in realistic UAV-enabled IoT applications.

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R2D-SLAM: A Real-Time and Robust Visual SLAM Framework for Autonomous Navigation in Dynamic Environments

Sun J., Xutian Y., Shen X., Yin Y., Zhao H., Ohtsuki T., Gui G.

IEEE Transactions on Vehicular Technology 2026年

ISSN  00189545

　概要を見る

Classical Simultaneous Localization and Mapping (SLAM) algorithms, fundamental to autonomous navigation, often assume a static environment, an assumption that fails in many real-world scenarios, resulting in significant trajectory drift. This creates a long-standing trade-off in dynamic SLAM: high-accuracy methods are typically too slow for real-time deployment, while real-time systems often suffer from reduced robustness. To address this challenge, we propose R²D-SLAM, a novel real-time visual SLAM framework that achieves both high accuracy and efficiency in dynamic environments. Our system integrates a lightweight and effective dynamic object detector, Detection Transformer (DETR), with an enhanced matching strategy, termed DEIM_Dyna, into the robust ORB-SLAM3 framework. A dedicated parallel thread performs dynamic region detection concurrently with the main SLAM pipeline, enabling early rejection of contaminated features during tracking. This ensures that pose estimation is conducted on a purified, static feature set, significantly improving robustness and accuracy. Extensive experiments on the challenging TUM and BONN dynamic datasets show that R²D-SLAM achieves new state-of-the-art performance in terms of Absolute Trajectory Error (ATE) and Relative Pose Error (RPE), while maintaining real-time processing speeds exceeding 25 FPS. R²D-SLAM effectively bridges the gap between accuracy and efficiency, offering a practical and robust solution for dynamic scene perception in autonomous vehicles and Unmanned Aerial Vehicles (UAVs).

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Optimal Carbon Emission Reduction Modeling Considering Energy Consumer Satisfaction in Cyber-Physical Energy System

Wang C., Guan X., Wang N., Chen H., Ohtsuki T., Han Z.

IEEE Internet of Things Journal 13 （ 4 ） 6508 - 6521 2026年

　概要を見る

Renewable energy has become a viable alternative to fossil fuels owing to its environmental benefits. However, its inherent uncertainty pose significant challenges. Demand response (DR) mechanisms have been developed to address these issues, facilitating renewable energy integration through consumer-side flexible resources. However, these mechanisms often affect consumer satisfaction, necessitating precise measurement and control of these impacts. In this article, we propose a two-stage electricity trading and load dispatch optimization model aimed at reducing carbon emission by promoting renewable energy accommodation, and the proposed optimization model takes into account multicategory energy consumer satisfaction. We begin by classifying consumers into distinct categories and designing tailored satisfaction functions that reflect their unique power consumption pReferences. The electricity trading and load dispatch processes are formulated as a two-stage optimization problem, which is then transformed into Markov decision processes (MDPs). A model-free framework applying two state-of-the-art deep reinforcement learning (DRL) algorithms is proposed to solve the optimization problem without requiring complex environmental modeling and prior knowledge. Numerical results demonstrate that the proposed framework outperforms benchmark algorithms regarding both consumer satisfaction preservation and carbon emission reduction.

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