論文 - 大槻　知明

An Intelligent Path Planning Mechanism for Firefighting in Wireless Sensor and Actor Networks

F. H. Panahi, F. H. Panahi, and T. Ohtsuki

in IEEE Internet of Things Journal 2023年

研究論文（学術雑誌）, 共著, 最終著者

Distributed Feature Selection Considering Data Pricing Based on Edge Computing in Electricity Spot Markets

Y. Hu, X. Guan, B. Hu, Y. Liu, H. Chen and T. Ohtsuki

in IEEE Internet of Things Journal 10 （ 3 ） 2231 - 2244 2023年02月

研究論文（学術雑誌）, 共著, 最終著者

SAR Image Change Detection in Spatial-Frequency Domain Based on Attention Mechanism and Gated Linear Unit

C. Zhao, L. Ma, L. Wang, T. Ohtsuki, P.T. Mathiopoulos, and Y. Wang

IEEE Geoscience and Remote Sensing Letters 2023年

研究論文（学術雑誌）, 共著

Graph Learning Empowered Situation Awareness in Internet of Energy with Graph Digital Twin

L. Sui, X. Guan, C. Cui, H. Jiang, H. Pan and T. Ohtsuki

in IEEE Transactions on Industrial Informatics 2022年

研究論文（学術雑誌）, 共著, 最終著者

Make Smart Decisions Faster: Deciding D2D Resource Allocation via Stackelberg Game Guided Multi-Agent Deep Reinforcement Learning

D. Shi, L. Li, T. Ohtsuki, M. Pan, Z. Han and H. V. Poor

in IEEE Transactions on Mobile Computing 21 （ 12 ） 4426 - 4438 2022年12月

研究論文（学術雑誌）, 共著

Unsupervised Learning-Based Non-Invasive Fetal ECG Muti-Level Signal Quality Assessment

X. Shi, K. Yamamoto, T. Ohtsuki, Y. Matsui, and K. Owada

Bioengineering 2023年

研究論文（学術雑誌）, 共著

Fetal Arrhythmia Detection based on Labeling Considering Heartbeat Interval

S. Nakatani, K. Yamamoto, and T. Ohtsuki

Bioengineering 2023年

研究論文（学術雑誌）, 共著, 最終著者

A Reinforcement Learning-based Fire Warning and Suppression System Using Unmanned Aerial Vehicles

F.H. Panahi, F.H. Panahi, and T. Ohtsuki

IEEE Transactions on Instrumentation and Measurement 2022年

研究論文（学術雑誌）, 共著, 最終著者

Heart Action Monitoring From Pulse Signals Using A Growing Hybrid Polynomial Network

L. Wang, C. Zhao, P.T. Mathiopoulos, and T. Ohtsuki

Engineering Applications of Artificial Intelligence 117 2023年01月

研究論文（学術雑誌）, 共著, 最終著者

SAR image change detection based on saliency region guidance and SIFT keypoint extraction

Wang L., Sun B., Zhao C., Mazhar S., Ohtsuki T., Takis Mathiopoulos P., Adachi F.

Pattern Recognition 172 2026年04月

ISSN  00313203

　概要を見る

Synthetic Aperture Radar (SAR) can operate under all-weather, all-day conditions, playing a crucial role in regional change detection (CD). However, due to its unique imaging principles, SAR images contain significant speckle noise and blurred boundary and detail features, which reduces the detection accuracy and leads to missed detection and false detection. To address these issues, this paper proposes a SAR image CD method based on saliency region guidance and Scale-Invariant Feature Transform (SIFT) keypoint extraction to reduce the interference of speckle noise. First, a saliency region guidance method is introduced to analyze the saliency of local features in SAR images, extracting potentially changed regions and reducing the interference of speckle noise. Second, the SIFT is employed to extract keypoints in regions significantly different from the background in the difference map, leveraging its robustness to speckle noise. By extracting keypoints, the approximate location and extent of the changed regions are determined. These are, then, fused with the saliency region information, enhancing the saliency weights of pixels around keypoints for more extraction of change regions. Finally, a Vision Transformer (ViT) detection network is used for SAR image CD, utilizing the combined saliency information from the original saliency map and SIFT keypoints. This approach effectively integrates SIFT's stable description of local features with ViT's modeling capability for global features, improving the model's accuracy and robustness.

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Depthwise-Attentive Hierarchical Cross-Modal Knowledge Distillation Network for Rail Surface Defect Detection

Zhou X., Guan X., Peng Y., Zhang Z., Zhou X., Chen H., Ohtsuki T., Han Z.

IEEE Internet of Things Journal 13 （ 6 ） 10915 - 10928 2026年03月

　概要を見る

Accurate detection of surface defects on railway tracks is critical for safe railway operation. Most existing models rely solely on red-green-blue (RGB) images, limiting their ability to capture structural information. Incorporating depth features provides richer spatial cues, significantly improving detection accuracy. However, current RGB and depth (RGB-D) dual-stream models suffer from high computational complexity and hardware dependencies, making them impractical for real-world deployment. To address these limitations, we propose DAHNet, an asymmetric knowledge distillation model with a teacher-student architecture. DAHNet-T serves as the teacher network, taking RGB-D inputs and integrating a cross-modal attention feature enhancer (CAFE) module to capture contextual information, along with a depthwise feature interaction block (DFIB) for efficient cross-modal fusion. DAHNet-S is the student network, a lightweight single-stream RGB model employing depthwise separable convolutions to reduce computation. We introduce a multilevel distillation strategy with dynamic temperature scaling to balance coarse-grained and fine-grained knowledge transfer, while incorporating contrastive learning and structural loss to improve pixel-level accuracy. Extensive experiments on the NEU RSDDS-AUG dataset demonstrate that our distilled model DAHNet-KD outperforms state-of-the-art methods. Compared to DAHNet-T, the number of parameters is reduced from 87.72 to 13.97 MParams, and the computational cost decreases from 19.79 to 5.41 GFLOPs. The proposed model achieves superior performance across various evaluation metrics and also generalizes well on other public datasets. Therefore, the model provides a lightweight and high-accuracy solution for deployment on mobile devices in real-world industrial scenarios.

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MuECNet: A Lightweight Multiuser Enhanced Convolutional Architecture for Robust CSI-Based Human Activity Recognition in Real-World IoT Environments

Miao F., Takyu O., Shan L., Yin Y., Zhao O., Ohtsuki T., Gui G.

IEEE Internet of Things Journal 13 （ 6 ） 12209 - 12219 2026年03月

　概要を見る

Wi-Fi channel state information (CSI)-based human activity recognition (HAR) leverages rich channel propagation characteristics to enable nonintrusive, privacy-preserving, and device-free sensing. In multiuser wireless environments, however, HAR faces significant challenges, including multipath interference, signal overlap, label ambiguity, cross-domain channel variability, and constraints imposed by real-time deployment on resource-limited edge devices. This article presents multiuser enhanced convolutional network (MuECNet), a lightweight and modular deep learning framework designed to operate under realistic multiuser, multiactivity CSI sensing conditions. MuECNet integrates three key components: 1) an enhanced convolutional encoding module (ECEM) for fine-grained temporal-spectral feature extraction that preserves channel propagation signatures; 2) a branch-wise feature normalization (BFN) module for user-specific channel representation learning; and 3) an adaptive decision module for multilabel activity inference. To improve robustness under diverse and dynamic channel conditions, we introduce MixUp-based data augmentation to emulate activity overlap and reduce label ambiguity. Evaluations on the WiMANS dataset show that MuECNet achieves 64.46% (2.4 GHz) and 64.15% (5 GHz) accuracy with only 1.19-M parameters, 1.74-G FLOPs, and 0.28-s inference latency, outperforming baseline models such as ABLSTM and THAT while reducing model size by up to 75%. Ablation studies confirm the contribution of each module, with accuracy drops of up to 6.41% when removed. These results demonstrate that MuECNet provides a robust and communication-efficient solution for integrating CSI-based sensing into future wireless networks and Internet of Things (IoT) systems.

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Synthetic aperture radar image change detection based on multi-scale deep adaptive convolution and spatial-frequency dual-domain feature extraction

Wang L., Jiahui E., Zhao C., Gao W., Mathiopoulos P., Ohtsuki T.

Expert Systems with Applications 301 2026年03月

ISSN  09574174

　概要を見る

Synthetic Aperture Radar (SAR) change detection is the process of identifying image changes by comparing SAR images of the same area captured at different time intervals. Considering the interference of speckle noise and the lack of spatial adaptability, this paper proposes a spatial-frequency domain feature fusion cooperative detection framework. This method extracts local structural features in the spatial domain while incorporating high-frequency components from the frequency domain to enhance edge feature representation and improve the discrimination of change regions. Additionally, a multi-region dynamic selection mechanism is designed to adaptively capture multi-scale contextual information through cascaded convolutional layers, overcoming the limitations of fixed receptive fields in complex dynamic scenarios. Furthermore, by integrating an attention mechanism with the Swish-Gated Linear Unit (SWiGLU), the network’s feature representation capability is optimized, effectively enhancing the precision of subtle change detection and significantly reducing the impact of speckle noise. This further improves the stability and reliability of SAR change detection. Through performance evaluation on five datasets, we compare the proposed method with state-of-the-art SAR change detection techniques. The results demonstrate that our method exhibits significant advantages in terms of accuracy and kappa coefficient.

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Clock Asynchronous Traffic Signal Timing for Multi-Intersections Based on a Joint Traffic Prediction and Control Method

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

IEEE Transactions on Vehicular Technology 75 （ 1 ） 1111 - 1125 2026年01月

ISSN  00189545

　概要を見る

One major challenge in improving traffic efficiency for multi-intersection signal timing is the synchronous iteration across all intersections. To deal with this problem, in this paper, a novel clock asynchronous traffic signal timing method for multi-intersections based on a joint traffic prediction and control method is proposed and its performance is analyzed. To improve traffic signal timing efficiency, each intersection is viewed as an agent which by following this joint approach designs, in a flexible way, actions, states and rewards. Using traffic prediction and control data, each intersection first computes a value which corresponds to a change in the vehicle position in its lane under control and then adds up this value to other values obtained from all the lanes under control to produce the reward. Traditional methods cannot effectively coordinate multi-intersections with different signal timing time. This has led to the introduction of a novel clock consistency mechanism the operation of which is implemented through a Multi-Agent Coordinated Reinforcement Learning (MACRL) algorithm. For any traffic intersection, the mechanism can estimate, within the duration of the previous action, the impact of adjacent intersections. This is accomplished by considering the previous intersections' reward information and quantify it as its joint reward. The proposed MACRL algorithm optimizes multi-intersections traffic by jointly considering these rewards. Using real traffic data, extensive simulations show that the proposed method outperforms existing schemes under the same conditions in traffic efficiency. The method also delivers more accurate traffic signal timing, reducing congestion and improving overall efficiency.

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A Comprehensive Survey on Self-Supervised Learning for Specific Emitter Identification

Liu C., Gui G., Wang Y., Ohtsuki T., Niyato D., Shen X.

IEEE Communications Surveys and Tutorials 28   1749 - 1775 2026年

　概要を見る

The rapid proliferation of the Internet of Things (IoT) has intensified the need for strong authentication mechanisms to ensure the integrity and reliability of connected devices. Recent advancements in Deep Learning (DL)-based Specific Emitter Identification (SEI) have demonstrated significant potential in leveraging unique Radio Frequency Fingerprints (RFF) for accurate device identification and authentication. However, the efficacy of these DL-based SEI methods is critically dependent on the availability of extensive labeled datasets, which are often scarce and expensive to obtain in practical applications. To address this limitation, Self-Supervised Learning (SSL) becomes a promising solution, capable of harnessing unlabeled data to learn effective representations. Furthermore, current surveys and reviews on SEI are generally summarized from a high-level perspective, lacking a detailed discussion of SEI methods under label-limited scenarios. This article comprehensively surveys SSL-based SEI, including its motivation, definition, paradigms, related work, challenges, and future direction combined with large models. To help readers quickly engage with this field, this paper also undertakes two specific efforts: collecting and organizing currently available open-source datasets with download links and comparing various SSL-based SEI methods with related codes.

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Long-Term Sum-Rate Maximization in Irregular RIS-Aided V2X Communications Using Lyapunov Optimization

Ganeshkumar L., Ramachandran R., Ohtsuki T.

IEEE Open Journal of the Communications Society 7   2446 - 2463 2026年

　概要を見る

In emerging smart cities and intelligent transportation systems (ITS) ecosystems, sixth-generation (6G) vehicular networks demand sustainable wireless environments capable of adapting to high mobility and dynamic propagation conditions. In dynamic vehicular networks, irregular reconfigurable intelligent surface (IRIS) with non-uniformly distributed elements over a larger surface offer improved coverage, reduced interference, and lower power consumption. This article undertakes a rigorous investigation into IRIS-assisted downlink vehicular communication in the context of Doppler shift, with the objective of optimizing the long-term net weighted average sum-rate under both perfect and imperfect channel state information (CSI). This task entails a comprehensive joint optimization of IRIS topology selection, phase-shift matrix, and beamforming vectors, while ensuring queue stability. To address this stochastic network optimization problems involving time-varying system dynamics, a Lyapunov optimization-based framework is proposed. This framework transforms the stochastic problem into a deterministic form, ensuring queue stability without requiring knowledge of future channel conditions. Subsequently, a joint optimization strategy based on the block coordinate descent (BCD) algorithm is applied to make optimal decisions of IRIS topology selection, phase-shift matrix, and beamforming vectors in each time slot. To validate the effectiveness of the proposed framework, extensive simulations were conducted and benchmarked against two established schemes: one employing conventional IRIS configurations in wireless networks and the other utilizing non-orthogonal multiple access (NOMA) techniques. The results consistently demonstrate that the proposed IRIS-assisted Lyapunov optimization framework significantly outperforms both benchmarks in terms of achievable sum-rate and maintains queue stability.

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Lightweight Regularized Network for Multilabel Indoor HAR in Multiuser CSI Environments With Uncertainty Quantification

Miao F., Liu C., Lu Z., Shan L., Takyu O., Ohtsuki T., Gui G.

IEEE Internet of Things Journal 13 （ 4 ） 6475 - 6484 2026年

　概要を見る

Human activity recognition (HAR) with Wi-Fi channel state information (CSI) is attractive for privacy-preserving, device-free sensing; yet, real deployments still struggle with three coupled issues: robustness across rooms and bands, efficiency on edge hardware, and unified support for multiple tasks. We present UN-2DCNN, a lightweight 2-D convolutional neural network (CNN) pipeline tailored to indoor, multiuser CSI sensing. The design reduces temporal redundancy via a simple temporal skipping augmentation, learns a compact 128-D representation with a small CNN + global average pooling (GAP) backbone, and injects reliability feedback through uncertainty-aware feature scaling (UAFS): Stage-1 predictive entropy is mapped to a gating weight that rescales features before a second decision head. A channel-attention MLP further suppresses spurious subcarrier responses. Evaluated on a recent multiuser CSI benchmark across classrooms, meeting rooms, and empty environments at 2.4/5 GHz, UN-2DCNN consistently outperforms competitive recurrent neural network (RNN)/Transformer baselines while using only ~1M parameters and maintaining sub-2-s test-time latency. Beyond higher accuracy, the model exhibits faster, smoother convergence and improved calibration (fewer overconfident errors). Ablations confirm that removing attention, UAFS, or the second-stage head yields consistent drops, and simple temporal skipping on the data side complements model-side selectivity. These results indicate that reliability-aware, lightweight designs can deliver practical accuracy–efficiency tradeoffs for CSI-based perception on edge/IoT platforms.

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Integrated Deployment and Resource Allocation in Multi-Layer UAV-Enabled NOMA Wireless Caching Networks

Yin Y., Bouazizi M., Hu B., Gui G., Ohtsuki T.

IEEE Internet of Things Journal 13 （ 7 ） 14898 - 14911 2026年

　概要を見る

Conventional multi-unmanned aerial vehicle (UAV) assisted non-orthogonal multiple access (NOMA) wireless caching networks (WCNs) usually operate in a distributed and non-collaborative manner, where each UAV serves users independently without coordination or relay support. When UAVs move beyond the communication range of ground base stations (BSs), backhaul disruption occurs, leading to high user latency and limited system scalability. To address these issues, we propose a multi-layer UAV-assisted NOMA WCN architecture, where a primary UAV (PUAV) communicates with the BS and cooperates with multiple secondary UAVs (SUAVs). The PUAV not only acts as a control and coordination hub but also serves as a relay for content transmission to SUAVs when necessary. To minimize user transmission latency, we propose a joint iterative algorithm that integrates user clustering, user pairing, power allocation, and UAV deployment. First, we develop an Advanced Balanced K-Means++ (ABKM) algorithm to ensure that each cluster contains a balanced number of users and to reduce the distance between users and their serving SUAVs. Next, we derive the NOMA power allocation factor that minimizes user transmission latency, ensuring efficient resource distribution among all paired users. Furthermore, we analyze the impact of PUAV and SUAV placement on user latency and propose a two-stage particle swarm optimization (PSO)-based algorithm to iteratively optimize the deployment of all UAVs. Finally, the user pairs and power allocation are jointly optimized based on the updated deployment of all UAVs to further reduce user latency. Simulation results show that, compared with a single-layer UAV architecture and benchmark schemes, the proposed multi-layer design with joint optimization achieves lower user latency. Additionally, comparisons with the optimal power allocation search method confirm the validity of the derived NOMA power allocation factor.

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Handover Optimization for UAV-Assisted LEO Satellite Networks Based on IPPO and Three-Sided Matching Theory

Yang F., Li M., Wang K., Si P., Ohtsuki T., Richard Yu F.

IEEE Internet of Things Journal 13 （ 1 ） 152 - 166 2026年

　概要を見る

Due to the triple mobility of mobile users (MUs), unmanned aerial vehicle (UAV) relays, and low Earth orbit (LEO) satellites, handover becomes a critical and challenging issue for maintaining the continuity and quality of communication services in UAV-assisted LEO satellite networks. This article proposes a distributed handover decision-making process aimed at improving scalability and reducing communication overhead. The handover problem is modeled as a decentralized Markov decision process (DEC-MDP) with the objective of maximizing the total end-to-end (E2E) throughput. We design an independent proximal policy optimization-based distributed intelligent handover (IPPO-DIH) algorithm within a centralized training with decentralized execution framework to solve the DEC-MDP. To analyze the theoretical optimal E2E throughput, we eliminate the correlation between handover decisions at different time steps. A three-sided matching algorithm with theoretical convergence guarantees is designed to obtain a stable matching among MUs, UAV relays, and LEO satellites at each time step. These stable matchings are combined to provide a theoretical performance benchmark for the handover algorithms. Simulation results validate the convergence of the proposed IPPO-DIH and three-sided matching algorithms. Additionally, the total E2E throughput achieved by the IPPO-DIH algorithm approaches the theoretical performance benchmark and outperforms typical handover algorithms.

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Generative Model-Aided Continual Learning for CSI Feedback in FDD mMIMO-OFDM Systems

Liu G., Cao Y., Ohtsuki T., He J., Mumtaz S.

IEEE Signal Processing Letters 33   1013 - 1017 2026年

ISSN  10709908

　概要を見る

Deep autoencoder (DAE) frameworks have demonstrated their effectiveness in reducing channel state information (CSI) feedback overhead in massive multiple-input multiple-output (mMIMO) orthogonal frequency division multiplexing (OFDM) systems. However, existing CSI feedback models struggle to adapt to dynamic environments caused by user mobility, requiring retraining when encountering new CSI distributions. Moreover, returning to previously encountered environments often leads to performance degradation due to catastrophic forgetting. Continual learning involves enabling models to incorporate new information while maintaining performance on previously learned tasks. To address these challenges, we propose a generative adversarial network (GAN)-based learning approach for CSI feedback. By using a GAN generator as a memory unit, our method preserves knowledge from past environments and ensures consistently high performance across diverse scenarios without forgetting. Simulation results show that the proposed approach enhances the generalization capability of the DAE framework while maintaining low memory overhead. Furthermore, it can be seamlessly integrated with other advanced CSI feedback models, highlighting its robustness and adaptability.

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