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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

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

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

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

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

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

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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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

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

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

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

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

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

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

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

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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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

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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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

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

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

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

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

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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