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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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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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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Energy-Efficient Resource Allocation Scheme Based on Reinforcement Learning in Distributed LoRa Networks †

Ariyoshi R., Li A., Hasegawa M., Ohtsuki T.

Sensors 25 （ 16 ）  2025年08月

　概要を見る

The rapid growth of Long Range (LoRa) devices has led to network congestion, reducing spectrum and energy efficiency. To address this problem, we propose an energy-efficient reinforcement learning method for distributed LoRa networks, enabling each device to independently select appropriate transmission parameters, i.e., channel, transmission power (TP), and bandwidth (BW) based on acknowledgment (ACK) feedback and energy consumption. Our method employs the Upper Confidence Bound (UCB)1-tuned algorithm and incorporates energy metrics into the reward function, achieving lower power consumption and high transmission success rates. Designed to be lightweight for resource-constrained IoT devices, it was implemented on real LoRa hardware and tested in dense network scenarios. Experimental results show that the proposed method outperforms fixed allocation, adaptive data rate low-complexity (ADR-Lite), and (Formula presented.) -greedy methods in both transmission success rate and energy efficiency.

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A Novel Deep Learning Model for Human Skeleton Estimation Using FMCW Radar †

Rantelinggi P.H., Shi X., Bouazizi M., Ohtsuki T.

Sensors 25 （ 13 ）  2025年07月

　概要を見る

Human skeleton estimation using Frequency-Modulated Continuous Wave (FMCW) radar is a promising approach for privacy-preserving motion analysis. However, the existing methods struggle with sparse radar point cloud data, leading to inaccuracies in joint localization. To address this challenge, we propose a novel deep learning framework integrating convolutional neural networks (CNNs), multi-head transformers, and Bi-LSTM networks to enhance spatiotemporal feature representations. Our approach introduces a frame concatenation strategy that improves data quality before processing through the neural network pipeline. Experimental evaluations on the MARS dataset demonstrate that our model outperforms conventional methods by significantly reducing estimation errors, achieving a mean absolute error (MAE) of 1.77 cm and a root mean squared error (RMSE) of 2.92 cm while maintaining computational efficiency.

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Model-Driven Meta-Learning-Aided Fast Beam Prediction in Millimeter-Wave Communications

Lu W., Jiang X., Cao Y., Ohtsuki T., Bai E.

Electronics Switzerland 14 （ 13 ）  2025年07月

　概要を見る

Beamforming plays a key role in improving the spectrum utilization efficiency of multi-antenna systems. However, we observe that (i) conventional beam prediction solutions suffer from high model training overhead and computational latency and thus cannot adapt quickly to changing wireless environments, and (ii) deep-learning-based beamforming may face the risk of catastrophic oblivion in dynamically changing environments, which can significantly degrade system performance. Inspired by the above challenges, we propose a continuous-learning-inspired beam prediction model for fast beamforming adaptation in dynamic downlink millimeter-wave (mmWave) communications. More specifically, we develop a meta-empirical replay (MER)-based beam prediction model. It combines empirical replay and optimization-based meta-learning. This approach optimizes the trade-offs between transmission and interference in dynamic environments, enabling effective fast beamforming adaptation. Finally, the high-performance gains brought by the proposed model in dynamic communication environments are verified through simulations. The simulation results show that our proposed model not only maintains a high-performance memory for old tasks but also adapts quickly to new tasks.

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Heterogeneous multi-agent deep reinforcement learning based low carbon emission task offloading in mobile edge computing

Zhou X., Guan X., Sun D., Zhang X., Zhang Z., Ohtsuki T.

Computer Communications 234 2025年03月

ISSN  01403664

　概要を見る

Mobile edge computing is an emerging computing paradigm in the Internet of Things. Task offloading is a critical method in mobile edge computing to alleviate computational resource constraints. Nowadays, the rising number of tasks is placing greater demands on computing resources. The increasing consumption of computing resources leads to high carbon emission. Achieving environmentally friendly mobile edge computing while effectively managing low-carbon task offloading poses a significant challenge. Recently, deep reinforcement learning has made certain progress in many research fields. However, there are few deep reinforcement learning methods that consider the carbon emission in task offloading. In this paper, we propose a deep reinforcement learning based low carbon emission task offloading algorithm for minimizing carbon emission in mobile edge computing. Firstly, since different base stations exist in the mobile edge computing environment, we consider the mobile edge computing environment with multiple heterogeneous agents. Secondly, to minimize carbon emission, we consider the carbon intensity of the base station as an optimization factor. We conclude the task offloading strategy to minimize carbon emission, consequently achieving the minimization of carbon emission. Moreover, our proposed algorithm allows user devices to decide their own preference for task offloading. Based on the specific requirements and preferences of user devices, our proposed algorithm can dynamically adjust the weights of delay, energy consumption, and carbon emission, respectively. Experiments indicate that the proposed algorithm can accurately and quickly conclude the task offloading strategy to minimize carbon emission.

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An Ensemble Learning-Based Fault Detection Method for Vehicular Ad Hoc Networks in Intelligent Transportation Systems

Liu J., Zhao H., Han P., Gui G., Ohtsuki T., Sari H., Adachi F.

IEEE Transactions on Vehicular Technology 74 （ 3 ） 5114 - 5124 2025年03月

研究論文（学術雑誌）,  ISSN  00189545

　概要を見る

Vehicular ad hoc networks (VANETs) are integral to Intelligent Transportation Systems (ITS), serving as a crucial platform for deploying applications. The overall performance of VANETs is highly dependent on the vehicles, emphasizing the importance of effective fault (vehicle crash or exit) detection methods. Traditional fault detection faces challenges due to the high mobility of vehicles and prevalent software, hardware, and communication link faults, necessitating a robust solution for timely and accurate fault detection. This paper introduces ET-FD, a novel fault detection method based on ensemble learning designed to address the unique challenges in VANETs. ET-FD satisfies stringent accuracy requirements and aims to reduce system overhead significantly. We present experimental results demonstrating the superior performance of ET-FD in terms of fault detection accuracy and false positive rates, alongside comparisons with other classification methods that highlight its reduced training time. This confirms the potential of ET-FD as an efficient and reliable solution for fault detection in VANET environments.

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Accurate Cardiac Duration Detection for Remote Blood Pressure Estimation Using mm-Wave Doppler Radar †

Wang S., Bouazizi M., Yang S., Ohtsuki T.

Sensors 25 （ 3 ）  2025年02月

　概要を見る

This study introduces a radar-based model for estimating blood pressure (BP) in a touch-free manner. The model accurately detects cardiac activity, allowing for contactless and continuous BP monitoring. Cardiac motions are considered crucial components for estimating blood pressure. Unfortunately, because these movements are extremely subtle and can be readily obscured by breathing and background noise, accurately detecting these motions with a radar system remains challenging. Our approach to radar-based blood pressure monitoring in this research primarily focuses on cardiac feature extraction. Initially, an integrated-spectrum waveform is implemented. The method is derived from the short-time Fourier transform (STFT) and has the ability to capture and maintain minute cardiac activities. The integrated spectrum concentrates on energy changes brought about by short and high-frequency vibrations, in contrast to the pulse-wave signals used in previous works. Hence, the interference caused by respiration, random noise, and heart contractile activity can be effectively eliminated. Additionally, we present two approaches for estimating cardiac characteristics. These methods involve the application of a hidden semi-Markov model (HSMM) and a U-net model to extract features from the integrated spectrum. In our approach, the accuracy of extracted cardiac features is highlighted by the notable decreases in the root mean square error (RMSE) for the estimated interbeat intervals (IBIs), systolic time, and diastolic time, which were reduced by 87.5%, 88.7%, and 73.1%. We reached a comparable prediction accuracy even while our subject was breathing normally, despite previous studies requiring the subject to hold their breath. The diastolic BP (DBP) error of our model is (Formula presented.) mmHg (mean absolute difference ± standard deviation), and the systolic BP (SBP) error is (Formula presented.) mmHg.

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A Comparison Study of Person Identification Using IR Array Sensors and LiDAR

Liu K., Bouazizi M., Xing Z., Ohtsuki T.

Sensors 25 （ 1 ） 271 2025年01月

研究論文（学術雑誌）, 最終著者

　概要を見る

Person identification is a critical task in applications such as security and surveillance, requiring reliable systems that perform robustly under diverse conditions. This study evaluates the Vision Transformer (ViT) and ResNet34 models across three modalities—RGB, thermal, and depth—using datasets collected with infrared array sensors and LiDAR sensors in controlled scenarios and varying resolutions (16 × 12 to 640 × 480) to explore their effectiveness in person identification. Preprocessing techniques, including YOLO-based cropping, were employed to improve subject isolation. Results show a similar identification performance between the three modalities, in particular in high resolution (i.e., 640 × 480), with RGB image classification reaching 100.0%, depth images reaching 99.54% and thermal images reaching 97.93%. However, upon deeper investigation, thermal images show more robustness and generalizability by maintaining focus on subject-specific features even at low resolutions. In contrast, RGB data performs well at high resolutions but exhibits reliance on background features as resolution decreases. Depth data shows significant degradation at lower resolutions, suffering from scattered attention and artifacts. These findings highlight the importance of modality selection, with thermal imaging emerging as the most reliable. Future work will explore multi-modal integration, advanced preprocessing, and hybrid architectures to enhance model adaptability and address current limitations. This study highlights the potential of thermal imaging and the need for modality-specific strategies in designing robust person identification systems.

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Dementia and MCI Detection Based on Comprehensive Facial Expression Analysis From Videos During Conversation

Okunishi T., Zheng C., Bouazizi M., Ohtsuki T., Kitazawa M., Horigome T., Kishimoto T.

IEEE Journal of Biomedical and Health Informatics 29 （ 5 ） 3537 - 3548 2025年01月

研究論文（学術雑誌）, 責任著者,  ISSN  21682194

　概要を見る

The development of a cost-effective digital biomarker for detecting dementia is highly needed. While numerous studies have explored dementia detection through speech and natural language analysis, only a few studies have focused on dementia detection using face video recordings, and more in-depth research is needed. In this paper, we propose a method for detecting dementia and mild cognitive impairment (MCI), a pre-dementia stage, by utilizing four types of facial expression features extracted from recorded videos of participants. These features include Action Units, emotion categories, Valence-Arousal, and face embeddings. From the above features obtained from each video frame, various statistical information was extracted and used as features, and predictions were performed using a decision tree-based model. Our method was evaluated using face video recordings during conversations. The method achieved an area under the receiver operating characteristic curve (AUC) of 0.933 for dementia detection and 0.889 for MCI detection. Statistical analysis of facial expression features revealed that participants with dementia had fewer positive emotions, more negative emotions, and lower valence and arousal than healthy participants. These results indicate that the proposed method could serve as an explainable screening tool for the early detection of dementia and MCI.

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Accurate Cardiac Duration Detection for Remote Blood Pressure Estimation Using mm-Wave Doppler Radar

S. Wang, M. Bouazizi, Siyuan Yang, and T. Ohtsuki

Sensors 25 （ 3 ） 619 2025年01月

研究論文（学術雑誌）, 最終著者