論文 - 大槻　知明

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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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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Correlation Analysis of Objective Features and Online Meeting Quality

Oba R., Zheng C., Akahori W., Yamada M., Yokoyama M., Ishii M., Ohtsuki T.

Lecture Notes in Computer Science 15805 LNCS   172 - 183 2025年

ISSN  03029743

　概要を見る

Meetings are vital for team activities, supporting both decision-making and information sharing, making their quality a critical factor in boosting productivity and reducing costs. Therefore, establishing objective methods for measuring meeting quality is essential to identify specific issues and verify the effectiveness of solutions. However, no well-defined objective features have yet been identified to accurately assess meeting quality. In this study, we explored features that can be extracted from meeting video and audio data to objectively assess the quality of meetings. We conducted online meetings and extracted features from video data, including Action Units, Mel-Frequency Cepstral Coefficients (MFCC) and utterances. We then examined the correlations between these features and participant responses regarding psychological safety and perceived meeting effectiveness. As a result, we found that certain features such as the number of utterances and the Action Units such as Lip Part and Dimpler showed significant correlations with psychological safety and perceived meeting effectiveness.

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Channel-Robust Few-Shot Specific Emitter Identification Using Meta-Feature Augmentation

Fu X., Meneghello F., Wang Y., Ohtsuki T., Yuen C., Gui G., Sari H.

IEEE Wireless Communications and Networking Conference Wcnc 2025年

ISSN  15253511

　概要を見る

The rapid increase in wireless devices has raised significant security and privacy concerns, positioning Specific Emitter Identification (SEI) as a crucial physical-layer security technology. While Deep Learning (DL) has been widely applied to SEI, it often requires large amounts of high-quality signal examples, which are laborious and expensive to obtain. Moreover, the DL-enabled SEI models have difficulties in extracting features from the signal examples in the testing process that are consistent with those from the signal examples in the training phase due to the wireless channel variations, further resulting in a significant reduction in identification performance. To address these challenges, we propose a channel-robust Few-Shot SEI (FS-SEI) method based on Meta-Feature Augmentation (MFA). Our approach utilizes datasets from base emitters to construct a meta-feature embedding function that can extract generalizable features from a few signal examples of target emitters. We then calculate and calibrate the statistics of these extracted features to describe the feature distribution of target emitters. A Multi-Layer Perceptron (MLP) is subsequently trained on both original and augmented features derived from this distribution, achieving a robust FS-SEI model. Experiments conducted on a Wi-Fi dataset comprising 16 emitter categories - 10 as base emitters and 6 as target emitters - demonstrate that our method achieves 93.75% identification accuracy with only 5 examples per target emitter, maintaining 92.56% accuracy even under varying wireless channel conditions. Code is available at https://github.com/lovelymimola/MFA-based-FS-SEI.

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Beamforming and Trajectory Planning Method Under Fixed-Footprint Conditions for Multi-HAPS Systems

Mori T., Ohtsuki T., Pan M., Han Z.

IEEE International Conference on Communications    2236 - 2241 2025年

ISSN  15503607

　概要を見る

High Altitude Platform Station (HAPS) is a new airborne communications platform that provides wide-area communications services from the stratosphere and has the potential for coverage extension in 6G networks. In particular, it is a practical scenario where multiple HAPS work together to provide communications in densely populated urban areas and extensive regions. However, changes in ground coverage due to HAPS movement cause frequent handovers, which pose challenges to communication quality and stability. To solve this problem, 'footprint fixation,' which maintains constant ground coverage even when the HAPS moves, is expected to reduce handovers and improve communication quality. How we should design beamforming and trajectory under fixed footprint conditions has not been clarified. In this paper, we propose a beamforming and trajectory planning method under fixed-footprint conditions for multiple HAPS systems. For footprint fixation, beamforming dynamically adapts to HAPS motion to eliminate ground coverage shifts, reduce handover frequency, and improve communication stability. Under fixed-footprint conditions, trajectory planning aims to improve the throughput of UEs with low (5th percentile) and medium (50th percentile) communication quality simultaneously through sequential multi-objective optimization. Simulation results in major Japanese cities with different UE distributions show that the proposed method improves both low and medium communication quality UEs, achieving a 20 - 36% improvement in 5th percentile throughput and an 8-20% improvement in 50th percentile throughput compared to other methods. In addition, the footprint fixation increases coverage stability, reducing outage probability to less than 2% and significantly reducing handover frequency.

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A Plug-and-Play Module for Enhancing Fault-Tolerant Distributed Inference Based on Gaussian Dropout

Hou Z., Ohtsuki T.

IEEE International Conference on Communications    4553 - 4558 2025年

ISSN  15503607

　概要を見る

Deploying deep neural network (DNN) models to the Internet of Things (IoT) for distributed inference (DI) is becoming an increasingly common requirement for AI applications. When IoT transmissions occur over lossy networks, data loss can significantly affect the accuracy of inference tasks. Therefore, we propose a plug-and-play module called Loss-Adapter, which can improve the accuracy of DI on lossy networks. To simulate network packet loss, we design a Gaussian distribution sampling dropout. This idea is utilized during the training of Loss-Adapter to acquire fault-tolerant inference capability on unreliable communication links. Moreover, we use the Tree-structured Parzen Estimator algorithm to optimize the parameters of the Gaussian dropout layer. To avoiding catastrophic forgetting, we implement freeze training. In addition, as a plug-and-play module, it is very easy to train and use. Our experiments show that by applying the Loss-Adapter, the inference accuracy of DNN models on lossy IoT is significantly improved.

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A Novel Physical Spoofing Technique Using Radio Frequency Fingerprint Emulation and Model Fitting

Yao Z., Wang Y., Gui G., Otsuki T., Mao S., Wang X., Sari H.

IEEE International Conference on Communications    2629 - 2634 2025年

ISSN  15503607

　概要を見る

With the increasing demand for secure communication in 5G and beyond, authentication of wireless devices has become a crucial task for communication security. Radio frequency fingerprint identification (RFFI) leverages the hardware-specific features in radio frequency (RF) signals, known as radio frequency fingerprints (RFF), to achieve highprecision device identification. However, the dependence of RFFI on the physical characteristics of devices makes it vulnerable to physical spoofing attacks. This paper proposes an innovative physical spoofing attack framework that combines spoofed transmitter and legitimate transmitter models. It performs RFF modeling, RFF concealment (RFFC), and RFF spoofing (RFFS) sequentially to achieve precise spoofing of the original baseband signal. We validate the effectiveness of the proposed physical spoofing mechanism through simulations of seven types of transmitters using MATLAB Simulink. The performance is further evaluated on an RFFI model based on complexvalued convolutional neural networks (CVCNN). Experimental results show that neural networks (NN) significantly outperform the generalized memory polynomial (GMP) model in nonlinear data fitting and temporal relationship modeling. Consequently, NN-based physical spoofing methods exhibit superior attack effectiveness. Specifically, under the signal-to-noise ratio (SNR) of 15 dB, the NN-based physical spoofing method achieves a target attack success rate (TSR) as high as 98%, which is superior to adversarial attack methods. NN-based methods also enhanced performance in terms of stealthiness metrics.

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A Novel MIMO FMCW Radar-Based Approach for Heart Rate Estimation Using Positional Feature Selection

Nakatani S., Bouazizi M., Ohtsuki T.

IEEE International Conference on Communications    916 - 921 2025年

ISSN  15503607

　概要を見る

This paper proposes a heart rate estimation method using Variational Mode Decomposition (VMD) employing Multiple-Input Multiple-Output (MIMO) Frequency Modulated Continuous Wave (FMCW) radar. The proposed method first estimates the human position within the radar's coverage area, then reduces noise by focusing on the signal from these positions. The signal is decomposed into Intrinsic Mode Function (IMF) signals using VMD, and only the IMF signal related to the heartbeat is retained. Heart rate signal is reconstructed by weighting IMF signals based on their energy within the specific spatial area in which the human is located. The reconstructed signals are utilized for heart rate estimation through peak detection. The estimation is done over consecutive time windows. Until the fourth time window, among the estimated heart rates from the different cells, the selection is based on energy and periodicity. From the fifth time window onwards, the heart rate closest to the average of the previous four estimates is chosen. This approach considers the gradual transition of heart rate estimation over time to mitigate extraneous variations. Validation experiments involved 4 subjects being seated and stationary within the radar coverage area, with heart rate observed via MIMO FMCW radar. Results showed an average Mean Absolute Error (MAE) of 2.54 BPM, with an exclusion rate of 2.12%.

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A Deep Transfer Learning-Based Low-Overhead Beam Prediction in Vehicle Communications

Xiao Z., Cao Y., Bouazizi M., Ohtsuki T., Mumtaz S.

IEEE Wireless Communications Letters 14 （ 12 ） 4147 - 4151 2025年

ISSN  21622337

　概要を見る

Existing transfer learning-based beam prediction approaches primarily rely on simple fine-tuning. When there is a significant difference in data distribution between the target domain and the source domain, simple fine-tuning limits the model’s performance in the target domain. To tackle this problem, we propose a transfer learning-based beam prediction method that combines fine-tuning with domain adaptation. We integrate a domain classifier into fine-tuning the pre-trained model. The model extracts domain-invariant features in adversarial training with domain classifier, which can enhance model performance in the target domain. Simulation results demonstrate that the proposed transfer learning-based beam prediction method achieves better achievable rate performance than the pure fine-tuning method in the target domain, and close to those when the training is done from scratch on the target domain.

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Deep Learning-Based Human Joint Localization Using mmWave Radar and Sequential Frame Fusion

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

Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society EMBS 2025年

ISSN  1557170X

　概要を見る

Unlike conventional techniques relying typically on RGB and depth cameras, millimeter wave (mmWave) radars, especially Frequency-Modulated Continuous Wave (FMCW) radar, have significant advantages in human monitoring. These advantages are more pronounced when detecting human joint positions, as FMCW radars rely solely on collected point cloud data. Compared with camera-based systems, this technology is efficient because it can operate in low-light conditions, and does not present a privacy threat. However, conventional approaches employing FMCW radar, such as voxel-based gathering techniques, still have limitations in optimally capturing spatiotemporal features, making it difficult to detect human joint points with high accuracy. To overcome this obstacle, we propose a novel framework that introduces sequential concatenation of multiple frames in the preprocessing stage to maintain spatiotemporal continuity, thereby improving feature representation. Furthermore, we design a deep learning architecture consisting of Convolutional Neural Network (CNN) for spatial feature extraction, Transformer for capturing long-term dependencies, and Bidirectional Long Short-Term Memory (Bi-LSTM) for temporal sequence modeling. The proposed approach makes optimal use of spatiotemporal data, improves the accuracy and reliability in detecting human joint positions, and accelerates model convergence. Through experimental validation, this method shows a Mean Absolute Error (MAE) of 1.77 cm and a Root Mean Squared Error (RMSE) of 2.92 cm between the actual human joint positions and the predicted ones. This reaffirms the effectiveness of the model in estimating human joint positions.

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User Clustering and Trajectory Optimization for UAV-Assisted Wireless Sensor Network

Zhang X., Yin Y., Ohtsuki T.

2025 30th Asia Pacific Conference on Communications Apcc 2025 2025年

　概要を見る

With the advances in flexibility, wide coverage, and cost-effectiveness, integrating unmanned aerial vehicles (UAVs) with wireless sensor networks (WSNs) has been regarded as a promising technology in data collection and transmission within complex environments. Due to the limited battery capacity of sensor nodes (SNs) and UAVs, UAV-assisted WSNs face challenges in achieving energy efficiency and optimizing resource allocation. To reduce the total energy consumption of UAV-assisted WSNs, we design a multifactor fitness function which includes the distance between SNs, energy levels of SNs, and UAV trajectory length. Based on that, we propose a balanced clustering and cluster head (CH) selection strategy. As for UAV trajectory design, we employ the branch and bound method according to the selection of CHs. Simulation results show that the proposed algorithm reduces the average energy consumption of all SNs and achieves short UAV trajectory length compared to Low-Energy Adaptive Clustering Hierarchy (LEACH), Hybrid Energy-Efficient Distributed clustering (HEED), Particle Swarm Optimization (PSO), and Stable Election Protocol (SEP).

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Towards Robust and Real-Time State Estimation: A Neural Kalman Filter Framework with Mamba Architecture

Sun J., Shen J., Tong J., Tang T., Yin Y., Ohtsuki T., Gui G.

IEEE Transactions on Vehicular Technology 2025年

ISSN  00189545

　概要を見る

The rapid advancement of intelligent perception systems and autonomous navigation technologies imposes increasingly stringent requirements on state estimation algorithms regarding modeling capacity, robustness, and real-time performance. While traditional Kalman Filter (KF) demonstrates computational efficiency and mathematical interpretability for linear Gaussian systems, their effectiveness diminishes in nonlinear, non-Gaussian, and non-stationary dynamic scenarios. Recent developments in neural state space modeling offer new perspectives for recursive estimation, yet existing approaches suffer from structural complexity, training difficulties, and inadequate real-time capability. This paper presents Mamba-KF, a novel state estimation framework integrating deep modeling with recursive filtering. Leveraging the cutting-edge Mamba state space neural network, our method dynamically generates state transition parameters while establishing a differentiable covariance propagation pathway, enabling end-to-end optimization. We further design a lightweight multi-modal fusion architecture tailored for high-frequency sensory data processing in real-time applications. Evaluated on the EuRoC Micro Aerial Vehicle (MAV) dataset, Mamba-KF attains the lowest errors across states, reducing position Root Mean Square Error (RMSE) by up to 33.3% relative to Extended Kalman Filter (EKF) and 24.1% relative to Unscented Kalman Filter (UKF) at 10 dB Signal-to-Noise Ratio (SNR), with an average velocity RMSE of 0.097 m/s and an orientation error of 2.3°. It also achieves superior consistency, with a mean Normalized Estimation Error Squared (NEES) of 6.02 and 93.7% of estimates within the χ²-based 95% confidence interval. Under robustness evaluations, Mamba-KF yields the lowest RMSEs and suppresses divergence observed in EKF. Computationally, it maintains near real-time performance with 0.65 ms per step, offering a favorable accuracy-efficiency trade-off for visual-inertial navigation and autonomous systems, while providing significant theoretical insights and promising engineering applications.

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Stress Detection Using Eye Activity via Recurrent Neural Networks

Bouazizi M., Feghoul R., Feghoul K., Ohtsuki T.

Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society EMBS 2025年

ISSN  1557170X

　概要を見る

Chronic stress has become a significant issue in modern society, negatively affecting individuals' overall well-being and potentially leading to severe physiological and psychological disorders. Most existing methods for automatic stress detection rely on physiological data, which requires direct skin contact with sensors. Not only does this make them intrusive, but it also limits their practicality for everyday use due to accuracy constraints. In this paper, we propose a novel approach using eye data captured by a standard video camera -specifically, eye gaze direction and eye landmarks- for stress detection. We use two types of Recurrent Neural Networks (RNNs), namely a Long Short-Term Memory (LSTM) network and a Gated Recurrent Unit (GRU) network. Through experiments on the UBFC-Phys dataset, we show that our method achieves a stress detection accuracy of up to 83.69% using gaze data, while eye landmarks further improve accuracy to 90.12%. Combined, these two modalities reach accuracy of 90.83%. These results highlight the effectiveness of our approach in enabling accurate and non-intrusive stress detection.

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Stress Detection Through Eye Tracking Incorporating Custom Lasso Stress Index

Meng X., Bouazizi M., Ohtsuki T.

IEEE International Conference on Communications    3174 - 3179 2025年

ISSN  15503607

　概要を見る

Stress has become an increasingly common issue, significantly impacting individuals' health and productivity. Traditional methods for assessing stress levels often rely on physiological signals, such as heart rate and skin conductance. However, these methods can be impractical for continuous, non-invasive monitoring. To address these limitations, we propose a machine learning approach for stress assessment using eye-tracking data. We collected eye-tracking and Electrocardiogram (ECG) data under different stress conditions. From this dataset, we extracted relevant features and conducted preliminary analysis. We further proposed a novel stress index, the Least Absolute Shrinkage and Selection Operator Stress Index (Lasso Stress Index), specifically tailored for stress detection in our dataset. In previous studies, researchers commonly used predefined subjective stress index or Baevsky's Stress Index (BSI) for stress classification. We compared these methods and found that our proposed Lasso Stress Index yielded the highest accuracy, achieving up to 90.6% in stress classification.

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Source Channel Model Selection for Downlink CSI Feedback Estimation using Transfer Learning in Massive MIMO System

Jingyu L., Bouazizi M., Ohtsuki T.

2025 International Conference on Computing Networking and Communications Icnc 2025    276 - 281 2025年

　概要を見る

Transfer learning-based methods for Channel State Information (CSI) feedback can achieve a good CSI reconstruction performance with small amounts of computation time and cost. However, for higher efficacy, such methods need to fine-tune a properly selected source channel model each time. This is because fine-tuning a model trained on drastically different data will suffer from slow convergence and non-optimal performance, which defies the purpose of transfer-learning. In this paper, we use a one-stage algorithm utilizing k-nearest neighbors (k-NN) and Deep Neural Network (DNN) methods to classify the downlink Clustered Delay Line (CDL) channels into one of 5 channel models with the help of a selected set of features. Based on the classification results, the appropriate source channel model for fine-tuning is selected, and the model is fine-tuned on the received data. To demonstrate the usefulness of our method, we compare its results to those of existing methods. Through simulation, we show that the overall classification accuracy for 5 CDL channel models using the one-step algorithm is 80.73%. Upon classification, and by selecting the proper source model, our method can reach a better normalized mean square error (NMSE) of CSI feedback performance in a shorter time and with fewer samples.

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Semantic Communication in Vehicular Networks: A Multi-Modal Approach for Faithful Image Transmission

Bouazizi M., Nagase R., Yin Y., Yang S., Ohtsuki T.

IEEE Vehicular Technology Conference 2025年

ISSN  15502252

　概要を見る

Semantic communication (SC) has emerged as a promising paradigm to address the bandwidth limitations of traditional wireless communication systems by transmitting only the essential meaning of data. This paper investigates an advanced SC framework for vehicular communication, employing diverse feature extraction techniques to encode multi-modal information, such as textual descriptions, object poses, semantic segmentation, and sketches, into compact semantic representations. These semantic encoders are evaluated based on their output size, faithfulness of reconstruction, and resilience to data loss. The proposed system model considers vehicular communication scenarios where vehicles transmit important information extracted from camera-collected data to other vehicles and road users, in bandwidth-constrained environments. Simulation results show the effectiveness of the proposed SC framework, with a reconstruction performance reaching 17.24 in Fréchet Inception distance (FID) and a an RMSE equal to 0.029 between the transmitted image and the reconstructed one. This performance is achieved while the data saving between the size of the original image and the transmitted semantics is equal to 92.25%.

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Remote RRI Estimation: A Signal Reconstruction Approach with Temporal Modeling

Wang S., Bouazizi M., Ohtsuki T.

2025 30th Asia Pacific Conference on Communications Apcc 2025 2025年

　概要を見る

We propose a neural network-based framework for radar-based heart rate monitoring and inter-beat interval (IBI) estimation that reconstructs the underlying cardiac waveform, rather than relying on Conventional peak detection methods. Conventional approaches typically estimate IBI by detecting peaks in radar signals after extensive signal processing, which can be sensitive to noise and transient signal degradation. Building on our previous work, which utilized a U-Net architecture with cross-channel attention, we introduce a enhanced model by incorporating Bidirectional Long Short-Term Memory (BiLSTM) layers and temporal self-attention into the bottleneck of the U-Net. This modification allows the network to explicitly model temporal dependencies and heartbeat periodicity, improving resilience to transient input degradation and physiological variability. The results are evaluated on a public radar-Electrocardiogram(ECG) dataset under Valsalva, Tilt Up, and Tilt Down conditions, our model achieves an average IBI estimation error of 22.54 ms Root Mean Square Error(RMSE), which outperforming both conventional peak-based methods and our prior U-Net-based design. These results demonstrate improved temporal generalization and signal stability, advancing the feasibility of radar-based cardiac monitoring in clinical and home settings.

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