論文 - 大槻　知明

Max-Min Fair 3D Trajectory Design and Transmission Scheduling for Solar-Powered Fixed-Wing UAV-Assisted Data Collection

C. Sun, X. Xiong, Z. Zhai, W. Ni, T. Ohtsuki, and X. Wang

IEEE Trans. on Wireless Communications 2023年

研究論文（学術雑誌）, 共著

Deep Learning and Image Super-Resolution-Guided Beam and Power Allocation for mmWave Networks

Cao Y., Ohtsuki T., Maghsudi S., Quek T.Q.S.

IEEE Transactions on Vehicular Technology 72 （ 11 ） 15080 - 15085 2023年

研究論文（学術雑誌）, 共著,  ISSN  00189545

　概要を見る

In this article, we develop a deep learning (DL)-guided hybrid beam and power allocation approach for multiuser millimeter-wave (mmWave) networks, which facilitates swift beamforming at the base station (BS). The following persisting challenges motivated our research: (i) User and vehicular mobility, as well as redundant beam-reselections in mmWave networks, degrade the efficiency; (ii) Due to the large beamforming dimension at the BS, the beamforming weights predicted by the cutting-edge DL-based methods often do not suit the channel distributions; (iii) Co-located user devices may cause a severe beam conflict, thus deteriorating system performance. To address the aforementioned challenges, we exploit the synergy of supervised learning and super-resolution technology to enable low-overhead beam- and power allocation. In the first step, we propose a method for beam-quality prediction. It is based on deep learning and explores the relationship between high- and low-resolution beam images (energy). Afterward, we develop a DL-based allocation approach, which enables high-accuracy beam and power allocation with only a portion of the available time-sequential low-resolution images. Theoretical and numerical results verify the effectiveness of our proposed framework.

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Investigation of Data Leakage in Deep Learning-based Blood Pressure Estimation using Photoplethysmogram/ Electrocardiogram

R. Yoshizawa, K. Yamamoto, and T. Ohtsuki

IEEE Sensors Journal 2023年

研究論文（学術雑誌）, 共著, 最終著者

Intelligent Cellular Offloading with VLC-enabled Unmanned Aerial Vehicles

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

IEEE Internet of Things Journal 2023年

研究論文（学術雑誌）, 最終著者

A Robust Approach Assisted by Signal Quality Assessment for Fetal Heart Rate Estimation from Doppler Ultrasound Signal

Shi X., Niida N., Yamamoto K., Ohtsuki T., Matsui Y., Owada K.

Sensors 23 （ 24 ）  2023年

研究論文（学術雑誌）, 責任著者,  ISSN  14248220

　概要を見る

Fetal heart rate (FHR) monitoring, typically using Doppler ultrasound (DUS) signals, is an important technique for assessing fetal health. In this work, we develop a robust DUS-based FHR estimation approach complemented by DUS signal quality assessment (SQA) based on unsupervised representation learning in response to the drawbacks of previous DUS-based FHR estimation and DUS SQA methods. We improve the existing FHR estimation algorithm based on the autocorrelation function (ACF), which is the most widely used method for estimating FHR from DUS signals. Short-time Fourier transform (STFT) serves as a signal pre-processing technique that allows the extraction of both temporal and spectral information. In addition, we utilize double ACF calculations, employing the first one to determine an appropriate window size and the second one to estimate the FHR within changing windows. This approach enhances the robustness and adaptability of the algorithm. Furthermore, we tackle the challenge of low-quality signals impacting FHR estimation by introducing a DUS SQA method based on unsupervised representation learning. We employ a variational autoencoder (VAE) to train representations of pre-processed fetal DUS data and aggregate them into a signal quality index (SQI) using a self-organizing map (SOM). By incorporating the SQI and Kalman filter (KF), we refine the estimated FHRs, minimizing errors in the estimation process. Experimental results demonstrate that our proposed approach outperforms conventional methods in terms of accuracy and robustness.

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Detecting Dementia from Face-Related Features with Automated Computational Methods," Bioengineering

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

Bioengineering 2023年

研究論文（学術雑誌）, 責任著者

Specific Emitter Identification Using Adaptive Signal Feature Embedded Knowledge Graph

Hua M., Zhang Y., Sun J., Adebisi B., Ohtsuki T., Gui G., Wu H.C., Sari H.

IEEE Internet of Things Journal 11 （ 3 ） 4722 - 4734 2023年

研究論文（学術雑誌）, 責任著者

　概要を見る

Specific emitter identification (SEI) plays an important role in secure Industrial Internet of Things (IIoT). In recent years, many SEI methods based on machine learning (ML) and deep learning (DL) have been proposed due to their great performance. However, DL-based SEI methods are accompanied by huge computation overhead, which is not suitable for IIoT applications. In addition, the existing ML-based SEI methods rely on feature extraction and a heavy and redundant classifier, which do not ensure optimal feature combination and efficient computation. To solve the above problem, we propose an improved DL-based SEI method using a signal feature embedded knowledge graph (KG) composed of universal features. To the best of our knowledge, this is the first attempt to apply KG for SEI technology. Specifically, we explore an adaptive feature combination (AFC) strategy through the attention mechanism to realize an efficient SEI classifier. The simulation results show that the proposed KG-AFC algorithm outperforms existing SEI methods in identification performance and computation overhead. At the same time, under the optimal compression rate, the average accuracy of the proposed SEI algorithm is higher than 99.2% and can effectively reduce complexity. The code and the data set can be downloaded from https://github.com/Lollipophua/KG-AFC.

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Detecting Dementia from Face-Related Features with Automated Computational Methods

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

Bioengineering 10 （ 7 ）  2023年

研究論文（学術雑誌）, 責任著者

　概要を見る

Alzheimer’s disease (AD) is a type of dementia that is more likely to occur as people age. It currently has no known cure. As the world’s population is aging quickly, early screening for AD has become increasingly important. Traditional screening methods such as brain scans or psychiatric tests are stressful and costly. The patients are likely to feel reluctant to such screenings and fail to receive timely intervention. While researchers have been exploring the use of language in dementia detection, less attention has been given to face-related features. The paper focuses on investigating how face-related features can aid in detecting dementia by exploring the PROMPT dataset that contains video data collected from patients with dementia during interviews. In this work, we extracted three types of features from the videos, including face mesh, Histogram of Oriented Gradients (HOG) features, and Action Units (AU). We trained traditional machine learning models and deep learning models on the extracted features and investigated their effectiveness in dementia detection. Our experiments show that the use of HOG features achieved the highest accuracy of 79% in dementia detection, followed by AU features with 71% accuracy, and face mesh features with 66% accuracy. Our results show that face-related features have the potential to be a crucial indicator in automated computational dementia detection.

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A Novel Approach for Heartbeat Rate Estimation Using 3-D Lidar and Multibeam MIMO Doppler Radar

Bouazizi M., Yamamoto K., Endo K., Ohtsuki T.

IEEE Sensors Journal 23 （ 24 ） 31259 - 31277 2023年

研究論文（学術雑誌）, 最終著者,  ISSN  1530437X

　概要を見る

Noncontact heartbeat detection and heart rate (HR) estimation have been hot topics of research over the last few years. By employing wireless sensors, such as Doppler sensors, the goal of these tasks is to detect subtle movements associated with heart movements. The detection is either used to reconstruct the R-peaks, thus the heartbeat detection, or simply identify the HR. However, due to the nature of the faint movements of the chest caused by the heart beats, unless the person is still, such movements are overwhelmingly obscured by the motion of the person. That being the case, we are interested in this work in identifying such instances where the person is not in motion, in which case, we measure their heartbeats and HR. To do so, we use a combination of a 3-D light detection and ranging (LiDAR) and a multiple-input multiple-output (MIMO) Doppler Radar. The former's objective is to recognize when the person is still, in which case their position and distance from the sensors are reported. The latter's objective is to create a beam directed toward the person's detected chest and measure the reflected signal, for heartbeat detection, HR, or respiration rate (RR) estimation. Our experiments demonstrate that determining the subject's location and distance and identifying their chest position using LiDAR, then collecting the data accordingly using the Doppler radar leads to better HR detection and R-R intervals (RRIs) estimation. For five different scenarios, the RRI estimation error reaches values between 112 and 231 ms.

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Resource Allocation for Secure MIMO-SWIPT Systems in the Presence of Multi-Antenna Eavesdropper in Vehicular Networks

Ganapathy V., Ramachandran R., Ohtsuki T.

Sensors 23 （ 19 ）  2023年

研究論文（学術雑誌）, 最終著者,  ISSN  14248220

　概要を見る

In this paper, we optimize the secrecy capacity of the legitimate user under resource allocation and security constraints for a multi-antenna environment for the simultaneous transmission of wireless information and power in a dynamic downlink scenario. We study the relationship between secrecy capacity and harvested energy in a power-splitting configuration for a nonlinear energy-harvesting model under co-located conditions. The capacity maximization problem is formulated for the vehicle-to-vehicle communication scenario. The formulated problem is non-convex NP-hard, so we reformulate it into a convex form using a divide-and-conquer approach. We obtain the optimal transmit power matrix and power-splitting ratio values that guarantee positive values of the secrecy capacity. We analyze different vehicle-to-vehicle communication settings to validate the differentiation of the proposed algorithm in maintaining both reliability and security. We also substantiate the effectiveness of the proposed approach by analyzing the trade-offs between secrecy capacity and harvested energy.

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Online Trajectory Optimization for Energy-Efficient Cellular-Connected UAVs with Deep Image Prior Map Reconstruction

H. Zhao, Q. Hao, H. Huang, G. Gui, T. Ohtsuki, H. Sari, and F. Adachi

IEEE Trans. on Vehicular Technology 2023年

研究論文（学術雑誌）, 責任著者

Mobility-Aware Routing and Caching in Small Cell Networks Using Federated Learning

Cao Y., Maghsudi S., Ohtsuki T., Quek T.Q.S.

IEEE Transactions on Communications 72 （ 2 ） 815 - 829 2023年

研究論文（学術雑誌）, 最終著者, 責任著者,  ISSN  00906778

　概要を見る

We consider a service cost minimization problem for resource-constrained small-cell networks with caching, where the challenge mainly stems from (i) the insufficient backhaul capacity and limited network bandwidth and (ii) the limited storing capacity of small-cell base stations (SBSs). Besides, the optimization problem is NP-hard since both the users' mobility patterns and content preferences are unknown. In this paper, we develop a novel mobility-Aware joint routing and caching strategy to address the challenges. The designed framework divides the entire geographical area into small sections containing one SBS and several mobile users (MUs). Based on the concept of one-stop-shop (OSS), we propose a federated routing and popularity learning (FRPL) approach in which the SBSs cooperatively learn the routing and preference of their respective MUs and make a caching decision. The FRPL method completes multiple tasks in one shot, thus reducing the average processing time per global aggregation of learning. By exploiting the outcomes of FRPL together with the estimated service edge of SBSs, the proposed cache placement solution greedily approximates the minimizer of the challenging service cost optimization problem. Theoretical and numerical analyses show the effectiveness of our proposed approaches.

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Automatic Modulation Classification Based on Decentralized Learning and Model Averaging

Xu Y., Ma M., Fu X., Wang Y., Gui G., Ohtsuki T.

IEEE INFOCOM 2023 Conference on Computer Communications Workshops INFOCOM Wkshps 2023 2023年

　概要を見る

Automatic modulation classification (AMC) is a promising technology to identify the modulation mode of the received signal in non-cooperative communication scenarios. Recently, benefitting from the outstanding classification performance of deep learning (DL), various deep neural networks (DNNs) have been introduced into AMC methods. Most current AMC methods are based on local framework (LocalAMC) where there is only one device, or centralized framework (CentAMC) where multiple local devices (LDs) upload their data to the only one central server (CS). LocalAMC may not get ideal results due to insufficient data and finite computational power. CentAMC carries a significant risk of privacy leakage and the final data for training model in CS is quite massive. In this paper, we propose an AMC method based on decentralized learning with residual network (ResNet). Simulation results show that ResNet-based decentralized AMC (DecentAMC) method achieves similar classification performance to CentAMC while improving training efficiency and protecting data privacy.

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Arterial Blood Pressure Waveform Estimation from Photoplethysmogram Under Inter-Subject Paradigm Using Subject-Distinguishable Dataset by U-Net and Domain Adversarial Training

Yoshizawa R., Yamamoto K., Ohtsuki T.

IEEE International Conference on Communications 2023-May   3401 - 3406 2023年

ISSN  15503607

　概要を見る

Blood pressure (BP) estimation methods using photoplethysmogram (PPG) based on deep learning models have been actively studied. These methods are also the basis of non-contact BP estimation methods using a camera or a Doppler radar. However, most previous studies are under data leakage, where subjects are not separated between training and test data. In this paper, we propose a method for BP estimation from PPG under the condition that subjects are separated between training and test data (inter-subject paradigm) using a subject-distinguishable large public dataset. Our BP estimation method estimates an 8-second BP waveform called arterial BP (ABP) from an 8-second PPG segment using U-Net. From the estimated ABP, systolic BP (SBP), diastolic BP (DBP), and mean BP (MBP) are calculated, which are the discrete single values for the 8-second ABP. In addition, we apply domain adversarial training, which facilitates the BP estimation model to extract subject-invariant features to improve the BP estimation accuracy under the inter-subject paradigm. Moreover, we use a considerably larger amount of training data than the previous studies and evaluate our model with smaller model sizes for better generalizability. Our experimental results showed that our method can estimate BP with moderate accuracy under the inter-subject paradigm, particularly MBP. The mean absolute errors for the estimated SBP, DBP, MBP, and ABP were 15.21, 7.12, 8.20, and 10.14 mmHg, respectively. The Pearson's correlation coefficients between the true and estimated values were 0.49, 0.39, 0.54, and 0.84, respectively.

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An LSTM-Based Approach for Fall Detection Using Accelerometer-Collected Data

Uotani Y., Yamamoto K., Ye C., Bouazizi M., Ohtsuki T.

Proceedings 2023 28th Asia Pacific Conference on Communications Apcc 2023    250 - 255 2023年

　概要を見る

Over the past few years, there has been a significant rise in the number of fall accidents occurring among elderly individuals, a problem that has been accentuated with to the aging population. Researchers and developers have focused their efforts on investigating and creating various fall detection methods that utilize an accelerometer. However, conventional fall detection methods typically target specific positions where accelerometers are placed. In addition, they suffer from low accuracy which can be attributed to the fact that the classification algorithms commonly employed, such as the support vector machine (SVM) and the random forest (RF), are not specialized in making predictions based on time series data. In this paper, we propose the fall detection method based on a long short-term memory (LSTM) neural network, using an accelerometer. In the proposed method, four kinds of possession positions are set: (i) in hand, (ii) inside a chest pocket, (iii) inside a waist pocket, and (iv) in a bag. The acceleration data collected are classified using the LSTM classifies into one of four classes: (i) standing, (ii) walking, (iii) falling, and (iv) lying down. The results of the multi-class classification are further reclassified into two classes, i.e., fall and non-fall. The experimental results demonstrate that our approach outperforms the conventional methods in terms of fall detection accuracy.

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Adversarial Learning Aided Malicious Network Traffic Detection Based on EDE Framework

Zhang L., Wang Y., Ohtsuki T., Adebisi B., Wu H.C., Gui G.

2023 IEEE Cic International Conference on Communications in China Iccc 2023 2023年

　概要を見る

With the increasing number of new malicious software and network attacks, anomalous network traffic detection technology needs to continuously identify unknown attacks in order to adapt to complex network environments. In this paper, we propose a novel adversarial learning aided malicious network traffic detection (AMD) method based on encoder-decoder-encoder (EDE) framework. The proposed AMD method detects unknown anomalies in a single type of semi-supervised form and converts network traffic data into pictures for later processing. First, an anomalous network traffic detection model consisting of two encoders, a decoder and a discriminator is introduced. Second, by using the visibility of normal samples and the unrecognizability of unknown anomaly samples, the difference between the two samples is enlarged to facilitate effective sample detection. Finally, experiments show that the proposed model detects anomalies in targeted space and is robust to complex and noisy network environments. At the same time, compared to traditional machine learning methods, AMD's shows performance improvement of up to 40%.

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A Robust Multi-Frame mmWave Radar Point Cloud-Based Human Skeleton Estimation Approach with Point Cloud Reliability Assessment

Shi X., Ohtsuki T.

Proceedings of IEEE Sensors 2023年

ISSN  19300395

　概要を見る

Millimeter-Wave (mmWave) radar-based skeleton estimation has gained significant attention in the field of human motion analysis and sensing. It offers distinct advantages over RGB camera-based, depth camera-based, and inertial sensor-based approaches. It operates independently of lighting conditions. Additionally, it overcomes limitations of depth camera-based methods such as reflections and occlusions, and provides precise real-time tracking, enhancing its overall performance. However, existing human skeleton estimation methods utilizing point cloud data mostly rely on single-frame inputs or incorporate voxelization, which introduces drawbacks. This paper proposes a novel Convolutional Neural Network (CNN) and Bi-directional Long Short-Term Memory (BiLSTM)-based model for multi-frame point cloud data without voxelization. Furthermore, an Long Short-Term Memory (LSTM)-based neural network is introduced to assess point cloud reliability, enhancing robustness. Experimental results demonstrate improved accuracy and robustness in human skeleton estimation compared with the conventional single frame-based methods.

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A Novel Approach for Activity, Fall and Gait Detection Using Multiple 2D LiDARs

Bouazizi M., Feghoul K., Lorite A., Ohtsuki T.

Proceedings IEEE Global Communications Conference Globecom    1997 - 2002 2023年

ISSN  23340983

　概要を見る

A key concept in health monitoring systems for elderly people is the continuous and non-intrusive detection of their activities to identify when hazardous events such as sudden falling occur/are about to occur. The existence of obstacles in the environment largely limits the detection performance of existing approaches of activity detection relying on non-contact sensors. A simple, yet effective, approach to address this issue is the use of multiple sensors which collaborate with one another. In this paper, we propose an approach that relies on 2D Light Detection and Ranging (LiDAR) technology for activity detection. We employ multiple 2D LiDARs placed at different locations in a single room with difference obstacles (e.g., furniture) and working in coordination to construct a fuller representation of the activities being performed. Our approach transforms the concatenation of the different LiDAR data into a more comprehensible data format (i.e., images). The generated images are then processed using a Convolutional LSTM Neural Network to perform the classification. For 3 different tasks, namely activity detection, fall detection, and unsteady gate detection, our proposed approach reaches an accuracy equal to 96.10%, 99.13% and 93.13%, respectively.

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A GAN-Based Approach for ECG Reconstruction from Doppler Sensor Signals

Bouazizi M., Yu D., Feghoul K., Ohtsuki T.

Proceedings IEEE Global Communications Conference Globecom    3867 - 3872 2023年

ISSN  23340983

　概要を見る

An Electrocardiograms (ECG) is a recording of the heart's electrical activity. It can help detect problems with one's heart rate or heart rhythm. Traditional methods to measure and collect ECG are not practical for daily use due to their invasive nature and inaccessibility. Thus, a less intrusive and more accessible method is needed. In this paper, we propose a method that uses attention-based Wasserstein Generative Adversarial Networks-Gradient Penalty (aWGAN-GP) to reconstruct ECG signals from ones collected using a Doppler sensor. GANs can capture pertinent features in the Doppler signals to enable such reconstruction. Our approach uses the integrated spectrum of the Doppler signal to identify R-peaks, which are then employed to train the aWGAN-GP to reconstruct the ECG signal. We evaluated this method on 19 healthy subjects and obtained a correlation coefficient of 0.88 between the reconstructed and actual ECG signals, outperforming the conventional CNN-based method which reached only 0.35. Our results demonstrate that it is possible to reconstruct an ECG signal from a heartbeat signal collected via a Doppler sensor using aWGAN-GP.

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A Few-Shot Malware Traffic Classification Method Using Knowledge Transfer Learning

Qin M., Zhang X., Wang Y., Ohtsuki T., Sari H., Gui G.

International Conference on Communication Technology Proceedings ICCT    15 - 19 2023年

ISSN  25767844

　概要を見る

Malware traffic classification (MTC) has become one of the important tasks to protect network and system security. However, it is a difficult task to obtain large-scale labeled malware traffic data in practice due to the wide variety and ever-changing malware. Existing MTC techniques require a large number of samples for training, the classification performance is poor when the training samples are few. Therefore, classification techniques based on few-shot learning (FSL) have attracted much attention in MTC. In this paper, we propose a few-shot MTC method based on knowledge transfer learning. Specifically, generic malware feature representation can be learned by pre-training the deep learning model on source tasks. Then, the parameters of the pre-trained model are used as initial parameters to fine-tune on the target task to adapt to feature distribution of the target task. The simulation results show that the few-shot MTC method based on knowledge transfer learning proposed in this paper has a 20-shot average accuracy of 95.23% when using GoogLeNet network as the backbone network, compared with CNN, VGG, and ResNet models, GoogLeNet has the best performance.

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