論文 - 大槻　知明

Data Augmentation Aided Few-Shot Learning for Specific Emitter Identification

Zhang X., Wang Y., Zhang Y., Lin Y., Gui G., Tomoaki O., Sari H.

IEEE Vehicular Technology Conference （IEEE Vehicular Technology Conference）  2022-September 2022年

ISSN  15502252

　概要を見る

Specific emitter identification (SEI) extracts the fingerprint characteristics of emitters according to the subtle differences of transmitted signals, to distinguish different emitter individuals and prevent unauthorized network access. Deep learning (DL) based SEI methods have been proposed to achieve a good identification performance in recent years. However, the existing methods need a massive specific emitter dataset to alleviate model overfitting during the training stage. In this paper, we propose data augmentation (DA) aided few-shot learning method and validate the proposed method using automatic dependent surveillance-broadcast (ADS-B) signals. Specifically, according to the characteristics of ADS-B signals, four DA methods, i.e., flip, rotation, shift, and noise are studied for the proposed method. Experimental results are provided to show that the proposed method improves the recognition accuracy and the model robustness.

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Cross-Person Activity Recognition Method Using Snapshot Ensemble Learning

Xu S., He Z., Shi W., Wang Y., Ohtsuki T., Guiy G.

IEEE Vehicular Technology Conference （IEEE Vehicular Technology Conference）  2022-September 2022年

ISSN  15502252

　概要を見る

Human activity recognition (HAR) is one of the most promising technologies in the smart home, especially radio frequency (RF-based) method, which has the advantages of low cost, few privacy concerns and wide coverage. In recent years, deep learning (DL) has been introduced into HAR and these DL-based HAR methods usually have outstanding performance. However, as the recognition scenarios and target change, the model performance drops sharply. To solve this problem, we propose a generalized method for cross-person activity recognition (CPAR), which is called snapshot ensemble learning based an attention with bidirectional long short-term memory (SE-ABLSTM). Specifically, by defining the cosine annealing learning rate, the models with diversity are saved and integrated in the same training process. In addition, we provide a dataset for CPAR and simulation results show that our method improves generalization performance by 5% compared to the original method. The source code and dataset for all the experiments can be available at https://github.com/NJUPT-Sivan/Cross-person-HAR.

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Co-Evolutionary Dynamic Cell optimization Algorithm for HAPS Mobile Communications

Shibata Y., Takabatake W., Hoshino K., Nagate A., Ohtsuki T.

IEEE Vehicular Technology Conference （IEEE Vehicular Technology Conference）  2022-June 2022年

ISSN  15502252

　概要を見る

High-Altitude Platform Stations (HAPSs) are attracting much attention as novel mobile communication platforms for ultra-wide coverage areas and disaster-resilient networks. Single-cell frequency reuse using multiple cells can increase capacity to cover a wide area. Thus far, we have proposed a cell configuration optimization method based on a genetic algorithm (GA). We clarified the optimal cell configuration in terms of spectral efficiency depending on the number of cells under a uniform user distribution scenario. Whereas user distributions differ depending on location. Thus, cell configuration optimization is required for a non-uniform user distribution. Each cell needs different antenna parameters for non-uniform user distributions, resulting in an increase in the number of parameters compared with a uniform user distribution, making the optimization difficult even by GA in some cases. To address this problem, we propose a co-evolutionary dynamic cell optimization algorithm. Co-evolution is one of the divide-and-conquer methods. The proposed method divides multiple cells into several groups to decrease the number of parameters to optimize at a time, and each group is optimized in order. The simulation results show that the cell configuration with the proposed method can increase the sum of the square root of throughput for non-uniform user distributions compared to that for uniform user distributions. Furthermore, the proposed method with three sub-areas can improve the sum of the square root of throughput while reducing the number of combinations performed compared to the method without subarea division for a nine-cell scenario.

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Autoencoder-based Pilot Pattern Design for CDL Channels

Yamada Y., Ohtsuki T.

APCC 2022 - 27th Asia-Pacific Conference on Communications: Creating Innovative Communication Technologies for Post-Pandemic Era （APCC 2022 - 27th Asia-Pacific Conference on Communications: Creating Innovative Communication Technologies for Post-Pandemic Era）     76 - 80 2022年

　概要を見る

In the pilot-based channel estimations, a large number of pilot signals enable an improvement in the channel estimation accuracy but force a decrease in the data transmission efficiency. Therefore, the pilot pattern design schemes on the resource grid have been researched to achieve high channel estimation accuracy with a small number of pilot signals. In the conventional scheme for pilot pattern design, the autoencoder which enables discrete feature selection is utilized to design pilot patterns for Vehicular-A channels. However, the pilot pattern for other channels is not reported, and it is not clear whether this scheme has good performance compared to uniform pilot patterns. In this paper, we derive the pilot patterns for Clustered Delay Line (CDL) channels specified in the 5th Generation Mobile Communication System (5G) standard using the autoencoder-based pilot pattern design scheme. We also derive the pilot patterns for CDL-A and CDL-D, which model respectively NLOS (Non-Line-of-Sight) and LOS (Line-of-Sight) environments, with different three speeds of User Equipment (UE). Through computer simulation, we show that the autoencoder-based pilot patterns improve the channel estimation accuracy compared to the uniform pilot pattern.

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Attention-CNN-Aided Specific Emitter Identification Method with Limited Radio Frequency Dataset

Wang S., Zhang Y., Sun J., Ohtsuki T., Gui G.

International Conference on Communication Technology Proceedings, ICCT （International Conference on Communication Technology Proceedings, ICCT）  2022-November-November   1536 - 1540 2022年

　概要を見る

Specific emitter identification (SEI) plays a more and more important role to recognize unknown radio frequency (RF) devices in the field of physical layer security. Existing deep learning (DL)-based SEI methods have been proposed for solving problems that enough RF dataset can be obtained at the model training stage. In many real application scenarios, however, only the limited dataset can be collected which causes the conventional DL methods unstable. To solve this problem, in this paper, we propose a robust SEI method using a self-attention mechanism and convolutional neural network (attention-CNN). To be specific, we collect a real RF dataset from the automatic dependent surveillance-broadcast (ADS-B) signal. Here, the ADS-B dataset is limited in some special cases and data augmentation is used to improve the features of the dataset. Simulation results show that the proposed method achieves better identification performance than the benchmark.

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Arterial Blood Pressure Estimation Method from Electrocardiogram Signals using U-Net

Yoshizawa R., Yamamoto K., Ohtsuki T.

Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS （Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS）  2022-July   2689 - 2692 2022年

ISSN  1557170X

　概要を見る

Previous works proposed deep learning models to estimate blood pressure from electrocardiogram (ECG) signals. However, they can only estimate max, min, and mean arterial blood pressures and cannot estimate arterial blood pressure (ABP). This paper presents the ABP estimation method from ECG signals using the deep learning model of U-Net. Through the performance evaluation with signals of about 185 hours, we observed that the proposed method estimated ABP with high accuracy. Furthermore, the accuracies of the calculated max\min and mean ABPs were comparable to those in the previous works, even though our method also estimated ABP. In the end, we discussed the subject-overfitting problem and future work toward practical use of daily blood pressure monitoring.

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An Evaluation on Information Composition in Dementia Detection Based on Speech

Zheng C., Bouazizi M., Ohtsuki T.

IEEE Access （IEEE Access）  10   92294 - 92306 2022年

　概要を見る

In recent years, scientists are paying much attention to the research on automatic dementia detection that could be applied to the speech samples of dementia patients. In a related context, recent research has seen the fast development of Deep Learning (DL) and Natural Language Processing (NLP). The techniques developed for text classification or sentiment analysis have been applied to the field of early dementia detection by many researchers. However, text classification and sentiment analysis are different tasks from dementia detection, which makes us believe that for dementia detection, some adjustments would help improve the performance of the machine learning models. In this work, we implemented experiments with various language models including traditional n-gram language models, Average stochastic gradient descent Weight-Dropped Long Short-Term Memory (AWD-LSTM) models, and attention-based models to evaluate the speech data of dementia patients. Unlike traditional works where the text is stripped from stop words, we propose the idea of exploiting the stop words themselves, since they offer non-context information which helps to identify dementia. As a result, 3 different language models are prepared in this work: a model processing only context words, a model processing stop words and Part-of-Speech (PoS) tag sequences, and a model processing both of them. By performing the aforementioned experiments, we show that both grammar and vocabulary contribute equally to classification: The 3 models achieve an accuracy equal to 70.00%, 76.16%, and 81.54%, respectively.

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An Effective Radio Frequency Signal Classification Method Based on Multi-Task Learning Mechanism

Liu H., Hao C., Peng Y., Wang Y., Ohtsuki T., Gui G.

IEEE Vehicular Technology Conference （IEEE Vehicular Technology Conference）  2022-September 2022年

ISSN  15502252

　概要を見る

With the increasing popularity of Internet of things (IoT), the emergence of many IoT devices has led to security vulnerabilities. The classification of wireless signals is very important for secure communications. Most of existing signal classification tasks only focus on single signal classification task, while ignoring the relationship between radio frequency fingerprinting identification (RFFI) and automatic modulation classification (AMC). To solve the multi-task classification problem, this paper designs a multi-task learning convolutional neural networks (MTL-CNN). Real-radio datasets are generated by Signal Hound VSG60A and collected by Signal Hound BB60C to solve the lack of RFF samples with numerous modulation types. Experimental results confirm that the MTL-CNN method can work well by using the generated dataset. The MTL network designed in this paper improves the accuracy of RFFI by 1xs% relative to the single-task learning (STL) network. The keras code is released at https://github.comLiuK1288/1hw-000.

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Adaptive DNN-based CSI Feedback with Quantization for FDD Massive MIMO Systems

Gao J., Bouazizi M., Ohtsuki T., Gui G.

IEEE Vehicular Technology Conference （IEEE Vehicular Technology Conference）  2022-September 2022年

ISSN  15502252

　概要を見る

Accessing the accurate downlink channel state information (CSI) is essential to take full advantage of frequency division duplex (FDD) massive multiple-input multiple-output (MIMO) systems due to its weak channel reciprocity. Meanwhile, great computational burdens will happen, which is accompanied by continuous CSI feedback. The existing compressive sensing (CS)-based and deep learning (DL)-based methods try to solve such problems, but do not achieve desired effect to get ideal CSI feedback or decrease the overhead. An adaptive deep neural network (DNN)-based CSI feedback method is proposed in this paper to address this. A classification block of the compression ratio is adopted and modified to apply to a more complex channel model named Clustered-Delay-Line (CDL), which helps decrease the computational overhead of the network. Besides, the reconstruction accuracy of the CSI feedback is further improved by proposing a new structure of the encoder. Quantization and dequantization modules are also applied to make the whole network more robust and effectively minimize the quantization distortion in the real communication scenario, respectively. The simulation results show that the proposed method performs better than the conventional ones on the CSI reconstruction accuracy in terms of normalized mean square error (NMSE), even though the quantization module is added.

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A Semi-Supervised Federated Learning Scheme via Knowledge Distillation for Intrusion Detection

Zhao R., Yang L., Wang Y., Xue Z., Gui G., Ohtsuki T.

IEEE International Conference on Communications （IEEE International Conference on Communications）  2022-May   2688 - 2693 2022年

ISSN  15503607

　概要を見る

Federated learning (FL) has become an increasingly popular solution for intrusion detection to avoid data privacy leakage in Internet of Things (IoT) edge devices. However, most of the current FL-based intrusion detection methods still suffer from three limitations: (1) model parameters transmitted in each round may be used to recover private data which leads to security risks, (2) not independent and identically distributed (non-IID) private data seriously adversely affects the training of FL (especially distillation-based FL), and (3) high communication overhead caused by the large model size greatly hinders the actual deployment of the solution. To address these problems, this paper develops an intrusion detection method based on semi-supervised FL scheme via knowledge distillation. First, our proposed method leverages unlabeled data via distillation method to enhance the classifier performance. Second, we build a CNN-based model for extracting deep features of the traffic packets, and take this model as both the classifier network and discriminator network. Third, discriminator is designed to improve the quality of each client's predicted labels, to avoid the failure of distillation training caused by a large number of incorrect predictions under private non-IID data. Moreover, the combination of hard-label strategy and voting mechanism further reduces communication overhead. Experimental results on the real-world traffic dataset show that our proposed method can achieve better classification performance as well as lower communication overhead than state-of-the-art methods.

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Sarcasm Over Time and Across Platforms: Does the Way We Express Sarcasm Change?

Bouazizi M., Ohtsuki T.

IEEE Access （IEEE Access）  10   55958 - 55987 2022年

　概要を見る

Sarcasm is a sophisticated form of speech used to convey a message other than the apparent one. To date, there are numerous papers that have discussed the idea of automatic sarcasm detection and how it could be used for sentiment analysis improvement. The objective of this paper is to provide non-experts with a comprehensive overview of the state of research in this field and the main findings regarding sarcasm detection. Therefore, in this paper, we survey the state-of-the-art work done in this field, we recapitulate the research effort done, with focus on the more recent works, and we present the expected performance out of the proposed works. Nevertheless, we study in detail how this form of speech is used in different platforms, and how the way we express it evolves over time. We also discuss the proposition that suggests that sarcasm is a polarity switcher for sentiment analysis. To achieve these goals, we run some experiments on 3 different data sets, collected from 3 different platforms, and compare how sarcasm is employed in each. These platforms are Twitter, Reddit, and some news websites. Our experiments show that the way sarcasm is expressed is highly dependent on language mastery and the platform used. For instance, in the Twitter data set, whose users vary widely in age, language mastery, and understanding what sarcasm means, the overall precision of detection of sarcastic statement reaches 89.31%. In the reddit data set, the precision of detection of such statements is about 55.33%, and in the news data set, the precision reaches over 96.67%. Our experiments also show that, to a great extent, it is safe to affirm that sarcasm, when employed, switches the polarity of a given piece of text: for the 3 platforms presented above, sarcasm has been a polarity switcher for 89.3%, 89.1%, and 92.0% of their respective instances.

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Unsupervised Representation Learning-based Doppler Ultrasound Signal Quality Assessment

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

2022 IEEE Global Communications Conference, GLOBECOM 2022 - Proceedings （2022 IEEE Global Communications Conference, GLOBECOM 2022 - Proceedings）     2254 - 2259 2022年

ISSN  23340983

　概要を見る

The Doppler ultrasound (DUS) transducer has been widely used for fetal heart rate (FHR) monitoring. However, the fetal DUS signals from the transducers can be corrupted by several interference sources such as maternal and fetal movements, which makes FHR estimation using fetal DUS signals challenging. Fetal DUS signal quality assessment (SQA) can help to remove or interpolate unreliable FHRs estimated from noisy signals to improve the accuracy of FHR estimation. There are some existing approaches for fetal DUS SQA, and most of these approaches with high accuracy are based on supervised learning-based algorithms and human-defined properties. Nonetheless, the fetal DUS datasets with quality-level annotations are limited, and human-defined properties place a limitation on mining more deep information related to signal quality in fetal DUS signals. In this paper, we propose an unsupervised representation learning-based fetal DUS SQA for the improvement of FHR estimation performance. We firstly learn representations of pre-processed fetal DUS data from variational autoencoder (VAE) and then combine these representations as one signal quality index (SQI) using a self-organizing map (SOM). Finally, we apply the combined SQI and a Kalman filter (KF) to estimate fetal RR intervals (FRRI) for reducing the errors of FHR estimation. The experimental results showed that our proposed method could reduce the averaged root mean squared error (RMSE) of FRRI and averaged absolute error (AAE) of FHR.

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Two-Step Dynamic Cell Optimization Algorithm for HAPS Mobile Communications

Shibata Y., Takabatake W., Hoshino K., Nagate A., Ohtsuki T.

IEEE Access （IEEE Access）  10   68085 - 68098 2022年

　概要を見る

High altitude platform stations are attracting much attention as novel mobile communication platforms for ultra-wide coverage areas and disaster-resilient networks. Multi-cell configurations can increase communication capacity in a wide area. Conventional work optimizes the cell configuration for a multi-cell configuration based on a genetic algorithm (GA). This method identifies an optimal cell configuration in terms of spectral efficiency depending on the number of cells under a uniform user distribution. However, user distributions differ depending on location; thus, cell configuration optimization is also required for non-uniform user distributions. The problem is an increased number of parameters because each cell requires different antenna parameters for non-uniform user distribution compared to a uniform user distribution. This increases the difficulty of optimization, even when using a GA in some cases. Therefore, we propose a GA-based two-step cell optimization algorithm that comprises both search space reduction and antenna parameter optimization steps to address this problem. The proposed method employs the concept of co-evolution, i.e., a divide-and-conquer method. The proposed method divides multiple cells into several groups to reduce the number of optimized parameters and optimizes each group in order. In addition, the search space is reduced based on a marginal histogram obtained via optimization with a large step size. Simulation results demonstrate that this technique can reduce the number of combinations compared to the case without sub-area division. In addition, there are cases where the search space reduction further reduces the number of combinations without degrading the sum of the square root of throughput.

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Social Robot Detection Using RoBERTa Classifier and Random Forest Regressor with Similarity Analysis

Chen Y., Bouazizi M., Ohtsuki T.

2022 IEEE Global Communications Conference, GLOBECOM 2022 - Proceedings （2022 IEEE Global Communications Conference, GLOBECOM 2022 - Proceedings）     6433 - 6438 2022年

ISSN  23340983

　概要を見る

Twitter has skyrocketed over the past few years and has become a major social media platform. At the same time, the number of social robots on Twitter has also increased significantly. These bot accounts imitate the speeches of normal users to manipulate public opinions, affect the normal communication of users. Therefore, bot account detection came into being. Despite extensive research efforts, bots on Twitter are still evolving to evade detection. Most of the current bot detection methods have a single structure and cannot detect and identify different types of bot accounts well. In this paper, we propose a new system for social robot detection that uses a RoBERTa (Robustly Optimized Bidirectional Encoder Representations from Transformers Pretraining Approach) classifier and a random forest regressor with similarity analysis. In particular, the system considers the similarity of tweets and uses a voting system in addition to a set of features extracted from the user profile information and the tweets themselves. We conduct experiments using the largest dataset of bots available and show that the accuracy of our system is up to 0.8588, which is higher than that of all the other baseline methods.

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Smartphone-Aided Human Activity Recognition Method using Residual Multi-Layer Perceptron

Shi S., Wang Y., Dong H., Gui G., Ohtsuki T.

INFOCOM WKSHPS 2022 - IEEE Conference on Computer Communications Workshops （INFOCOM WKSHPS 2022 - IEEE Conference on Computer Communications Workshops）  2022年

　概要を見る

Human activity recognition (HAR) has received intensely attention in many applications, such as healthcare, human-computer interaction, and smart home. Existing HAR methods based on deep learning (DL) have been proposed in the last several years. However, these DL-based HAR methods are hard to balance between performance and cost, which truly limited the applications in practical scenarios. To solve this problem, this paper proposes a smartphone-aided HAR method using the residual multi-layer perceptron (Res-MLP). It composes of two linear layers and Gaussian error linear unit (GELU) activation function, and obtains Res-MLP network through residual. Experimental results show that the proposed HAR method can achieve a high classification accuracy of 96.72% based on the public UCI HAR dataset.

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A Denoising Method Using Deep Image Prior to Human-Target Detection Using MIMO FMCW Radar

K. Endo, K. Yamamoto, and T. Ohtsuki

Sensors 2022 2022年

研究論文（学術雑誌）, 共著, 最終著者

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

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

IEEE Transactions on Instrumentation and Measurement 2022年

研究論文（学術雑誌）, 共著, 最終著者

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

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

in IEEE Transactions on Industrial Informatics 2022年

研究論文（学術雑誌）, 共著, 最終著者

Downlink CSI Feedback Algorithm with Deep Transfer Learning for FDD Massive MIMO Systems

Zeng J., Sun J., Gui G., Adebisi B., Ohtsuki T., Gacanin H., Sari H.

IEEE Transactions on Cognitive Communications and Networking （IEEE Transactions on Cognitive Communications and Networking）  7 （ 4 ） 1253 - 1265 2021年12月

　概要を見る

In this paper, a channel state information (CSI) feedback method is proposed based on deep transfer learning (DTL). The proposed method addresses the problem of high training cost of downlink CSI feedback network in frequency division duplexing (FDD) massive multiple-input multiple-output (MIMO) systems. In particular, we obtain the models of different wireless channel environments at low training cost by fine-tuning the pre-trained model with a relatively small number of samples. In addition, the effects of different layers on training cost and model performance are discussed. Furthermore, a model-agnostic meta-learning (MAML)-based method is proposed to solve the problem associated with large number of samples of a wireless channel environment required to train a deep neural network (DNN) as a pre-trained model. Our results show that the performance of the DTL-based method is comparable with that of the DNN trained with a large number of samples, which demonstrates the effectiveness and superiority of the proposed method. At the same time, although there is a certain performance loss compared with the DTL-based method, the MAML-based method shows good performance in terms of the normalized mean square error (NMSE).

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Joint UL/DL Resource Allocation for UAV-Aided Full-Duplex NOMA Communications

Shi W., Sun Y., Liu M., Xu H., Gui G., Ohtsuki T., Adebisi B., Gacanin H., Adachi F.

IEEE Transactions on Communications （IEEE Transactions on Communications）  69 （ 12 ） 8474 - 8487 2021年12月

ISSN  00906778

　概要を見る

This paper proposes an unmanned aerial vehicle (UAV)-aided full-duplex non-orthogonal multiple access (FD-NOMA) method to improve spectrum efficiency. Here, UAV is utilized to partially relay uplink data and achieve channel differentiation. Successive interference cancellation algorithm is used to eliminate the interference from different directions in FD-NOMA systems. Firstly, a joint optimization problem is formulated for the uplink and downlink resource allocation of transceivers and UAV relay. The receiver determination is performed using an access-priority method. Based on the results of the receiver determination, the initial power of ground users (GUs), UAV, and base station is calculated. According to the minimum sum of the uplink transmission power, the Hungarian algorithm is utilized to pair the users. Secondly, the subchannels are assigned to the paired GUs and the UAV by a message-passing algorithm. Finally, the transmission power of the GUs and the UAV is jointly fine-tuned using the proposed access control methods. Simulation results confirm that the proposed method achieves higher performance than state-of-the-art orthogonal frequency division multiple-access method in terms of spectrum efficiency, energy efficiency, and access ratio of the ground users.

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