Published papers

© 2013 IEEE. Blink duration is one of the useful indicators to estimate drowsiness and fatigue. A Doppler sensor could be a key device to realize the non-contact blink duration estimation, which is very useful for the drowsiness and fatigue monitoring in real life. However, none of the blink duration estimation methods has been proposed so far. In this paper, we propose a novel Doppler sensor-based blink duration estimation method based on the analysis of eyelids closing and opening behavior on a spectrogram. When one blinks, the energies caused by eyelids closing and opening behavior appears on a spectrogram. Hence, the blink duration can be estimated by integrating such energies and then detecting the timings when the integrated energy caused by the eyelids closing behavior appears and when the energy caused by the eyelids opening behavior disappears. To precisely detect such timings, a spectrogram with a short time window is better. However, thorough the preliminary experiments, we confirmed that when a shorter time window is used, more noise appears around such timings, which makes it difficult to detect such timings. To deal with this issue, the proposed method multiplies several integrated energies for spectrograms with different time windows. By multiplying the integrated energies with such noise and the ones without such noise, the energy level of such noise gets significantly declined over the product. Furthermore, the levels of the redundant blink energies also decline to the noise level, where the redundant blink energies denote the ones that appear before and after the eyelids closing and opening timings because of a long time window. The results of the experiments on 8 subjects watching a movie showed that our method achieved an average root mean squared error (RMSE) of 49.3 ms.

© 2018 IEEE. It is known that the blink duration is highly related to drowsiness, where the blink duration is the entire duration of one blink. Hence, it is important to measure the blink duration without any special wearable devices in various application, e.g., driver's monitoring. Although a Doppler sensor could be a key device to realize it, no such blink duration estimation method has been realized so far, since estimating the blink duration is difficult because of the low SNR (Signal to Noise Ratio) of the signal reflected from eyelids. In this paper, we propose a Doppler sensor-based method that estimates the duration, tblink, that is proportional to the actual blink duration. In the proposed method, a spectrogram is firstly calculated from a received signal, and then the timings when eyelids close and open are extracted from that. More specifically, tblink is calculated based on the center-of-gravity of the energy on a spectrogram. We conducted the experiments on five subjects to show the estimation accuracy of our proposed method. We confirmed that our method achieved the average correlation coefficient R of 0.95, furthermore, the average RMSE (Root Mean Square Error) of 46 ms.

© 2017 IEEE. Blink is a physiological signal that reflects drowsiness and concentration. It is important to detect driver's blinks without any wearable devices. For this purpose, a Doppler sensor has been used and several blink detection methods where a subject sits in front of such sensor have been proposed. However, it is challenging to detect driver's blinks because of face and body movement. In this paper, we propose a Doppler sensor-based driver's blink detection method in existence of face and body movement in a car. In the proposed method, blinks are detected through two steps: pre-detection and classification. In the first step which we call pre-detection, the time candidates of subject's blinks are detected based on spectrograms calculated from a received signal. Then, in the second one which we call classification, a set of features are calculated from a spectrogram and are fed into a supervised machine learning classifier to identify which time candidates are truly blinks. We leverage the fact that the distribution of the energy on a spectrogram differs between a blink and non-blink. Specifically, features are extracted based on the distribution of energy on a spectrogram. We conducted a series of experiments for the evaluation in the situation where a subject drives a real car in public road. As a result, we confirmed our method outperforms the conventional one in terms of F-measure calculated from recall rate and precision rate.

© 2019 IEEE. In this paper, we investigate the multiple access spatial modulation (MASM) in multiple-input multiple-output (MIMO) systems with the aid of dual-ascent inspired iterative transmit precoding (TPC) methods. To find a global optimum against the maximum minimum Euclidean distance (MMD) problem, as well as to reach a fast convergence rate with low-complexity, we first study the peculiarities of the convex optimization methods that take the dual- ascent method into account. This beneficially provides us an insight into developing a new dual- ascent method which is capable of bringing robustness to the existing dual-ascent method. Then, we introduce an evolving MASM-MIMO system by imposing non-stationary time-varying TPC parameters, and thereby resulting in a guaranteed Euclidean distance (GED) aided TPC method. Numerical results reveal that our proposals are capable of promoting a significant bit error rate (BER) performance improvement comparing with the existing methods for the MASM-MIMO systems, as well as facilitating a fast convergence with low- complexity.

© 1972-2012 IEEE. In this article, we investigate the dual-ascent inspired transmit precoding (TPC) for multiple-access spatial modulation (MASM) in multiple-input multiple-output (MIMO) systems. Note that several novel TPC techniques have been developed in earlier works, to provide a solution for either the maximal-minimum Euclidean distance, or the quadratically constrained quadratic program problems. However, numerical results expose that the system performances degrade distinctly when applying these TPC techniques into MASM-MIMO. The main reason behind is that these TPC techniques are sensitive to the system dimensions and the quadratic constraints. In this context, we first recast the above challenging problems as an unconstrained problem by imposing a penalty over the quadratic constraints. Based on the primal-dual optimality theory, we next propose a Broyden-Fletcher-Goldfarb-Shanno (BFGS) aided dual-ascent approach for finding a global optimum solution to the unconstrained problem. Further, we introduce non-stationary time-varying TPC parameters to characterize an evolving MASM-MIMO in which the signals are multiplexed over a small coherence time, and thereby resulting in dual-ascent aided non-stationary TPC approach. Numerical results manifest that the proposed algorithms possess an inherent robustness to the increasing system dimension and quadratic constraint. Besides, simulation results show the benefits of our algorithms under different kinds of performance metrics.

© 2013 IEEE. An Electrocardiogram (ECG) is a typical method used to detect heartbeat, and an ECG signal analysis enables the detection of some heart diseases. However, the ECG-based heartbeat detection requires device attachment, which is not preferred for daily use. A Doppler sensor could be a device used to enable the non-contact heartbeat detection. In this paper, we propose a Doppler sensor-based ECG signal reconstruction method by a hybrid deep learning model with Convolutional Neural Network (CNN) and Long Short-Term Memory (LSTM). An ECG signal can be reconstructed by relating features of a heartbeat signal obtained by a Doppler sensor to those of the ECG signal. Thus, we construct the deep learning model that extracts the spatial and temporal features from the heartbeat signal by CNN and LSTM. Based on the extracted features, the ECG signal is reconstructed. We conducted experiments to observe heartbeat against 9 healthy subjects without heart disease. The experimental results showed that our method performed ECG signal reconstruction with a correlation coefficient of 0.86 between the reconstructed and actual ECG signals, even without attaching devices. The results indicate that it is possible to remotely reconstruct an ECG signal from a heartbeat signal via a Doppler sensor.

© 2013 IEEE. Small cells (SCs) offer a promising approach to meeting the exponentially growing data rate demands. However, dense deployment of SCs can degrade the energy-efficiency (EE) of the network due to the additional deployed base stations (BSs). Under heterogeneous network (HetNet) deployments, SCs can be dynamically switched off for energy saving when traffic load decreases. We do this by defining a load-dependent transmission power coefficient (TPC) for SC BSs. In addition, with Device-to-Device (D2D) communication, mobile users in proximity can establish a direct link and bypass the BSs, thereby offloading the network infrastructure and providing further improvement of EE. In this paper, the objective is to rely on both D2D communications and sleeping SC BSs to offload traffic from the mains powered macrocell BSs leading to energy saving in the network. Furthermore, in order to consider D2D requirements under the practical application scenarios, we assume a framework for wireless video sharing, where users can store popular video files and share files via D2D communication. To begin with, we derive the EE expression of the cache-enabled D2D-aided HetNet. Then, to maximize the EE of the cache-enabled D2D-aided HetNet, the optimal load-dependent TPC for SC BSs is obtained under constraints on both network's coverage and rate. Using simulations, we will investigate the potential effects of the TPC defined for SC BSs as well as the introduced D2D layer on the network EE.

© 2018 IEEE. In Cognitive Radio Networks (CRNs), the available channels for the unlicensed Secondary Users (SUs) may be varying. When SUs want to communicate with each other, they must first access the same channel simultaneously. The process of accessing the same channel is referred to as a rendezvous process, by which SUs can exchange control information for establishing data transmission link. Channel Hoping (CH) is one of the most representative techniques for letting SUs rendezvous with each other. At the beginning of each time slot, SUs access available channels according to their CH Sequences (CHSs) generated by the CH algorithm. In our previous work, we have proposed a Heterogeneous Radio Rendezvous (HRR) algorithm to address the rendezvous problem for heterogeneous CRNs, where SUs may be equipped with different numbers of radios. In this paper, we propose an Enhanced HRR (EHRR) algorithm, which can further shorten the length of period for the CHSs. Compared with the HRR algorithm, the EHRR algorithm lowers the upper bounds of Maximum Time To Rendezvous (MTTR). Moreover, the upper bounds of MTTR for the EHRR algorithm are derived by theoretical analysis. In addition, the performance of the EHRR algorithm in terms of MTTR is evaluated by simulation. Simulation results show the superiority of the EHRR algorithm compared with the HRR algorithm in terms of MTTR.

© Springer Nature Switzerland AG 2019. In Japan a lot of children stay home alone after school. Safety of children is a big issue. In addition, a stability of their emotion is a big issue. For the stability of their emotion, it is important to know the psychological state of children, particularly stress so that we can try to relieve their stress. As for the detection of the stress, it is preferred to detect in a less constrained or non-contact way. The nasal skin temperature is known to be affected by emotion, which can be detected in a non-contact way with an infrared thermography camera. In this paper, we report our experimental results about the detection of stress condition of children by the nasal skin temperature with the infrared thermography camera. In estimating the stress condition, we use a heart rate variability as a physiological index. With our experimental results, we show that the stress condition of children can be estimated by the nasal skin temperature. We also show that when the stress index rises, the change of the nasal skin temperature differs depending on their ages.

© 2019 Elsevier B.V. Due to environment-friendliness, renewable energy like solar power and wind power is more and more introduced to energy systems all over the world. Simultaneously, high penetrations of wind and solar generation also have brought severe curtailment of wind and solar. How to alleviate curtailment of wind and solar is a crucial problem in evaluating accommodation capability of renewable energy, which reflects the extent of utilization of renewable energy and economic benefits. The uncertainty of renewable energy brings challenges to precisely describe renewable generation, which leads to difficulty in designing effective mechanisms for accommodation capability of renewable energy. Existing work suffers from high computation overhead from frequently updated data, and low precision of describing renewable energy, which leads to less effective policies for renewable energy accommodation and underestimated accommodation capability. To make the most of renewable energy, an algorithm AccCap-DRL based on deep reinforcement learning is proposed. AccCap-DRL partitions a distribution into segments by time intervals, employs WGAN to describe distributions of renewable energy data, and employs DDPG to obtain approximate policies for renewable energy accommodation in different scenarios. Simulation results from real power generation and users’ demand data show high effectiveness of the proposed algorithm, and high efficiency of evaluating accommodation capability.

© 2018 IEEE. Injuries caused by falls are one of the major threats for the elderly. Thus, the demand for a fall detection system without requiring a user to wear or equip any devices is rapidly increasing. In this paper, a fall detection system using Ultra-High Frequency (UHF) passive Radio-Frequency IDentification (RFID) is presented. With the RFID reader on the ceiling and multiple RFID tags on the floor, anomaly scores are computed based on the neighborhood preservation principle. This focuses only on the correlation between RSSI fluctuations between a pair of tags. Therefore, unlike the conventional methods, our method manages to detect falls without requiring a large number of reference data. To detect falls in several areas of the room, our system only requires a simple reference data with one subject walking around the room. Experiments are conducted in an indoor environment with two different setups to show the effectiveness of our method. Our method improves the conventional one, and achieves a high Area Under Curve (AUC) of 0.98.

© 2019 IEEE. With the increase in the population of elderly, a technique for monitoring them is receiving more and more attention all over the world. In particular, it is highly demanded to develop the system that detects a fainting elderly in a toilet, since such an accident is likely to get worse due to the late detection. In this paper, we propose an anomaly detection method in toilet using an FMCW(Frequency Modulated Continuous Wave) radar, which can measure the distance between an object and an FMCW radar. As anomalies, we determine as the situations where a subject faints while stooping on the toilet seat, leaning on the backrest, leaning on the side wall, and lying on the floor after the fall. With an FMCW radar, a fainting subject could be detected based on whether the estimated distance between a subject and an FMCW radar is almost constant. However, a subject might be sitting still, and also the distance to not a subject but a floor could be estimated depending on a subject's posture. To address this problem, in the proposed method, the time when the estimated distance is almost constant is detected, and then the detected time is classified into the time due to a faint or a non-faint by a supervised machine learning classifier. The used features are extracted based on the data, e.g., the estimated distance, just before the detected time. To evaluate the anomaly detection accuracy of the proposed method, we conducted the experiments to observe not only the aforementioned abnormal behavior but also the normal behavior, e.g., the button operation and sitting down. As a result, in terms of the classification of abnormal and normal behavior, our proposed method achieved the F- measure of 0.91, where F-measure is the overall score of recall and precision rates.

© 2018 IEEE. Device-to-device (D2D) underlay massive multipleinput multiple-output (MIMO) is a notable technology to improve spectrum efficiency by using the same frequency among both cellular user equipments (CUEs) and D2D user equipments (DUEs). In terms of communication resources, it is required to avoid assigning orthogonal pilot sequences to all CUEs and DUEs. Although sharing the same pilot sequences among user equipments (UEs) can save the communication resources, interference arises among the same pilot sequences, which is known as pilot contamination. Because the impact of this interference is still large in the conventional pilot reuse, it is highly demanded to mitigate the effect of pilot contamination. In this paper, we propose a novel pilot reuse scheme based on a graph coloring technique to mitigate the effect of pilot contamination. By leveraging the fact that the interference is alleviated when the number of antennas is large and the angle of arrivals (AOAs) of the same pilot sequences does not overlap, our method assigns the same pilot sequences so that they are not overlapped. Furthermore, to improve the channel estimation accuracy between D2D pairs, our scheme restricts reusable pilot sequences by constructing an interference graph based on graph coloring. In this graph, the nodes near to each other are connected and assigned orthogonal pilot sequences. Simulation results show that the proposed scheme outperforms the conventional one in terms of channel estimation accuracy for UEs and spectral efficiency of the system.

© 2017 IEEE. Increased environmental awareness coupled with the rising cost of energy have sparked a keen interest in the deployment of energy-efficient communication technologies over the infrastructure of cellular networks. Base stations (BSs) are responsible for the largest portion of power consumption and energy usage in cellular networks. Thus, sleep/wake-up scheduling strategies for BSs can significantly improve energy-efficiency (EE) of cellular networks. In this paper, we propose a Fuzzy Q-Learning (FQL) based energy-efficient sleep/wake-up mechanism for BSs in a heterogeneous network (HetNet). The goal is to save energy, without compromising the offered Quality of Service (QoS), by switching off the redundant BSs according to the local traffic profile and depending on the required area coverage and cell EE. The introduction of sleep mode for BSs may lead to a large-scale coverage loss, unless a specific remedial solution is exploited at the same time. To this end, we also propose to use device-to-device (D2D) communications to extend network coverage to the service areas of the switched-off BSs. Simulation results validate that the proposed framework provides significant improvements in power consumption and the EE.

© 2020 SAE International. All Rights Reserved. In recent years, the number of reported traffic accidents due to sudden deterioration in driver's physical condition has been increasing, it is expected to develop a system that prevents accidents even if physical condition suddenly changes while driving, or reduces damage through vehicle body control. For this purpose, it is necessary to detect sudden changes of the driver's physical condition, and research is being conducted widely. Among them, it is reported that some of such changes may appear in the heartbeat interval. In other words, by acquiring the driver's heartbeat interval in real time, it may be possible to detect the sudden changes, and reduce traffic accident. Even if a traffic accident occurs, the damage can be reduced by emergency evacuation immediately after detecting sudden changes. Therefore, we focused on the technology to detect the heartbeat interval with 24GHz microwave Doppler radar, which can detect heartbeat non-contactly while maintaining the interior design and passenger's privacy. Doppler radar with microwave is sensitive enough to detect heartbeat, however vibration noise is also superimposed on the sensor signal easily. Thus, in general algorithm studied widely, the problem is that the accuracy of detecting the heartbeat interval deteriorates particularly while driving, because the car vibration is superimposed on the frequency band used for heartbeat analysis, and the SNR (Signal to Noise power Ratio) decreases. To solve this problem, we developed an algorithm that extracts power spectrum over wide band including harmonic components of heartbeat and detects the peaks. This time, our new peak-searching method, in which multiple peak intervals are selected simultaneously based on the previous estimation results, suppresses false detection of peaks caused by vibration noise. As a result, the error rate of the heartbeat interval detection was reduced by half compared to the conventional algorithm in which a peak is detected based on previous heartbeat intervals.

© 2019 IEEE. Bitcoin is a decentralized digital currency whose transactions are recorded in a common ledger, so called blockchain. Due to the anonymity and lack of law enforcement, Bitcoin has been misused in darknet markets which deal with illegal products, such as drugs and weapons. Therefore from the security forensics aspect, it is demanded to establish an approach to identify newly emerged darknet markets' transactions and addresses. In this paper, we thoroughly analyze Bitcoin transactions and addresses related to darknet markets and propose a novel identification method of darknet markets' addresses. To improve the identification performance, we propose a voting based method which decides the labels of multiple addresses controlled by the same user based on the number of the majority label. Through the computer simulation with more than 200K Bitcoin addresses, it was shown that our voting based method outperforms the nonvoting based one in terms of precision, recal, and F1 score. We also found that DNM's addresses pay higher fees than others, which significantly improves the classification.

© 2018 IEEE. Recently, a sparse adaptive algorithm termed zero-attracting sign least-mean-square (ZA-SLMS), has been clarified to be able to reconstruct robustly heartbeat spectrum by Doppler radar signal. However, since the strengths of noise evidently differ under different body motions, the sparse heartbeat spectra cannot be always acquired accurately by the constant regularization parameter (REPA) that balances the gradient correction and the sparse penalty, applying in the ZA-SLMS algorithm. In this paper, an improved ZA-SLMS algorithm is proposed by introducing adaptive REPA (AREPA), where the proportion of sparse penalty is adjusted based on the standard deviation of radar data. Moreover, to enhance the stability of heartbeat detection, a time-window-variation (TWV) technique is further introduced in the improved ZA-SLMS algorithm, considering the fact that the position of spectral peak associated with the heart rate (HR) is stable when the length of time window changes within a short period. Experimental results measured against five subjects validated that our proposal reliably improves the error of HR estimation than the standard ZA-SLMS algorithm.

© 2020 IEEE. LTE-LAA (LTE-Licensed Assisted Access) is the LTE (Long Term Evolution) to use an unlicensed band. However, in addition to the currently used license band, to use the unlicensed band, interference control with Wi-Fi that also uses the unlicensed bandwidth is required. A technique called LBT (Listen Before Talk) is considered as a technique of LTE-LAA interference control. However, since the channel occupation time (TXOP) of LTE-LAA is larger than that of Wi-Fi, there is a problem that Wi-Fi channel access is hindered. As a solution to this problem, one way is to increase the chances of channel access to Wi-Fi by establishing a waiting time of Tmute in LTE-LAA to reduce TXOP of LTE-LAA and postpone transmission. In this paper we propose an interference control method for LTE-LAA using Q-learning, which is one of the reinforcement learning techniques. In the proposed method, we select TXOP and Tmute using Q-learning. In the proposed method, the number of ACKs and that of NACKs in HARQ (Hybrid Automatic Repeat Request) are used as reward. In LTE-A the user terminal generates ACK / NACK according to the received radio frame and transmits it to the BS (base station). BS retransmits according to the number of ACKs / NACKs. Learning values of TXOP, Tmute of the LTE-LAA BS can be obtained so as to increase the number of ACKs or NACKs. When the LTE-LAA BS learns to increase the number of ACKs, the throughput of LTE-LAA is controlled to be high and that of Wi-Fi is controlled to be low. On the other hand, when the LTE-LAA BS learns to increase the number of NACKs, the throughput of LTE-LAA is controlled to be low and that of Wi-Fi is controlled to be high. Simulation result shows the proposed method using LTE-LAA UE (User Equipment) and Wi-Fi UE.

© 2020 IEEE. Despite the enormous potential applications, non-invasive recordings have not yet made enough satisfaction for fetal disease detection. This is mainly due to the fetal ECG signal is contaminated by the maternal electrocardiograph (ECG) interference, muscle contractions, and motion artifacts. In this paper, we propose a joint multiple subspace-based blind source separation (BSS) approach to extract the fetal heart rate (HR), so that it could greatly reduce the effect of maternal ECG and motion artifacts. The approach relies on the estimation of the coefficient matrix formulated as the tensor decomposition in terms of multiple datasets. Since the objective function takes the coupling information from the stacking of the covariance matrix for multiple datasets into account, estimating the coefficient matrices is fulfilled not only on dependence across multiple datasets, but also can combine the extracted components across four different datasets. Numerical results demonstrate that the proposed method can achieve a high extracted HR accuracy for each dataset, when compared to some conventional methods.

© 2013 IEEE. In a massive multiple-input multiple-output (MIMO) system, belief propagation (BP) detection is known as a method to separate and detect received signals. In BP detection, a MIMO channel is represented by a factor graph and the transmitted symbols are estimated by message passing. However, the convergence property of BP deteriorates due to multiple loops included in the MIMO channel. As a method to improve the convergence property and the detection performance, the damped BP that averages two successive messages with a weighing factor (called damping factor) is known. To train the damping factors off-line for each antenna configuration, deep neural network-based damped BP (DNN-dBP) has been reported. The problem with DNN-dBP is that the detection performance deteriorates when there is a difference of the channel correlation between training and test. This is because the optimal damping factors vary with the channel correlation. In this paper, to solve this issue, we derive the damping factors of BP with the node selection (NS) method that selects nodes to be updated to lower spatial correlation using DNN-dBP. By applying the NS method, the channel correlation among the selected nodes in BP detection is lowered. Therefore, the proposed method can improve the detection performance deterioration due to the mismatches of the channel correlations between training and test in DNN-dBP. In addition, the convergence property of BP is improved by applying the NS method. Therefore, the proposed method has the same detection performance with low computational complexity as the conventional DNN-dBP. By computer simulation, it is shown that the proposed method significantly improves the bit error rate (BER) performance deterioration due to the mismatches of the channel correlations between training and test in DNN-dBP. The results also show that the proposed method can show the same BER performance with low computational complexity as the conventional DNN-dBP. We also investigate the distribution of the trained damping factors and evaluate the tendency of that.