Details of a Researcher - Ohtsuki, Tomoaki

Papers - Ohtsuki, Tomoaki

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Fetal Heart Rate Detection Using First Derivative of ECG Waveform and Multiple Weighting Functions

Niida N., Wang L., Ohtsuki T., Owada K., Honma N., Hayashi H.

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) 434 - 438 2021

ISSN 1557170X

Fetal heart rate monitoring using the abdominal electrocardiograph (ECG) is an important topic for the diagnosis of heart defects. Many studies on fetal heart rate detection have been presented, however, their accuracy is still unsatisfactory. That is because the fetal ECG waveform is contaminated by maternal ECG interference, muscle contractions, and motion artifacts. One of the conventional methods is to detect the R-peaks from the integrated power of the frequency corresponding to the fetal heartbeats. However, the detection accuracy of the R-peaks is not enough. In this paper, we propose a method to generate the candidates of R-peaks using the first derivative of the signal and to pick up the estimated heartbeats by a multiple weighting function. The proposed multiple weighting function is designed by the Gaussian distribution, of which parameters are set from a grid search with the goal of minimizing the standard deviation of RR intervals (neighboring R-peaks intervals). The validation for the proposed framework has been evaluated on real-world data, which got the better accuracy than the conventional method that detects R-peaks from the integrated power and uses the weighting function produced by a fixed parameter of Gaussian distribution [12]. The averaged absolute error (AAE) which compares the estimated fetal heart rate and the reference fetal heart rate has been decreased by 17.528 bpm.

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Weighted-Beam Superposition for mmWave Massive MIMO-NOMA Systems

Dai H., Yin Y., Huang H., Yang J., Ohtsuki T., Sari H., Adachi F.

IEEE Vehicular Technology Conference (IEEE Vehicular Technology Conference) 2021-September 2021

ISSN 15502252

Millimeter wave (mmWave) and massive multiple input multiple output (MIMO) are recognized as key technologies in the forthcoming beyond the fifth-generation (B5G) and the sixth-generation (6G) wireless networks. In this paper, a multibeam MIMO non-orthogonal multiple access (NOMA) scheme with weighted beam superposition for mmWave is proposed to enhance the system sum rate while ensuring fairness of users as much as possible. Specifically, a method of power allocation is adopted to guarantee the minimum demanded for the quality of service (QoS) of the weak users (lower channel gain)in each group. Furthermore, to improve further the sum rate after the QoS of the weak users is satisfied, the coefficient of strong users' beam gain are set to the largest value. In system simulations, we compare the performance of three multi-beam schemes, i.e, beam splitting, beam superposition and the proposed scheme, together with a single beam scheme, and a TDMA scheme at different levels of the SNR. The simulation results demonstrate that the system sum rate of the proposed method is much higher than TDMA scheme and competitive compared to the best scheme.

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Spatial degrees of freedom exploration and analog beamforming designs for signature spatial modulation

Cao Y., Ohtsuki T.

IEICE Transactions on Communications (IEICE Transactions on Communications) E104B ( 8 ) 934 - 941 2021

ISSN 09168516

In this paper, we focus on developing efficient multi-configuration selection mechanisms by exploiting the spatial degrees of freedom (DoF), and leveraging the simple design benefits of spatial modulation (SM). Notably, the SM technique, as well as its variants, faces the following critical challenges: (i) the performance degradation and difficulty in improving the system performance for higher-level QAM constellations, and (ii) the vast complexity cost in precoder designs particularly for the increasing system dimension and amplitude-phase modulation (APM) constellation dimension. Given this situation, we first investigate two independent modulation domains, i.e., the original signal- and spatial-constellations. By exploiting the analog shift weighting and the virtual spatial signature technologies, we introduce the signature spatial modulation (SSM) concept, which is capable of guaranteing superior trade-offs among spectral- and cost-efficiencies, and system bit error rate (BER) performance. Besides, we develop an analog beamforming for SSM by solving the introduced unconstrained Lagrange dual function minimization problem. Numerical results manifest the performance gain brought by our developed analog beamforming for SSM.

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Remote Sensing of Heartbeat based on Space Diversity Using MIMO FMCW Radar

Yamamoto K., Endo K., Ohtsuki T.

2021 IEEE Global Communications Conference, GLOBECOM 2021 - Proceedings (2021 IEEE Global Communications Conference, GLOBECOM 2021 - Proceedings) 2021

Remote sensing of heartbeat offers various applications in the medical and health care fields. To realize non-contact heartbeat detection, an FMCW (Frequency Modulated Continuous Wave) radar-based heartbeat detection method has been investigated. The conventional FMCW radar-based heartbeat detection method estimates a range from an FMCW radar to a subject and extracts heartbeat components from phase changes for the range. However, the range suitable for extracting heartbeat components can change over time due to respiration and body fluctuation. Thus, when the SNR (Signal-to-Noise Ratio) of heartbeat components over phase changes is low at the estimated range, the accuracy of heartbeat detection tends to degrade. In this paper, we propose a MIMO (Multiple-Input Multiple-Output) FMCW radar-based heartbeat detection method based on space diversity. A MIMO FMCW radar can estimate the range for multiple beam directions and obtain phase changes for a space specified with the range and the beam direction. The SNR of heartbeat components over phase changes differs from one space to another. Taking it into account, the proposed method detects heartbeat by exploiting the space diversity of phase changes. The experimental results showed that compared to the detection method using only one phase change, the proposed method using phase changes for multiple spaces detected heartbeat accurately, which is brought by the diversity effect of phase changes for multiple spaces.

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Overloadedwireless mimo switching for information exchanging through untrusted relay in secure wireless communication

TAKAHASHI A., TAKYU O., FUJIWARA H., FUJII T., OHTSUKI T.

IEICE Transactions on Communications (IEICE Transactions on Communications) E104B ( 10 ) 1249 - 1259 2021

ISSN 09168516

Information exchange through a relay node is attracting attention for applying machine-to-machine communications. If the node demodulates the received signal in relay processing confidentially, the information leakage through the relay station is a problem. In wireless MIMO switching, the frequency spectrum usage efficiency can be improved owing to the completion of information exchange within a short time. This study proposes a novel wireless MIMO switching method for secure information exchange. An overloaded situation, in which the access nodes are one larger than the number of antennas in the relay node, makes the demodulation of the relay node difficult. The access schedule of nodes is required for maintaining the overload situation and the high information exchange efficiency. This study derives the equation model of the access schedule and constructs an access schedule with fewer time periods in the integer programming problem. From the computer simulation, we confirm that the secure capacity of the proposed MIMO switching is larger than that of the original one, and the constructed access schedule is as large as the ideal and minimum time period for information exchange completion.

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Noncontact heartbeat detection by Viterbi algorithm with fusion of beat-beat interval and deep learning-driven branch metrics

Yamamoto K., Ohtsuki T.

ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings (ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings) 2021-June 8308 - 8312 2021

ISSN 15206149

Heartbeat is one of essential vital signs to assess our health condition. Noncontact heartbeat detection is thus receiving a lot of attention in recent years, which motivates many researchers to investigate heartbeat detection via a Doppler radar. In this paper, to detect heartbeat with a high accuracy, we propose a Doppler radar-based heartbeat detection method by the Viterbi algorithm with a fusion of Beat-Beat Interval (BBI) and deep learning-driven Branch Metrics (BM). The Viterbi algorithm is a technique to estimate a sequence with maximum likelihood by using a pre-defined metric, namely, a BM. In the proposed method, we combine two BMs defined based on (i) a difference between two adjacent BBIs and (ii) an output probability of a deep learning model that judges whether a peak is caused by heartbeat or not. We apply the VIterbi algorithm with the fusion of the two BMs to the signal obtained by some signal processing. We experimentally confirmed that our method performed heartbeat detection with small Root Mean Squared Error (RMSE) between the estimated and actual BBIs.

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Non-contact Heartbeat Detection Based on Beam Diversity Using Multibeam Doppler Sensor

Kitagawa T., Yamamoto K., Ohtsuki T.

2021 IEEE Global Communications Conference, GLOBECOM 2021 - Proceedings (2021 IEEE Global Communications Conference, GLOBECOM 2021 - Proceedings) 2021

Heartbeat detection is a promising technology for health care and is in great demand in many applications. For non-contact and non-invasive heartbeat detection, Doppler sensor-based heartbeat detection methods have been investigated extensively. However, since the conventional methods use a single beam Doppler sensor, the accuracy of heartbeat detection tends to degrade, when the SNR (Signal-to-Noise Ratio) of heartbeat components for the beam direction is low. In this paper, we propose a non-contact heartbeat detection method based on beam diversity using a multibeam Doppler sensor. The effects of noises and the corresponding SNRs of heartbeat components over the received signal differ from one beam to another. Based on this fact, the proposed method detects heartbeat by exploiting beam diversity of the received signals. The experimental results showed that compared to the detection method using a single beam, our proposed method using multiple beams detected heartbeat accurately, which indicates that exploiting the beam diversity can lead to the improvement of heartbeat detection accuracy.

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Multi-Rate Compression for Downlink CSI Based on Transfer Learning in FDD Massive MIMO Systems

Wang Y., Sun J., Wang J., Yang J., Ohtsuki T., Adebisi B., Gacanin H.

IEEE Vehicular Technology Conference (IEEE Vehicular Technology Conference) 2021-September 2021

ISSN 15502252

Accurate downlink channel state information (CSI) is one of the essential requirements for harnessing the potential advantages of frequency-division duplexing (FDD) massive multi-input multi-output (MIMO) systems. The current state-of-art in this vibrant research area include the use of deep learning to compress and feedback downlink CSI at the user equipments (UEs). These approaches focus mainly on achieving CSI feedback with high reconstruction performance and low complexity, but at the expense of inflexible compression rate (CR). High training overheads and limited storage capacity requirements are some of the challenges associated with the design of dynamic CR, which instantaneously adapt to propagation environment. This paper applies transfer learning (TL) to develop a multi-rate CSI compression and recovery neural network (TL-MRNet) with reduced training overheads. Simulation results are presented to validate the superiority of the proposed TL-MRNet over traditional methods in terms of normalized mean square error and cosine similarity.

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Lightweight Network Design Based on ResNet Structure for Modulation Recognition

Lu X., Tao M., Fu X., Gui G., Ohtsuki T., Sari H.

IEEE Vehicular Technology Conference (IEEE Vehicular Technology Conference) 2021-September 2021

ISSN 15502252

The problem of unknown modulation signal recognition has been received intensely attentions in next-generational intelligent wireless communications. The deep learning (DL) has been widely used in unknown modulation signal recognition due to its excellent performance in solving classification problems and the DL-based automatic modulation classification (AMC) had been proposed. However, DL-based AMC method usually has high space complexity and computational complexity, which limits DL-based AMC to miniaturized devices with limited storage and computing capability. Therefore, a lightweight residual neural network (LResNet) for AMC is proposed in this paper. The simulation results show that the model parameters of LResNet is about 4.8% of the traditional CNN network, and about 14.9% of the ResNet and the classification performance of LResNet improves more than 3% compared with the traditional CNN network and decreases less than 1.5% compared to the ResNet.

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Lightweight network and model aggregation for automatic modulation classification in wireless communications

Fu X., Gui G., Wang Y., Ohtsuki T., Adebisi B., Gacanin H., Adachi F.

IEEE Wireless Communications and Networking Conference, WCNC (IEEE Wireless Communications and Networking Conference, WCNC) 2021-March 2021

ISSN 15253511

This paper proposes a decentralized automatic modulation classification (DecentAMC) method using light network and model aggregation. Specifically, the lightweight network is designed by separable convolution neural network (S-CNN), in which the separable convolution layer is utilized to replace the standard convolution layer and most of the fully connected layers are cut off, the model aggregation is realized by a central device (CD) for edge device (ED) model weights aggregation and multiple EDs for ED model training. Simulation results show that the model complexity of S-CNN is decreased by about 94% while the average CCP is degraded by less than 1% when compared with CNN and that the proposed AMC method improves the training efficiency when compared with the centralized AMC (CentAMC) using S-CNN.

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HSRRS Classification Method Based on Deep Transfer Learning and Multi-Feature Fusion

Wang Z., Li Z., Wang Y., Li W., Yang J., Ohtsuki T.

IEEE Vehicular Technology Conference (IEEE Vehicular Technology Conference) 2021-September 2021

ISSN 15502252

Convolutional neural network (CNN) is one of the most important tools to accomplish high-spatial-resolution remote sensing (HSRRS) image classification tasks with their unique feature extraction and feature expression capabilities. However, the CNN-based classification method is very limited due to the acquisition of HSRRS images is difficult and the sample size is limited. In addition, the extraction of features by a single model is very limited, which limits the further improvement of classification performance. To solve the above problems, we propose ResNet50-InceptionV3 based on deep transfer learning and multi-feature fusion (TLMFFRI) model to apply for high-spatial-resolution remote sensing image classification. First, both ResNet50 and InceptionV3 are trained on the ImageNet dataset. Then, transfer the trained convolutional layers weights to the TLMFFRI model to fuse the features and realize the HSRRS image classification. Finally, we evaluate the method on the HSRRS dataset. Compared with ResNet50 based on transfer learning (TL-ResNet50) and InceptionV3 based on transfer learning (TL-InceptionV3), the proposed method achieved better classification performance.

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Surveillance Plane Aided Air-Ground Integrated Vehicular Networks: Architectures, Applications, and Potential

Sun J., Liu F., Zhou Y., Gui G., Ohtsuki T., Guo S., Adachi F.

IEEE Wireless Communications (IEEE Wireless Communications) 27 ( 6 ) 122 - 128 2020.12

ISSN 15361284

© 2002-2012 IEEE. Future air-ground integrated vehicular network (AGVN) is a vital part of the expected ubiquitous communication system. The resources in AGVN are geographically separated and are often heterogeneous. Therefore, distributed management and cooperative management are key issues in a multi-tier based topology of AGVN. Recently, artificial intelligence (AI) shows advantages in facilitating the management of AGVN; however, adequate, real-time, and secure information for driving AI is still a big concern. In this article, we propose a conceptual enhanced AGVN by introducing a so-called surveillance plane to promote the management in the control plane. Thus, components such as virtual network functions (VNFs) and service function chaining (SFC) can get profits. Concepts and trials of utilizing the surveillance plane and side information are presented from three different aspects, namely networking, security, and application. We also summarize research directions in designing a more flexible management scheme by coupling the service, surveillance, and control planes of the enhanced AGVN.

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Dual-Ascent Inspired Transmit Precoding for Evolving Multiple-Access Spatial Modulation

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

IEEE Transactions on Communications (IEEE Transactions on Communications) 68 ( 11 ) 6945 - 6961 2020.11

ISSN 00906778

© 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.

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Evaluating smart grid renewable energy accommodation capability with uncertain generation using deep reinforcement learning

Liu Y., Guan X., Li J., Sun D., Ohtsuki T., Hassan M.M., Alelaiwi A.

Future Generation Computer Systems (Future Generation Computer Systems) 110 647 - 657 2020.09

ISSN 0167739X

© 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.

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Semi-Supervised Machine Learning Aided Anomaly Detection Method in Cellular Networks

Lu Y., Wang J., Liu M., Zhang K., Gui G., Ohtsuki T., Adachi F.

IEEE Transactions on Vehicular Technology (IEEE Transactions on Vehicular Technology) 69 ( 8 ) 8459 - 8467 2020.08

ISSN 00189545

© 1967-2012 IEEE. The ever-increasing amount of data in cellular networks poses challenges for network operators to monitor the quality of experience (QoE). Traditional key quality indicators (KQIs)-based hard decision methods are difficult to undertake the task of QoE anomaly detection in the case of big data. To solve this problem, in this paper, we propose a KQIs-based QoE anomaly detection framework using semi-supervised machine learning algorithm, i.e., iterative positive sample aided one-class support vector machine (IPS-OCSVM). There are four steps for realizing the proposed method while the key step is combining machine learning with the network operator's expert knowledge using OCSVM. Our proposed IPS-OCSVM framework realizes QoE anomaly detection through soft decision and can easily fine-Tune the anomaly detection ability on demand. Moreover, we prove that the fluctuation of KQIs thresholds based on expert knowledge has a limited impact on the result of anomaly detection. Finally, experiment results are given to confirm the proposed IPS-OCSVM framework for QoE anomaly detection in cellular networks.

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An Infrared Array Sensor-Based Method for Localizing and Counting People for Health Care and Monitoring

Bouazizi M., Ohtsuki T.

ISSN 9781728119908

© 2020 IEEE. To build a system for monitoring elderly people living alone, an important step needs to be done: identifying the presence/absence of the person being monitored and his location. Such task has several applications that we discuss in this paper, and remains very important. Several techniques were proposed in the literature. However, most of them suffer from issues related to privacy, coverage or convenience. In the current paper, we propose an infrared array sensor-based approach to detect the presence/absence of a person in a room. We used a wide angle low resolution sensor (i.e., 32×24 pixels) to collect heat-related information from the area monitored, and used Deep Learning (DL) to identify the presence of up to 3 people with an accuracy reaching 97%. Our approach also detects of the presence or absence of a person with a 100% accuracy. Nevertheless, it allows identifying the location of the detected people within a room of dimensions 4×7.4 m with a margin of 0.3 m.

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Online Recursive ICA Algorithm Used for Motor Imagery EEG Signal

Lin X., Wang L., Ohtsuki T.

ISSN 9781728119908

© 2020 IEEE. Electroencephalogram (EEG) signals are important to study the activities of human brains. The independent component analysis (ICA) algorithm is a practical blind source separation (BSS) technique that can separate EEG sources from artifacts effectively. However, most traditional ICA algorithms assume that the mixing process is instantaneous and off-line. In this paper, a novel framework based on the extension of the online recursive ICA algorithm (ORICA) is proposed to apply for motor imagery (MI) EEG recording. The contributions are as follows. Firstly, we show ORICA's adaptability to accurate and effective source separation used for artifact-contaminated MI EEG recording. Secondly, to identify EOG signals on the output of source separation, the topographic map is presented to distinguish the target signals. The experimental results show that the proposed framework is able to be applied to process MI EEG recording in real-time situations.

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Joint Multiple Subspace-based BSS Method for Fetal Heart Rate Extraction from Non-invasive Recordings

Wang L., Ohtsuki T., Owada K., Honma N., Hayashi H.

ISSN 9781728119908

© 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.

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Deep Reinforcement Learning for Economic Dispatch of Virtual Power Plant in Internet of Energy

Lin L., Guan X., Peng Y., Wang N., Maharjan S., Ohtsuki T.

IEEE Internet of Things Journal (IEEE Internet of Things Journal) 7 ( 7 ) 6288 - 6301 2020.07

© 2014 IEEE. With the high penetration of large-scale distributed renewable energy generation, the power system is facing enormous challenges in terms of the inherent uncertainty of power generation of renewable energy resources. In this regard, virtual power plants (VPPs) can play a crucial role in integrating a large number of distributed generation units (DGs) more effectively to improve the stability of the power systems. Due to the uncertainty and nonlinear characteristics of DGs, reliable economic dispatch in VPPs requires timely and reliable communication between DGs, and between the generation side and the load side. The online economic dispatch optimizes the cost of VPPs. In this article, we propose a deep reinforcement learning (DRL) algorithm for the optimal online economic dispatch strategy in VPPs. By utilizing DRL, our proposed algorithm reduced the computational complexity while also incorporating large and continuous state space due to the stochastic characteristics of distributed power generation. We further design an edge computing framework to handle the stochastic and large-state space characteristics of VPPs. The DRL-based real-time economic dispatch algorithm is executed online. We utilize real meteorological and load data to analyze and validate the performance of our proposed algorithm. The experimental results show that our proposed DRL-based algorithm can successfully learn the characteristics of DGs and industrial user demands. It can learn to choose actions to minimize the cost of VPPs. Compared with the deterministic policy gradient algorithm and DDPG, our proposed method has lower time complexity.

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Deep Clustering with LSTM for Vital Signs Separation in Contact-free Heart Rate Estimation

Ye C., Gui G., Ohtsuki T.

IEEE International Conference on Communications (IEEE International Conference on Communications) 2020-June 2020.06

ISSN 9781728150895

© 2020 IEEE. So far, most separation approaches of vital signs such as heartbeat and respiration, are implemented based on linear mixtures. However, some literatures have reported that non-linear mixtures actually occur in the associated applications, e.g., heart rate (HR) estimation with Doppler radar, where the simple linear demixing architecture may limit the effect of source separation. In addition, the human motions during HR measurement further complicate the mixing processes. The issue motivates us to exploit a more suitable separation approach to deal with contact-free HR estimation, considering non-linear mixtures including motions. A semi-supervised deep clustering (DC) is proposed to separate the three mixed sources of heartbeat, respiration, and motions, by segmenting the spectrogram of Doppler signal. First, through training a deep recurrent neural network (RNN) with long short-term memory (LSTM) via heartbeat/respiration-only data, the embeddings to each frame-sample from spectrogram can be acquired, which enables feature optimization in a lower dimensional space. Then, in the test phase, K-means clusters the embeddings associated with each source, to infer the masks used for spectrogram segmentation. The proposed deep clustering has three main strengths: It (i) gets rid of the restriction of mixture class, relying on data mining; (ii) can handle three-source mixtures by training two sorts of source-independent samples; (iii) only requires the mixtures from single-channel. The HR measurement experiments on subjects' sitting still and typing, validate the improvements of accuracy and robustness by our proposal, over some prevailing approaches in signal decomposition or separation.

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