Set Of Signals Research Articles

Smart Transportation Systems: IOT-enabled Traffic Management & Vehicle-to-infrastructure Communication

Recently, there has been a substantial increase in the number of cars on the road. Escalating traffic congestion is a widespread problem that folks face on a daily basis. The manual control of traffic-by-traffic police has shown to be ineffective. “Furthermore, the signal's set duration has failed to effectively address this issue, regardless of the level of traffic congestion. This research presents a communication system called Vehicle-to-Blockchain (V2B), which utilizes blockchain technology to enable transparent and decentralized communications. The aim is to optimize the incorporation of blockchain technology into V2X (Vehicle-to-Everything) and IoT (Internet of Things) systems, therefore enhancing the efficiency and effectiveness of future transportation systems. We present a range of advanced applications for blockchain technology, such as a car ownership system that utilizes the multi-token standard, a vehicle grading system, the integration of blockchain with the Internet of Things (IoT), and a decentralized ticket administration system for transportation services. The design places a high emphasis on essential elements such as the seamless integration of data, precise precision, and the establishment of secure connections. Furthermore, it includes a decentralized payment system and marketplace for transportation inside smart cities. Our main focus is on the technical execution of smart contracts in these particular scenarios, highlighting their importance in guaranteeing strong and dependable interactions. Our decentralized approach creates a transportation ecosystem that has the ability to adjust and fulfill the evolving requirements of intelligent urban areas. It is groundbreaking, durable, and capable

Cyclostationarity blind deconvolution via eigenvector screening and its applications to the condition monitoring of rotating machinery

Maximum second-order cyclostationarity blind deconvolution (CYCBD) is accomplished by maximizing the second-order cyclostationarity of signals through the indicator of second-order cyclostationarity (ICS2). It is a significant method for extracting weak periodic pulses related to bearing faults. However, since the interference spectral lines generated by the interference signals in squared envelope spectrum do not be considered by ICS2, the method can be invalid for certain applications. In addition, when the detected signal contains compound faults, only one fault can be enhanced, and misdiagnosis is easily caused. This article proposes cyclostationarity blind deconvolution via eigenvector screening, abbreviated as ESCYCBD, for fault feature enhancement and extraction of rotating machinery in order to solve these problems. Different from existing deconvolution methods which use the maximum value of the index as a criterion to select the filter coefficients, this method proposes the concept of eigenvector subspace, which contains the optimal eigenvector as filter coefficients that are ignored by CYCBD. Subsequently, the Fourier coefficients related to a series of cyclic frequencies are extended for compound fault signals. Besides, by using the harmonic characteristics of fault features in the envelope spectrum, fault feature recognition and optimal selection are carried out in eigenvector subspaces. At the same time, the concept of signals set is created, by which means different feature modes of different fault can be extracted at one time. The analysis results of simulation signals and experimental data show that the proposed ESCYCBD has robustness and accuracy in extracting fault feature modes, whether it is single fault diagnosis or compound fault diagnosis.

Research on ultrasonic non-destructive evaluation algorithm for ultimate tensile strength of stainless-steel welded pipe welds based on width learning

ABSTRACT The regression prediction between ultrasonic signal and ultimate tensile strength of stainless-steel pipe weld is studied in this paper. First, ultrasonic signals were collected from the weld of AISI304 austenitic stainless steel welded pipe sample, and its ultimate tensile strength was tested. The data sets of ultrasonic signal and ultimate tensile strength of weld were constructed. The random forest algorithm is used to extract the characteristic value set of the ultrasonic signal set of the welding seam and reduce the dimension of the characteristic value set. Then, the set of eigenvalues after dimensionality reduction is input into the Bayesian BLS network for training. The results of the training were compared with those of other regression models. The results show that the model established by Bayes BLS algorithm is feasible and reliable to predict the ultimate tensile strength of stainless-steel pipe weld. The results also show that the model established by the Bayesian BLS algorithm has high accuracy and stability in predicting the ultimate tensile strength of stainless-steel pipe weld. This method can be used to predict the ultimate tensile strength of welds in different types of materials.

Optimized wavelet and feature set of EEG signal for Parkinson disease classification

Parkinson disease (PD) is a type of neurodegenerative disorder that affects the motor movement of the patient. But each technique has its own advantages or disadvantages. In gene, speech and handwriting data model, the feature extraction and reduction is an important step for efficient classification. These two steps require proper attention for selection and also require high processing time as compared to other data model like images. Because in image modality, the deep learning algorithm can be applied that can perform all process and automate the classification. As compared to these domains, the signal produces better and best results. Because the electroencephalogram (EEG) signal are taken from the brain using electrodes and it helps to observe the brain signals effectively and immediately as compared to the other data modals. Hence, in this paper, the wavelet transform will be used to decompose the signals and statistical features will be extracted from the transformed signal. Here, the satin bower bird optimization will be used for both type of wavelet selection and feature reduction process for final classification. The reduced feature set will be classified using Ensemble Neural Network type including InceptionV3, DenseNet, MobileNet, Xception, and NasNet) recently proposed for medical image classification. The whole process will be realized using MATLAB R2021a software and its performance will be evaluated in terms of Accuracy and is compared against Automated Tunable Q-wavelet transform performance. The proposed ensemble method, employing EEG signal processing and neural networks, achieved a 97% success rate in discriminating PD datasets, surpassing Convolutional Neural Network (CNN) and Machine Learning (ML) classifications (88% –92%). Utilizing MATLAB R2021a, its superiority over Q-wavelet transform was evident, signifying improved PD dataset discrimination.

A Fully Self-Powered Ocean Wave Observation System Empowered by Natural Light Modulated by a Friction-Driven Polymer Network Liquid Crystal.

Conventional ocean wave observation instruments are powered by batteries, limiting the continuous observation time. Besides, the waste of batteries brings environmental contaminations. Triboelectric nanogenerators (TENGs) can reveal ocean wave information through their electrical output, taking the triboelectric charge as the information carrier. However, charge amplification is necessary, consuming additional energy. Herein, taking the photons rather than electrons as the information carrier, we developed a fully self-powered natural light-enabled sensing system for ocean wave monitoring by coupling two rotary-freestanding sliding TENGs (RFS-TENGs) and a polymer network liquid crystal (PNLC)-triggered optical system. The natural light is modulated by the PNLC driven by ocean wave-induced friction. With the assistance of a one-way bearing, the rise and fall of the wave will trigger different RFS-TENGs to power the PNLC in different voltage drops, leading to different transmitted natural light intensities. The wave height information can be obtained through the number of pulse signals with the same trough light intensity, while the wave period can be obtained through the duration between the same two sets of pulse signals. The effectiveness of the developed sensing paradigm in practical applications was verified by flume-based experiments, with the highest accuracies of 90.7% in wave height and 99.8% in wave period.

Stability of Traffic Equilibria in a Day-to-Day Dynamic Model of Route Choice and Adaptive Signal Control

Adaptive traffic signal control has the potential to promote the efficient use of road intersections, thus contributing to the effectiveness of urban traffic management schemes. However, the reaction of drivers to repeatedly updated signal settings and the ensuing route choice dynamics may trigger the emergence of various kinds of network instability. In this study, the joint evolution of traffic flows and adaptive signal settings in a road network is investigated at the level of day-to-day dynamics with an explicit focus on the stability issue. We show how a Logit form signal control policy can be used, in interaction with route choice, to counter the emergence of instabilities possibly arising as a consequence of various behavioral factors and network conditions. After providing a general formulation of the model as a discrete time, deterministic nonlinear dynamical system, an explicit analysis of fixed-point stability is carried out for a simple network. Numerical results obtained from the implementation of the model on two example networks are presented in order to support the analytical findings of this study. We conclude that, in an integrated traffic management and information system, a properly calibrated adaptive signal control policy has the potential to offset the destabilizing effect of highly accurate driver information supplied by navigational aids. Our findings also suggest that the Logit-like control policy performs better than the Equisaturation signal setting method, in terms of average intersection delay at equilibrium, for all levels of driver information and travel demand tested in the experiment.

Pros and cons of a bone-conduction device implanted in the worse hearing ear of patients with asymmetric hearing loss

PurposeBone-conduction devices can be considered a viable treatment option for patients experiencing asymmetric hearing loss (AHL), especially those with severe to profound hearing loss (HL) present in one ear. However, there are only a few reports on the effects of bone-anchored hearing aids (BAHAs) on patients with AHL. This retrospective study analyzed in detail the effects of BAHA on softer sounds than other hearing aids and identified situations in which BAHA had a negative effect.MethodsPatients with AHL, characterized by severe to profound hearing loss in one ear with a difference of ≥45 dB from that of the contralateral ear, underwent BAHA implantation in the ear with worse hearing. The BAHA effects were evaluated by assessing the word recognition score (WRS) and speech reception threshold (SRT) using the Japanese Oldenburg Sentence Test for various signal settings and noise directions. For a subjective analysis, the Speech, Spatial and Qualities of Hearing Scale (SSQ12) score was determined.ResultsThirteen patients who underwent BAHA implantation at the Miyazaki University Hospital between 2007 and 2021 were included. The BAHA demonstrated a significant improvement in the WRS from 40 to 70 dB sound pressure levels. Although the SRT showed significant improvement in noisy environments when speech was presented to the BAHA-wearing side, it worsened significantly when noise was presented to this side. In the survey of subjective hearing ability, both the total and subscale SSQ12 scores improved significantly after wearing the BAHA.ConclusionThis study identified scenarios in which BAHAs were beneficial and detrimental to individuals with AHL. Generally used audiological tests, such as the WRS with fixed sound pressure, may underestimate the effectiveness of BAHAs for softer sounds. In addition, depending on the direction of the noise, BAHAs may have adverse effects. These results could help patients comprehend the potential benefits and limitations of bone-conduction devices for their hearing.

Performance Optimization for 5GWireless Network

Fifth Generation (5th G) wireless network is a challengeable research area. As it is intended to serve growing number of subscribers, and to handle various types of data. Therefore, the need for more number of channels and higher performance is increasing. In this paper, we focus on the adopted modulation scheme by 5th G (256 QAM), where the signals set have been arranged among the constellation diagram in order to reach minimum Bit Error Rate (BER) using Gradient Search Method [1]. The novelty of this study comes from adding permutation loop, which is applied on the resulting signals set of Gradient Algorithm, in order to achieve better performance (lower BER).The results show that acceptable BER under given Signal to Noise Ratio (SNR) could be reached with the relatively large amount of data that is required by this digital modulation scheme.

Research on intelligent identification algorithm of coal and rock strata based on Hilbert transform and amplitude stacking

AbstractThe high precision identification of coal–rock layers is a significant challenge in intelligent mining. There is a large amount of electromagnetic noise and metal reflector signals in the full space detection environment of mining roadway, which makes it hard to distinguish the reflected waves at interface from a set of echo signals generated by the interface due to the similar amplitudes among them. So the method of identifying layers solely based on amplitude characteristics has poor stability and accuracy in coal mining environments. This paper proposes a method for identifying coal–rock layers based on Hilbert transform and tracking–scanning–stacking technology. There are two steps to achieve the recognition of air–coal–rock interfaces. First, by analysing the instantaneous amplitude spectrum obtained from the Hilbert transform, the first extreme point that is always the maximum value within a wavelength range is determined as the rough position of the air–coal interface. To solve the problem of recognition errors caused by noise and energy dispersion, the density difference method is used to remove discrete points. Second, the precise position of the air–coal interface is determined by tracking the extreme points within the 1.5 wavelength range around the rough position, and using the amplitude stacking method to quantitatively analyse and compare the degree of energy concentration. The data between zero time and the reflected waves at the air–coal interface is removed to avoid the impact of them on the recognition of the coal–rock interface. Results of physical model experiments and actual coal mine experiments show that this method yields better results and has high stability compared to conventional recognition method. Moreover, the average relative thickness errors are 4.5% for air layer and 4.2% for coal layer.

Investigating the Performance of Gammatone Filters and Their Applicability to Design Cochlear Implant Processing System

Commercially available cochlear implants are designed to aid profoundly deaf people in understanding speech and environmental sounds. A typical cochlear implant uses a bank of bandpass filters to decompose an audio signal into a set of dynamic signals. These filters’ critical center frequencies f0 imitate the human cochlea’s vibration patterns caused by audio signals. Gammatone filters (GTFs), with two unique characteristics: (a) an appropriate “pseudo resonant” frequency transfer function, mimicking the human cochlea, and (b) realizing efficient hardware implementation, could demonstrate them as unique candidates for cochlear implant design. Although GTFs have recently attracted considerable attention from researchers, a comprehensive exposition of GTFs is still absent in the literature. This paper starts by enumerating the impulse response of GTFs. Then, the magnitude spectrum, |H(f)|, and bandwidth, more specifically, the equivalent rectangular bandwidth (ERB) of GTFs, are derived. The simulation results suggested that optimally chosen filter parameters, e.g., critical center frequencies,f0; temporal decay parameter, b; and order of the filter, n, can minimize the interference of the filter bank frequencies and very likely model the filter bandwidth (ERB), independent of f0b. Finally, these optimized filters are applied to delineate a filter bank for a cochlear implant design based on the Clarion processor model.

Support matrix machine with truncated pinball loss for classification

With the expansion of vector-based classifiers to matrix-based classifiers, noise insensitivity and sparsity have always been the focal points. Existing SMM and Pin-SMM enjoy the former and the latter separately. To remedy the shortcoming, we propose a support matrix machine based on truncated pinball loss (TPin-SMM) in this paper, which integrates noise insensitivity and sparsity simultaneously. Thanks to the adding of two quantiles, it possesses precious properties including Bayes rule and bounding misclassification error as well. Concerning the non-convexity of TPin-SMM, a targeted CCCP-ADMM algorithm is established, which decomposes the problem into three sub-problems of each sub-iteration. To verify the validity of TPin-SMM, we have conducted numerical experiments on image datasets, EEG signal sets and Daimler Pedestrian Classification Benchmark dataset with different noises, all of them pass the statistical tests.

Conversational facial signals combine into compositional meanings that change the interpretation of speaker intentions

Human language is extremely versatile, combining a limited set of signals in an unlimited number of ways. However, it is unknown whether conversational visual signals feed into the composite utterances with which speakers communicate their intentions. We assessed whether different combinations of visual signals lead to different intent interpretations of the same spoken utterance. Participants viewed a virtual avatar uttering spoken questions while producing single visual signals (i.e., head turn, head tilt, eyebrow raise) or combinations of these signals. After each video, participants classified the communicative intention behind the question. We found that composite utterances combining several visual signals conveyed different meaning compared to utterances accompanied by the single visual signals. However, responses to combinations of signals were more similar to the responses to related, rather than unrelated, individual signals, indicating a consistent influence of the individual visual signals on the whole. This study therefore provides first evidence for compositional, non-additive (i.e., Gestalt-like) perception of multimodal language.

Random Sampling of Mellin Band-Limited Signals

In this paper, we address the random sampling problem for the class of functions in the space of Mellin band-limited functions B T , which are concentrated on a bounded cube. It is established that any Mellin band-limited function can be approximated by an element in a finite-dimensional subspace of B T . Utilizing the notion of covering number and Bernstein’s inequality to the sum of independent random variables, we prove that the probabilistic sampling inequality holds for the set of concentrated signals in B T with an overwhelming probability provided the sampling size is large enough.

Beyond antibiotic resistance: The whiB7 transcription factor coordinates an adaptive response to alanine starvation in mycobacteria

Pathogenic mycobacteria are a significant cause of morbidity and mortality worldwide. The conserved whiB7 stress response reduces the effectiveness of antibiotic therapy by activating several intrinsic antibiotic resistance mechanisms. Despite our comprehensive biochemical understanding of WhiB7, the complex set of signals that induce whiB7 expression remain less clear. We employed a reporter-based, genome-wide CRISPRi epistasis screen to identify a diverse set of 150 mycobacterial genes whose inhibition results in constitutive whiB7 expression. We show that whiB7 expression is determined by the amino acid composition of the 5' regulatory uORF, thereby allowing whiB7 to sense amino acid starvation. Although deprivation of many amino acids can induce whiB7, whiB7 specifically coordinates an adaptive response to alanine starvation by engaging in a feedback loop with the alanine biosynthetic enzyme, aspC. These findings describe a metabolic function for whiB7 and help explain its evolutionary conservation across mycobacterial species occupying diverse ecological niches.

Distributed Sensitivity and Critical Interference Power Analysis of Multi-Degree-of-Freedom Navigation Interference for Global Navigation Satellite System Array Antennas.

Current research on the interference of GNSS (Global Navigation Satellite System) array antennas focuses on the single interference effect and the improvement of interference hardware capability, while the multi-degree-of-freedom (DOF) interference model and mechanism remain to be fully studied. Aiming at this problem, this paper analyzes the preconditions for the definition of anti-jamming degrees of freedom and the characteristics of super-DOF interference through formula derivation and simulation. First, by analyzing the influence of the number of interfering signals on the angular resolution, the prerequisite of the definition of anti-interference degrees of freedom in the airspace is proposed. Second, the definition of anti-interference degrees of freedom is used to calculate the change rule of the critical power of the interference under different numbers of interfering signals. Finally, the influence of super-DOF interference on the array antenna is analyzed. The results show that the prerequisite for the anti-interference freedom of the array antenna is that the distribution interval of the interfering signal is greater than 15°, taking a four-array element uniform circular array antenna as an example. The critical interference power of the array antenna decreases by about 15 dB when the number of interfering signals exceeds the degrees of freedom of the array antenna's interference immunity, provided that the interference resolution is satisfied. The conclusions of this paper give the critical power change rule of multi-DOF interference and the effect of super-DOF interference, as well as the prerequisites for the setting of interference signals, which can be used, for example, in the deployment of distributed interference sources and the development of anti-jamming algorithms.

Physics-informed deep learning for scattered full wavefield reconstruction from a sparse set of sensor data for impact diagnosis in structural health monitoring

This paper presents a physics-informed deep learning framework for the reconstruction of full scattered spatiotemporal Lamb wavefields (video images) in plate-like structures from a sparse set of time-series sensor data. The reconstructed scattered wavefield contains a wealth of information about the wave propagation phenomenon including any interactions of the propagating wave with damage in the structure. This information is paramount for damage diagnosis as is demonstrated in this paper via impact diagnosis—a key structural health monitoring application. A physics-informed neural network (PINN) that encodes the underlying elastodynamic field equations into the learning/training process in the neural network is proposed for this purpose. This prior wavefield physics knowledge embedded in the loss function acts as a regularization agent for the minimization problem in the neural network training, thereby enabling the extrapolation of a sparse set of one-dimensional time-series signals into two-dimensional scattered wavefield. The wavefield reconstruction capabilities of the proposed supervised forward PINN framework are first verified both numerically and experimentally for a stiffened aluminum panel under a couple of narrowband ultrasonic-frequency excitations, and the results confirm its robustness to low spatial resolution and substantial noise in the measured sensor data. The PINN requires far fewer sensors for scattered wavefield reconstruction, thereby permitting for a higher sensor spacing or lower spatial sampling. To this end, it is shown that a sensor spacing of 5λ generates good wavefield reconstruction accuracy, which is a 10-fold increase over the Nyquist–Shannon sampling limit (λ/2). Two sets of experiments are then conducted on a long-stiffened aluminum panel to validate the proposed framework via low-velocity impact diagnosis in the near-ultrasonic frequency range. The first set of experiments, with the known excitation force incorporated into the PINN, allows the wavefields to be accurately reconstructed with the sensor spacing up to 5λ as expected. The second set of experiments assumes unknown impact force history—a classical case of impact diagnosis where the impact force history is not known a priori. It is shown that the wavefield reconstruction through PINN still provides good accuracy albeit with a less generous sensor spacing of 2λ. A convolutional neural network long short-term memory (CNN-LSTM) model then solves the mathematically inverse problem of inferring the impact location and impact force history by analyzing the reconstructed impact generated wavefield. The impact location is predicted well with 93% accuracy, and the impact force history is reconstructed with 90% accuracy, further validating the proposed framework.

Hard Magnetic Graphene Nanocomposite for Multimodal, Reconfigurable Soft Electronics.

Soft electronics provide effective means for continuous monitoring of a diverse set of biophysical and biochemical signals from the human body. However, the sensitivities, functions, spatial distributions, and many other features of such sensors remain fixed after deployment and cannot be adjusted on demand. Here, laser-induced porous graphene is exploited as the sensing material, and dope it with permanent magnetic particles to create hard magnetic graphene nanocomposite (HMGN) that can self-assemble onto a flexible carrying substrate through magnetic force, in a reversible and reconfigurable manner. A set of soft electronics in HMGN exhibits enhanced performances in the measurements of electrophysiological signals, temperature, and concentrations of metabolites. All these flexible HMGN sensors can adhere to a carrying substrate at any position and in any spatial arrangement, to allow for wearable sensing with customizable sensitivity, modality, and spatial coverage. The HMGN represents a promising material for constructing soft electronics that can be reconfigured for various applications.

Dual Attention Relation Network With Fine-Tuning for Few-Shot EEG Motor Imagery Classification.

Recently, motor imagery (MI) electroencephalography (EEG) classification techniques using deep learning have shown improved performance over conventional techniques. However, improving the classification accuracy on unseen subjects is still challenging due to intersubject variability, scarcity of labeled unseen subject data, and low signal-to-noise ratio (SNR). In this context, we propose a novel two-way few-shot network able to efficiently learn how to learn representative features of unseen subject categories and classify them with limited MI EEG data. The pipeline includes an embedding module that learns feature representations from a set of signals, a temporal-attention module to emphasize important temporal features, an aggregation-attention module for key support signal discovery, and a relation module for final classification based on relation scores between a support set and a query signal. In addition to the unified learning of feature similarity and a few-shot classifier, our method can emphasize informative features in support data relevant to the query, which generalizes better on unseen subjects. Furthermore, we propose to fine-tune the model before testing by arbitrarily sampling a query signal from the provided support set to adapt to the distribution of the unseen subject. We evaluate our proposed method with three different embedding modules on cross-subject and cross-dataset classification tasks using brain-computer interface (BCI) competition IV 2a, 2b, and GIST datasets. Extensive experiments show that our model significantly improves over the baselines and outperforms existing few-shot approaches.

Channel Estimation for Multiple-Input Multiple-Output Orthogonal Chirp-Division Multiplexing Systems

In multiple-input multiple-output (MIMO) systems, channel estimation is of crucial importance to guarantee reliable recovery of ultra-high-speed MIMO signals. This paper proposes a novel channel estimation algorithm for the emerging MIMO-based orthogonal chirp-division multiplexing (OCDM) systems by utilizing the unique features of OCDM signals. In the proposed algorithm, a set of pilot signals is designed based on the Fresnel basis, which is essentially a family of orthogonal linear chirps. The pilots are assigned to different antennas for transmission occupying the same time slot and bandwidth. According to the convolution-preservation theorem of the Fresnel transforms, the transfer matrices of MIMO-OCDM systems can be readily estimated at the receiver without any inter-antenna interference, even if the pilots overlap in both the time and frequency domains. The proposed algorithm avoids bandwidth waste in conventional channel estimators, in which silent pilots will be required in time and/or frequency to ensure the received MIMO pilots separable. We show that the proposed algorithm is unbiased for the unique OCDM pilots and has better estimate accuracy and system performance. Finally, analysis and numerical results are provided to validate its advantages as a promising algorithm for emerging wireless access technology based on MIMO-OCDM.

Features gradient-based signals selection algorithm of linear complexity for convolutional neural networks

<abstract><p>Recently, convolutional neural networks (CNNs) for classification by time domain data of multi-signals have been developed. Although some signals are important for correct classification, others are not. The calculation, memory, and data collection costs increase when data that include unimportant signals for classification are taken as the CNN input layer. Therefore, identifying and eliminating non-important signals from the input layer are important. In this study, we proposed a features gradient-based signals selection algorithm (FG-SSA), which can be used for finding and removing non-important signals for classification by utilizing features gradient obtained by the process of gradient-weighted class activation mapping (grad-CAM). When we defined $ n_ \mathrm{s} $ as the number of signals, the computational complexity of FG-SSA is the linear time $ \mathcal{O}(n_ \mathrm{s}) $ (i.e., it has a low calculation cost). We verified the effectiveness of the algorithm using the OPPORTUNITY dataset, which is an open dataset comprising of acceleration signals of human activities. In addition, we checked the average of 6.55 signals from a total of 15 signals (five triaxial sensors) that were removed by FG-SSA while maintaining high generalization scores of classification. Therefore, FG-SSA can find and remove signals that are not important for CNN-based classification. In the process of FG-SSA, the degree of influence of each signal on each class estimation is quantified. Therefore, it is possible to visually determine which signal is effective and which is not for class estimation. FG-SSA is a white-box signal selection algorithm because it can understand why the signal was selected. The existing method, Bayesian optimization, was also able to find superior signal sets, but the computational cost was approximately three times greater than that of FG-SSA. We consider FG-SSA to be a low-computational-cost algorithm.</p></abstract>