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With the dramatic progress of wearable devices, continuous collection of single lead ECG wave is able to be implemented in a comfortable fashion. Data mining on single lead ECG wave is therefore attracting increasing attention, where atrial fibrillation (AF) detection is a hot topic. In this paper, we propose a dual-channel neural network for AF detection from a single lead ECG wave. Two primary phases are included, the data preprocessing part followed by a dual-channel neural network. A two-stage denoising procedure is developed for data preprocessing, so as to tackle the high noise and disturbance which generally resides in the ECG wave collected by wearable devices. Then the time-frequency spectrum and Poincare plot of the denoised ECG signal are imported into the developed dual-channel neural network for feature extraction and AF detection. On the 2017 PhysioNet/CinC Challenge database, the F1 values were 0.83, 0.90, and 0.75 for AF rhythm and normal rhythm, and other rhythm, respectively. The results well validate the effectiveness of the proposed method for AF detection from a single lead ECG wave, and also indicate its performance advantages over some state-of-the-art counterparts.

Degree-of-freedom (DoF)-based models have been proven to be highly successful in modeling and analysis of MIMO systems. Among existing DoF-based models, the number of DoFs used for interference cancellation (IC) is solely based on the number of interfering data streams. However, from both experimental and simulation results, we find that signal strengths of an interference link vary significantly in different directions in the eigenspace. In this paper, we exploit the difference in interference signal strengths in the eigenspace and perform IC with DoFs only on those directions with strong signals. To differentiate interference signal strengths on an interference link, we introduce a novel concept called ‘`effective rank threshold.’' Based on this threshold, DoFs are consumed only to cancel strong interferences in the eigenspace while weak interferences are treated as noise in throughput calculation. To better understand the benefits of this approach, we study a fundamental trade-off between network throughput and effective rank threshold for an MU-MIMO network. Our simulation results show that network throughput under optimal rank threshold is significantly higher than that under existing DoF IC models. To ensure the new DoF IC model is feasible at PHY layer, we propose an algorithm to set the weights for all nodes that can offer our desired DoF allocation.

As anaesthetists we rely on the monitoring that we attach to our patients to be accurate, responsive and reliable. The display screen we examine closely daily with its beeps and alarms represents the processing of a large volume of biological signals of disparate types into something easily recognizable that can rapidly alert us to changes in a patient's physiology. This article uses some common examples of physiological measurements that undergo the pathway of detection, transduction, processing, amplification and filtering before being displayed on a monitoring screen and stored. We explain key components of this pathway such as Fourier analysis, gain and analogue to digital conversion; and how confounding factors such as noise, interference and damping are dealt with. We also discuss some newer technologies that are finding their way into common anaesthetic practice such as depth of anaesthesia monitors and electronic anaesthesia records.

The basic polite NLMS adaptive channel calculation is ordinarily utilized in applications where a framework needs to adjust to its condition. Structures are analyzed as far as the accompanying criteria: speed, power utilization and FPGA utilized. Present day FPGAs contain numerous assets that help signal processing applications. This posits are actualized in the programmable ICs texture and enhanced for superior and less power consumption The LMS adaptive finite impulse channelsarevery well known adaptive calculation process and is probably going to keep on so within a reasonable time-frame. In spite of theories that the LMS calculation is losing it is built up status as the workhorse for the plan of straight adaptive frameworks, there are as yet various continuous looks into and best in class propels right now. An expected new book titled — Least Mean-Square Adaptive Filters, altered by Bernard Widrow (originator of LMS) and Simon Haykin is a decent delegate of gave interests right now appeared by scientists around the globe. The LMS calculation can be effectively altered to a standardized advance size adaptation known as the Normalized LMS (NLMS) calculation. NLMS, gives a possibly quicker adaptive calculation, yet in addition ensures an increasingly steady combination in light of varieties of input signal power. Following this methodology, the fundamental goals of this postulation is to execute the NLMS-based adaptive calculation for awardee framework in outlaying communication to show its great gathering figure and reduce the error. Fartheriproposedone more design for DFS consumption it transfer about the usage of reduced memory use. The fundamental downside of the “absolute” least mean square computation this one touchy to the measuring the original signal X(N). It creates a difficult to pick a known rate µ that ensures good measurement. This is very useful for lowering the power consumption.

In this article, a novel composite hierarchical antidisturbance control (CHADC) algorithm aided by the information-theoretic learning (ITL) technique is developed for non-Gaussian stochastic systems subject to dynamic disturbances. The whole control process consists of some time-domain intervals called batches. Within each batch, a CHADC scheme is applied to the system, where a disturbance observer (DO) is employed to estimate the dynamic disturbance and a composite control strategy integrating feedforward compensation and feedback control is adopted. The information-theoretic measure (entropy or information potential) is employed to quantify the randomness of the controlled system, based on which the gain matrices of DO and feedback controller are updated between two adjacent batches. In this way, the mean-square stability is guaranteed within each batch, and the system performance is improved along with the progress of batches. The proposed algorithm has enhanced disturbance rejection ability and good applicability to non-Gaussian noise environment, which contributes to extending CHADC theory to the general stochastic case. Finally, simulation examples are included to verify the effectiveness of theoretical results.

Non-destructive testing & evaluation techniques play an essential role in ensuring safety of materials in operation at various industry sectors. Pulse compressed favourable thermal wave imaging is one of the widely used non-destructive testing techniques due to its excellent noise rejection capabilities. However, the high dimensional thermal imaging data needs to be encoded into lossless compressed form to highlight the hidden defects inside the materials. This paper proposes a novel constrained and regularized autoencoder based thermography approach for sub-surface defect detection in a mild steel specimen. Certain properties such as non-correlation of encoded data, weight orthogonality, and weights with unit norm length have been highlighted which are non-existent in linear autoencoders but are responsible for better defect detection inside the materials inspected by frequency modulated thermal wave imaging. Novel constraints are formulated for autoencoder cost function to incorporate these significant properties. The proposed approach is able to provide better defect detection, in terms of signal to noise ratio of defects, than linear autoencoder as well as traditional principal component thermography approach. Also, non-correlation of encoded data is found to be the most significant factor in achieving better defect detection followed by properties ensuring weight orthogonality and weights with unit norm length.

The instantaneous frequency (IF) is an important feature for the analysis of non-stationary signals. However, extracting multiple IF ridges, crossing IF ridges, and discontinuous IF ridges simultaneously is still a challenging task. To solve this problem, an adaptive IF ridge extraction method is proposed. This method regards the IF ridge values at each time point as targets to be tracked. The tracking targets are defined as three states: survival, death, and birth. Firstly, the potential peaks in the time-frequency representation are found to reduce the noise effect. Secondly, an adaptive penalty function is constructed to determine the target state and track the ridge target. Thirdly, the crossing ridges are separated base on the slope information. This method is capable of simultaneously extracting multiple IF ridges, crossing IF ridges, and discontinuous IF ridges. The performance of the proposed method is investigated by a simulated signal and two experimental signals.

The rapidly increasing volumes of data and the need for big data analytics have emphasized the need for algorithms that can accommodate incomplete or noisy data. The concept of recurrency is an important aspect of signal processing, providing greater robustness and accuracy in many situations, such as biological signal processing. Probabilistic fuzzy neural networks (PFNN) have shown potential in dealing with uncertainties associated with both stochastic and nonstochastic noise simultaneously. Previous research work on this topic has addressed either the fuzzy-neural aspects or alternatively the probabilistic aspects, but currently a probabilistic fuzzy neural algorithm with recurrent feedback does not exist. In this article, a PFNN with a recurrent probabilistic generation module (designated PFNN-R) is proposed to enhance and extend the ability of the PFNN to accommodate noisy data. A back-propagation-based mechanism, which is used to shape the distribution of the probabilistic density function of the fuzzy membership, is also developed. The objective of the work was to develop an approach that provides an enhanced capability to accommodate various types of noisy data. We apply the algorithm to a number of benchmark problems and demonstrate through simulation results that the proposed technique incorporating recurrency advances the ability of PFNNs to model time-series data with high intensity, random noise.

Fault diagnosis has a great significance in preventing serious failures of rotating machinery and avoiding huge economic losses. The performance of the existing fault diagnosis approaches might be affected by two factors, i.e., the quality of fault features extracted from monitoring signals and the capability of fault diagnosis model. This paper proposes a new fault diagnosis method combined mel-frequency cepstral coefficients (MFCC) with a designed parallel multi-fusion convolutional neural network (MFCNN) Specifically, a MFCC-based feature extraction methos is defined to reduce the noise components in monitoring signal of rotating machinery and extract more useful low-frequency fault information for downstream task. Furthermore, a novel MFCNN is designed to enrich the high-level features after each convolution operation by using multiple activation functions, so as to improve the quality of the obtained fault features. Meanwhile, a new parallel MFCNN is constructed by using a defined structural ensemble operation to improve its diagnostic performance in different noise environments. Two typical bearing and gearbox failure datasets are applied to evaluate the performance of the proposed fault diagnosis method. The experimental results indicate that the proposed parallel MFCNN has the better diagnostic performance than other methods.

The ability to understand speech in complex environments depends on the brain's ability to preserve the precise timing characteristics of the speech signal. Age-related declines in temporal processing may contribute to the older adult's experience of communication difficulty in challenging listening conditions. This study's purpose was to evaluate the effects of rate discrimination training on auditory temporal processing. A double-blind, randomized control design assigned 77 young normal-hearing, older normal-hearing, and older hearing-impaired listeners to one of two treatment groups: experimental (rate discrimination for 100- and 300-Hz pulse trains) and active control (tone detection in noise). All listeners were evaluated during pre- and post-training sessions using perceptual rate discrimination of 100-, 200-, 300-, and 400-Hz band-limited pulse trains and auditory steady-state responses (ASSRs) to the same stimuli. Training generalization was evaluated using several temporal processing measures and sentence recognition tests that included time-compressed and reverberant speech stimuli. Results demonstrated a session × training group interaction for perceptual and ASSR testing to the trained frequencies (100 and 300 Hz), driven by greater improvements in the training group than in the active control group. Further, post-test rate discrimination of the older listeners reached levels that were equivalent to those of the younger listeners at pre-test. Generalization was observed in significant improvement in rate discrimination of untrained frequencies (200 and 400 Hz) and in correlations between performance changes in rate discrimination and sentence recognition of reverberant speech. Further, non-auditory inhibition/attention performance predicted training-related improvement in rate discrimination. Overall, the results demonstrate the potential for auditory training to partially restore temporal processing in older listeners and highlight the role of cognitive function in these gains.

The simulation of the target capture process of space manipulator is of great significance for the manipulator and task verification. By integrating the physical and numerical simulation, the hardware-in-the-loop (HIL) simulation is very suitable to simulate the complicated contact dynamics of the target capture process of the multi-body manipulator in 3D space. However, the time delays in the system loop lead to HIL simulation error and even system instability. In this study, a distributed delay compensation approach is proposed for the hybrid simulation system of space manipulator capture. The force delay, calculation delay, actuation delay and mechanism delay in the system loop are compensated in situ. Compared with lumped compensation, distributed compensation has stronger robustness to the signal noise and better adaptability to the varying contact natural frequency. Simulations and experiments show that the proposed compensation approach can guarantee the system stability and satisfactory simulation accuracy.

Background Carotid intima-media thickness (IMT) measured in B-mode ultrasound image is an important indicator of Atherosclerosis disease. Speckle noise inherently present in ultrasounds’ thereby degrades the visual evaluation and limits the automated segmentation performance. The objective of this study is to investigate the effects of three despeckle filters on the segmentation of carotid IMT in ultrasound image. Methods Automated segmentation of IMT is achieved by utilizing fast fuzzy c-mean clustering and distance-regularized level set without re-initialization techniques. Manual segmentation has been done by an experienced radiologist. The performances of median, hybrid median and improved adaptive complex diffusion (IACDF) filters are examined and a quantitative and qualitative comparison among these filters has been reported on 151 DICOM images. Bland–Altman plots were used to compare IMT results of these filters. Furthermore, performances of above three filters are evaluated under different noise levels by individually adding speckle and salt and pepper noise in ten randomly selected images from 151 DICOM dataset. Plots between noise and quality evaluation metric parameters are used to compare de-noising performance of these filters. Results The average processing time per image of proposed IMT measurement technique without-filter and with filter is approx 15.39 s max. Conclusion It is shown that the median filter (window 5 × 5) measures better than hybrid median and IACDF filters. Finally, concluded that de-noising of ultrasound image before segmentation procedure certainly improves segmentation accuracy. Furthermore, it is observed that these filters do not impose serious computational burden and entail moderate processing time.

Bearing plays an important role in industrial equipment and it may operate under varying conditions. When the speed of shaft changes, whether monotonous or non-monotonous speed, common diagnostic approaches cannot effectively extract fault features. But encoders and tachometers are not always available. Therefore, tacholess order tracking methods which can directly extract the instantaneous rotating frequency (IRF) from vibration signal are very useful in bearing fault diagnosis under varying speed. Among these methods, the generalized linear chirplet transform (GLCT) can produce time-frequency representation without constructing any mathematical model, but there are two parameters must be set in advance. The parameters have great influence on the analysis result. To reduce the dependence on the prior knowledge of presetting the parameters in varying conditions, two different improved GLCT methods are proposed in this paper. To do with the situation where the trend of speed changes is monotonous, the scale-space is introduced to lift GLCT which can adaptively set a vital parameter, and the other parameter is set to default value. When faced with non-monotonous speed, the second method is proposed which the grey wolf optimizer (GWO) and Gini index are introduced to search the optimal parameters of GLCT without any prior knowledge. With the help of the proposed methods, the IRF can be extracted directly from vibration signal. Then, the raw signal can be resampled based on the IRF to eliminate the influences of speed. The morphological filtering is adopted to remove the noise and extract the fault characteristics order (FCO). Another two typical time-frequency analysis methods are used for comparisons. Three different signals are used for analysis to demonstrate the superiority of the proposed methods.

The rapid development of autonomous vehicles (AVs) has depicted a promising future of a safer and more efficient transportation system. To better induce this revolution, massive efforts have been spent on the technical competence of AVs. However, the human comfort in AVs has been an under-discussed yet important topic to the user acceptance of AVs. For the detection of human comfort, existing studies focus more on the physical influential factors of comfort such as sitting posture, vibration, and noise. With the introduction of AVs, psychological factors also have gained greater influence on human comfort. Despite existing studies of exploring correlations between human comfort and some physiological signals in automated driving contexts, there is few study on how human comfort level in AVs can be detected with these physiological signals. In this paper, we developed effective human comfort study approaches in autonomous vehicles with wearable sensors. We also proposed a machine learning based approach with adaptive feature selection to detect human comfort levels based on the wearable sensing data. The experimental results illustrated the effectiveness of the proposed approaches in studying human comfort in AVs.

This paper presents a fast model-agnostic method for recovering noisy Phasor Measurement Unit (PMU) datastreams with missing entries. The measurements are first transformed into a Page matrix, and the original signals are reconstructed using low-rank matrix estimation based on optimal singular value thresholding. Two variations of the recovery algorithm are shown- a) an offline block-processing method for imputing past measurements, and b) an online method for predicting future measurements. Information within a PMU channel (temporal correlation) as well as from different PMUchannels in a network (spatial correlation) are utilized to recover degraded data. The proposed method is fast and needs no explicit knowledge of the underlying system model or measurement noise distribution. The performance of the recovery algorithms is illustrated using simulated measurements from the IEEE 39-bus test system as well as real measurements from an anonymized U.S. electric utility. Extensive numeric tests show that the original signals can be accurately recovered in the presence of additive noise, consecutive data drop as well as simultaneous data erasure across multiple PMU channels.

Differential protection, as the key protection element in the power transformers, has always been threatened with sending false trips subjected to external transient disturbances. As a result, differential protection needs an additional block to distinguish between internal faults and external transient disturbances. The protection system should i) be able to perform based on raw data, ii) be able to learn fully temporal features and sudden changes in the transient signals, and iii) impose no assumption on noise. To address these challenges, a fast recurrent neural network, namely fast gated recurrent neural network (FGRNN). By removing the reset gate in the gated recurrent unit (GRU), the proposed network is capable of learning abrupt changes in addition to significantly reducing the computational time. Furthermore, a loss function based on an information theory concept is formulated in this paper to enhance the learning ability as well as robustness against non-Gaussian/Gaussian noises. A generalized form of mutual information is also adopted to form a noise model-free loss function, then incorporated with the designed deep network. Simulated and experimental examinations engaging various external factors, in addition to a comparison between the proposed fast GRNN, GRU, and seven firmly-established methods indicates the faster and more reliable performance of the proposed algorithm. Post-print / Final draft

Despite the simplicity of the Duffing oscillator, its dynamical behaviour is extremely rich. Hence, the Duffing equations are used to describe the dynamic behaviour of many real-world nonlinear systems for a wide range of frequency bands and amplitude of the excitation signal in basic sciences and engineering. For example, the Duffing oscillator has been successfully used to model a variety of physical processes such as stiffening springs, beam buckling, nonlinear electronic circuits, superconducting Josephson parametric amplifiers, and ionisation waves in plasmas etc. Therefore, the identification of the Duffing oscillator model/parameters directly from the measured input–output data is a topic of active research in many scientific fields In this paper, we use the concept of stochastic differential equations (SDEs) to identify a model of the Duffing oscillator. SDE-based grey-box models allow us to capture the underlying mathematical structure describing the physics of the system (e.g. the original Duffing equations) using the drift term and explicitly handling of model uncertainty (or the process noise) using the diffusion term whereas the measurement uncertainty is modelled using the measurement noise term respectively. In this paper, we propose a slight variation of the maximum likelihood estimation framework used for the identification of SDEs based grey-box models yielding improved performance for long-term predictions. The proposed framework is combined with an iterative residual analysis to develop a grey-box model of the forced Duffing oscillator. The benchmark data from the so-called Brussels “Silverbox system”, which is an electrical circuit mimicking the forced Duffing oscillator dynamics is used for the identification purpose. Finally, the identified model performance (the simulation errors) is compared with the existing results available in the literature.

Steel wire rope (SWR) can be frequently damaged by various factors. Although weak defect signal detection is the key to guarantee safety, current classic stochastic resonance (SR) models and methods are unavailable due to the complicated interference of noise. Thus, a novel E-exponential SR model and weak signal detection method are proposed in this paper. The principle of this model is first introduced from the perspective of Taylor expansion and nonlinear system analysis. Then, the model performance is simulated and evaluated by output signal analysis and chaos characterizations. Afterwards, model comparison with different system parameters, experiments and case studies regarding three typical wire rope defects inspection and weak signal recognition are conducted to demonstrate the validity of the novel method. Finally, the advantages and limitations of the proposed SR model are discussed.

This article develops an evolving type-2 quantum fuzzy neural network (eT2QFNN) control scheme for achieving trajectory tracking with unmanned aerial vehicles (UAVs). The proposed approach involves quantum membership functions, automatic rule growing process, and parameter adjustment learning scenario to deal with the problems of inadequacy, uncertainties, and noise in conventional control techniques. Furthermore, the proposed approach is operated in a parallel structure with the proportional derivative (PD) controller to compensate the transients in performance and learn the dynamic characteristics of the system. Besides, a sliding theory-based adaptive law is equipped with the control approach to compensate for the nonlinearity of the UAV. To assess the performance, numerical simulations and real-time experiments are carried for pitch and yaw axes control of two degrees of freedom (2DoF) helicopter test rig with the proposed approach. The simulations and experiments are aimed at achieving an offline path tracking with an objective to minimize the deviation error and improve the time response characteristics of the UAVs. The results depict the robustness of the proposed approach in terms of integral time absolute error for a helicopter following various trajectories.

Frequency selective surface (FSS) is critical for electromagnetic (EM) radiation protection due to its high spatial filtering performance, especially for active FSS. Recently, artificial neural network (ANN) has shown great potential in solving EM inverse problems and rapid industrial design. In such an inverse model with ANN, it establishes the relationship between the given inputs of S-parameters and the desired structure parameters or material parameters. However, faced with applications where S-parameters vary in a large frequency range with different curve shapes, such as multi-band microwave devices, equal interval sampling may result in high dimensional inputs and will require more complicated neural network. In this work, we present a Fourier subspace-based deep learning method (FS-BDLM) for FSS inverse design, where the dimension of the input is largely reduced by using Fourier subspace to represent the most salient features of the desired S-parameter performance. Compared with existing deep learning methods, the proposed technique makes inverse neural models more compact and more stable to noise contaminations. The validation of the proposed FS-BDLM is conducted both numerically and experimentally through two dual-passband FSS design examples, where the well-designed FSS is fabricated to validate the technique.

Mathematical morphology spectrum entropy is a signal feature extraction method based on information entropy and mathematical morphology. The scale of structure element is a critical parameter, whose value determines the accuracy of feature extraction. Existing scale selection methods depend on experiment parameters or external indicators including noise ratio, fault frequencies, etc. In many cases, existing methods obtain fix scale and they are not suitable for quantifying the performance degradation and the fault degree of bearings. There are few researches on scale selection based on the properties of mathematical morphology spectrum. In this study, a scale-adaptive mathematical morphology spectrum entropy (AMMSE) is proposed to improve the scale selection. To support the proposed method, two properties of the mathematical morphology spectrum (MMS), namely non-negativity and monotonic decreasing, are proved. It can be concluded from the two properties that the feature loss of MMS decreases with the increase of scale. Based on the conclusion, two adaptive scale selection strategies are proposed to automatically determine the scale by reducing the feature loss of MMS. AMMSE is the integration of two strategies. Compare to the existing methods, AMMSE is not constrained by the information of the experiment and the signal. The scale of AMMSE changes with the signal characteristics and is no longer fixed by experimental parameters. The parameters of AMMSE are more generalizable as well. The presented method is applied to identify fault degree on CWRU bearing data set and evaluate performance degradation on IMS bearing data set. The experiment result shows that AMMSE has better results in both experiments with the same parameters.

Flexible joint robot (FJR) manipulators can offer many attractive features over rigid manipulators, including light weight, safe operation, and high power efficiency. However, the tracking control of the FJR is challenging due to its inherent problems, such as underactuation, coupling, nonlinearities, uncertainties, and unknown external disturbances. In this article, a terminal sliding mode control (TSMC) is proposed for the FJR system to guarantee the finite-time convergence of the systems output, and to achieve the total robustness against the lumped disturbance and estimation error. By using two coordinate transformations, the FJR dynamics is turned into a canonical form. A cascaded finite-time sliding mode observer (CFTSMO) is constructed to estimate states and lumped disturbance in a finite time based on two measurable states, which not only attenuates the measurement noise but also reduces the peaking phenomenon. The closed-loop stability and the finite-time convergence are rigorously proved by using Lyapunov theorem. The upper bound of the finite convergence time is derived for the reaching and sliding phase. Comparative study is conducted experimentally in real time on the FJR manipulator to verify the effectiveness of the proposed control method.

In this paper, the problem of the distributed $H_\infty$ -consensus filtering in finite-time horizon is investigated for a class of Takagi-Sugeno (T-S) fuzzy systems with sensor saturation and unknown bounded noise. Considering the network bandwidth limitation, a dynamic event-triggered mechanism (ETM) is proposed, which leads to lower transmission frequency than the static ETM. Considering the dynamic ETM induces the asynchronous premise variables, a novel piecewise T-S fuzzy distributed $H_\infty$ -consensus filtering is derived. By constructing the Lyapunov function, the sufficient conditions for the finite-time $H_\infty$ performance of the estimation error system are given. Furthermore, to minimize the interference from disturbance to {distributed filtering}, the optimal problem of disturbance attenuate parameter $\gamma$ is solved. Finally, a simulation example is presented to verify the effectiveness of the proposed algorithm.

Frequency is a useful parameter for ensuring the proper functioning of the power system operation, control, and protection. In this paper, a fast and accurate frequency estimator is developed using a Zero-crossing and a Peak, occurring in the buffered data instead of two Zero crossings, which is commonly used to measure the frequency. Prior to this, the signal is preprocessed using the Sliding Goertzel Filter (SGF) that mitigates the impact of undesirable features such as harmonics, noise, etc. from the signal. The two frequency estimators, with and without SGF, then work in parallel to form the hybrid estimator and named as $Z_{C}P_{k}-HFE$. The estimated frequency is chosen based on the minimum error computed from the two parallel estimators. The performance of the estimator is, then, validated using the simulated signals polluted with harmonics, noise, transients etc. Moreover, the evaluation results with the IEEE-13 Active Distribution Network (ADN) incorporated with Photovoltaic DG using and dSPACE1104 and Real Time Digital Simulator (RTDS), show its capability to estimate real-time frequency with satisfactory output.

This paper introduces a new, easy, fast and highly efficient PLL algorithm, that it does not need to adjust every time the input signal changes. This makes it independent of the input signal it receives and, therefore, and in a certain way, universally applicable. In addition, the proposal is implemented exclusively by software, housed in a microcontroller, which also represents another novelty. As another remarkable feature, the proposed algorithm is very simple, with a very low computational cost, which makes it very stable, practically insensitive to noise, and very fast. These characteristics and the performance shown over a wide range of input signal frequencies, make the proposed algorithm suitable for use in different applications, from the electricity grid synchronization to the demodulation of frequency modulation, DC motor drives, etc. In addition to frequency and phase tracking, the developed algorithm generates the signal corresponding to the fundamental frequency of the input signal. Furthermore, the algorithm behavior is not affected by the input signal distortion and is independent of its initial phase. The performance and excellent behavior of the developed algorithm are evaluated through simulations and experimental tests.

Online parameter identification is vital for boosting the accuracy of the battery Equivalent Circuit Model (ECM) under dynamic profiles. However, traditional Recursive Least Squares (RLS) method easily decays with the noise corruption from sensors or insufficient exciting signal in reality, which further limits the performance of ECM in battery modeling and states estimation. This paper thus proposes a reliable online parameter identification method for battery ECM, which utilizes a well-designed Information Appraisal Procedure (IAP) based on the Fisher-information-based Cramer-Rao Lower Bound (CRLB). Without increasing much computing complexity, a comprehensive appraisal indicator, derived recursively from CRLB, enables a new mechanism for online parameter updating. Simulation and experimental results prove the validity of the proposed method under different driving cycles, temperatures, and aging conditions. The results show that the identification accuracy of the proposed method has been significantly improved comparing with a typical RLS and a Multiple Adaptive Forgetting Factors RLS (MAFF-RLS) method.

LoRa, a representative Low-Power Wide Area Network (LPWAN) technology, has been shown as a promising platform to connect Internet of Things. Practical LoRa deployments, however, suffer from collisions, especially in dense networks and wide coverage areas expected by LoRa applications. Existing collision resolution approaches do not exploit the coding properties of LoRa and thus cannot work well for low SNR LoRa signals. We propose NScale to decompose concurrent transmissions by leveraging subtle inter-packet time offsets for low SNR LoRa collisions. NScale (1) translates subtle time offsets, which are vulnerable to noise, to robust frequency features, and (2) further amplifies the time offsets by non-stationary signal scaling, i.e., scaling the amplitude of a symbol differently at different positions. In practical implementation, we propose a noise resistant iterative symbol recovery method to combat symbol distortion in low SNR, and address frequency shifts incurred by CFO and packet time offsets in decoding. We theoretically show that NScale introduces

In recent years, LoRa has been deployed in countless Internet of Things (IoT) applications across the globe. However, as LoRa is a proprietary technology, research into its physical-layer performance has been challenging. Implementing LoRa on software defined radio (SDR) platforms yields valuable insight into the physical layer of the LoRa standard and paves the way for improvements in packet reception capabilities for LoRa receivers. This paper presents an independently developed packet reception algorithm, which drastically improves the physical performance of LoRa communication links. The advanced signal presence detection, synchronization and symbol detection strategies are shown to significantly increase packet reception ratios in extremely adverse noise conditions. Multiple algorithm variations are presented and compared in terms of bit error rate (BER) performance and computational cost. In comparison to a theoretical system with perfect channel state information, the simulated bit error rate performance of the best performing algorithm only requires an increase of 1.6 dB in signal-to-noise ratio (SNR) to exhibit the same performance. Finally, SDR implementations of the algorithms exhibit average SNR performance gains up to 4.7 dB when compared to commercially available hardware.

In this paper, a sequential ensemble of intelligence-based methods is used for fault detection and classification in microgrids. The proposed scheme is recommended because of the impracticality of conventional fault detection and protection due to microgrid dynamic behavior and dependency of traditional methods on faults current level or impedance. These methods use the collective decision of learners to increase their accuracy. This is done by distributing the knowledge among the classifiers. The proposed ensemble method is called Brownboost. Its main advantage over its counterparts is that it uses a nonconvex optimization method. This makes it robust to overfitting and applicable and practical for a real-world noisy or misclassified data. In addition, a signal processing method called the Hilbert-Huang transform was chosen for feature extraction from signals transient behavior to reduce the noise sensitivity. In this method, current difference of the both ends of line is selected to decompose various types of adaptive basis signal processing method. The results are validated in IEC test microgrid with various noise penetration level and synchronization delays. Compared to traditional strong classifiers, this method is easy to tune and program and is robust to overfitting. Moreover, it can be justified for real-world imperfect data.

Insufficient signal-to-noise ratio (SNR) limits the quality of cardiac pulses measured by multitap CMOS image sensors (CISs). We present an in-pixel temporal redundant samplings (TRS) method for enhancing the SNR through sampling the charges originated by one individual optical scene into the multiple storage taps in turn with the identical time window, enabling magnification of the image signal in shot noise domain. Modeling indicates that the linear SNR increases with a factor square root of TRS sampling number, which is also verified by characterization results utilizing a four-tap CIS. Application results show that the high-frequency (HF) noise components observed in the measured cardiac pulses can be attenuated by 67.9%, 48%, and 53.6% with heart rate detection accuracies up to 99.7%, 99.3%, and 98.7% by applying TRS modes under given conditions of stable ambient, background light (BGL) variation, and motion disturbance, respectively. Moreover, the proposed method is as well foreseeable to be extended to other multitap-involved imaging applications.

OBJECTIVE: Reducing time-to-treatment and providing acute management in stroke are essential for patient recovery. Electrical bioimpedance (EBI) is an inexpensive and non-invasive tissue measurement approach that has the potential to provide novel continuous intracranial monitoring—something not possible in current standard-of-care. While extensive previous work has evaluated the feasibility of EBI in diagnosing stroke, high-impedance anatomical features in the head have limited clinical translation. METHODS: The present study introduces novel electrode placements near highly-conductive cerebral spinal fluid (CSF) pathways to enhance electrical current penetration through the skull and increase detection accuracy of neurologic damage. Simulations were conducted on a realistic finite element model (FEM). Novel electrode placements at the tear ducts, soft palate and base of neck were evaluated. Classification accuracy was assessed in the presence of signal noise, patient variability, and electrode positioning. RESULTS: Algorithms were developed to successfully determine stroke etiology, location, and size relative to impedance measurements from a baseline scan. Novel electrode placements significantly increased stroke classification accuracy at various levels of signal noise (e.g. p < 0.001 at 40 dB). Novel electrodes also amplified current penetration, with up to 30% increase in current density and 57% increased sensitivity in central intracranial regions (p

The Nonlinear Noisy Leaky Integrate and Fire (NNLIF) model is widely used to describe the dynamics of neural networks after a diffusive approximation of the mean-field limit of a stochastic differential equation system. When the total activity of the network has an instantaneous effect on the network, in the average-excitatory case, a blow-up phenomenon occurs. This article is devoted to the theoretical study of the NNLIF model in the case where a delay in the effect of the total activity on the neurons is added. We first prove global-in-time existence and uniqueness of “strong” solutions, independently of the sign of the connectivity parameter, that is, for both cases: excitatory and inhibitory. Secondly, we prove some qualitative properties of solutions: asymptotic convergence to the stationary state for weak interconnections and a non-existence result for periodic solutions if the connectivity parameter is large enough. The proofs are mainly based on an appropriate change of variables to rewrite the NNLIF equation as a Stefan-like free boundary problem, constructions of universal super-solutions, the entropy dissipation method and Poincaré’s inequality.

The dynamic estimation of epidemic spreading on networks is essential for controlling morbidity. Nonlinear Kalman filters, which are capable of estimating the hidden state of a nonlinear system, can be utilized for dynamic state estimation of epidemic spreading. Traditional nonlinear Kalman filters perform optimization using the minimum mean square error (MMSE) criterion. Since observable measurements are generally corrupted by non-Gaussian noise, maximum correntropy criterion (MCC)-based nonlinear Kalman filters have been used for improving robustness against non-Gaussian noise. In comparison with the MCC, generalized correntropy is more robust and flexible in the presence of non-Gaussian noise. In this article, epidemic spreading is described by a compartmental model, i.e., susceptible-infected-recovered-susceptible model. Based on the compartmental model, the generalized correntropy-based sparse Gauss-Hermite quadrature filter (GCSGHQF) is proposed for epidemic tracking on homogeneous networks. The GCSGHQF approximates the Gaussian-weighted integrals by utilizing the sparse Gauss-Hermite quadrature rule. In addition, the generalized correntropy is applied to enhancing robustness in the presence of non-Gaussian noise. Simulation results show that the GCSGHQF exhibits a higher filtering accuracy and improves robustness compared to traditional nonlinear Kalman filters.

Positron emission tomography (PET) imaging is used to track biochemical processes in the human body. PET image quality is limited by noise and several methods have been implemented to improve the quality. Kernel-based image reconstruction is among the methods implemented to increase PET image quality and commonly uses a Gaussian kernel to include spatial correlations from image priors into the forward projection model of PET. Unfortunately, the Gaussian kernel tends to smooth details in the reconstructed image. To reduce noise without losing contrast details, a different kernel is needed. Wavelet kernels can be more efficient than the Gaussian kernel in reducing noise while keeping contrast details 1 by better separating signal from noise and thus it does not over smooth peak values in the final reconstructed images. In this work, we evaluate a wavelet kernel for kernel-based PET image reconstruction. For this goal, a wavelet kernel approach has been tested using simulated brain data, physical phantom data and patient data. Reconstruction results are presented and discussed in detail comparing the wavelet kernel method with the Gaussian kernel method. Our results suggest that a wavelet kernel performs better in contrast recovery for phantoms and also results in higher signal-to-noise ratio (SNR) for real patient data.

With the rapid development of microwave photonics technology, high-speed processing and ultra-weak signal detection capability have become the main bottlenecks in many applications. Thanks to the ultra-weak signal detection capability and the extremely low timing jitter properties of single-photon detectors, the combination of single-photon detection and classical microwave photonics technology may provide a solution to break the above bottlenecks. In this paper, we first report a novel concept of single-photon microwave photonics (SP-MWP), a SP-MWP signal processing system with phase shifting and frequency filtering functionalities is demonstrated based on a superconducting nanowire single photon detector (SNSPD) and a successive time-correlated single photon counting (TCSPC) module. Experimental results show that an ultrahigh optical sensitivity down to −100 dBm has been achieved, and the signal processing bandwidth is only limited by the timing jitter of single-photon detectors. In the meantime, the proposed system demonstrates an ultrahigh anti-interference capability, only the signal which is phase locked by the trigger signal in TCSPC can be extracted from the detected signals combining with noise and strong interference. The proposed SP-MWP concept paves a way to a novel interdisciplinary field of microwave photonics and quantum mechanism, named by quantum microwave photonics.

The fundamental current component at the secondary side of current transformer (CT) is often accompanied with decaying direct current (DC) component produced by the system and the CT. The Discrete Fourier transform (DFT) is known as a powerful tool in digital protection for estimation of the fundamental component in the presence of harmonics. The decaying DC component, unlike harmonics, is not limited to a distinct frequency but include a wide range of low frequencies affecting the DFT output by undesirable oscillations. This paper proposes a method based on two selective interval integration for accurate estimation and elimination of the decaying DC component from the fundamental component of fault current. The two data intervals are separated by half a cycle minus one interval. The proposed algorithm is first tested using data derived by mathematical formulation under different conditions such as sampling frequency, noise, and characteristics of the DC component. The algorithm is also tested by simulation study using PSCAD/EMTDC for different fault conditions in a typical transmission system. In order to test the algorithm performance for practical applications, a Mho distance relay is simulated in the Matlab/Simulink environment. All the test cases verify efficiency, accuracy and speed of the proposed algorithm.

A novel fault location scheme using feeder terminal units (FTUs) for high-impedance grounding faults in resonant grounding sys-tems (RGSs) is presented. The parallel resonance for a high-impedance fault (HIF) in an RGS with inverter-interfaced dis-tributed generators (IIDGs) is analyzed. The process exhibits unique distribution features for transient zero-sequence compo-nents involving underdamped and overdamped states. We con-clude that the transient zero-sequence admittances (TZSAs) from FTUs upstream of the fault point have the maximum value, while the TZSAs obtained from FTUs downstream of the fault point are small for HIFs. Therefore, the proposed location scheme uses FTUs to calculate the maximum values of the TZSA. By compar-ing with action criteria, the distribution automation (DA) master station utilizes the above results of each FTU to locate the faulty feeder section. The accuracy of the fault characteristics and the proposed method are validated by applying a real-time digital simulator (RTDS) and an FTU-based monitoring system. Addi-tionally, the proposed scheme demonstrates an excellent and reli-able performance, considering various faulty feeder sections, fault resistances, arcing faults, fault inception angles, imbalances and the presence of IIDGs and noise.

This article proposed a new multi-sensor fusion fault diagnosis method for gearbox, namely residual gated dynamic sparse network, to improve multi-sensor feature learning and fusion ability. Considering that the fault sensitivity of the sensor varies with mounted location and complex transfer path modulation causes information from multi-sensor redundant, the lightweight channel attention unit was designed to strengthen the feature extraction ability of the network. The developed gated dynamic sparse unit was inserted into the deep architecture to eliminate ineffective components caused by high noise interference. Besides, the loss function was improved with multiple activation criteria to enhance convergence ability. The results of experiments and the engineering application showed that the proposed method was more effective than other methods under varying degrees of noise interference.

The development of the IIOT paradigm brings the possibility of a significant transformation within the manufacturing industry. This paradigm is based on sensing large amounts of data, so that it can be employed by intelligent control systems eliciting optimal decisions in real time. Ensuring the accuracy and reliability of intelligent wireless sensing and control systems is crucial towards achieving this goal. In this paper, a novel solution is proposed to denoise data while simultaneously preserving actual anomalies. The proposed approach operates by measuring both the neighbor and background contrasts to compute a noise score. The trust level of each data point is then calculated through a correlation measure, to purge spurious data. Extensive experiments on real data-sets demonstrate that the proposed approach yields effective performance, as compared to existing methods, and it meets the requirements of low latency, facilitating the normal operation of the monitored control systems.

Due to the motor parameter mismatches, the errors of current sampling circuit, converter nonlinearities and noise, DC bias and high order harmonics exist in flux model, which make the traditional flux observer estimation inaccurate. In this paper, an interior permanent magnet synchronous motors (IPMSM) sensorless drive method based on a five order flux observer is proposed to suppress DC bias and high order harmonics. The proposed five order flux observer can completely remove DC bias and has strong filtering ability for high order harmonics. Additionally, the parameters of the five order flux observer are set through s-domain analysis. Then, the discrete five order flux observer is obtained to better implementation in digital systems, and the discrete five order flux observer is easy to implement. The proposed five order flux observer is verified by experiments on a 2.0-kW IPMSM drive platform.

To improve the feature extraction of ship-radiated noise in a complex ocean environment, a novel feature extraction method for ship-radiated noise based on complete ensemble empirical mode decomposition with adaptive selective noise (CEEMDASN) and refined composite multiscale fluctuation-based dispersion entropy (RCMFDE) is proposed. CEEMDASN is proposed in this paper which takes into account the high frequency intermittent components when decomposing the signal. In addition, RCMFDE is also proposed in this paper which refines the preprocessing process of the original signal based on composite multi-scale theory. Firstly, the original signal is decomposed into several intrinsic mode functions (IMFs) by CEEMDASN. Energy distribution ratio (EDR) and average energy distribution ratio (AEDR) of all IMF components are calculated. Then, the IMF with the minimum difference between EDR and AEDR (MEDR) is selected as characteristic IMF. The RCMFDE of characteristic IMF is estimated as the feature vectors of ship-radiated noise. Finally, these feature vectors are sent to self-organizing map (SOM) for classifying and identifying. The proposed method is applied to the feature extraction of ship-radiated noise. The result shows its effectiveness and universality.

In industrial internet-of-things (IIoT), conventional event-triggered control method requires sensors continuously monitoring control states in periodical time. This would consume a huge amount of energy resource, which impedes the application of such conventional methods since the sensors are powered by batteries in most cases. To deal with this issue, this paper proposes a new state-to-noise-ratio (SNR) based transmission scheduling method from wireless communication perspective. Specifically, we first adopt an Additive White Gaussian Noise (AWGN) channel model to represent the event-triggered control model, by which we can convert the original event-triggered control problem into a wireless transmission scheduling problem from the sensor to the controller. Based on that, the mutual information is proposed to measure the value of the control states. More importantly, a new control SNR is defined based on mutual information. Using control SNR as the transmission activating criterion, perfect state observation assumption and sensor’s monitoring with periodical time in traditional event-triggered control are no longer needed. Furthermore, we provide an analytical expression of dynamic SNR thresholds to determine whether the transmission should be activated. Finally, we prove the mean-square stability of our proposed method. Numerical comparisons are provided to demonstrate the effectiveness of our proposed method.

This article describes a novel technique for removing the influence of a continuous wave (or narrowband) electromagnetic disturbance in orthogonal frequency division multiplexing (OFDM) systems with quadrature amplitude modulation or phase-shift keying modulation schemes. The technique relies on a mathematical derivation of how a continuous wave electromagnetic disturbance induces spectral leakage in the OFDM system. Using this derivation, an algorithm is obtained that aims to cancel the continuous wave electromagnetic disturbance by estimating its frequency, retrieving its amplitude and phase from the corrupted OFDM frame. Note that the algorithm does not require any prior information about the disturbance. The proposed algorithm is validated through thorough simulations, covering different modulations, noise variations, and spectral leakage cases, and compared with standard OFDM performance without the algorithm. Through our experimentation, it has been demonstrated that for a disturbance frequency not equal to one of the OFDM subcarriers, the algorithm can estimate the disturbance frequency with high precision, resulting in a gain of more than 80 dB when compared to the case without the algorithm. For a disturbance frequency equal to one of the OFDM subcarriers, a simple coding technique such as Hamming & interleaving enables the user to remove the disturbance. Though the paper is accepted, it will only be included in the next issue of the journal, so the pagination field remains blank. The paper has been published in the Early Access area on IEEE Xplore ispartof: IEEE Transactions on Electromagnetic Compatibility vol:64 issue:2 pages:338-347 status: Published online

On the basis of the double coupled Duffing oscillator, a differential double coupled Duffing oscillator method, called the proposed method 1, is proposed. The large scale state is judged by the differential timing sequence diagram, and its detection result is more intuitive. To overcome the frequency limitation of differential double coupled Duffing oscillator in detecting unknown frequency periodic signal, according to the proposed method 1, based on the intermittent chaos phenomenon, the differential double coupled Duffing oscillator variable step size detection method, called the proposed method 2, is proposed. This method can be used to detect Gaussian noise background periodic signal and K-distributed noise background periodic signal. Compared with the double Duffing method and the double coupled Duffing method, its average signal-to-noise ratio (SNR) threshold is reduced by 4.94 dB and 1.68 dB respectively, and the detection result is more intuitive to judge through the differential timing sequence diagram. The accuracy and universality of the proposed method 2 are proved by the detection of three measured underwater weak target signals. Experiments show that it has some characteristics such as intuitive detection result, high sensitivity, low SNR threshold, few oscillators, low system complexity and so on.

This paper explores a novel composite signal extraction method (SEM) to extract high-precision angular position from the signals detected by Hall sensors in the gimbal servo system of single gimbal magnetically suspended control moment gyro (SGMSCMG). Due to magnetic field distortion and other factors, the detected signals involve lots of harmonics and unequal amplitude which decrease the position resolution accuracy significantly. In the proposed method, normalization is firstly performed to obtain signals with equal amplitude. Then, on the basis of off-line analysis, signal reconstructed model is established to suppress the most harmonics. Finally, the errors of the models and un-modeled noise are restrained by applying the discrete extended state observer (ESO). Simulated results show that the influence of harmonics and unequal amplitude on the angular position can be almost attenuated by the proposed method. Besides, plentiful experiments on a SGMSCMG demonstrate the validity of the proposed method, and the desired angular position of the ultra-low speed servo system can be extracted effectively.

The dual-active-bridge (DAB) dc-dc converter is a promising candidate for the isolated dc-dc power transferred applications, such as in dc distribution system, the solid-state transformer and the energy storage system. In these applications, the fast-dynamic response is usually a core requirement, especially under load changes. In order to improve dynamic performance of DAB dc-dc converter, this paper proposes a simple load current estimating (LCE) scheme with delay compensation for extremely fast dynamic performance. Based on the current flowing model of DAB dc-dc converter, the LCE strategy is proposed with single-phase-shift (SPS) modulation method. Moreover, the inherent switching-period delay phenomenon of the LCE scheme is analyzed. Therefore, the corresponding delay compensation method is proposed for further boosting dynamic responses, and the dynamic limitation of the LCE scheme may be obtained for DAB dc-dc converter. Then, for the proposed LCE scheme, a damping coefficient is introduced to restrict the potential instability caused by the measurement noise. In addition, the extended rule for the optimized triple-phase-shift modulation method is also discussed. Finally, the simulation result and the experimental result both validate the excellent performance of this proposed LCE strategy with delay compensation.

Massive multiple-input multiple-output (MIMO) systems can support a large number of industrial Internet of things (IIoT) devices using many service antennas that are equipped at a base station (BS). Even though Massive MIMO can increase both spectral efficiency (SE) and energy efficiency (EE), to support numerous IIoT devices, a drastic amount of downlink power consumption can be required. We investigate the performance of downlink signal transmission for massive IIoT networks using Massive MIMO, and propose a downlink signal transmission scheme that can significantly reduce the transmission power consumption with little SE loss. We consider a system in which the number of IIoT devices is much larger than the number of service antennas, and in order to simultaneously support a large number of IIoT devices, every orthogonal uplink reference signal (RS) may be reused by a few IIoT devices. In this situation, we derive simple SE approximations, and based on the approximation, a target data rate is determined. The target data rate is used to determine a target signal-to-interference and noise ratio (SINR), and the corresponding signal power. In addition, the SE approximation can be used as an upper bound and the corresponding signal power can be determined by setting an adjustable parameter which is multiplied to the upper bound. The maximum allowable uplink/downlink signal power is pre-defined to prevent unexpected high power requirements. The simulation results are provided to show the effectiveness of the proposed schemes.

The method based on machine vision image processing is used to detect the surface defects of Si3N4 bearing roller. Owing to the variety of defects, small area and low contrast, it is easy to miss or error detection. In this paper, an adaptive update template defect enhancement algorithm based on Gaussian model is proposed. First, a large number of surface images of Si3N4 bearing roller are collected to obtain the non-defect background statistical feature, and the background characteristic curve is fitted by Gaussian model. Further, the initial background template is gained according to the Gaussian curve. Then, combined with the gray distribute of defect images and initial background template, unique adaptive update template can be established. Finally, subtraction operation and nonlinear enhancement are used to improve the comparison of defect information and background. Through inverse sorting, adaptive threshold segmentation and Canny operation, the precise positioning of defects is realized. The enhancement algorithm can effectively enhance the contrast and eliminate the influence of noise. The average detection time is 0.84s, and the detection accuracy is 96.2%.

The pattern recognition of surface electromyography (sEMG) signal is an important application in the realization of human-machine interface. However, due to the disturbance of human body, sensors and environment, sEMG signal usually contains lots of noise, which brings great challenges to the high-accuracy sEMG pattern recognition. In addition, embedded human wearable devices are becoming more and more popular nowadays. How to realize the sEMG recognition method with low power consumption and high noise immunity has also become a difficult and very meaningful research topic. In this paper, a spiking neural network (SNN) classification method based on second-order information bottleneck training is proposed. Firstly, the training loss function for classification neural networks is constructed based on the proposed second-order information bottleneck. The method is used to train the conventional continuous-valued neural network and convert it into an SNN model with equivalent structure and connection weights. Then, the converted SNN is used to classify the sEMG signal patterns. Through a series of theoretical analysis and experimental results, it is proved that this method has significant advantages in terms of generalization of network determination and computational efficiency. The experimental code can be accessed from https://github.com/anvien/2OIB-for-sEMG-Recognition.

Phase-generated carrier (PGC) demodulation technology has been widely used in fiber-optic interferometric sensors system in recent years. Nonlinear errors caused by interference noise and parasitic amplitude modulation (AM) are key factors that affect the reliability of traditional PGC demodulation system based on arctangent (PGC-Arctan) algorithm. In order to enhance the reliable performance of PGC demodulation system under the conditions of low signal-to-noise ratio (SNR) and high parasitic AM interference, an ellipse fitting algorithm based on the Gauss–Newton iteration (GNI) is proposed, which is called the PGC-Arctan-GNI demodulation algorithm. The proposed algorithm uses the Euclidean distance to accurately estimate the geometric parameters of ellipse, which can correct the nonlinear distortion of the demodulated signal. In addition, the PGC demodulation system designed based on GNI algorithm has the advantages of strong antinoise ability, good antiparasitic AM ability, and widely dynamic range of the input signal amplitude. Experimental results show that, compared with the direct demodulation algorithm and the ellipse fitting algorithm based on the least squares (LS) method, the SINAD value of the proposed algorithm is always greater than 30 dB and higher than the other two algorithms under the condition of low SNR. Under the condition that the parasitic AM index $m$ changes from 0 to 0.5, the signal-to-noise and distortion ratio (SINAD) curve of the proposed algorithm is more stable than the curves of the other two algorithms, and the fluctuation range of the SINAD value does not exceed 3 dB. This algorithm also has a wider input signal amplitude range than the LS algorithm. Especially, when the amplitude of the measured signal is less than $\pi$ /2, the relative amplitude error of the GNI algorithm is less than 2% and significantly smaller than the LS algorithm. Finally, the demodulation results using real measured data in the actual system show that the frequency and amplitude of the demodulated signal are same as the original signal. At the same time, the SNIAD value of this system reaches 67.40 dB, the spurious-free dynamic range value reaches 68.82 dB, and the total harmonic distortion value reaches -68.40 dB@1 KHz. In summary, the proposed PGC-Arctan-GNI algorithm can eliminate system nonlinear errors caused by the parasitic AM, harmonics, and noise interference by estimating the ellipse parameters, thereby effectively enhancing the stability and reliability of the PGC demodulation system.