Signal Detection Method Research Articles

Modulated signal detection method for fault diagnosis

Envelope analysis is a dominant approach in detecting modulated fault signals in rotating machinery. The Hilbert transform is a popular method used to obtain envelopes. However, envelopes based on the Hilbert transform are only suitable for narrowband signals. Signals are usually filtered before envelope demodulation, which leads to the challenging issue of filter selection. A modulated fault signal detection method using the upper and lower envelopes of the signals is proposed. This method can demodulate the modulated fault signals directly from the raw data, which can include wideband and narrowband signals. The proposed method uses a simple algebraic operation instead of a transform function, so it is convenient in practical applications. It is tested with some simulated signals and some real-measured data. The test results show that the method can effectively demodulate a modulated fault signal directly from the raw data.

Study on the mean first-passage time and stochastic resonance of a multi-stable system with colored correlated noises

The mean first-passage time (MFPT) and the weak signal detection method of stochastic resonance (SR) on multi-stable nonlinear system under color correlated noise are studied. Using the uniform color noise approximation method, the Fokker-Planck equation of the system is obtained, and the steady-state probability density function of the multi-stable system driven by the multiplicative noise and additive noise is derived. On the basis of this, the formula of MFPT is derived, and the influence of parameters on the MFPT is analyzed. The problem of weak signal detection under color noise background is studied based on multi-stable SR. The results of simulation and experiment show that the method can effectively extract the frequency feature of weak signal in the background of color noise.

End-to-end LDA-based automatic weak signal detection in web news

An extremely competitive business environment requires every company to monitor its competitors and anticipate future opportunities and risks, creating a dire need for competitive intelligence. In response to this need, foresight study became a prominent field, especially the concept of weak signal detection. This research area has been widely studied for its utility, but it is limited by the need of human expert judgments on these signals. Moreover, the increase in the volume of information on the Internet through blogs and web news has made the detection process difficult, which has created a need for automation. Recent studies have attempted topic modeling techniques, specifically latent Dirichlet allocation (LDA), for automating the weak signal detection process; however, these approaches do not cover all parts of the process. In this study, we propose a fully automatic LDA-based weak signal detection method, consisting of two filtering functions: the weakness function aimed at filtering topics, which potentially contains weak signals, and the potential warning function, which helps to extract only early warning signs from the previously filtered topics. We took this approach with a famous daily web news dataset, and we could detect the risk of the COVID19 pandemic at an early stage.

Low-Complexity 1-bit Detection of Parametric Signals for IoT Sensing Applications

A detection method for parametric signals in noise is proposed. Since the detection is performed by processing 1-bit quantized data, the method can be implemented using low-complexity systems. A generalized likelihood ratio test (GLRT) is derived, which is based on parametric estimates of the amplitude and phase of a periodic signal. The asymptotic properties of the test statistic of the GLRT are derived analytically. Such properties are then employed to study the performance of the detector, by comparing them with the clairvoyant detector. The performance of the developed detector is evaluated by numerical simulations and validated by a simple experimental setup. The impact of noise and threshold selection on detection performance and the promptness of response are also numerically analyzed. Given its implementation simplicity, the proposed detector does not require high-resolution circuitry nor computationally intensive hardware. Therefore, it could enable novel applications in the Internet-of-Things domain, such as low-cost radar systems and sensor networks.

Analysis and Optimization of Dynamic Performance for Resonant Integrated Optical Gyroscope

In this article, a closed-loop signal detection method is proposed to improve the dynamic performance of the resonant integrated optical gyroscope. We firstly establish a closed-loop error model of the angular velocity tracking system with optical nonlinearity and parameter uncertainty based on triangular phase modulation technology. Then, we optimize the loop gains of the signal processing to improve the signal-to-noise ratio of the closed-loop error signal about angular velocity tracking. Secondly, we design a timing sequence control method to effectively eliminate the time-delay of signal processing for optimizing the dynamic performance of the angular velocity tracking system. Finally, a control algorithm is designed to suppress the influence of the optical nonlinearity and parameter uncertainty for improving the dynamic tracking performance of resonant integrated optical gyroscopes. The experimental results demonstrate that the resonant integrated optical gyroscope has a rise time less than 36 μs, a high frequency response with the bandwidth 13.1 kHz, and a low scale factor nonlinearity less than 269.2 ppm, which verifies the effectiveness of the proposed closed-loop signal detection method for the resonant integrated optical gyroscope.

All-optical modulation and detection using a gain medium in a pulse shaper

We demonstrate all-optical modulation and ultrafast detection using an on-resonance optical gain medium, combined with spectral splitting in a Fourier transform pulse shaper. Multiple spectral channels of one optical beam can be independently modulated in time by another beam, allowing high-rate modulation and multiplexing without requiring ultrafast response from the gain medium. For detection of sub-picosecond signals we demonstrate a method of ultrafast signal detection (temporal imaging with no spatial resolution) that utilizes the spatio-temporal tilt of an optical pulse in a pulse shaper. The proposed methods can find applications in optical information technology and ultrafast imaging.

Molecular Signal Tracking and Detection Methods in Fluid Dynamic Channels

This method and data paper sets out the macro-scale experimental techniques to acquire fluid dynamic knowledge to inform molecular communication performance and design. Fluid dynamic experiments capture latent features that allow the receiver to detect coherent signal structures and infer transmitted parameters for optimal decoding. This paper reviews two powerful fluid dynamical measurement methodologies that can be applied beneficially in the context of molecular signal tracking and detection techniques. The two methods reviewed are Particle Image Velocimetry (PIV) and Planar Laser-Induced Fluorescence (PLIF). Step-by-step procedures for these techniques are outlined as well as comparative evaluation in terms of performance accuracy and practical complexity is offered. The relevant data is available on IEEE DataPort to help in better understanding of these methods.

Extracting weak multi-frequency signal in heavy colored noise

The extraction of weak multi-frequency signal in the background of heavy colored noise is studied. For the multi-frequency signal which cannot be directly decomposed by ensemble empirical mode decomposition (EEMD), further processing is made in the paper. Based on the normalized autocorrelation analysis and EEMD, a new method of weak multi-frequency signal detection is proposed in the article. Firstly, the adaptive noise reduction of multi-frequency signal with strong colored noise is realized by the normalized autocorrelation analysis. Then, the denoised multi-frequency signal is decomposed by the EEMD method. A series of monochromatic intrinsic mode functions are obtained to representing the characteristics of multi-frequency signal. Finally, two mechanical fault experiments are designed to verify the applicability of the proposed method in the fault diagnosis. The vibration signals with bearing pedestal bolt looseness fault, bearing outer raceway and rolling element compound fault were collected, respectively. The experimental results show that the weak multi-frequency fault features in the vibration signals with heavy colored noise are effectively extracted by the new method. The proposed method has a good application prospect in the field of signal processing.

Uplink NOMA signal transmission with convolutional neural networks approach

Non-orthogonal multiple access (NOMA), featuring high spectrum efficiency, massive connectivity and low latency, holds immense potential to be a novel multi-access technique in fifth- generation (5G) communication. Successive interference cancellation (SIC) is proved to be an effective method to detect the NOMA signal by ordering the power of received signals and then decoding them. However, the error accumulation effect referred to as error propagation is an inevitable problem. In this paper, we propose a convolutional neural networks (CNNs) approach to restore the desired signal impaired by the multiple input multiple output (MIMO) channel. Especially in the uplink NOMA scenario, the proposed method can decode multiple users' information in a cluster instantaneously without any traditional communication signal processing steps. Simulation experiments are conducted in the Rayleigh channel and the results demonstrate that the error performance of the proposed learning system outperforms that of the classic SIC detection. Consequently, deep learning has disruptive potential to replace the conventional signal detection method.

Extended multi-item gamma Poisson shrinker methods based on the zero-inflated Poisson model for postmarket drug safety surveillance.

Bayesian signal detection methods, including the multiitem gamma Poisson shrinker (MGPS), assume a Poisson distribution for the number of reports. However, the database of the adverse event reporting system often has a large number of zero-count cells. A zero-inflated Poisson (ZIP) distribution can be more appropriate in this situation than a Poisson distribution. Few studies have considered ZIP-based models for Bayesian signal detection. In addition, most studies on Bayesian signal detection methods include simulation studies conducted assuming a gamma distribution for the prior. Herein, we extend the MGPS method using the ZIP model and apply various prior distributions. We evaluated the extended methods through an extensive simulation using more varied settings for the model and prior than existing methods. We varied the total number of reports, the number of true signals, the relative reporting rate, and the probability of observing a true zero. The results show that as the probability of observing a zero count increased, methods based on the ZIP model outperformed the Poisson model in most cases. We also found that using the mixture log-normal prior resulted in more conservative detection than other priors when the relative reporting rate is high. Conversely, more signals were found when using the mixture truncated normal distributions. We applied the Bayesian signal detection methods to data from the Korea Adverse Event Reporting System from 2012 to 2016.

Sensitive Feature Extraction of Telemetry Vibration Signal Based on Referenced Manifold Spatial Fusion Learning

The telemetry vibration signal contains a large number of state characteristic information in the test process of the aircraft, which has the characteristics of nonlinearity, nonstationary, and strong noise. How to effectively extract the weak sensitive features on the operating state of the system and accurately detect the abnormal pattern are directly related to the accuracy of state analysis for flights. To solve these problems, a sensitive feature extraction and anomaly detection method for the telemetry vibration signal is proposed based on referenced manifold spatial fusion learning. First, the multiscale analysis method is used to orthogonally decompose the signals into each of the scale bands and the multiscale feature is extracted to construct the high-dimensional feature set. Then, the same normal signal sample is combined with the same type of abnormal sample to establish the exclusive reference model, and the linear manifold learning is used to obtain the multiscale manifold feature difference of each reference model to enhance the sensitivity of anomalous features. The projection matrix for each reference model is used to make the original feature set robust and obtain the low-dimensional multiscale sensitive manifold feature. Finally, the input to the classifier is used to realize the state recognition of the unknown sample. The measured signal is used to verify the effectiveness of the algorithm.

Machine Fault Detection under the Background of Strong Interference Based on Line Learning Music Algorithm

First, this article introduces the importance of fault diagnosis in social production, and analyzes many existing machine fault detection methods. Considering the use conditions of the algorithm, these methods may be complicated and may not be suitable for online applications. Especially for the background of strong interference, this phenomenon occurs in the early stage of machine failure, and the traditional weak signal detection method loses its function. The characteristics of random noise modulation are given, and then an improved online learning evolutionary MUSIC algorithm is designed. First, it constructs a data array based on the sampling sequence, and then analyzes the signal spectrum under the condition that the number of signals is underestimated. Because traditional analysis methods are limited to current data, it is difficult to obtain stable analysis results under strong interference. Finally, some historical analysis data is weighted to obtain the true spectrum. The effectiveness of the algorithm can be seen through simulation. For weak fault signals under strong interference background, this method has better detection performance than FFT algorithm.

Post-Marketing Safety Surveillance for the Adjuvanted Recombinant Zoster Vaccine: Methodology.

A diligent, systematic, regular review of aggregate safety data is essential, particularly early after vaccine introduction, as this is when safety signals not identified during clinical development may emerge. In October 2017, the US Centers for Disease Control and Prevention Advisory Committee on Immunization Practices recommended the adjuvanted recombinant zoster vaccine (RZV; Shingrix, GSK) as the preferred vaccine for preventing herpes zoster (HZ) and related complications in immunocompetent adults aged ≥ 50 years. Subsequently, GSK experienced an unprecedented high demand for RZV. In this methodology paper, we summarize the enhanced measures undertaken to assess RZV safety during its early post-marketing experience in the USA, Canada and Germany. In addition to the routine signal-detection methods already in place for all vaccines, GSK established tailored and enhanced safety monitoring for RZV based on aggregate data of spontaneous reports and manufacturing data. Proactive, near real-time detection and evaluation of signals was a key objective. A dedicated in-house signal-detection tool customized for RZV was employed on a weekly (rather than the routine monthly) basis, allowing for a centralized, more frequent review of data on a single web-based platform. We also identified the background incidence rates of preselected medical events of interest in the first countries to introduce RZV (USA, Canada and Germany) to perform observed-to-expected analyses. This approach may offer a solution to the challenges associated with the assessment and monitoring of vaccine safety in an efficient and timely manner in the context of high vaccine uptake.Electronic supplementary materialThe online version of this article (10.1007/s40264-020-00989-2) contains supplementary material, which is available to authorized users.

Spectral Convolution Feature-Based SPD Matrix Representation for Signal Detection Using a Deep Neural Network.

Convolutional neural networks have powerful performances in many visual tasks because of their hierarchical structures and powerful feature extraction capabilities. SPD (symmetric positive definition) matrix is paid attention to in visual classification, because it has excellent ability to learn proper statistical representation and distinguish samples with different information. In this paper, a deep neural network signal detection method based on spectral convolution features is proposed. In this method, local features extracted from convolutional neural network are used to construct the SPD matrix, and a deep learning algorithm for the SPD matrix is used to detect target signals. Feature maps extracted by two kinds of convolutional neural network models are applied in this study. Based on this method, signal detection has become a binary classification problem of signals in samples. In order to prove the availability and superiority of this method, simulated and semi-physical simulated data sets are used. The results show that, under low SCR (signal-to-clutter ratio), compared with the spectral signal detection method based on the deep neural network, this method can obtain a gain of 0.5–2 dB on simulated data sets and semi-physical simulated data sets.

Multiple Access in Cell-Free Networks: Outage Performance, Dynamic Clustering, and Deep Reinforcement Learning-Based Design

In future cell-free (or cell-less) wireless networks, a large number of devices in a geographical area will be served simultaneously in non-orthogonal multiple access scenarios by a large number of distributed access points (APs), which coordinate with a centralized processing pool. For such a centralized cell-free network with static predefined beamforming design, we first derive a closed-form expression of uplink outage probability for a user/device. To reduce the complexity of joint processing of received signals in presence of a large number of devices and APs, we propose a novel dynamic cell-free network architecture. In this architecture, the distributed APs are clustered (i.e. partitioned) among a set of subgroups with each subgroup acting as a virtual AP in a distributed antenna system (DAS). The conventional static cell-free network is a special case of this dynamic cell-free network when the cluster size is one. For this dynamic cell-free network, we propose a successive interference cancellation (SIC)-enabled signal detection method and an inter-user-interference (IUI)-aware receive diversity combining scheme. We then formulate the general problem of clustering the APs and designing the beamforming vectors with an objective such as maximizing the sum rate or maximizing the minimum rate. To this end, we propose a hybrid deep reinforcement learning (DRL) model, namely, a deep deterministic policy gradient (DDPG)-deep double Q-network (DDQN) model to solve the optimization problem for online implementation with low complexity. The DRL model for sum-rate optimization significantly outperforms that for maximizing the minimum rate in terms of average per-user rate performance. Also, in our system setting, the proposed DDPG-DDQN scheme is found to achieve around 78% of the rate achievable through an exhaustive search-based design.

Detection for Multisatellite Downlink Signal Based on Generative Adversarial Neural Network

A method for satellite downlink signal detection based on a generative adversarial network is proposed. The generator adversarial network and adversarial network are established, respectively. The generator network realizes the local generator of satellite signals, and the adversarial network is used for high-precision signal detection. The error network is generated by the error signal to form the satellite link downlink. The network reconstructs the optimal weights by generating errors, forms an error matrix for different satellite downlink, and then forms an adaptive matrix weight adjustment. Through the reconstruction of the optimal detection matrix, detection for the downlink signals of multiple satellites is completed. The proposed generative adversarial network can realize the high-precision detection for the downlink signal.

Reduced Complexity Detection of Narrowband Secondary Synchronization Signal for NB-IoT Communication Systems

Narrowband Internet of Things is one of the most promising technologies to support low cost, massive connection, deep coverage, and low power consumption. In this paper, a computationally efficient narrowband secondary synchronization signal detection method is proposed in the narrowband Internet of Things system. By decoupling the detection of complementary sequence and Zadoff–Chu sequence that make up the synchronization signal sequence, the search space of narrowband secondary synchronization signal hypotheses is reduced. Such a design strategy along with the use of the symmetric property of synchronization signals allows reduced-complexity synchronization signal detection in the narrowband Internet of Things system. Both theoretical and simulation results are provided to verify the usefulness of the proposed detector. It is shown via simulation results that the complexity of the proposed detection method is significantly reduced while producing some performance degradation, compared to the conventional detection method.

A Support Vector Machine Classification-Based Signal Detection Method in Ultrahigh-Frequency Radio Frequency Identification Systems

Noise and distortion in real signals have posed challenges in signal detection in ultrahigh-frequency (UHF) radio frequency identification (RFID) systems, with the exception of large frequency deviation. In this article, signal detection in a UHF RFID system was treated as a time-series classification problem. A support vector machine-based detector was proposed for real-time signal detection to optimize the system performance. The improved system was able to achieve frame synchronization detection and time-series data detection with a frequency deviation of ± 22%. With the analysis of the frame format, the frame synchronization was based on the sequence detection with the most likely selection, and the data detection was performed using a symbol-by-symbol method with two-step adjustments in order to reduce time and hardware costs. The algorithm was mainly trained offline using the experimental data, and then efficiently implemented in a UHF RFID system based on a software-defined radio platform. The system was validated on real-time signals of commercial tags. The experimental results showed that the learned detector had displayed improvements in detection accuracy and stability when compared to the existing method based on a correlation algorithm.

Comparison of Data Mining Methods for the Signal Detection of Adverse Drug Events with a Hierarchical Structure in Postmarketing Surveillance.

There are several different proposed data mining methods for the postmarketing surveillance of drug safety. Adverse events are often classified into a hierarchical structure. Our objective was to compare the performance of several of these different data mining methods for adverse drug events data with a hierarchical structure. We generated datasets based on the World Health Organization’s Adverse Reaction Terminology (WHO-ART) hierarchical structure. We evaluated different data mining methods for signal detection, including several frequentist methods such as reporting odds ratio (ROR), proportional reporting ratio (PRR), information component (IC), the likelihood ratio test-based method (LRT), and Bayesian methods such as gamma Poisson shrinker (GPS), Bayesian confidence propagating neural network (BCPNN), the new IC method, and the simplified Bayesian method (sB), as well as the tree-based scan statistic through an extensive simulation study. We also applied the methods to real data on two diabetes drugs, voglibose and acarbose, from the Korea Adverse event reporting system. Only the tree-based scan statistic method maintained the type I error rate at the desired level. Likelihood ratio test-based methods and Bayesian methods tended to be more conservative than other methods in the simulation study and detected fewer signals in the real data example. No method was superior to the others in terms of the statistical power and sensitivity of detecting true signals. It is recommended that those conducting drug‒adverse event surveillance use not just one method, but make a decision based on several methods.

Combining Random Forests and a Signal Detection Method Leads to the Robust Detection of Genotype-Phenotype Associations.

Genome wide association studies (GWAS) are a well established methodology to identify genomic variants and genes that are responsible for traits of interest in all branches of the life sciences. Despite the long time this methodology has had to mature the reliable detection of genotype–phenotype associations is still a challenge for many quantitative traits mainly because of the large number of genomic loci with weak individual effects on the trait under investigation. Thus, it can be hypothesized that many genomic variants that have a small, however real, effect remain unnoticed in many GWAS approaches. Here, we propose a two-step procedure to address this problem. In a first step, cubic splines are fitted to the test statistic values and genomic regions with spline-peaks that are higher than expected by chance are considered as quantitative trait loci (QTL). Then the SNPs in these QTLs are prioritized with respect to the strength of their association with the phenotype using a Random Forests approach. As a case study, we apply our procedure to real data sets and find trustworthy numbers of, partially novel, genomic variants and genes involved in various egg quality traits.