Signal Detection Method Research Articles

A Correlation Detection Method of Low SNR Based on Multi-Channelization

This letter proposes a correlation detection algorithm based on multi-channelization. The existing method has poor performance for detecting a time-varying signal with a low signal-to-noise ratio(SNR). To overcome this problem, we use the method of correlation summation after multi-channelization. According to the difference between the correlation between signals and noise in the received data, the single-channel data is converted into multi-channel data by a multi-channelization method without changing the signals. Then the noise is filtered by correlation calculation of multi-channel data, which can improve the detection probability of signals under low SNR. Based on the theory of multi-channelization, we propose two different methods to detect signals. To better reflect the performance of the algorithm, we compared several classical signal detection methods, such as energy detection (ED), correlation detection (CD), and cyclic spectrum detection (CSD). Simulation analysis and experimental results show that the multi-channelization correlation detection algorithm has better detection performance when the SNR is low.

Intelligent Signal Detection Under Spatially Correlated Noise

Detecting the signal of the antenna array is a major problem in theoretical research and practical application. In this paper, several new methods are given for the number of signals detection at first, secondly, a new method called Principal Component Analysis of Signal Estimation (PCASE) will be introduced which can simultaneously detect the number of signals and the direction of arrival. In recent decades, the signal detection method based on the information theory criterion has been widely studied. The problem has been adequately solved under the assumption of uncorrelated white noise. However, considering the actual situation of wireless communication, the noise is spatially correlated or the noise information is unknown. In this case, traditional methods such as Akaike’s information criteria (AIC), minimum descriptive length (MDL) and sparse and parameter approach (SPA) will often lead to a wrong estimation. Therefore, this paper introduces an improved eigenvalue correction method for the number of signals, and applies it to two new methods: the improved eigenvalue gradient method (Im-EGM) and the improved eigen-increment stop rule (Im-EISR), and studies a new estimation algorithm based on signal cancellation (SC). In addition, previous algorithms for estimating the direction of arrival (such as MUSIC, ESPRIT) require the number of known signals to estimate the direction of arrival. Therefore, this paper proposes a new method called PCASE, which can estimate the number of signals and the direction of arrival at the same time. This method combines the SPA and the Principal Component Analysis method (PCA) in machine learning. Compared with the existing methods, the accuracy of these new methods is verified by Monte Carlo simulation.

An Improved Underdamped Asymmetric Bistable Stochastic Resonance Method and its Application for Spindle Bearing Fault Diagnosis

High-precision spindle bearing is one of the most critical and vulnerable parts in a motorized spindle. Its unexpected failure may lead to production loss. Stochastic resonance (SR) is a weak signal detection method, which can obtain noise energy in strong background noise and enhance incipient fault characteristics of spindle bearing. Based on the fact that asymmetry can improve the enhancement ability of asymmetric bistable SR in weak feature extraction, we introduce an underdamped well-width asymmetric bistable SR (UABSR) method to the field of bearing fault diagnosis for the first time. However, the engineering application of UABSR can still be limited by two aspects. Firstly, the SNR index can take effect only when the actual fault frequency is obtained in advance, so the UABSR method is at high-cost in real practices. Secondly, an appropriate band-pass filter band range of the bearing faults can hardly be obtained due to the massive impulsive noise in operations. Here an improved UABSR method for spindle bearing fault diagnosis is proposed. Infogram method is used to process and analysis the original vibration signal for resisting the influence from the impulsive noise and obtaining more accurate frequency range of spindle bearing fault. In addition, time domain zero-crossing (TDZC) index, as the index of the improved UABSR method, can directly reflect the fault characteristics of spindle bearings without knowing the accurate fault characteristic frequency in advance. Besides, the Quantum Genetic Algorithms (QGAs) and the fourth-order Runge-Kutta algorithm are combined to simultaneously obtain the optimal system parameter, the asymmetric ratio, the damping factor and the rescaling factor of the improved UABSR model. Comparing the Infogram and original UABSR methods, the improved UABSR method performs better effect in incipient spindle bearing fault diagnosis.

A rolling bearing fault detection method based on compressed sensing and a neural network.

The high sampling frequency of traditional Nyquist sampling theory not only puts greater requirements on the sampling equipment, but also generates a large amount of data, which increases the difficulty of information transmission and storage. To this end, this paper proposes a rolling bearing fault signal detection method based on compressed sensing combined with a neural network. Based on the theory of compressed sensing, the observations obtained from compression sampling are divided into two sets of data. Given the one set of data, the predictive ability of the nonlinear time series through the neural network can predict the second set of observed values. The predicted observations are used to reconstruct the signal, thereby reducing the amount of data to be stored and transmitted and realizing secondary compression of the signal.

Learning-Based Signal Detection for Wireless OAM-MIMO Systems With Uniform Circular Array Antennas

This paper presents a neural-like network-based signal detection method for orbital angular momentum multiplexing systems with uniform circular array antennas. The signal detection network is derived by unfolding the alternating direction method of multipliers (ADMM), and in addition, a parallel interference cancellation (PIC) function is integrated, which enhances the tolerance to inter-mode interference while keeping the complexity feasible. The number of parameters to be learned in each layer of the network is a linear order of the number of antenna elements. Simulation results show that the ADMM-PIC detector exhibits excellent error performance, which cannot be achieved by a conventional minimum mean square error-based detector.

Propensity score-adjusted three-component mixture model for drug-drug interaction data mining in FDA Adverse Event Reporting System.

With increasing trend of polypharmacy, drug‐drug interaction (DDI)‐induced adverse drug events (ADEs) are considered as a major challenge for clinical practice. As premarketing clinical trials usually have stringent inclusion/exclusion criteria, limited comedication data capture and often times small sample size have limited values in study DDIs. On the other hand, ADE reports collected by spontaneous reporting system (SRS) become an important source for DDI studies. There are two major challenges in detecting DDI signals from SRS: confounding bias and false positive rate. In this article, we propose a novel approach, propensity score‐adjusted three‐component mixture model (PS‐3CMM). This model can simultaneously adjust for confounding bias and estimate false discovery rate for all drug‐drug‐ADE combinations in FDA Adverse Event Reporting System (FAERS), which is a preeminent SRS database. In simulation studies, PS‐3CMM performs better in detecting true DDIs comparing to the existing approach. It is more sensitive in selecting the DDI signals that have nonpositive individual drug relative ADE risk (NPIRR). The application of PS‐3CMM is illustrated in analyzing the FAERS database. Compared to the existing approaches, PS‐3CMM prioritizes DDI signals differently. PS‐3CMM gives high priorities to DDI signals that have NPIRR. Both simulation studies and FAERS data analysis conclude that our new PS‐3CMM is a new method that is complement to the existing DDI signal detection methods.

A New Method of Low Amplitude Signal Detection and Its Application in Acoustic Emission

A novel methodology is proposed to enhance the reliability of detection of low amplitude transients in a noisy time series. Such time series often arise in a wide range of practical situations where different sensors are used for condition monitoring of mechanical systems, integrity assessment of industrial facilities and/or microseismicity studies. In all these cases, the early and reliable detection of possible damage is of paramount importance and is practically limited by detectability of transient signals on the background of random noise. The proposed triggering algorithm is based on a logarithmic derivative of the power spectral density function. It was tested on the synthetic data, which mimics the actual ultrasonic acoustic emission signal recorded continuously with different signal-to-noise ratios (SNR). Considerable advantages of the proposed method over established fixed amplitude threshold and STA/LTA (Short Time Average / Long Time Average) techniques are demonstrated in comparative tests.

RETRACTED ARTICLE: Data transmission and real-time information acquisition based on fine-grained algorithm and wireless network

With the advent of the 5G era, wireless network data transmission and real-time information acquisition face certain technical challenges. Simultaneously, how to ensure fast and safe information transmission becomes the research topic of the information age. The improvement of this research is based on the traditional fine-grained algorithm, and this study uses spatial constraints and evaluation scores to obtain the bounding box positions of objects and objects. Simultaneously, Part-based R-CNN is used to extract convolution features for each filtered bounding box and join the local features to form a feature representation vector. In addition, the research is based on the application of high-resolution acquisition of information entropy, abnormal signal detection and transient signal measurement methods and techniques in typical time domain test instruments, that is, digital oscilloscope, and this study is experimentally verified. The research indicates that the method proposed in this paper is scientific and can provide theoretical reference for subsequent related research.

Review of the initial post-marketing safety surveillance for the recombinant zoster vaccine

BackgroundThe adjuvanted recombinant zoster vaccine (RZV) received its first marketing authorization in October 2017, for prevention of herpes zoster in individuals aged ≥50 years. MethodsWe summarized safety information, following RZV administration, received by GSK via spontaneous adverse event (AE) reports submitted by healthcare providers, vaccine recipients and other reporters. Observed-to-expected (O/E) analyses were performed for selected outcomes: reports of death, Guillain–Barré syndrome and Bell’s palsy. Standard case definitions were used to assess individual case reports. Data mining, using proportional reporting ratio and time-to-onset signal detection methods, was employed to identify RZV-AE pairs with disproportionate reporting or unexpected time-to-onset distribution. ResultsBetween October 13, 2017 and February 10, 2019, an estimated 9.3 million doses were distributed and GSK received 15,638 spontaneous AE reports involving RZV. Most reports were classified as non-serious (95.3%) and originated from the United States (81.7%), where the majority of doses were distributed. Among reports with age or sex reported, individuals were mainly 50–69-year-olds (62.1%) and female (66.7%). Of all reports, 3,579 (22.9%) described vaccination errors, of which 82.7% were without associated symptoms. Of all vaccination error reports, most described errors of vaccine preparation and reconstitution (29.7%), inappropriate schedule or incomplete course of administration (26.7%), incorrect route of administration (16.4%), and storage errors (12.9%). The most commonly reported symptoms were consistent with the known RZV reactogenicity profile observed in clinical trials, including injection site reactions, pyrexia, chills, fatigue, headache. O/E analyses for selected outcomes and data mining analyses for all reported AEs did not identify any unexpected patterns. ConclusionsReview of the initial data from the post-marketing safety surveillance showed that the safety profile of RZV is consistent with that previously observed in pre-licensure clinical trials. Other studies are ongoing and planned, to continue generating real-world safety data and further characterize RZV.

Towards early detection of adverse drug reactions: combining pre-clinical drug structures and post-market safety reports

BackgroundAdverse drug reaction (ADR) is a major burden for patients and healthcare industry. Early and accurate detection of potential ADRs can help to improve drug safety and reduce financial costs. Post-market spontaneous reports of ADRs remain a cornerstone of pharmacovigilance and a series of drug safety signal detection methods play an important role in providing drug safety insights. However, existing methods require sufficient case reports to generate signals, limiting their usages for newly approved drugs with few (or even no) reports.MethodsIn this study, we propose a label propagation framework to enhance drug safety signals by combining drug chemical structures with FDA Adverse Event Reporting System (FAERS). First, we compute original drug safety signals via common signal detection algorithms. Then, we construct a drug similarity network based on chemical structures. Finally, we generate enhanced drug safety signals by propagating original signals on the drug similarity network. Our proposed framework enriches post-market safety reports with pre-clinical drug similarity network, effectively alleviating issues of insufficient cases for newly approved drugs.ResultsWe apply the label propagation framework to four popular signal detection algorithms (PRR, ROR, MGPS, BCPNN) and find that our proposed framework generates more accurate drug safety signals than the corresponding baselines. In addition, our framework identifies potential ADRs for newly approved drugs, thus paving the way for early detection of ADRs.ConclusionsThe proposed label propagation framework combines pre-clinical drug structures with post-market safety reports, generates enhanced drug safety signals, and can potentially help to accurately detect ADRs ahead of time.AvailabilityThe source code for this paper is available at: https://github.com/ruoqi-liu/LP-SDA.

Matrix Information Geometry for Signal Detection via Hybrid MPI/OpenMP

The matrix information geometric signal detection (MIGSD) method has achieved satisfactory performance in many contexts of signal processing. However, this method involves many matrix exponential, logarithmic, and inverse operations, which result in high computational cost and limits in analyzing the detection performance in the case of a high-dimensional matrix. To address these problems, in this paper, a high-performance computing (HPC)-based MIGSD method is proposed, which is implemented using the hybrid message passing interface (MPI) and open multiple processing (OpenMP) techniques. Specifically, the clutter data are first modeled as a Hermitian positive-definite (HPD) matrix and mapped into a high-dimensional space, which constitutes a complex Riemannian manifold. Then, the task of computing the Riemannian distance on the manifold between the sample data and the geometric mean of these HPD matrices is assigned to each MPI process or OpenMP thread. Finally, via comparison with a threshold, the signal is identified and the detection probability is calculated. Using this approach, we analyzed the effect of the matrix dimension on the detection performance. The experimental results demonstrate the following: (1) parallel computing can effectively optimize the MIGSD method, which substantially improves the practicability of the algorithm; and (2) the method achieves superior detection performance under a higher dimensional HPD matrix.

Hardware Resource Optimized Detection of LFM Signals with Unknown Start Frequency and Frequency Rate

Detection of very low-SNR LFM signals with unknown start frequency and frequency rate is of great interest both in electronic support measure (ESM), and radio astronomy. The direct method for LFM signal detection needs a bank of matched-filters which is a really hardware consuming solution. As another solution, a bank of de-ramping blocks, followed by FFT units, can be used with the same performance as matched-filters bank. In such an alternative solution, with no optimization constraint, it is quite likely to reach a hardware extensive solution with limited processing gain. In this paper, a novel method based on de-ramping bank is proposed. Also, an optimization problem is developed, which could determine the optimum values for detection structure’s parameters, e.g. number of channels, as well as FFT length. It is shown that, the optimized detector features better processing gain in comparison to the non-optimized versions. Furthermore, adding a moving average at the output of the FFT could make remarkable improvement on detection performance. Moreover, the proposed detector is compared against the conventional methods in terms of detection performance and computational complexity characteristic, which aptly prove the superiority of the proposed method.

Comparison of signal detection of tumour necrosis factor-α inhibitors using the Korea Adverse Events Reporting System Database, 2005-2016.

There are no pharmacovigilance studies on adverse event (AE) data for tumour necrosis factor alpha (TNFα) inhibitors in South Korea. We analysed AEs induced by adalimumab, infliximab, and etanercept METHODS: We used data from the Korea Institute of Drug Safety and Risk Management-Korea Adverse Events Reporting System Database (KIDS-KD) collected between 2005 and 2016. We used three different signal detection methods: proportional reporting ratio (PRR), reporting odds ratio (ROR), and information component (IC). The drug was compared with drug labels in the USA and Korea. Logistic regression analysis identified AEs that are more likely to occur with drug use. Of the 5594 AEs identified, 350, 452, and 71 were related to adalimumab, infliximab, and etanercept, respectively. We identified seven new signals, which were not listed on drug labels in either Korea or the USA, for AEs associated with the study drugs: two for adalimumab (medication error and drug failure), two for infliximab (palpitation and temperature sensation change), and three for etanercept (hyperkeratosis, acne, and thyroid neoplasm malignant). Injection site pain (OR 6.14, 95% CI 1.15-32.74) and alopecia (OR 4.54, 95% CI 1.16-17.77) for adalimumab, chest pain (OR 6.01, 95% CI 1.35-26.77) for infliximab, and uveitis (OR 10.11, 95% CI 1.13-90.77) for etanercept were more likely to be reported in patients with each TNFα inhibitor than in those without, respectively. Seven new signals that were not included in the current label were identified for TNFα inhibitors and should be updated and monitored.Key Points• Large-scale spontaneous AE reporting data and data mining techniques are useful for detecting signals of rare AEs as well as common AEs induced by drugs.• Drug labels should be updated to reflect signals that are newly discovered by continuous monitoring.

Effects of Plasma Sheath on Parameter Estimations of Linear Frequency Modulation Pulse Signal

A space vehicle traveling in the atmosphere at hypersonic speed is enveloped by a time-varying plasma sheath, and the performance of onboard radar reconnaissance system will be significantly interrupted. Plasma sheath would generate parasitic modulations of the amplitude and phase of radar signals. Therefore, based on the transmission coefficient of radar wave in time-varying plasma sheath, the Wigner–Hough transform and autocorrelation time-domain signal detection method are used to estimate the frequency- and time-domain parameters of the linear frequency modulation (LFM) pulse signal statistically. A variation rule of the estimation errors of signal parameters is established under various plasma characteristics parameter and frequency and bandwidth of LFM waves. The critical signal-to-noise ratio that is the threshold value for failure of signal parameter estimation has been accurately obtained for different electron densities and radar frequencies. The calculation results could provide useful information for onboard electronic reconnaissance system in further applications.

Empirically defining treatment response and remission in body dysmorphic disorder.

The number of clinical trials in body dysmorphic disorder (BDD) has steadily increased in recent years. As the number of studies grows, it is important to define the most empirically useful definitions for response and remission in order to enhance field-wide consistency and comparisons of treatment outcomes across studies. In this study, we aim to operationally define treatment response and remission in BDD. We pooled data from three randomized controlled trials of cognitive-behavior therapy (CBT) for BDD (combined n = 153) conducted at four academic sites in Sweden, the USA, and England. Using signal detection methods, we examined the Yale-Brown Obsessive Compulsive Scale modified for BDD (BDD-YBOCS) score that most reliably identified patients who responded to CBT and those who achieved remission from BDD symptoms at the end of treatment. A BDD-YBOCS reduction ⩾30% was most predictive of treatment response as defined by the Clinical Global Impression (CGI) - Improvement scale (sensitivity 0.89, specificity 0.91, 91% correctly classified). At post-treatment, a BDD-YBOCS score ⩽16 was the best predictor of full or partial symptom remission (sensitivity 0.85, specificity 0.99, 97% correctly classified), defined by the CGI - Severity scale. Based on these results, we propose conceptual and operational definitions of response and full or partial remission in BDD. A consensus regarding these constructs will improve the interpretation and comparison of future clinical trials, as well as improve communication among researchers, clinicians, and patients. Further research is needed, especially regarding definitions of full remission, recovery, and relapse.

Theoretical research of point-measurement laser absorption spectroscopy

The point measurement laser absorption spectroscopy (PMLAS) based on saturated absorption theory could surpass the defect of 'line-of-sight' measurement in traditional tunable diode laser absorption spectroscopy (TDLAS) and achieve the 'point' measurement with millimeter spatial resolution. It is realized by crossing with two frequency synchronized laser beams: one named probe beam as in traditional TDLAS and the other named saturated beam with higher power. In this paper, the theory of PMLAS was firstly analyzed by the theoretical deduction of saturated absorption coefficients with arbitrary cross angles and the numerical calculations of point absorbance under different saturation parameters. Next, a weak signal detection method based on high-frequency sinusoidal modulation of the saturated beam intensity was proposed, in which the first-order harmonic signal was theoretically deduced and verified by numerical demonstration. Furthermore, it is found that the FWHMs (full width at half maximum) of different order harmonics are all the same and equal to the width of the absorption signal without modulation, which implied that the superposition of multi-harmonics could enhance the signal-to-noise ratio (SNR) in measuring the spectrum line-width.

Better-than-chance classification for signal detection.

The estimated accuracy of a classifier is a random quantity with variability. A common practice in supervised machine learning, is thus to test if the estimated accuracy is significantly better than chance level. This method of signal detection is particularly popular in neuroimaging and genetics. We provide evidence that using a classifier's accuracy as a test statistic can be an underpowered strategy for finding differences between populations, compared to a bona fide statistical test. It is also computationally more demanding than a statistical test. Via simulation, we compare test statistics that are based on classification accuracy, to others based on multivariate test statistics. We find that the probability of detecting differences between two distributions is lower for accuracy-based statistics. We examine several candidate causes for the low power of accuracy-tests. These causes include: the discrete nature of the accuracy-test statistic, the type of signal accuracy-tests are designed to detect, their inefficient use of the data, and their suboptimal regularization. When the purpose of the analysis is the evaluation of a particular classifier, not signal detection, we suggest several improvements to increase power. In particular, to replace V-fold cross-validation with the Leave-One-Out Bootstrap.

Comparison of estimated 20-Hz pulse fin whale source levels from the tropical Pacific and Eastern North Atlantic Oceans to other recorded populations.

Passive acoustic monitoring, mitigation, animal density estimation, and comprehensive understanding of the impact of sound on marine animals all require accurate information on vocalization source level to be most effective. This study focused on examining the uncertainty related to passive sonar equation terms that ultimately contribute to the variability observed in estimated source levels of fin whale calls. Differences in hardware configuration, signal detection methods, sample size, location, and time were considered in interpreting the variability of estimated fin whale source levels. Data from Wake Island in the Pacific Ocean and off Portugal in the Atlantic Ocean provided the opportunity to generate large datasets of estimated source levels to better understand sources of uncertainty leading to the observed variability with and across years. Average seasonal source levels from the Wake Island dataset ranged from 175 to 188 dB re 1 μPa m, while the 2007-2008 seasonal average detected off Portugal was 189 dB re 1 μPa m. Owing to the large inherent variability within and across this and other studies that potentially masks true differences between populations, there is no evidence to conclude that the source level of 20-Hz fin whale calls are regionally or population specific.

Graph representation of random signal and its application for sparse signal detection

In this paper, a novel graph-based adequate and concise signal representation paradigm is explored. Amplitudes of a signal can be quantized first. Then the quantization levels and transitions among these levels can be specified as vertices and edges so that the signal waveform (spectrum) can be converted to a graph thereby. This new signal representation framework can provide a promising alternative for manifesting the essential structure of random signals in the graph domain. The randomness of signal samples can be characterized by the topological features of the converted graph. In this work, we explore the connectivity of a graph to measure such randomness. A typical application, namely sparse communication signal detection, can be undertaken by the graph-connectivity metric. New pertinent theoretical analyses are also conducted here. First, the analysis on the minimum sample size for constructing a fully-connected graph from random samples is established. Second, the probability analysis of constructing a fully-connected graph with respect to a particular sample size is also derived. Third, if a graph is not fully connected, the probability distribution function of the number of edges is derived. According to Monte Carlo simulations, our proposed graph-based signal-detection method leads to outstanding performance, compared to a popular existing signal-detection technique especially when the signal-to-noise ratio is rather small.

Information divergence‐based low probability of intercept waveform detection for multi‐antenna intercept receivers

Based on the asymptotic spectral distribution of sample covariance matrix, a new low probability of interception (LPI) signal detection method for multi-antenna intercept receiver is proposed via reforming the noise sequence into a Wishart matrix. The authors deduce the asymptotic cumulative distribution function (CDF) of eigenvalues of sample covariance matrix. Also a numerical Kullback–Leibler divergence of the empirical spectral CDF based on test samples from the deduced asymptotic CDF is established, which is treated as the test statistic. By comparing with other signal detection methods, simulation results show the new LPI signal detection method is superior and robust.