Signal Detection Method Research Articles

A novel weak signal detection method for Linear Frequency Modulation signal based on bistable system and Fractional Fourier Transform

Fractional Fourier Transform (FRFT) is regarded as an effective method for detection of Linear Frequency Modulation (LFM) signals in recent years. However, the performance of FRFT detection will deteriorate sharply in the weak noise scene. An alternative solution is the use of bistable system, which can generate stochastic resonance (SR), and is relatively easy to achieve. This paper proposes a novel LFM weak signal detection algorithm based on bistable system and FRFT, named BSFRFT. We use the SR effect of bistable system to amplify LFM signal, and then apply FRFT to the previous output. We also present an evaluation criteria to measure the ability of detection methods and to compare performance of FRFT and BSFRFT. Numerical simulations show that the algorithm is effective for two cases – the LFM signal covered by white noise or colored noise (including pink noise).

Epileptic seizure detection from EEG signals using logistic model trees

Reliable analysis of electroencephalogram (EEG) signals is crucial that could lead the way to correct diagnostic and therapeutic methods for the treatment of patients with neurological abnormalities, especially epilepsy. This paper presents a novel analysis system for detecting epileptic seizure from EEG signals, which uses statistical features based on optimum allocation technique (OAT) with logistic model trees (LMT). The analysis involves applying the OAT to select representative EEG signals that reflect the entire database. Then, some statistical features are extracted from these EEG signals and the obtained feature set is fed into the LMT classification model to detect epileptic seizure. To test the consistency of the proposed method, all experiments are carried out on a benchmark EEG dataset and repeated twenty times with the same parameters in the detection process, and the average values of the performance parameters are reported. The results show very high detection performances for each class, and also confirm the consistency of the proposed method in the repeating process. The proposed method outperforms some state-of-the-art methods of epileptic EEG signal detection using the same EEG dataset.

Detecting neuromagnetic synchrony in the presence of noise

BackgroundSynchrony between neuroelectric oscillations in distant brain areas is currently used as an indicator of functional connectivity between the involved neural substrates. Coherence measures, which quantify synchrony, are affected by concurrent brain activities, commonly subsumed as noise. New methodUsing Monte-Carlo simulation, we analysed the properties of circular statistics and how those are affected by noise. We considered three different models of neuroelectric signal generation, which are an additive model, phase-reset, and reciprocal phase-interaction. Using the receiver-operating characteristic method, we compared the performances of currently implemented algorithms for coherence detection such as phase-coherence or phase-locking factor, magnitude-squared coherence, and phase-lagging index, all based on circular statistics, and a more general approach to synchrony, using measures of mutual information. We compared inter-trial coherence as a method for signal detection with coherence between multiple sources as measure of source interaction and connectivity. ResultsCharts of performance characteristics showed that the choice of methods depend on the underlying signal generation model. Detection of coherence requires in general a higher signal-to-noise ratio than detection of the signal itself, and again, the difference in performance depends strongly on the underlying model of signal generation. Comparison with existing methodsPrevious comparisons of the performances of different algorithms for signal detection and coherence have not considered systematically the underlying neural generation mechanisms. ConclusionDetection of coherence generated by additive signals or a phase-reset requires largely higher signal-to-noise ratio compared to signal detection. Only in case of true phase interaction, signal detection and coherence measures are similarly sensitive.

Weak harmonic signal detection method from strong chaotic interference based on convex optimization

Noticing that the second-order matrix of chaotic signal is stationary, we propose a method for detecting weak harmonic signal from strong chaotic interference based on convex optimization. After a data matrix in frequency domain is composed by combining reference cells and the detection cell, we detect weak harmonic signals by searching each frequency channel based on an optimization filter. Then, the signal detection problem is boiled down to an optimization problem. Further, we design an optimization filter based on second-order cone programs. The filter can maintain the gain of signal from current frequency channel and suppress signals from other frequency channels. The harmonic signal can be detected by the output signal-to-interference-plus-noise ratio (SINR) of each frequency channel. Compared with the neural network methods, the proposed method has following advantages: (1) It can detect weak harmonic signal under lower SINR and (2) it is robust against white Gaussian noise.

Methods for detection of electromagnetic signals of lithospheric origin

The paper considers physical conditions of generation and distribution of electromagnetic radiation of lithospheric origin and the peculiarities of registration based on them. Methods for measurements of seismoelectromagnetic activity, parameters and procedures, are suggested.

Stochastic resonance based on frequency information exchange

In the past few decades, stochastic resonance (SR) has attracted considerable attention of researchers due to a curious phenomenon appearing in a nonlinear system:an input weak periodic signal can be amplified and optimized by the assistance of noise. It has been proved that the classical stochastic resonance (CSR) has the adiabatic limit, so the performance of CSR in high-frequency signal detection is restricted in practical engineering. To break the restriction, a number of methods have been suggested, such as re-scaling frequency stochastic resonance (RFSR), parameters normalized stochastic resonance, modulated stochastic resonance, etc. Although the high-frequency signal can be detected by the above methods in specific conditions, there are some problems that restrict their applications in different circumstances. In this paper, a new method, stochastic resonance based on frequency-information exchange (FIESR), is developed to deal with the adiabatic limit of CSR. The mechanism of FIESR is analyzed in detail by the theory of single-side band modulation (SSB) which is based on phase shift. The information in small-parameter frequency domain is swapped with the information of the high-frequency target signal. Then the amplitude and phase of the target signal are moved to the small-parameter frequency domain. Consequently the target signal can be enhanced and detected by CSR in small-parameter frequency domain. Besides, a necessary plan, narrow band spectrum exchange, is put forward to diminish the influence of the spectrum leakage of FIESR. It is well known that the RFSR is a method of detecting the practical signal with large-parameter frequency. Through rescaling the time interval of the signal and compressing its frequency according to the scale R, the large-parameter frequency is compressed into a small-parameter frequency. The RFSR has a good performance in mechanical incipient fault diagnosis. However, it has a high sampling ratio limitation. The ratio of sampling frequency to target signal frequency is more than 50. To overcome this weakness of RFSR, frequency-information exchange (FIE) is introduced into RFSR. A new signal detection method based on FIE and RFSR, named F-RFSR, is put forward simultaneously. The flow of F-RFSR consists of three steps. Firstly, the frequency of the original input signal is compressed linearly according to the estimated scale. Then, the frequency information is exchanged between the compressed target signal and the small-parameter signal in the frequency domain. Finally, the CSR is used to amplify and detect the weak target signal processed by re-scaling frequency and FIE. Performance analysis of signal detection and numerical simulation are carried out to demonstrate that F-RFSR has more efficient sampling ratio than RFSR for practical application.

A chaotic signal detection method based on the component permutation of the incomplete two-dimensional phase-space

Detection and identification of chaotic signal is very important in the chaotic time series analysis. It is not easy to distinguish chaotic time series from stochastic processes since they share some similar natures. The detection methods to capture and utilize the structure of state-space dynamics can be very effective. In practice, it is very hard to obtain full information about the structure, and accurate phase-space reconstruction from scalar time series data is also a real challenge. However, the chaotic signals also show fundamental dynamical structure in the incomplete two-dimensional phase-space for the reason that they are generated by the deterministic chaotic systems or maps. Based on the fact that the distribution of chaotic signals is quite different from that of the noise signals in the incomplete two-dimensional phase-space, a novel detection method, which depends on the component permutation of the incomplete two-dimensional phase-space, is proposed. The incomplete two-dimensional phase-space is first obtained through the time series. Then, the first component is sorted in the ascending order, and the second component is permutated accordingly. The permutated component shows more structure characteristics for chaotic signals because of the relation between these two components. But this phenomenon does not appear in the noise because these components are independent of each other. And then, the permutated component is segmented into several groups properly. Finally, the sample mean and sample variance of different groups are calculated to obtain the sequence of sample mean (SSM) and the sequence of sample variance (SSV). Meanwhile, by calculating the variance of the SSM and the mean of the SSV, the test statistic is obtained. Furthermore, it is proved that this test statistic follows the F distribution under the null hypothesis of Gaussian noise. The proposed method is first adopted for detecting the several chaotic signals under different data lengths in Gaussian noise conditions. The simulation results show that the proposed method can detect chaotic signals effectively under low signal-to-noise ratio and it also has a good robustness against noise compared with the permutation entropy test. The time consumptions of the proposed method under different data lengths are evaluated and also compared with the results of permutation entropy test, showing that the proposed method can detect chaotic signals quickly, and the time complexity is much lower than that of the permutation entropy test. The theoretical analysis and simulation results demonstrate that the proposed method not only outperforms the permutation entropy test with lower complexity, but also has a better robustness against noise.

Practical Implementation of Spectrum Sensing and Signal Detection for Satellite Broadcasting Systems

In the European satellite broadcasting specifications, the symbol rate and the carrier frequency are not regulated. Furthermore, the first generation format DVB-S does not have any control signals. In a practical environment, the received signal condition is not stable due to the imperfect reception environment, i.e., unterminated receiver ports, cheap indoor wiring cables etc. These issues prevent correct detection of the satellite signals. For this reason, the conventional signal detection method uses brute force search for detecting the received signal's cyclostationarity, which is an extremely time-consuming approach. A coarse estimation method of the carrier frequency and the bandwidth was proposed by us based on the power spectrum. We extend this method to create a new method for detecting satellite broadcasting signals, which can significantly reduce the search range. In other words, the proposed method can detect the signals in a relatively short time. In this paper, the proposed method is applied to signals received in an actual environment. Our analysis shows that the proposed method can effectively reduce the detection time at almost a same detection performance.

Signal Detection and their Assessment in Pharmacovigilance

Signal detection and its assessment is the most important aspect in pharmacovigilance which plays a key role in ensuring that patients receive safe drugs. For detection of adverse drug reactions, clinical trials usually provide limited information as they are conducted under strictly controlled conditions. Some of the adverse drug reactions can be detected only after long term use in larger population and in specific patient groups due to specific concomitant medications or disease. The detection of unknown and unexpected safety signals as early as possible from post marketing data is one of the major challenge of pharmacovigilance. The current method of detecting a signal is predominantly based on spontaneous reporting, which is mainly helpful in detecting type B adverse effects and unusual type A adverse effects. Other sources of signals detection are prescription event monitoring, case control surveillance and follow up studies. Signal assessment is mainly performed by using Upsala Monitoring scale & Naranjo scale of probability to analyze the cause and effect analysis. Signal detection and their assessment is very vital and complex process. Thus, the main objective of this review is to provide a summary of the most common methods of signal detection and their assessment used in pharmacovigilance to confirm the safety of a drug. Recent developments, challenges, & future needs have also been discussed.

Bayesian Inference on Gravitational Waves

The Bayesian approach is increasingly becoming popular among the astrophysics data analysis communities. However, the Pakistan statistics communities are unaware of this fertile interaction between the two disciplines. Bayesian methods have been in use to address astronomical problems since the very birth of the Bayes probability in eighteenth century. Today the Bayesian methods for the detection and parameter estimation of gravitational waves have solid theoretical grounds with a strong promise for the realistic applications. This article aims to introduce the Pakistan statistics communities to the applications of Bayesian Monte Carlo methods in the analysis of gravitational wave data with an overview of the Bayesian signal detection and estimation methods and demonstration by a couple of simplified examples.

Design and Implementation of Time and Frequency Synchronization in LTE

A novel architecture for efficient time and frequency synchronization, applied to the long-term evolution (LTE) standard, is proposed. For symbol timing, we propose applying a symbol-folding method on top of the sign-bit reduction technique, leading to a novel algorithm for the cyclic prefix-type recognition in LTE. Following the symbol timing, the fractional carrier frequency offset is estimated and compensated using an adaptive gain loop, which allows for a high-accuracy compensation in a short interval. In the frequency domain, for cell search, we propose a sign-bit reduction technique on top of the matched filter method for the primary synchronization signal detection. In addition, we propose the sign-bit maximum-likelihood sequence detection algorithm for the secondary synchronization signal analysis. These methods result in >90% hardware reduction in the cell search compared with the state-of-the-art design. Moreover, possible structures for the frequency tracking in LTE are investigated leading to an experimental comparison, used to choose the best hardware-efficient structure. The proposed architecture is fabricated in a 130-nm CMOS technology occupying 0.68 $\mathrm{mm}^{2}$ of silicon area. At 25 °C and 1.2 V supply voltage, the fabricated chip consumes 34.4 mW at 42 MHz in the time domain and 34.6 mW at 188 MHz in the frequency domain. The fabricated and tested synchronizer core proves to have an outstanding performance for all defined communication modes in LTE.

Gravitational wave astrophysics, data analysis and multimessenger astronomy

This paper reviews gravitational wave sources and their detection. One of the most exciting potential sources of gravitational waves are coalescing binary black hole systems. They can occur on all mass scales and be formed in numerous ways, many of which are not understood. They are generally invisible in electromagnetic waves, and they provide opportunities for deep investigation of Einstein's general theory of relativity. Sect. 1 of this paper considers ways that binary black holes can be created in the universe, and includes the prediction that binary black hole coalescence events are likely to be the first gravitational wave sources to be detected. The next parts of this paper address the detection of chirp waveforms from coalescence events in noisy data. Such analysis is computationally intensive. Sect. 2 reviews a new and powerful method of signal detection based on the GPU-implemented summed parallel infinite impulse response filters. Such filters are intrinsically real time alorithms, that can be used to rapidly detect and localise signals. Sect. 3 of the paper reviews the use of GPU processors for rapid searching for gravitational wave bursts that can arise from black hole births and coalescences. In sect. 4 the use of GPU processors to enable fast efficient statistical significance testing of gravitational wave event candidates is reviewed. Sect. 5 of this paper addresses the method of multimessenger astronomy where the discovery of electromagnetic counterparts of gravitational wave events can be used to identify sources, understand their nature and obtain much greater science outcomes from each identified event.

A Multiagent System for Integrated Detection of Pharmacovigilance Signals.

Pharmacovigilance is the scientific discipline that copes with the continuous assessment of the safety profile of marketed drugs. This assessment relies on diverse data sources, which are routinely analysed to identify the so-called "signals", i.e. potential associations between drugs and adverse effects, that are unknown or incompletely documented. Various computational methods have been proposed to support domain experts in signal detection. However, recent comparative studies illustrated that current methods exhibit high false-positive rates, significantly variable performance across different datasets used for analysis and events of interest, but also complementarity in their outcomes. In this regard, in order to reinforce accurate and timely signal detection, we elaborated through an agent-based approach towards systematic, joint exploitation of multiple heterogeneous signal detection methods, data sources and other drug-related resources under a common, integrated framework. The approach relies on a multiagent system operating based on a collaborative agent interaction protocol, aiming to implement a comprehensive workflow that comprises of method selection and execution, as well as outcomes' aggregation, filtering, ranking and annotation. This paper presents the design of the proposed multiagent system, discusses implementation issues and demonstrates the applicability of the proposed solution in an example signal detection scenario. This work constitutes a step towards large-scale, integrated and knowledge-intensive computational signal detection.

Accurate detection of low signal-to-noise ratio neuronal calcium transient waves using a matched filter

BackgroundCalcium imaging has become a fundamental modality for studying neuronal circuit dynamics both in vitro and in vivo. However, identifying calcium events (CEs) from spectral data remains laborious and difficult, especially since the signal-to-noise ratio (SNR) often falls below 2. Existing automated signal detection methods are generally applied at high SNRs, leaving a large need for an automated algorithm that can accurately extract CEs from fluorescence intensity data of SNR 2 and below. New methodIn this work we develop a Matched filter for Multi-unit Calcium Event (MMiCE) detection to extract CEs from fluorescence intensity traces of simulated and experimentally recorded neuronal calcium imaging data. ResultsMMiCE reached perfect performance on simulated data with SNR ≥ 2 and a true positive (TP) rate of 98.27% (± 1.38% with a 95% confidence interval), and a false positive(FP) rate of 6.59% (± 2.56%) on simulated data with SNR 0.2. On real data, verified by patch-clamp recording, MMiCE performed with a TP rate of 100.00% (± 0.00) and a FP rate of 2.04% (± 4.10). Comparison with existing method(s)This high level of performance exceeds existing methods at SNRs as low as 0.2, which are well below those used in previous studies (SNR ≃ 5–10). ConclusionOverall, the MMiCE detector performed exceptionally well on both simulated data, and experimentally recorded neuronal calcium imaging data. The MMiCE detector is accurate, reliable, well suited for wide-spread use, and freely available at sites.uci.edu/aggies or from the corresponding author.

Plasmonic SERS biosensing nanochips for DNA detection.

The development of rapid, cost-effective DNA detection methods for molecular diagnostics at the point-of-care (POC) has been receiving increasing interest. This article reviews several DNA detection techniques based on plasmonic-active nanochip platforms developed in our laboratory over the last 5years, including the molecular sentinel-on-chip (MSC), the multiplex MSC, and the inverse molecular sentinel-on-chip (iMS-on-Chip). DNA probes were used as the recognition elements, and surface-enhanced Raman scattering (SERS) was used as the signal detection method. Sensing mechanisms were based on hybridization of target sequences and DNA probes, resulting in a distance change between SERS reporters and the nanochip's plasmonic-active surface. As the field intensity of the surface plasmon decays exponentially as a function of distance, the distance change in turn affects SERS signal intensity, thus indicating the presence and capture of the target sequences. Our techniques were single-step DNA detection techniques. Target sequences were detected by simple delivery of sample solutions onto DNA probe-functionalized nanochips and measuring the SERS signal after appropriate incubation times. Target sequence labeling or washing to remove unreacted components was not required, making the techniques simple, easy-to-use, and cost-effective. The usefulness of the nanochip platform-based techniques for medical diagnostics was illustrated by the detection of host genetic biomarkers for respiratory viral infection and of the dengue virus gene.

Method of signal detection from silicon photomultipliers using fully differential Charge to Time Converter and fast shaper

The paper presents an implementation of fully differential readout method for Silicon Photomultipliers (SiPM). Front-end electronics consists of a fast and slow path. The former creates the trigger signal while the latter produces a pulse of width proportional to the input charge. The fast shaper generates unipolar pulse and utilizes the pole-zero cancelation circuit. The peaking time for single photoelectron is equal to 3.6ns and the FWHM is 3.8ns. The pulse width of the Charge to Time Converter (QTC) depends on the number of photons entering the SiPM at the moment of measurement. The QTC response is nonlinear but it allows us to work with signals in a wide dynamic range. The proposed readout method is effective in measurements of random signals where frequent events tend to pile-up. Thermal generation and afterpulses have a strong influence on the width of pulses from QTC. The proposed method enables us to distinguish those overlapping signals and get the reliable information on the number of detected photons.

Reference set for performance testing of pediatric vaccine safety signal detection methods and systems

BackgroundSafety signal detection in spontaneous reporting system databases and electronic healthcare records is key to detection of previously unknown adverse events following immunization. Various statistical methods for signal detection in these different datasources have been developed, however none are geared to the pediatric population and none specifically to vaccines. A reference set comprising pediatric vaccine-adverse event pairs is required for reliable performance testing of statistical methods within and across data sources. MethodsThe study was conducted within the context of the Global Research in Paediatrics (GRiP) project, as part of the seventh framework programme (FP7) of the European Commission. Criteria for the selection of vaccines considered in the reference set were routine and global use in the pediatric population. Adverse events were primarily selected based on importance. Outcome based systematic literature searches were performed for all identified vaccine-adverse event pairs and complemented by expert committee reports, evidence based decision support systems (e.g. Micromedex), and summaries of product characteristics. Classification into positive (PC) and negative control (NC) pairs was performed by two independent reviewers according to a pre-defined algorithm and discussed for consensus in case of disagreement. ResultsWe selected 13 vaccines and 14 adverse events to be included in the reference set. From a total of 182 vaccine-adverse event pairs, we classified 18 as PC, 113 as NC and 51 as unclassifiable. Most classifications (91) were based on literature review, 45 were based on expert committee reports, and for 46 vaccine-adverse event pairs, an underlying pathomechanism was not plausible classifying the association as NC. ConclusionA reference set of vaccine-adverse event pairs was developed. We propose its use for comparing signal detection methods and systems in the pediatric population.

Multi-frequency weak signal detection based on wavelet transform and parameter compensation band-pass multi-stable stochastic resonance

In actual fault diagnosis, useful information is often submerged in heavy noise, and the feature information is difficult to extract. A novel weak signal detection method aimed at the problem of detecting multi-frequency signals buried under heavy background noise is proposed based on wavelet transform and parameter compensation band-pass multi-stable stochastic resonance (SR). First, the noisy signal is processed by parameter compensation, with the noise and system parameters expanded 10 times to counteract the effect of the damping term. The processed signal is decomposed into multiple signals of different scale frequencies by wavelet transform. Following this, we adjust the size of the scaled signals' amplitudes and reconstruct the signals; the weak signal frequency components are then enhanced by multi-stable stochastic resonance. The enhanced components of the signal are processed through a band-pass filter, leaving the enhanced sections of the signal. The processed signal is analyzed by FFT to achieve detection of the multi-frequency weak signals. The simulation and experimental results show that the proposed method can enhance the signal amplitude, can effectively detect multi-frequency weak signals buried under heavy noise and is valuable and usable for bearing fault signal analysis.

Efficient Variational Bayesian Method for Joint Channel Estimation and Signal Detection in OFDM-Based AF Relay Networks

We propose improved joint channel estimation and data detection in orthogonal frequency division multiplexing (OFDM) amplify-and-forward (AF) relay network in the case of imperfect partial channel knowledge at the destination. To improve the detection accuracy, we adopt the variational Bayesian approximation (VBA) which is an iterative Bayesian method for channel estimation. We derive a modified iterative estimator that takes into account the channel estimation errors at the previous iteration, to reduce the impact of channel uncertainty at the next iteration, and hence, to improve the overall detection accuracy. By comparison with state of the art VBA-based estimators through numerical analysis, we highlight the superiority of our new VBA method and demonstrate a notable improvement in terms of bit error rate and mean square error between the true and the estimated channel.

A Blind OFDM Signal Detection Method Based on Cyclostationarity Analysis

In this paper, we present a novel technique to sense, blindly infer signal features [FFT size, cyclic prefix (CP) length], and detect OFDM signals based on second order cyclostationarity analysis. First, we infer accurate FFT size and CP length from the sensed signal based on cross correlation, through considering FFTs of different size (2L) and CPs length. In our experimental study, we assume that CP length in the sensed OFDM signal could be 5---15 % of the FFT size {64, 128, 256, 512, 1024, 2048 and 4096} used at primary user level. We successfully estimate accurate FFT size and CP length, and carry out performance analysis of the proposed approach at various channel conditions, and the effect of increase in sample length (frames) of the sensed signal. Additionally, we derive a recursive procedure to calculate the cross-correlation at sample (l + 1), using the cross-correlation value at sample (l) and a few mathematical operations. We have also tested MAX values distribution for FFT size and CP, whether inferred parameters are valid or not, by finding the confidence of estimation. With experimental results we evaluated that the proposed approach can successfully measure unknown OFDM signal parameters and detect OFDM signals.