Signal Detection Method Research Articles

Research on the Single-Double Interval Closed-Loop Method of Resonant Fiber Optic Gyro

Resonant fiber optic gyro (RFOG) is a type of inertial sensor with great application potential. Its closed-loop signal detection methods include single closed-loop, double closed-loop and other methods to ensure high system stability and high-precision signal output. In view of the respective advantages of single and double closed-loop systems, a single-double interval closed-loop scheme for RFOG is proposed in this letter. By introducing the threshold trigger mechanism, the advantages of low control signal noise of the single closed-loop system and large dynamic range of the double closed-loop system are fully combined. In addition, in order to improve the linearity of the demodulation curve, a linear fitting method is applied to the system. The experiment shows that the short-term and long-term stability of the system have been improved effectively.

Automated and accurate focal EEG signal detection method based on the cube pattern

Automated and accurate focal EEG signal detection method based on the cube pattern

Digital inter-symbol interference mitigation method for Nyquist OTDM signal detection with coherent sampling and KNN classifier

We propose and experimentally demonstrate a digital method to mitigate inter-symbol interference (ISI) in demultiplexing Nyquist optical time-division multiplexing (N-OTDM) signal using a simple but powerfully serviceable k-nearest neighbors (KNN) classifier. The proposed method is evaluated under four types of sampling pulse-width corresponding to various extents of ISI distortion in 160 Gbaud down to 40 Gbaud demultiplexing experiments, and the mitigation performance is found to be better along with the increasement of sampling pulse-width. Compared with the traditional decision-directed least mean square (DD-LMS) equalization method, a 3.9 dB required-OSNR (at a BER of 10−4) improvement is observed under the pulse-width of 5.3 ps. Demultiplexing performance is investigated under the pulse-widths of 3.2 ps and 3.9 ps as well, in which the improvement of required-OSNR are found to be 1.33 dB and 2.95 dB, respectively. The performance improvement clearly indicates the strong ISI mitigation ability of the KNN classifier for N-OTDM signal demultiplexing. In addition, experimental results also confirm that the proposed digital ISI mitigation method could release the pulse-width requirement of the N-OTDM demultiplexing, which paves the way for future practical application of N-OTDM technique.

Blind Detection of Broadband Signal Based on Weighted Bi-Directional Feature Pyramid Network.

With the development of wireless technology, signals propagating in space are easy to mix, so blind detection of communication signals has become a very practical and challenging problem. In this paper, we propose a blind detection method for broadband signals based on a weighted bi-directional feature pyramid network (BiFPN). The method can quickly perform detection and automatic modulation identification (AMC) on time-domain aliased signals in broadband data. Firstly, the method performs a time-frequency analysis on the received signals and extracts the normalized time-frequency images and the corresponding labels by short-time Fourier transform (STFT). Secondly, we build a target detection model based on YOLOv5 for time-domain mixed signals in broadband data and learn the features of the time-frequency distribution image dataset of broadband signals, which achieves the purpose of training the model. The main improvements of the algorithm are as follows: (1) a weighted bi-directional feature pyramid network is used to achieve a simple and fast multi-scale feature fusion approach to improve the detection probability; (2) the Efficient-Intersection over Union (EIOU) loss function is introduced to achieve high accuracy signal detection in a low Signal-Noise Ratio (SNR) environment. Finally, the time-frequency images are detected by an improved deep network model to complete the blind detection of time-domain mixed signals. The simulation results show that the method can effectively detect the continuous and burst signals in the broadband communication signal data and identify their modulation types.

Chromatographic printed array strip (C-PAS) method for cultivar-specific identification of sweetpotato cultivars 'Beniharuka' and 'Fukumurasaki'.

Sweetpotato (Ipomoea batatas) cultivars grown in Japan are highly valued for their excellent sweetness, high quality, and good texture. The export volume of sweetpotato from Japan has been rising rapidly, with a 10-fold increase on a weight basis over the last 10 years. However, since sweetpotato is propagated vegetatively from storage roots, it is easy to cultivate and propagate this crop, prompting concerns that Japanese sweetpotato cultivars/lines are being exported overseas, cultivated without permission, or reimported. Therefore, a rapid and accurate cultivar identification methodology is needed. In this study, we comprehensively analyzed the insertion sites of Cl8 retrotransposon to develop a cultivar identification technique for the Japanese cultivars 'Beniharuka' and 'Fukumurasaki'. These two cultivars were successfully distinguished from other cultivars using a minimum of two marker sets. Using the chromatographic printed array strip (C-PAS) method for DNA signal detection, 'Beniharuka' and 'Fukumurasaki' can be precisely identified using a single strip of chromatographic paper based on multiplex DNA signals derived from the amplicons of the Cl8 insertion sites. Since this method can detect DNA signals in only ~15 minutes, we expect that our method will facilitate rapid, reliable, and convenient cultivar discrimination for on-site inspection of sweetpotato.

Fast Detection Method for Frequency Scanning Interference Signals of Non-Cooperative Targets

Fast Detection Method for Frequency Scanning Interference Signals of Non-Cooperative Targets

A Detection Method for Vortex Precession Frequency Based on MEMS Three-Axis Acceleration Sensor

Swirlmeters are widely used in the wellhead metering of low permeability gas fields in China. However, the swirlmeter’s piezoelectric sensor has difficult distinguishing the vortex precession signal from fluid pulsation noise, pipeline mechanical vibration, and other interference. In this article, in order to detect the vortex precession frequency signal effectively and accurately from the interference noises, a signal detection method based on micro-electro-mechanical systems (MEMS) three-axis acceleration sensor was proposed. According to the difference between the force direction of vortex signal and interference signals, a dual-differential technique based on three-axis measurement is proposed to eliminate interference noises. The radiated interference of electromagnetic noise was eliminated by adding a notch circuit and radio frequency interference filter to the measuring circuit. Then, the sensitivity characteristics of vortex precession with different swirl angles were investigated by numerical simulation and the performance of the method was evaluated by experiments. The results of experiments show that the method proposed in this article can improve the sensor’s capability of anti-interference and widen the measurement range. Further wet gas experimental results show that the vortex precession frequency signal detected by MEMS-three-axis acceleration sensor (TAAS) does not change its linear output law in spite of the presence of liquid phase. Finally, a correction model of the instrument coefficient of the swirlmeter for wet gas measurement was established. The relative errors of the fitting model are within ±2.0%, the mean percentage error (MPE) is 0.17%, and the mean absolute percentage error (MAPE) is 0.81%. The method proposed in this article can improve the ratio of signal to noise of swirlmeter, overcome the influence of interference noise on the precession frequency, and broaden the gas flow measurement range. It realizes the efficient and accurate measurement of the gas flow rate for wet gas flow.

Field PMU Test and Calibration Method—PartII: Test Signal Identification Methods and FieldTest Applications

Synchrophasor measurement units (PMUs) provide synchronized measurement data for wide-area applications. To improve the effectiveness of synchrophasor-based applications, field PMUs must be tested to ensure their performance and data quality. In the companion paper (Part I), we proposed a field PMU test and calibration framework consisting of a PMU calibrator and analysis center. Part I presents the development and test of the PMU calibrator. This paper focuses on the analysis center and field test applications. First, the critical component of the analysis center is the signal identification module, for which the step and oscillation signal identification methods are proposed. Here, the performance evaluation criteria of PMU in these two cases are different from others. The methods include a step signal detection method based on singular value decomposition (SVD), which has the capability of weak step detection to account for energy leakage of the signal during the step process, and an oscillation signal identification method based on SVD and fast Fourier transform, which can accurately extract oscillation components that benefit from the adaptive threshold setting method. Second, the analysis center software is implemented based on identification results. By integrating the PMU calibrator in Part I with the analysis center in Part II, we can examine in depth the field PMU test applications in three test scenarios, including standard, playback, and field signal test. Results demonstrate the effectiveness and applicability of the proposed field PMU test methods from both Parts I and II.

HybNet: A Hybrid Deep Learning-Matched Filter Approach for IoT Signal Detection

Random access schemes are widely used in IoT wireless access networks. They enable a reduced complexity and overcome power consumption constraints. Nevertheless, random access results in high packet losses which are caused by overlapping transmissions. Signal detection methods for digital modulation techniques are typically based on the well-established <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">matched filter</i> , which is proven as the optimal filter under additive white Gaussian noise for minimizing error probability. However, with the <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">colored</i> interference arising from the overlapping IoT transmissions, deep learning approaches are being considered a suitable alternative. In this paper, we present a hybrid framework, dubbed as <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">HybNet</i> , that alternates between deep learning and match filter pathways based on the perceived interference level. This helps the detector to work in a broader range of conditions, optimally leveraging both the matched filter and deep learning advantages. We compare the performance of several possible data modalities and detection architectures concerning the interference-to-noise ratio, demonstrating that leveraging domain knowledge by pre-processing the input data in conjunction with the proposed HybNet surpasses the complex conjugate matched filter performance under interference-limited scenarios.

Ways of ensuring effective detection of multiposition OFDM signals

The problem of processing various stages of signal detection in interference conditions was investigated, statistical processing methods were analyzed. The task of information processing is to detect the moment of change in the input signal (change in signal level, change in frequency, occurrence of a pulse signal, etc.). External interference distorts the input signal, so it can be distinguished based on the statistical difference between interference and mixing. Additional interference that distorts the input signal in the equipment of the telecommunications system is introduced during the conversion of the signal before entering the digital processing equipment. Various methods of statistical processing are studied: optimal, adaptive, non-classical optimization methods based on the principles of non-displacement, invariance, similarity, etc. The application of non-parametric methods was investigated when the functional form of the distribution of the input data was unknown and only the general difference between the presence and absence of the signal was indicated. The ranking method of signal detection processing deserves special attention. Adaptive methods are used if the distribution of input data is known with accuracy up to the array of unknown parameters. Nonparametric methods are used when the functional form of the distribution of input data is unknown, and only the general differences between the situations of the presence and absence of a signal are specified. The analysis of signal detection methods shows that one of the promising directions of research is the development of methods that use the invariant properties of the system and ensure the maximum possible storage of signal information. It was studied that the flexibility of the ranking procedure makes it possible to solve a wide range of problems in the detection of multi-position signals under conditions of non-parametric a priori uncertainty.

Frequency hopping signal detection based on optimized generalized S transform and ResNet.

The performance of traditional frequency hopping signal detection methods based on time frequency analysis is limited by the tradeoff of time-frequency resolution and spectrum leakage. Machine learning-based frequency hopping signal detection techniques have a high level of complexity. Therefore, this paper proposes a residual network and the optimized generalized S transform to detect frequency hopping signals. First, based on the time-frequency aggregation measure, the generalized S transform parameters $ \lambda $ and $ p $ are optimized using a multi-population genetic algorithm. Second, the optimized generalized S transform is used to determine a signal's time-frequency spectrum, which is then normalized to make this robust to noise power uncertainty. Finally, a residual network structure is designed which receives the time-frequency spectrum. To detect frequency hopping signals, the network automatically learns the time-frequency properties of signals and noise. Simulated findings indicate that the multi-population genetic algorithm not only increases optimization efficiency when compared to a regular genetic algorithm, but also has faster convergence and more stable optimization results. Compared with a hybrid convolutional network/recurrent neural network algorithm, the proposed technique is better at detection and has less computational and storage complexity.

A matched-filter technique with an objective threshold

We propose an objective threshold determination method for detecting outliers from the empirical distribution of cross-correlation coefficients among seismic waveforms. This method is aimed at detecting seismic signals from continuous waveform records. In our framework, detectability is automatically determined from Akaike’s Information Criterion (AIC). We applied the method of seismic signal detection to continuous records collected over two years. The results show that the maximum value of network cross-correlation coefficients sampled from each constant interval can be approximated by the theory of extreme value statistics, which provides a parametric probability density function of maxima. By using the function, outliers can be considered with a reasonable criterion.

Knowledge-Enhanced Compressed Measurements for Detection of Frequency-Hopping Spread Spectrum Signals Based on Task-Specific Information and Deep Neural Networks

The frequency-hopping spread spectrum (FHSS) technique is widely used in secure communications. In this technique, the signal carrier frequency hops over a large band. The conventional non-compressed receiver must sample the signal at high rates to catch the entire frequency-hopping range, which is unfeasible for wide frequency-hopping ranges. In this paper, we propose an efficient adaptive compressed method to measure and detect the FHSS signals non-cooperatively. In contrast to the literature, the FHSS signal-detection method proposed in this paper is achieved directly with compressed sampling rates. The measurement kernels (the non-zero coefficients in the measurement matrix) are designed adaptively, using continuously updated knowledge from the compressed measurement. More importantly, in contrast to the iterative optimizations of the measurement matrices in the literature, the deep neural networks are trained once using task-specific information optimization and repeatedly implemented for measurement kernel design, enabling efficient adaptive detection of the FHSS signals. Simulations show that the proposed method provides stably low missing detection rates, compared to the compressed detection with random measurement kernels and the recently proposed method. Meanwhile, the measurement design in the proposed method is shown to provide improved efficiency, compared to the commonly used recursive method.

A Bayesian method for safety signal detection in ongoing blinded randomised controlled trials.

Sponsors have a responsibility to minimise risk to participants in clinical studies through safety monitoring. The FDA Final Rule for IND Safety Reporting requires routine aggregate safety evaluation, including in ongoing blinded studies. We are interested in estimating the probability that the true adverse event rate in the experimental arm exceeds that in the control arm. We developed a Bayesian approach that specifies an informative meta-analytic predictive prior on the event probability in the control arm and an uninformative prior on that in the experimental arm. We combined these priors with a mixture likelihood that considers each patient in the ongoing blinded study may belong to the experimental or control arm. This allowed us to estimate the quantity of interest without unblinding. We evaluated our method by simulation, pairing scenarios that differed only in whether a safety signal was present or missing, and quantifying the ability of our model to discriminate using signal detection theory. Our approach shows benefit. It detects safety signals more reliably with greater sample sizes and for common rather than rare events. Performance does not deteriorate markedly when historical studies exhibit heterogeneous hazards or non-constant hazards. Our method will allow us to monitor safety signals in ongoing blinded studies with the goal of earlier identification and risk mitigation. Our method could be adapted to use informative priors on both arms or predictive covariates where pertinent data exist. We stress that ongoing safety monitoring should involve a multi-disciplinary team where statistical methods are paired with medical judgement.

Concurrent Low-power Listening: A New Design Paradigm for Duty-cycling Communication

In this article, we explore a new design paradigm of duty-cycling mechanism that supports low-power devices to fully turn channel contention into transmission opportunities. To achieve this goal, we propose Concurrent Low-power Listening (CLPL) to enable contention-tolerant and concurrent media access control (MAC) for widely deployed low-power devices. The fundamental principle behind CLPL is that frequency modulated receiver can reliably demodulate the strongest signal even if cochannel interference and noise exist. By using CLPL, a sender inserts a series of tailor-made signals (namely, wake-up signal) between adjacent data frames to awaken appointed receiver, making it capable to receive the next data frame. According to system-defined maximum transmission power level, CLPL adopts an adaptive algorithm to adjust the transmission power of wake-up signals so that its signal strength is above receiver sensitivity and will not interfere with the other data frames in transit. By exploiting the spatial-temporal correlation, we further develop a light-weight wake-up signal detection method to enable a waiting sender to accurately identify the current channel condition. Then, it schedules the sender’s data frame transmissions by overlapping with those wake-up signals, without conflicting with existing data frame transmissions. We have implemented the prototype of CLPL and conducted extensive experiments on a real testbed. In comparison with the state-of-the-art low-power MAC schemes, such as ContikiMAC, A-MAC, BoX-MAC, and opportunistic scheme ORW, CLPL can improve the throughput by 2–6 times and halve the end-to-end transmission delay.

Deep Learning-Based Signal Detection for Underwater Acoustic OTFS Communication

Orthogonal time frequency space (OTFS) is a novel two-dimensional (2D) modulation technique that provides reliable communications over time- and frequency-selective channels. In underwater acoustic (UWA) channel, the multi-path delay and Doppler shift are several magnitudes larger than wireless radio communication, which will cause severe time- and frequency-selective fading. The receiver has to recover the distorted OTFS signal with inter-symbol interference (ISI) and inter-carrier interference (ICI). The conventional UWA OTFS receivers perform channel estimation explicitly and equalization to detect transmitted symbols, which requires prior knowledge of the system. This paper proposes a deep learning-based signal detection method for UWA OTFS communication, in which the deep neural network can recover the received symbols after sufficient training. In particular, it cascades a convolutional neural network (CNN) with skip connections (SC) and a bidirectional long short-term memory (BiLSTM) network to perform signal recovery. The proposed method extracts feature information from received OTFS signal sequences and trains the neural network for signal detection. The numerical results demonstrate that the SC-CNN-BiLSTM-based OTFS detection method performs with a lower bit error rate (BER) than the 2D-CNN, FC-DNN, and conventional signal detection methods.

OFDM Spectrum Sensing Analysis and Implementation Method

OFDM is a carrier modulation technology, which realizes the parallel transmission of high-speed serial data through frequency division multiplexing. OFDM has an excellent ability to resist multipath fading and is commonly used in wireless communication transmission technology. Spectrum sensing technology is a technology that allows cognitive users to accurately obtain the authorized spectrum usage in wireless networks through signal detection and other processing methods. It is the basis for effectively using different idle channels in different time domains. This paper mainly discusses the OFDM technology, deduces the algorithm of the implementation principle and mathematical characteristics of OFDM, analyses its spectrum characteristics and performance advantages, focuses on the principle and method of OFDM spectrum sensing based on spectrum sensing technology, combs the characteristics of several existing commonly used OFDM spectrum sensing methods, and analyses the implementation cost, hardware requirements, transmission performance The performances of interference factors and other aspects are sorted out, the advantages and disadvantages of several existing OFDM spectrum sensing methods are compared, their respective applicable environments are compared, and their possible problems in the future development prospect of OFDM technology and the direction that needs further research and improvement are put forward.

Physical Layer Security Analysis of Multi-Hop Hybrid RF/FSO System in Presence of Multiple Eavesdroppers

In this paper, the physical layer security (PLS) analysis for a decode-and-forward (DF) protocol based multi-hop hybrid radio frequency (RF)/ free space optics (FSO) is presented. Herein, two different scenarios are considered for detecting the secured information at the destination. In the first scenario, at each hop, the received signals with higher secrecy capacity are selected. However, in the second scenario, the RF and FSO signals are simply decoded and forwarded to the next hop and selection is done at the last hop only. Each node in the system is connected to its subsequent node through parallel RF and FSO links. The FSO links and RF links are characterized by Málaga ( <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\mathcal {M}$</tex-math></inline-formula> ) and Nakagami- <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$m$</tex-math></inline-formula> composite distributions respectively. For both scenarios, the secrecy outage probability (SOP), strictly positive secrecy capacity (SPSC), intercept probability (IP), and effective secrecy throughput (EST) are obtained as performance metrics. These performance metrics are obtained by considering pointing error, different optical signal detection methods, turbulence effect, and the RF link fading parameter ( <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$m$</tex-math></inline-formula> ). Using the results, the security and reliability trade-off analysis is discussed for two scenarios. In addition, the asymptotic results are also obtained to study the system in depth. Further, the secrecy diversity order (SDO) and secrecy coding gain (SCG) are obtained to analyse the system in depth. Finally, Monte-Carlo Simulation is performed to verify the obtained results. The results manifest that the scenario with selection at each hop provides better secrecy performance than the scenario with selection at the last hop.

Signal detection and imaging methods for MEMS electron microscope

In the paper, new detection methods that can be used to detect signals in a miniature MEMS (Micro-Electro-Mechanical System) electron microscope were presented. The methods were designed to fit the structure of the developed MEMS microscope, equipped with an electron-optical microcolumn and a scanning system based on an octupole deflector. In the experiments carried out, imaging was performed using a system of three silicon detectors placed above, below, and at the sample level and integrated with the octupole deflection system. Measurements were carried out under different pressure conditions (vacuum and atmospheric pressure) and for different detector bias voltage and electron beam energy. Good quality images were obtained using all three detectors, both in vacuum and in air. The results presented indicate that in the final implementation of the MEMS electron microscope, all three detection systems can be successfully incorporated.

Two-Stage Ultrasound Signal Recognition Method Based on Envelope and Local Similarity Features

Accurate identification of ultrasonic signals can effectively improve the accuracy of a defect detection and inversion. Current methods, based on machine learning and deep learning have been able to classify signals with significant differences. However, the ultrasonic internal detection signal is interspersed with a large number of anomalous signals of an unknown origin and is affected by the time shift of echo features and noise interference, which leads to the low recognition accuracy of the ultrasonic internal detection signal, at this stage. To address the above problems, this paper proposes a two-stage ultrasonic signal recognition method, based on the envelope and local similarity features (TS-ES). In the first stage, a normal signal classification method, based on the envelope feature extraction and fusion is proposed to solve the problem of the low ultrasonic signal classification accuracy under the conditions of the echo feature time shift and noise interference. In the second stage, an abnormal signal detection method, based on the local similarity feature extraction and enhancement is proposed to solve the problem of detecting abnormal signals in ultrasound internal detection data. The experimental results show that the accuracy of the two-stage ultrasonic signal recognition method, based on the envelope and local similarity features (TS-ES) in this paper is 97.43%, and the abnormal signal detection accuracy and recall rate are as high as 99.7% and 97.81%.