Waveform Detection Research Articles

Analysis of Generation Mechanism of Unimodal and Bimodal Waveform Detection Signals of a Whole Roll Flatness Meter

Analysis of Generation Mechanism of Unimodal and Bimodal Waveform Detection Signals of a Whole Roll Flatness Meter

Balanced air-biased detection of terahertz waveforms.

A novel, to the best of our knowledge, balanced air-biased coherent detection scheme for capturing ultrabroadband terahertz (THz) waveforms is implemented. The balanced detection scheme allows for coherent detection at the full repetition rate of the laser system without requiring bias modulation, signal generators, or lock-in amplifiers while doubling the dynamic range and quadrupling the signal-to-noise ratio compared to conventional air-biased coherent detection. These advantages are achieved by rotating the bias electrodes by 90° relative to the conventional scheme. With a 1 kHz driving laser, the scheme enables sub-second, high-fidelity waveform acquisition with a continuously moving delay stage, demonstrated by collecting 200 waveforms in 100 s. The balanced detection scheme paves the way for much faster and higher quality 2D ultrabroadband terahertz spectroscopy.

Construction method of fault function test case for Primary-Secondary fusion switch in distribution network

Test case design is the key to effective quality inspection of electrical equipment. To tackle the challenges of inadequate scenario coverage and disparate difficulty levels in the detection of single-phase ground faults in power distribution primary and secondary fusion switch, we propose a case construction method utilizing Latin hypercube sampling (LHS) based on NSGA-II double-objective optimization. Initially, the method investigates the primary factors influencing waveform detection difficulty and determines their respective weights through measurement and calculation, thus establishing a comprehensive model for calculating difficulty coefficients. Then, based on the dual objectives of uniformity and difficulty balance, we introduce a double-objective optimization LHS generation algorithm to construct a multi-dimensional detection cases of single-phase ground fault in power distribution primary and secondary fusion switch. Finally, the difficulty coefficient calculation model and the constructed test case undergo validation using actual test results, demonstrating a 43.59% efficiency improvement on the premise of ensuring the detection efficacy.

A deep learning-assisted skin-integrated pulse sensing system for reliable pulse monitoring and cardiac function assessment

Long-term pulse monitoring plays a vital role in the prevention and diagnosis of cardiovascular diseases since pulse signals can provide rich characteristics of cardiac conditions. Flexible sensors mounting on the wrist for pulse monitoring have drawn great attention due to the advantages of soft, skin-interfaced and non-invasive features. However, slight wrist movement and transposition of the sensor on skin would dramatically impact the signal quality because of motion artifacts and interfacial properties. Here, we report an intelligent skin-integrated system by combining a sensitive pressure sensor array and a signal enhancement algorithm to provide reliable pulse signal monitoring. The sensors have the advantages of low cost, simple fabrication, reusability and high sensitivity. The multiple sensor channels offer rich information of vital signals, which can facilitate deep learning-based algorithms to denoise and reconstruct pulses more accurately and thus enable precise heart rate monitoring and heart variability assessments. The reported technology suggests a novel approach for pulse waveform detection in static and dynamic conditions, allowing users to more easily achieve reliable pulse monitoring.

Spectral recovery of broadband waveforms via cross-phase modulation based tunable Talbot amplifier

Physical processes in the Fourier domain play a crucial role in various applications such as spectroscopy, quantum technology, ranging, radio-astronomy, and telecommunications. However, the presence of stochastic noise poses a significant challenge in the detection of broadband spectral waveforms, especially those with limited power. In this study, we propose and experimentally demonstrate a cross-phase modulation (XPM) based spectral Talbot amplifier to recover the broadband spectral waveforms in high fidelity. Through the combination of spectral phase filtering and XPM nonlinear effect in an all-fiber configuration, we demonstrate spectral purification of THz-bandwidth spectral waveforms submerged in strong noise. The proposed spectral Talbot amplifier provides tunable amplification factors from 3 to 10, achieved by flexible control on the temporal waveform of the pump and the net dispersion. We demonstrate up to 10-dB remarkable improvement on optical signal-to-noise ratio (OSNR) while preserving the spectral envelope. Furthermore, our system allows frequency-selective reconstruction of noisy input spectra, introducing a new level of flexibility for spectral recovery and information extraction. We also evaluate numerically the impact of pump intensity deviation on the reconstructed spectral waveforms. Our all-optical approach presents a powerful means for effective recovery of broadband spectral waveforms, enabling information extraction from a noise-buried background.

Investigation of Characteristics of Ultraviolet Light Pulse Weak Signal Communication System Based on Fourth-Order Frequency-Shift Keying Modulation

In ultraviolet (UV) communication, On–Off Keying (OOK) is the primary modulation technique. Compared to OOK, frequency modulation offers stronger resistance to path attenuation. Currently, research on frequency modulation demodulation schemes for UV communication is limited, mainly employing waveform detection and laser pulse response methods, which require high detection sensitivity to light. This study presents a novel frequency modulation communication scheme using discrete Poisson channel distribution and optical pulse signal processing algorithms, enhancing the signal processing sensitivity of the existing frequency modulation scheme to the level of photons. The proposed system model is rigorously evaluated through theoretical derivations and simulations. Additionally, a hardware system integrating optical pulse counting, frequency detection, and clock data recovery algorithms is developed. Experimental results show the system achieving a 5 kbps transmission rate under frequency modulation. In non-line-of-sight (NLOS) scenarios, communication reaches up to 65 m, with the receiver elevation angle ranging from 10° to 25° and the bit error rate (BER) stabilized at 10−4, while in line-of-sight (LOS) situations, the BER remains at 10−5 up to 400 m and 10−4 up to 700 m, achieving the farthest distance and fastest communication rate achievable in the current FSK modulation scheme of ultraviolet communication systems. The integrated components enhance its applicability in communication systems. This study offers a valuable addition to UV communication technology.

Research on the key technology of high-precision waveform synchronization identification and measurement.

High-precision waveform identification and measurement are effective for waveform detection and evaluation in signal processing. The accuracy of waveform identification, precision of measurement, and speed of response are important indicators of waveform measurement instruments. To detect the waveform accurately, a hold and attenuation circuit divided into two is designed, and the STM32F4 microcontroller is used to accurately capture and perform spectrum analysis using a high-precision analog-to-digital converter based on fast Fourier transform technology to identify key parameters, such as waveform type, frequency, peak-to-peak value, and duty cycle. To improve the recognition accuracy and response speed, technical solutions, such as high-frequency sampling and over-zero detection, are used to improve the system efficiency. Algorithm simulation, circuit simulation, and physical testing show that the high-precision waveform synchronization recognition circuit and algorithm can accurately recognize various essential waveforms in the voltage and frequency ranges of 50 mV ≤ VPP ≤ 10 V and 1 Hz ≤ f ≤ 50 kHz, respectively, and simultaneously measure important parameters, such as frequency, peak-to-peak value, and duty cycle with an accuracy within ±1%. Intelligent linkage, no intermediate parameter setting, and a response speed of approximately 0.3 s make it suitable for such applications as fast and high-precision waveform intelligent detection and display. The method is highly integrated, simple to operate, cost-effective, and practical.

Four-wave mixing based spectral Talbot amplifier for programmable purification of optical frequency combs

Optical frequency combs (OFCs) with programmable free spectral range and high optical carrier-to-noise ratio (CNR) play a crucial role in diverse research fields, including telecommunications, spectroscopy, quantum information, astronomy, sensing, and imaging. Unfortunately, the presence of stochastic noise often results in degraded optical CNR, leading to limited communication performance and measurement accuracy in comb-based systems. There is a lack of effective and flexible methods to improve the CNR of OFCs contaminated by broadband noise, hampering their widespread utilization. To address this challenge, we propose a four-wave mixing based spectral Talbot amplifier to purify OFCs flexibly. Our approach employs programmable spectral phase filters followed by a nonlinear Kerr medium to regenerate an OFC with superior CNR. In our experimental demonstration, we regenerated a 165-GHz spaced CNR enhanced OFC from a noise-dominated comb source spaced at 11 GHz, achieving up to ∼11-dB CNR improvement. The technique allows for a user-defined purification factor m to range from 7 to 15. Furthermore, our scheme demonstrates flexibility in adjusting the wavelengths of the regenerated comb lines via a tunable optical delay line without the need for a tunable seed laser. We also investigated the impact of the pump and signal on the regenerated comb experimentally and studied the influence of dispersion mismatch on the suppression of undesired sidebands numerically. Our proposed scheme presents a powerful alternative for programmable purification, manipulation, and detection of noise-dominated spectral waveforms.

Reliable 5G waveform detection from an optical fibre transmitter system

Reliable 5G waveform detection from an optical fibre transmitter system

Chirp Spread Spectrum-Based Waveform Design and Detection Mechanisms for LPWAN-Based IoT: A Survey

Chirp Spread Spectrum-Based Waveform Design and Detection Mechanisms for LPWAN-Based IoT: A Survey

Joint Waveform Design and Detection in Symbiotic Ambient Backscatter NOMA Systems

Non-orthogonal multiple access (NOMA) and symbiotic ambient backscatter communications (AmBC) are both considered promising technologies for beyond 5G mobile communication technology by enabling low-powered and spectrum-efficient access in large-scale Internet of Things. In this paper, we investigate the uplink symbiotic communication in AmBC enabled NOMA system. In contrast with existing works, we assume the carrier transmitter (CT) transmits information while providing energy to the backscatter devices (BDs), which improves the energy efficiency. However, the signal power transmitted by the CT to integrated receiver (IR) through the direct link is much larger than the signal reflected by the BD, which can seriously affect the detection of the reflected signal by BD. Therefore, we jointly design the signal waveforms of CT and BDs, and propose a multi-user blind detection algorithm based on interference cancellation at IR. The simulation results demonstrate that the proposed multi-user detection algorithm achieves improved performance even the active BD number is unknown or without direct link.

Evaluation of 5G techniques affecting the deployment of smart hospital infrastructure: Understanding 5G, AI and IoT role in smart hospital

Smart Hospital will play a key role in improving the quality of services (QoS) related to the health sector. In the present scenario, it is impossible to accommodate an ageing population with the current infrastructure and facilities provided by conventional hospitals. However, the integration of technologies with the conventional hospital is enhancing the medical services and also making it easier for the public, who can save time and money by getting all the necessary medical facilities while sitting at home. The deployment of the fifth generation (5G) is taking place all over the world. The 5G network offers a better service that can be used in hospitals to make it a smart health service provider with online facilities such as video monitoring, suggesting a proper medical prescription based on medical data, remote surgery, and so on. High bandwidth, a fast data rate, low latency, and a low peak-to-average power ratio (PAPR) are some requirements that need to be fulfilled by the 5G radio. In this work, we focus on improving the power savings, spectral access, latency, and Bit error rate (BER) of the advanced 5G waveforms such as non-orthogonal multiple access (NOMA), filter bank multi carrier (FBMC), and orthogonal frequency division multiplexing (OFDM). In the first part of the article, we proposed a PAPR reduction algorithm to improve the power efficiency of the power amplifier (PA) for 5G waveforms. Several algorithms are applied to the 5G waveforms, and their performances are estimated in terms of PAPR curves. In the second part, we have utilised advanced detection algorithms to improve the signal detection of the 5G waveforms. The simulation results reveal that the proposed algorithms efficiently enhance the throughput of the framework. In the third part, we improve the spectral efficiency of the 5G waveforms by applying spectrum sensing (SS) algorithms. It is seen that the SS methods efficiently reduce the spectrum leakage of the multi-carrier waveforms (MCW). Finally, we examined the role and challenges of the Internet of Things (IoT) and artificial intelligence (AI) in smart hospitals. Overall, it is concluded that hospitals can become more effective, cut expenses, and provide better patient care with the aid of AI and IoT. Hospitals can improve patient outcomes and the state of the healthcare system as a whole by utilising these technologies.

Reconstruction of central arterial pressure waveform based on CBi-SAN network from radial pressure waveform

The central arterial pressure (CAP) is an important physiological indicator of the human cardiovascular system which represents one of the greatest threats to human health. Accurate non-invasive detection and reconstruction of CAP waveforms are crucial for the reliable treatment of cardiovascular system diseases. However, the traditional methods are reconstructed with relatively low accuracy, and some deep learning neural network models also have difficulty in extracting features, as a result, these methods have potential for further advancement. In this study, we proposed a novel model (CBi-SAN) to implement an end-to-end relationship from radial artery pressure (RAP) waveform to CAP waveform, which consisted of the convolutional neural network (CNN), the bidirectional long-short-time memory network (BiLSTM), and the self-attention mechanism to improve the performance of CAP reconstruction. The data on invasive measurements of CAP and RAP waveform were used in 62 patients before and after medication to develop and validate the performance of CBi-SAN model for reconstructing CAP waveform. We compared it with traditional methods and deep learning models in mean absolute error (MAE), root mean square error (RMSE), and Spearman correlation coefficient (SCC). Study results indicated the CBi-SAN model performed great performance on CAP waveform reconstruction (MAE: 2.23 ± 0.11 mmHg, RMSE: 2.21 ± 0.07 mmHg), concurrently, the best reconstruction effect was obtained in the central artery systolic pressure (CASP) and the central artery diastolic pressure(CADP) (RMSECASP: 2.94 ± 0.48 mmHg, RMSECADP: 1.96 ± 0.06 mmHg). These results implied the performance of the CAP reconstruction based on CBi-SAN model was superior to the existing methods, hopped to be effectively applied to clinical practice in the future.

An ultrasonic in-line inspection data processing method considering invalid data caused by sensor failure

Ultrasonic in-line inspection is one of the most widely adopted nondestructive testing methods for defect detection in pipelines. In practical industrial scenes, pipeline specification varies, sensor failure occurs frequently, and the number of pipeline defect samples is scarce. How to detect defects without false detection caused by invalid data and with limited labeled samples is a challenging problem in this area. An ultrasonic in-line inspection data processing method considering invalid data caused by sensor failure is proposed in this paper to enhance the accuracy of the defect and its profile detection. Firstly, the multi-channel data is aggregated according to sampling time. The data dimension is reduced, and the accuracy of the invalid waveform detection adopting isolation forest arithmetic is improved. The methods are adopted for the invalid waveforms replacement with two-dimensional cubic spline interpolation using adjacent sensors. Secondly, a natural breakpoint method is adopted to locate the echo peaks. Residual wall thickness is evaluated by calculating the time difference between the echo peaks. A fast pseudo-colorization method based on sliding windows and a morphological image processing method are proposed to detect defects and their profiles efficiently utilizing the residual wall thickness. Finally, practical in-line inspection data is utilized to evaluate the performance. The experiment results illustrate that the detection accuracy is enhanced on different sizes of pipelines without requiring labeled samples.

Detection and characterization of geomagnetic anomaly waveforms

Geomagnetic anomalies are abnormal changes in the geomagnetic field caused by changes in the stress of the underground rock. Research on geomagnetic anomaly signals can help explore earthquake prediction. This study proposes a window-weighted correlation degree (WWC) method to detect geomagnetic vertical component anomaly waveforms, addressing the limitations of traditional methods. The WWC method calculates the similarity between geomagnetic and reference data in daytime and nighttime windows, identifying abnormal signals based on change values. This method achieves a 0.976 accuracy in identifying geomagnetic anomaly waveforms, effectively detecting and recognizing complex waveform features in the geomagnetic field. Further, based on this method, a dataset of geomagnetic anomaly waveforms under various earthquake events was built, identifying seven typical anomalies. Five types of features were selected and constructed to measure the non-random deviation of diurnal geomagnetic variation from the baseline signal, improving the precision and recall of the random forest classification model. The machine learning method can classify and predict geomagnetic anomaly waveforms related to earthquake occurrence with superior precision and recall compared to traditional methods. This approach offers a productive means for identifying geomagnetic anomaly waveforms associated with earthquake occurrence and analyzing the potential correlations between anomalies and seismic events. It also provides a possibility for exploring the physical mechanism involved in geomagnetic anomalies' generation and evolution process. It provides effective data support for further analysis of seismic-magnetic relationships and helps to promote the deep integration of artificial intelligence and earthquake prediction research.

Design and implementation of a modular pulse wave preprocessing and analysis system based on a new detection algorithm

As one of the standard electrophysiological signals in the human body, the photoplethysmography contains detailed information about the blood microcirculation and has been commonly used in various medical scenarios, where the accurate detection of the pulse waveform and quantification of its morphological characteristics are essential steps. In this paper, a modular pulse wave preprocessing and analysis system is developed based on the principles of design patterns. The system designs each part of the preprocessing and analysis process as independent functional modules to be compatible and reusable. In addition, the detection process of the pulse waveform is improved, and a new waveform detection algorithm composed of screening-checking-deciding is proposed. It is verified that the algorithm has a practical design for each module, high accuracy of waveform recognition and high anti-interference capability. The modular pulse wave preprocessing and analysis software system developed in this paper can meet the individual preprocessing requirements for various pulse wave application studies under different platforms. The proposed novel algorithm with high accuracy also provides a new idea for the pulse wave analysis process.

An NPE-PCA-based Detection Method of Distribution Transformer Closing Surge Waveform

In order to effectively detect the waveform characteristics of the closing inrush current of distribution transformers and distinguish between the magnetizing inrush current and the fault current when closing, this paper proposes a new method based on neighborhood preserving embedding (NPE) and principal component analysis (PCA) transformer closing inrush current detection method. This method can detect and process the global and local feature information of the data. First, the NPE-PCA algorithm is used to reduce the current data to two-dimensional space, and then the fitting error σ is obtained by fitting the two-dimensional space data. The relationship between σ and a given threshold is used to identify the magnetizing inrush current when closing. Finally, a model is built on the ATP/EMTP platform to test the proposed method for detecting the waveform characteristics of the closing inrush current. The simulation results show that the NPE-PCA inrush current waveform detection algorithm proposed in this paper can effectively identify the waveform characteristics of the transformer closing inrush current, which is consistent with the second harmonic wave. Algorithm comparison analysis shows that the performance of this algorithm is better.

Radar Signal Recognition Based on Multilayer Perceptron Neural Network

Low Probability of Intercept (LPI) radars are developed on an advanced architecture by making use of coded waveforms. Detection and classification of radar waveforms are important in many critical applications like electronic warfare, threat to radar and surveillance. Precise estimation of parameter and classification of the type of waveform will provide information about the threat to the radar and also helps to develop sophisticated intercept receiver. The present work is on classification of modulation waveforms of LPI radar using multilayer perceptron neural (MLPN) network. The classification approach is based on the following two steps. In the first step, the waveforms are analysed using cyclstationary technique which models the signal in bi-frequency (BF) plane. Using this algorithm, the BF images of the signals are obtained. In the second step, the BF images are fed to a feature extraction unit to get the salient features of the waveform and then to the multilayer perceptron neural (MLPN) network for classification. Nine types of noise free modulation waveforms (Frank, four polyphase codes and four poly time codes) are classified using the images obtained in the first step. The success rate achieved is 100 % for noise free signals. The experiment is repeated for various noise levels up to -12dB SNR. The noisy signals, before feeding to the MLPN network, are denoised using two types of denoising filters connected in cascade and the classification success rate achieved is 93.3% for signals up to -12dB SNR.

Combining Local PWV and Quantified Arterial Changes for Calibration-Free Cuffless Blood Pressure Estimation: A Clinical Validation

This study proposes a novel calibration-free cuffless blood pressure monitor (BPM). Unlike the cuffless BPM that requires timing correction with the traditional BPM, the calibration-free cuffless BPM can directly estimate the arterial blood pressure (BP) based on the hemodynamics mathematical model. Our proposed device integrates a pair of piezoelectric ceramics as pressure sensors to sense the pressure wave in the radial artery. It calculates the local pulse wave velocity (PWV) with our waveform detection algorithm of our proposed one. A photoplethysmogram (PPG) probe is set between the pair of pressure wave sensors. This PPG probe is used to monitor the radial artery’s PPG intensity ratio (PIR). We design PPG signal processing algorithms to quantify the PIR. We recruited 129 participants for the BP monitoring experiment. Compared with the reference sphygmomanometer, the error mean ± standard deviations (STDs) systolic BP (SBP) was 2.1 ± 3.4, and the correlation r-value was 0.97. The diastolic BP (DBP) was 0.8 ± 4.2, the correlation r-value was 0.90, and p < 0.05 is taken as statistically significant. A new type of wearable continuous calibration-free BPM can replace the situation that requires the use of traditional ambulatory BPM and reduce patient discomfort. Our proposed BP measurement passed all ANSI/AAMI/ISO 81060-2:20181_5.2.4.1.2 data analysis criterion 1 and 2 standard requirements.

Amplitude–Temporal and Spectral Characteristics of Pulsed UHF-SHF Radiation of a High-Voltage Streamer Discharge in Air under the Atmospheric Pressure

A special experimental setup with a three-electrode discharge gap was used to study the dynamic characteristics of the ultra-high- and super-high-frequency (UHF-SHF) electromagnetic radiation of a high-voltage discharge having the streamer form with reference to the dynamics of individual streamers at the nanosecond time resolution. We performed synchronous detection of the radiation waveforms using a wideband horn antenna, on the one hand, and high-speed photography of the discharge development in the discharge gap using an ICCD camera, on the other hand. It was found that the high-voltage discharge is a source of radiation in the frequency band up to 10 GHz, which is a series of individual ultrawideband (UWB) bursts having durations of less than 1 ns and leading fronts less than 100 ps long and appears when the streamers moving from the discharge anode (thin wire) meet the discharge cathode (plane). By the order of magnitude, the number of radiation bursts corresponds to the number of streamers that reach the electrode, according to the high-speed photography data. The qualitative data confirmed by simple theoretical estimations show that the sources of UWB radiation pulses are individual streamers at the moment of their contact with the electrode, and the radiation of the streamers can be regarded as transition radiation.