Differential Detection Method Research Articles

Ultra-compact and high-precision differential detection method based on liquid crystal polarization grating for miniature atomic magnetometer

Ultra-compact and high-precision differential detection method based on liquid crystal polarization grating for miniature atomic magnetometer

A differential low frequency eddy current test method for detecting moisture content of high voltage cable water-blocking tape

ABSTRACT The excessive moisture content in the water-blocking tape has been one of the main causes of high voltage cable erosion failures in recent years. However, the 2–4 mm thick aluminium sheath wrapped around the water-blocking tape makes it challenging to detect the moisture content. This study introduces a method for detecting the moisture content of high voltage cable water-blocking tapes based on low-frequency eddy currents. Initially, longitudinal and transverse impedance measurement experiments of the water-blocking tape were conducted to establish the relationship between impedance and moisture content. Since the impedance of the water-blocking tape is significantly higher compared to the aluminium sheath, the eddy current signal was obtained using a differential detection method. An excitation frequency of 10 Hz was chosen to achieve a deeper skin depth. Additionally, an eddy current finite element analysis model was developed, showing that moisture content has an almost linear relationship with the amplitude and phase of the eddy current signal. Finally, an experiment was conducted on an equivalent high voltage cable structure, and the experimental results aligned well with the simulation results.

A coherent population trapping magnetometer using a phase-delayed differential detection method

We propose an improved Coherent population trapping (CPT) magnetometer using a phase-delayed differential detection (PDD) method. The PDD approach capable of generating two linearly-polarized beams with orthogonal states that can interfere with each other can radically improve the slope of the CPT dispersion signal curve and offer the possibility of improving the CPT magnetometer sensitivity. We experimentally observed the simultaneous existence of constructive interference states and destructive interference states in the dispersion curves for different sideband excitations in a bichromatic light field and obtained an approximately two-fold increase in the slope of the dispersion curves after interference. We also utilize a differential scheme to probe the change in the slope of the dispersion curve and measure that the slope of the dispersion curve is increased by about a factor of 3 compared to the slope of the dispersion curve of the single-transmitted light. The results indicated that the proposed method has about a three-fold sensitivity enhancement effect on the CPT magnetometer.

Development and Application of Droplet Digital PCR Assay for the Detection of Watermelon Silver Mottle Virus and Melon Yellow Spot Virus

Watermelon silver mottle virus (WSMoV) and melon yellow spot virus (MYSV) (Tospoviridae, Orthotospovirus) are responsible for silver mottle mosaic and yellow spot symptoms, posing threats to melon (Cucumis melo), watermelon (Citrullus lanatus), and cucumber and leading to significant economic losses in China. Early disease detection and monitoring of these two viruses are necessary for disease management, for which a rapid, reliable, and adaptable diagnostic method is required. In this study, using a droplet digital PCR (ddPCR) method, the conserved region of the nucleocapsid gene (N gene) sequence was detected in WSMoV and MYSV. The probes and primers for WSMoV and MYSV did not detect other relevant cucurbit viruses, and the specificity reached 100%. Although both qPCR and ddPCR exhibited good reproducibility, the reproducibility of ddPCR was better than that of qPCR. The reproducibility of ddPCR was proved to be 100%. Moreover, ddPCR exhibited a good linear correlation with varying concentrations of targets. The detection limits of WSMoV and MYSV in ddPCR were 18 and 9 copies/μL and were approximately 12- and 18-times more than those in qPCR, respectively. Finally, 62 samples collected from the field (including infected melon, watermelon, and weeds) were further evaluated for the presence of WSMoV and MYSV. The field samples exhibited 91.94% and 51.61% positivity rates in ddPCR assays for WSMoV and MYSV, respectively; the rates were higher than those in qPCR (59.68% and 43.39%, respectively). The results indicated that ddPCR has a higher accuracy than qPCR. Therefore, ddPCR could be used in the clinical diagnosis of early infections of WSMoV and MYSV. To the best of our knowledge, this is the first study to establish a ddPCR method for the detection of WSMoV and MYSV. The application of this method for differential detection of MYSV and WSMoV will help in understanding the epidemics caused by these two important viruses and provide important information for the early detection, monitoring, and rapid extermination of infection.

Research on differential pulse voltammetry detection method for low concentration glucose based on machine learning model

In the electrochemical detection process, determining the optimal experimental conditions for the standard curve often requires multiple iterations. A low-concentration glucose detection method that combines electrochemical principles with machine learning was introduced to address the challenge of determining the optimal reactant addition in electrochemical detection. We developed a model that incorporates electrochemical curve features and various experimental conditions as variables. Concentration regression analysis was conducted using five machine learning models: Multiple Linear Regression (MLR), Decision Trees (DT),Artificial Neural Networks (ANN), Random Forest (RF), and Extreme Gradient Boosting (XGBoost). The results show that the performance of the XGBoost model surpasses that of the other models. Compared to the second place—Random Forest model, it reduces the Mean Absolute Error (MAE) by 21.6 % and the Root Mean Square Error (RMSE) by 25.3 %. This method can be used for low-concentration glucose detection, addressing the challenge of determining the optimal experimental conditions. It mitigates the impact of experimental conditions on detection results, enhances concentration determination accuracy, and provides new methods for determining the optimal experimental conditions and improving detection accuracy in the field of detection and analysis.

Ultrahigh Sensitive Trace Gas Sensing System with Dual Fiber-Optic Cantilever Multiplexing-Based Differential Photoacoustic Detection.

An ultrahigh sensitive trace gas sensing system was presented with dual cantilever-based differential photoacoustic detection. By combining the double enhancement of multipass absorption and optical differential detection, the gas detection sensitivity was significantly improved. The dual-channel synchronous photoacoustic detection was realized by fiber-optic Fabry-Perot interference spectrum multiplexing. The photoacoustic signals detected by two fiber-optic cantilever microphones installed in a differential photoacoustic cell (DPAC) were out of phase, while the detected gas flow noises were in phase. The optical differential detection method achieved both highly sensitive optical interference measurement and differential noise suppression. In the multipass configuration, the interaction path between excitation light and target gas achieved 4.1 m, which improved the photoacoustic signal by an order of magnitude compared with a single reflection. The maximum gas flow allowed by the system based on the DPAC was 250 sccm, which realized the dynamic monitoring of H2S in the SF6 background. The detection limit for H2S in SF6 background was 5.1 ppb, which corresponds to the normalized noise equivalent absorption coefficient of 9 × 10-10 cm-1 W Hz-1/2.

Optimizing an Optical Cavity-Based Biosensor for Enhanced Sensitivity.

The rapid advancement of biosensor technology has revolutionized healthcare, offering improved sensitivity, specificity, and portability. We have developed an optical cavity-based biosensor (OCB) as a promising solution due to its label-free detection, high sensitivity, real-time monitoring, multiplexing capability, and versatility. The OCB consists of an optical cavity structure (OCS), optical components, and a low-cost camera. The OCS is created by two partially reflective surfaces separated by a small gap, where the interaction between target analytes and immobilized receptors leads to a shift in the resonance transmission spectrum, caused by minute changes in the local refractive index (RI). In our previous work, we successfully detected these small changes with a simple OCS and cost-effective components using a differential detection method. Building upon these achievements, this study focuses on optimizing the OCS, improving the camera settings, and enhancing the differential detection approach. By increasing the reflectance of the surfaces and optimizing the optical cavity widths correspondingly, we achieved an improved limit of detection (LOD). We also investigated how the charge-coupled device (CCD) camera shutter time affects the LOD. Additionally, we introduced a new differential equation to further enhance the sensitivity of our system. Through these advancements, we could improve the LOD of the OCB by 7.2 times, specifically for an OCS with a cavity thickness of 9.881 μm and a silver thickness of 46.87 nm. These findings not only contribute to the ongoing effort of optimizing the OCB, but also pave the way for the development of advanced point-of-care biosensors with enhanced detection capabilities.

Molten salt Cu(I) intercalation into the poly(triazine imide) for the electrochemical sensing of nitrite

AbstractSlight modification of the molten salt synthesis of poly(triazine imide) (PTI) with introduction of copper (I) chloride yielded a novel copper modified material. Energy‐dispersive x‐ray spectroscopy together with electron microscopy confirmed homogenous distribution of the copper within PTI lattice. X‐ray photoelectron spectroscopy suggests the copper to coordinate randomly at bridging nitrogen atoms. Thus obtained material exhibited excellent sensitivity to nitrite anions together with low limits of detection. Under previously optimized experimental conditions and pH 4 of supporting electrolyte, differential pulse voltammetric detection method showed linear response in wide range from 5 to 2200 μM with detection limit of 1.3 μM. Practical applicability for the developed method was scrutinized in wastewater and pipe water samples. Developed method of metal ion intercalation open doors to widespread introduction of metal ions in the PTI structure for practical utilization.

Atomic Spin Detection Method Based on Spin‐Selective Beam‐Splitting Metasurface

AbstractThe current polarization differential detection method used for atomic co‐magnetometers and other atomic sensors usually depends on the beam splitting plane at 45° from the incident light, so the beam splitter needs a certain thickness and a second optical path perpendicular to the original optical axis for detection, which restricts the integration degree of the beam splitter and photodetectors on the probe optical path. To address this issue, a new spin‐selective beam‐splitting metasurface‐based technique for atomic spin detection is proposed in this study, which utilizes a silicon nitride (SiN) metasurface for chiral beam‐splitting detection. The experimental results show that the 2.8 × 2.8 mm fabricated metasurface supports the beam splitting focusing of the left and right circular partial incident light with a distance up to 1.340 mm. It has a remarkable ability to detect optical rotation angles, achieving an angular measurement sensitivity of 3.0526 × 10−5 rad at 70 kHz. Compared with the traditional beam splitter, the probe's optical path volume can be reduced by at least 80%. This method can enhance the integration of atomic sensors and provide a novel approach for the development of atom sensors on chips.

Error Characterization of Differential Detection and Non-Differential Detection for MIMO UWOC Systems in Seawater Turbulent Channels

Ocean turbulence is an important factor affecting the development of underwater wireless optical communication (UWOC). To improve the error characteristics of the underwater optical communication system, we propose a differential detection-based multi-receiver-multi-transmitter (MIMO) underwater laser communication transmission method. Additionally, we derive the expressions for calculating the average BER of the MIMO underwater wireless optical communication system with differential detection and non-differential detection in the case that the two transmitted beams are completely uncorrelated. The error characteristics of the MIMO system are simulated and analyzed from the perspective of ocean turbulence intensity and link distance. The simulation results show that the differential detection method has a lower average BER compared to the non-differential detection method in the case of moderate-to-strong ocean turbulence. In addition, the differential detection methods do not have the error floor effect, and non-differential detection methods have the error floor effect. The more the turbulence intensity affects the average BER of the MIMO UWOC system with the increase of the communication link distance, the more obvious is the effect of the turbulence intensity on the average BER of the MIMO UWOC system. Accordingly, the simulation analysis shows that the differential detection method is more suitable for the construction of communication links under long-distance and medium-strong turbulence.

Using Phase-Sensitive Optical Time Domain Reflectometers to Develop an Alignment-Free End-to-End Multitarget Recognition Model

This pattern recognition method can effectively identify vibration signals collected by a phase-sensitive optical time-domain reflectometer (Φ-OTDR) and improve the accuracy of alarms. An alignment-free end-to-end multi-vibration event detection method based on Φ-OTDR is proposed, effectively detecting different vibration events in different frequency bands. The pulse accumulation and pulse cancellers determine the location of vibration events. The local differential detection method demodulates the vibration event time-domain variation signals. After the extraction of the signal time-frequency features by sliding window, the convolution neural network (CNN) further extracts the signal features. It analyzes the temporal relationship of each group of signal features using a bidirectional long short-term memory network (Bi-LSTM). Finally, the connectionist temporal classification (CTC) is used to label the unsegmented sequence data to achieve single detection of multiple vibration targets. Experiments show that using this method to process the collected 8563 data, containing 5 different frequency bands of multi-vibration acoustic sensing signal, the system F1 score is 99.49% with a single detection time of 2.2 ms. The highest frequency response is 1 kHz. It is available to quickly and efficiently identify multiple vibration signals when a single demodulated acoustic sensing signal contains multiple vibration events.

Identification and classification of bearing steel bars based on low-frequency eddy current detection method

Raw material testing is of great significance to guarantee the reliability of bearing ring production. Traditional testing methods such as spark identification are unable to run online, leading to the risk of raw material misuse in bearing ring production. To this end, this paper develops a low-frequency differential eddy current detection method for identifying and classifying raw materials of bearing steel bars. Firstly, the distribution of eddy current density inside bars and effects of excitation signals on the induced signals are discussed via numerical simulation. Then, the low-frequency eddy current detection experimental system with a phase-locked amplifier algorithm is established. Experiment results shows that the method developed in this study can distinguish between carbon steel and bearing steel reliably, identify the furnace number and heat treatment modes of bearing steel bars effectively, laying the foundation for online detection of raw material during bearing production.

Effect of spectrum broadening on photon-counting fiber Bragg grating sensing.

In a photon-counting fiber Bragg grating (FBG) sensing system, a shorter probe pulse width reaches a higher spatial resolution, which inevitably causes a spectrum broadening according to the Fourier transform theory, thus affecting the sensitivity of the sensing system. In this work, we investigate the effect of spectrum broadening on a photon-counting FBG sensing system with a dual-wavelength differential detection method. A theoretical model is developed, and a proof-of-principle experimental demonstration is realized. Our results give a numerical relationship between the sensitivity and spatial resolution at the different spectral widths of FBG. In our experiment, for a commercial FBG with a spectral width of 0.6 nm, an optimal spatial resolution of 3 mm and a corresponding sensitivity of 2.03 nm-1 can be achieved.

A low complexity distributed differential scheme based on orthogonal space time block coding for decode-and-forward wireless relay networks

<span lang="EN-US">This work proposes a new differential cooperative diversity scheme with high data rate and low decoding complexity using the decode-and-forward protocol. The proposed model does not require either differential encoding or channel state information at the source node, relay nodes, or destination node where the data sequence is directly transmitted and the differential detection method is applied at the relay nodes and the destination node. The proposed technique enjoys a low encoding and decoding complexity at the source node, the relay nodes, and the destination node. Furthermore, the performance of the proposed strategy is analyzed by computer simulations in quasi-static Rayleigh fading channel and using the decode-and-forward protocol. The simulation results show that the proposed differential technique outperforms the corresponding reference strategies.</span>

DTD: “Detect–Track–Detect” Method for Infrared Marine Object Detection

Infrared object detection is a crucial technology in searching and rescuing missions. In infrared marine images, the objects are usually dim and small, easily interfered with or blocked by waves or other clutters. The stable real-time detection of sequential images is challenging. Therefore, this paper proposes a “detect-track-detect" method based on the joint multi-domain features of the object in the image. First, we design a differential Gaussian local peak single-frame detection method to screen out potential objects. Then, we remove most false objects and predict the position of missing objects through dual-threshold pipeline filter multi-frame selection and trajectory prediction. At last, we re-detect predicted patches to recover the missing objects. Experiments prove that the proposed method improves the stability and accuracy of infrared sequence object detection while ensuring real-time performance.

DiffCircaPipeline: a framework for multifaceted characterization of differential rhythmicity.

Circadian oscillations of gene expression regulate daily physiological processes, and their disruption is linked to many diseases. Circadian rhythms can be disrupted in a variety of ways, including differential phase, amplitude and rhythm fitness. Although many differential circadian biomarker detection methods have been proposed, a workflow for systematic detection of multifaceted differential circadian characteristics with accurate false positive control is not currently available. We propose a comprehensive and interactive pipeline to capture the multifaceted characteristics of differentially rhythmic biomarkers. Analysis outputs are accompanied by informative visualization and interactive exploration. The workflow is demonstrated in multiple case studies and is extensible to general omics applications. R package, Shiny app and source code are available in GitHub (https://github.com/DiffCircaPipeline) and Zenodo (https://doi.org/10.5281/zenodo.7507989). Supplementary data are available at Bioinformatics online.

High-sensitivity dynamic analysis of dissolved gas in oil based on differential photoacoustic cell

In order to realize real-time online monitoring of dissolved trace gas in transformer oil, a high-sensitivity dynamic analysis technology based on a low-noise differential photoacoustic (PA) cell (DPAC) is proposed. The characteristic gas dissolved in oil is separated by the means of headspace degassing and then pumped into the DPAC. The emitted laser light is amplified by an erbium-doped fiber amplifier (EDFA) and reflected in the DPAC to form a double-pass excitation enhancement. The DPAC with two expansion mufflers is equipped. The airflow noise in the two PA tubes with symmetrical design is basically the same. Differential processing is used to reduce the noise. In addition, the airflow noise with the same frequency as the PA signal is filtered by optimizing the band-stop filtering characteristic of mufflers. The experimental results show that both the differential detection method and the muffler reduce the noise during headspace degassing by more than 80%. The detection limit of the system for dissolved acetylene (C2H2) in oil reaches 0.1 μL/L. The technical solution has the advantages of high detection accuracy and fast response speed, which provides a technical solution for dynamic dissolved gas analysis.

Displacement measurement of sub-nanometer resolution using space-domain active fiber cavity ring-down technology

A displacement sensor of sub-nanometer resolution using space-domain active fiber cavity ring-down (FCRD) technology was developed. In our scheme, by combining active FCRD with frequency-shifted interferometry technology, a sensitivity of 0.05 km−1 ·nm−1 was achieved and the stability was improved mostly because this scheme is free from thepowerfluctuationofalight source, benefited to the use of low-noise Bi-EDFA and the differential detection method to reduce the direct current noise. Besides the first sensitivity amplification process is involved in this technique due to the multiple times rounds of interaction between the light pulse and the sensor head in the fiber cavity, the secondary sensitivity amplification process is realized by using a Bi-EDFA to compensate the inherent cavity loss of fiber cavity, which results in a significantly improvement in resolution. The experimental results show that our proposed displacement sensor can achieve a resolution of up to 0.3 nm with stability of 1.3 %.

Optical sensor using space-domain active fiber cavity ringdown technique

A novel active fiber cavity ringdown (FCRD) technique using frequency-shifted interferometry (FSI) is proposed for the first time. Using this scheme, external parameters can be monitored in the space domain by measuring the ringdown distance instead of ringdown time. A bidirectional erbium-doped fiber amplifier (Bi-EDFA) is employed to compensate the inherent cavity loss for achieving higher sensitivity. And two band-pass filters are used to reduce the amplified spontaneous emission (ASE) noise of the Bi-EDFA. Compared with the well-known time-domain active FCRD scheme, our proposed method enables us to avoid using pulsed laser needed in time-domain active FCRD, it uses continuous-wave laser to inject into the fiber cavity and stabilize the optical power in the fiber cavity, which can suppress the baseline drift of ringdown signal caused by the gain fluctuations of the EDFA and thus improve the detecting precision. Moreover, this novel method enables us to use differential detection method for further reducing the ASE noise, and thus eliminating the baseline drift of ringdown signal. A magnetic field sensor was developed as a proof-of-concept demonstration. The experimental results demonstrate that the proposed sensor with a sensitivity of 0.01537 (1/km·Gs) was achieved. This is the highest magnetic field sensitivity compared to the time-domain active FLRD method. Due to the reduced ASE noise, the stability of the proposed sensing system was also greatly improved.

Dense Temporally Multiplexed Fiber Bragg Grating Sensing Based on Single-Photon Detection

Temporal multiplexed fiber Bragg grating (FBG) sensing system for detecting physical parameters with a high spatial resolution is desirable for applications such as structure health monitoring, oil and gas exploration, geophysical research, etc. However, due to the trade-off between bandwidth and sensitivity of the linear photodetection, the wavelength resolution of dense temporally multiplexed FBG sensing system is severely affected under a spatial resolution of centimeters. In this paper, we propose and demonstrate a dense temporal multiplexed FBG sensing scheme with single-photon detection at 1.5 μm. A proof-of-principle demonstration is implemented by using three FBG sensors spliced along a piece of fiber. The spatial resolution can reach 5.0 cm which is determined by a time jitter of superconducting nanowire single-photon detector and a pulse width of laser. In our experiment, by using the dual-wavelength differential detection method, a wavelength resolution of 0.5 pm over a range of 550 pm is obtained with a corresponding temperature resolution of 0.05 °C, which could be further improved to 0.01 °C by increasing the repetition rate of laser pulse.